Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution 

from Locomotive Engines and Marine 

Compression-Ignition Engines Less than 

30 Liters per Cylinder 
Draft Regulatory Impact Analysis:
 

Control of Emissions of Air Pollution from 
 

Locomotive Engines and 
 

Marine Compression-Ignition Engines 
 

Less than 30 Liters per Cylinder
 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

EPA420-D-07-001 

March 2007 
Table of Contents 

Executive Summary ES-1 

Chapter 1: Industry Characterization 

Chapter 2: Air Quality, Health and Welfare 

Chapter 3: Inventory 

Chapter 4: Technological Feasibility 

Chapter 5: Cost/Cost per Ton 

Chapter 6: Benefits 

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 2-28 

AIRS Aerometric Information Retrieval System 

APHEA Air Pollution and Health: A European Approach 

AQ Air Quality 

AQCD Air Quality Criteria Document 

AQMTSD Air Quality Modeling Technical Support Document 

ARB (California) Air Resources Board 

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 

COI Cost of Illness 

iii 
COPD 

CPI-U 

C-R 

CSS 

CUA 

cyl 

D 

DE 

DEM 

diff 

disp 

DOC 

DOE 

DOT 

DPF 

DPM 

DR 

DRIA 

DV 

EAC 

EC 

EF 

EGR 

EIA 

EIA 

EIM 

EMD 

EMS-HAP 

EO 

EPA 

EPAct 

ESPN 

F 

FEM 

FEV 

FR 

FRA 

FRM 

FRP 

g 

g/bhp-hr 

g/kW-hr 

gal 

GAO 

GDP 

GEOS 

GETS 

GIS 

H2 

HAD 

HAP 

HC 

HD 

HEI 

HEP 

HES 

Chronic Obstructive Pulmonary Disease 

Consumer Price Index - All Urban Consumers 

Concentration Response 

Coastal Sage Scrub 

Cost Utility Analysis 

Cylinder 

Demand 

Diesel Exhaust 

Domestic Engine Manufacturer 

Difference 

Displacement 

Diesel Oxidation Catalyst 

Department of Energy 

Department of Transportation 

Diesel Particulate Filter 

Diesel Particulate Matter 

Discount Rate 

Draft Regulatory Impact Analysis 

Design Values 

Early Action Component 

Elemental Carbon 

Emission Factor 

Exhaust Gas Recirculation 

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

Economic Impact Analysis 

Economic Impact Model 

Electromotive Diesel 

Emissions Modeling System for Hazardous Air Pollution 

Executive Order 

Environmental Protection Agency 

Energy Policy Act of 2005 

EPA speciation network 

Fahrenheit 

Foreign Engine Manufacturer 

Functional Expiratory Volume 

Federal Register 

Federal Railroad Administration 

Final Rulemaking 

Fiberglass-Reinforced Plastic 

Gram 

Grams per Brake Horsepower Hour 

Grams per Kilowatt Hour 

Gallon 

Government Accountability Office 

Gross Domestic Product 

Goddard Earth Observing System 

General Electric Transportation Systems 

Geographic Information System 

Hydrogen Gas 

Diesel Health Assessment Document 

Hazardous Air Pollutant 

Hydrocarbon 

Heavy-Duty 

Health Effects Institute 

Head End Power 

Health Effects Subcommittee 

iv 
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 

NEI National Emissions Inventory 

NESCAUM Northeast States for Coordinated Air Use Management 

NESHAP National Emissions Standards for Hazardous Air Pollutants 

v 
NH3 Ammonia 

NIOSH National Institute of Occupational Safety and Health 

NLEV National Low Emission Vehicle 

NMHC Non-Methane 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 

RFS Renewable Fuels Standard 

RIA Regulatory Impact Analysis 

rpm Revolutions per Minute 

vi 
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 
Executive Summary 

Executive Summary 

The Environmental Protection Agency (EPA) is proposing 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 

proposed standards would achieve PM reductions of 90 percent and NOx reductions 

of 80 percent, compared to engines meeting the current Tier 2 standards. The 

proposed standards would also yield sizeable reductions in emissions of nonmethane 

hydrocarbons (NMHC), carbon monoxide (CO), and hazardous compounds known as 

air toxics. 

This proposal 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. 

This Regulatory Impact Analysis provides technical, economic, and 

environmental analyses of the proposed 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 the proposed 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 proposed 

rulemaking. Chapter 7 contains our estimates of the market impacts of the proposed 

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 proposal. 

1. Proposed Emission Standards 

The proposed program 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 

ES-1 
 
Draft Regulatory Impact Analysis 

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 proposing 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 marine 

diesel engines. These standards phase in over time, beginning in 2014. 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 proposing to tighten emission standards for existing locomotives when 

they are remanufactured. Also included in our proposal are provisions to eliminate 

emissions from unnecessary locomotive idling, and we are requesting comment on 

applying standards to certain existing marine diesel engines when they are 

manufactured. 

Locomotive Standards 

The proposed 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 would 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. The regulations also generally do not apply to existing 

locomotives owned by railroads that are classified as small businesses. In addition, 

engines used in locomotive-type vehicles with less than 750 kW (1006 hp) total 

power (used primarily for railway maintenance), engines used only for hotel power 

(for passenger railcar equipment), and engines that are used in self-propelled 

passenger-carrying railcars, are excluded from these regulations. The engines used in 

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 

these smaller locomotive-type vehicles are generally subject to our nonroad engine 

requirements (40 CFR Parts 89 and 1039). 

Table ES-1 – Proposed Standards for New and Existing Line-Haul and Passenger Locomotives 

(g/bhp-hr) 

STANDARDS 

APPLY TO: 

DATE PM NOx HC 

Remanufactured 

Tier 0 & 1 

2008 as available, 

2010 required 

0.22 7.4 a

 0.55 a 

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 PM and HC 2015 

NOx 2017 

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 for NOx and HC, respectively. 

Table ES-2 – Proposed Standards for New and Existing Switch Locomotives (g/bhp-hr) 

SWITCH 

LOCOMOTIVE 

STANDARDS 

APPLY TO: 

DATE 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 proposed standards for newly-built marine diesel engines are set out in 

Tables 3, 4, 5, and 6. The Tier 3 standards would apply to all marine diesel engines 

with per cylinder displacement up to 30 liters. The Tier 4 standards would apply only 

to commercial marine diesel engines above 600 kW and recreational marine diesel 

engines above 2,000 kW. 

ES-3 
Draft Regulatory Impact Analysis 

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). 

Table ES-3 – Proposed Tier 3 Standards for Marine Diesel C1 

RATED KW L/CYLIND 

ER 

PM 

G/BHP-HR 

NOx+HC 

G/BHP-HR 

MODEL 

YEAR 

<19 kW <0.9 0.30 5.6 2009 

19 - <75 kW <0.9 a 0.22 5.6 2009 

0.22 b

 3.5 b 2014 

<0.9 0.10 4.0 2012 

75 - 3700 kW 

0.9- <1.2 0.09 4.0 2013 

1.2- <2.5 0.08 c 4.2 2014 

2.5- <3.5 0.08 c 4.2 2013 

3.5- <7.0 0.08 c 4.3 2012 

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

(b) Option: 0.15 PM / 4.3 NOx in 2014. 

(c) This standard level drops to 0.07 in 2018 for <600 kW engines. 

Table ES-4 – Proposed Tier 3 Standards for Marine Diesel C1 Recreational and Commercial 
 

High Power Density 
 

RATED KW L/CYLIND 

ER 

PM 

G/BHP-HR 

NOx+HC 

G/BHP-HR 

MODEL 

YEAR 

<19 kW <0.9 0.30 5.6 2009 

19 - <75 kW <0.9 a 0.22 5.6 2009 

0.22 b

 3.5 b 2014 

<0.9 0.11 4.3 2012 

75 - 3700 kW 

0.9- <1.2 0.10 4.3 2013 

1.2- <2.5 0.09 4.3 2014 

2.5- <3.5 0.09 4.3 2013 

3.5- <7.0 0.09 4.0 2012 

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

(b) Option: 0.15 PM / 4.3 NOx+HC in 2014. 

ES-4 
Executive Summary 

Table ES-5 – Proposed Tier 3 Standards for Marine Diesel C2 

RATED KW L/CYLIN 

DER 

PM 

G/BHP-HR 

NOx+HC 

G/BHP-HR 

MODEL 

YEAR 

7- <15 0.10 4.6 2013 

=<3700 kW 

15- <20 0.20 a 6.5 a 2014 

20- <25 0.20 7.3 2014 

25- <30 0.20 8.2 2014 

(a) For engines at or below 3300 kW in this group, the PM / NOx+HC Tier 3 standards are 0.25 / 5.2. 

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

RATED KW PM 

G/BHP-HR 

NOx 

G/BHP-HR 

HC 

G/BHP-HR 

MODEL 

YEAR 

>3700 kW 0.09 a

 1.3 0.14 2014 

0.04 1.3 0.14 2016 b 

1400 - 3700 kW 0.03 1.3 0.14 2016 c 

600 - <1400 kW 0.03 1.3 0.14 2017 b 

(a) This standard is 0.19 for engines with 15-30 liter/cylinder displacement. 

(b) Optional compliance start dates are proposed within these model years; see discussion below. 

(c) Option for engines with 7-15 liter/cylinder displacement: Tier 4 PM and HC in 2015 and Tier 4 NOx in 2017. 

2. Projected Inventory and Cost Impacts 

Our analysis of the projected impacts of the proposed 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 

ES-5 
 
Draft Regulatory Impact Analysis 

2030, these engines would 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 the proposed standards would reduce annual NOx emissions 

by about 765,000 tons and PM2.5 and 28,000 tons in 2030. Table 7 shows the 

emissions reductions associated with today’s proposal for selected years, and the 

cumulative reductions through 2040 discounted at 3 and 7 percent. These reductions 

in PM and NOx levels would produce nationwide air quality improvements. 

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

Marine Standards (Short tons) 

YEAR PM2.5 PM10 

A NOx NMHC 

2015 7,000 7,000 84,000 14,000 

2020 15,000 15,000 293,000 25,000 

2030 28,000 29,000 765,000 39,000 

2040 38,000 40,000 1,123,000 50,000 

NPV at 3% 315,000 325,000 7,869,000 480,000 

NPV at 7% 136,000 140,000 3,188,000 216,000 

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. 

Engineering Costs 

The engineering cost analysis for the proposed standards can be found in 

Chapter 5. The total engineering costs associated with today’s proposal are the 

summation of the engine and equipment compliance costs, both fixed and variable, 

the operating costs, and the costs associated with the locomotive remanufacturing 

program. These costs are summarized in Table 8. 

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

YEAR ENGINE 

COSTS 

EQUIPMENT 

COSTS 

OPERATING 

COSTS 

COSTS OF 

REMANUFACTURING 

PROGRAM 

TOTAL 

COSTS 

2011 $99 $0 $11 $97 $207 

2012 $55 $0 $13 $75 $142 

2015 $100 $25 $25 $31 $181 

2020 $87 $10 $187 $15 $250 

2030 $105 $8 $407 $85 $605 

2040 $104 $8 $611 $153 $876 

NPV at 3% $1,678 $141 $4,039 $1,374 $7,233 

NPV at 7% $883 $71 $1,596 $682 $3,231 

ES-6 
 
Executive Summary 

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 urea-related costs associated with SCR technology. Since SCR is 

a technique for reduce 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 $93 $113 

2012 $62 $80 

2015 $93 $88 

2020 $836 $164 

2030 $159 $446 

2040 $218 $658 

NPV at 3% $2,222 $5,011 

NPV at 7% $1,068 $2,163 

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 proposed 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 remanufacturing program. 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. That estimates includes 

engineering costs and emission reductions that will occur from the new engine 

standards and locomotive remanufacturing program in that year. 

Table ES-10 – Proposed 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 $600 $630 $550 

PM $6,840 $7,640 $5,560 

ES-7 
 
Draft Regulatory Impact Analysis 

3. Estimated Benefits and Economic Impacts 

Estimated Benefits 

We estimate that the requirements in this proposal will result in substantial 

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

The benefits analysis performed for this proposal 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. 

EPA typically quantifies PM- and ozone-related benefits in its regulatory 

impact analyses (RIAs) when possible. In the analysis of past air quality regulations, 

ozone-related benefits have included morbidity endpoints and welfare effects such as 

damage to commercial crops. EPA has not recently included a separate and additive 

mortality effect for ozone, independent of the effect associated with fine particulate 

matter. For a number of reasons, including 1) advice from the Science Advisory 

Board (SAB) Health and Ecological Effects Subcommittee (HEES) that EPA consider 

the plausibility and viability of including an estimate of premature mortality 

associated with short-term ozone exposure in its benefits analyses and 2) conclusions 

regarding the scientific support for such relationships in EPA’s 2006 Air Quality 

Criteria for Ozone and Related Photochemical Oxidants (the CD), EPA is in the 

process of determining how to appropriately characterize ozone-related mortality 

benefits within the context of benefits analyses for air quality regulations. As part of 

this process, we are seeking advice from the National Academy of Sciences (NAS) 

regarding how the ozone-mortality literature should be used to quantify the reduction 

in premature mortality due to diminished exposure to ozone, the amount of life 

expectancy to be added and the monetary value of this increased life expectancy in 

the context of health benefits analyses associated with regulatory assessments. In 

addition, the Agency has sought advice on characterizing and communicating the 

uncertainty associated with each of these aspects in health benefit analyses. 

Since the NAS effort is not expected to conclude until 2008, the agency is 

currently deliberating how best to characterize ozone-related mortality benefits in its 

rulemaking analyses in the interim. For the analysis of the proposed locomotive and 

marine standards, we do not quantify an ozone mortality benefit. So that we do not 

provide an incomplete picture of all of the benefits associated with reductions in 

emissions of ozone precursors, we have chosen not to include an estimate of total 

ozone benefits in the proposed RIA. By omitting ozone benefits in this proposal, we 

acknowledge that this analysis underestimates the benefits associated with the 

proposed standards. Our analysis, however, indicates that the rule's monetized PM2.5 

benefits alone substantially exceed our estimate of the costs. 

The range of benefits associated with the proposed program are estimated 

based on the risk of several sources of PM-related mortality effect estimates, along 

with all other PM non-mortality related benefits information. These benefits are 

presented in Table ES-11. The benefits reflect two different sources of information 

ES-8 
 
Executive Summary 

about the impact of reductions in PM on reduction in the risk of premature death, 

including both the American Cancer Society (ACS) cohort 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. These estimates are in year 2005 dollars. 

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

Marine Engine Standards 

TOTAL BENEFITSA,B,C,D (BILLIONS 2005$) 

2020 2030 

PM mortality derived from the ACS cohort study; Morbidity functions from epidemiology literature 

Using a 3% discount rate $4.4+B $12+B

 Confidence Intervals (5th - 95th %ile) ($1.0 - $10) ($2.1 - $27) 

Using a 7% discount rate $4.0+B $11+B

 Confidence Intervals (5th - 95th %ile) ($1.0 - $9.2) ($1.8 - $25) 

PM mortality derived from lower bound and upper bound expert-based result;e

 Morbidity functions from 

epidemiology literature 

Using a 3% discount rate $1.7+B - $12+B $4.6+B - $33+B

 Confidence Intervals (5th - 95th %ile) ($0.2 - $8.5) – ($2.0 - $27) ($1.0 - $23) – ($5.4 - $72) 

Using a 7% discount rate $1.6+B - $11+B $4.3+B - $30+B

 Confidence Intervals (5th - 95th %ile) ($0.2 - $7.8) – ($1.8 - $24) ($1.0 - $21) – ($4.9 - $65) a

 Benefits include avoided cases of mortality, chronic illness, and other morbidity health endpoints. b

 PM-related mortality benefits estimated using an assumed PM threshold of 10 μg/m3. There is 

uncertainty about which threshold to use and this may impact the magnitude of the total benefits 

estimate. For a more detailed discussion of this issue, please refer to Section 6.6.1.3 of the RIA. 

c For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of 

additional monetary benefits and disbenefits. A detailed listing of unquantified health and welfare 

effects is provided in Chapter 6 of the RIA. 

d

 Results reflect the use of two different discount rates: 3 and 7 percent, which are 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. e The effect estimates of nine 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 two 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. Likewise the 

5th and 95th percentiles for these highest and lowest judgments of the effect estimate do not imply any 

particular distribution within those bounds. The distribution of benefits estimates associated with each 

of the twelve expert responses can be found in Tables 6.4-3 and 6.4-4 in the RIA. 

We estimate that the annual emission reductions associated with the proposed 

standards would annually prevent 1,500 premature deaths (based on the ACS cohort 

study), 170,000 work days lost, and 1,000,000 minor restricted-activity days. Using 

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

that the monetized benefits of this rule in 2030 would be approximately $12 billion, 

ES-9 
 
Draft Regulatory Impact Analysis 

assuming a 3 percent discount rate (or $11 billion assuming a 7 percent discount rate). 

Using the range of results derived from the expert elicitation, we estimate that the 

monetized benefits in 2030 would range from approximately $4.6 billion to $33 

billion, assuming a 3 percent discount rate (or $4.3 to $30 billion assuming a 7 

percent discount rate). These estimates would be increased substantially if we were 

to adopt the remanufactured marine engine program concept. The annual cost of the 

program in 2030 would be significantly less, at approximately $600 million. 

Economic Impact 

We also performed an economic impact analysis to estimate the market and 

social welfare impacts of the proposed standards. This analysis can be found in 

Chapter 7. According to this analysis, the average price of a locomotive in 2030 is 

expected to increase by less than three percent as a result of the proposed standards. 

The average price of a commercial marine diesel engine in 2030 is expected to 

increase by about 8.5 percent for Category 1 engines above 800 hp and about 19 

percent for Category 2 engines above 800 hp.B

 The average price of a marine vessel 

using those engines is expected increase by about 1 percent for vessels using 

Category 1 engines above 800 hp (about $16,000) and about 3.6 percent for vessels 

using Category 2 engines above 800 hp (about $142,000). Increases in engine and 

vessel prices for commercial engines below 800 hp and recreational engines are 

expected to be negligible. 

Overall, producers and consumers of rail and marine transportation services 

are expected to bear the majority of the social costs of the program, in large part 

because they bear the operating (urea) and remanufacturing costs that make up most 

of the compliance costs of the proposal. Providers of those transportation services are 

expected to bear about 42 percent of the social costs of the rule, and users are 

expected to bear about 50 percent. However, the price of rail and transportation 

services is expected to increase by less than 1 percent. Locomotive, marine diesel 

engine, and marine vessel manufacturers will bear the remainder of the social costs. 

4. Alternative Program Options 

In the course of designing our proposed program, 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. 

B Marine diesel engines are divided into three categories for the purposes of EPA’s standards. 

Category 1 are engines above 50 hp and up to 5 liters per cylinder displacement. Category 2 are 

engines from 5 to 30 liters per cylinder. Category 3 are engines at or above 30 liters per cylinder. See 

40 CFR 94.2. Note that we are proposing to change the definition of Category 1 and Category 2 

engines to reflect a 7 liter per cylinder cut-off. 

ES-10 
 
Chapter 1: Industry Characterization 

CHAPTER 1: INDUSTRY CHARACTERIZATION 

1.1 MARINE............................................................................................................. 1-2 
 

1.1.1 INTRODUCTION........................................................................................... 1-2 
 

1.1.2 MARINE DIESEL ENGINE MANUFACTURERS.................................... 1-6 
 

1.1.3 MARINE VESSEL MANUFACTURERS.................................................. 1-24 
 

1.2 LOCOMOTIVE ............................................................................................... 1-44 
 

1.2.1 CURRENT EMISSION REGULATIONS ................................................. 1-45 
 

1.2.2 SUPPLY: LOCOMOTIVE MANUFACTURING AND 
 

REMANUFACTURING ....................................................................................... 1-46 
 

1.2.3 DEMAND: RAILROADS ............................................................................ 1-56 
 

1.2.4 EXISTING REGULATIONS ...................................................................... 1-70 
 

1.2.5 FOREIGN RAILROADS IN US ................................................................. 1-72 
 

1-1 
 
Draft 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. The industries affected by these regulations include marine diesel engine 

manufacturers and marinizers, the manufacturers of marine vessels which have 

marine diesel marine engines installed on them, 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, and is provided for 

background purposes. 

1.1 Marine 

1.1.1 Introduction 

The regulations for marine diesel engines will directly impact three industries. 

These industries are the manufacturers of marine diesel engines, diesel engine 

marinizers, and the manufacturers of vessels which have marine diesel engines 

installed on them. Each of these industries is discussed in more detail in the 

following sections. 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 post-manufacturer 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 oceangoing 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 

1-2 
 
Chapter 1: Industry Characterization 

vessels which service offshore drilling platforms, and a variety of other specialized 

commercial vessels. 

Figure 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 Marine Diesel Market Segment Flow Chart 

1.1.1.2 Current Emission Regulations 

The first standards to take effect for commercial marine diesel engines are the 

Tier 1 emission standards, which were adopted in 2003, and 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 1-1 the standards vary 

depending on the rated speed of the engine. 

1-3 
 
Draft Regulatory Impact Analysis 

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

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

<130 17 

130-2000 45 X rpm-0.2 

>2000 9.8 

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 FR 

73300, December 29, 1999). These standards are shown in Table 1-2. 

Table 1-2 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/kWhr) 

CO 

(g/kWhr) 

1 Power ≥37 kW, disp. <0.9 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 

2.5 ≤ disp. < 5.0 2007 7.2 0.20 5.0 

2 5.0 ≤ disp. < 15.0 2007 7.8 0.27 5.0 

15.0 ≤ disp. < 20.0, and power < 

3300 kW 

2007 8.7 0.50 5.0 

15.0 ≤ disp. < 20.0, and power ≥

3300 kW 

2007 9.8 0.50 5.0 

20.0 ≤ disp. < 25.0 2007 9.8 0.50 5.0 

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 1-2 

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). 

There are currently no emission regulations specifically for marine diesel 

engines less than 37 kW. Rather, these engines are 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 
 
Chapter 1: 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 Programs in California and Europe 

The State of California has recently finalized a requirement that auxiliary 

engines on ocean-going vessels that operate in California waters use clean distillate 

fuel. Under California’s proposal, NOx, diesel PM and SOx emissions from a 

regulated auxiliary diesel engine would be limited to the emission rates that would 

have resulted had the engine been fueled with distillate fuel. The regulated auxiliary 

engines are typically below 30 liters per cylinder displacement, although the 

California program would also apply to indirect drive diesel engines of any size. The 

requirements could be met by using either distillate fuel or an alternative emission 

control strategy as evidenced by an Alternative Compliance Plan. The proposed 

controls are effective January 1, 2007, for a 5,000 ppm equivalent fuel sulfur limit, 

and January 1, 2010, for a 1,000 ppm equivalent fuel sulfur limit. The requirements 

would apply to auxiliary diesel engines on ocean-going vessels while they are 

operating within any of regulated California waters, which include all California 

inland waters, all California estuarine waters, and all waters within a zone 24 nautical 

miles seaward of the coastline. 

 California’s program is roughly patterned after the European Union’s 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 

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 
 
Draft Regulatory Impact Analysis 

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. 

In this proposal we are not considering similar programs for the fuels used by 

vessels while operating in U.S. territorial waters. We believe that the best approach 

for addressing emissions from auxiliary engines on foreign vessels that visit US ports 

is through the adoption of international standards that would reduce both NOx and 

PM emissions from these engines. We will continue to participate in discussions for 

the next tier of international standards at the International Maritime Organization, as 

part of the U.S. negotiating team. We will also reconsider this issue as part of our 

future Category 3 marine diesel engine action. 

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 compressionignition. 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. 

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. 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 should be less than 100 gross registered tons, should carry fewer than six 

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 

1-6 
 
Chapter 1: Industry Characterization 

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.2 

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 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 

1-7 
 
Draft Regulatory Impact Analysis 

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. 

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 

1-8 
 
Chapter 1: Industry Characterization 

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 proposal, 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. Table 1-5 shows a summary of the general characteristics of 

engines in each of the four categories considered in this industry characterization. 

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% 

1-9 
 
Draft Regulatory Impact Analysis 

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. 

1.1.2.2.1 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.3 

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 

1-10 
 
Chapter 1: Industry Characterization 

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 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 

1-11 
 
Draft Regulatory Impact Analysis 

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. 

1.1.2.2.3 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 form 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 

1-12 
 
c 

Chapter 1: Industry Characterization 

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.4 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, semidiesel, and dual-fuel 

engines (except 

automobile, highway truck, 

bus, tank) 

2,003 215 1,287 59 

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% 

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

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

NAICS codes 33152005, 33152003, 33631100. 

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 

1-13 
 
Draft Regulatory Impact Analysis 

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. 

Table 1-9 Marine Engine Manufacturers 

Domestic Engine 

Manufacturers 

Foreign Engine Manufacturers Postmanufacture 

Marinizers 

Caterpillar Deutz Bombardiera 

Cummins EQT (parent to DDC) Brunswick 

Deere & Company Greenbriar Equity, LLC (parent 

to EMD) 

Cummins 

General Electric MAN Daytona Marinea

 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. 

1-14 
 
Chapter 1: Industry Characterization 

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 Rebuilding 

Engines are often rebuilt to extend their service life. Engine rebuilding refers 

to overhauling an engine or otherwise performing extensive renovation on the engine 

(or on a portion of the engine or engine system). This involves disassembling the 

engine, inspecting and/or replacing many of the parts, and reassembling the engine in 

a way that extends its service life. Marine engines are typically rebuilt several times 

of the course of their service lives. 

Many of these marine engine rebuilds are performed by machine shops. The 

Engine Rebuilders Association lists over 2,500 machine shops in its member 

database. In 2003, Engine Builder magazine surveyed these machine shops for their 

2003 Machine Shop Market Profile. According to their results, 53 percent of these 

firms were involved in marine engine rebuilding in 2002. The rebuilding of gas and 

diesel marine engines accounted for 5.1 percent of the total 1.13 million engines 

rebuilt in 2002.5

 Finally, a large number of engine rebuilds are performed by ship 

and boat builders at their facilities. 

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 Marine Diesel Engine Production by Application and Use Type 

(2002) 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 

1-15 
 
Draft Regulatory Impact Analysis 

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 

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. 

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.6 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. 

1-16 
 
Chapter 1: Industry Characterization 

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 engine-powered commercial vessels built in the United States in 2004.7 

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 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 

Recreational ApplicationsFigure 1-2 and Figure 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 FEMs that oversee the entire production process. No DEMs 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. 

1-17 
 
Draft Regulatory Impact Analysis 

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. 

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

Dressers 

28% 

FEM

11% 

PMM

61% 

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

PMM

28% DEM

45% 

FEM

26% Dresser 

1% 

1-18 
 
Chapter 1: Industry Characterization 

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 1-4). As opposed to the 

recreational C1 market, dressers supply a larger portion of the commercial C1 market 

(19 percent), with FEMs supplying 5 percent. 

1-19 
 
Draft Regulatory Impact Analysis 

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

PMM

35% 

FEM

5% 

Dresser 

19% 

DEM

41% 

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 

1-20 
 
Chapter 1: Industry Characterization 

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) (D) 

Total Dressers — 0.0% 

Total C2 Market 277 

(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. 

1-21 
 
Draft Regulatory Impact Analysis 

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. 

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:8 

•	 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. 

1-22 
 
Chapter 1: Industry Characterization 

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% 

1.1.2.5.2 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% 

1-23 
 
Draft Regulatory Impact Analysis 

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 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.”9

 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. 

1-24 
 
Chapter 1: Industry Characterization 

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

government vessels, which are powered by gasoline outboard, stern-drive, or inboard 

engines. Figure I-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 1-5 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.10 It is very difficult to develop useful criteria which will 

allow the separation of vessels populations into those powered by the various engines 

categories. Nonetheless, this analysis provides some insight as to the relative 

proportion of vessel in the U.S. fleet powered by C1/C2 engines versus C3 engines. 

1-25 
 
Draft Regulatory Impact Analysis 

Figure 1-5 U.S. Commercial Fleet (1960 to 2003) 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

Number of Vessels 

1980 1990 1995 2000 2003 

Year 

Ships Boats 

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). 

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 

1-26 
 
Chapter 1: Industry Characterization 

establishments in both of these industry segments.11 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.12 

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. 

1.1.3.3.2 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 

1-27 
 
Draft Regulatory Impact Analysis 

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 

•	 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 

1-28 
 
Chapter 1: Industry Characterization 

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 dieselpowered 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.13 

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 to decrease as more and more of the larger recreational boats 

are being built overseas (e.g., Taiwan).14 

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Draft Regulatory Impact Analysis 

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).15 Because diesel engines are generally more expensive than gasoline engines, 

materials may represent an even larger share of diesel-powered 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 

Expenditures 

($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.16,17,18 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. 

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Chapter 1: 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 2001–2003.19 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. 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 2003.20 Imports continue 

to outpace exports, with the trade balance deficit roughly tripling between 1997 and 

1-31 
 
Draft Regulatory Impact Analysis 

2003. However, because of the substantial increase in exports, the deficit actually fell 

between 2002 and 2003. 

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. 

boatbuilders construct a wide variety of commercial vessels. Most of these 

boatbuilders are single-establishment 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. Cargo vessels on these routes must satisfy Jones Act 

requirements and, therefore, be built in the United States (U.S. Department of 

Transportation, 1998). As described above, the Jones Act (Section 27 of the 

Merchant Marine Act of 1920) requires that any vessel transporting merchandise 

between U.S. ports be built in the U.S., 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 building vessels for international trade do. Clients include American 

waterways operators (e.g., tugboats), 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 markets for commercial vessels 

have relatively low barriers to entry and exit. There are a significant number of 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.21 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 

1-32 
 
Chapter 1: Industry Characterization 

of some types. 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 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 1-24.22 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 Census23 estimated that 3,164 of 4,337 

towboats in existing databases had C1 engines. 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 Gulf Inland and Inland areas. 

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 

1-33 
 
c 

Draft Regulatory Impact Analysis 

Number of Boats Produced 

Total number of boats 446 512h 569 376 405 460 

a Supply boats were consolidated with crew/supply boats and pilot boats. 

b General workboats were consolidated with utility boats in the 1993 survey. 

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 

strongly built and have relatively large engines for their dimensions. All but the very 

smallest tugs and towboats are made of steel. 

Shipyards and boatyards building commercial ships including 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 typically 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 

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 cost of production, 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, 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 

1-34 
 
Chapter 1: Industry Characterization 

most of New England’s home heating oil and gasoline.24 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. 

1.1.3.4.2 Commercial Fishing Vessels 

Commercial fishing vessels are self-propelled vessels dedicated to procuring 

fish for market. Commercial fishing boats 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. 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 census25 

suggests that the actual number of C2 powered fishing vessels may be less than half 

this number. 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. 

1-35 
 
Draft Regulatory Impact Analysis 

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.26 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 patrols and 

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. 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 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 

demanders 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.27 The Workboat Construction Survey shows that 

contracts have risen from 48 in 1997 to 92 in 2005. 

1-36 
 
Chapter 1: Industry Characterization 

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 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. 

1.1.3.4.5 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 

1-37 
 
Draft Regulatory Impact Analysis 

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 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 heterogeneous 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, but some much 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 increased drastically, and day rates 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.28 

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 

1-38 
 
Chapter 1: Industry Characterization 

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 (≤37 kW), C1 and C2 engines. Boatbuilders are located along all 

coasts and major inland waterways of the United States. Figure 1-6 shows the 

geographic distribution of boatbuilders in the United States. A majority of these 

boatbuilders are located in the Gulf Coast, the Northeast, and the West Coast. The 

number of boatbuilders in these three regions account for approximately 30 percent, 

25 percent, and 26 percent of the boatbuilding industry, respectively. A majority of 

boatbuilders are located in the Gulf Coast (128), the Northeast (107), and the West 

Coast (110). Collectively, these three regions represent 345 boatbuilders, or 80 

percent of all companies in the 1998 Boatbuilder Database. 

Figure 1-6 Major Boatbuilding Regions of the United States 

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.29,30,31 The available data capture 

total company employment and sales figures including any subsidiaries and 

operations, such as boat repair, that may not be related to boatbuilding; similarly, 

because many companies may produce boats powered by both SI and CI engines, or 

1-39 
 
Draft Regulatory Impact Analysis 

may produce larger vessels powered by C3 engines, not all of the boatbuilding 

employment and revenues are related to 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 would be 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, domestic commercial boat manufacturers face markets that are 

contestable and therefore behave as if the markets were 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 Tclass boats 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 
 
Chapter 1: 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 in casino boats 

•	 Decisions by leading boatbuilders to reopen facilities and expand their labor 

forces are 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 boatbuilders 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. boatbuilders are cautiously optimistic about the future 

because almost every segment of the U.S. flag fleet is facing significant replacement 

requirements. The commercial boatbuilders are expected to continue to be a major 

consumer of marine diesel engines. 

1.2 Locomotive 

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 

locomotivesB

. 

Remanufacturers are companies that certify kits for remanufactured 

locomotives.C

 A brief overview of these industries follows, along with descriptions 

of the national economic impact of railroads and current regulations in effect for 

railroads. 

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 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. 

1-41 
 
Draft Regulatory Impact Analysis 

1.2.1 Current Emission Regulations 

The Agency’s 1998 Locomotive Rule (63 FR 18978; April 16, 1998) created a 

comprehensive program that both the large Class I and small Class II and III railroads 

were subject to, including 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. Electric locomotives, historic steam-powered locomotives, and 

locomotives freshly manufactured prior to 1973 were not covered by the 1998 

regulations. 

Several requirements are currently applicable to Class I railroads. First, 

railroads purchasing a new locomotive must insure it meets the current standards and 

has a valid certificate of conformity. Second, with regard to in-use testing, railroads 

must reasonably supply locomotives to the locomotive engine manufacturers for 

purposes of testing them under the manufacturer in-use testing program. In cases 

where the railroads fail to meet this requirement EPA could, under section 114 of the 

Act, require the railroads to perform the testing itself. Third, the railroads must also 

comply with the in-use testing requirements of the post-useful life railroad in-use 

testing program. Fourth, failure of a railroad to perform all proper maintenance on 

certified locomotives, so they continue to meet the applicable emissions standards, 

are subject to civil penalties for tampering. Railroads must also keep records of this 

maintenance. Finally, when remanufacturing all 1973 and later locomotives, 

railroads must remanufacture to new standards. (Note: small railroads are generally 

exempt from these provisions.) 

Small railroads have three requirements under the existing emission 

regulations. First, small railroads are subject to the prohibition against 

remanufacturing their locomotives without a valid certificate of conformity. 

However, the regulations exempted their existing noncompliant locomotives as well 

as any noncompliant locomotives that they purchase from other railroads in the 

future. The prohibition only applies to previously certified locomotives. For 

example, if a Class I railroad had a 1990 locomotive that was remanufactured in 2005 

to meet the Tier 0 standards, any small railroad that purchased that locomotive would 

need to comply with the Tier 0 requirements for all subsequent remanufacturing. 

Second, small railroads must properly maintain (with respect to emissions) all 

certified locomotives, and they must keep records of this maintenance. Finally, if any 

small railroad purchased a totally new locomotive, they would need to ensure that it 

meets the current standards and has a valid certificate of conformity. 

Three separate sets of emission standards (Tiers) have been adopted, with 

applicability of the standards dependent on the date a locomotive is manufactured. 

The first set of standards (Tier 0) applies to locomotives and locomotive engines 

originally manufactured from 1973 through 2001. The second set of standards (Tier 

1-42 
 
Chapter 1: Industry Characterization 

1) applies to locomotives and locomotive engines originally manufactured from 2002 

to 2004, and the final set of standards (Tier 2) applies 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 Class I and large Class II and II line-haul and 

switch duty-cycles are shown in Table 1-27. 

Table 1-27 Maximum Permissible NOx, CO, HC, and PM Rates by Tier 

(g/bhp/hr) 

Tier 0 LineHaul DutyCycle 

Tier 0 

Switch 

DutyCycle 

Tier 1 LineHaul DutyCycle 

Tier 1 

Switch 

DutyCycle 

Tier 2 LineHaul DutyCycle 

Tier 2 

Switch 

DutyCycle 

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 

1.2.1.1 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. Currently, engine manufacturers have only 

certified locomotives they manufactured themselves. Class I and all Class II and III 

railroads must purchase all new locomotives with a valid certificate of conformity, 

and when remanufacturing a locomotive must have a valid certificate of conformity. 

Small Class II and III railroads are, however, provided an exemption for their existing 

noncompliant locomotives as well as any noncompliant locomotives that they 

purchase from other railroads in the future. 

1.2.2 Supply: Locomotive Manufacturing and Remanufacturing 

1.2.2.1 Locomotive Manufacturing 

1.2.2.1.1 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 small trains. Some larger road 

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 6000hp or more. Freight or line-haul locomotives are the most powerful 

locomotives and are used to power freight train operations over long distances. Older 

line-haul locomotives are typically powered by engines of approximately 2000-3000 

1-43 
 
Draft Regulatory Impact Analysis 

hp, while newer line-haul locomotives are powered by engines of approximately 

3500-5000 hp. In some cases, older line-haul locomotives (especially lower powered 

ones) are used in switch applications. The industry has been producing higher 

powered locomotives, with some new models having 4400hp. 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. 

1.2.2.1.2 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 an overhead contact system, these types of 

locomotives have existed for over 125 years. An example of this type of locomotive 

is commonly seen on commuter trains. Power to operate the locomotive is not 

generated by an onboard engine. 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 still 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 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 for heating, air 

conditioning, lighting, etc. (i.e., "hotel 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 passenger cars. 

The third category "hybrid" is a combination of the "electric" and "enginepowered" groups, and was first developed and used in the 1920’s, although at the 

time it wasn’t very successful. Today’s technology is considered “battery dominant” 

and uses a small diesel engine and generator to charge a battery pack; the battery pack 

will then supply energy on demand to the traction motors.32 The engine can be 250­

640hp (200-480kW) and will typically operate at a constant speed, which is 

optimized for efficiency and will only run to keep the batteries at a certain charge 

D Essentially all "engine powered" locomotives used in the U.S. employ a diesel engine and the 

electrical drive system described. The term "diesel-electric" has therefore become the most common 

terminology for these locomotives. 

1-44 
 
Chapter 1: Industry Characterization 

level.33 This technology is currently only available for switcher locomotives, 

although it is being developed for use in line-hauls. 

1.2.2.1.3 Locomotive Design Features and Operation 

1.2.2.1.3.1 Sizing Constraints 

Similar to the variation in horsepower, locomotive size determines the work it 

will 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 that a locomotive has. In the past, the 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, since these larger six-axle locomotives are typically too long to be 

practical in most switch applications. 

1.2.2.1.3.2 Operation 

One unique feature of locomotives that makes them different than other, 

currently regulated mobile sources is the way that 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 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, which are described below. 

1-45 
 
Draft Regulatory Impact Analysis 

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. 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. On straight sections of rail, some 

locomotives have a built-in system that will put sand on the rails and in order to 

increase traction. 

 In dynamic braking the traction motors act as generators, with the generated 

power being 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, and is essentially 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 has 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”34. It is, however, 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.35 

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. This power can be provided by the lead locomotive, or by an 

additional engine, which is then 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 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 (1070 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 

1-46 
 
Chapter 1: Industry Characterization 

emissions characteristics when operating in hotel power mode than when operating in 

non-hotel power mode. 

1.2.2.1.3.3 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. By the 1950’s 

diesel-electric had replaced steam powered locomotives because they required less 

fuel, maintenance, and man-power.36 Locomotives use diesel engines because they 

are much more efficient, reliable, and can generate tremendous power. The diesel 

engine is the most efficient transportation power plant 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 ratio, and low thermal 

and mechanical losses. Many locomotive engines achieve the equivalent of one 

million miles before overhaul.35 Durability is critical as a locomotive breakdown on 

the tracks can bottleneck the entire system; road failures are very costly to the 

railroads because 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).37 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 the one 

following the lead locomotive will not have the same effective cooling as the one in 

front since the air it encounters will be warmer. The practice of following creates 

additional cooling problems especially in tunnels which call for special design 

considerations. 

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 

models/modifications and production of prototype models are done in much the same 

manner as is the case with other mobile sources. Locomotive manufacturers 

1-47 
 
Draft Regulatory Impact Analysis 

indicated that this process can be expected to require from 12 to 24 months for 

significant changes such as those required to comply with the new Tier 0 standards. 

Prototype locomotives are typically sold or leased to the railroads for extended field 

reliability testing, normally of one to two years duration. Only after this testing is 

completed can the new design/design change be certified and placed into normal 

production. 

1.2.2.2 Line-Haul Manufacturing 

1.2.2.2.1 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 Industries has produced some mid-horsepower locomotives suited for 

commuter or long-distance service using engines manufactured by Caterpillar, Inc,. 

MotivePower's Wabtec division also manufactures a switcher locomotive that runs on 

liquefied natural gas. The Cummins Engine Company, Inc. produces V12 and V16 

diesel engines for use in locomotives. The EPA has identified four locomotive diesel 

manufacturers, one of which can be considered a small business according to SBA 

guidelines. There are also a few companies such as Steward and Stevenson or 

Brookville Mining Equipment that manufacture small switch locomotives (under 700 

bhp) for use in mines or for companies who need to move a few cars around a local 

yard. 

EMD was founded in 1922 and acquired by General Motors in 1930; EMD 

was sold in 2005 by General Motors 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 started manufacturing a 4­

stroke engine in 1997. They currently produce five national models ranging from 

3000-6000hp, and have other international models as well as custom built 

locomotives.38 EMD employs approximately 2,600 people and designs, 

manufactures, market, sells and services freight and passenger diesel-electric 

locomotives worldwide. GE was formed by Thomas Edison who developed his first 

experimental electrical locomotive in 1880, they also built and put into the service the 

world’s first diesel-electric 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.39 GE’s Transportation 

division employs approximately 8,000 people and also engineers, manufactures, 

markets and services their diesel locomotive products worldwide. 

1-48 
 
Chapter 1: Industry Characterization 

1.2.2.2.2 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. EPA estimated a replacement rate for locomotives and locomotive engines 

based on historical data supplied by AAR, Table 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 as of the base year. Class II an III railroads 

generally buy used locomotives from Class I railroads, although some are purchasing 

new switchers and a few line-hauls. 

Table 1-28 Class I New Locomotive Turnover Rates40 

Year Number of New 

Locomotives Installed 

Number of Remanufactured 

Locomotives Installed 

Total Number of 

Locomotives in Service 

Percent 

Turnover of New 

1995 928 201 18,812 4.9% 

1996 761 60 19,269 3.9% 

1997 743 68 19,684 3.8% 

1998 889 172 20,261 4.4% 

1999 709 156 20,256 3.5% 

2000 640 81 20,028 3.2% 

2001 710 45 19,745 3.6% 

2002 745 33 20,506 3.6% 

2003 587 34 20,774 2.8% 

2004 1,121 5 22,015 5.1% 

1.2.2.2.3 Cost 

The cost of AC-traction locomotives can be as high as $2.2 million, while DC 

locomotives are usually less than 1.5$ million. Figure 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 

is the price of AC locomotives seems to be coming down, while DC locomotives 

remain about the same. 

1-49 
 
Draft Regulatory Impact Analysis 

Figure 1-7 Cost of New Locomotives1 

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 

1.2.2.3 Switcher Manufacturing 

1.2.2.3.1 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 wane over time because of the size and power of most new locomotives, which 

make them unsuitable for switching operations. While EMD does offer a traditional 

new switch locomotive, other companies are offering switchers with alternative 

power plants 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 will build on a core supplied 

by a railroad. 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 completely built by them from the ground up. They employ 

approximately 150 employees. RailPower Technologies, is headquartered in 

Brossard, Quebec but also has an American office in Erie, Pennsylvania; they employ 

approximately 100 people. RailPower manufactures the Green Goat® hybrid yard 

switcher and is developing a natural gas switcher locomotive as well, and they also 

use an old switcher locomotive core to build their platform on. 

1.2.2.3.2 Production 

Multi gen-set switchers are a falling back into favor; they were originally used 

in the late 1920's in some applications. The existing fleet of retired line-haul switcher 

locomotives turns over very slowly, and production of alternative technology 

switchers is beginning to increase. NREC is working with UP and is building sixty 

1-50 
 
Chapter 1: Industry Characterization 

2,100hp triple-engin GS21B gensets equipped with four-cycle, six-cylinder 700hp 

Cummins QSK-19 engines, these switchers are purported to reduce NOx and PM by 

80% as compared to reduce fuel consumption by up to 40%. Railpower has also been 

asked by UP to build 80 triple-engine switchers on the GreenGoat platform and they 

have noted that their system can reduce fuel consumption by up to 35% and NOx and 

PM emissions by 80%, Norfolk Southern has ordered two of these from RailPower in 

the form of rebuild kits where their own maintenance staff will install this tripleengine system during a switcher rebuild.41 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.42 The price of these 

and other switchers depends on whether or not a core is supplied and what features it 

will be built with. 

1.2.2.3.3 Trends 

Trends: remote control locomotives have been used in Canada and the U.S. 

for many years; however, Class I railroads have recently begun to implement this on a 

wider scale according to the FRA. Although this is mainly a switch yard function, 

this type of operation may be applied on line-hauls as well in the future. This may 

affect cab design and what necessary equipment is built into future switchers, for 

example if it is a remote control unit it wouldn’t need cab comfort equipment such as 

heaters or air conditioners. Many new switchers have been retrofitted with idle 

reduction devices to decrease fuel consumption and increase the railroads efficiency. 

1.2.2.4 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 parts that are 

designed to be readily replaceable on locomotive engines are the power assemblies( 

i.e., the 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 in general do not experience high wear rates, and may be reused after 

being inspected and requalified (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 may also be replaced.E

 Engine remanufactures may be 

performed either by the railroad that owns the locomotive or by the original 

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" 

1-51 
 
Draft Regulatory Impact Analysis 

manufacturer of the locomotive. Remanufactures are 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 the scheduled maintenance. It is standard practice for the Class I railroads 

within the railroad industry 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 usually are remanufactured 

less frequently because they are used less within each year. Such remanufacturing is 

necessary to insure 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 also 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 the 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 

F

 In some cases, some components are remanufactured by welding in new metal and remachining the 

component to the original specifications. 

1-52 
 
Chapter 1: Industry Characterization 

justifies the additional cost, and thus scrap the entire locomotive. Nevertheless, many 

smaller railroads, especially switching and terminal railroads, are still using 

locomotives that were originally manufactured in the 1940s. 

1.2.2.4.1 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 

contract 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 apparently 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 vary tremendously as some have as few as two 

employees, while others can have up to 5,000 employees. The cost of 

remanufacturing kits can vary depending on the model of locomotive and year of 

manufacture, an estimated range is $15,000 - $30,000 per kit. 

1.2.3 Demand: Railroads 

Railroads 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 

7.4% of all diesel fuel used in transportation and 2.1% of the total energy used by all 

forms of transportation to move 22.1% of all freight ton-miles (miles one ton of 

freight is moved). It also shows that this is less than 1% of the total U.S. energy use, 

but that locomotives currently emit slightly less than one million tons of NOx each 

year, which is about 4% of total NOx emitted by all sources. It is important to 

recognize, however, that the 2.1% of energy used by rail transportation (625.5 trillion 

BTUs) is the total of all rail sectors including: line-haul, switcher, Amtrak, commuter 

rail, and transit rail, as shown in Figure 1-9. This means that the freight railroads use 

approximately 1.86% of all energy consumed by every source of transportation to 

haul 22.1% of all ton-miles. 

1-53 
 
Draft Regulatory Impact Analysis 

Figure 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. Unlike most other methods of 

shipping, railroads are responsible to maintain 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.44 As locomotives 

grow larger and heavier, and as cars are designed to hold more weight, track is 

required that can handle this increased load, and this is quite costly. To date, of the 

549 short line and regional railroads in existence, 333 have track that cannot handle 

these increased loads.45 

1.2.3.1 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 revenue46. As of 2004, Class I railroads are those with annual 

carrier operating revenues of at least $289.4 million, Class II railroads are those with 

annual carrier operating revenues between $23.1-$289.3 million, Class III railroads 

are those with annual carrier operating revenues of $23.1 or less. The AAR further 

subdivides Class II and III railroads based on the miles of track over which they 

operate and their revenue. These categories are then called Short Line and Regional 

Railroads and usually belong to the American Short Line and Regional Railroad 

Association (ASLRRA). 

1-54 
 
Chapter 1: Industry Characterization 

Figure 1-9 Freight Rail Industry Overview 

Freight Rail Industry Overview 

100% 95% (% of Industry Totals in 2003) 

90% (Source: Assoc. of American Railroads. Overview of U.S. Freight Railroads, Feb.2005) 

80% 

70% 

70%
 

60%
 56%
 

50%
 

40%
 37%

30%
 

20%
 14% 

11% 

10% 6% 5% 

1% 1.70% 1.70% 1.70% 

0% 

Class I Regional Local Linehaul Switching & Terminal 

Number of Railroads Track Miles Operated Fuel Consumption 

1.2.3.2 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.48 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 Gross National Product in 2004. Thus, 

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. 

The Class I railroads are the 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 country, two of which are Canadian owned. Class I 

1-55 
 
Draft Regulatory Impact Analysis 

railroads operated approximately 22,400 locomotives in the U.S., over 97,662G miles 

of track and 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 2004.47,48 Of these, 

the two largest Class I railroads, BNSF, and Union Pacific, accounted for the vast 

majority (63%) of the Class I locomotives in service in the U.S as of the end of 

2004.49 According to the 2004 AAR’s’ Analysis of Class I Railroads, Class I 

railroads paid on average $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 whom are unionized, and as of 

2004 receive an average compensation of $65,500.49 

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 1-10 . 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. Being a primary source/mode of transporting 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.50 

Figure 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. 

1-56 
 
Chapter 1: Industry Characterization 

Figure 1-12 and Table 1-29 show the long term growth trends for the amount 

of freight carried by Class I railroads and the amount of fuel consumed in carrying 

that freight.51 As can be seen from these data, 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.i The reason for this is 

that 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 1-29 Annual Fuel Consumption and Revenue Freight For Class I Railroads 

Annual Fuel Consumption and Revenue Freight 

For Class I Railroads 

Year Revenue Freight 

(Million Ton-Miles) 

Fuel Consumption 

(Million Gallons) 

Ton-Miles of Freight 

moved per gallon of 

fuel 

1960 572,309 3,463 165 

1970 764,809 3,545 216 

1980 918,958 3,904 235 

1990 1,033,969 3,115 332 

1995 1,305,969 3,480 375 

2000 1,456,960 3,700 394 

2001 1,495,472 3,710 403 

2002 1,507,011 3,730 404 

2003 1,551,438 3,826 405 

2004 1,662,598 4,059 410 

Figure 1-11 Fuel Consumption and Revenue Ton-Miles for Class I Railroads 

1-57 
 
Draft Regulatory Impact Analysis 

1.2.3.2.1 Class I Market Share 

Union Pacific (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-milesH by BNSF (569 billion). UP owns 

more miles of track than any other Class I (27,123), and operates the most 

locomotives (7,680), as show in Table I-30. 

Table 1-30 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 

1.2.3.2.2 Locomotive Fleet 

Purchasing practices have historically been for Class I railroads to buy 

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 would therefore actually be 

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 would be longer. 

Additionally, independent of cyclic changes in the industry, future locomotive 

replacement rates could actually decrease. Locomotive manufacturers are now 

producing locomotives that have significantly more horsepower than older 

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) 

1-58 
 
Chapter 1: Industry Characterization 

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 would be more fuel-efficient to use two 

6000 hp locomotives, rather than three 4000 hp locomotives, to pull the same weight 

train, because the weight of an entire locomotive can be eliminated. Thus, whereas 

three old locomotives may be scrapped, only two new locomotives may need to be 

bought 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 costcompetitive, 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. 

1.2.3.2.3 Operation Profile 

1.2.3.2.3.1 Fuel consumption.52 

Class I railroads consumed 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. 

Amtrak traveled 37 million train miles in 2004, and consumed 69.9 million gallons of 

fuel. The 4.1 billion gallons of diesel fuel used by the Class I railroad's 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 11% of total operating 

expenses on fuel in 2004. The railroads are continually trying to reduce their fuel 

consumption through efforts such as idle reduction, and other operational 

improvements. 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 

for a coal train, intermodal cars that are double stacked also carry an aerodynamic 

fuel consumption penalty of about 30% loss due to drag. Experiments have 

developed some 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. 

1.2.3.2.3.2 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. 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 

1-59 
 
Draft Regulatory Impact Analysis 

range from repair or replacement of major engine components (such as power 

assemblies) to a complete 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 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, and a few of the smaller railroads also have 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 (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. 

1.2.3.2.4 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 ranged between 30 days and 5 

years, with the average being 3 years 

As can be seen from Figure 1-12 leasing has been a continuing trend among 

Class I railroads, with almost two-thirds of the locomotives placed in service in 2004 

being leased. Leasing among Class II and III railroads is not nearly as widespread. 

1-60 
 
Chapter 1: Industry Characterization 

Figure 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 

1.2.3.2.5 Traffic52 

Between 1993-2002 the value of goods being transported by all modes of 

transportation increased by 43.6% to $8,397.2 billion, and the ton miles increased 

over that period by 29.6% to 3,137.9 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%. Ton-miles 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 1-13 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.48 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 where 862 miles is the average haul 

length, commuter rail has not really 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. 

1-61 
 
Draft Regulatory Impact Analysis 

Figure 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 

1.2.3.3 Hauling Statistics52 

Class I railroads hauled 1,603,564 million ton-miles of freight in 2003, which 

was 37% of all freight hauled in the US; they also carried 19.8 billion ton-miles of 

crude oil and petroleum products, which was 2.2% of all those products, trucks 

carried 3.8%, but the bulk is transported via pipeline (66.8%) or water carriers 

(27.2%). As of 2002, railroads transported 72.1 billion ton miles of hazardous 

materials, or 22.1% of all hazardous material being shipped an average of 695 miles 

per shipment (BTS 2006). Railroads and trucks carry roughly equal hazmat tonmileage, but trucks have nearly 16 times more hazmat releases than railroads.56 

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 then number of trailer and container loadings has risen from 

3.4 million to 11.0 million in 2004. Figure 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 the STB, at least 55% of all traffic moves 

under contract, which allows railroads to increase efficiency by permitting better 

planning. 

1-62 
 
Chapter 1: Industry Characterization 

Figure 1-14 Class I Intermodal Traffic Source: AAR Railroad Facts 2006 Edition 

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 

1.2.3.4 Track Statistics 

As of 2004, Class I owned 97,662 miles of road Since 1980, capital 

expenditures on roadway and structures has increased 88% from 2.6 billion in 1990 to 

4.9 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 doublestacking containers has helped to reduce traffic to some degree, this is still a concern 

due to the continual growth in ton-miles. 

1.2.3.5 Class II & III Characteristics53 

In the 1970's, deregulation allowed the Class I railroads to stop serving many 

smaller lines that were unprofitable to them. 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 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 approximately 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 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 

1-63 
 
Draft Regulatory Impact Analysis 

equipment, road and structures, and $221 million on fuel. More than half of the short 

line and regional railroads connect to two or more other railroads, and over 80% 

operate in one state only. 

Statistics compiled by the American Short Line and Regional Railroad 

Association (ASLRRA) 2004 show that there are approximately 549 Class II and III 

railroads (not including commuter and insular railroads). A more detailed breakdown 

of these can be found in Table 1-31. They consist primarily of regional and local 

line-haul and switching railroadsI

, which operate in a much more confined 

environment than do the Class I railroads. Class II and III railroads operate 

approximately 3,777 locomotives. In a recent survey taken 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, 5.4% 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 substantially since the 1990's, but all 

railroads are predicting growth in hiring. 

Table 1-31 Profile of Railroad Industry -200454 

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 18 25,29655 

Shipper-Owned Railroads 68 

Government Owned 

Railroads 

28 

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. Such locomotives typically use a few thousand gallons of diesel 

 "Regional railroad" and "local railroad" are terms used by AAR that are similar, but not identical, to 

"Class II" and "Class III", respectively. 

1-64 
 

I 
Chapter 1: Industry Characterization 

fuel each year, and thus are not a particularly significant source of emissions. 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. 

1.2.3.6 Passenger Rail 

1.2.3.6.1 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 massive losses, many of the freight 

railroads donated equipment to Amtrak as well as $200 million in startup capital.56 

Amtrak is operated by the National Railroad Passenger Corporation of Washington, 

D.C. The Secretary of Transportation has the authority to designate Amtrak's 

destinations, which as of 2004 included 527 cities; other transit rail serves 2,909, 

some of which may be shared with Amtrak (STB 2006). 52 On average, 777,000 

people each day depend on commuter rail services operated under contract by 

Amtrak, or that use Amtrak-owned infrastructure, shared operations and dispatching; 

an average of 69,000 people ride on up to 300 Amtrak trains each day. Amtrak relies 

on receiving federal subsidies in order to operate, although it continually working to 

become independent and profitable. 

Although Amtrak's rates are not regulated, they do 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. Their only source of competition is other modes of 

transportation, and this also affects their rates. Fuel costs can dramatically affect 

rates and Amtrak’s need for subsidies; between 2004 and 2005, Amtrak’s fuel costs 

increased 149%, and continue to increase substantially. Despite an increase in 

passengers between 2004-2005 and improved fuel conservation methods that reduced 

their fuel consumption by nearly 10%, their fuel cost increased by $43 million.57 

Amtrak is the sole large-scale provider of inter-city passenger transport. Their 

fleet includes 436 locomotives, of which 360 are diesel locomotives that used a 

reported 69.958 million of gallons of fuel in 2005, and 76 are electric locomotives. 

The FRA provided Amtrak with funding to purchase Acela locomotives, which are 

4,000 horsepower gas turbine locomotives. These trains consume about the same 

amount of fuel as a diesel locomotive but produce about 1/10th of the NOx 

They offer service to 46 states on 21,00059 miles of routes, only 745 miles of 

which are actually owned by Amtrak, primarily in Michigan, and between Boston and 

Washington DC.60 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 

1-65 
 
Draft Regulatory Impact Analysis 

time in 2003, but the 65% of that delay was caused by a host railroad. A host railroad 

is a freight or commuter railroad over which 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 their use of this track and any other 

resources, in 2005, those payments were for more than 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/intercity fair was $55.15, the average revenue per 

passenger-mile is $0.249 for Amtrak, and the average length of haul was 231 miles61 

In 2006, Amtrak was able to obtain an additional subsidy in order to remain 

operational in 2006, in the amount of $1.1 billion62, but the future of Amtrak 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 corporation. 

1.2.3.6.2 Commuter63 

There are also 21 independent commuter rail systems operating in 16 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 length of haul for commuter rail in 2004 was 23.5 miles, an 

average of 414 million people use commuter rail each year to result in over 9.7 billion 

passenger-miles. The average commuter rail fair in 2004 was $3.90, with an average 

$0.154 revenue per passenger-mile. The commuter rail is also a young fleet and has 

remained younger than 17 years old since 1985. 

1.2.4 Existing Regulations 

1.2.4.1 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 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 inspection increases in complexity. The inspections are usually 

performed by the railroad which owns or leases the locomotive. FRA personnel 

1-66 
 
Chapter 1: Industry Characterization 

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 at these times. It appears likely that each locomotive is out of service 

for 12 to 24 hours during each FRA safety inspection and 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. 

1.2.4.2 Federal 

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 

1.2.4.3 Rates 

Rail transportation accounts for 8.7% of all for-hire transportation services 

that are a measured in the GDP, or 0.2% of the total U.S. GDP. The average freight 

revenue per ton-mile for Class I rail in 2004 was $0.0235, and average operating 

revenue of $40.5 billion. Freight rates 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 1-15.64 

J

 Values are an approximate estimate by FRA personnel. 

1-67 
 
Draft Regulatory Impact Analysis 

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. 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. 

Figure 1-15 Railroad Rate Trends Before and After Staggers Act of 198064 

1.2.5 Foreign Railroads in US 

Locomotives that operate extensively within the U.S. are subject to the 

existing provisions of 40 CFR Part 92. 

1.2.5.1 Mexico 

In 2004, the 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 16,415 miles of track have been privately owned since a 

Constitutional amendment was passed in 1995 (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. 

1-68 
 
Chapter 1: Industry Characterization 

1.2.5.2 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. 

References 

1

 RTI International, “Industry Profile for Small, Category 1, and Category 2 Marine Diesel Engines 
 

and Marine Vessels,” Final Report, May 2006. Prepared for U.S Environmental Protection Agency, 
 

Office of Transportation and Air Quality.. 
 2

 Power Systems Research (PSR). 2004. OELinkTM. <http://www.powersys.com/OELink.htm>. 
 3

 U.S. Environmental Protection Agency (EPA). 1995. EPA Office of Compliance Sector Notebook 
 

Project: Profile of the Motor Vehicle Assembly Industry. EPA310-R-95-009. Washington, DC: U.S. 
 

EPA. 
 4

 U.S. Census Bureau. 2004. Economic Manufacturing Industry Series: 2002. EC02-31I-333618.
 

Washington, DC: U.S. Census Bureau. Tables 1, 5, and 7. 
 5

 Wooldridge, David. 2003. “2003 Marine Shop Profile.” Engine Builder (June): 23-40.
 

(http://www.babcox.com/editorial/ar/60323.pdf). As obtained on May 10, 2006. 
 6

 National Marine Manufacturers’ Association (NMMA). January 12, 2006. Facsimile from John
 

McKnight (NMMA) to Dave Reeves (RTI).
 7

 Workboat. January 2005. Workboat Construction Survey. Last obtained March 15, 2006. 
 

(http://www.workboat.com/pdfs/WB_2004const_survey.pdf) 
 8

 Viscusi, W.K., J.M. Vernon and J.E. Harrington. 1992. Economics of Regulation and Antitrust. 
 

Lexington, MA: D.C. Heath and Co. 
 9

 U.S. Census Bureau. 2004. Economic Manufacturing Industry Series: 2002. EC02-31I-333618.
 

Washington, DC: U.S. EPA. 
 10 U.S. Department of Transportation. 1998 and 2006. National Transportation Statistics 1997 and
 

2005. Washington, DC: U.S Department of Transportation.
 11 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. 
 12 U.S. Maritim Administration (MARAD). 2003. Report on the survey of U.S. Shipbuilding and 
 

Repair Facilities. U.S. Department of Transportation, Maritime Administration.
 13 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical 
 

Abstract. Chicago, IL. 
 14 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical 
 

Abstract. Chicago, IL. 
 15 U.S. Bureau of the Census. 2002 Economic Census. 
 

http://www.census.gov/prod/ec02/ec0231i336612.pdf. 
 16 Hoover’s Online. 2006. Online company database. <http://www.hoovers.com/>. 
 17 Dun & Bradstreet. 2006a. D&B Million Dollar Directory. Bethlehem, Pennsylvania: Dun &
 

Bradstreet, Inc. 
 18 Dun & Bradstreet. 2006b. Small Business Database. <http://smallbusiness.dnb.com/>. 
 19 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical 
 

Abstract. Chicago, IL. 
 

1-69 
 
Draft Regulatory Impact Analysis 

20 National Marine Manufacturers Association (NMMA). 2004. 2004 Recreational Boating Statistical 
 

Abstract. Chicago, IL. 
 21 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. 
 22 WorkBoat. Annual Construction Survey. January/February 1992, January/February 1994, 
 

January/February 1995, January 1998, and January 2004. Portland, ME: Workboat. 
 23 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. 
 24 American Waterways Operators (AWO). 2006. Facts About the American Tugboat, Towboat, and 
 

Barge Industry. http://www.americanwaterways.com/. Arlington, VA. 
 25 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. 
 26 U.S. Department of Labor Statistics. 2006. Occupational Outlook Handbook. 
 

http://www.bls.gov/oco/ocos177.htm. 
 27 WorkBoat.com. February 2006. 2005 Construction Survey. 
 

<http://www.workboat.com/pdfs/wb06_constructionsurvey.pdf> 
 28 WorkBoat.com. February 2006. 2005 Construction Survey. 
 

<http://www.workboat.com/pdfs/wb06_constructionsurvey.pdf> 
 29 Hoover’s Online. 2006. Online company database. <http://www.hoovers.com/>. 
 30 Dun & Bradstreet. 2006a. D&B Million Dollar Directory. Bethlehem, Pennsylvania: Dun &
 

Bradstreet, Inc. 
 31 Dun & Bradstreet. 2006b. Small Business Database. <http://smallbusiness.dnb.com/>. 
 32 Frank W. Donnelly, Raymond L. Cousineau, R. Nigel M. Horsley, "Hybrid Technology for the Rail
 

Industry" ASME/IEEE Document RTD2004-66041, April 2004. 
 33 www.railpower.com/products_hl_howitworks.html 
 34 https://www.getransportation.com/general/locomotives/hybrid/hybrid_default.asp
 35 "Railroad and Locomotive Technology Roadmap" Argonne National Laboratory, December 2002 
 36 en.wikipedia.org/wiki/Diesel_locomotive 
 37 Steven Fritz, "On Track Toward Cleaner Large Engines: New emissions reduction strategies focus 
 

on locomotives and ferry boats" Southwest Research Technology Today, Spring 2004. 
 38 http://www.emdiesels.com/lms/en/company/history 
 39 www.getransportation.com/general/freight_rail/models 
 40 AAR Railroad Ten-Year Trends 1995-2004 
 41 William C. Vantuono, "New power plays to watch" Railway Age, August 2006. 
 42 http://www.railpower.com/dl/greensavesgreen.pdf#search='price%20hybrid%20green%20goat' 
 43 "Railroad and Locomotive Technology Roadmap" Argonne National Laboratory, December 2002
 44 nationalatlas.gov/articles/transportation/a_freightrr.html 
 45 American Short Line and Regional Railroad Association 2005 Facts and Figures.
 46 STB Railroad Revenue Deflator Formula: Current Year's Revenues x (1991 Average Index/Current
 

Year's Average Index) 
 47 AAR Railroad Facts 2005
 48 Transportation Energy Data Book, Edition 25, 2006
 49 Based on 2005 Railroad Annual Reports filed with STB.
 50 Quoted from May 15, 1997 testimony by Bruce Wilson representing AAR. Docket item #A-94-31­

IV-D-7. 

51 "Railroad Facts", Association of American Railroads, 2006 edition. 

52 National Transportation Statistics 2006, Bureau of Transportation 

53 Short Line and Regional Railroad Facts and Figures, 2005 Edition 

54 AAR "Class Railroad Ten-Year Trends" 1995-2004 

55 apta.com 

1-70 
 
63 

Chapter 1: Industry Characterization 

56 http://nationalatlas.gov/articles/transportation/a_freightrr.html 57 www.amtrak.com 

58 amtrak.com report 

59 Amtrak Media Relations "National Fact Sheet" July, 2006 

60 Railroad Facts 2005 

61 Bureau of Transportation National Statistics 2006 62 Andrew Taylor, "Amtrak Supporters Aim to Ease Budget Cuts" 06/14/06, Associated Press. 

 "2006 Transit Fact Book", American Public Transportation Association. 

64 FRA "Freight Railroad Overview" www.fra.dot.gov 

1-71 
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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 ......................................................................2-5 
 

2.1.2 Health Effects of PM Pollution ........................................................... 2-10 
 

2.1.3 Attainment and Maintenance of the PM2.5 NAAQS ....................... 2-12 
 

2.1.4 Source Apportionment Studies of PM2.5 .......................................... 2-16 
 

2.1.5 Risk of Future Violations .................................................................... 2-18 
 

2.1.6 Environmental Effects of PM Pollution.............................................2-27 
 

2.2 Ozone............................................................................................................. 2-40 
 

2.2.1 Science of Ozone Formation ............................................................... 2-40 
 

2.2.2 Health Effects of Ozone....................................................................... 2-42 
 

2.2.3 Current 8-Hour Ozone Levels ............................................................ 2-43 
 

2.2.4 Projected 8-Hour Ozone Levels.......................................................... 2-45 
 

2.2.5 Environmental Effects of Ozone Pollution ........................................ 2-53 
 

2.3 Air Toxics......................................................................................................2-55 
 

2.3.1 Diesel Exhaust PM ...............................................................................2-56 
 

2.4 Gaseous Air Toxics—benzene, 1,3-butadiene, formaldehyde, 
 

acetaldehyde, acrolein, POM, naphthalene ..................................................... 2-67 
 

2-1
 
Draft Regulatory Impact Analysis 

CHAPTER 2: Air Quality and Resulting Health and Welfare 

Effects of Air Pollution from Mobile Sources 

Locomotive and marine diesel engines subject to today's proposal 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 which are associated 

with serious adverse health effects. Emissions from locomotive and marine diesel engines 

also cause harm to public welfare and contribute to visibility impairment and other harmful 

environmental impacts across the US. Therefore, EPA is proposing to adopt 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 market system itself cannot correct this externality because 

firms in the market are rewarded for minimizing their production costs, including the costs of 

pollution control. 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. 

To correct this market failure and reduce the negative externality from these emissions, it is 

necessary to give producers the market signals for the social costs generated from the 

emissions. The standards EPA is proposing will accomplish this by mandating that 

locomotives and marine diesel engines reduce their emissions to a technologically feasible 

limit. In other words, with this proposed rule the costs of the transportation services 

produced by these engines and equipment will reflect social costs more efficiently. 

Today millions of Americans continue to live in areas with unhealthful 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). With regard to PM2.5 nonattainment, EPA recently 

finalized PM2.5 nonattainment designations (70 FR 943, Jan 5, 2005) and as of October 2006 

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 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 2006 there are approximately 157 

million people living in 116 areas (461 full or partial 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 maintain or achieve the 8-hour ozone 

NAAQS. Figure 2-1 illustrates the widespread nature of these problems highlighting 

counties which are currently designated in nonattainment for the 8-hour ozone, PM2.5 

2-2
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

NAAQS, or for both pollutants. It also shows the location of mandatory class I federal areas 

for visibility. 

Figure 2.1-1 Air Quality Problems are Widespread (October 2006) 

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]. Over time, the relative 

contribution of these engines to air quality problems will increase unless EPA takes action to 

reduce their pollution levels. 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 today=s proposed comprehensive standards annual NOx emissions would be 

reduced by more than 765,000 tons and annual PM2.5 emissions by about 28,000 tons in 

2-3
 
Draft Regulatory Impact Analysis 

2030. We estimate that the reduced PM2.5 levels would produce nationwide air quality 

improvements. According to air quality modeling performed in conjunction with this 

proposed rule, if finalized, all current PM2.5 nonattainment areas would experience a resulting 

decrease in their 2020 and 2030 PM2.5 design values (DV). In addition, all 116 monitored 

mandatory class I federal areas would also 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 would on average decrease by 0.06 μg/m3 

in 2020 and by 0.14 μg/m3 in 2030. The maximum decrease for future-year annual PM2.5 

DVs in these nonattainment areas would be 0.35μg/m3

 in 2020 and 0.90μg/m3 in 2030. 

This rule would also result in ozone benefits in 2030 for 114 of the current 116 ozone 

nonattainment areas. According to air quality modeling performed for this rulemaking, the 

proposed 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 would decrease by 0.29 ppb in 2020 and 

0.80 ppb in 2030. Within projected ozone nonattainment areas, the average decrease would 

be somewhat higher: -0.30 ppb in 2020 and - 0.88 ppb in 2030.A The maximum decrease for 

future-year DVs over the U.S. would be -1.10 ppb in 2020 and -2.90 ppb in 2030 

While EPA has already adopted many emission control programs that are expected to 

reduce both ambient ozone and PM levels, including the Clean Air Interstate Rule (CAIR) 

(70 FR 25162, May 12, 2005), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 

2004), the additional PM2.5 and NOx emissions reductions resulting from this locomotive and 

marine diesel engine rule would be important to states’ efforts in attaining and maintaining 

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 proposed 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 recent EPA rulemakings and 

other state and local air quality information. 

A This is in spite of the fact that NOx reductions can at certain times in some areas cause ozone levels to 

increase. Such "disbenefits" are predicted in our modeling, but these results make clear that the overall effect of 

the proposed rule is positive. The two nonattainment areas that show slight increases in 2030 as a result of the 

rule are Los Angeles / South Coast Air Basin (0.1 ppb) and Norfolk-Virginia Beach-Newport News (0.8 ppb) 

2-4
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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. PM is further described 

by breaking it down into size fractions. PM10 refers to particles generally less than or equal 

to 10 micrometers (μm). PM2.5 refers to fine particles, those 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). 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 particles’ ability to 

shift between solid/liquid and gaseous phases, which is influenced by concentration and 

meteorology, especially temperature. 

Particles are made up of different chemical components. The major chemical 

components include carbonaceous materials (carbon soot and organic compounds), and 

inorganic 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 (soot and accompanying organics)— 

emitted from cars, trucks, heavy equipment, forest fires, some industrial processes and 

burning waste—and both combustion and process related 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. 

2-5
 
Draft Regulatory Impact Analysis 

Figure 2-2 National Average of Source Contribution to Fine Particle Levels 

Cars, trucks, industrial 

combustion and 

processes, heavy 

equipment, wildfires, Suspended soils, industrial wood/waste burning, metallurgical operations 

Cars, trucks, 

industrial combustion, and Mobile power generation, 

power generation industrial combustion and 

processes 

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 2-3. Data on PM2.5 composition are available from the EPA Speciation Trends 

Network and the IMPROVE Network, covering both urban and rural areas in numerous 

regions of 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 discussed below. For the Eastern and 

mid U.S., these data show that both sulfates and carbonaceous PM2.5 are major contributors 

to ambient PM2.5 in both 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 

2-6
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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 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 B

 assumed to be representative of regional background) from those 

measured at an urban location. 

As shown in Figure 2-4, we observe a large urban excess across the U.S. for most 

PM2.5 species but especially for total carbon mass. All of these locations have consistently 

high urban excess for total carbon mass with Fresno, CA and Birmingham, AL having the 

B

 Regional concentrations are derived from the rural IMPROVE monitoring network Interagency Monitoring of 

Protected Visual Environments. See http://vista.cira.colostate.edu/improve. 

2-7
 
Draft Regulatory Impact Analysis 

largest observed measures. Larger urban excess of nitrates is seen in the western U.S. with 

Fresno, CA and Salt Lake City, UT 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. The measured PM2.5 concentration is not 

necessarily the maximum for each urban area. As expected for a predominately regional 

pollutant, only a modest urban excess is observed for sulfates 

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. 

In the East, regional pollution contributes more than half of total PM2.5 

concentrations. 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 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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. 

2.1.1.3 Composition of PM2.5 in 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 2-5 shows the relative contribution of elemental and organic 

carbon to PM emissions for six Tier 0, Tier 1, and Tier 2 locomotives (three locomotive 

engines were 2-stroke while 3 locomotive engines were 4- stroke). This recent data, while 

limited to six locomotives, suggest that locomotives, regardless of when it was 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 6 locomotives tested using 3000 ppm sulfur nonroad diesel fuel. 

2-9
 
Draft Regulatory Impact Analysis 

The proposed locomotive and marine engine standards would reduce emissions of 

carbonaceous PM. NOx emissions, a prerequisite for formation of secondary nitrate aerosols, 

would also be reduced. The proposed standards would also reduce VOC emissions. The 

emission inventories are discussed in detail in Chapter 3 for primary PM2.5 emissions from 

these sources. This proposed rule would also reduce secondary PM produced from these 

engines emissions. 

As discussed in Sections 2.2 diesel PM also 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 

represent a very small portion of the national emissions of metals (less than one percent) and 

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 engines also emit polycyclic organic matter (POM), including 

polycyclic aromatic hydrocarbons (PAH), which can be present in both gas and particle 

phases of diesel exhaust. Many PAH compounds are classified by EPA as probable human 

carcinogens. 

2.1.2 Health Effects of PM Pollution 

As stated in the EPA Particulate Matter Air Quality Criteria Document (PM AQCD), 

available scientific findings “demonstrate well that human health outcomes are associated 

with ambient PM.”C 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.1,2 We also present additional 

recent studies published after the cut-off date for the PM AQCD.D3 Taken together this 

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. 

C

 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. 

D 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 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. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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 studies 

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.4,5 

Another recent study in 14 U.S. cities examined the effect of PM10 exposures on daily 

hospital admissions for cardiovascular disease. They 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.6 These studies provide evidence that PM-related 

emissions, specifically from mobile sources, are associated with adverse health effects. 

In terms of morbidity, short-term studies have shown associations between ambient 

PM2.5 and cardiovascular and respiratory hospital admissions (PM AQCD, p. 9-93), 

decreased lung function (PM AQCD Table 8-34), and physiological cardiac changes (PM 

AQCD, Section 8.3.1.3.4). 

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 proposal, 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 

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).7,8,9 These studies indicate that there 

are significant associations for all-cause, cardiopulmonary, and lung cancer mortality with 

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Draft Regulatory Impact Analysis 

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.10 

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.11 

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.12 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.13,14,15 

2.1.2.4 Marine Ports and Rail Yard Studies 

Recently, new studies from the State of California provides evidence that PM2.5 

emissions within marine ports and rail yards contribute significantly to elevated ambient 

concentrations near these sources16 and that a substantial number of people experience 

exposure to fresh locomotive and marine diesel engine emissions, raising potential health 

concerns. Additional information on near roadway, marine port, and rail yard emissions and 

potential health effects can be found in Section 2.3.1.4 of this draft RIA. 

2.1.3 Attainment and Maintenance of the PM2.5 NAAQS 

EPA has recently amended the NAAQS for PM2.5 (71 FR 61144, October 17, 2006). 

The final rule, signed on September 21, 2006 and published on October 17, 2006, addressed 

revisions to the primary and secondary NAAQS for PM to provide increased protection of 

public health and welfare, respectively. The primary PM2.5 NAAQS include a short-term 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

(24-hour) and a long-term (annual) standard. The level of the 24-hour PM2.5 NAAQS has 

been revised from 65μg/m3

to 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 shall be within 10 percent of the spatially 

averaged annual mean, and (2) the daily values for each monitoring site pair shall yield a 

correlation coefficient of at least 0.9 for each calendar quarter. 

With regard to the secondary PM2.5 standards, 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. 

The proposed emission reductions from this rule would assist PM2.5 nonattainment 

areas in reaching the standard by each area’s respective attainment date and assist PM2.5 

maintenance areas in maintaining the PM2.5 standards in the future. The emission reductions 

will also help continue to lower ambient PM levels and resulting health impacts into the 

future. In this section we present information on current and future PM2.5 levels. 

2.1.3.1 Current PM2.5 Air Quality 

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 (using 2001-2003 or 2002-2004 measurements) and a number of other 

factors.E

(70 FR 943, January 5, 2005; 70 FR 19844, April 14, 2005). 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 2006, 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. 

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 

E

 The full details involved in calculating a PM2.5 design value are given in Appendix N of 40 CFR Part 50. 

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Draft Regulatory Impact Analysis 

time frame and then be required to maintain the 1997 PM2.5 NAAQS thereafter.F 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 

proposed in this action would become effective between 2008 and 2017. The expected PM2.5 

and PM2.5 precursor inventory reductions from the standards being proposed in this action 

will be needed by states to attain or maintain the PM2.5 NAAQS. 

Table 2-1 provides an estimate of the counties violating the 2006 PM2.5 NAAQS 

based on 2003-05 air quality data. The areas designated as nonattainment for the 2006 

PM2.5 NAAQS will be based on three years of air quality data from later years. 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. Currently more than 106 million people live in counties 

where monitors show violation of the 2006 standards. 

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 Population 

1997 PM2.5 Standards: 39 areas currently designated 208 88,394,000 

2006 PM2.5 Standards: Counties with violating 

monitors 

49 18,198,676 

Total 257 106,592,676 

2.1.3.2 Current and Projected Composition of Urban PM2.5 for Selected Areas 

Based on CMAQ modeling for the new PM NAAQS standard, a local perspective of 

PM2.5 levels and composition was developed by EPA to elaborate further on the nature of the 

PM2.5 air quality problem after implementation of the CAIR/CAMR/CAVR rules, the 

national mobile rules for light and heavy-duty vehicles and nonroad mobile sources, and 

current state programs that were on the books as of early 2005.17 As an illustrative example, 

the PM NAAQS RIA developed a localized analysis of current ambient and future-year 

speciation for two cities, one in the East (Detroit) and one in the West (Salt Lake City).18 

Figure 2-6 shows projected PM2.5 component species concentrations (i.e., sulfate, 

nitrate, elemental carbon, organic aerosols, crustal, and uncontrollable PM2.5) for current 

ambient data (5 year weighted average, 1999–2003) and a 2020 regulatory base case with the 

F

 The EPA finalized PM2.5 attainment and nonattainment areas in April 2005. The EPA finalized the PM 

Implementation rule in Nov. 5, 2005, 70 FR 65984). 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

addition of the controls mentioned in the previous paragraph. Note that organic aerosols 

include directly emitted organic carbon and organic carbon particles formed in the 

atmosphere from anthropogenic sources and biogenic sources. Uncontrollable PM2.5 is based 

upon a 0.5 μg/m3

 PM2.5 blank mass correction used in the Speciated Modeled Attainment 

Test (SMAT) approach, in which a number of adjustments and additions were made to the 

measured species data to provide for consistency with the chemical components retained on 

the FRM Teflon filter.19 The analysis provided here specifically looks at one area in the East 

(Detroit), and one in the West (Salt Lake City). 

Figure 2-6. Base Case and Projected PM2.5 Component Species Concentrations in Detroit and Salt Lake 

City 

Ambient and Projected 2020 Base Annual Average PM2.5 Species 

Concentration in Detroit and Salt Lake City 

Nitrate 

Elemental Carbon 

Organic Aersols 

Sulfate 

Soil 

Uncontrollable 

0 

2 

4 

6 

8 

10 

12 

14 

16 

18 

PM2.5 (ug/m3) 

Ambient 2020 Base Ambient 2020 Base
 

Detroit Salt Lake City
 

Note: The ambient and projected 2020 base case annual design values above are averages taken across 

multiple urban area monitors. Thus, while the average 2020 Detroit base case design value reflected above is 

lower than the projected base case design values at certain Detroit monitors. 

Notably, organic aerosols constitute a large fraction of the overall remaining PM2.5 

mass in Detroit and Salt Lake City. Sulfate is a considerable part of the total PM2.5 mass in 

both cities and is the largest contributor to PM2.5 mass in Detroit. Nitrate is a relatively small 

source of PM2.5 for Detroit but nitrate is the second largest contributor to the remaining PM2.5 

problem in Salt Lake City; the exception is that on higher days, nitrate represents the largest 

contributor in Salt Lake City. The relatively large contribution of sulfate to PM2.5 mass in 

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Draft Regulatory Impact Analysis 

Detroit is characteristic of the urban air pollution mixture in the East, while the nitrate 

contribution to PM2.5 mass in Salt Lake City is characteristic of that found in the West. 

2.1.4 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 

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 

engines20, 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 both 

qualifying and quantifying 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 sources. Receptor modeling techniques are 

observation-based 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. 21 These techniques are very useful in 

characterizing 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 and completeness 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. 

A literature compilation summarizing source apportionment studies was conducted as 

part of a research and preparation program for the CAIR (EPA, 2005) rule, which was 

focused on PM2.5 transport.22 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 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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 can lead to better separation of blended “factors” such as 

secondary commingled sulfates and organic aerosols 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.23,24,25,26,27,28,29,30,31,32 More routine monitoring for 

specific tracer compounds that are unique to individual sources can lead to better separation 

of blended “factors” such as secondary commingled sulfates and organic aerosols which are 

more attributed to emissions from vehicles and vegetation. 

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 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 these 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 along with mobile sources including locomotive and marine diesel 

engines. 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 2.1.1.3 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 VOC and 

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 

2-17
 
Draft Regulatory Impact Analysis 

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. Other sources 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. The key 

for individual areas is to understand the nature of the problem (i.e., determining the 

relationship between various types of emissions sources and elevated levels of PM2.5 at 

ambient monitoring) in order to develop effective and efficient emission control strategies to 

reduce PM2.5 ambient concentrations through local control program scenarios 

2.1.5 Risk of Future Violations 

States with PM2.5 nonattainment areas will be required to take action to bring those 

areas into compliance in the future. Based on the final rule designating and classifying 1997 

PM2.5 nonattainment areas, most of these areas will be required to attain the 1997 PM2.5 

NAAQS in the 2009 to 2014 time frame and then be required to maintain the PM2.5 NAAQS 

thereafter. 

As mentioned in Section 2.1.3, the 1997 PM2.5 NAAQS was recently revised (71 FR 

61144, October 17, 2006) and the 2006 NAAQS, effective on December 18, 2006, revised 

the level of the 24-hour PM2.5 standard to 35 μg/m3 from the old standards of 65 μg/m3 and 

retained the level of the annual PM2.5 standard at 15 μg/m3.

33 The nonattainment areas will be 

designated with respect to the 2006 PM NAAQS in early 2010. The attainment dates 

associated with the potential new PM2.5 nonattainment areas would likely be in the 2015 to 

2020 timeframe. The emission standards being proposed in this action will become effective 

between 2008 and 2017 and it is anticipated that the expected PM2.5 inventory reductions 

from the standards being proposed will be useful to states seeking to attain or maintain both 

the 1997 PM2.5 NAAQS as well as the 2006 PM standards. 

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 projected to be U.S. counties violating the PM2.5 NAAQS well into the future. EPA 

modeling conducted as part of the final PM NAAQS rule projects that in 2015, with all 

current controls in effect, up to 52 counties, with a population of 53 million people, may not 

attain some combination of the annual standard of 15 μg/m3 and the daily standard of 35 

μg/m3

, and that even in 2020 up to 48 counties with a population of 54 million people may 

still not be able to attain either the annual, daily, or both the annual and daily PM2.5 

standards.34 This does not account for additional areas that have air quality measurements 

within 10 percent of the 2006 PM2.5 standard. These areas, although not violating the 

standards, would also benefit from the emissions reductions being proposed, ensuring long 

term maintenance of the PM NAAQS. For example, in 2015, an additional 27 million people 

are projected to live in 54 counties that have air quality measurements within 10 percent of 

the 2006 PM NAAQS. In 2020, 25 million people, in 50 counties, will continue to have air 

quality measurements within 10 percent of the revised standards. The expected PM2.5 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

reductions from this proposed in this action will be needed by states to both attain and 

maintain the PM2.5 NAAQS. 35 

States and state organizations have told EPA that they will need the reductions 

proposed in this proposed rule in order to be able to attain or maintain the 1997 PM2.5 

standards as well as necessary to attain the 2006 PM2.5 NAAQS.36 

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 

five emissions scenarios: 

(1) 2001/2002 baseline projection, 

(2) 2020 baseline projection, 

(3) 2020 projection with locomotive/marine diesel engine controls, 

(4) 2030 baseline projection, and 

(5) 2030 projection with locomotive/marine diesel engine controls. 

Further discussion of this modeling, including evaluations of model performance 

relative to predicted future air quality, occur in section 2.1.5.2 of this RIA and also in the AQ 

Modeling TSD. 

The model outputs from the2001/2002, 2020 and 2030 baselines, combined with 

current air quality data, were used to identify areas expected to exceed the PM2.5 NAAQS in 

2020 and 2030. These areas became candidates for being determined to be residual 

excedence areas which would require additional emission reductions to attain and maintain 

the PM2.5 NAAQS. 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. 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 in 2020 and 2030. Emission reductions from locomotive and 

marine diesel engines will be helpful for these counties in attaining and maintaining the 

PM2.5 NAAQS. 

2.1.5.1 Air Quality Modeling Results for PM2.5 

According to air quality modeling performed for this rulemaking, the proposed 

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 for all counties is expected to decrease by 0.06 μg/m3 in 2020 

and 0.13 μg/m3 in 2030. In counties predicted to have annual design values greater than 15 

μg/m3 the average decrease would be somewhat higher: 0.16 μg/m3 in 2020 and 0.36 μg/m3 

in 2030. In addition, those counties that are within 10 percent of the annual PM2.5 design 

value would see their average DV decrease by 0.06 μg/m3 in 2020 and 0.23 μg/m3 in 2030. 

The maximum decrease for future-year annual PM2.5 design values in 2020 would be 

2-19
 
Draft Regulatory Impact Analysis 

0.35μg/m3 and 0.90μg/m3 in 2030. Note that for the current 39 PM2.5 nonattainment areas 

the average population weighted modeled future-year annual PM2.5 design values would on 

average decrease by 0.06μg/m3 in 2020 and by 0.14 μg/m3 in 2030. 

The geographic impact of the proposed locomotive and marine diesel engine controls 

in 2030 on annual PM2.5 design values (DV) in counties across the US, can be seen in Figure 

2-7. A complete set of maps illustrating the geographic impact of various alternatives 

explored as part of this rulemaking are available in Air Quality Modeling TSD for this 

rulemaking. 

Figure 2-7 Impact of Proposed Locomotive/Marine controls on annual PM2.5 Design Values (DV) in 2030 
 

Legend Number of Counties 

-0.90 to -0.50 3
 

-0.49 to -0.25 13
 

-0.24 to -0.10 153
 

-0.09 to -0.05 172
 

-0.04 to no change 216
 2030 diff 

2-20 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Figure 2-7 illustrates that the greatest emission reductions in 2030 are projected to 

occur in Southern California where three counties would experience reductions in their PM2.5 

design values of -0.50 to -0.90 μg/m3. The next level of emission reductions would occur 

among 13 counties geographically dispersed along the Gulf Coast, near St. Louis, and again 

Southern California. An additional 325 counties spread across the US would see a decrease 

in PM2.5 DV ranging from -0.05 to -0.24 μg/m3

. 

Table 2-2 lists the counties with 2020 and 2030 projected annual PM2.5 design values 

that violate the annual standard or are within 10 percent of it. 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 design values are greater than 

or equal to 13.55 μg/m3

, but less than 15.05 μg/m3. These are counties that are not projected 

to violate the standard, but to be close to it, so the proposed rule will help assure that these 

counties continue to meet the standard. The current design values of these counties are also 

listed. 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 and 2030 Projected Annual PM2.5Design Values in Violation or within 10 

percent of the Annual PM2.5 Standard. In the Base and Control cases. 

State County 1999 – 2003 

Average Design 

2020 2030 2000 Population 

Value (μg/m3

) 

Base Control Base Control 

AL Jefferson 19.05 V V V V 662,046 

CA Fresno 21.85 V V V V 799,406 

CA Imperial 15.22 X X X X 142,360 

CA Kern 22.74 V V V V 661,644 

CA Kings 18.52 V V V V 129,460 

CA Los Angeles 24.21 V V V V 9,519,334 

CA Merced 16.73 V V V V 210,553 

CA Orange 20.39 V V V V 2,846,288 

CA Riverside 28.82 V V V V 1,545,386 

CA San Bernardino 25.27 V V V V 1,709,433 

CA San Diego 16.44 X X V X 2,813,831 

CA San Joaquin 15.46 V V V V 563,597 

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Draft Regulatory Impact Analysis 

CA Stanislaus 17.87 V V V V 446,996 

CA Tulare 23.06 V V V V 368,020 

GA Bibb 16.42 X X X X 153,887 

GA Clayton 17.51 X X X X 236,516 

GA Floyd 16.67 X X X X 90,565 

GA Fulton 19.51 V V V V 816,005 

IL Cook 18.00 V V V V 5,376,739 

IL Madison 17.40 V X V V 258,940 

IL St. Clair 16.87 X X X X 256,081 

KY Jefferson 17.07 X X X 693,603 

MI Wayne 19.62 V V V V 2,061,161 

MT Lincoln 16.24 X X X X 18,837 

NY New York 16.67 X X X X 1,537,194 

OH Cuyahoga 19.25 V V V V 1,393,977 

OH Hamilton 18.55 X X X X 845,302 

OH Jefferson 18.36 X X X X 73,894 

OH Scioto 19.53 V V V V 79,195 

PA Allegheny 21.17 V V V V 1,281,665 

PA Philadelphia 16.39 X X 1,517,549 

TX Harris 14.13 X X 3,400,577 

WV Cabell 17.22 X 96,784 

WV Kanawha 17.75 X X X X 200,072 

2.1.5.2 PM Air Quality Modeling and Methods 

2.1.5.2.1 Air Quality Modeling Overview 

A national scale air quality modeling analysis was performed to estimate future year 

annual and daily PM2.5 concentrations and visibility. These projections were used as inputs 

to the calculation of expected benefits from the locomotive and marine emissions controls 

considered in this assessment. The 2001-based CMAQ modeling platform was used as the 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

tool for the air quality modeling of future baseline emissions and control scenarios. In 

addition to the CMAQ model, the modeling platform includes the emissions, meteorology, 

and initial and boundary condition data which are inputs to this model. 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.).37 38 39 

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. 

The CMAQ model was peer-reviewed in 2003 for EPA as reported in “Peer Review 

of CMAQ Model”.40 The latest version of CMAQ (Version 4.5) was employed for this 

modeling analysis. This version reflects updates in a number of areas to improve the 

underlying science and address comments from the peer-review including (1) use of a stateof-the-science inorganic nitrate partitioning module (ISORROPIA) and updated gaseous, 

heterogeneous chemistry in the calculation of nitrate formation, (2) a state-of-the-science 

secondary organic aerosol (SOA) module that includes a more comprehensive gas-particle 

partitioning algorithm from both anthropogenic and biogenic SOA, (3) an in-cloud sulfate 

chemistry module that accounts for the nonlinear sensitivity of sulfate formation to varying 

pH, and (4) an updated CB-IV gas-phase chemistry mechanism and aqueous chemistry 

mechanism that provide a comprehensive simulation of aerosol precursor oxidants.41 

2.1.5.2.2 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 (Figure 2.1-6). The domain extends from 126 

degrees to 66 degrees west longitude and from 24 degrees north latitude to 52 degrees north 

latitude. The horizontal grid cells are approximately 36 km by 36 km. The modeling domain 

contains 14 vertical layers with the top of the modeling domain at about 16,200 meters, or 

100 mb. 

Figure 2-8. Map of the CMAQ modeling domain. 

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Draft Regulatory Impact Analysis 

2.1.5.2.3 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 Model42 for the entire year 

of 2001. 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. For this analysis, version 3.6.1 of MM5 was used. The 

horizontal domain consisted of a single 36 x 36 km grid with 165 by 129 cells, selected to 

maximize the coverage of the ETA model analysis region and completely cover the CMAQ 

modeling domain with some buffer to avoid boundary effects. The meteorological outputs 

from MM5 were processed to create model-ready inputs for CMAQ using the MeteorologyChemistry Interface Processor (MCIP) version 3.1 to derive the specific inputs to CMAQ: 

horizontal wind components (i.e., speed and direction), temperature, moisture, vertical 

diffusion rates, and rainfall rates for each grid cell in each vertical layer.43 

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.44 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2001 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 

CMAQ simulations. 

A complete description of the development and processing of model-ready 

meteorological inputs and initial and boundary condition inputs used for this analysis are 

discussed in the CAIR TSD.45 In addition, the development of the gridded, hourly modelready emissions inputs used for the 2001 base year and each of the future year base cases and 

control scenarios are summarized above in Chapter 2. 

2.1.5.2.4 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 2001 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.46 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 

studies47. Overall, the performance for this application is within the range or better than 

these other applications. A detailed summary of the 2001 CMAQ model performance 

evaluation is available within the PM NAAQS RIA, Appendix O. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

2.1.5.2.5 Model Simulation Scenarios 

As part of our analysis the CMAQ modeling system was used to calculate daily and 

annual PM2.5 concentrations and visibility estimates for each of the following eleven 

emissions scenarios: 

2001 base year 

2020 base line projection 2020 with projection of impact of primary 

locomotive/marine control case, low control option, high control option, and a locomotive 

only control case 

2030 base line projection 

2030 with projection of impact of primary locomotive/marine control case, low 

control option, high control option, and a locomotive-only control case 

We use the predictions from the model in a relative sense by combining the 2001 

base-year predictions with predictions from each future-year scenario and speciated ambient 

air quality observations to determine PM2.5 concentrations and visibility for each of the 2020 

and 2030 scenarios. After completing this process, we then calculated daily and seasonal PM 

air quality metrics as inputs to the health and welfare impact functions of the benefits 

analysis. The following sections provide a more detailed discussion of our air quality 

projection method and a summary of the results. 

2.1.5.2.6 Projection Methodology for Annual Average Design Values 

The procedures used to project the annual design values are generally consistent with 

the projection techniques used in the Clean Air Interstate Rule (CAIR). The projected annual 

design values were calculated using the Speciated Modeled Attainment Test (SMAT) 

approach. The SMAT uses an 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 used in the CAIR analysis 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)".48 For this 

latest analysis, several datasets and techniques were updated. The changes and updates 

include: 

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Draft Regulatory Impact Analysis 

1) Revised database of PM2.5 speciation data which includes data from 2002 and 

2003. 

2) Revised interpolations of PM2.5 species data using updated techniques. 

3) An updated equation to calculate particle bound water. 

4) Revised treatment of ambient ammonium data. 

Documentation of these updates and changes can be found in “Procedures for 

Estimating Future PM2.5 Values for the PM NAAQS Final Rule by Application of the 

Speciated Modeled Attainment Test (SMAT)" (EPA, 2006). 49 Below are the steps we 

followed for projecting future PM2.5 concentrations. These steps were performed to estimate 

future case concentrations at each FRM monitoring site. The starting point for these 

projections is a 5 year weighted average design value for each site. The weighted average is 

calculated as the average of the 1999-2001, 2000-2002, and 2001-2003 design values at each 

monitoring site. By averaging 1999-2001, 2000-2002, and 2001-2003, the value from 2001 

is weighted three times, whereas, values for 2000 and 2002 are each weighted twice, and 

1999 and 2003 are each weighted once. This approach has the desired benefits of (1) 

weighting the PM2.5 values towards the middle year of the five-year period (2001), which is 

the Base Year for our emissions projections, and (2) smoothing out the effects of year-to-year 

variability in emissions and meteorology that occurs over the full five-year period. This 

approach provides a robust estimate of current air quality for use as a basis for future year 

projections. 

Step 1: Calculate quarterly mean ambient concentrations for each of the major 

components of PM2.5 (i.e., sulfate, nitrate, ammonium, elemental carbon, organic carbon, 

water, and crustal material) using the component species concentrations estimated for each 

FRM site. 

The component species concentrations were estimated using an average of 2002 and 

2003 ambient data from speciation monitors. The speciation data was interpolated to provide 

estimates for all FRM sites across the country. The interpolated component concentration 

information was used to calculate species fractions at each FRM site. The estimated 

fractional composition of each species (by quarter) was then multiplied by the 5 year 

weighted average 1999-2003 FRM quarterly mean concentrations at each site (e.g., 20 

percent sulfate multiplied by 15.0 μg/m3

 of PM2.5 equals 3 μg/m3 sulfate). The end result is a 

quarterly concentration for each of the PM2.5 species at each FRM site. 

Step 2: Calculate quarterly average Relative Reduction Factors (RRFs) for sulfate, 

nitrate, elemental carbon, organic carbon, and crustal material. The species-specific RRFs 

for the location of each FRM are the ratio of the 2015 (or 2020) future year cases to the 2001 

Base Year quarterly average model predicted species concentrations. The species-specific 

quarterly RRFs are then multiplied by the corresponding 1999-2003 quarterly species 

concentration from Step 1. The result is the future case quarterly average concentration for 

each of these species for each future year model run. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Step 3: Calculate future case quarterly average concentrations for ammonium and 

particle-bound water. The future case concentrations for ammonium are calculated using the 

future case sulfate and nitrate concentrations determined from Step 2 along with the degree 

of neutralization of sulfate (held constant from the base year). Concentrations of particlebound water are calculated using an empirical equation derived from the AIM model using 

the concentrations of sulfate, nitrate, and ammonium as inputs. 

Step 4: Calculate the mean of the four quarterly average future case concentrations to 

estimate future annual average concentration for each component species. The annual 

average concentrations of the components are added together to obtain the future annual 

average concentration for PM2.5. 

Step 5: For counties with only one monitoring site, the projected value at that site is 

the future case value for that county. For counties with more than one monitor, the highest 

future year value in the county is selected as the concentration for that county. 

2.1.6 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. 

2.1.6.1 Visibility Impairment 

Visibility can be defined as the degree to which the atmosphere is transparent to 

visible light.50 Visibility impairment manifests in two principal ways: as local visibility 

impairment and as regional haze.51 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 

from 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 areas 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. For more information on visibility see the PM AQCD as well as the 2005 PM 

Staff Paper.52,53 

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 set secondary PM2.5 

standards which would work in conjunction with the establishment of a regional haze 

program. The secondary (welfare-based) PM2.5 NAAQS was established as equal to the suite 

2-27
 
Draft Regulatory Impact Analysis 

of primary (health-based) NAAQS. Furthermore, Section 169 of the Act provides additional 

authority to remedy existing visibility impairment and prevent future visibility impairment in 

the 156 national parks, forests and wilderness areas labeled as mandatory class I federal areas 

(62 FR 38680-81, July 18, 1997). 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. In July 1999 

the regional haze rule (64 FR 35714) was put in place to protect the visibility in mandatory 

class I federal areas. A list of the mandatory class I federal areas is included in Appendix 

2D. Visibility can be said to be 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 PM and NOx emissions from locomotive and marine diesel engines 

subject to this proposed rule either directly emit PM2.5 or contribute to formation of 

secondary PM-precursors and contribute to these visibility effects. This is evident in the 

PM2.5 visibility modeling completed for this rulemaking. In this section we present current 

information and projected estimates about both 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 projected 

emission reductions from this proposed action will help improve visibility conditions across 

the country and in mandatory class I federal areas. More detailed discussions on visibility 

are contained in the EPA PM AQCD and the revised PM NAAQS rule RIA.54, 55 

2.1.6.1.1 Current Visibility Impairment 

The need for reductions in the levels of PM2.5 is widespread. Currently, high ambient 

PM2.5 levels are measured throughout the country. Fine particles may remain suspended for 

days or weeks and travel hundreds to thousands of kilometers, and thus fine particles emitted 

or created in one county may contribute to ambient concentrations in a neighboring region.56 

As mentioned above the secondary PM2.5 standards were set as equal to the suite of 

primary PM2.5 standards. Recently designated PM2.5 nonattainment areas indicate that almost 

90 million people live in 208 counties that are in nonattainment for the 1997 PM2.5 NAAQS, 

(see Appendix 2A for the complete list of current nonattainment areas). Thus, at least these 

populations (plus others who travel to these areas) would likely be experiencing visibility 

impairment. 

As discussed in the Staff Paper (EPA 2004, section 6.2), in mandatory class I federal 

areas, visibility levels on the 20 percent haziest days in the West are about equal to levels on 

the 20 percent best days in the East. Despite improvement through the 1990’s, visibility in 

the rural East remains significantly impaired, with an average visual range of approximately 

20 km on the 20 percent haziest days (compared to the naturally occurring visual range in the 

eastern US of about 150 ±45km). In the rural West, the average visual range showed little 

change over this period, with an average visual range of approximately 100km on the 20 

percent haziest days (compared to the naturally occurring visual range in the western US of 

about 230 ±40km). 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

In urban areas, visibility levels show far less difference between eastern and western 

regions. For example, the average visual ranges on the 20 percent haziest days in eastern 

and western urban areas are approximately 20 km and 27 km, respectively (Schmidt et al., 

2005). Even more similarity is seen in considering 4-hour (12 to 4 pm.) average PM2.5 

concentrations for which the average visual ranges on the 20 percent haziest days in eastern 

and western urban areas are approximately 26 km and 31 km, respectively (Schmidt et al., 

2005). 

2.1.6.1.2 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.57,58 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.59 

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. 60 Table 2-3 below provides the current visibility deciviews for each of the 

116 monitored federal class 1 areas along with the natural background values for each area. 

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. 

2.1.6.1.3 Future Visibility Impairment 

Additional emission reductions will be needed from a broad set of sources, including 

those proposed in this action, as part of the overall strategy to achieve the visibility goals of 

the Act and the regional haze program. 

Modeling conducted for this proposed rule was used to project visibility conditions in 

116 of the mandatory class I federal areas across the US in 2020 and 2030 as a result of the 

proposed locomotive and marine diesel standards. The results indicate that improvements in 

visibility would occur in all 116 mandatory class I federal areas, although all these areas 

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Draft Regulatory Impact Analysis 

would continue to have annual average deciview levels above background in both 2020 and 

2030. 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 proposed standards. In 2030, the greatest visibility 

improvement due to this proposed rule would occur at Agua Tibia (-0.24 deciview) located in 

San Diego County, California followed by San Georgonio (-0.22 deciview) in San Bernadino 

County, California. 

Table 2-3 Current and Future projected Visibility Conditions With and Without Proposed Locomotive 

and Marine Diesel Rule in Mandatory Class I Federal Areas (Annual Average Deciview) 

Annual Results DeciViewsa 

Site name state 1998-2002 

Baseline 

Visibility 

(deciviews) 

2020base 

case 

without 

controls 

2020 base 

case with 

proposed 

controls 

2030 base 

case 

without 

controls 

2030 base 

case with 

proposed 

controls 

Natural 

Background 

(deciviews) 

Acadia ME 22.7 12.84 12.83 12.88 12.87 11.5 

Agua Tibia CA 23.2 16.03 15.94 15.98 15.74 7.2 

Anaconda - Pintler MT 18.0 7.53 7.52 7.53 7.51 7.9 

Arches UT 12.3 8.19 8.18 8.22 8.19 7.3 

Badlands SD 12.0 11.42 11.39 11.38 11.32 7.0 

Bandelier NM 17.3 8.63 8.62 8.66 8.63 7.3 

Big Bend TX 13.2 12.16 12.15 12.17 12.15 7.0 

Black Canyon of the 

Gunnison 

CO 18.4 6.84 6.83 6.83 6.81 6.9 

Desolation CA 11.6 7.63 7.61 7.59 7.55 7.1 

Bob Marshall MT 14.2 9.25 9.24 9.24 9.21 7.4 

Boundary Waters Canoe 

Area 

MN 20.0 12.06 12.04 12.10 12.04 11.2 

Bryce Canyon UT 11.5 7.53 7.51 7.53 7.51 7.1 

Bridger WY 27.6 6.98 6.97 6.97 6.95 11.3 

Brigantine NJ 12.0 18.49 18.46 18.61 18.55 7.0 

Cabinet Mountains MT 13.8 8.55 8.53 8.57 8.52 7.4 

Caney Creek AR 25.9 17.52 17.47 17.52 17.43 11.3 

Canyonlands UT 12.0 8.06 8.06 8.09 8.08 7.0 

Caribou CA 25.9 7.64 7.62 7.60 7.55 11.4 

Carlsbad Caverns NM 14.8 11.74 11.73 11.74 11.71 7.3 

Chassahowitzka FL 17.6 18.54 18.52 18.62 18.58 7.0 

Chiricahua NM AZ 25.7 8.60 8.59 8.59 8.57 11.5 

Chiricahua W AZ 13.9 8.60 8.59 8.59 8.57 6.9 

Craters of the Moon ID 13.9 8.74 8.72 8.71 8.66 6.9 

Dome Land CA 14.7 11.89 11.87 11.73 11.66 7.1 

Dolly Sods WV 12.9 16.79 16.77 16.84 16.80 7.1 

Eagles Nest CO 27.6 6.26 6.25 6.26 6.24 11.3 

Emigrant CA 20.3 9.50 9.49 9.41 9.37 7.1 

Everglades FL 19.6 14.33 14.32 14.40 14.38 7.3 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Fitzpatrick WY 11.3 6.98 6.97 6.97 6.95 7.1 

Flat Tops CO 17.6 6.32 6.31 6.33 6.31 7.1 

Galiuro AZ 20.3 8.58 8.57 8.58 8.55 11.2 

Gates of the Mountains MT 11.5 6.43 6.42 6.43 6.40 7.1 

Gila NM 11.3 8.20 8.19 8.20 8.18 7.1 

Glacier MT 13.9 12.38 12.32 12.40 12.29 6.9 

Glacier Peak WA 11.2 7.61 7.59 7.67 7.63 7.2 

Grand Teton WY 13.5 7.55 7.54 7.53 7.51 7.0 

Great Gulf NH 19.5 12.87 12.87 12.90 12.89 7.6 

Great Sand Dunes CO 14.0 8.52 8.51 8.51 8.50 7.8 

Great Smoky Mountains TN 12.1 18.16 18.12 18.19 18.11 7.1 

Guadalupe Mountains TX 23.2 11.76 11.74 11.76 11.72 11.3 

Hells Canyon OR 13.1 10.66 10.63 10.64 10.56 7.1 

Isle Royale MI 29.5 12.48 12.46 12.50 12.45 11.4 

Jarbidge NV 17.6 7.11 7.10 7.11 7.08 7.0 

James River Face VA 18.1 17.89 17.84 17.93 17.83 7.3 

Joshua Tree CA 21.1 12.35 12.30 12.34 12.20 11.2 

Joyce Kilmer – Slickrock NC 28.5 18.16 18.12 18.19 18.11 11.2 

Kalmiopsis OR 12.6 9.02 9.01 9.02 8.99 7.1 

Kings Canyon CA 19.5 16.46 16.44 16.36 16.30 7.1 

Lava Beds CA 29.5 8.21 8.18 8.18 8.12 11.5 

La Garita CO 14.8 7.19 7.18 7.19 7.18 7.7 

Lassen Volcanic CA 23.5 7.68 7.66 7.64 7.59 7.1 

Linville Gorge NC 11.6 16.84 16.80 16.87 16.80 7.1 

Lostwood ND 14.8 13.24 13.22 13.19 13.15 7.3 

Lye Brook VT 16.6 12.71 12.70 12.75 12.73 7.5 

Mammoth Cave KY 27.9 19.95 19.91 19.97 19.87 11.4 

Marble Mountain CA 19.6 9.13 9.11 9.09 9.04 7.3 

Maroon Bells – 

Snowmass 

CO 23.9 6.15 6.14 6.16 6.14 11.3 

Mazatzal AZ 30.2 9.38 9.37 9.43 9.40 11.5 

Medicine Lake MT 17.1 12.38 12.35 12.34 12.28 7.7 

Mesa Verde CO 11.3 8.16 8.15 8.18 8.16 7.1 

Mingo MO 13.1 19.15 19.09 19.15 19.02 6.9 

Mission Mountains MT 17.7 8.91 8.90 8.89 8.87 7.3 

Mount Hood OR 12.8 7.55 7.53 7.63 7.56 7.1 

Mokelumne CA 27.5 7.69 7.68 7.63 7.60 11.3 

Moosehorn ME 14.2 13.23 13.23 13.26 13.25 7.4 

Mount Rainier WA 12.9 10.31 10.28 10.37 10.30 7.1 

Mount Jefferson OR 21.4 8.21 8.20 8.25 8.20 11.4 

Mount Washington OR 14.0 8.31 8.29 8.36 8.32 7.8 

Mount Zirkel CO 15.7 7.70 7.69 7.72 7.70 7.8 

North Cascades WA 18.9 7.76 7.75 7.81 7.79 7.9 

Okefenokee GA 15.7 17.83 17.80 17.87 17.80 7.9 

Otter Creek WV 11.7 16.74 16.71 16.77 16.73 7.1 

Pasayten WA 14.0 7.67 7.65 7.67 7.62 7.8 

Petrified Forest AZ 26.4 8.54 8.50 8.55 8.48 11.5 

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Draft Regulatory Impact Analysis 

Pine Mountain AZ 27.6 9.30 9.29 9.29 9.26 11.3 

Presidential Range – Dry NH 14.7 12.61 12.61 12.66 12.66 7.8 

Rawah CO 13.5 7.55 7.54 7.55 7.53 7.0 

Red Rock Lakes WY 13.1 7.53 7.52 7.51 7.49 6.9 

Redwood CA 23.2 9.49 9.46 9.46 9.38 11.3 

Cape Romain SC 11.7 17.14 17.10 17.28 17.17 7.1 

Rocky Mountain CO 12.1 8.36 8.34 8.37 8.33 7.1 

Roosevelt Campobello ME 16.5 13.35 13.34 13.37 13.37 7.8 

Salt Creek NM 14.1 12.12 12.09 12.07 12.02 7.1 

San Gorgonio CA 21.4 13.72 13.63 13.65 13.43 11.4 

San Jacinto CA 17.7 13.33 13.22 13.12 12.85 7.0 

San Pedro Parks NM 21.5 7.20 7.19 7.20 7.18 7.1 

Sawtooth ID 21.5 8.49 8.48 8.48 8.46 7.1 

Scapegoat MT 11.4 9.09 9.07 9.08 9.06 7.0 

Selway - Bitterroot MT 13.6 7.53 7.51 7.54 7.48 7.2 

Seney MI 14.2 13.22 13.20 13.27 13.21 7.3 

Sequoia CA 12.3 15.96 15.93 15.73 15.66 7.3 

Shenandoah VA 23.8 16.26 16.23 16.27 16.20 11.4 

Sierra Ancha AZ 23.5 9.50 9.49 9.50 9.47 7.1 

Sipsey AL 27.6 19.15 19.10 19.16 19.06 11.3 

Alpine Lakes WA 13.4 10.92 10.88 11.03 10.92 6.9 

South Warner CA 28.7 8.31 8.29 8.27 8.23 11.4 

Eagle Cap OR 16.6 11.25 11.21 11.24 11.14 7.3 

Strawberry Mountain OR 19.6 11.35 11.33 11.34 11.28 7.5 

Swanquarter NC 14.7 16.39 16.37 16.43 16.39 6.9 

Sycamore Canyon AZ 24.6 10.71 10.66 10.72 10.64 11.2 

Teton WY 16.1 7.71 7.70 7.70 7.68 7.0 

Theodore Roosevelt ND 12.1 11.96 11.89 11.91 11.79 7.1 

Three Sisters OR 17.6 8.31 8.29 8.36 8.32 7.3 

Superstition AZ 14.8 9.89 9.87 9.86 9.84 7.3 

Thousand Lakes CA 15.7 7.68 7.66 7.64 7.59 7.9 

UL Bend MT 14.7 9.16 9.15 9.13 9.10 7.2 

Upper Buffalo AR 25.5 16.89 16.85 16.88 16.79 11.3 

Voyageurs MN 18.4 11.25 11.23 11.25 11.21 11.1 

Weminuche CO 11.6 6.90 6.89 6.89 6.88 7.1 

West Elk CO 11.3 6.18 6.17 6.19 6.17 7.1 

Wind Cave SD 16.0 9.56 9.52 9.55 9.47 7.2 

Wolf Island GA 26.4 18.14 18.11 18.18 18.13 11.4 

Yellowstone WY 12.1 7.69 7.67 7.67 7.65 7.1 

Yolla Bolly - Middle Eel CA 17.1 9.31 9.30 9.28 9.23 7.4 

Yosemite CA 17.6 9.30 9.28 9.21 9.17 7.1 

Zion UT 13.5 8.92 8.89 8.95 8.90 7.0 

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. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

2.1.6.1.4 Visibility Modeling Methodology 

The modeling platform described in Section 2.1.5 above 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 

110 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 only 81 of these sites have complete data for 2001. For 

this analysis, we quantified visibility improvement at the 116 mandatory class I federal areas 

which have complete IMPROVE ambient data for 2001 or are represented by IMPROVE 

monitors with complete data.G 

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 (1996), 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 light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental 

carbon, and soil (Sisler, 1996). 

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 

G There are 81 IMPROVE sites with complete data for 2001. Many of these sites collect data that is 

“representative” of other nearby unmonitored mandatory class I federal areas. There are a total of 116 

mandatory class I federal areas that are represented by the 81 sites. The matching of sites to monitors is taken 

from “Guidance for Tracking Progress Under the Regional Haze Rule”. 

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Draft Regulatory Impact Analysis 

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). 

2.1.6.2 Other PM Related Welfare Effects 

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 

(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 Staff Paper notes that some of 

these other components are regulated under separate statutory authorities, e.g., section 112 of 

the CAA.) The following characterizations of the nature of these welfare effects are based on 

the information contained in the PM AQCD and Staff Paper. 

2.1.6.2.1 Effects on Vegetation and Ecosystems 

Potentially adverse PM-related effects on vegetation and ecosystems are principally 

associated with particulate nitrate and sulfate deposition. In characterizing such effects, it is 

important to recognize that nitrogen and sulfur are necessary and beneficial nutrients for 

most organisms that make up ecosystems, with optimal amounts of these nutrients varying 

across organisms, populations, communities, ecosystems and time scales. Therefore, it is 

impossible to generalize to all species in all circumstances as to the amount at which inputs 

of these nutrients or acidifying compounds become stressors. The Staff Paper recognizes the 

public welfare benefits from the use of nitrogen (N) and sulfur (S) nutrients in fertilizers in 

managed agricultural and commercial forest settings. 

2.1.6.2.1.1 Vegetation Effects 

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 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

with the leaf effects described above are currently found in some locations in the eastern US 

(EPA 2003). 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 risks of leaf 

injury occurring from acid deposition in some areas of the eastern U.S. is high. However, 

based on currently available information, the contribution of particulate sulfates and nitrates 

to the total acidity found at these locations is not clear. 

2.1.6.2.1.2 Ecosystem Effects 

The nitrogen and sulfur containing components of PM have been associated with a 

broad spectrum of ecosystems impacts that result from either the nutrients or acidifying 

characteristics of the deposited compounds. 

Reactive nitrogen is the form of nitrogen that is available to support the growth of 

plants and microorganisms. Since the mid-1960’s reactive nitrogen creation through natural 

processes has been overtaken by reactive nitrogen creation as a result of human processes, 

and is now accumulating in the environment on the local, regional and global scale. Some 

reactive nitrogen emission are transformed into ambient PM and deposited onto sensitive 

ecosystems. Some of the most significant detrimental effects associated with excess reactive 

nitrogen deposition are those associated with a syndrome known as “nitrogen saturations.: 

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) alternation of 

ecosystem processes such as nutrient and energy cycles through changes in the functioning 

and species composition of beneficial soil organisms (Galloway and Cowling, 2002). Thus, 

through its effects on habitat suitability, genetic diversity, community dynamics and 

composition, nutrient status, energy and nutrient cycling, and frequency and intensity of 

natural disturbance regimes (fire), exceed reactive nitrogen deposition is have profound and 

adverse impact on essential ecological attributes associated with terrestrial ecosystems. In 

the US numerous forests now show severe symptoms of nitrogen saturation. For other 

forested locations, ongoing expansion in nearby urban areas will increase the potential for 

nitrogen saturation unless there are improved emissions controls. 

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or 

oceans) either form 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. 

In the U.S., forests that are now showing severe symptoms of nitrogen saturation 

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 

2-35
 
Draft Regulatory Impact Analysis 

National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern 

California and the southwestern Sierra Nevada in Central California; the alpine 

tundra/subalpine conifer forests of the Colorado Front Range; and red alder forests in the 

Cascade Mountains in Washington. 

2.1.6.2.1.2.1 Eutrophication, Nitrification, and Fertilization 

In recent decades, human activities have greatly accelerated nutrient impacts, such as 

nitrogen deposition in both aquatic and terrestrial systems. Nitrogen deposition in aquatic 

systems can cause excessive growth of algae and lead to degraded water quality and 

associated impairment of fresh water and estuarine resources for human uses. 61 Nitrogen 

deposition on terrestrial systems can cause fertilization and lead to ecosystem stress and 

species shift. 

Eutrophication is the accelerated production of organic matter, particularly algae, in a 

water body. This increased growth can cause numerous adverse ecological effects and 

economic impacts, including nuisance algal blooms, dieback of underwater plants due to 

reduced light penetration, and toxic plankton blooms. Algal and plankton blooms can also 

reduce the level of dissolved oxygen, which can adversely affect fish and shellfish 

populations. 

Deposition of nitrogen contributes to elevated nitrogen levels in waterbodies. The 

NOx reductions from today’s promulgated standards will help reduce the airborne nitrogen 

deposition that contributes to eutrophication of watersheds, particularly in aquatic systems 

where atmospheric deposition of nitrogen represents a significant portion of total nitrogen 

loadings. 

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 the 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. 625 

In its Third Report to Congress on the Great Waters, EPA reported that atmospheric 

deposition contributes from 2 to 38 percent of the nitrogen load to certain coastal waters. 63 

A review of peer reviewed literature in 1995 on the subject of air deposition suggests a 

typical contribution of 20 percent or higher.64 Human-caused nitrogen loading to the Long 

Island Sound from the atmosphere was estimated at 14 percent by a collaboration of federal 

and state air and water agencies in 1997.65 The National Exposure Research Laboratory, 

U.S. EPA, estimated based on prior studies that 20 to 35 percent of the nitrogen loading to 

the Chesapeake Bay is attributable to atmospheric deposition.66 The mobile source portion of 

atmospheric NOx contribution to the Chesapeake Bay was modeled at about 30 percent of 

total air deposition.10 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

In U.S. terrestrial systems, the nutrient whose supply most often sets the limit of 

possible plant based productivity at a given site is nitrogen. By increasing available nitrogen, 

overall ecosystem productivity may be expected to increase for a time, and then decline as 

nitrogen saturation is reached. However, because not all vegetation, organisms, or 

ecosystems react in the same manner to increased nitrogen fertilization, those plants or 

organisms that are predisposed to capitalize on any increases in nitrogen availability gain an 

advantage over those that are not as responsive to added nutrients, leading to a change in 

plant community composition and diversity. Changes to plant community composition and 

structure within an ecosystem are of concern because plants in large part determine the food 

supply and habitat types available for use by other organisms. Further, in terrestrial systems, 

plants serve as the integrators between above-ground and below-ground environments and 

influence nutrient, energy and water cycles. Because of these linkages, chronic excess 

nutrient nitrogen additions can lead to complex, dramatic, and severe ecosystem level 

responses such as changes in habitat suitability, genetic diversity, community dynamics and 

composition, nutrient status, energy and nutrient cycling, and frequency and intensity of 

natural disturbance regimes such as fire. 

These types of effects have been observed both experimentally and in the field. For 

example, experimental additions of nitrogen to a Minnesota grassland dominated by native 

warm-season grasses produced a shift to low-diversity mixtures dominated by coolseason 

grasses over a 12 year period at all but the lowest rate of nitrogen addition. 67 Similarly, the 

coastal sage scrub (CSS) community in California has been declining in land area and in 

drought deciduous shrub density over the past 60 years, and is being replaced in many areas 

by the more nitrogen responsive Mediterranean annual grasses. Some 25 plant species are 

already extinct in California, most of them annual and perennial forbs that occurred in sites 

now experiencing conversion to annual grassland. As CSS converts more extensively to 

annual grassland dominated by invasive species, loss of additional rare species may be 

inevitable. Though invasive species are often identified as the main threat to rare species, it is 

more likely that invasive species combine with other factors, such as excess N deposition, to 

promote increased productivity of invasive species and resulting species shifts. 

Deposition of nitrogen from the engines covered in this proposal contributes to 

elevated nitrogen levels in bodies of water and on land. The NOx reductions proposed in this 

action will reduce the airborne nitrogen deposition that contributes to eutrophication of 

watersheds and nitrogen saturation on land. 

2.1.6.2.1.2.2 Atmospheric Deposition 

Wet and dry deposition of ambient particulate matter delivers a complex mixture of 

metals (e.g., mercury, zinc, lead, nickel, aluminum, and cadmium), organic compounds (e.g., 

POM, dioxins, and furans) and inorganic compounds (e.g., nitrate, sulfate) to terrestrial and 

aquatic ecosystems. The chemical form of the compounds deposited is impacted by a variety 

of factors including ambient conditions (e.g., temperature, humidity, oxidant levels) and the 

sources of the material. Chemical and physical transformations of the particulate compounds 

occur in the atmosphere as well as the media onto which they deposit. These transformations 

in turn influence the fate, bioavailability and potential toxicity of these compounds. 

Atmospheric deposition has been identified as a key component of the environmental and 

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Draft Regulatory Impact Analysis 

human health hazard posed by several pollutants including mercury, dioxin and PCBs.68 

Adverse impacts on water quality can occur when atmospheric contaminants deposit 

to the water surface or when material deposited on the land enters a water body through 

runoff. Potential impacts of atmospheric deposition to water bodies include those related to 

both nutrient and toxic inputs. Adverse effects to human health and welfare can occur from 

the addition of excess particulate nitrate nutrient enrichment which contribute to toxic algae 

blooms and zones of depleted oxygen that can lead to fish kills, frequently in coastal waters. 

Particles contaminated with heavy metals or other toxins may lead to the ingestion of 

contaminated fish, ingestion of contaminated water, damage to the marine ecology, and 

limited recreational uses. Several studies have been conducted in U.S. coastal waters and in 

the Great Lakes Region in which the role of ambient PM deposition and runoff is 

investigated.69,70,71,72,73 

Adverse impacts on soil chemistry and plant life have been observed for areas heavily 

impacted by atmospheric deposition of nutrients, metals and acid species, resulting in species 

shifts, loss of biodiversity, forest decline and damage to forest productivity. Potential 

impacts also include adverse effects to human health through ingestion of contaminated 

vegetation or livestock (as in the case for dioxin deposition), reduction in crop yield, and 

limited use of land due to contamination. 

In the following subsections, atmospheric deposition of heavy metals and particulate 

organic material is discussed. 

2.1.6.2.1.2.2.1 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).74 

Investigation of trace metals near roadways and industrial facilities indicate that a substantial 

burden 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).75 

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.76,77 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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.78,79 Over fifty 

percent of the mercury in the Chesapeake Bay has been attributed to atmospheric 

deposition.80 Overall, the National Science and Technology Council (NSTC, 1999) identifies 

atmospheric deposition as the primary source of mercury to aquatic systems. 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.81,82 

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.83 Plant uptake of platinum has been observed at 

these locations. 

2.1.6.2.1.2.2.2 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.84 Polycyclic aromatic hydrocarbons (PAHs) are a 

class of POM that contain 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.85 

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.86,87 Analyses of PAH deposition to 

Chesapeake and Galveston Bay indicate that dry deposition and gas exchange from the 

atmosphere to the surface water predominate.88,89 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.90 

PAHs that enter a water body through gas exchange likely partition into organic rich particles 

and be biologically recycled, while dry deposition of aerosols containing PAHs tends to be 

more resistant to biological recycling.91 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. 

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Draft Regulatory Impact Analysis 

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.92 Van Metre et al. (2000) noted PAH 

concentrations in urban reservoir sediments have increased by 200-300% over the last forty 

years and correlates with increases in automobile use.93 

Cousins et al. (1999) estimates that greater than ninety percent of semi-volatile 

organic compound (SVOC) emissions in the United Kingdom deposit on soil.94 An analysis 

of polycyclic aromatic hydrocarbon (PAH) concentrations near a Czechoslovakian roadway 

indicated that concentrations were thirty times greater. 

2.1.6.2.1.2.2.3 Materials Damage and Soiling 

The deposition of airborne particles can also reduce the aesthetic appeal of buildings 

and culturally important articles through soiling, and can contribute directly (or in 

conjunction with other pollutants) to structural damage by means of corrosion or erosion.95 

Particles affect materials principally by promoting and accelerating the corrosion of metals, 

by degrading paints, and by deteriorating building materials such as concrete and limestone. 

Particles contribute to these effects because of their electrolytic, hygroscopic, and acidic 

properties, and their ability to sorb corrosive gases (principally sulfur dioxide). The rate of 

metal corrosion depends on a number of factors, including the deposition rate and nature of 

the pollutant; the influence of the metal protective corrosion film; the amount of moisture 

present; variability in the electrochemical reactions; the presence and concentration of other 

surface electrolytes; and the orientation of the metal surface. 

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 volatile organic compounds (VOCs) 

from locomotive and marine diesel engines subject to this proposed 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 proposed emission reductions from this rule would 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 resulting 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 and nonroad vehicles, power plants, chemical plants, refineries, makers of consumer 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

and commercial products, and smaller area sources. 

The science of ozone formation, transport, and accumulation is complex.96 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 

man-made VOC emissions are relatively low can be NOx -limited. 

When NOx levels are relatively high and VOC levels 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 are not 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. 

Ozone concentrations in an area also 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. 

The current ozone National Ambient Air Quality Standards (NAAQS) has an 8-hour 

averaging time.H The 8-hour ozone NAAQS, established by EPA in 1997, is based on welldocumented science demonstrating that more people were experiencing adverse health 

effects at lower levels of exertion, over longer periods, and at lower ozone concentrations 

than addressed by the previous one-hour ozone NAAQS. The current ozone NAAQS 

H EPA’s review of the ozone NAAQS is underway and a proposal is scheduled for May 2007 with a final rule 

scheduled for February 2008. 

2-41
 
Draft Regulatory Impact Analysis 

addresses ozone exposures of concern for the general population and populations most at 

risk, including children active outdoors, outdoor workers, and individuals with pre-existing 

respiratory disease, such as asthma. The 8-hour ozone 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 0.084 ppm. 

2.2.2 Health Effects of Ozone 

Exposure to ambient ozone contributes to a wide range of adverse health effectsI. 

These health effects are well documented and are critically assessed in the EPA ozone air 

quality criteria document (ozone AQCD) and EPA staff paper.97,98 We are relying on the 

data and conclusions in the ozone AQCD and staff paper, regarding the health effects 

associated with ozone exposure. 

Ozone-related health effects include lung function decrements, respiratory symptoms, 

aggravation of asthma, increased hospital and emergency room visits, increased asthma 

medication usage, inflammation of the lungs and a variety of other respiratory effects and 

cardiovascular effects. People who are more susceptible to effects associated with exposure 

to ozone include children, asthmatics and the elderly. There is also suggestive evidence that 

certain people may have greater genetic susceptibility. Those with greater exposures to 

ozone, for instance du to time spent outdoors (e.g. 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.99, 100, 101, 

102, 103, 104 Repeated exposure to ozone can increase susceptibility to respiratory infection and 

lung inflammation and can aggravate preexisting respiratory diseases, such as asthma.105, 106, 

107, 108, 109 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.110, 111, 112, 113 

Children and adults who are outdoors and active during the summer months, such as 

construction workers and other outdoor workers, are among those most at risk of elevated 

ozone exposures.

114 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.115 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.116, 117, 118, 119, 120, 121, 122, 123 

I

 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. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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.124, 125, 126, 127 

EPA typically quantifies ozone-related health impacts in its regulatory impact 

analyses (RIAs) when possible. In the analysis of past air quality regulations, ozone-related 

benefits have included morbidity endpoints and welfare effects such as damage to 

commercial crops. EPA has not recently included a separate and additive mortality effect for 

ozone, independent of the effect associated with fine particulate matter. For a number of 

reasons, including 1) advice from the Science Advisory Board (SAB) Health and Ecological 

Effects Subcommittee (HEES) that EPA consider the plausibility and viability of including 

an estimate of premature mortality associated with short-term ozone exposure in its benefits 

analyses and 2) conclusions regarding the scientific support for such relationships in EPA’s 

2006 Air Quality Criteria for Ozone and Related Photochemical Oxidants (the CD), EPA is 

in the process of determining how to appropriately characterize ozone-related mortality 

benefits within the context of benefits analyses for air quality regulations. As part of this 

process, we are seeking advice from the National Academy of Sciences (NAS) regarding 

how the ozone-mortality literature should be used to quantify the reduction in premature 

mortality due to diminished exposure to ozone, the amount of life expectancy to be added 

and the monetary value of this increased life expectancy in the context of health benefits 

analyses associated with regulatory assessments. 

Since the NAS effort is not expected to conclude until 2008, the agency is currently 

deliberating how best to characterize ozone-related mortality benefits in its rulemaking 

analyses in the interim. For the analysis of the proposed locomotive and marine standards, 

we do not quantify an ozone mortality benefit. So that we do not provide an incomplete 

picture of all of the benefits associated with reductions in emissions of ozone precursors, we 

have chosen not to include an estimate of total ozone benefits in the proposed RIA. By 

omitting ozone benefits in this proposal, we acknowledge that this analysis underestimates 

the benefits associated with the proposed standards. For more information regarding the 

quantified benefits included in this analysis, please refer to Chapter 6. 

2.2.3 Current 8-Hour Ozone Levels 

The proposed 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 assist in maintaining the 8-hour ozone standard in the future. In this section and the next 

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) lays out the 

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Draft Regulatory Impact Analysis 

factors 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 2006 there are approximately 157 million people living in 116 areas 

designated as not in attainment with the 8-hour ozone NAAQS. There are 461 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 achieve the 8­

hour ozone NAAQS. Figure 2-1 illustrates the widespread nature of these current problems. 

Shown in this figure are counties designated as nonattainment for the 8-hour ozone NAAQS, 

PM2.5 nonattainment counties, and mandatory class I federal areas. The current 8-hour ozone 

nonattainment areas, nonattainment counties, and populations are listed in Appendix 2C to 

this draft RIA. 

Counties designated as 8-hour ozone nonattainment were classified, on the basis of 

their one-hour ozone design value, as Subpart 1 or Subpart 2 (69 FR 23951, April 30, 2004). 

Areas classified as Subpart 2 were then further classified, on the basis of their 8-hour ozone 

design value, as marginal, moderate, serious, severe or extreme. The maximum attainment 

date assigned to an ozone nonattainment area is based on the area’s classification. 

States with 8-hour ozone nonattainment areas will be required to take action to bring 

those areas into compliance in the future. Based on the final rule designating and classifying 

8-hour ozone nonattainment areas (69 FR 23951, April 30, 2004), 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 emission 

reduction programs. The expected NOx and VOC reductions from the standards proposed in 

this action would be useful to states as they seek to either attain or maintain the 8-hour ozone 

NAAQS. 

Further insight into the need for reductions from this rule can be gained by evaluating 

counties at various levels above the level of the 8-hour ozone NAAQS. As shown in Table 

2-4 below, of the 158 million people living in counties with 2001-2003 design value 

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 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. Therefore, our analysis may underestimate the number of counties with design values above 

the level of NAAQS. 

K The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area will have to attain before June 15, 

2021. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

measurements above the 8-hour ozone NAAQS, almost 90 million live in counties with 

2001-2003 8-hour ozone design values above 95 ppb. 

Table 2-4 Population Living in Counties with 2001-2003 8-hour Ozone Design Values Shown 

2001-2003 8-hour Ozone Design 

Value (ppb) 

Number of Counties Within The 

Concentration Range 

2000 Population Living in 

Counties Within The 

Concentration Range (Millions, 

2000 Census Data) 

>95 25 89.7 

>90 <=95 47 40.0 

>85 <= 90 54 29.6 

EPA’s review of the ozone NAAQS is currently underway and a proposal is 

scheduled for June 2007 with a final rule scheduled for March 2008. If the ozone NAAQS is 

revised then new nonattainment areas could be designated. While EPA is not relying on it 

for purposes of justifying this proposal, the emission reductions from this proposed 

rulemaking would also be helpful to states if there is an ozone NAAQS revision. 

2.2.4 Projected 8-Hour Ozone Levels 

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 fail to meet the 8-hour ozone NAAQS in the future is expected to decrease. 

The base case air quality modeling completed for this proposed 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 this level will persist 

into the future. Those interested in greater detail should review the air quality modeling 

TSD. 

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 31 counties where 35 million people are 

projected to live. In addition, in 2020, 89 counties where 60 million people are projected to 

live, will be within 10 percent of violating the 8-hour ozone NAAQS. Table 2- 5 below 

provides the full list of counties in 2020 projected to have design values at or above 85 ppb 

as well as the 89 counties within 10 percent of violating the NAAQS in 2020. By 2030 27 

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Draft Regulatory Impact Analysis 

current ozone nonattainment counties would still remain impacting 37 million people. Even 

in 2030, 75 million people, living in 108 counties would continue to be within 10 percent of 

the current 8-hour ozone standard. 

Clearly the almost 300,000 tons of annual NOx reductions in 2020 and the more than 

7650,000 NOx tons reduced in 2030 would be very important to these areas as they struggle 

to attain the 8-hour ozone standard or continue to maintain the standards. Table 2-5 below 

shows the current 8-hour ozone nonattainment areas which are projected to be in 

nonattainment in 2020 and 2030 as well as those current nonattainment areas, which will be 

in attainment but within 10 percent of not meeting the standard. The table also presents 

ozone design values and populations in 2020 and 2030. 

Table 2-5 Counties with 2020 and 2030 projected Annual 8-hour Ozone Design Values in Violation or within 

10 percent of the Annual Ozone Standard in the Base and Control Cases. 

State County 2001-2003 

Average 

Ozone DV 

(ppb) 

2020 2030 2020 population 

base control base control 

AZ Maricopa 85.0 X X X X 4,609,780 

CA Amador 88.0 X X X X 52,471 

CA Calaveras 92.3 X X X 58,261 

CA El Dorado 105.7 X X X X 236,310 

CA Fresno 111.3 V V V X 1,066,878 

CA Imperial 87.0 V V V V 161,555 

CA Kern 112.0 X X X X 876,131 

CA Kings 97.3 V V V V 173,390 

CA Los Angeles 110.0 V V X X 10,376,013 

CA Madera 90.7 V V V V 173,940 

CA Mariposa 88.3 X X X X 22,272 

CA Merced 101.3 V V X X 277,863 

CA Nevada 97.7 V V V V 131,831 

CA Orange 82.7 X X X X 3,900,599 

CA Placer 100.3 X X X 451,620 

CA Riverside 108.7 V V X X 2,252,510 

CA Sacramento 99.7 V V V V 1,640,590 

CA San Bernardino 129.3 X X X X 2,424,764 

CA San Diego 94.0 V V V V 3,863,460 

CA Stanislaus 94.0 X X X X 607,766 

CA Tehama 84.3 X X 64,298 

CA Tulare 105.3 X X 477,296 

CA Tuolumne 91.5 V V V V 70,570 

CA Ventura 97.7 V X X X 1,023,136 

CO Douglas 82.5 V V V V 303,846 

CO Jefferson 83.7 X X X X 655,782 

CT Fairfield 98.7 X X X X 962,824 

CT Hartford 89.3 V V V V 942,284 

CT Middlesex 98.0 X X X X 177,500 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

CT New Haven 99.0 V V V V 898,415 

CT New London 90.7 V V V V 280,729 

CT Tolland 93.0 X X X X 152,653 

DC Washington 94.3 X X X X 554,330 

DE Kent 91.3 X X X X 153,635 

DE New Castle 95.3 X X 584,627 

DE Sussex 93.3 X X X X 202,387 

GA De Kalb 95.3 X X X 801,817 

GA Fulton 99.0 X X X 929,278 

IL Cook 87.7 X X X X 5,669,479 

IN Hamilton 93.3 X X X X 279,537 

IN Lake 90.7 X X X 509,293 

IN Marion 90.0 X V X X 935,610 

IN Porter 89.0 X 188,604 

IN Shelby 93.5 X 50,387 

KY Campbell 91.7 X X 95,622 

LA East Baton Rou 87.3 X X X 522,399 

LA Iberville 86.7 X X X 33,130 

MD Anne Arundel 101.0 X X 596,924 

MD Baltimore 93.0 X X V X 855,464 

MD Cecil 102.7 X X X X 109,425 

MD Harford 103.7 V V V V 317,847 

MD Kent 99.0 V V V V 21,407 

MD Montgomery 88.7 X X X X 1,060,716 

MD Prince Georges 95.0 X X X X 944,987 

MA Barnstable 94.7 X X X X 283,735 

MA Bristol 92.7 X X X X 605,591 

MI Allegan 92.0 X X X X 141,851 

MI Macomb 91.0 X X 894,095 

MI Muskegon 92.0 X X X X 183,444 

MI Oakland 87.0 X X X X 1,443,380 

MI Wayne 88.0 X X X X 1,908,196 

MO St Louis 89.3 X X X X 1,057,171 

MO St Louis City 87.0 X X X X 303,712 

NJ Bergen 92.5 X X X 944,507 

NJ Camden 102.3 X X X X 547,817 

NJ Cumberland 96.7 V V V V 161,512 

NJ Gloucester 100.3 X X X X 304,105 

NJ Hudson 88.0 V V V V 694,357 

NJ Hunterdon 97.3 X X X X 160,989 

NJ Mercer 102.3 X X X X 392,236 

NJ Middlesex 100.7 V V V V 934,654 

NJ Monmouth 95.7 V V V V 741,640 

NJ Morris 97.7 V X V X 548,694 

NJ Ocean 109.0 X X X X 644,323 

NY Erie 96.0 V V V V 959,145 

NY Jefferson 91.7 X X X X 119,264 

NY Niagara 91.0 X X X 220,989 

NY Putnam 91.3 X X X X 124,395 

NY Richmond 96.0 X X X X 561,360 

NY Suffolk 98.5 X X X X 1,598,742 

NY Westchester 92.0 V V V V 1,027,798 

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Draft Regulatory Impact Analysis 

OH Ashtabula 94.0 X X X X 108,355 

OH Geauga 98.3 X X X X 114,438 

OH Lake 92.7 X X X X 250,353 

PA Allegheny 93.0 X X X X 1,242,587 

PA Beaver 90.7 X X X X 186,566 

PA Bucks 103.0 X X X X 711,275 

PA Chester 96.5 V V V V 528,797 

PA Delaware 93.7 X X X X 548,283 

PA Lancaster 94.0 X X X X 568,258 

PA Lehigh 93.3 X X X 351,875 

PA Montgomery 96.3 X X X 805,003 

PA Northampton 93.0 X X X X 301,041 

PA Philadelphia 97.5 X X X 1,394,176 

RI Kent 95.3 V V V V 183,833 

RI Providence 90.3 X X X X 648,008 

RI Washington 93.3 X X X X 156,286 

TX Brazoria 91.0 X X X X 322,385 

TX Dallas 91.0 X X X X 2,828,339 

TX Denton 99.0 X X X X 715,168 

TX Galveston 92.0 X X X 318,966 

TX Gregg 88.3 X X X X 132,922 

TX Harris 105.0 X X X X 4,588,812 

TX Jefferson 90.5 V V V V 272,075 

TX Tarrant 98.3 X X X X 2,137,957 

VA Alexandria Cit 90.0 X X X X 132,893 

VA Arlington 95.7 X X X X 208,368 

VA Charles City 89.3 V V V V 8,086 

VA Fairfax 96.3 X X X 1,281,265 

VA Hampton City 88.7 X X X X 161,913 

VA Hanover 94.0 X X X X 109,984 

VA Suffolk City 87.3 X X X X 72,313 

WI Door 92.7 X X X 34,106 

WI Kenosha 98.7 X X X 184,825 

WI Kewaunee 90.0 V V V V 21,040 

WI Manitowoc 90.0 X X X 85,187 

WI Milwaukee 91.3 X X X X 927,845 

WI Ozaukee 95.3 X X X X 110,294 

WI Racine 91.7 X X X X 212,351 

WI Sheboygan 98.0 X X X X 128,777 

2.2.4.1 Ozone Modeling Results with proposed controls 

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 proposed in this 

action. Specifically, we compare baseline scenarios to scenarios with the proposed controls. 

Our modeling indicates that the reductions from this proposed rule will contribute to 

reducing ambient ozone concentrations and potential exposures in future years. 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

According to air quality modeling performed for this rulemaking, the proposed 

locomotive and marine diesel engines standards are expected to provide nationwide 

improvements in ozone levels for the vast majority of areas. Specifically, this proposed rule 

would result in ozone benefits for all but two U.S. ozone nonattainment areas in both their 

2020 and 2030 ozone design values. There are two areas with small (i.e., less than 1 ppb) 

increases in their annual 8- hour ozone design values due to the NOx disbenefits which 

occurs in some VOC-limited ozone nonattainment areas. Briefly NOx reductions can at 

certain times and in some areas cause ozone levels to increase slightly. Section 2.2.4.1.1 

provides additional detail about NOx disbenefits. 

Despite of the localized areas that experience small increases, the overall effect of this 

proposed rule is positive with 454 (of 473) counties experiencing at least a 0.1 ppb decrease 

in both their 2020 and 2030 ozone design values. On a population-weighted basis, the 

average modeled future-year 8-hour ozone design values would decrease by 0.29 ppb in 2020 

and 0.80 ppb in 2030. Within projected ozone nonattainment areas in 2030, the average 

decrease would be somewhat higher: -0.30 ppb in 2020 and - 0.88 ppb in 2030 while the 

maximum decrease for future-year design values would be -1.10 ppb in 2020 and -2.90 ppb 

in 2030. 

Table 2-6 shows the average change in future year eight-hour ozone design values. 

Average changes are shown 1) for all counties with 2020 baseline design values, 2) for 

counties with baseline design values that exceeded the standard in 2001-2003 (“violating” 

counties), and 3) for counties that did not exceed the standard, but were within 10 percent of 

it in 2001-2003. 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. The average 

and population-weighted average over all counties demonstrates a broad improvement in 

ozone air quality. The average across violating counties shows that the proposed rule will 

help bring these counties into attainment. Since some of the VOC and NOx emission 

reductions expected from this proposed rule will go into effect during the period when areas 

will need to attain the 8-hour ozone NAAQS, the projected reductions in emissions are 

expected to assist States and local agencies in their effort to attain and maintain the 8-hour 

ozone standard. The average over counties within ten percent of the standard shows that the 

proposed rule will also help those counties to maintain the standard. All of these metrics 

show a decrease in 2020 and a larger decrease in 2030, indicating in four different ways the 

overall improvement in ozone air quality. 

Table 2-6 Average change in projected future year 8-hour ozone design value 

Averagea Number of US 

Counties 

Change in 2020 

design valueb

 (ppb) 

Change in 2030 

design valueb (ppb) 

All 473 0.32 0.86 

All, population-weighted 473 0.29 0.80 

Violating countiesc

 277 0.33 0.88 

Violating countiesc 

, 

population-weighted 

277 0.29 0.87 

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Draft Regulatory Impact Analysis 

Counties within 10 146 0.35 0.94 

percent of the standardd

Counties within 10 146 0.32 1.02 

percent of the standardd 

, 

population-weighted 

a) averages are over counties with 2020 modeled design values 

b) assuming the nominal modeled control scenario 

c) counties whose 2001 baseline design values exceeded the 8-hour ozone standard (>= 85 ppb) 

d) counties whose 2001 baseline values were less than but within 10 percent of the 8-hour ozone standard. 

The impact of the proposed reductions has also been analyzed with respect to those 

areas that have the highest projected design values. We project that there will be 27 US 

counties with design values at or above 85 ppb in 2030. After implementation of this 

proposed action, we project that 3 of these 27 counties will attain the standard. Further, 17 of 

the 27 counties will be at least 10 percent closer to a design value of less than 85 ppb, and on 

average all 27 counties will be 29 percent closer to a design value of less than 85 ppb. 

The geographic impact of these emissions reductions in 2030 on annual ozone design 

values in counties across the US, can be seen in Figure 2-9. 

Figure 2-9 Impact of Proposed Locomotive/Marine controls on annual Ozone Design Values (DV) in 2030 

Number of Counties 

<= -2.0 7 

-1.0 to -1.9 187 

-0.5 to -0.9 217 

-0.1 to -0.4 56 

no change 1 

> 0 5 2030bn_p 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Figure 2.9 shows those US counties in 2030 which are projected to experience a 

change in their ozone design values as a result of this proposed rule. The most significant 

decreases, equal or greater than -2.0 ppb, would occur in 7 counties across the US including: 

Grant (-2.1ppb) and Lafayette (-2.0 ppb) Counties in Louisiana; Montgomery (-2.0 ppb), 

Galveston (-2.0ppb), and Jefferson (-2.0 ppb) Counties in Texas; Warren County (-2.9 ppb) 

in Mississippi; and Santa Barbara County (-2.7 ppb) in California. One hundred eighty-seven 

(187) counties would see annual ozone design value reductions from -1.0 to -1.9 ppb while 

an estimated 217 additional counties would see annual design value reductions from -0.5 to ­

0.9 ppb. Note that 5 counties including: Suffolk (+1.5 ppb) and Hampton (+ 0.8 ppb) 

Counties in Virginia; Cook County (+ 0.7 ppb) in Illinois; Lake County (+ 0.2 ppb) in 

Indiana; and San Bernardino County (+ 0.1 ppb) in California are projected to experience 

increased ozone design values because of the NOx disbenefit that occurs under certain 

conditions. 

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 proposed. 

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 inputs 

and resulting emission inventories between the preliminary assumptions used for the air 

quality modeling and the final proposed regulatory scenario. These refinements to the 

proposed program would not significantly change the results summarized here or our 

conclusions drawn from this analysis. 

2.2.4.1.1 Potentially Counterproductive Impacts on Ozone Concentrations from NOx 

Emissions Reductions 

While the proposed rule would reduce ozone levels generally and provide significant 

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 

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 

NARSTO Ozone Assessment, these disbenefits are generally limited to small regions within 

specific urban cores and are surrounded by larger regions in which NOx control is beneficial. 

In the context of ozone disbenefits, some have postulated that present-day weekend 

conditions serve as a demonstration of the effects of future NOx reduction strategies because 

NOx emissions decrease more than VOC emissions on weekends, due to a disproportionate 

decrease in the activity of heavy-duty diesel trucks and other diesel equipment. Recent 

research indicates that ambient ozone levels are higher in some metropolitan areas on 

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Draft Regulatory Impact Analysis 

weekends than weekdays. 129, 130 There are other hypotheses for the cause of the “weekend 

effect.” 131 For instance, the role of ozone and ozone precursor carryover from previous days 

is difficult to evaluate because of limited ambient data, especially aloft. The role of the 

changed timing of emissions is difficult to evaluate because of limited ambient and emissions 

inventory information. It is also important to note that in many areas with “weekend effects” 

(e.g., Los Angeles and San Francisco) significant ozone reductions have been observed over 

the past 20 years for all days of the week, during a period in which both NOx and VOC 

emissions have been greatly reduced. 

EPA maintains that the best available approach for determining the value of a particular 

emissions reduction strategy is the net air quality change projected to result from the rule, 

evaluated on a nationwide basis and for all pollutants that are health and/or welfare concerns. 

The primary tool for assessing the net impacts of this rule are the air quality simulation 

models. Model scenarios of 2020 and 2030 with and without the proposed controls are 

compared to determine the expected changes in future pollutant levels resulting from the 

proposed rule. There are several factors related to the air quality modeling and inputs which 

should be considered regarding the disbenefit issue. First, our future year modeling 

conducted does not contain any local governmental actions beyond the controls proposed in 

this rule. It is possible that significant local controls of VOC and/or NOx could modify the 

conclusions regarding ozone changes in some areas. Second, recent work by CARB has 

indicated that model limitations and uncertainties may lead to overestimates of ozone 

disbenefits attributed to NOx emission reductions. While EPA maintains that the air quality 

simulations conducted for the rule represent state-of-the-science analyses, any changes to the 

underlying chemical mechanisms, grid resolution, and emissions/meteorological inputs could 

result in revised conclusions regarding the strength and frequency of ozone disbenefits. 

A wide variety of ozone metrics were considered in the assessment of the proposed 

emissions reductions. Three of the most important assessments are: 1) the effect of the 

proposed rule on projected future-year ozone design values, 2) the effect of the proposed 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. 

Based only on the reductions from today’s rule, our modeling predicts that in 2020 and 

2030 periodic ozone disbenefit would occur in up to five counties: Suffolk and Hampton 

Counties in Virginia, Cook County in Illinois, Lake County in Indiana, and San Bernardino 

County in California. Despite these localized increases, the net ozone impact of the rule 

nationally is positive for the majority of the analysis metrics as described in section 2.2.4.1 

above. 

 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 emissions reductions proposed today will significantly 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 aspect of overall emissions 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 any localized disbenefit. EPA will 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

continue to rely on local attainment measures to ensure that the NAAQS are not violated in 

the future. Many organizations with an interest in improved air quality support the rule 

because they believe the resulting NOx reductions would reduce both ozone and PM 132 . 

EPA believes that a balanced air quality management approach that includes NOx emissions 

reductions from nonroad engines is needed as part of the Nation’s progress toward clean air. 

Another category of potential effects that may change in response to ozone reduction 

strategies results from the shielding provided by ozone against the harmful effects of 

ultraviolet radiation (UV-B) derived from the sun. The great majority of this shielding 

results from naturally occurring ozone in the stratosphere, but the 10 percent of total 

“column”ozone present in the troposphere also contributes.133 A variable portion of this 

tropospheric fraction of UV-B shielding is derived from ground level ozone related to 

anthropogenic air pollution. Therefore, strategies that reduce ground level ozone could, in 

some small measure, increase exposure to UV-B from the sun. 

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. 

2.2.4.2 Ozone Air Quality Modeling Methodology 

To model the ozone air quality benefits of this rule we also used the CMAQ model. 

CMAQ simulates the numerous physical and chemical processes involved in the formation, 

transport, and destruction of ozone. This model is commonly used in developing attainment 

demonstration State Implementation Plans as well as for estimating the ozone reductions 

expected to occur from a reduction in emitted pollutants. The model was applied for two 

separate domains: a) a 36 km continental U.S. domain as described in Section 2.1.5, and b) a 

smaller easter U.S. grid with a grid resolution of 12 km. 

For ozone modeling results over the western U.S. the 36 km modeling results were 

used, but only for those periods within the months from May to October. Over the eastern 

U.S. we utilized two periods of episodic modeling to generate the projections: June 15-30, 

2001 and July 15–August 10, 2001. Model configurations for the finer-scale episodic 

modeling was identical to that described in Section 2.1.5.2 except for the use of finer-scale 

MM5 meteorological inputs and that the boundary conditions were taken from the 

appropriate 36 km continental U.S. simulations. 

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.134 In this section we discuss the impact of ozone on plants, including trees, 

agronomic crops and urban ornamentals. 

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Draft Regulatory Impact Analysis 

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”.135 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.136,137 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.138 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 

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.139,140 

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).141,142,143 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.144 

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.145,146 In terms of forest productivity and ecosystem diversity, ozone may be the 

pollutant with the greatest potential for regional-scale forest impacts. Studies have 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

demonstrated repeatedly that ozone concentrations commonly observed in polluted areas can 

have substantial impacts on plant function.147, 148 

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.149 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.150,151,152 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 long-term 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 

typical of those found in the Unites States.”153 In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.154, 155, 156 

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.157 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 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. Mobile sources were responsible for 68 percent of benzene 

emissions in 1999. In response, EPA has proposed a series of mobile source and fuel 

controls that address this serious problem.L Although the 1999 NATA did not quantify 

L

 U.S. EPA (2006). Control of Hazardous Air Pollutants From Mobile Sources. 71 FR 15804; March 29, 2006. 

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Draft Regulatory Impact Analysis 

cancer risks associated with exposure to this diesel exhaust, EPA has concluded that diesel 

exhaust ranks with the other air toxic substances that the national-scale assessment suggests 

pose the greatest relative risk. 

 At the same time, nearly the entire U.S. population was exposed to an average level 

of air toxics that has the potential for adverse respiratory health effects (noncancer). This 

will continue to be the case in 2030, even though toxics levels 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 acrolein, and formaldehyde also contributed to the 

risk of respiratory health effects. Mobile sources will continue to be responsible for the 

majority of noncancer risk from outdoor air toxics in 2030. Although not included in 

NATA's estimates of noncancer risk, PM from gasoline and diesel mobile sources contribute 

significantly to the health effects associated with ambient PM. 

It should be noted that 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.158 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. 

2.3.1 Diesel Exhaust PM 

Locomotive and marine diesel engine PM2.5 emissions include 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 area 

which makes them an excellent medium for adsorbing organics as well as their small size 

makes them highly respirable and able to reach the deep lung. Many of the organic 

compounds present on the particles and in the gases are individually known to have 

mutagenic and carcinogenic properties. 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). 159 Also, there are emission differences between on-road and nonroad engines 

because the nonroad engines are generally of older technology. This is especially true for 

locomotive and marine diesel engines. 

After emission from the tailpipe, 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. Although the 1999 National-Scale Air 

Toxics Assessment (NATA) did not quantify cancer risks associated with exposure to this 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

pollutant, EPA has concluded that diesel exhaust ranks with the other air toxic substances 

that the national-scale assessment suggests pose the greatest relative risk. Following is a 

discussion of the health risks associated with diesel exhaust. 

A number of health studies have been conducted regarding diesel exhaust including 

epidemiologic studies of lung cancer in groups of workers, and animal studies focusing on 

non-cancer effects specific to diesel exhaust. 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. 

2.3.1.1 Potential Cancer Effects of Diesel Exhaust 

In addition to its contribution to ambient PM inventories, diesel exhaust is of specific 

concern because it has been judged to pose a lung cancer hazard for humans as well as a 

hazard from noncancer respiratory effects such as pulmonary inflammation. 

EPA’s 2002 final “Health Assessment Document for Diesel Engine Exhaust” (the 

EPA Diesel HAD classified 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.160,161 In accordance with earlier EPA guidelines, diesel exhaust would be 

similarly classified as a probable human carcinogen (Group B1). 162,163 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.164, 165,166,167,168 

The Health Effects Institute has also made numerous studies and report on the potential 

carcinogenicity of diesel exhaust.169, 170, 171 Numerous animal and bioassay/genotoxic tests 

have been done on diesel exhaust.172, 173 Also, case-control and cohort studies have been 

conducted on railroad engine exposures174, 175,176 in addition to studies on truck workers. 177, 

178,179, 180 Also, there are numerous other epidemiologic studies including some studying mine 

workers and fire fighters. 181, 182 

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 

Diesel HAD acknowledges that the studies were done on engines with older technologies 

generally for onroad 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 nonroad 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 workers exposed 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 within several industries, including railroad 

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Draft Regulatory Impact Analysis 

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, which found statistically significant increases in smoking-adjusted relative lung 

cancer risk associated with diesel exhaust, of 1.33 to 1.47. 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. 183,184,185 

Retrospective health studies of railroad workers have played an important part in 

finding that diesel exhaust is a likely human carcinogen. Key evidence of the diesel exhaust 

exposure linkage to lung cancer comes from two retrospective case-control studies of railroad 

workers. The Garshick railroad study 186 looked at more than 55,000 railroad workers post1959 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. 187 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. 188 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 possible currently 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. 

However, in the absence of a cancer unit risk, the EPA Diesel HAD sought to provide 

additional insight into the possible ranges of risk that might be present in the population. 

Such insights, while not confident or definitive, nevertheless contribute to an understanding 

of the possible public health significance of the lung cancer hazard. An exploratory analysis 

was used to characterize a possible risk range by comparing a typical environmental 

exposure level to a selected range of occupational exposure levels and then proportionally 

scaling the occupationally observed risks 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 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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. 

The first step in this process is to note that the occupational relative risk of 1.4, or a 

40 percent from increased risk compared to the typical 5 percent lung cancer risk in the U.S. 

population, translates to an increased risk of 2 percent (or 10-2) for these diesel exhaust 

exposed workers. The Diesel HAD derived a typical nationwide average environmental 

exposure level of 0.8 μg/m3

 for diesel PM from on-highway sources for 1996. This estimate 

was based on national exposure modeling; the derivation of this exposure is discussed in 

detail in the EPA Diesel HAD. Diesel PM is a surrogate for diesel exhaust and, as mentioned 

above, has been classified as a carcinogen by some agencies. 

The possible environmental risk range was estimated by taking the relative risks in 

the occupational setting, EPA selected 1.4 and converting this to absolute risk of 2% and then 

ratioing this risk by differences in the occupational versus environmental exposures of 

interest. A number of calculations are needed to accomplish this, and these can be seen in 

the EPA Diesel HAD. The outcome was that environmental risks from diesel exhaust using 

higher estimates of occupational exposure could range from a low of 10-4 to 10-5 or be as 

high as 10-3 if lower estimates of occupational exposure were used. Note that the 

environmental exposure of interest (0.8 μg/m3

) remains constant in this analysis, while the 

occupational exposure is a variable. The range of possible environmental risk is a reflection 

of the range of occupational exposures that could be associated with the relative and related 

absolute risk levels observed in the occupational studies. 

While these risk estimates are exploratory and not intended to provide a definitive 

characterization of cancer risk, they are useful in gauging the possible range of risk based on 

reasonable judgment. It is important to note that the possible risks could also be higher or 

lower and a zero risk cannot be ruled out. Some individuals in the population may have a 

high tolerance to exposure from diesel exhaust and low cancer susceptibility. Also, one 

cannot rule out the possibility of a threshold of exposure below which there is no cancer risk, 

although no evidence is available on this point. As discussed in the Diesel HAD, there is a 

relatively small difference between some occupational studies where increased lung cancer 

risk is reported and concentrations sometimes seen in ambient settings. 

EPA recently assessed air toxic emissions and their associated risk (the NationalScale Air Toxics Assessment or NATA for 1996 and 1999), and we concluded that diesel 

exhaust ranks with substances that the national-scale assessment suggests pose the greatest 

relative risk.189,190 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. The EPA Diesel HAD states that use of the 1996 NATA exposure estimates 

instead of the 0.8 μg/m3

 estimate results in a similar risk perspective. 

In summary, even though EPA does not have a specific carcinogenic potency with 

which to accurately estimate the carcinogenic impact of diesel exhaust, the likely hazard to 

humans together with the potential for significant environmental risks leads us to conclude 

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Draft Regulatory Impact Analysis 

that diesel exhaust emissions from locomotive and marine engines present public health 

issues of concern to this proposal. 

2.3.1.2 Other Health Effects of Diesel Exhaust 

Noncancer health effects of acute and chronic exposure to diesel exhaust emissions 

are also of concern to the Agency. The Diesel HAD established an inhalation Reference 

Concentration (RfC) specifically based on animal studies of diesel exhaust. 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.191, 192, 193,

194 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 

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 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 diesel PM alone, diesel PM is frequently part of the ambient particles 

studied in numerous epidemiologic studies. Conclusions that health effects associated with 

ambient PM in general is 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 include 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 notes that acute or short-term exposure to diesel exhaust can cause acute 

irritation (e.g., eye, throat, bronchial), neurophysiological symptoms (e.g., lightheadedness, 

nausea), and respiratory symptoms (cough, phlegm). There is also evidence for an 

immunologic effect such as the exacerbation of allergenic responses to known allergens and 

asthma-like symptoms.195,196,197 The Diesel HAD lists numerous other studies as well. Also, 

as discussed in more detail previously, in addition to its contribution to ambient PM 

inventories, diesel PM is of special concern because it has been associated with an increased 

risk of lung cancer. 

Diesel exhaust has been shown to cause serious noncancer effects in occupational 

exposure studies. One recent study 198 of a small group of railroad workers and electricians 

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Chapter 2: Air Quality and Resulting Health and Welfare Effects 

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 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 such study, published in 2006199 

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 also briefly summarizes health effects associated with ambient PM 

and discusses the EPA’s annual NAAQS of 15 μg/m3

. There is a much more extensive body 

of human data showing a wide spectrum of adverse health effects associated with exposure to 

ambient PM, 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, of which diesel PM is a constituent. 

Also, as mentioned earlier in the health effects discussion for PM2.5, there are a 

number of other health effects associated with PM in general, and mobile source exhaust 

including diesels in particular, that provide additional evidence for the need for significant 

emission reductions from locomotive and marine diesel sources. 

As indicated earlier, a number of recent studies have associated living near roadways 

with adverse health effects. Two of the studies cited earlier will be mentioned again here as 

examples of the type of work that has been done. A Dutch study (discussed earlier by G. 

Hoek and others) 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. In a 

review discussed earlier of studies (by R. Delfino) 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. These studies offer evidence 

that people exposed most directly to emissions from mobile sources including those from 

diesels face an elevated risk of illness or death. 

All of these health effects plus the designation of diesel exhaust as a likely human 

carcinogen provide ample health justification for control. 

2.3.1.3 Diesel Exhaust PM Ambient Levels 

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 instead using one of three approaches: 1) ambient air 

quality modeling based on diesel PM emission inventories; 2) using elemental carbon 

concentrations in monitored data as surrogates; or 3) using the chemical mass balance (CMB) 

model in conjunction with ambient PM measurements. (Also, in addition to CMB, 

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Draft Regulatory Impact Analysis 

UNMIX/PMF have also been used). Estimates using these three approaches are described 

below. In addition, estimates developed using the first two approaches above are subjected 

to a statistical comparison to evaluate overall reasonableness of estimated concentrations 

from ambient air quality modeling. It is important to note that, while there are 

inconsistencies in some of these studies on the relative importance of gasoline and diesel PM, 

the studies which are discussed in the Diesel HAD all show that diesel PM is a significant 

contributor to overall ambient PM. Some of the studies differentiate nonroad from onhighway diesel PM. 

2.3.1.3.1 Toxics Modeling and Methods 

In addition to the general ambient PM modeling conducted for this proposal, diesel 

PM concentrations for 1999 were recently estimated as part of the second National-Scale Air 

Toxics Assessment (NATA; EPA, 2006). Ambient impacts of mobile source emissions were 

predicted using the Assessment System for Population Exposure Nationwide (ASPEN) 

dispersion model. 

From the NATA 1999 modeling, overall medium annual national ambient diesel PM 

levels of .91 μg/m3

 were calculated with a medium of 1.06 in urban counties and 0.43 in rural 

counties. Table 2-8 below summarizes the distribution of medium ambient concentrations to 

diesel PM at the national scale. Over half, 62 percent, of the diesel PM and diesel exhaust 

organic gases can be attributed to nonroad diesels. A map of county median ambient 

concentrations is provided in Figure 2-8. While the high median concentrations are 

clustered in the Northeast, Great Lake States California, and the Gulf Coast States, areas of 

high median concentrations are distributed throughout the U.S. 

Table 2-8 Distribution of Median Ambient Concentrations of Diesel PM at the National Scale in the 

1999 NATA Assessment. 

 Nationwid 

e (μg/m3

) 

Urban 

(μg/m3) 

Rural 

(μg/m3) 

5th Percentile 0.21 0.22 0.08 

25th Percentile 0.54 .70 0.28 

Medium 0.91 1.06 0.43 

75th Percentile 1.41 1.56 0.62 

95th Percentile 2.91 3.21 .96 

Onroad Contribution to Mean 0.43 0.49 0.20 

Nonroad Contribution to Mean 0.78 0.90 0.28 

2-62
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Figure 2-10 Estimated County Median Ambient Concentration of Diesel Particulate Matter 

2.3.1.4 Diesel Exhaust PM Exposures 

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 accounts for a person 

moving from location to location, proximity to the emission source, and whether the 

exposure occurs in an enclosed environment. 

2-63
 
Draft Regulatory Impact Analysis 

2.3.1.4.1 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 1,280 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).200 A 1988 study201 

estimated that U.S. railroad workers received an estimated occupational 

exposure/concentration of between 39 -191 μg/m3 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. 

2.3.1.4.1.1	 Elevated Concentrations and Ambient Exposures in Mobile SourceImpacted Areas 

While occupational studies indicate that those 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, 202 rail yards and marine 

ports 203 are also likely to experience greater diesel exhaust exposure levels than the overall 

population putting them at greater health risks. 

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 recent study conducted by the California Air Resources Board (CARB) examined 

the air quality impacts of railroad operations at the J.R. Davis Rail Yard, the largest rail 

facility in the western United States. 204 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 concentrations of 

diesel PM using a well-accepted dispersion model (ISCST3). 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. 

2-64
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Another study from CARB evaluated air quality impacts of diesel engine emissions 

within the Ports of Long Beach and Los Angeles in California, one of the largest ports in the 

U.S. 205 Like the earlier rail yard study, the port study employed the ISCST3 dispersion 

model. Also using local meteorological data, annual average concentrations were 

substantially elevated over an area exceeding 200,000 acres. Because they 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 directly from the port. Figure 2-11 provides an aerial 

shot of the Port of Long beach and Los Angeles in California. 

Figure 2-11 Aerial Shot – Port of LA and Long Beach, California 

While these studies focus on two large marine port and one large rail yard facility, 

these studies do highlight the substantial contribution these facilities make to elevated 

ambient concentrations in large, densely populated areas. 

We have recently initiated a study to better understand the populations that are living 

near rail yards and marine ports. As part of the study, a computer geographic information 

system (GIS) is being used to identify the locations and property boundaries of a sampling of 

these facilities nationally, and to determine the size and demographic characteristics of the 

population living near these facilities. We anticipate that the results of this study will be 

complete in early 2007 and we intend to add this report to the public docket in advance of the 

final rulemaking. Figure 2.-12 to 2.-14 provides a sampling of aerial photos of the rail yards 

and marine ports that are part of this study. 

2-65
 
Draft Regulatory Impact Analysis 

Figure 2-12 2006 aerial photo Port of Cleveland, Cleveland Ohio 

Figure 2-13 2006 aerial photo Argentine Rail Yard, Kansas City, Missouri 

2-66
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Figure 2-14. 2006 aerial photo DeButts Rail Yard, Chattanooga, Tennessee 

2.4 Gaseous 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. These other compounds include benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. All of 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 contributions 

associated with these compounds. The reductions in locomotive and marine diesel engine 

emissions proposed in this rulemaking would help reduce exposure to these harmful 

substances. 

Table 2-9 Mobile Source Contribution to 1999 NATA Risk Drivers 

1999 NATA Risk Drivers Percent Contribution from 

ALL Mobile Sources 

Percent Contribution for 

Non-road Mobile Sources 

Benzene 68% 19% 

1,2-Butadiene 58% 17% 

Formaldehyde 47% 20% 

Acrolein 25% 11% 

2-67
 
Draft Regulatory Impact Analysis 

Polycyclic organic matter 

(POM)* 

6% 3% 

Naphthalene 27% 6% 

Diesel PM and Diesel 100% 62% 

exhaust organic gases 

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. 

Air toxics can cause a variety of cancer and noncancer health effects. A number of the 

mobile source air toxic pollutants described in this section are known or likely to pose a 

cancer hazard in humans. Many of these compounds also cause adverse noncancer health 

effects resulting from chronic,206 subchronic,207 or acute208 inhalation exposures. These 

include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects 

on the immune and reproductive systems. 

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.209, 210, 211 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.212, 213 The most sensitive noncancer effect observed in 

humans, based on current data, is the depression of the absolute lymphocyte count in 

blood.214, 215 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. 216, 217, 218, 219 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.220, 221 The specific mechanisms of 1,3-butadiene-induced carcinogenesis are 

unknown. However, it is virtually certain that the carcinogenic effects are mediated by 

genotoxic metabolites of 1,3-butadiene. 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.222 

Formaldehyde: Since 1987, EPA has classified formaldehyde as a probable human 

carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys.223 EPA’s 

current IRIS summary provides an upper bound cancer unit risk estimate of 1.3x10-5 per 

2-68
 
Chapter 2: Air Quality and Resulting Health and Welfare Effects 

μg/m3. In other words, there is an estimated risk of about thirteen excess leukemia cases in 

one million people exposed to 1 μg/m3 of formaldehyde over a lifetime. 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.224, 225 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.226 

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. 227 

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. The Agency is currently conducting a reassessment of the human hazard and 

dose-response associated with formaldehyde. 

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. 228, 229,230 , CIIT’s risk assessment of 

formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. The 

risk assessment analyzed carcinogenic risk from inhaled formaldehyde using approaches that 

are consistent with EPA’s draft guidelines for carcinogenic risk assessment. In 2001, 

Environment Canada relied on this cancer dose-response assessment in their assessment of 

formaldehyde. 231 In 2004, EPA also relied on this cancer unit risk estimate during the 

development of the plywood and composite wood products national emissions standards for 

hazardous air pollutants (NESHAPs). 232 In these rules, EPA concluded that the CIIT work 

represented the best available application of the available mechanistic and dosimetric science 

on the dose-response for portal of entry cancers due to formaldehyde exposures. 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.233 The primary acute effect of exposure to acetaldehyde vapors 

is irritation of the eyes, skin, and respiratory tract.234 The agency is currently conducting a 

reassessment of the health hazards from inhalation exposure to acetaldehyde. 

Acrolein: Acrolein is intensely irritating to humans when inhaled, with acute 

exposure resulting in upper respiratory tract irritation and congestion. EPA determined in 

2-69
 
Draft Regulatory Impact Analysis 

2003 using the 1999 draft cacner guidelines 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.235 

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, 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.236, 

237 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.238 The draft reassessment recently completed external peer review.239 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.240 

Naphthalene also causes a number of chronic non-cancer effects in animals, including 

abnormal cell changes and growth in respiratory and nasal tissues.241 

In addition to reducing substantial amounts of NOx and PM2.5 emissions from 

locomotive and marine diesel engines the standards being proposed today would also reduce 

air toxics emitted from these engines thereby helping to mitigate some of the adverse health 

effects associated with operation of these engines. 

2-70
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

Appendix 2A PM2.5 Nonattainment 

Table 2A PM2.5 Nonattainment Areas and Populations (Data is current throughOctober 2006 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 

A2-1
 
Draft Regulatory Impact Analysis 

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 

A2-2
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

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 

A2-3
 
Draft Regulatory Impact Analysis 

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 

A2-4
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

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 

A2-5
 
Draft Regulatory Impact Analysis 

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 

A2-6
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

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 

A2-7
 
Draft Regulatory Impact Analysis 

Appendix 2B: Current 8-Hour Ozone Nonattainment Areas 

Table 2B 8-Hour Ozone Nonattainment Areas and Populations (Data is current through October 2006 

and Population Numbers are from 2000 Census Data) 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

NY Subpart 1 Albany Co W 294,565 

Albany-Schenectady-Troy 

Area NY Subpart 1 Greene Co W 48,195 

Albany-Schenectady-Troy 

Area NY Subpart 1 Montgomery Co W 49,708 

Albany-Schenectady-Troy 

Area NY Subpart 1 Rensselaer Co W 152,538 

Albany-Schenectady-Troy 

Area NY Subpart 1 Saratoga Co W 200,635 

Albany-Schenectady-Troy 

Area NY Subpart 1 Schenectady Co W 146,555 

Albany-Schenectady-Troy 

Area NY Subpart 1 Schoharie Co W 31,582 

Allegan County Area MI Subpart 1 Allegan Co W 105,665 

Allentown-Bethlehem-Easton 

Area PA Subpart 1 Carbon Co W 58,802 

Allentown-Bethlehem-Easton 

Area PA Subpart 1 Lehigh Co W 312,090 

Allentown-Bethlehem-Easton 

Area PA Subpart 1 Northampton Co W 267,066 

Altoona Area PA Subpart 1 Blair Co W 129,144 

Amador and Calaveras 

Counties (Central Mountain 

Counties) Area 

CA Subpart 1 Amador Co W 35,100 

Amador and Calaveras 

Counties (Central Mountain 

Counties) Area 

CA Subpart 1 Calaveras Co W 40,554 

Atlanta Area GA Barrow Co W 46,144 

Atlanta Area GA Subpart 2/Marginal Bartow Co W 76,019 

Atlanta Area GA Subpart 2/Marginal Carroll Co W 87,268 

Atlanta Area GA Subpart 2/Marginal Cherokee Co W 141,903 

Atlanta Area GA Subpart 2/Marginal Clayton Co W 236,517 

Atlanta Area GA Subpart 2/Marginal Cobb Co W 607,751 

Atlanta Area GA Subpart 2/Marginal Coweta Co W 89,215 

Atlanta Area GA Subpart 2/Marginal De Kalb Co W 665,865 

Atlanta Area GA Subpart 2/Marginal Douglas Co W 92,174 

Atlanta Area GA Subpart 2/Marginal Fayette Co W 91,263 

Atlanta Area GA Subpart 2/Marginal Forsyth Co W 98,407 

Atlanta Area GA Subpart 2/Marginal Fulton Co W 816,006 

Atlanta Area GA Subpart 2/Marginal Gwinnett Co W 588,448 

Atlanta Area GA Subpart 2/Marginal Hall Co W 139,277 

Atlanta Area GA Subpart 2/Marginal Henry Co W 119,341 

Atlanta Area GA Subpart 2/Marginal Newton Co W 62,001 

Atlanta Area GA Subpart 2/Marginal Paulding Co W 81,678 

A2-8
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Atlanta Area GA Subpart 2/Marginal Rockdale Co W 70,111 

Atlanta Area GA Subpart 2/Marginal Spalding Co W 58,417 

Atlanta Area GA Subpart 2/Marginal Walton Co W 60,687 

Baltimore Area MD Subpart 2/Moderate Anne Arundel Co W 489,656 

Baltimore Area MD Subpart 2/Moderate Baltimore (City) W 651,154 

Baltimore Area MD Subpart 2/Moderate Baltimore Co W 754,292 

Baltimore Area MD Subpart 2/Moderate Carroll Co W 150,897 

Baltimore Area MD Subpart 2/Moderate Harford Co W 218,590 

Baltimore Area MD Subpart 2/Moderate Howard Co W 247,842 

Baton Rouge Area LA Subpart 2/Marginal Ascension Par W 76,627 

Baton Rouge Area LA Subpart 2/Marginal East Baton Rouge Par W 412,852 

Baton Rouge Area LA Subpart 2/Marginal Iberville Par W 33,320 

Baton Rouge Area LA Subpart 2/Marginal Livingston Par W 91,814 

Baton Rouge Area LA Subpart 2/Marginal West Baton Rouge Par W 21,601 

Beaumont-Port Arthur Area TX Subpart 2/Marginal Hardin Co W 48,073 

Beaumont-Port Arthur Area TX Subpart 2/Marginal Jefferson Co W 252,051 

Beaumont-Port Arthur Area TX Subpart 2/Marginal Orange Co W 84,966 

Benton Harbor Area MI Subpart 1 Berrien Co W 162,453 

Benzie County Area MI Subpart 1 Benzie Co W 15,998 

Berkeley and Jefferson 

Counties Area WV Subpart 1 - EAC Berkeley Co W 75,905 

Berkeley and Jefferson 

Counties Area WV Subpart 1 - EAC Jefferson Co W 42,190 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Barnstable Co W 222,230 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Bristol Co W 534,678 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Dukes Co W 14,987 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Essex Co W 723,419 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Middlesex Co W 1,465,396 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Nantucket Co W 9,520 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Norfolk Co W 650,308 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Plymouth Co W 472,822 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Suffolk Co W 689,807 

Boston-Lawrence-Worcester 

(E. Mass) Area MA Subpart 2/Moderate Worcester Co W 750,963 

Boston-ManchesterPortsmouth (SE) Area NH Subpart 2/Moderate Hillsborough Co P 336,518 

Boston-ManchesterPortsmouth (SE) Area NH Subpart 2/Moderate Merrimack Co P 11,721 

Boston-ManchesterPortsmouth (SE) Area NH Subpart 2/Moderate Rockingham Co P 266,340 

A2-9
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Boston-ManchesterPortsmouth (SE) Area NH Subpart 2/Moderate Strafford Co P 82,134 

Buffalo-Niagara Falls Area NY Subpart 1 Erie Co W 950,265 

Buffalo-Niagara Falls Area NY Subpart 1 Niagara Co W 219,846 

Canton-Massillon Area OH Subpart 1 Stark Co W 378,098 

Cass County Area MI Subpart 2/Marginal Cass Co W 51,104 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Cabarrus Co W 131,063 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Gaston Co W 190,365 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Iredell Co P 39,885 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Lincoln Co W 63,780 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Mecklenburg Co W 695,454 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Rowan Co W 130,340 

Charlotte-Gastonia-Rock Hill 

Area NC Subpart 2/Moderate Union Co W 123,677 

Charlotte-Gastonia-Rock Hill 

Area SC Subpart 2/Moderate York Co P 102,000 

Chattanooga Area GA Subpart 1 - EAC Catoosa Co W 53,282 

Chattanooga Area TN Subpart 1 - EAC Hamilton Co W 307,896 

Chattanooga Area TN Subpart 1 - EAC Meigs Co W 11,086 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Cook Co W 5,376,741 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Du Page Co W 904,161 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Grundy Co P 6,309 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Kane Co W 404,119 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Kendall Co P 28,417 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Lake Co W 644,356 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Mc Henry Co W 260,077 

Chicago-Gary-Lake County 

Area IL Subpart 2/Moderate Will Co W 502,266 

Chicago-Gary-Lake County 

Area IN Subpart 2/Moderate Lake Co W 484,564 

Chicago-Gary-Lake County 

Area IN Subpart 2/Moderate Porter Co W 146,798 

Chico Area CA Subpart 1 Butte Co W 203,171 

Cincinnati-Hamilton Area IN Subpart 1 Dearborn Co P 10,434 

Cincinnati-Hamilton Area KY Subpart 1 Boone Co W 85,991 

Cincinnati-Hamilton Area KY Subpart 1 Campbell Co W 88,616 

Cincinnati-Hamilton Area KY Subpart 1 Kenton Co W 151,464 

A2-10
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Cincinnati-Hamilton Area OH Subpart 1 Butler Co W 332,807 

Cincinnati-Hamilton Area OH Subpart 1 Clermont Co W 177,977 

Cincinnati-Hamilton Area OH Subpart 1 Clinton Co W 40,543 

Cincinnati-Hamilton Area OH Subpart 1 Hamilton Co W 845,303 

Cincinnati-Hamilton Area OH Subpart 1 Warren Co W 158,383 

Clearfield and Indiana 

Counties Area PA Subpart 1 Clearfield Co W 83,382 

Clearfield and Indiana 

Counties Area PA Subpart 1 Indiana Co W 89,605 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Ashtabula Co W 102,728 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Cuyahoga Co W 1,393,978 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Geauga Co W 90,895 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Lake Co W 227,511 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Lorain Co W 284,664 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Medina Co W 151,095 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Portage Co W 152,061 

Cleveland-Akron-Lorain Area OH Subpart 2/Moderate Summit Co W 542,899 

Columbia Area SC Subpart 1 - EAC Lexington Co P 181,265 

Columbia Area SC Subpart 1 - EAC Richland Co P 313,253 

Columbus Area OH Subpart 1 Delaware Co W 109,989 

Columbus Area OH Subpart 1 Fairfield Co W 122,759 

Columbus Area OH Subpart 1 Franklin Co W 1,068,978 

Columbus Area OH Subpart 1 Knox Co W 54,500 

Columbus Area OH Subpart 1 Licking Co W 145,491 

Columbus Area OH Subpart 1 Madison Co W 40,213 

Dallas-Fort Worth Area TX Subpart 2/Moderate Collin Co W 491,675 

Dallas-Fort Worth Area TX Subpart 2/Moderate Dallas Co W 2,218,899 

Dallas-Fort Worth Area TX Subpart 2/Moderate Denton Co W 432,976 

Dallas-Fort Worth Area TX Subpart 2/Moderate Ellis Co W 111,360 

Dallas-Fort Worth Area TX Subpart 2/Moderate Johnson Co W 126,811 

Dallas-Fort Worth Area TX Subpart 2/Moderate Kaufman Co W 71,313 

Dallas-Fort Worth Area TX Subpart 2/Moderate Parker Co W 88,495 

Dallas-Fort Worth Area TX Subpart 2/Moderate Rockwall Co W 43,080 

Dallas-Fort Worth Area TX Subpart 2/Moderate Tarrant Co W 1,446,219 

Dayton-Springfield Area OH Subpart 1 Clark Co W 144,742 

Dayton-Springfield Area OH Subpart 1 Greene Co W 147,886 

Dayton-Springfield Area OH Subpart 1 Miami Co W 98,868 

Dayton-Springfield Area OH Subpart 1 Montgomery Co W 559,062 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Adams Co W 348,618 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Arapahoe Co W 487,967 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Boulder Co W 269,814 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Broomfield Co W 38,272 

Denver-Boulder-Greeley-Ft. CO Subpart 1 - EAC Denver Co W 554,636 

A2-11
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Collins-Love. Area 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Douglas Co W 175,766 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Jefferson Co W 525,507 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Larimer Co P 239,000 

Denver-Boulder-Greeley-Ft. 

Collins-Love. Area CO Subpart 1 - EAC Weld Co P 172,000 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Lenawee Co W 98,890 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Livingston Co W 156,951 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Macomb Co W 788,149 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Monroe Co W 145,945 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Oakland Co W 1,194,156 

Detroit-Ann Arbor Area MI Subpart 2/Marginal St Clair Co W 164,235 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Washtenaw Co W 322,895 

Detroit-Ann Arbor Area MI Subpart 2/Marginal Wayne Co W 2,061,162 

Door County Area WI Subpart 1 Door Co W 27,961 

Erie Area PA Subpart 1 Erie Co W 280,843 

Essex County (Whiteface 

Mtn.) Area NY Subpart 1 Essex Co P 1,000 

Fayetteville Area NC Subpart 1 - EAC Cumberland Co W 302,963 

Flint Area MI Subpart 1 Genesee Co W 436,141 

Flint Area MI Subpart 1 Lapeer Co W 87,904 

Fort Wayne Area IN Subpart 1 Allen Co W 331,849 

Franklin County Area PA Subpart 1 Franklin Co W 129,313 

Frederick County Area VA Subpart 1 - EAC Frederick Co W 59,209 

Frederick County Area VA Subpart 1 - EAC Winchester W 23,585 

Grand Rapids Area MI Subpart 1 Kent Co W 574,335 

Grand Rapids Area MI Subpart 1 Ottawa Co W 238,314 

Greater Connecticut Area CT Subpart 2/Moderate Hartford Co W 857,183 

Greater Connecticut Area CT Subpart 2/Moderate Litchfield Co W 182,193 

Greater Connecticut Area CT Subpart 2/Moderate New London Co W 259,088 

Greater Connecticut Area CT Subpart 2/Moderate Tolland Co W 136,364 

Greater Connecticut Area CT Subpart 2/Moderate Windham Co W 109,091 

Greene County Area PA Subpart 1 Greene Co W 40,672 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Alamance Co W 130,800 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Caswell Co W 23,501 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Davidson Co W 147,246 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Davie Co W 34,835 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Forsyth Co W 306,067 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Guilford Co W 421,048 

A2-12
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Randolph Co W 130,454 

Greensboro-Winston-SalemHigh Point Area NC Subpart 2/Marginal - EAC Rockingham Co W 91,928 

Greenville-SpartanburgAnderson Area SC Subpart 1 - EAC Anderson Co W 165,740 

Greenville-SpartanburgAnderson Area SC Subpart 1 - EAC Greenville Co W 379,616 

Greenville-SpartanburgAnderson Area SC Subpart 1 - EAC Spartanburg Co W 253,791 

Hancock, Knox, Lincoln and 

Waldo Counties (Central 

Maine Coast) Area 

ME Subpart 1 Hancock Co P 29,805 

Hancock, Knox, Lincoln and 

Waldo Counties (Central 

Maine Coast) Area 

ME Subpart 1 Knox Co P 33,563 

Hancock, Knox, Lincoln and 

Waldo Counties (Central 

Maine Coast) Area 

ME Subpart 1 Lincoln Co P 28,504 

Hancock, Knox, Lincoln and 

Waldo Counties (Central 

Maine Coast) Area 

ME Subpart 1 Waldo Co P 604 

Harrisburg-Lebanon-Carlisle 

Area PA Subpart 1 Cumberland Co W 213,674 

Harrisburg-Lebanon-Carlisle 

Area PA Subpart 1 Dauphin Co W 251,798 

Harrisburg-Lebanon-Carlisle 

Area PA Subpart 1 Lebanon Co W 120,327 

Harrisburg-Lebanon-Carlisle 

Area PA Subpart 1 Perry Co W 43,602 

Haywood and Swain Counties 

(Great Smoky NP) Area NC Subpart 1 Haywood Co P 28 

Haywood and Swain Counties 

(Great Smoky NP) Area NC Subpart 1 Swain Co P 260 

Hickory-Morganton-Lenoir 

Area NC Subpart 1 - EAC Alexander Co W 33,603 

Hickory-Morganton-Lenoir 

Area NC Subpart 1 - EAC Burke Co P 69,970 

Hickory-Morganton-Lenoir 

Area NC Subpart 1 - EAC Caldwell Co P 64,254 

Hickory-Morganton-Lenoir 

Area NC Subpart 1 - EAC Catawba Co W 141,685 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Brazoria Co W 241,767 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Chambers Co W 26,031 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Fort Bend Co W 354,452 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Galveston Co W 250,158 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Harris Co W 3,400,578 

A2-13
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Liberty Co W 70,154 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Montgomery Co W 293,768 

Houston-Galveston-Brazoria 

Area TX Subpart 2/Moderate Waller Co W 32,663 

Huntington-Ashland Area KY Subpart 1 Boyd Co W 49,752 

Huron County Area MI Subpart 1 Huron Co W 36,079 

Imperial County Area CA Subpart 2/Marginal Imperial Co W 142,361 

Indianapolis Area IN Subpart 1 Boone Co W 46,107 

Indianapolis Area IN Subpart 1 Hamilton Co W 182,740 

Indianapolis Area IN Subpart 1 Hancock Co W 55,391 

Indianapolis Area IN Subpart 1 Hendricks Co W 104,093 

Indianapolis Area IN Subpart 1 Johnson Co W 115,209 

Indianapolis Area IN Subpart 1 Madison Co W 133,358 

Indianapolis Area IN Subpart 1 Marion Co W 860,454 

Indianapolis Area IN Subpart 1 Morgan Co W 66,689 

Indianapolis Area IN Subpart 1 Shelby Co W 43,445 

Jamestown Area NY Subpart 1 Chautauqua Co W 139,750 

Jefferson County Area NY Subpart 2/Moderate Jefferson Co W 111,738 

Johnson City-KingsportBristol Area TN Subpart 1 - EAC Hawkins Co W 53,563 

Johnson City-KingsportBristol Area TN Subpart 1 - EAC Sullivan Co W 153,048 

Johnstown Area PA Subpart 1 Cambria Co W 152,598 

Kalamazoo-Battle Creek Area MI Subpart 1 Calhoun Co W 137,985 

Kalamazoo-Battle Creek Area MI Subpart 1 Kalamazoo Co W 238,603 

Kalamazoo-Battle Creek Area MI Subpart 1 Van Buren Co W 76,263 

Kent and Queen Anne's 

Counties Area MD Subpart 2/Marginal Kent Co W 19,197 

Kent and Queen Anne's 

Counties Area MD Subpart 2/Marginal Queen Annes Co W 40,563 

Kern County (Eastern Kern) 

Area CA Subpart 1 Kern Co P 99,251 

Kewaunee County Area WI Subpart 1 Kewaunee Co W 20,187 

Knoxville Area TN Subpart 1 Anderson Co W 71,330 

Knoxville Area TN Subpart 1 Blount Co W 105,823 

Knoxville Area TN Subpart 1 Cocke Co P 20 

Knoxville Area TN Subpart 1 Jefferson Co W 44,294 

Knoxville Area TN Subpart 1 Knox Co W 382,032 

Knoxville Area TN Subpart 1 Loudon Co W 39,086 

Knoxville Area TN Subpart 1 Sevier Co W 71,170 

La Porte County Area IN Subpart 2/Marginal La Porte Co W 110,106 

Lancaster Area PA Subpart 2/Marginal Lancaster Co W 470,658 

Lansing-East Lansing Area MI Subpart 1 Clinton Co W 64,753 

Lansing-East Lansing Area MI Subpart 1 Eaton Co W 103,655 

Lansing-East Lansing Area MI Subpart 1 Ingham Co W 279,320 

A2-14
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Las Vegas Area NV Subpart 1 Clark Co P 1,348,864 

Lima Area OH Subpart 1 Allen Co W 108,473 

Los Angeles and San 

Bernardino Counties (W 

Mojave Desert) Area 

CA Subpart 2/Moderate Los Angeles Co P 297,058 

Los Angeles and San 

Bernardino Counties (W 

Mojave Desert) Area 

CA Subpart 2/Moderate San Bernardino Co P 359,350 

Los Angeles-South Coast Air 

Basin Area CA Subpart 2/Severe 17 Los Angeles Co P 9,222,280 

Los Angeles-South Coast Air 

Basin Area CA Subpart 2/Severe 17 Orange Co W 2,846,289 

Los Angeles-South Coast Air 

Basin Area CA Subpart 2/Severe 17 Riverside Co P 1,194,859 

Los Angeles-South Coast Air 

Basin Area CA Subpart 2/Severe 17 San Bernardino Co P 1,330,159 

Louisville Area IN Subpart 1 Clark Co W 96,472 

Louisville Area IN Subpart 1 Floyd Co W 70,823 

Louisville Area KY Subpart 1 Bullitt Co W 61,236 

Louisville Area KY Subpart 1 Jefferson Co W 693,604 

Louisville Area KY Subpart 1 Oldham Co W 46,178 

Macon Area GA Subpart 1 Bibb Co W 153,887 

Macon Area GA Subpart 1 Monroe Co P 50 

Manitowoc County Area WI Subpart 1 Manitowoc Co W 82,887 

Mariposa and Tuolumne 

Counties (Southern Mountain 

Counties) Area 

CA Subpart 1 Mariposa Co W 17,130 

Mariposa and Tuolumne 

Counties (Southern Mountain 

Counties) Area 

CA Subpart 1 Tuolumne Co W 54,501 

Mason County Area MI Subpart 1 Mason Co W 28,274 

Memphis Area AR Subpart 2/Marginal Crittenden Co W 50,866 

Memphis Area TN Subpart 2/Marginal Shelby Co W 897,472 

Milwaukee-Racine Area WI Subpart 2/Moderate Kenosha Co W 149,577 

Milwaukee-Racine Area WI Subpart 2/Moderate Milwaukee Co W 940,164 

Milwaukee-Racine Area WI Subpart 2/Moderate Ozaukee Co W 82,317 

Milwaukee-Racine Area WI Subpart 2/Moderate Racine Co W 188,831 

Milwaukee-Racine Area WI Subpart 2/Moderate Washington Co W 117,493 

Milwaukee-Racine Area WI Subpart 2/Moderate Waukesha Co W 360,767 

Murray County 

(Chattahoochee Nat Forest) 

Area 

GA Subpart 1 Murray Co P 1,000 

Muskegon Area MI Subpart 2/Marginal Muskegon Co W 170,200 

Nashville Area TN Subpart 1 - EAC Davidson Co W 569,891 

Nashville Area TN Subpart 1 - EAC Rutherford Co W 182,023 

Nashville Area TN Subpart 1 - EAC Sumner Co W 130,449 

Nashville Area TN Subpart 1 - EAC Williamson Co W 126,638 

Nashville Area TN Subpart 1 - EAC Wilson Co W 88,809 

A2-15
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Nevada County (Western 

part) Area CA Subpart 1 Nevada Co P 77,735 

New York-N. New JerseyLong Island Area CT Subpart 2/Moderate Fairfield Co W 882,567 

New York-N. New JerseyLong Island Area CT Subpart 2/Moderate Middlesex Co W 155,071 

New York-N. New JerseyLong Island Area CT Subpart 2/Moderate New Haven Co W 824,008 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Bergen Co W 884,118 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Essex Co W 793,633 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Hudson Co W 608,975 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Hunterdon Co W 121,989 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Middlesex Co W 750,162 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Monmouth Co W 615,301 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Morris Co W 470,212 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Passaic Co W 489,049 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Somerset Co W 297,490 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Sussex Co W 144,166 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Union Co W 522,541 

New York-N. New JerseyLong Island Area NJ Subpart 2/Moderate Warren Co W 102,437 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Bronx Co W 1,332,650 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Kings Co W 2,465,326 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Nassau Co W 1,334,544 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate New York Co W 1,537,195 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Queens Co W 2,229,379 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Richmond Co W 443,728 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Rockland Co W 286,753 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Suffolk Co W 1,419,369 

New York-N. New JerseyLong Island Area NY Subpart 2/Moderate Westchester Co W 923,459 

Norfolk-Virginia BeachNewport News (Hampton VA Subpart 2/Marginal Chesapeake W 199,184 

A2-16
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Roads) Area 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Gloucester Co W 34,780 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Hampton W 146,437 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Isle Of Wight Co W 29,728 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal James City Co W 48,102 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Newport News W 180,150 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Norfolk W 234,403 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Poquoson W 11,566 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Portsmouth W 100,565 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Suffolk W 63,677 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Virginia Beach W 425,257 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal Williamsburg W 11,998 

Norfolk-Virginia BeachNewport News (Hampton 

Roads) Area 

VA Subpart 2/Marginal York Co W 56,297 

Parkersburg-Marietta Area OH Subpart 1 Washington Co W 63,251 

Parkersburg-Marietta Area WV Subpart 1 Wood Co W 87,986 

Philadelphia-WilmingtonAtlantic City Area DE Subpart 2/Moderate Kent Co W 126,697 

Philadelphia-WilmingtonAtlantic City Area DE Subpart 2/Moderate New Castle Co W 500,265 

Philadelphia-WilmingtonAtlantic City Area DE Subpart 2/Moderate Sussex Co W 156,638 

Philadelphia-WilmingtonAtlantic City Area MD Subpart 2/Moderate Cecil Co W 85,951 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Atlantic Co W 252,552 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Burlington Co W 423,394 

A2-17
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Camden Co W 508,932 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Cape May Co W 102,326 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Cumberland Co W 146,438 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Gloucester Co W 254,673 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Mercer Co W 350,761 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Ocean Co W 510,916 

Philadelphia-WilmingtonAtlantic City Area NJ Subpart 2/Moderate Salem Co W 64,285 

Philadelphia-WilmingtonAtlantic City Area PA Subpart 2/Moderate Bucks Co W 597,635 

Philadelphia-WilmingtonAtlantic City Area PA Subpart 2/Moderate Chester Co W 433,501 

Philadelphia-WilmingtonAtlantic City Area PA Subpart 2/Moderate Delaware Co W 550,864 

Philadelphia-WilmingtonAtlantic City Area PA Subpart 2/Moderate Montgomery Co W 750,097 

Philadelphia-WilmingtonAtlantic City Area PA Subpart 2/Moderate Philadelphia Co W 1,517,550 

Phoenix-Mesa Area AZ Subpart 1 Maricopa Co P 3,054,504 

Phoenix-Mesa Area AZ Subpart 1 Pinal Co P 31,541 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Allegheny Co W 1,281,666 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Armstrong Co W 72,392 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Beaver Co W 181,412 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Butler Co W 174,083 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Fayette Co W 148,644 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Washington Co W 202,897 

Pittsburgh-Beaver Valley 

Area PA Subpart 1 Westmoreland Co W 369,993 

Portland Area ME Subpart 2/Marginal Androscoggin Co P 3,390 

Portland Area ME Subpart 2/Marginal Cumberland Co P 252,907 

Portland Area ME Subpart 2/Marginal Sagadahoc Co W 35,214 

Portland Area ME Subpart 2/Marginal York Co P 164,997 

Poughkeepsie Area NY Subpart 2/Moderate Dutchess Co W 280,150 

Poughkeepsie Area NY Subpart 2/Moderate Orange Co W 341,367 

Poughkeepsie Area NY Subpart 2/Moderate Putnam Co W 95,745 

Providence (all of RI) Area RI Subpart 2/Moderate Bristol Co W 50,648 

Providence (all of RI) Area RI Subpart 2/Moderate Kent Co W 167,090 

Providence (all of RI) Area RI Subpart 2/Moderate Newport Co W 85,433 

A2-18
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Providence (all of RI) Area RI Subpart 2/Moderate Providence Co W 621,602 

Providence (all of RI) Area RI Subpart 2/Moderate Washington Co W 123,546 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Chatham Co P 21,320 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Durham Co W 223,314 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Franklin Co W 47,260 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Granville Co W 48,498 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Johnston Co W 121,965 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Orange Co W 118,227 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Person Co W 35,623 

Raleigh-Durham-Chapel Hill 

Area NC Subpart 1 Wake Co W 627,846 

Reading Area PA Subpart 1 Berks Co W 373,638 

Richmond-Petersburg Area VA Subpart 2/Marginal Charles City Co W 6,926 

Richmond-Petersburg Area VA Subpart 2/Marginal Chesterfield Co W 259,903 

Richmond-Petersburg Area VA Subpart 2/Marginal Colonial Heights W 16,897 

Richmond-Petersburg Area VA Subpart 2/Marginal Hanover Co W 86,320 

Richmond-Petersburg Area VA Subpart 2/Marginal Henrico Co W 262,300 

Richmond-Petersburg Area VA Subpart 2/Marginal Hopewell W 22,354 

Richmond-Petersburg Area VA Subpart 2/Marginal Petersburg W 33,740 

Richmond-Petersburg Area VA Subpart 2/Marginal Prince George Co W 33,047 

Richmond-Petersburg Area VA Subpart 2/Marginal Richmond W 197,790 

Riverside County (Coachella 

Valley) Area CA Subpart 2/Serious Riverside Co P 324,750 

Roanoke Area VA Subpart 1 - EAC Botetourt Co W 30,496 

Roanoke Area VA Subpart 1 - EAC Roanoke W 94,911 

Roanoke Area VA Subpart 1 - EAC Roanoke Co W 85,778 

Roanoke Area VA Subpart 1 - EAC Salem W 24,747 

Rochester Area NY Subpart 1 Genesee Co W 60,370 

Rochester Area NY Subpart 1 Livingston Co W 64,328 

Rochester Area NY Subpart 1 Monroe Co W 735,343 

Rochester Area NY Subpart 1 Ontario Co W 100,224 

Rochester Area NY Subpart 1 Orleans Co W 44,171 

Rochester Area NY Subpart 1 Wayne Co W 93,765 

Rocky Mount Area NC Subpart 1 Edgecombe Co W 55,606 

Rocky Mount Area NC Subpart 1 Nash Co W 87,420 

Sacramento Metro Area CA Subpart 2/Serious El Dorado Co P 124,164 

Sacramento Metro Area CA Subpart 2/Serious Placer Co P 239,978 

Sacramento Metro Area CA Subpart 2/Serious Sacramento Co W 1,223,499 

Sacramento Metro Area CA Subpart 2/Serious Solano Co P 197,034 

Sacramento Metro Area CA Subpart 2/Serious Sutter Co P 25,013 

Sacramento Metro Area CA Subpart 2/Serious Yolo Co W 168,660 

A2-19
 
Draft Regulatory Impact Analysis 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

San Antonio Area TX Subpart 1 - EAC Bexar Co W 1,392,931 

San Antonio Area TX Subpart 1 - EAC Comal Co W 78,021 

San Antonio Area TX Subpart 1 - EAC Guadalupe Co W 89,023 

San Diego Area CA Subpart 1 San Diego Co P 2,813,431 

San Francisco Bay Area CA Subpart 2/Marginal Alameda Co W 1,443,741 

San Francisco Bay Area CA Subpart 2/Marginal Contra Costa Co W 948,816 

San Francisco Bay Area CA Subpart 2/Marginal Marin Co W 247,289 

San Francisco Bay Area CA Subpart 2/Marginal Napa Co W 124,279 

San Francisco Bay Area CA Subpart 2/Marginal San Francisco Co W 776,733 

San Francisco Bay Area CA Subpart 2/Marginal San Mateo Co W 707,161 

San Francisco Bay Area CA Subpart 2/Marginal Santa Clara Co W 1,682,585 

San Francisco Bay Area CA Subpart 2/Marginal Solano Co P 197,508 

San Francisco Bay Area CA Subpart 2/Marginal Sonoma Co P 413,716 

San Joaquin Valley Area CA Subpart 2/Serious Fresno Co W 799,407 

San Joaquin Valley Area CA Subpart 2/Serious Kern Co P 550,220 

San Joaquin Valley Area CA Subpart 2/Serious Kings Co W 129,461 

San Joaquin Valley Area CA Subpart 2/Serious Madera Co W 123,109 

San Joaquin Valley Area CA Subpart 2/Serious Merced Co W 210,554 

San Joaquin Valley Area CA Subpart 2/Serious San Joaquin Co W 563,598 

San Joaquin Valley Area CA Subpart 2/Serious Stanislaus Co W 446,997 

San Joaquin Valley Area CA Subpart 2/Serious Tulare Co W 368,021 

Scranton-Wilkes-Barre Area PA Subpart 1 Lackawanna Co W 213,295 

Scranton-Wilkes-Barre Area PA Subpart 1 Luzerne Co W 319,250 

Scranton-Wilkes-Barre Area PA Subpart 1 Monroe Co W 138,687 

Scranton-Wilkes-Barre Area PA Subpart 1 Wyoming Co W 28,080 

Sheboygan Area WI Subpart 2/Moderate Sheboygan Co W 112,646 

South Bend-Elkhart Area IN Subpart 1 Elkhart Co W 182,791 

South Bend-Elkhart Area IN Subpart 1 St Joseph Co W 265,559 

Springfield (W. Mass) Area MA Subpart 2/Moderate Berkshire Co W 134,953 

Springfield (W. Mass) Area MA Subpart 2/Moderate Franklin Co W 71,535 

Springfield (W. Mass) Area MA Subpart 2/Moderate Hampden Co W 456,228 

Springfield (W. Mass) Area MA Subpart 2/Moderate Hampshire Co W 152,251 

St. Louis Area IL Subpart 2/Moderate Jersey Co W 21,668 

St. Louis Area IL Subpart 2/Moderate Madison Co W 258,941 

St. Louis Area IL Subpart 2/Moderate Monroe Co W 27,619 

St. Louis Area IL Subpart 2/Moderate St Clair Co W 256,082 

St. Louis Area MO Subpart 2/Moderate Franklin Co W 93,807 

St. Louis Area MO Subpart 2/Moderate Jefferson Co W 198,099 

St. Louis Area MO Subpart 2/Moderate St Charles Co W 283,883 

St. Louis Area MO Subpart 2/Moderate St Louis W 348,189 

St. Louis Area MO Subpart 2/Moderate St Louis Co W 1,016,315 

State College Area PA Subpart 1 Centre Co W 135,758 

Steubenville-Weirton Area OH Subpart 1 Jefferson Co W 73,894 

Steubenville-Weirton Area WV Subpart 1 Brooke Co W 25,447 

Steubenville-Weirton Area WV Subpart 1 Hancock Co W 32,667 

A2-20
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

8-hour Ozone Nonattainment State Classificationa,b County Name Whole 

/Part 2000 Cty Pop 

Sutter County (part) (Sutter 

Buttes) Area CA Subpart 1 Sutter Co P 1 

Tioga County Area PA Subpart 1 Tioga Co W 41,373 

Toledo Area OH Subpart 1 Lucas Co W 455,054 

Toledo Area OH Subpart 1 Wood Co W 121,065 

Ventura County (part) Area CA Subpart 2/Moderate Ventura Co P 753,197 

Washington Area DC Subpart 2/Moderate Entire District W 572,059 

Washington Area MD Subpart 2/Moderate Calvert Co W 74,563 

Washington Area MD Subpart 2/Moderate Charles Co W 120,546 

Washington Area MD Subpart 2/Moderate Frederick Co W 195,277 

Washington Area MD Subpart 2/Moderate Montgomery Co W 873,341 

Washington Area MD Subpart 2/Moderate Prince George's Co W 801,515 

Washington Area VA Subpart 2/Moderate Alexandria W 128,283 

Washington Area VA Subpart 2/Moderate Arlington Co W 189,453 

Washington Area VA Subpart 2/Moderate Fairfax W 21,498 

Washington Area VA Subpart 2/Moderate Fairfax Co W 969,749 

Washington Area VA Subpart 2/Moderate Falls Church W 10,377 

Washington Area VA Subpart 2/Moderate Loudoun Co W 169,599 

Washington Area VA Subpart 2/Moderate Manassas W 35,135 

Washington Area VA Subpart 2/Moderate Manassas Park W 10,290 

Washington Area VA Subpart 2/Moderate Prince William Co W 280,813 

Washington County 

(Hagerstown) Area MD Subpart 1 - EAC Washington Co W 131,923 

Wheeling Area OH Subpart 1 Belmont Co W 70,226 

Wheeling Area WV Subpart 1 Marshall Co W 35,519 

Wheeling Area WV Subpart 1 Ohio Co W 47,427 

York Area PA Subpart 1 Adams Co W 91,292 

York Area PA Subpart 1 York Co W 381,751 

Youngstown-Warren-Sharon 

Area OH Subpart 1 Columbiana Co W 112,075 

Youngstown-Warren-Sharon 

Area OH Subpart 1 Mahoning Co W 257,555 

Youngstown-Warren-Sharon 

Area OH Subpart 1 Trumbull Co W 225,116 

Youngstown-Warren-Sharon 

Area PA Subpart 1 Mercer Co W 120,293 

A2-21
 
Draft Regulatory Impact Analysis 

References 

1

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. 

2

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. 

3 U.S. EPA 2006. Provisional Assessment of Recent Studies on Health Effects of Particulate 

Matter Exposure. EPA/600/R-06/063. 

4 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. 

5 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. 

6 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. 

7 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. 

8 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. 

9 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. 

10 Jerrett, M; Burnett, RT; Ma, R; et al. 2005. “Spatial Analysis of Air Pollution and 

Mortality in Los Angeles.” Epidemiology. 16(6):727-736. 

11 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/]. 

A2-22
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

12 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. 

13 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. 

14 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. 

15 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. 

16 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 and 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 

17 Multi-pollutant legislation modeling. (Multi-pollutant analyses and technical support 

documents. http://www.epa.gov/airmarkets/mp/.) 

18 U.S. Environmental Protection Agency (2006), Regulatory Impact Analysis for the Review 

of the Particulate Matter National Ambient Air Quality Standards, [Docket No. EPA –HQOAR-2006-0834]. October 6, 2006. Available Electronically at: 

http:www.epa.gov.ttn/ecas/ria.html. 

19 Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Applications 

of the Speciated Modeling Attainment Test (SMAT), Updated November 8, 2004 (EPA 

Docket #: OAR-2003-0053-1907). 

20 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. 

21 Currently, two established receptor models are widely used for source apportionment 

studies: the Chemical Mass Balance (CMB) model 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 

A2-23
 
Draft Regulatory Impact Analysis 

PMF technique decomposes the ambient measurement data matrix into source profiles and 

contributions by utilizing the underlying relationship (i.e., correlations) between the 

individually measured species. 

22 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 

23 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. 

24 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. 

25 Schauer, J. J.; Cass, G. R. Environ. Sci. Technol. 2000, 34 (9), 1821-1832. 

26Chow, J. C.; Watson, J. G.; Lowenthal, D. H.; Countess, R. J. Atmos. Environ. 1996, 30 

(9), 1489-1499. 

27 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. 

28 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. 

29 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. 

30 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. 

31 Malm, W. C.; Gebhart, K. A. JAWMA 1997, 47 (3), 250-268. 

32 Eatough, D. J.; Farber, R. J.; Watson, J. G. JAWMA 2000, 50 (5), 759-774. 

33 U.S. EPA (2006) National Ambient Air Quality Standards for Particulate Matter; Final 

Rule. October 17, 2006; Vol.71, No. 200; pp. 61144-61232. 

34 Final RIA PM NAAQS, Chapter 2: Defining the PM2.5 Air Quality Problem. October 17, 

2006. 

A2-24
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

35 Final RIA PM NAAQS, Chapter 2: Defining the PM2.5 Air Quality Problem. October 17, 

2006. 

36 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. 

37 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). 

38 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. 

39 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. 

40 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. 

41 Please see the Community Modeling and Analysis System (CMAS) Center website for 

complete details on CMAQ version 4.5: http://www.cmascenter.org/ 

42 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. 

43 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/ 

44 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry 

Modeling Group, Harvard University, Cambridge, MA, October 15, 2004. 

A2-25
 
Draft Regulatory Impact Analysis 

45 U.S. Environmental Protection Agency, March 2005. Technical Support Document for the 

Clean Air Interstate Rule: Air Quality Modeling, Office of Air Quality Planning and 

Standard, Research Triangle Park, NC. (Docket No. OAR-2005-0053-2151). 

46 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). 

47 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. 

48 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”. 

49 U.S. EPA, (2006), “Procedures for Estimating Future PM2.5 Values for the PM NAAQS 

by Application of the Speciated Modeled Attainment Test (SMAT)”. 

50 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 can be viewed on the National 

Academy Press Website at http://www.nap.edu/books/0309048443/html/ and is available in 

Docket EPA-HQ-OAR-2004-0008. 

51 See discussion in 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 

52 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. 

53 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. 

54 U.S. EPA (2004) Air Quality Criteria for Particulate Matter. Document Nos. EPA/600/P99/002aF and EPA/600/P-99/002bF. This document is available in Docket OAR-2004-0008, 

Document Nos. OAR-2004-0008-0042 and 0043. [Available online at 

http://cfpub.epa.gov/ncea/cfm/partmatt.cfm]. 

A2-26
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

55 CAND rule RIA. 

56 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. 

57 U.S. EPA. 1993. Effects of the 1990 Clean Air Act Amendments on Visibility in Class I 

Areas: An EPA Report to Congress. EPA452-R-93-014. 

58 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

454/K-03-001. 

59 National Park Service. Air Quality in the National Parks, Second edition. NPS, Air 

Resources Division. D 2266. September 2002. 

60 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

454/K-03-001. 

61 Deposition of Air Pollutants to the Great Waters, Third Report to Congress, June 2000, 

EPA-453/R-00-005. This document can be found in Docket No. OAR-2002-0030, 

Document No. OAR-2002-0030-0025. It is also available at 

www.epa.gov/oar/oaqps/gr8water/3rdrpt/obtain.html. 

62 Bricker, Suzanne B., et al., National Estuarine Eutrophication Assessment, Effects of 

Nutrient Enrichment in the Nation’s Estuaries, National Ocean Service, National Oceanic 

and Atmospheric Administration, September, 1999. 

63 Deposition of Air Pollutants to the Great Waters, Third Report to Congress, June, 2000. 

64 Valigura, Richard, et al., Airsheds and Watersheds II: A Shared Resources Workshop, Air 

Subcommittee of the Chesapeake Bay Program, March, 1997. 

65 The Impact of Atmospheric Nitrogen Deposition on Long Island Sound, The Long Island 

Sound Study, September, 1997. 

66 Dennis, Robin L., Using the Regional Acid Deposition Model to Determine the Nitrogen 

Deposition Airshed of the Chesapeake Bay Watershed, SETAC Technical Publications 

Series, 1997. 

67 Wedin, D.A. and D. Tilman. 1996. Influence of nitrogen loading and species composition 

on the carbon balance of grasslands. Science 274:1720-1723. 

68 U.S. EPA (2000) Deposition of Air Pollutants to the Great Waters: Third Report to 

Congress. Office of Air Quality Planning and Standards. EPA-453/R-00-0005. 

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Draft Regulatory Impact Analysis 

69 U.S. EPA (2004) National Coastal Condition Report II. Office of Research and 

Development/ Office of Water. EPA-620/R-03/002. 

70 Gao, Y., E.D. Nelson, M.P. Field, et al. 2002. “Characterization of atmospheric trace 

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71 Kim, G., N. Hussain, J.R. Scudlark, and T.M. Church. 2000. “Factors influencing the 

atmospheric depositional fluxes of stable Pb, 210Pb, and 7Be into Chesapeake Bay.” J. 

Atmos. Chem. 36: 65-79. 

72 Lu, R., R.P. Turco, K. Stolzenbach, et al. 2003. “Dry deposition of airborne trace metals 

on the Los Angeles Basin and adjacent coastal waters.” J. Geophys. Res. 108(D2, 4074): 

AAC 11-1 to 11-24. 

73 Marvin, C.H., M.N. Charlton, E.J. Reiner, et al. 2002. “Surficial sediment contamination 

in Lakes Erie and Ontario: A comparative analysis.” J. Great Lakes Res. 28(3): 437-450. 

74 Smith, W.H. 1991. “Air pollution and Forest Damage.” Chemical Engineering News, 

69(45): 30-43. 

75 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. 

76 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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77 Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. “Effect of heavy metals and storage 

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78 Mason, R.P. and Sullivan, K.A. 1997. “Mercury in Lake Michigan.” Environmental 

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79 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. 

80 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. 

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81 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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82 Rice, K.C. 1999. “Trace Element Concentrations in Streambed Sediment Across the 

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83 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. 

84 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. 

85 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. 

86 Simcik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. “Atmospheric Loading of 

Polycyclic Aromatic Hydrocarbons to Lake Michigan as Recorded in the Sediments.” 

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87 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.” 

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88 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. “Fate of Atmospherically 

Deposited Polycyclic Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental 

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89 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. 

90 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. 

91 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. 

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92 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. 

93 Van Metre, P.C.; Mahler, B.J.; and Furlong, E.T. 2000. “Urban Sprawl Leaves its PAH 

Signature.” Environmental Science & Technology, 34: 4064-4070. 

94 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. “A review of the processes involved in the 

exchange of semi-volatile organic compounds across the air-soil interface.” The Science of 

the Total Environment, 228: 5-24. 

95 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. 

96 U.S. EPA. 1996. Air Quality Criteria for Ozone and Related Photochemical Oxidants, 

EPA600-P-93-004aF. 

97. U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

98 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. 

99.Bates, D.V.; Baker-Anderson, M.; Sizto, R. (1990) Asthma attack periodicity: a study of 

hospital emergency visits in Vancouver. Environ. Res. 51: 51-70. 

100.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. 

101.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. 

102.Lipfert, F.W.; Hammerstrom, T. (1992) Temporal patterns in air pollution and hospital 

admissions. Environ. Res. 59: 374-399. 

103.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: 

172-194. 

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104. U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

105. U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

106.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. 

107.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. 

108.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) 

Ozone-induced inflammation in the lower airways of human subjects. Am. Rev. Respir. Dis. 

139: 407-415. 

109.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. 

110.U.S. EPA (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants, 

EPA600-P-93-004aF; (See page 7-171). 

111.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. 

112.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. 

Environ. Health 43: 279-285. 

113.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. 

114. 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. 

115. U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

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Draft Regulatory Impact Analysis 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

116. U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

117. 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. 

118. 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. 

119. 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. 

120. 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. 

121. 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). 

122. 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. 

123.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. 

124 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

125 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. 

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126 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. 

127 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. 

128 NARSTO Synthesis Team (2000). An Assessment of Tropospheric Ozone Pollution: A 

North American Perspective. 

129 Fujita, E.M., W.R. Stockwell, D.E. Campbell, R.E. Keislar, and D.R. Lawson (2003). 

Evolution of the Magnitude and Spatial Extent of the Weekend Ozone Effect in California's 

South Coast Air Basin from 1981 to 2000, Submitted to the J. Air & Waste Manage. Assoc. 

130 Marr, L.C. and R.A. Harley (2002). Modeling the Effect of Weekday-Weekend 

Differences in Motor Vehicle Emissions on Photochemical Air Pollution in Central 

California, Environ. Sci. Technol., 36, 4099-4106. 

131 Larsen, L.C. (2003). The Ozone Weekend Effect in California: Evidence Supporting NOx 

Emissions Reductions, Submitted to the J. Air & Waste Manage. Assoc. 

132 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.). 

133 (NAS, 1991). 

134 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. 

135 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. 

136 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 

2006.This document is available in Docket EPA-HQ-OAR-2005-0036. 

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137 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. 

138 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 

2006.This document is available in Docket EPA-HQ-OAR-2005-0036. 

139 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

140 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

141 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

142 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. 

143 Winner, W.E., 1994. “Mechanistic analysis of plant responses to air pollution.” 

Ecological Applications, 4(4):651-661. 

144 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

145 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

146 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. 

147 De Steiguer, J., J. Pye, C. Love. 1990. “Air Pollution Damage to U.S. Forests.” Journal of 

Forestry, Vol 88 (8) pp. 17-22. 

148 Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of 

Environmental Quality 17 pp.347-360. 

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149 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

150 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

151 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. 

152 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). 

153 This document is available in Docket EPA-HQ-OAR-2005-0036.153 U.S. EPA. Air 

Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Environmental 

Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is 

available in Docket EPA-HQ-OAR-2005-0036. 

154 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. 

155 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. 

156 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. 

157 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. 

158 U.S. EPA (2006) National-Scale Air Toxics Assessment for 1999. 

http://www.epa.gov/ttn/atw/nata1999. 

159 U.S. EPA (2002) Health Assessment Document for Diesel Engine Exhaust. EPA/600/8­

90/057F Office of Research and Development, Washington DC. Pp1-1 1-2. This document 

is available electronically at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. 

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160 U.S. EPA (2002) Health Assessment Document for Diesel Engine Exhaust. EPA/600/8­

90/057F Office of Research and Development, Washington DC. This document is available 

electronically at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. 

161 U.S. EPA. (1999). Guidelines for Carcinogen Risk Assessment. Review Draft. NCEA-F0644, July. Risk Assessment Forum, Washington, DC. 

http://www.epa.gov/ncea/raf/cancer.htm. 

162 U.S. EPA. (1986) .Guidelines for carcinogen risk assessment. Federal Register 

51(185):33992-34003. 

163 National Institute for Occupational Safety and Health (NIOSH). (1988). Carcinogenic 

effects of exposure to diesel exhaust. NIOSH Current Intelligence Bulletin 50. DHHS 

(NIOSH) Publication No. 88-116. Atlanta, GA: Centers for Disease Control. 

164 International Agency for Research on Cancer - IARC. (1997). Monographs on the 

evaluation of carcinogenic risks to humans. Vol. 68. Silica, some silicates, coal dust and 

para-aramid fibrils. Lyon, France: IARC, pp. 362-375. 

165 National Institute for Occupational Safety and Health (NIOSH). (1988). Carcinogenic 

effects of exposure to diesel exhaust.. NIOSH Current Intelligence Bulletin 50. DHHS 

(NIOSH) Publication No. 88-116. Atlanta, GA: Centers for Disease Control. 

166 World Health Organization International Program on Chemical Safety (1996). 

Environmental Health Criteria 171. Diesel fuel and exhaust emissions. Geneva: World 

Health Organization, pp.172-176. 

167 California Environmental Protection Agency. (Cal EPA, OEHHA) (1998). Health risk 

assessment for diesel exhaust. Public and Scientific Review Draft. 

168 National Toxicology Program (NTP). (2000). 9th 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. 

169 Health Effects Institute (HEI). (1995). Diesel exhaust: a critical analysis of emissions, 

exposure, and health effects. Cambridge, MA. 

170 Health Effects Institute (HEI) (1999). Diesel emissions and lung cancer: epidemiology 

and quantitative risk assessment. A special report of the Institute's Diesel Epidemiology 

Expert Panel. Cambridge, MA. 

171 Health Effects Institute (HEI). (2002). Research directions to improve estimates of 

human exposure and risk assessment. A special report of the Institute's Diesel Epidemiology 

Working Group, Cambridge, MA. 

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172 Ishinishi, N., Kuwabara, N., Takaki, Y., et al. (1988). Long-term inhalation experiments 

on diesel exhaust. In: Diesel exhaust and health risks. Results of the HERP studies. Ibaraki, 

Japan: Research Committee for HERP Studies; pp. 11-84. 

173 Lewtas, J. (1983). Evaluation of the mutagenicity and carcinogenicity of motor vehicle 

emissions in short-term bioassays. Environ Health Perspect 47:141-152. 

174 Garshick, E., Schenker, M., Munoz, A, et al. (1987). A case-control study of lung 

cancer and diesel exhaust exposure in railroad workers. Am Rev Respir Dis 135:1242-1248. 

175 Garshick, E., Schenker, M., Munoz, A, et al. (1988). A retrospective cohort study of 

lung cancer and diesel exhaust exposure in railroad workers. Am Rev Respir Dis 137:820­

825. 

176 Woskie, SR; Smith, TJ; Hammond, SK; et al. (1988). Estimation of the diesel exhaust 

exposures of railroad workers. I. Current exposures. Am J Ind Med 13:381-394. 

177 Steenland, K., Silverman, D, Hornung, R. (1990). Case-control study of lung cancer and 

truck driving in the Teamsters Union. Am J Public Health 80:670-674. 

178 Steenland, K., Deddens, J., Stayner, L. (1998). Diesel exhaust and lung cancer in the 

trucking industry: exposure-response analyses and risk assessment. Am J Ind Med 34:220­

228. 

179 Steenland, K., Deddens, J., Stayner, L. (1998). Diesel exhaust and lung cancer in the 

trucking industry: exposure-response analyses and risk assessment. Am J Ind Med 34:220­

228. 

180 Zaebst, DD; Clapp, DE; Blake, LM; et al. (1991). Quantitative determination of trucking 

industry workers' exposures to diesel exhaust particles. Am Ind Hyg Assoc J 52:529-541. 

181 Saverin, R. (1999). German potash miners: cancer mortality. Health Effects Institute 

Number 7. March 7-9, Stone Mountain, GA, pp. 220-229. 

182 Saverin, R. (1999). German potash miners: cancer mortality. Health Effects Institute 

Number 7. March 7-9, Stone Mountain, GA, pp. 220-229. 

183 U.S. EPA (2002) Health Assessment Document for Diesel Engine Exhaust. EPA/6008­

90/057F Office of Research and Development, Washington DC. 9-11. 

184 Bhatia, R., Lopipero, P., Smith, A. (1998) Diesel exposure and lung cancer. 

Epidemiology 9(1):84-91. 

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185 Lipsett, M: Campleman, S; (1999) Occupational exposure to diesel exhaust and lung 

cancer: a meta-analysis. Am J Public Health 80(7): 1009-1017. 

186 Garshcik, E; Schenker, MB; Munoz,A; et al. (1988) A retrospective cohort study of lung 

cancer and diesel exhaust exposure in railroad workers. Am Rev Respir Dis 137; 820-825. 

187 Swanson, GM; Lin, CS; Burn, PB. (1993) Diversity in the association between occupation 

and lung cancer among black and white men. Cancer Epidemiol Biomarkers Prev 2:313-320. 

188 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. 

189 U.S. EPA (2002), National-Scale Air Toxics Assessment for 1996. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata/. 

190 U.S. EPA (2006), National-Scale Air Toxics Assessment for 1999. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata1999/. 

191 Ishinishi, N; Kuwabara, N; Takaki, Y; et al. (1988) Long-term inhalation experiments on 

diesel exhaust. In: Diesel exhaust and health risks. Results of the HERP studies. Ibaraki, 

Japan: Research Committee for HERP Studies; pp.11-84. 

192 Heinrich, U; Fuhst, R; Rittinghausen, S; et al. (1995) Chronic inhalation exposure of 

Wistar rats and two different strains of mice to diesel engine exhaust, carbon black, and 

titanium dioxide. Inhal Toxicol 7:553-556. 

193 Mauderly, JL; Jones, RK; Griffith, WC; et al. (1987) Diesel exhaust is a pulmonary 

carcinogen in rats exposed chronically by inhalation. Fundam Appl Toxicol 9:208-221. 

194 Nikula, KJ; Snipes, MB; Barr, EB; et al. (1995) Comparative pulmonary toxicities and 

carcinogenicities of chronically inhaled diesel exhaust and carbon black in F344 rats. 

Fundam Appl Toxicol 25:80-94. 

195 Reger, R; Hancock, J; Hankinson, J; et al. (1982) Coal miners exposed to diesel exhaust 

emissions. Ann OccupHyg 26:799-815. 

196 Attfield, MD. (1978) The effect of exposure to silica and diesel exhaust in underground 

metal and nonmetal miners. In: Industrial hygiene for mining and tunneling: proceedings of a 

topical symposium; November; Denver, CO. Kelley, WD, ed. Cincinnati, OH: The American 

Conference of Governmental Industrial Hygienists, Inc. 

197 Wade, JF, III; Newman, LS. (1993) Diesel asthma: reactive airways disease following 

overexposure to locomotive exhaust. J Occup Med 35:149-154. 

A2-38
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

198 Kilburn (2000) See HAD Chapter 5-7. 

199 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. 

200 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. 

201 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. 

202 Kinnee,E.J.; Touman, J.S.; Mason, R.; Thurman,J.; Beidler, A.; Bailey, C.; Cook, R. 

(2004) Allocation of onroad mobile emissions to road segments for air toxics modeling in an 

urban area. Transport. Res. Part D 9: 139-150. 

203 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 and 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 

204 Hand, R.; Pingkuan, D.; Servin, A.; Hunsaker, L.; Suer, C. (2004) Roseville rail yard 

study. California Air Resources Board. [Online at 

http://www.arb.ca.gov/diesel/documents/rrstudy.htm] 

205 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 

Resources Board. [Online at 

http://www.arb.ca.gov/msprog/offroad/marinevess/marinevess.htm] 

206 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 used laboratory animal species). 

207 Defined in the IRIS database as exposure to a substance spanning approximately 10 of the 

lifetime of an organism. 

208 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 

hours or less. 

A2-39
 
Draft Regulatory Impact Analysis 

209 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 

210 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. 

211 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. 

212 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health 

Perspect. 82: 193-197. 

213 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art 

Reviews. 3: 541-554. 

214 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. 

215 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. 

216 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. 

217 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. 

218 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers 

Exposed to Low Levels of Benzene. Science 306: 1774-1776. 

219 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. 

A2-40
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

220 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. 

221 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. 

222 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. 

223 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. 

224 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. 

225 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. 

226 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to 

formaldehyde: an update. Occup. Environ. Med. 61: 193-200. 

227 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. 

228 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. 

229 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. 

A2-41
 
Draft Regulatory Impact Analysis 

230 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. 

231 Health Canada (2001) Priority Substances List Assessment Report. Formaldehyde. 

Environment Canada, Health Canada, February 2001. The document may be accessed at 

http://www.hc-sc.gc.ca/ewh-semt/pubs/contaminants/psl2-lsp2/formaldehyde/index_e.html. 

232 U.S. EPA (2004) National Emission Standards for Hazardous Air Pollutants for Plywood 

and Composite Wood Products Manufacture: Final Rule. (69 FR 45943, 7/30/04) 

233 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. 

234 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. 

235 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 

236 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. 

237 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. 

238 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. 

239 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the 

IRIS Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004. 

http://cfpub2.epa.gov/ncea/cfm/recordisplay.cfm?deid=86019 

240 International Agency for Research on Cancer (IARC). (2002). Monographs on the 

Evaluation of the Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France. 

A2-42
 
Chapter 2 Appendices: Air Quality and Resulting Health and Welfare Effects 

241 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 

http://www.epa.gov/iris/subst/0436.htm 

A2-43
 
Chapter 3: Inventory 

1
 
Draft Regulatory Impact Analysis 

Emission Inventory 

This chapter presents our analysis of the emission impact of the proposed rule for the 

three source categories affected: commercial marine diesel engines, recreational marine 

diesel engines, and locomotives. The proposed control requirements include NOx and PMa 

emission standards for Category 1 and Category 2 commercial marine diesel engines (both 

above and below 37 kilowatts [kW]). New NOx and PM emission standards would also 

apply to all recreational marine diesel engines and locomotives. There are no new standards 

for HC or CO; however, the PM standards are also expected to decrease HC emissions. 

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 proposed 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 proposed 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 proposed 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. 

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 proposed 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 

a

 PM in this document refers to PM10, which are particles less than 10 microns in diameter. 

2
 
Chapter 3: Inventory 

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 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 

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 1 I = N*P*L*A*EF 

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 in any given 

simulation year. 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 proposed regulations, as well as emissions 

deterioration. PM emission factors also have an additional adjustment to account for the inuse 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. 

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. 

3
 
Draft Regulatory Impact Analysis 

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 2 DF = 1 + A*(age/ML) 

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: 

Equation 3 SPM adj =FC * 7.1 * 0.02247 * 224/32 * (soxdsl - soxbas) * 1/2000 

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 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. 

4
 
Chapter 3: Inventory 

Estimation of fuel consumption. Annual fuel consumption is estimated using the 

following equation: 

Equation 4 FC = (BSFC * N * P * L *A)/(7.1 * 454) 

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: 

Equation 5 SO2 = FC * 7.1 * (1-0.02247) * 64/32 * soxdsl * 1/2000 

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.4 

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 model3,2 and the Clean Air Nonroad 

Diesel Rule.4 

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.5

 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. 

5
 
Draft Regulatory Impact Analysis 

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 0-1 and Table 0-2. 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 proposed 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 proposed 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 0-1 and the inputs for the high power density engines in Table 0-2. 

The proposed Tier 4 standards that apply to Category 1 engines vary by the following 

kW categories: <600kW, 600≤kW<1000, 1000≤kW<1400, 1400≤kW<3700, and ≥3700kW. 

As a result, these power categories were also added, with the exception of the ≥3700kW 

category, 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 

6
 
Chapter 3: Inventory 

activity estimates used were 0.79 and 4,503 hours per year. These latter estimates were taken 

from the 1999 Marine Diesel FRM.6

 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.7 This power break point is not related to 

the kW categories in the proposed 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. 

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.8 

7
 
Draft Regulatory Impact Analysis 

Table 0-1 Inventory Inputs for C1 Propulsion Standard Power Density Engines 

DISPLACEMENT CATEGORY 

<35 W/L 

<=600KW 600<KW≤1000 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 

DISPLACEMENT CATEGORY 

<35 KW/L 

1000<KW≤1400KW >1400KW* 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 * No populations ≥3700KW 

8
C

Table 0-2 Inventory Inputs for C1 Propulsion High Power Density Engines 

DISPLACEMENT CATEGORY 

≥35 KW/L 

<=600KW 600<KW≤1000 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 

DISPLACEMENT CATEGORY 

≥35 KW/L 

1000<KW≤1400KW >1400KW* 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 * No populations ≥3700KW 

9
Draft Regulatory Impact Analysis 

The baseline emission factors are given in Table 0-3 and Table 0-4. 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 begin 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 0-3 Baseline PM10 and NOx Emission Factors for C1 Propulsion Engines* 

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 

* Deterioration is applied to the PM emission factors (EFs); see text for details. The NOx EFs are not 

subject to deterioration. 

Table 0-4 Baseline HC and CO Emission Factors for C1 Propulsion Engines* 

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 

* 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.6 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 

b

 The E3 duty cycle is designated for propulsion marine diesel engines. 

10
 
Chapter 3: Inventory 

emission factors. For Tier 2, the same 2006 certification data were used to estimate 

PM, NOx, and HC emission factors. 

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 precontrol 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.2 

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 heavyduty diesel engines.9 

The resulting baseline 50-state emission inventories for Category 1 propulsion 

engines are given in Table 0-5. 

11
 
Draft Regulatory Impact Analysis 

Table 0-5 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 

12
 
Chapter 3: Inventory 

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. The inventory inputs of base 

year population, average power, load factor, and activity for C1 auxiliary engines are 

given in Table 0-6 and Table 0-7. The baseline emission factors are given in Table 

0-8 and Table 0-9. 

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.6 

The cut point of 560kW is that used for propulsion engines. 

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 0-10. 

c

 The D2 steady-state duty cycle is designated for constant-speed engines. 

13
 
Draft Regulatory Impact Analysis 

Table 0-6 Inventory Inputs for C1 Auxiliary Standard Power Density Engines 

DISPLACEMENT CATEGORY 

<35 KW/L 

<=600KW 600<KW≤1000 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 

DISPLACEMENT CATEGORY 

<35 KW/L 

1000<KW≤1400 >1400KW* 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 * No populations ≥3700KW 

14 
C

Table 0-7 Inventory Inputs for C1 Auxiliary High Power Density Engines 

DISPLACEMENT CATEGORY 

≥35 KW/L 

<=600KW 600<KW≤1000 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 

DISPLACEMENT CATEGORY 

≥35 KW/L 

1000<KW≤1400 >1400KW* 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

ACTIVITY, 

HOURS 

2002 

POPULATION 

AVG 

KW 

LOAD 

FACTOR 

AC

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 * No populations ≥3700KW 

15 
Draft Regulatory Impact Analysis 

Table 0-8 Baseline PM10 and NOx Emission Factors for C1 Auxiliary Engines* 

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 

* Deterioration is applied to the PM emission factors (EFs); see text for details. The NOx EFs are not 

subject to deterioration. 

Table 0-9 Baseline HC and CO Emission Factors for C1 Auxiliary Engines* 

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 

* The HC and CO emission factors (EFs) are not subject to deterioration. 

16
 
Chapter 3: Inventory 

Table 0-10 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 

17
 
Draft Regulatory Impact Analysis 

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. However, the engine 

population, average rated power, load factor and engine activity terms shown in 

Equation 1 of that section were consolidated into a single term for total kW-hr/year 

for all C2 vessels.10 The total kW-hr value for C2 propulsion engines in 2002 was 

estimated at 30,246,809,539 kW-hr. The total kW-hr value was then allocated to the 

necessary displacement and horsepower categories, using the PSR engine data. 

The median life for all C2 propulsion engines is 23 years.11 The emission 

factors used for all C2 propulsion engines are largely those we used for the original 

commercial marine rulemaking analysis.6

 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 0-11. 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 0-11 Baseline Emission Factors for C2 Engines (g/kW-hr)* 

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 

* 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 0-12. 

18
 
Chapter 3: Inventory 

Table 0-12 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 

19
 
Draft Regulatory Impact Analysis 

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 0-13 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 0-13 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 0-14 and Table 0-15. These 

engines are subject to EPA nonroad diesel regulations that have established two tiers 

of emission standards.12 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. 

20
 
Chapter 3: Inventory 

Table 0-14 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 0-15 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.2

 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.2 

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.2

 The annual population growth rate is 1.009, which is the growth rate 

used for all commercial diesel engines. 

The resulting baseline 50-state emission inventories for <37kW commercial 

marine engines (propulsion and auxiliary combined) are given in Table 0-16. 

21
 
Draft Regulatory Impact Analysis 

Table 0-16 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 

22
 
Chapter 3: Inventory 

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 0-17 thru Table 0-22 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)5 and are 

provided in Table 0-23. Inventories are provided for calendar years 1999, 2010, 

2015, 2020, and 2030. 

23
 
Draft Regulatory Impact Analysis 

Table 0-17 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 

24
 
Chapter 3: Inventory 

Table 0-18 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 

25
 
Draft Regulatory Impact Analysis 

Table 0-19 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 

26
 
Chapter 3: Inventory 

Table 0-20 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 

27
 
Draft Regulatory Impact Analysis 

Table 0-21 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 

28
 
Chapter 3: Inventory 

Table 0-22 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 

29
 
Draft Regulatory Impact Analysis 

Table 0-23 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 controlled 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 proposed PM and 

either combined HC+NOx or NOx only standards for the control scenario that was 

modeled. 

The proposed emission standards for Category 1 engines are summarized in 

Table 0-24 and Table 0-25. These standards apply to both propulsion and auxiliary 

engines. There are separate emission standards 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 proposed emission standards for Category 2 engines are summarized in 

Table 0-26. 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 proposed emission standards for <37kW propulsion and auxiliary engines 

are given in Table 0-27. This category is subject to Tier 3 standards which begin in 

2009. 

30
 
C

Table 0-24 Proposed Standards (g/kW-hr) for C1 Standard Power Density Engines 

DISPLACEMENT CATEGORY 

<35 KW/L 

<=600KW 600<KW≤1000 

TIER 3 TIER 4 TIER 3 

YEAR NOX PM YEAR NOX PM YEAR NOX PM YEA

DISP<0.9 AND 37<KW<=75 

2009 7.5 0.30 

NO ENGINES IN THESE C

2014 4.7 

DISP<0.9 AND >75KW 2012 5.4 0.13 

0.9<=DISP<1.2 2013 5.4 0.12 1.2<=DISP<2.5 2014 5.6 0.11 NO TIER 4 STANDARDS 2014 5.6 0.11 201

2018 0.09 

2.5<=DISP<3.5 2013 5.6 0.11 2013 5.6 0.11 201

2018 0.09 

3.5<=DISP<5.0 2012 5.8 0.11 2012 5.8 0.11 201

2018 0.09 

DISPLACEMENT CATEGORY 

<35 KW/L 

1000<KW≤1400 >1400KW 

TIER 3 TIER 4 TIER 3 

YEAR NOX PM YEAR NOX PM YEAR NOX PM YE

DISP<0.9 AND 37<KW<=75 NO ENGINES IN THESE CATEGORIES 

NO ENGINES IN THESE 

DISP<0.9 AND >75KW 0.9<=DISP<1.2 1.2<=DISP<2.5 2014 5.6 0.11 2017 1.7 0.04 

2.5<=DISP<3.5 2013 5.6 0.11 2017 1.7 0.04 

3.5<=DISP<5.0 2012 5.8 0.11 2017 1.7 0.04 2012 5.8 0.11 2

31
Draft Regulatory Impact Analysis 

Table 0-25 Proposed Standards (g/kW-hr) for C1 High Power Density Engines 

DISPLACEMENT CATEGORY 

≥35 KW/L 

<=600KW 600<KW≤10

TIER 3 TIER 4 TIER 3 

YEAR NOX PM YEAR NOX PM YEAR NOX PM YE

DISP<0.9 AND 37<KW<=75 

2009 7.5 0.30 

NO TIER 4 STANDARDS 

NO ENGINES IN THESE 

2014 4.7 

DISP<0.9 AND >75KW 2012 5.8 0.15 

0.9<=DISP<1.2 2013 5.8 0.13 1.2<=DISP<2.5 2014 5.8 0.12 2014 5.6 0.11 2

2.5<=DISP<3.5 NO ENGINES NO ENGINES IN THESE 3.5<=DISP<5.0 DISPLACEMENT CATEGORY 

≥35 KW/L 

1000<KW≤1400 >1400KW 

TIER 3 TIER 4 TIER 3 

YEAR NOX PM YEAR NOX PM YEAR NOX PM YE

DISP<0.9 AND 37<KW<=75 NO ENGINES IN THESE CATEGORIES NO ENGINES IN THESE DISP<0.9 AND >75KW 0.9<=DISP<1.2 2013 5.4 0.12 2017 1.7 0.04 

1.2<=DISP<2.5 NO ENGINES IN THESE CATEGORIES 2014 5.6 0.11 22.5<=DISP<3.5 NO ENGINES IN THIS 3.5<=DISP<5.0 2012 5.8 0.11 2017 1.7 0.04 2012 5.8 0.11 2

32
Chapter 3: Inventory 

Table 0-26 Proposed Standards (g/kW-hr) for C2 Engines 

DISPLACEMENT CATEGORY TIER 3 TIER 4 

YEAR NOX+HC PM YEAR 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 1.7 0.04 

5.0<=DISP<15 AND 1000≤KW<1400 2013 6.2 0.13 2017 1.7 0.04 

5.0<=DISP<15 AND 1400≤KW<3700 2013 6.2 0.13 2016 1.7 0.04 

5.0<=DISP<15 AND ≥3700KW 2014 1.7 0.12 

2017 0.05 

15.0<=DISP<20.0 AND <1400KW NO ENGINES IN THIS CATEGORY 

15.0<=DISP<20.0 AND 1400≤KW<3300 2014 7.0 0.34 2016 1.7 0.04 

15.0<=DISP<20.0 AND 3300≤KW<3700 NO ENGINES IN THIS CATEGORY 

15.0<=DISP<20.0 AND ≥3700KW 2014 1.7 0.25 

2017 0.05 

20.0<=DISP<30.0 NO ENGINES IN THIS CATEGORY 

Table 0-27 Proposed 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 

25-50 2009 5.6 0.22 

2014 3.5 0.22 

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 0-28 and Table 0-29. 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 Tier 3 EF = (Tier 3 std/Tier 2 std) x Tier 2 EF 

Equation 4 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. 

The resulting control case 50-state emission inventories for Category 1 

propulsion engines are given in Table 0-30. 

33
 
Draft Regulatory Impact Analysis 

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 0-31 and Table 0-32. The methodology described above 

for Category 1 propulsion engines was used to derive these emission factors. 

The resulting control case 50-state emission inventories for Category 1 

auxiliary engines are given in Table 0-33. 

34
 
C

Table 0-28 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Propulsion Standard Power Density 

DISPLACEMENT 

<35 KW/L 

<=600KW 600<KW≤1000 

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 3 

YEAR 

HC NOX PM HC NOX PM HC 

DISP<0.9 AND 37<KW<=75 

2009 0.30 5.70 0.17 

NO ENGINES IN THESE CATEGORIES 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 1.2<=DISP<2.5 2014 0.10 4.69 0.07 2014 0.10 4.69 0.07 2018 0.04 

2018 0.061 

2.5<=DISP<3.5 2013 0.10 4.69 0.07 2013 0.10 4.69 0.07 2018 0.04 

2018 0.061 

3.5<=DISP<5.0 2012 0.10 4.81 0.07 2012 0.10 4.81 0.07 2018 0.04 

2018 0.061 

DISPLACEMENT 

<35 KW/L 

1000<KW≤1400 >1400K

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 4 

YEAR 

TIER 3 

Y

HC NOX PM HC NOX PM HC NOX PM 

DISP<0.9 AND 37<KW<=75 

NO ENGINES IN THESE CATEGORIES 

NO ENGINES IN THES

DISP<0.9 AND >75KW 0.9<=DISP<1.2 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 

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 

35
Draft Regulatory Impact Analysis 

Table 0-29 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Propulsion High Power Density E

DISPLACEMENT 

≥35 KW/L 

<=600KW 600<KW≤1000 

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 3 

YEAR 

HC NOX PM HC NOX PM HC 

DISP<0.9 AND 37<KW<=75 NO ENGINES NO ENGINES IN THESE CATEGORIES DISP<0.9 AND >75KW 2012 0.15 4.38 0.08 

0.9<=DISP<1.2 2013 0.14 4.89 0.05 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 NO ENGINES NO ENGINES IN THESE CATEGORIES 3.5<=DISP<5.0 

DISPLACEMENT 

≥35 KW/L 

1000<KW≤1400 >1400K

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 4 

YEAR 

TIER 3 

Y

HC NOX PM HC NOX PM HC NOX PM 

DISP<0.9 AND 37<KW<=75 

NO ENGINES IN THESE CATEGORIES NO ENGINES IN THES

DISP<0.9 AND >75KW 0.9<=DISP<1.2 1.2<=DISP<2.5 2.5<=DISP<3.5 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 

36
Chapter 3: Inventory 

Table 0-30 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 9,409 9,127 276,209 8,708 8,270 49,497 1,862 

2013 8,859 8,593 267,453 8,433 8,008 48,574 664 

2014 8,291 8,042 257,691 8,042 7,637 47,680 799 

2015 7,700 7,469 248,317 7,658 7,273 46,827 857 

2016 7,065 6,853 236,292 7,228 6,864 46,023 865 

2017 6,463 6,269 223,265 6,784 6,443 45,368 872 

2018 5,911 5,734 209,717 6,334 6,015 44,879 879 

2019 5,388 5,226 196,847 5,898 5,601 44,482 886 

2020 4,938 4,790 185,242 5,496 5,219 44,301 893 

2021 4,562 4,425 174,843 5,126 4,868 44,329 900 

2022 4,208 4,082 164,971 4,772 4,532 44,423 907 

2023 3,873 3,756 155,589 4,433 4,210 44,571 915 

2024 3,552 3,446 146,696 4,111 3,904 44,760 923 

2025 3,263 3,165 138,521 3,826 3,634 44,987 931 

2026 3,013 2,923 131,195 3,589 3,408 45,248 939 

2027 2,808 2,724 124,763 3,400 3,229 45,539 946 

2028 2,644 2,565 119,185 3,252 3,089 45,861 954 

2029 2,512 2,436 114,708 3,134 2,976 46,209 962 

2030 2,417 2,344 111,660 3,049 2,896 46,583 970 

2031 2,352 2,282 109,766 2,991 2,841 46,975 978 

2032 2,310 2,241 108,624 2,953 2,804 47,385 986 

2033 2,284 2,215 107,896 2,927 2,780 47,811 995 

2034 2,265 2,197 107,443 2,911 2,764 48,241 1,006 

2035 2,254 2,186 107,233 2,902 2,756 48,675 1,015 

2036 2,248 2,181 107,236 2,901 2,755 49,114 1,023 

2037 2,250 2,182 107,444 2,906 2,760 49,556 1,032 

2038 2,256 2,189 107,834 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 

37
 
Draft Regulatory Impact Analysis 

Table 0-31 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Auxiliary Standard Power Density 

DISPLACEMENT CATEGORY 

<35 KW/L 

≤600KW 600<KW≤1000 

TIER 3 TIER 3 

YEAR HC NOX PM YEAR HC NOX PM YEAR HC 

DISP<0.9 AND 37<KW<=75 

2009 0.30 5.70 0.17 

NO ENGINES IN THESE CATEGORI

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 

1.2<=DISP<2.5 2014 0.11 4.77 0.08 2014 0.11 4.77 0.08 2018 0.04 

2018 0.070 

2.5<=DISP<3.5 2013 0.11 4.77 0.08 2013 0.11 4.77 0.08 2018 0.04 

2018 0.070 

3.5<=DISP<5.0 2012 0.11 4.89 0.08 2012 0.11 4.89 0.08 2018 0.04 

2018 0.070 

DISPLACEMENT 

<35 KW/L 

1000<KW≤1400 >1400K

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 4 

YEAR 

TIER 3 

Y

HC NOX PM HC NOX PM HC NOX PM 

DISP<0.9 AND 37<KW<=75 

NO ENGINES IN THESE CATEGORIES NO ENGINES IN THES

DISP<0.9 AND >75KW 0.9<=DISP<1.2 1.2<=DISP<2.5 2.5<=DISP<3.5 2013 0.11 4.77 0.08 2017 0.04 1.3 0.03 

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 

38
C

Table 0-32 Control PM10, NOx, and HC Emission Factors (g/kW-hr) for C1 Auxiliary High Power Density E

DISPLACEMENT CATEGORY 

≥35 KW/L 

≤600KW 600<KW≤1000 

TIER 3 TIER 3 

YEAR HC NOX PM YEAR HC NOX PM YEAR HC 

DISP<0.9 AND 37<KW<=75 

2009 0.30 5.70 0.17 

NO ENGINES IN THESE CATEGORI

2014 3.56 

DISP<0.9 AND >75KW 2012 0.15 4.38 0.08 0.9<=DISP<1.2 

NO ENGINES IN THESE 

CATEGORIES 

1.2<=DISP<2.5 2.5<=DISP<3.5 3.5<=DISP<5.0 

DISPLACEMENT 

≥35 KW/L 

1000<KW≤1400 >1400K

CATEGORY 

YEAR 

TIER 3 

YEAR 

TIER 4 

YEAR 

TIER 3 

Y

HC NOX PM HC NOX PM HC NOX PM 

DISP<0.9 AND 37<KW<=75 NO ENGINES IN THESE CATEGORIES DISP<0.9 AND NO ENGINES IN THES>75KW 0.9<=DISP<1.2 2013 0.13 4.02 0.08 2017 0.04 1.3 0.03 

1.2<=DISP<2.5 2014 0.11 4.77 0.08 2.5<=DISP<3.5 NO ENGINES IN THESE CATEGORIES NO ENGINES IN THES3.5<=DISP<5.0 

39
Draft Regulatory Impact Analysis 

Table 0-33 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 2,042 1,981 52,949 1,753 1,664 8,489 337 

2013 1,971 1,912 51,796 1,727 1,640 8,327 120 

2014 1,902 1,845 50,317 1,677 1,593 8,167 145 

2015 1,829 1,774 48,863 1,628 1,546 8,010 155 

2016 1,751 1,698 47,349 1,577 1,497 7,857 157 

2017 1,663 1,613 45,754 1,523 1,446 7,708 158 

2018 1,561 1,514 43,895 1,463 1,389 7,563 159 

2019 1,458 1,414 42,089 1,403 1,333 7,426 160 

2020 1,354 1,314 40,347 1,345 1,278 7,298 162 

2021 1,261 1,224 38,787 1,290 1,225 7,198 163 

2022 1,184 1,149 37,444 1,239 1,176 7,134 164 

2023 1,116 1,082 36,210 1,188 1,129 7,088 166 

2024 1,054 1,022 35,096 1,141 1,083 7,066 167 

2025 998 968 34,089 1,095 1,040 7,067 169 

2026 945 917 33,138 1,052 999 7,077 170 

2027 895 868 32,243 1,010 959 7,094 171 

2028 847 822 31,399 970 921 7,117 173 

2029 803 779 30,630 935 888 7,145 174 

2030 764 741 29,948 905 859 7,178 176 

2031 733 711 29,388 882 838 7,215 177 

2032 708 687 28,939 866 823 7,257 179 

2033 687 667 28,572 853 810 7,303 180 

2034 669 649 28,303 843 801 7,353 182 

2035 656 637 28,159 836 794 7,407 184 

2036 647 628 28,117 832 790 7,464 185 

2037 641 622 28,123 830 788 7,524 187 

2038 637 618 28,176 829 787 7,588 188 

2039 635 616 28,259 829 788 7,654 190 

2040 635 616 28,367 831 789 7,721 192 

40
 
Chapter 3: Inventory 

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 0-34. The methodology described above for Category 1 

propulsion engines was used to derive these emission factors. 

The resulting control case 50-state emission inventories for Category 2 

propulsion engines are given in Table 0-35. 

Table 0-34 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<3700 2013 0.07 5.97 0.11 2016 0.02 1.3 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<3300 2014 0.09 6.77 0.30 2016 0.01 1.3 0.04 

15.0<=DISP<20.0 AND >3700KW 2014 0.07 1.3 0.23 

2017 0.01 1.3 0.05 

41
 
Draft Regulatory Impact Analysis 

Table 0-35 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 14,436 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 15,569 15,102 404,127 5,150 4,891 85,665 39,905 

2014 14,031 13,610 392,503 5,082 4,826 85,701 21,334 

2015 12,996 12,606 380,939 5,014 4,761 85,746 7,888 

2016 12,865 12,479 365,582 4,896 4,650 85,800 7,817 

2017 12,482 12,107 350,179 4,729 4,491 85,864 5,901 

2018 12,130 11,766 334,823 4,563 4,333 85,937 4,574 

2019 11,748 11,396 319,586 4,396 4,175 86,020 2,963 

2020 11,394 11,052 304,523 4,230 4,017 86,116 1,888 

2021 11,108 10,775 289,618 4,066 3,861 86,222 1,976 

2022 10,804 10,480 274,971 3,901 3,705 86,341 1,995 

2023 10,465 10,151 261,143 3,738 3,550 86,475 1,975 

2024 10,094 9,791 248,136 3,576 3,396 86,626 1,954 

2025 9,710 9,419 235,393 3,415 3,243 86,790 1,934 

2026 9,315 9,035 222,855 3,254 3,090 86,974 1,913 

2027 8,909 8,641 210,526 3,094 2,938 87,178 1,894 

2028 8,493 8,238 198,433 2,935 2,787 87,406 1,874 

2029 8,071 7,829 186,645 2,777 2,637 87,672 1,855 

2030 7,644 7,414 175,655 2,622 2,490 88,078 1,836 

2031 7,211 6,995 165,474 2,468 2,344 88,623 1,818 

2032 6,776 6,573 155,629 2,317 2,200 89,207 1,800 

2033 6,342 6,152 146,134 2,169 2,060 89,820 1,783 

2034 5,909 5,732 136,983 2,025 1,923 90,457 1,766 

2035 5,482 5,318 128,247 1,885 1,790 91,119 1,750 

2036 5,089 4,936 120,169 1,757 1,669 91,799 1,735 

2037 4,756 4,613 113,689 1,651 1,568 92,500 1,721 

2038 4,466 4,332 108,659 1,562 1,484 93,219 1,709 

2039 4,220 4,093 104,710 1,488 1,413 93,956 1,700 

2040 4,039 3,918 101,729 1,434 1,362 94,707 1,699 

42
 
Chapter 3: Inventory 

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 0-36. 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 

proposed rule does not result in NOx control for this category, the Tier 3 NOx 

emission factors were set equal to Tier 2. 

Table 0-36 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 

11-16 2009 0.21 3.63 0.19 

2014 0.21 2.32 0.19 

16-25 2009 0.21 3.63 0.19 

2014 0.21 2.32 0.19 

25-50 2009 0.41 3.71 0.18 

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 0-37. 

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 0-38 thru Table 0-43 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 0-44. The gaseous air toxics are assumed to be controlled 

proportionately to VOC, whereas POM is controlled proportionately to PM. 

43
 
Draft Regulatory Impact Analysis 

Table 0-37 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 

44
 
Chapter 3: Inventory 

Table 0-38 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 14,436 551 28,211 

2009 10,709 2,229 12,939 14,706 524 28,169 

2010 10,304 2,169 12,472 14,975 495 27,942 

2011 9,916 2,113 12,029 15,245 466 27,740 

2012 9,409 2,042 11,451 15,515 437 27,404 

2013 8,859 1,971 10,830 15,569 409 26,808 

2014 8,291 1,902 10,192 14,031 383 24,606 

2015 7,700 1,829 9,528 12,996 357 22,881 

2016 7,065 1,751 8,816 12,865 334 22,015 

2017 6,463 1,663 8,126 12,482 317 20,925 

2018 5,911 1,561 7,472 12,130 303 19,905 

2019 5,388 1,458 6,845 11,748 291 18,885 

2020 4,938 1,354 6,292 11,394 280 17,967 

2021 4,562 1,261 5,824 11,108 271 17,203 

2022 4,208 1,184 5,393 10,804 263 16,460 

2023 3,873 1,116 4,988 10,465 257 15,710 

2024 3,552 1,054 4,606 10,094 252 14,952 

2025 3,263 998 4,262 9,710 248 14,219 

2026 3,013 945 3,959 9,315 244 13,518 

2027 2,808 895 3,704 8,909 242 12,855 

2028 2,644 847 3,491 8,493 241 12,225 

2029 2,512 803 3,315 8,071 240 11,626 

2030 2,417 764 3,181 7,644 240 11,065 

2031 2,352 733 3,085 7,211 240 10,537 

2032 2,310 708 3,019 6,776 241 10,036 

2033 2,284 687 2,971 6,342 242 9,555 

2034 2,265 669 2,934 5,909 244 9,087 

2035 2,254 656 2,910 5,482 245 8,638 

2036 2,248 647 2,896 5,089 247 8,232 

2037 2,250 641 2,891 4,756 249 7,895 

2038 2,256 637 2,894 4,466 251 7,611 

2039 2,268 635 2,903 4,220 253 7,376 

2040 2,282 635 2,917 4,039 255 7,211 

45
 
Draft Regulatory Impact Analysis 

Table 0-39 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 14,003 534 27,364 

2009 10,388 2,162 12,551 14,264 509 27,324 

2010 9,995 2,104 12,098 14,525 480 27,104 

2011 9,618 2,049 11,668 14,787 452 26,908 

2012 9,127 1,981 11,107 15,050 424 26,582 

2013 8,593 1,912 10,505 15,102 397 26,004 

2014 8,042 1,845 9,887 13,610 371 23,868 

2015 7,469 1,774 9,242 12,606 346 22,195 

2016 6,853 1,698 8,551 12,479 324 21,354 

2017 6,269 1,613 7,882 12,107 308 20,297 

2018 5,734 1,514 7,248 11,766 294 19,308 

2019 5,226 1,414 6,640 11,396 282 18,318 

2020 4,790 1,314 6,103 11,052 272 17,428 

2021 4,425 1,224 5,649 10,775 263 16,687 

2022 4,082 1,149 5,231 10,480 255 15,966 

2023 3,756 1,082 4,838 10,151 249 15,239 

2024 3,446 1,022 4,468 9,791 244 14,503 

2025 3,165 968 4,134 9,419 240 13,793 

2026 2,923 917 3,840 9,035 237 13,113 

2027 2,724 868 3,592 8,641 235 12,469 

2028 2,565 822 3,386 8,238 234 11,858 

2029 2,436 779 3,215 7,829 233 11,277 

2030 2,344 741 3,086 7,414 233 10,733 

2031 2,282 711 2,993 6,995 233 10,221 

2032 2,241 687 2,928 6,573 234 9,735 

2033 2,215 667 2,882 6,152 235 9,269 

2034 2,197 649 2,846 5,732 236 8,815 

2035 2,186 637 2,823 5,318 238 8,378 

2036 2,181 628 2,809 4,936 240 7,985 

2037 2,182 622 2,804 4,613 242 7,658 

2038 2,189 618 2,807 4,332 244 7,383 

2039 2,200 616 2,816 4,093 246 7,155 

2040 2,214 616 2,829 3,918 248 6,995 

46
 
Chapter 3: Inventory 

Table 0-40 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 57,030 365,554 423,601 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 276,209 52,949 329,158 408,943 4,351 742,453 

2013 267,453 51,796 319,249 404,127 4,234 727,609 

2014 257,691 50,317 308,007 392,503 4,073 704,584 

2015 248,317 48,863 297,181 380,939 3,917 682,037 

2016 236,292 47,349 283,640 365,582 3,777 652,999 

2017 223,265 45,754 269,020 350,179 3,658 622,856 

2018 209,717 43,895 253,612 334,823 3,556 591,991 

2019 196,847 42,089 238,936 319,586 3,462 561,984 

2020 185,242 40,347 225,589 304,523 3,377 533,489 

2021 174,843 38,787 213,630 289,618 3,301 506,550 

2022 164,971 37,444 202,415 274,971 3,240 480,625 

2023 155,589 36,210 191,800 261,143 3,188 456,131 

2024 146,696 35,096 181,792 248,136 3,144 433,072 

2025 138,521 34,089 172,610 235,393 3,103 411,106 

2026 131,195 33,138 164,333 222,855 3,070 390,259 

2027 124,763 32,243 157,006 210,526 3,042 370,574 

2028 119,185 31,399 150,584 198,433 3,018 352,035 

2029 114,708 30,630 145,338 186,645 2,998 334,981 

2030 111,660 29,948 141,608 175,655 2,982 320,245 

2031 109,766 29,388 139,154 165,474 2,978 307,605 

2032 108,624 28,939 137,563 155,629 2,983 296,175 

2033 107,896 28,572 136,468 146,134 2,993 285,596 

2034 107,443 28,303 135,746 136,983 3,007 275,735 

2035 107,233 28,159 135,392 128,247 3,022 266,661 

2036 107,236 28,117 135,352 120,169 3,040 258,561 

2037 107,444 28,123 135,566 113,689 3,058 252,314 

2038 107,834 28,176 136,009 108,659 3,079 247,747 

2039 108,376 28,259 136,635 104,710 3,100 244,445 

2040 109,054 28,367 137,421 101,729 3,123 242,273 

47
 
Draft Regulatory Impact Analysis 

Table 0-41 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 5,150 688 15,998 

2014 8,042 1,677 9,719 5,082 636 15,437 

2015 7,658 1,628 9,286 5,014 586 14,885 

2016 7,228 1,577 8,805 4,896 545 14,246 

2017 6,784 1,523 8,307 4,729 515 13,551 

2018 6,334 1,463 7,796 4,563 492 12,851 

2019 5,898 1,403 7,302 4,396 472 12,169 

2020 5,496 1,345 6,841 4,230 454 11,526 

2021 5,126 1,290 6,416 4,066 438 10,920 

2022 4,772 1,239 6,010 3,901 423 10,335 

2023 4,433 1,188 5,621 3,738 411 9,771 

2024 4,111 1,141 5,252 3,576 401 9,229 

2025 3,826 1,095 4,922 3,415 393 8,729 

2026 3,589 1,052 4,640 3,254 387 8,281 

2027 3,400 1,010 4,410 3,094 383 7,887 

2028 3,252 970 4,223 2,935 381 7,538 

2029 3,134 935 4,068 2,777 379 7,225 

2030 3,049 905 3,953 2,622 378 6,953 

2031 2,991 882 3,874 2,468 378 6,720 

2032 2,953 866 3,819 2,317 380 6,516 

2033 2,927 853 3,781 2,169 381 6,331 

2034 2,911 843 3,754 2,025 384 6,162 

2035 2,902 836 3,738 1,885 387 6,010 

2036 2,901 832 3,733 1,757 389 5,880 

2037 2,906 830 3,736 1,651 392 5,779 

2038 2,919 829 3,748 1,562 395 5,705 

2039 2,936 829 3,765 1,488 398 5,652 

2040 2,957 831 3,787 1,434 402 5,623 

48
 
Chapter 3: Inventory 

Table 0-42 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 

49
 
Draft Regulatory Impact Analysis 

Table 0-43 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 

50
 
Chapter 3: Inventory 

Table 0-44 Control Case (50-State) Air Toxic Emissions for Commercial Marine Diesel Engines 

(short tons) 

HAP 2010 2015 2020 2030 

BENZENE 556 515 410 252 

FORMALDEHYDE 4,088 3,785 3,018 1,857 

ACETALDEHYDE 2,032 1,881 1,500 923 

1,3-BUTADIENE 6 5 4 3 

ACROLEIN 79 73 58 36 

NAPHTHALENE 38 34 26 16 

POM 10 9 7 4 

3.1.4 Projected Commercial Marine Emission Reductions of Proposal 

The PM2.5, NOx, and VOC emission reductions for each category and calendar 

year are presented in Table 0-45 thru Table 0-47. The air toxic emission reductions 

by pollutant and calendar year are given in Table 0-48. 

51
 
Draft Regulatory Impact Analysis 

Table 0-45 Projected Commercial Marine PM2.5 Emission Reductions (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2008 0 0 0 0 0 0 

2009 0 1 1 0 2 3 

2010 0 2 2 0 4 6 

2011 0 2 3 0 5 8 

2012 60 9 69 0 6 76 

2013 140 21 161 153 8 321 

2014 288 49 336 431 9 776 

2015 441 76 518 620 11 1,149 

2016 634 106 740 988 13 1,740 

2017 856 139 995 1,459 15 2,469 

2018 1,112 179 1,292 1,937 16 3,245 

2019 1,375 220 1,595 2,405 18 4,019 

2020 1,643 262 1,905 2,882 20 4,808 

2021 1,917 305 2,221 3,400 22 5,644 

2022 2,194 348 2,542 3,925 24 6,491 

2023 2,473 392 2,865 4,456 26 7,347 

2024 2,751 437 3,188 4,995 28 8,210 

2025 3,012 482 3,494 5,541 29 9,064 

2026 3,245 528 3,773 6,096 31 9,899 

2027 3,449 573 4,021 6,658 32 10,711 

2028 3,625 618 4,243 7,227 33 11,503 

2029 3,782 661 4,442 7,801 33 12,277 

2030 3,914 700 4,613 8,380 34 13,027 

2031 4,022 733 4,755 8,962 35 13,752 

2032 4,115 761 4,876 9,546 35 14,458 

2033 4,198 787 4,985 10,130 36 15,151 

2034 4,274 811 5,085 10,712 37 15,834 

2035 4,343 832 5,175 11,288 37 16,500 

2036 4,407 850 5,257 11,831 38 17,126 

2037 4,465 867 5,332 12,316 38 17,686 

2038 4,518 882 5,400 12,759 39 18,198 

2039 4,568 897 5,465 13,160 39 18,664 

2040 4,614 911 5,525 13,498 40 19,063 

52
 
Chapter 3: Inventory 

Table 0-46 Projected Commercial Marine NOx Emission Reductions (short tons) 

YEAR 

C1 

PROPULSION 

C1 

AUXILIARY 

C1 

TOTAL 

C2 

PROPULSION <37KW TOTAL 

2008 0 0 0 0 0 0 

2009 0 0 0 0 0 0 

2010 0 0 0 0 0 0 

2011 0 0 0 0 0 0 

2012 1,342 121 1,463 0 0 1,463 

2013 3,311 322 3,633 1,301 0 4,935 

2014 6,944 868 7,812 9,467 47 17,326 

2015 10,562 1,414 11,976 17,654 94 29,723 

2016 17,246 2,051 19,297 29,714 141 49,151 

2017 26,061 2,835 28,896 41,922 188 71,006 

2018 36,621 3,954 40,576 54,165 235 94,975 

2019 47,117 5,071 52,187 66,413 281 118,882 

2020 57,522 6,184 63,705 78,633 328 142,666 

2021 67,833 7,292 75,126 90,840 374 166,339 

2022 78,019 8,397 86,416 103,020 420 189,855 

2023 88,051 9,495 97,546 115,170 465 213,181 

2024 97,867 10,586 108,453 127,293 510 236,257 

2025 107,215 11,667 118,882 139,391 555 258,828 

2026 115,946 12,737 128,683 151,488 599 280,771 

2027 123,957 13,792 137,749 163,559 643 301,951 

2028 131,290 14,829 146,119 175,606 685 322,410 

2029 137,676 15,822 153,498 187,573 726 341,797 

2030 142,790 16,755 159,545 199,471 764 359,780 

2031 146,842 17,592 164,434 211,253 794 376,481 

2032 150,228 18,343 168,571 222,938 815 392,324 

2033 153,285 19,039 172,324 234,439 835 407,598 

2034 156,089 19,659 175,748 245,767 852 422,367 

2035 158,671 20,173 178,844 256,829 869 436,542 

2036 161,061 20,604 181,665 267,350 884 449,899 

2037 163,268 21,004 184,271 276,408 899 461,578 

2038 165,314 21,377 186,692 284,135 913 471,739 

2039 167,230 21,732 188,962 290,899 926 480,787 

2040 169,033 22,069 191,102 296,798 938 488,838 

53
 
Draft Regulatory Impact Analysis 

Table 0-47 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 37 27 383 

2014 564 88 652 152 32 837 

2015 849 135 984 268 38 1,290 

2016 1,187 185 1,372 433 43 1,848 

2017 1,563 237 1,800 648 49 2,497 

2018 1,970 297 2,267 863 54 3,183 

2019 2,374 356 2,730 1,078 59 3,867 

2020 2,773 415 3,188 1,293 64 4,545 

2021 3,167 474 3,640 1,508 70 5,218 

2022 3,555 532 4,087 1,722 75 5,883 

2023 3,934 590 4,524 1,936 79 6,539 

2024 4,303 647 4,950 2,149 84 7,183 

2025 4,639 704 5,343 2,362 89 7,794 

2026 4,934 760 5,694 2,575 92 8,360 

2027 5,184 814 5,998 2,787 95 8,880 

2028 5,397 867 6,264 2,999 97 9,360 

2029 5,585 917 6,501 3,210 99 9,811 

2030 5,743 961 6,704 3,420 101 10,225 

2031 5,876 998 6,874 3,628 103 10,605 

2032 5,993 1,029 7,022 3,834 105 10,960 

2033 6,099 1,058 7,157 4,037 106 11,300 

2034 6,197 1,084 7,281 4,237 108 11,625 

2035 6,287 1,107 7,394 4,433 109 11,936 

2036 6,371 1,128 7,499 4,618 111 12,228 

2037 6,449 1,147 7,596 4,781 112 12,490 

2038 6,521 1,166 7,687 4,928 114 12,728 

2039 6,589 1,183 7,772 5,060 115 12,947 

2040 6,654 1,200 7,854 5,173 116 13,143 

54
 
Chapter 3: Inventory 

Table 0-48 Projected Commercial Marine Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 0 45 162 371 

FORMALDEHYDE 4 328 1,190 2,730 

ACETALDEHYDE 2 163 591 1,357 

1,3-BUTADIENE 0 0 2 4 

ACROLEIN 0 6 23 53 

NAPHTHALENE 0 3 10 24 

POM 0 0 2 5 

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 proposed 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 proposed standards 

as well as updated baseline data.13 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 5 I = N*P*L*A*EF 

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) 

55
 
Draft Regulatory Impact Analysis 

A = engine activity (operating hours/year) 

EF = emission factor (gram/kW-hr) 

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. 14 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.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 6 DF = 1 + A*(age/ML) 

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 

56
 
Chapter 3: Inventory 

expended.2 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. 

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 SPM adj =FC * 7.1 * 0.02247 * 224/32 * (soxdsl - soxbas) * 1/2000 

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 Rule.4

, 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.4 

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.5

 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. 

57
 
Draft Regulatory Impact Analysis 

Table 0-49 and Table 0-50 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. 

Table 0-49 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 0-50 for breakout by individual power ranges. 

Table 0-50 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 0-51 and Table 0-52. "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 

58 
Chapter 3: Inventory 

used to calculate how emissions change as the engine and emission control system 

deteriorate over time, as explained above in Equation 2. Engines under 50 hp are 

subject to EPA nonroad diesel regulations that have established two tiers of emission 

standards.12 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 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.2

 Tier 2 emission factors were updated 

from those in the NONROAD model using all the nonroad engine certification data 

available in mid-2006. 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.2 

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.2 

Table 0-51 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 

59
 
Draft Regulatory Impact Analysis 

Table 0-52 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 0-53 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 rule5

 and are summarized in Table 0-54. Inventories are 

provided for calendar year 1999, and are projected for 2010, 2015, 2020, and 2030. 

60
 
Chapter 3: Inventory 

Table 0-53 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 

61
 
Draft Regulatory Impact Analysis 

Table 0-54 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 0-55 shows the control case exhaust emission standards that were 

modeled for recreational marine diesel engines. 

Table 0-55 Modeled Standards (g/hp-hr) for Recreational Marine Diesel Engines 

HP TIER 3 TIER 4 

RANGE YEAR NOX+HC PM YEAR NOX PM 

0-25 2009 5.6 0.30 

NO TIER 4 

STANDARDS 

25-100 2009 5.6 0.22 

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 

1200-2680 2012 4.0 0.09 

>2680 2012 4.0 0.09 2016 1.27 0.03 

3.2.3.2 Control Inventory Inputs 

Table 0-56 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 0-55. No sulfur adjustment is applied to the Tier 3 or Tier 4 

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. The 

Tier 4 modeled emission factors are identical to the Tier 4 emission factors used for 

Category 1 standard power density propulsion engines. However, the NONROAD 

62
 
Chapter 3: Inventory 

model does not have a hp bin corresponding to greater than 2000 kW (2680 hp), so 

the 2000-3000 hp bin was used to model the effects of the Tier 4 standard. 

All other modeling inputs are the same as shown above for the base case 

inventory development. Table 0-49 and Table 0-50 list the base engine populations, 

average hp by power range, annual activity, load factor, and median life. These are 

unchanged from the default inputs in the NONROAD model. 

Table 0-56 Control Emission Factors for Recreational Marine Diesel Engines 

HP RANGE 

TIER 3 EMISSION FACTORS 

G/HP-HR 

TIER 4 EMISSION FACTORS 

G/HP-HR 

PM10 NOX HC CO PM10 NOX HC CO 

0-11 0.24 4.39 0.43 4.11 

NO TIER 4 STANDARDS 

11-16 0.19 3.63 0.21 2.16 

16-25 0.19 3.63 0.21 2.16 

25-50 0.18 3.71 0.41 1.53 

0.18 2.32 0.41 1.53 

50-75 0.13 3.82 0.20 0.95 

0.13 2.39 0.20 0.95 

75-100 0.13 3.82 0.20 0.95 

0.13 2.39 0.20 0.95 

100-175 0.088 3.34 0.13 0.95 

175-300 0.080 3.90 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 3.70 0.29 0.95 

1200-2000 0.072 3.70 0.29 0.95 

>2000 0.072 3.70 0.29 0.95 0.022 0.97 0.03 0.95 

DF ("A") 0.473 0.009 0.034 0.101 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 0-57. 

63
 
Draft Regulatory Impact Analysis 

3.2.3.3.2 Air Toxics Emissions 

The control case air toxics inventories for recreational marine diesel engines 

are provided in Table 0-58. Gaseous air toxics and POM are reduced proportionately 

to VOC and PM2.5, respectively. 

64
 
Chapter 3: Inventory 

Table 0-57 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 44,648 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 44,931 2,104 1,998 8,150 311 

2013 930 902 44,864 2,159 2,051 8,308 113 

2014 944 916 44,681 2,206 2,095 8,464 136 

2015 957 928 44,490 2,252 2,139 8,618 150 

2016 967 938 44,248 2,294 2,179 8,771 153 

2017 976 947 43,998 2,337 2,219 8,922 156 

2018 985 955 43,742 2,379 2,259 9,073 156 

2019 993 963 43,479 2,421 2,300 9,223 159 

2020 1,001 971 43,218 2,465 2,341 9,374 162 

2021 1,008 978 42,957 2,508 2,382 9,525 165 

2022 1,015 985 42,697 2,552 2,423 9,675 168 

2023 1,022 991 42,443 2,595 2,465 9,825 171 

2024 1,028 997 42,206 2,638 2,505 9,975 174 

2025 1,033 1,002 42,001 2,680 2,545 10,124 177 

2026 1,041 1,009 41,955 2,717 2,581 10,279 180 

2027 1,049 1,018 42,072 2,751 2,613 10,439 183 

2028 1,058 1,026 42,237 2,784 2,644 10,601 186 

2029 1,068 1,036 42,443 2,816 2,674 10,765 189 

2030 1,077 1,045 42,683 2,847 2,704 10,930 193 

2031 1,088 1,055 42,946 2,879 2,734 11,095 196 

2032 1,098 1,066 43,241 2,911 2,765 11,262 199 

2033 1,110 1,077 43,584 2,946 2,797 11,429 202 

2034 1,123 1,089 43,979 2,983 2,832 11,596 205 

2035 1,136 1,102 44,412 3,021 2,869 11,765 208 

2036 1,150 1,115 44,875 3,061 2,907 11,933 211 

2037 1,164 1,129 45,359 3,102 2,946 12,102 214 

2038 1,179 1,143 45,864 3,143 2,985 12,272 217 

2039 1,193 1,158 46,382 3,185 3,025 12,442 220 

2040 1,208 1,172 46,915 3,227 3,064 12,613 223 

65
 
Draft Regulatory Impact Analysis 

Table 0-58 Control Case Air Toxic Emissions for Recreational Marine Diesel Engines 

(short tons) 

HAP 2010 2015 2020 2030 

BENZENE 34 33 31 30 

FORMALDEHYDE 198 192 181 174 

ACETALDEHYDE 89 86 81 78 

1,3-BUTADIENE 3 3 3 3 

ACROLEIN 5 5 5 4 

NAPHTHALENE 0 0 0 0 

POM 0 0 0 0 

3.2.4 Projected Recreational Marine Emission Reductions of Proposal 

The PM2.5, NOx, and VOC emission reductions by calendar year are shown in 

Table 0-59. The air toxic emission reductions by pollutant and calendar year are 

given in Table 0-60. 

66
 
Chapter 3: Inventory 

Table 0-59 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 47 8 

2013 5 200 20 

2014 8 458 40 

2015 12 718 61 

2016 16 1,022 86 

2017 21 1,328 111 

2018 25 1,637 137 

2019 30 1,947 163 

2020 35 2,260 188 

2021 41 2,574 215 

2022 46 2,889 241 

2023 52 3,206 267 

2024 58 3,524 294 

2025 63 3,842 320 

2026 70 4,160 347 

2027 76 4,477 373 

2028 82 4,793 400 

2029 88 5,108 426 

2030 95 5,419 452 

2031 101 5,725 477 

2032 107 6,016 501 

2033 112 6,277 523 

2034 116 6,498 541 

2035 120 6,693 558 

2036 124 6,873 573 

2037 127 7,039 587 

2038 130 7,199 600 

2039 133 7,353 613 

2040 136 7,502 626 

67
 
Draft Regulatory Impact Analysis 

Table 0-60 Projected Air Toxic Reductions from Recreational Marine Diesel Engines (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 0 1 2 5 

FORMALDEHYDE 0 5 14 28 

ACETALDEHYDE 0 2 6 12 

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. 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 II/III Switch railroads owned by Class 

I railroads) 

•	 Other Line-Haul Locomotives (i.e., local and regional 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 are tracked separately by 

model year and then the activity is 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: 

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. 

68
 
Chapter 3: Inventory 

5.	 Determine the number of freshly manufactured 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 rebuild (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 hp 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. 

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. 

69
 
Draft Regulatory Impact Analysis 

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 7 Wi = H*LF*Ni*Pi*RUFi 

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 newly 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 8 New Sales = (Total Fuel/BSFC- Wr)/H/LF/P 

Where BSFC is the estimated brake specific fuel consumption rate (Gal/MWhr) 

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 

70
 
Chapter 3: Inventory 

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 9 Total emissions = Total Work * Emission factor 

The emission factors used are the estimated average in-use emissions for each 

tier of standards, which are shown in Table 0-61 and Table 0-62. 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 0-61 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 0-62 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 locomotives operating on nominally 500 ppm 

sulfur fuel will be 0.029 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 0.033 g/bhp-hr 

lower than the PM10 emission rate for locomotives operating on 3000 ppm sulfur fuel. 

71
 
Draft Regulatory Impact Analysis 

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 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.8

 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. 

Table 0-63 Summary of Locomotive Emission Analysis Inputs 

Large 

Line-Haul 

Large 

Switch 

Small 

Line-Haul 

Small 

Switch 

Passenger/ 

Commuter 

2005 FUEL 

CONSUMPTION 

(GAL/YR) 

3.910 

BILLION 

310 

MILLION 

105 

MILLION 

39 

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/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 proposed. 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 

72 
Chapter 3: Inventory 

for Class I railroads. However, as described in 3.3.2.3, the total fuel includes onethird of the estimated Class II and Class III fuel use to account for those Class II and 

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. This was reduced by 7 

percent to reflect fuel used in switching rather than line-haul operation. 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.) Usage and scrappage patterns were developed to fit the fuel 

use and 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 0-63, 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 then for line-haul. We also 

estimate that switching operation will use approximately seven 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. 

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: 

d

 Branch service includes short-haul operations that would be considered intermediate to 

intercity line-haul service and switch service. 

73
 
Draft Regulatory Impact Analysis 

•	 They do not purchase newly manufactured locomotive. 

•	 They use their locomotives at a constant rate. 

•	 They replace their existing locomotives at a constant rate of 3 percent per 

year. 

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 only 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 II and 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 

II and 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 the fraction small railroad activity should be 

characterized as line-haul service versus switching service. We estimate that Class II 

railroads use 7 percent of their fuel in switching service (the same as Class I 

railroads), but that Class III railroads use 50 percent of their fuel in switching service. 

When combined, these factors result in our estimate that small railroads used a total 

of 105 million gallons of diesel fuel in line-haul service in 2005, and 39 million 

gallons of diesel fuel in switching service, as shown in Table 3-64. 

Table 0-64 Distribution of annual fuel consumption by Class II and Class III railroads (million 

gallons per year) 

Amount of fuel used by railroads 

that qualify as small railroads 

Fuel used by other Class II and 

Class III railroads 

LINE-HAUL SWITCH LINE-HAUL SWITCH 

CLASS II 71.5 5.4 35.7 2.7 

CLASS III 33.7 33.7 16.8 16.8 

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 

74 
Chapter 3: Inventory 

in the table, we believe that passenger/commuter locomotives tend to have an average 

age that is slightly newer then 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 0-65 through Table 0-71. 

The baseline air toxics inventories for locomotives were taken from the 

MSAT rule and are provided in Table 0-72. Inventories are provided for calendar 

years 1999, 2010, 2015, 2020, and 2030. 

75
 
Draft Regulatory Impact Analysis 

Table 0-65 Baseline (50-State) PM10 Emissions for Locomotives (short tons) 

Calendar 

Year 

Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter Total 

2006 27,919 2,270 935 1,023 32,147 

2007 27,873 2,295 950 1,011 32,129 

2008 25,078 2,162 883 901 29,023 

2009 24,965 2,185 897 888 28,934 

2010 24,831 2,208 911 874 28,824 

2011 24,686 2,232 926 859 28,703 

2012 24,536 2,256 940 845 28,577 

2013 24,015 2,258 944 817 28,033 

2014 23,874 2,282 959 802 27,916 

2015 23,724 2,306 974 787 27,791 

2016 23,561 2,330 990 771 27,653 

2017 23,398 2,355 1,006 756 27,515 

2018 23,240 2,380 1,022 741 27,383 

2019 23,081 2,405 1,038 726 27,251 

2020 22,918 2,431 1,055 711 27,114 

2021 22,750 2,457 1,071 696 26,974 

2022 22,579 2,483 1,088 681 26,831 

2023 22,407 2,490 1,106 666 26,668 

2024 22,244 2,489 1,124 651 26,508 

2025 22,080 2,483 1,141 636 26,340 

2026 21,944 2,472 1,160 624 26,200 

2027 21,836 2,456 1,178 614 26,084 

2028 21,755 2,434 1,197 607 25,993 

2029 21,703 2,410 1,216 602 25,931 

2030 21,685 2,380 1,223 598 25,886 

2031 21,696 2,343 1,230 597 25,866 

2032 21,735 2,301 1,237 598 25,871 

2033 21,800 2,257 1,243 600 25,901 

2034 21,894 2,209 1,250 603 25,957 

2035 22,023 2,161 1,256 608 26,049 

2036 22,187 2,113 1,263 613 26,176 

2037 22,378 2,066 1,269 618 26,331 

2038 22,597 2,018 1,275 623 26,513 

2039 22,846 1,971 1,281 628 26,726 

2040 23,126 1,924 1,287 633 26,969 

76
 
Chapter 3: Inventory 

Table 0-66 Baseline (50-State) PM2.5 Emissions for Locomotives (short tons) 

Calendar 

Year 

Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 27,082 2,202 907 992 31,183 

2007 27,037 2,226 922 981 31,166 

2008 24,325 2,097 856 874 28,153 

2009 24,216 2,120 870 861 28,066 

2010 24,086 2,142 884 847 27,959 

2011 23,946 2,165 898 833 27,842 

2012 23,800 2,188 912 819 27,720 

2013 23,294 2,190 916 792 27,192 

2014 23,157 2,213 930 778 27,079 

2015 23,012 2,237 945 763 26,957 

2016 22,854 2,260 960 748 26,823 

2017 22,696 2,284 975 734 26,690 

2018 22,542 2,309 991 719 26,561 

2019 22,389 2,333 1,007 704 26,433 

2020 22,230 2,358 1,023 690 26,301 

2021 22,067 2,383 1,039 675 26,165 

2022 21,902 2,409 1,056 660 26,026 

2023 21,734 2,415 1,073 646 25,868 

2024 21,577 2,415 1,090 631 25,713 

2025 21,417 2,408 1,107 617 25,550 

2026 21,286 2,398 1,125 605 25,414 

2027 21,181 2,382 1,143 596 25,301 

2028 21,102 2,361 1,161 589 25,213 

2029 21,052 2,338 1,180 584 25,153 

2030 21,034 2,308 1,186 581 25,109 

2031 21,045 2,273 1,193 579 25,090 

2032 21,083 2,232 1,200 580 25,094 

2033 21,146 2,190 1,206 582 25,124 

2034 21,238 2,143 1,212 585 25,178 

2035 21,362 2,096 1,219 590 25,267 

2036 21,521 2,050 1,225 595 25,391 

2037 21,707 2,004 1,231 600 25,541 

2038 21,919 1,958 1,237 604 25,718 

2039 22,160 1,912 1,243 609 25,925 

2040 22,432 1,866 1,248 614 26,160 

77
 
Draft Regulatory Impact Analysis 

Table 0-67 Baseline (50-State) NOx Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 779,842 86,861 37,690 38,466 942,858 

2007 770,409 87,803 38,293 36,409 932,914 

2008 761,768 87,623 38,906 34,361 922,658 

2009 755,490 88,573 39,528 32,338 915,929 

2010 745,431 88,625 40,161 30,370 904,587 

2011 735,641 89,586 40,803 28,459 894,490 

2012 730,031 88,909 41,456 27,212 887,608 

2013 726,116 89,872 42,119 26,017 884,125 

2014 722,365 89,090 42,793 24,872 879,121 

2015 718,800 90,055 43,168 24,382 876,405 

2016 714,893 89,682 43,544 23,325 871,445 

2017 711,364 90,653 43,921 22,922 868,860 

2018 708,525 90,875 44,299 22,559 866,258 

2019 706,475 91,859 44,609 22,197 865,139 

2020 704,353 89,367 44,917 21,836 860,474 

2021 702,449 90,332 45,224 21,477 859,481 

2022 700,505 89,231 45,529 21,119 856,383 

2023 698,881 89,395 45,832 20,797 854,905 

2024 697,737 87,896 46,134 20,510 852,277 

2025 696,922 85,521 46,433 20,256 849,133 

2026 696,845 85,305 46,730 20,066 848,946 

2027 697,488 84,961 46,863 19,935 849,248 

2028 698,814 84,538 46,989 19,860 850,202 

2029 700,893 84,058 47,107 19,836 851,894 

2030 703,847 83,458 47,062 19,859 854,226 

2031 707,554 82,732 47,002 19,926 857,214 

2032 711,989 81,917 46,929 20,033 860,868 

2033 717,100 81,067 46,842 20,160 865,168 

2034 722,959 80,141 46,739 20,305 870,144 

2035 729,705 79,228 46,622 20,468 876,023 

2036 737,374 78,332 46,488 20,631 882,826 

2037 745,744 77,455 46,339 20,797 890,334 

2038 754,836 76,596 46,172 20,963 898,567 

2039 764,711 75,766 45,989 21,131 907,596 

2040 775,388 74,931 45,788 21,300 917,407 

78
 
Chapter 3: Inventory 

Table 0-68 Baseline (50-State) VOC Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/ 

Commuter Total 

2006 43,874 5,501 2,891 1,609 53,874 

2007 43,762 5,566 2,937 1,589 53,853 

2008 43,636 5,630 2,984 1,568 53,818 

2009 43,486 5,696 3,032 1,546 53,759 

2010 43,301 5,763 3,080 1,523 53,667 

2011 43,100 5,830 3,129 1,500 53,559 

2012 42,891 5,898 3,179 1,476 53,445 

2013 42,700 5,967 3,230 1,453 53,349 

2014 42,518 6,037 3,282 1,429 53,265 

2015 42,323 6,108 3,335 1,404 53,169 

2016 42,107 6,179 3,388 1,380 53,054 

2017 41,892 6,252 3,442 1,356 52,941 

2018 41,684 6,325 3,497 1,332 52,838 

2019 41,478 6,399 3,553 1,307 52,738 

2020 41,265 6,475 3,610 1,283 52,633 

2021 41,044 6,551 3,668 1,259 52,522 

2022 40,820 6,628 3,726 1,235 52,410 

2023 40,596 6,664 3,786 1,212 52,259 

2024 40,391 6,686 3,847 1,188 52,112 

2025 40,185 6,696 3,908 1,165 51,954 

2026 40,027 6,697 3,971 1,146 51,841 

2027 39,916 6,685 4,034 1,132 51,768 

2028 39,850 6,665 4,099 1,121 51,735 

2029 39,833 6,639 4,164 1,114 51,750 

2030 39,873 6,600 4,231 1,110 51,813 

2031 39,961 6,547 4,299 1,109 51,917 

2032 40,098 6,485 4,367 1,111 52,062 

2033 40,278 6,419 4,437 1,116 52,250 

2034 40,507 6,345 4,508 1,123 52,483 

2035 40,793 6,271 4,580 1,132 52,776 

2036 41,139 6,197 4,654 1,141 53,131 

2037 41,531 6,125 4,728 1,150 53,534 

2038 41,969 6,053 4,804 1,159 53,986 

2039 42,459 5,983 4,881 1,169 54,491 

2040 43,000 5,912 4,959 1,178 55,049 

79
 
Draft Regulatory Impact Analysis 

Table 0-69 Baseline (50-State) HC Emissions for Locomotives (short tons) 

Calendar Year Large Linehaul Large Switch Small 

Railroads 

Passenger/Co 

mmuter 

Total 

2006 41,665 5,225 2,745 1,528 51,163 

2007 41,559 5,285 2,789 1,509 51,143 

2008 41,439 5,347 2,834 1,489 51,109 

2009 41,297 5,409 2,879 1,468 51,053 

2010 41,122 5,473 2,925 1,446 50,965 

2011 40,930 5,537 2,972 1,424 50,863 

2012 40,733 5,601 3,019 1,402 50,755 

2013 40,550 5,667 3,068 1,379 50,664 

2014 40,378 5,733 3,117 1,357 50,584 

2015 40,192 5,800 3,167 1,334 50,493 

2016 39,988 5,868 3,217 1,311 50,384 

2017 39,783 5,937 3,269 1,288 50,277 

2018 39,586 6,007 3,321 1,265 50,179 

2019 39,391 6,077 3,374 1,242 50,084 

2020 39,188 6,149 3,428 1,219 49,984 

2021 38,978 6,221 3,483 1,196 49,879 

2022 38,766 6,294 3,539 1,173 49,772 

2023 38,553 6,329 3,595 1,151 49,628 

2024 38,358 6,350 3,653 1,129 49,489 

2025 38,162 6,359 3,711 1,107 49,339 

2026 38,013 6,360 3,771 1,089 49,232 

2027 37,907 6,349 3,831 1,075 49,162 

2028 37,844 6,330 3,892 1,064 49,131 

2029 37,828 6,305 3,955 1,058 49,145 

2030 37,866 6,268 4,018 1,054 49,205 

2031 37,950 6,218 4,082 1,053 49,304 

2032 38,079 6,159 4,148 1,055 49,441 

2033 38,250 6,096 4,214 1,060 49,621 

2034 38,468 6,025 4,281 1,067 49,841 

2035 38,740 5,955 4,350 1,075 50,120 

2036 39,068 5,885 4,419 1,084 50,457 

2037 39,440 5,817 4,490 1,092 50,839 

2038 39,857 5,748 4,562 1,101 51,269 

2039 40,322 5,682 4,635 1,110 51,749 

2040 40,836 5,614 4,709 1,119 52,278 

80
 
Chapter 3: Inventory 

Table 0-70 Baseline (50-State) CO Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 116,584 9,620 5,805 4,201 136,211 

2007 118,450 9,774 5,898 4,234 138,356 

2008 120,345 9,930 5,993 4,268 140,536 

2009 122,271 10,089 6,089 4,302 142,751 

2010 124,227 10,251 6,186 4,337 145,000 

2011 126,215 10,415 6,285 4,371 147,286 

2012 128,234 10,581 6,386 4,406 149,607 

2013 130,286 10,751 6,488 4,442 151,966 

2014 132,370 10,923 6,592 4,477 154,362 

2015 134,488 11,097 6,697 4,513 156,796 

2016 136,640 11,275 6,804 4,549 159,268 

2017 138,826 11,455 6,913 4,585 161,780 

2018 141,047 11,639 7,024 4,622 164,332 

2019 143,304 11,825 7,136 4,659 166,924 

2020 145,597 12,014 7,250 4,696 169,558 

2021 147,927 12,206 7,366 4,734 172,233 

2022 150,293 12,402 7,484 4,772 174,951 

2023 152,698 12,600 7,604 4,810 177,712 

2024 155,141 12,802 7,725 4,849 180,517 

2025 157,624 13,006 7,849 4,887 183,366 

2026 160,146 13,215 7,975 4,926 186,261 

2027 162,708 13,426 8,102 4,966 189,202 

2028 165,311 13,641 8,232 5,006 192,189 

2029 167,956 13,859 8,364 5,046 195,224 

2030 170,643 14,081 8,497 5,086 198,308 

2031 173,374 14,306 8,633 5,127 201,440 

2032 176,148 14,535 8,771 5,168 204,622 

2033 178,966 14,768 8,912 5,209 207,855 

2034 181,830 15,004 9,054 5,251 211,139 

2035 184,739 15,244 9,199 5,293 214,475 

2036 187,695 15,488 9,346 5,335 217,864 

2037 190,698 15,736 9,496 5,378 221,307 

2038 193,749 15,987 9,648 5,421 224,805 

2039 196,849 16,243 9,802 5,464 228,359 

2040 199,999 16,503 9,959 5,508 231,969 

81
 
Draft Regulatory Impact Analysis 

Table 0-71 Baseline (50-State) SO2 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 83,769 6,637 3,085 3,018 96,510 

2007 85,110 6,743 3,134 3,042 98,030 

2008 10,088 799 372 358 11,617 

2009 10,250 812 377 361 11,800 

2010 10,414 825 384 364 11,986 

2011 10,580 838 390 366 12,175 

2012 10,750 852 396 369 12,367 

2013 312 25 11 11 359 

2014 317 25 12 11 365 

2015 322 26 12 11 370 

2016 327 26 12 11 376 

2017 333 26 12 11 382 

2018 338 27 12 11 388 

2019 343 27 13 11 394 

2020 349 28 13 11 400 

2021 354 28 13 11 407 

2022 360 29 13 11 413 

2023 366 29 13 12 420 

2024 372 29 14 12 426 

2025 378 30 14 12 433 

2026 384 30 14 12 440 

2027 390 31 14 12 447 

2028 396 31 15 12 454 

2029 402 32 15 12 461 

2030 409 32 15 12 468 

2031 415 33 15 12 476 

2032 422 33 16 12 483 

2033 429 34 16 12 491 

2034 435 35 16 13 499 

2035 442 35 16 13 506 

2036 450 36 17 13 515 

2037 457 36 17 13 523 

2038 464 37 17 13 531 

2039 471 37 17 13 539 

2040 479 38 18 13 548 

82
 
Chapter 3: Inventory 

Table 0-72 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 proposed regulations would apply in largely the same manner as the 

existing program. Thus, the control scenario can be defined simply by the proposed 

standards and the model years for which they would become effective. Two new sets 

of emission standards are being proposed: line-haul locomotive standards and switch 

locomotive standards. The line-haul standards would apply for freight and passenger 

line-haul locomotives, while the switch standards would 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 proposed 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 not 

proposing changes to 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 0-73 and Table 0-74. 

83
 
Draft Regulatory Impact Analysis 

Table 0-73 Projected Line-Haul Emission Factors with Proposed Standards 

Tier Initial Model 

Year 

NOx 

(g/bhp-hr) 

PM10 

(g/bhp-hr) 

HC 

(g/bhp-hr) 

TIER 0 2008/2010A 8.60 0.20 0.30 

TIER 1 2008/2010 6.70 0.20 0.29 

TIER 2 2013 4.95 0.09 0.13 

TIER 3 2012 4.95 0.09 0.13 

TIER 4 2015/2017B 1.00 0.027 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 a 40/80/100 phase-in. 

B The Tier 4 NOx standard would not apply until 2017, while the other standards would apply starting in 

2015. The Tier 4 NOx standard would apply, however, at remanufacture for model year 2015 and 2016 

locomotives. 

Table 0-74 Projected Switch Emission Factors with Proposed Standards 

Tier Initial Model 

Year 

NOx 

(g/bhp-hr) 

PM10 

(g/bhp-hr) 

HC 

(g/bhp-hr) 

TIER 0 2008 12.60 0.25 0.57 

TIER 1 2008 9.90 0.25 0.57 

TIER2 2013 7.30 0.09 0.26 

TIER3 2012 5.40 0.09 0.26 

TIER4 2015 1.00 0.02 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 0-75 through Table 0-79. See section 3.3.2.5 for CO and SO2 

inventories which are not projected to change as a result of the proposed standards. 

The control air toxic inventories for locomotives are provided in Table 0-80. 

The gas phase air toxics are assumed to be controlled proportionately to VOC, while 

POM is controlled proportionately to PM. 

84
 
Chapter 3: Inventory 

Table 0-75 Control Case PM10 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 27,919 2,270 935 1,023 32,147 

2007 27,873 2,295 950 1,011 32,129 

2008 24,919 2,111 883 901 28,814 

2009 24,393 2,134 897 888 28,311 

2010 23,777 2,109 911 848 27,645 

2011 22,544 2,128 926 809 26,407 

2012 21,311 2,068 940 761 25,081 

2013 20,030 2,069 944 707 23,750 

2014 19,279 2,015 959 663 22,916 

2015 18,377 2,029 974 623 22,003 

2016 17,108 1,968 990 574 20,639 

2017 15,849 1,981 1,006 527 19,363 

2018 14,965 1,954 1,022 480 18,422 

2019 14,113 1,968 1,038 435 17,554 

2020 13,567 1,851 1,055 402 16,874 

2021 13,014 1,862 1,071 379 16,326 

2022 12,427 1,793 1,088 355 15,664 

2023 11,831 1,774 1,106 332 15,043 

2024 11,246 1,687 1,124 309 14,366 

2025 10,656 1,557 1,141 286 13,641 

2026 10,098 1,518 1,160 265 13,041 

2027 9,561 1,473 1,178 247 12,459 

2028 9,045 1,425 1,197 230 11,896 

2029 8,553 1,374 1,216 215 11,358 

2030 8,092 1,321 1,223 201 10,837 

2031 7,656 1,263 1,230 189 10,337 

2032 7,243 1,200 1,237 178 9,858 

2033 6,851 1,136 1,243 168 9,398 

2034 6,501 1,069 1,250 158 8,978 

2035 6,181 1,001 1,256 150 8,589 

2036 5,905 934 1,263 143 8,244 

2037 5,661 866 1,269 136 7,933 

2038 5,451 799 1,275 131 7,656 

2039 5,277 733 1,281 127 7,417 

2040 5,140 665 1,287 124 7,216 

85
 
Draft Regulatory Impact Analysis 

Table 0-76 Control Case PM2.5 Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 27,082 2,202 907 992 31,183 

2007 27,037 2,226 922 981 31,166 

2008 24,171 2,048 856 874 27,950 

2009 23,661 2,070 870 861 27,462 

2010 23,063 2,046 884 823 26,816 

2011 21,868 2,064 898 785 25,614 

2012 20,672 2,006 912 738 24,329 

2013 19,429 2,007 916 686 23,037 

2014 18,701 1,954 930 643 22,228 

2015 17,826 1,968 945 604 21,343 

2016 16,594 1,909 960 557 20,020 

2017 15,373 1,922 975 511 18,782 

2018 14,516 1,896 991 466 17,869 

2019 13,690 1,909 1,007 422 17,027 

2020 13,160 1,795 1,023 390 16,368 

2021 12,623 1,806 1,039 367 15,836 

2022 12,054 1,740 1,056 345 15,194 

2023 11,476 1,721 1,073 322 14,592 

2024 10,909 1,637 1,090 300 13,935 

2025 10,336 1,511 1,107 277 13,232 

2026 9,795 1,473 1,125 257 12,650 

2027 9,274 1,429 1,143 239 12,085 

2028 8,773 1,382 1,161 223 11,539 

2029 8,297 1,332 1,180 208 11,017 

2030 7,849 1,281 1,186 195 10,512 

2031 7,426 1,225 1,193 183 10,027 

2032 7,026 1,164 1,200 172 9,562 

2033 6,645 1,102 1,206 163 9,116 

2034 6,306 1,037 1,212 154 8,709 

2035 5,996 971 1,219 145 8,331 

2036 5,728 906 1,225 138 7,997 

2037 5,491 840 1,231 132 7,695 

2038 5,287 775 1,237 127 7,426 

2039 5,118 711 1,243 123 7,195 

2040 4,985 645 1,248 120 6,999 

86
 
Chapter 3: Inventory 

Table 0-77 Control Case NOx Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul Large Switch Small Railroads 

Passenger/ 

Commuter Total 

2006 779,842 86,861 37,690 38,466 942,858 

2007 770,409 87,803 38,293 36,409 932,914 

2008 757,789 87,056 38,906 34,361 918,111 

2009 751,364 87,999 39,528 32,338 911,229 

2010 731,807 87,513 40,161 29,845 889,326 

2011 705,203 88,324 40,803 27,408 861,738 

2012 692,606 86,614 41,456 25,933 846,609 

2013 679,298 87,409 42,119 24,545 833,372 

2014 673,879 85,623 42,793 23,239 825,533 

2015 670,297 86,221 43,168 22,879 822,565 

2016 658,944 84,610 43,544 21,717 808,815 

2017 628,992 85,186 43,921 20,575 778,674 

2018 608,010 84,612 44,299 19,496 756,417 

2019 588,239 85,177 44,609 18,438 736,463 

2020 569,144 80,769 44,917 17,662 712,492 

2021 549,859 81,278 45,224 16,903 693,264 

2022 529,725 78,845 45,529 16,144 670,243 

2023 490,882 78,025 45,832 14,732 629,471 

2024 451,535 74,751 46,134 13,316 585,735 

2025 431,091 70,098 46,433 12,558 560,179 

2026 411,268 68,538 46,730 11,833 538,369 

2027 391,811 66,724 46,863 11,182 516,581 

2028 372,842 64,743 46,989 10,555 495,130 

2029 354,485 62,635 47,107 9,948 474,175 

2030 336,949 60,285 47,062 9,355 453,651 

2031 320,021 57,681 47,002 8,775 433,480 

2032 303,667 54,892 46,929 8,204 413,692 

2033 287,812 52,013 46,842 7,641 394,307 

2034 272,853 48,969 46,739 7,082 375,643 

2035 258,735 45,924 46,622 6,527 357,807 

2036 246,204 42,882 46,488 6,048 341,622 

2037 234,905 39,846 46,339 5,623 326,713 

2038 224,870 36,814 46,172 5,270 313,127 

2039 216,190 33,806 45,989 4,986 300,970 

2040 208,892 30,761 45,788 4,765 290,205 

87
 
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Table 0-78 Control Case VOC Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 43,874 5,501 2,891 1,609 53,874 

2007 43,762 5,566 2,937 1,589 53,853 

2008 42,998 5,488 2,984 1,568 53,037 

2009 42,008 5,552 3,032 1,546 52,137 

2010 40,825 5,483 3,080 1,470 50,858 

2011 38,373 5,534 3,129 1,395 48,431 

2012 35,890 5,364 3,179 1,301 45,734 

2013 33,597 5,413 3,230 1,210 43,451 

2014 31,991 5,253 3,282 1,122 41,648 

2015 30,268 5,291 3,335 1,045 39,939 

2016 27,758 5,112 3,388 952 37,210 

2017 25,275 5,147 3,442 861 34,725 

2018 23,607 5,066 3,497 771 32,941 

2019 22,010 5,100 3,553 683 31,346 

2020 21,142 4,760 3,610 623 30,135 

2021 20,266 4,790 3,668 586 29,310 

2022 19,340 4,588 3,726 549 28,204 

2023 18,402 4,538 3,786 512 27,238 

2024 17,483 4,291 3,847 476 26,096 

2025 16,556 3,916 3,908 439 24,819 

2026 15,681 3,810 3,971 406 23,869 

2027 14,839 3,692 4,034 377 22,943 

2028 14,031 3,565 4,099 351 22,047 

2029 13,263 3,432 4,164 328 21,187 

2030 12,543 3,302 4,231 307 20,383 

2031 11,863 3,160 4,299 288 19,609 

2032 11,220 3,009 4,367 270 18,866 

2033 10,611 2,853 4,437 255 18,156 

2034 10,068 2,689 4,508 241 17,506 

2035 9,573 2,525 4,580 228 16,907 

2036 9,147 2,362 4,654 217 16,379 

2037 8,771 2,199 4,728 207 15,906 

2038 8,448 2,037 4,804 199 15,488 

2039 8,182 1,876 4,881 193 15,132 

2040 7,974 1,714 4,959 188 14,835 

88
 
Chapter 3: Inventory 

Table 0-79 Control Case HC Emissions for Locomotives (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2006 41,665 5,225 2,745 1,528 51,163 

2007 41,559 5,285 2,789 1,509 51,143 

2008 40,834 5,211 2,834 1,489 50,368 

2009 39,894 5,272 2,879 1,468 49,513 

2010 38,770 5,207 2,925 1,396 48,298 

2011 36,441 5,255 2,972 1,325 45,993 

2012 34,083 5,094 3,019 1,236 43,432 

2013 31,906 5,141 3,068 1,149 41,264 

2014 30,381 4,989 3,117 1,065 39,552 

2015 28,745 5,025 3,167 993 37,929 

2016 26,361 4,854 3,217 904 35,337 

2017 24,003 4,888 3,269 817 32,977 

2018 22,419 4,811 3,321 732 31,283 

2019 20,902 4,844 3,374 648 29,769 

2020 20,078 4,521 3,428 591 28,618 

2021 19,246 4,549 3,483 556 27,835 

2022 18,367 4,357 3,539 521 26,784 

2023 17,476 4,310 3,595 487 25,867 

2024 16,603 4,075 3,653 452 24,783 

2025 15,722 3,719 3,711 417 23,570 

2026 14,892 3,619 3,771 386 22,667 

2027 14,092 3,506 3,831 358 21,788 

2028 13,325 3,386 3,892 334 20,937 

2029 12,595 3,259 3,955 311 20,121 

2030 11,912 3,136 4,018 291 19,357 

2031 11,266 3,001 4,082 273 18,622 

2032 10,655 2,857 4,148 257 17,917 

2033 10,077 2,709 4,214 242 17,242 

2034 9,561 2,554 4,281 229 16,625 

2035 9,092 2,398 4,350 216 16,056 

2036 8,687 2,243 4,419 206 15,555 

2037 8,330 2,089 4,490 197 15,105 

2038 8,023 1,934 4,562 189 14,709 

2039 7,770 1,782 4,635 183 14,370 

2040 7,573 1,627 4,709 178 14,088 

89
 
Draft Regulatory Impact Analysis 

Table 0-80 Control Case Air Toxic Emissions for Locomotives (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 79 62 46 30 

FORMALDEHYDE 1,269 990 733 478 

ACETALDEHYDE 554 432 320 208 

1,3-BUTADIENE 92 72 53 35 

ACROLEIN 90 70 52 34 

NAPHTHALENE 40 30 22 13 

POM 24 19 14 9 

3.3.4 Projected Locomotive Emission Reductions from the Proposed Rule 

The projected emission reductions for PM 2.5, NOx and VOC for each category 

and calendar year are given in Table 0-81, Table 0-82, and Table 0-83. Table 0-84 

presents the air toxic emission reductions. 

90
 
Chapter 3: Inventory 

Table 0-81 Projected Locomotive PM2.5 Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 154 49 0 0 203 

2009 555 50 0 0 604 

2010 1,023 96 0 24 1,144 

2011 2,078 101 0 49 2,227 

2012 3,128 182 0 81 3,391 

2013 3,865 183 0 107 4,155 

2014 4,457 259 0 135 4,850 

2015 5,186 269 0 159 5,614 

2016 6,260 352 0 191 6,803 

2017 7,323 363 0 222 7,908 

2018 8,026 413 0 253 8,692 

2019 8,699 425 0 283 9,406 

2020 9,070 563 0 300 9,933 

2021 9,444 577 0 308 10,329 

2022 9,848 669 0 316 10,832 

2023 10,258 694 0 324 11,276 

2024 10,668 778 0 332 11,777 

2025 11,081 898 0 339 12,318 

2026 11,490 926 0 348 12,764 

2027 11,907 953 0 356 13,216 

2028 12,329 979 0 365 13,674 

2029 12,755 1,006 0 375 14,136 

2030 13,185 1,027 0 385 14,597 

2031 13,619 1,048 0 396 15,063 

2032 14,057 1,068 0 407 15,532 

2033 14,501 1,087 0 419 16,007 

2034 14,932 1,106 0 432 16,470 

2035 15,366 1,125 0 445 16,936 

2036 15,794 1,144 0 457 17,394 

2037 16,215 1,163 0 467 17,846 

2038 16,632 1,182 0 477 18,291 

2039 17,042 1,201 0 486 18,730 

2040 17,447 1,220 0 494 19,161 

91
 
Draft Regulatory Impact Analysis 

Table 0-82 Projected Locomotive NOx Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 3,978 568 0 0 4,546 

2009 4,126 575 0 0 4,700 

2010 13,624 1,111 0 526 15,261 

2011 30,439 1,261 0 1,051 32,751 

2012 37,425 2,295 0 1,278 40,999 

2013 46,819 2,463 0 1,472 50,753 

2014 48,487 3,468 0 1,634 53,588 

2015 48,503 3,834 0 1,503 53,840 

2016 55,949 5,072 0 1,608 62,630 

2017 82,372 5,467 0 2,347 90,186 

2018 100,515 6,263 0 3,063 109,841 

2019 118,236 6,681 0 3,759 128,676 

2020 135,209 8,598 0 4,175 147,982 

2021 152,589 9,054 0 4,574 166,217 

2022 170,780 10,386 0 4,975 186,141 

2023 207,999 11,370 0 6,065 225,434 

2024 246,202 13,144 0 7,195 266,541 

2025 265,831 15,424 0 7,699 288,954 

2026 285,577 16,767 0 8,233 310,577 

2027 305,677 18,237 0 8,753 332,667 

2028 325,972 19,795 0 9,305 355,071 

2029 346,408 21,423 0 9,888 377,719 

2030 366,898 23,173 0 10,504 400,575 

2031 387,533 25,050 0 11,151 423,735 

2032 408,322 27,025 0 11,828 447,175 

2033 429,288 29,054 0 12,519 470,861 

2034 450,106 31,171 0 13,223 494,501 

2035 470,970 33,304 0 13,941 518,215 

2036 491,170 35,451 0 14,584 541,204 

2037 510,838 37,609 0 15,173 563,621 

2038 529,966 39,782 0 15,693 585,440 

2039 548,521 41,960 0 16,145 606,626 

2040 566,497 44,171 0 16,534 627,202 

92
 
Chapter 3: Inventory 

Table 0-83 Projected Locomotive VOC Emission Reductions (short tons) 

Calendar Year Large 

Line-haul 

Large Switch Small 

Railroads 

Passenger/ 

Commuter 

Total 

2008 638 143 0 0 780 

2009 1,477 144 0 0 1,622 

2010 2,476 279 0 52 2,808 

2011 4,727 296 0 105 5,128 

2012 7,002 534 0 175 7,711 

2013 9,102 554 0 242 9,899 

2014 10,527 784 0 307 11,617 

2015 12,054 817 0 359 13,230 

2016 14,349 1,067 0 428 15,844 

2017 16,617 1,104 0 495 18,217 

2018 18,078 1,259 0 561 19,897 

2019 19,468 1,299 0 625 21,392 

2020 20,122 1,714 0 661 22,498 

2021 20,778 1,760 0 674 23,212 

2022 21,480 2,040 0 687 24,206 

2023 22,194 2,126 0 699 25,020 

2024 22,908 2,395 0 713 26,016 

2025 23,629 2,780 0 726 27,135 

2026 24,346 2,887 0 740 27,973 

2027 25,077 2,993 0 754 28,825 

2028 25,819 3,100 0 770 29,688 

2029 26,570 3,207 0 786 30,563 

2030 27,329 3,297 0 803 31,430 

2031 28,099 3,387 0 822 32,308 

2032 28,878 3,477 0 841 33,196 

2033 29,667 3,566 0 861 34,095 

2034 30,439 3,656 0 882 34,977 

2035 31,220 3,745 0 904 35,869 

2036 31,992 3,835 0 924 36,752 

2037 32,759 3,926 0 943 37,628 

2038 33,521 4,016 0 960 38,497 

2039 34,276 4,107 0 976 39,360 

2040 35,026 4,198 0 990 40,214 

Table 0-84 Projected Locomotive Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 4 20 34 46 

FORMALDEHYDE 70 328 547 736 

ACETALDEHYDE 31 143 239 321 

1,3-BUTADIENE 5 24 40 54 

ACROLEIN 5 23 39 52 

NAPHTHALENE 2 10 16 20 

POM 1 5 9 12 

93
 
Draft Regulatory Impact Analysis 

3.4 Projected Total Emission Reductions from the Proposed 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 0-85. The totals include 

emissions from the three major categories affected by this proposed 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 0-86 thru Table 0-88. 

The total air toxics reductions are provided in Table 0-89. 

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 

Base 50-State 

Control 50-State 

2005 2010 2015 2020 2025 2030 2035 2040 

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Chapter 3: Inventory 

Figure 2 Estimated NOx Reductions from Locomotive and Marine Diesel Engine Standards 

(short tons) 

2005 2010 2015 2020 2025 2030 2035 2040
 

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 

Base 50-State 

Control 50-State 

95
 
Draft Regulatory Impact Analysis 

Table 0-85 Total Emissions and Projected Reductions (short tons) 

Year PM2.5 NOx VOC 

Base Control Reduction Base Control Reduction Base Control Reduction 

2006 62,653 62,653 0 1,807,216 1,807,216 0 72,872 72,872 0 

2007 60,785 60,785 0 1,784,284 1,784,284 0 72,776 72,776 0 

2008 56,405 56,202 203 1,761,171 1,756,625 4,546 72,667 71,887 780 

2009 56,302 55,694 608 1,741,683 1,736,983 4,700 72,541 70,912 1,629 

2010 55,976 54,826 1,149 1,717,796 1,702,535 15,261 72,386 69,562 2,824 

2011 55,667 53,431 2,236 1,695,396 1,662,645 32,751 72,219 67,068 5,151 

2012 55,283 51,813 3,469 1,676,501 1,633,993 42,508 72,052 64,182 7,870 

2013 54,424 49,943 4,481 1,661,733 1,605,845 55,888 71,909 61,608 10,301 

2014 52,646 47,011 5,635 1,646,170 1,574,799 71,371 71,784 59,291 12,494 

2015 51,240 44,466 6,775 1,633,374 1,549,093 84,281 71,657 57,077 14,580 

2016 50,872 42,313 8,560 1,618,865 1,506,062 112,803 71,528 53,750 17,778 

2017 50,424 40,026 10,397 1,608,049 1,445,528 162,520 71,437 50,613 20,824 

2018 50,095 38,133 11,962 1,598,602 1,392,149 206,453 71,388 48,170 23,218 

2019 49,764 36,308 13,455 1,591,433 1,341,927 249,506 71,359 45,937 25,421 

2020 49,543 34,767 14,776 1,582,106 1,289,199 292,907 71,357 44,126 27,231 

2021 49,514 33,501 16,013 1,577,901 1,242,771 335,130 71,382 42,738 28,645 

2022 49,514 32,145 17,369 1,572,450 1,193,565 378,885 71,420 41,090 30,330 

2023 49,496 30,821 18,675 1,569,867 1,128,045 441,821 71,431 39,604 31,827 

2024 49,481 29,436 20,045 1,567,335 1,061,013 506,322 71,456 37,963 33,493 

2025 49,473 28,027 21,446 1,564,909 1,013,286 551,623 71,477 36,228 35,249 

2026 49,505 26,772 22,733 1,566,090 970,582 595,508 71,547 34,867 36,680 

2027 49,575 25,572 24,003 1,568,322 929,227 639,095 71,659 33,581 38,077 

2028 49,683 24,424 25,258 1,571,677 889,403 682,274 71,817 32,369 39,448 

2029 49,831 23,330 26,501 1,576,224 851,600 724,624 72,027 31,228 40,799 

2030 50,009 22,290 27,719 1,582,353 816,578 765,775 72,290 30,184 42,106 

2031 50,219 21,303 28,916 1,589,972 784,030 805,941 72,597 29,208 43,389 

2032 50,460 20,363 30,097 1,598,625 753,109 845,516 72,950 28,293 44,657 

2033 50,733 19,462 31,271 1,608,222 723,487 884,735 73,349 27,432 45,917 

2034 51,032 18,612 32,420 1,618,723 695,357 923,366 73,794 26,651 47,144 

2035 51,368 17,812 33,557 1,630,331 668,881 961,451 74,302 25,939 48,364 

2036 51,741 17,097 34,644 1,643,034 645,058 997,976 74,873 25,320 49,553 

2037 52,142 16,482 35,660 1,656,625 624,387 1,032,239 75,493 24,787 50,705 

2038 52,572 15,953 36,620 1,671,116 606,737 1,064,379 76,163 24,337 51,826 

2039 53,034 15,507 37,527 1,686,564 591,798 1,094,766 76,888 23,968 52,920 

2040 53,526 15,166 38,360 1,702,935 579,393 1,123,542 77,667 23,684 53,983 

96
 
Chapter 3: Inventory 

Table 0-86 Projected Total PM2.5 Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 0 0 203 203 

2009 3 0 604 608 

2010 6 0 1,144 1,149 

2011 8 1 2,227 2,236 

2012 76 3 3,391 3,469 

2013 321 5 4,155 4,481 

2014 776 8 4,850 5,635 

2015 1,149 12 5,614 6,775 

2016 1,740 16 6,803 8,560 

2017 2,469 21 7,908 10,397 

2018 3,245 25 8,692 11,962 

2019 4,019 30 9,406 13,455 

2020 4,808 35 9,933 14,776 

2021 5,644 41 10,329 16,013 

2022 6,491 46 10,832 17,369 

2023 7,347 52 11,276 18,675 

2024 8,210 58 11,777 20,045 

2025 9,064 63 12,318 21,446 

2026 9,899 70 12,764 22,733 

2027 10,711 76 13,216 24,003 

2028 11,503 82 13,674 25,258 

2029 12,277 88 14,136 26,501 

2030 13,027 95 14,597 27,719 

2031 13,752 101 15,063 28,916 

2032 14,458 107 15,532 30,097 

2033 15,151 112 16,007 31,271 

2034 15,834 116 16,470 32,420 

2035 16,500 120 16,936 33,557 

2036 17,126 124 17,394 34,644 

2037 17,686 127 17,846 35,660 

2038 18,198 130 18,291 36,620 

2039 18,664 133 18,730 37,527 

2040 19,063 136 19,161 38,360 

97
 
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Table 0-87 Projected Total NOx Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 0 0 4,546 4,546 

2009 0 0 4,700 4,700 

2010 0 0 15,261 15,261 

2011 0 0 32,751 32,751 

2012 1,463 47 40,999 42,508 

2013 4,935 200 50,753 55,888 

2014 17,326 458 53,588 71,371 

2015 29,723 718 53,840 84,281 

2016 49,151 1,022 62,630 112,803 

2017 71,006 1,328 90,186 162,520 

2018 94,975 1,637 109,841 206,453 

2019 118,882 1,947 128,676 249,506 

2020 142,666 2,260 147,982 292,907 

2021 166,339 2,574 166,217 335,130 

2022 189,855 2,889 186,141 378,885 

2023 213,181 3,206 225,434 441,821 

2024 236,257 3,524 266,541 506,322 

2025 258,828 3,842 288,954 551,623 

2026 280,771 4,160 310,577 595,508 

2027 301,951 4,477 332,667 639,095 

2028 322,410 4,793 355,071 682,274 

2029 341,797 5,108 377,719 724,624 

2030 359,780 5,419 400,575 765,775 

2031 376,481 5,725 423,735 805,941 

2032 392,324 6,016 447,175 845,516 

2033 407,598 6,277 470,861 884,735 

2034 422,367 6,498 494,501 923,366 

2035 436,542 6,693 518,215 961,451 

2036 449,899 6,873 541,204 997,976 

2037 461,578 7,039 563,621 1,032,239 

2038 471,739 7,199 585,440 1,064,379 

2039 480,787 7,353 606,626 1,094,766 

2040 488,838 7,502 627,202 1,123,542 

98
 
Chapter 3: Inventory 

Table 0-88 Projected Total VOC Emission Reductions (short tons) 

YEAR COMMERCIAL 

MARINE 

RECREATIONAL 

MARINE 

LOCOMOTIVES TOTAL 

2008 0 0 780 780 

2009 7 1 1,622 1,629 

2010 14 1 2,808 2,824 

2011 22 2 5,128 5,151 

2012 152 8 7,711 7,870 

2013 383 20 9,899 10,301 

2014 837 40 11,617 12,494 

2015 1,290 61 13,230 14,580 

2016 1,848 86 15,844 17,778 

2017 2,497 111 18,217 20,824 

2018 3,183 137 19,897 23,218 

2019 3,867 163 21,392 25,421 

2020 4,545 188 22,498 27,231 

2021 5,218 215 23,212 28,645 

2022 5,883 241 24,206 30,330 

2023 6,539 267 25,020 31,827 

2024 7,183 294 26,016 33,493 

2025 7,794 320 27,135 35,249 

2026 8,360 347 27,973 36,680 

2027 8,880 373 28,825 38,077 

2028 9,360 400 29,688 39,448 

2029 9,811 426 30,563 40,799 

2030 10,225 452 31,430 42,106 

2031 10,605 477 32,308 43,389 

2032 10,960 501 33,196 44,657 

2033 11,300 523 34,095 45,917 

2034 11,625 541 34,977 47,144 

2035 11,936 558 35,869 48,364 

2036 12,228 573 36,752 49,553 

2037 12,490 587 37,628 50,705 

2038 12,728 600 38,497 51,826 

2039 12,947 613 39,360 52,920 

2040 13,143 626 40,214 53,983 

99
 
Draft Regulatory Impact Analysis 

Table 0-89 Projected Total Air Toxic Emission Reductions (short tons) 

HAP 2010 2015 2020 2030 

BENZENE 5 66 198 422 

FORMALDEHYDE 74 661 1,751 3,494 

ACETALDEHYDE 32 308 836 1,691 

1,3-BUTADIENE 5 24 42 58 

ACROLEIN 5 30 62 105 

NAPHTHALENE 2 13 27 44 

POM 1 6 11 17 

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 II/III Switch railroads 

owned by Class I railroads), Other Line-Haul Locomotives (i.e., Class II/III local and 

regional 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, 

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Chapter 3: Inventory 

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 Model 

(NMIM) outputs for the final Mobile Source Air Toxics rulemaking, which includes 

the assumed implementation of Renewable Fuels Standards (RFS) and corrections for 

cold-start HC effects. Nonroad land-based diesel values are from the latest publicly 

released version of EPA's nonroad model (NONROAD2005a). Nonroad sparkignition (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 run at the nationwide/annual level using single default nationwide 

temperature & RVP, using the full 50-state equipment population including all 

California equipment. 

3.5.2 PM2.5 Contributions to Baseline 

Table 0-90 provides the contribution of locomotives and diesel-fueled 

recreational and commercial marine engines to mobile source diesel and to total manmade 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 59,000 

tons in 2002 to 50,000 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 0-91 provides the contribution of locomotives and diesel-fueled 

recreational and commercial marine engines to mobile source NOx and to total manmade 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 1,993,000 tons in 2002 to 

1,582,000 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 in 2025 and by 2035 exceed 2015 emission levels. 

3.5.4 VOC Contributions to Baseline 

Table 0-92 provides the contribution of locomotives and diesel-fueled 

recreational and commercial marine engines to mobile source VOC and to total manmade 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 

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Draft Regulatory Impact Analysis 

increase from 67,000 tons in 2002 to 71,000 tons in 2020 and 72,000 tons in 2030, 

since the existing emission standards are not aimed at controlling VOC. 

3.5.5 CO Contributions to Baseline 

Table 0-93 provides the contribution of locomotives and diesel-fueled 

recreational and commercial marine engines to mobile source carbon monoxide (CO) 

and to total man-made 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 

319,000 tons in 2020 and 353,000 tons in 2030, since the existing emission standards 

are not aimed at controlling CO. 

3.5.6 SO2 Contributions to Baseline 

Table 0-94 provides the contribution of locomotives and diesel-fueled 

recreational and commercial marine engines to mobile source SO2 and to total manmade 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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Chapter 3: Inventory 

Table 0-90 50-State Annual PM 2.5 Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

Category 2001* 2020 2030 

short tons % of % of short tons % of % of short tons % of % of 

diesel total diesel total diesel total 

mobile mobile mobile 

Locomotive 29,660 8.9% 1.2% 26,301 23.6% 1.3% 25,109 32.2% 1.2% 

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 20.0% 1.1% 23,760 30.5% 1.1% 

Land-Based Nonroad Diesel 164,180 49.2% 6.8% 46,075 41.4% 2.2% 17,934 23.0% 0.9% 

Commercial Marine (C3)** 20,023 - 0.8% 36,141 - 1.7% 52,682 - 2.5% 

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% 190,675 9.2% 184,130 8.9% 

Highway Diesel 109,952 33.0% 4.5% 15,800 14.2% 0.8% 10,072 12.9% 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 3.0% 66,806 3.2% 

Total Diesel (distillate) Mobile 333,619 100% 13.7% 111,418 100% 5.4% 78,015 100% 3.8% 

Total Mobile Sources 466,170 19.2% 253,829 12.3% 250,936 12.1% 

Stationary Point and Area 

Sources 

1,963,264 80.8% 1,817,722 87.7% 1,817,722 87.9% 

Total Man-Made Sources 2,429,434 100% 2,071,551 100% 2,068,658 100% 

* The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 
 

2002. 
 

** This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
 

on ocean-going vessels. 
 

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Draft Regulatory Impact Analysis 

Table 0-91 50-State Annual NOx Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

Category 2001* 2020 2030 

short tons % of % of short tons % of % of short tons % of % of 

mobile total mobile total mobile total 

source source source 

Locomotive 1,118,786 9.0% 5.1% 860,474 17.2% 7.8% 854,226 19.0% 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 13.6% 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)** 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% 2,980,523 59.7% 26.9% 2,940,064 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 40.3% 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% 4,988,760 100% 44.9% 4,490,759 100% 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% 11,100,626 - 100% 10,602,625 - 100% 

* The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 
 

2002. 
 

** This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
 

on ocean-going vessels. 
 

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Chapter 3: Inventory 

Table 0-92 50-State Annual VOC Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

Category 2001* 2020 2030 

short tons % of % of short tons % of % of short tons % of % of 

mobile total mobile total mobile total 

source source source 

Locomotive 50,665 0.6% 0.3% 52,633 1.0% 0.4% 51,813 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)** 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% 2,603,977 48.5% 18.8% 2,737,914 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 49.2% 19.1% 2,987,562 50.9% 20.8% 

Total Highway 4,540,134 55.1% 25.3% 2,769,812 51.5% 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% 5,373,789 100% 38.8% 5,864,234 100% 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% 13,849,232 - 100% 14,339,677 - 100% 

* The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 
 

2002. 
 

** This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
 

on ocean-going vessels. 
 

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Draft Regulatory Impact Analysis 

Table 0-93 50-State Annual CO Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

Category 2001* 2020 2030 

short tons % of % of short tons % of % of short tons % of % of 

mobile total mobile total mobile total 

source source source 

Locomotive 123,210 0.1% 0.1% 169,558 0.3% 0.2% 198,308 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)** 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% 32,626,663 49.9% 44.1% 36,909,725 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% 65,378,756 100% 88.3% 74,458,720 100% 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% 74,020,434 - 100% 83,100,398 - 100% 

* The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 
 

2002. 
 

** This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
 

on ocean-going vessels. 
 

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Chapter 3: Inventory 

 Table 0-94 50-State Annual SO2 Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

Category 2001* 2020 2030 

short tons % of % of short tons % of % of short tons % of % of 

mobile total mobile total mobile total 

source source source 

Locomotive 76,727 9.7% 0.5% 400 0.1% 0.0% 468 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)** 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% 302,255 88.7% 3.5% 433,745 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% 340,893 100% 4.0% 480,907 100% 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% 8,555,909 - 100% 8,695,923 - 100% 

* The locomotive, commercial marine (C1 & C2), and diesel recreational marine estimates are for calendar year 
 

2002. 
 

** This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
 

on ocean-going vessels. 
 

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Draft Regulatory Impact Analysis 

3.6 Contribution of Marine Diesel Engines and Locomotives to NonAttainment Area Emission Inventories 

Table 0-95 and Table 0-96 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).15 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. 

These percentages are the same as shown in Chapter 2 of the Preamble of this 

proposed rule. 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. 

Table 0-95 Locomotive and Diesel Marine Engine Contributions to Non-Attainment Area Mobile 

Source Diesel PM2.5 Emissions 

PM2.5 Metropolitan Area 2002 2020 2030 

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% 

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Chapter 3: Inventory 

Table 0-96 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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Draft Regulatory Impact Analysis 

3.7 Emission Inventories Used for Air Quality Modeling 

3.7.1 Comparison of Air Quality and Proposed Rule Inventories 

The emission inventory estimates used to demonstrate the effect of the 

proposed rule on air quality relied on the best estimates available at that time of the 

emission contributions from all sources in the base calendar year and projections into 

future calendar years. However, because of the long lead time necessary to prepare 

inputs for the air quality models and to run the models, the emission inventory 

estimates used in the air quality analysis are not the inventories that are now our best 

estimate of the impacts of the proposed rule. In all cases, the changes to the emission 

inventory estimates reflect improvements made to the inventories which reflect new 

information about the emission contributions from various sources that was not 

available at the time the air quality analysis inventories were prepared. This section 

describes the differences in the inventories used for the air quality analysis and the 

inventories used for the proposed 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 proposed rule. 

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 sources not directly affected by the proposed 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 aircraft, 

oceangoing vessels, stationary and area sources were not updated between the air 

quality analysis and the proposed rule. However, changes were made in the onroad 

and nonroad inventories and in the locomotive and commercial marine vessel 

inventories for both the base (uncontrolled) and proposed rule control cases. 

Table 0-97, Table 0-98, and Table 0-99 summarize the differences between 

the air quality inventories and the more updated proposed rule inventories for baseline 

VOC, NOx, and PM2.5. Similarly, Table 0-100, Table 0-101, and Table 0-102 

summarize the differences between the air quality inventories and the more updated 

proposed rule inventories for control case VOC, NOx, and PM2.5. Lastly, Table 

0-103, Table 0-104, and Table 0-105 summarize the differences in ton reductions for 

110
 
Chapter 3: Inventory 

these pollutants between the air quality inventories and the more updated proposed 

rule inventories. Only the years 2020 and 2030 are shown for the latter two sets of 

tables, since this proposal has no benefits prior to 2008. Although the actual 

inventories change up to 20% depending on pollutant and year between the air quality 

inventories and the later proposed rule inventories, the net effect of all the changes on 

ton reductions of these pollutants ranges only from -4 percent to +3 percent. For the 

final rule air quality analysis, we will be incorporating the changes described below, 

as well as any future updates to the baseline estimates and control programs, which 

we expect will have counterbalancing impacts on both baseline and control cases for 

the final rule. 

3.7.2 Onroad Inventory Changes 

The onroad (highway) emission inventory estimates used in the air quality 

analysis were taken directly from the estimates used for the recent Clean Air 

Interstate Rule (CAIR)16 using the National Mobile Inventory Model (NMIM) tool 

and the March 25, 2004, version of the NMIM County database (County20040325). 

The updated emission inventory estimates for onroad in the proposed rule 

were originally calculated for use in the proposed Mobile Source Air Toxics (MSAT) 

rule. The MSAT emission inventory estimates use the NMIM tool and the July 25, 

2006 version of the NMIM County database (NCD20060725MSATFinal). This new 

database includes important corrections to the inputs for 13 states regarding the 

implementation of California emission standards. The error in the old database 

resulted in significantly over-predicted NOx emissions for light-duty gasoline vehicles 

in the onroad emission inventory estimates used in the air quality analysis, especially 

in the projection years of 2020 (+995,000 tons, +60%) and 2030 (+995,000 tons, 

+60%). This resulted in an overprediction of the ozone levels in both the base and 

control cases, and probably also a small overprediction of the air quality benefits of 

this proposed rule. Using the corrected database, light-duty gasoline NOx emissions 

decrease by 434,000 tons (-24%) in 2020 and 464,000 tons (-26%) in 2030. 

The updated emission inventory estimates in the proposed rule for onroad also 

made use of an in-house version of the EPA MOBILE6.2 emission factor model 

which has been adapted to use new temperature correction factors for hydrocarbon 

(HC) emissions for light duty gasoline vehicles. These new temperature correction 

factors were developed as part of the MSAT rule. Using the new temperature 

correction factors significantly increases the HC inventories for light duty gasoline 

vehicles, especially in the projection years of 2020 (+995,000 tons, +60%) and 2030 

(+1,358,000 tons, +83%), during periods where temperatures are less than 75 degrees 

Fahrenheit. 

These changes do not affect the estimated ton reductions from this proposed 

rule, but they do affect the total emission inventory in both base and control cases. 

This is shown in Table 0-97 through Table 0-102 in combination with the inventory 

changes for nonroad equipment. 

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Draft Regulatory Impact Analysis 

3.7.3 Nonroad Inventory Changes 

The air quality analysis for the nonroad emission inventory estimates for all 

sources other than diesel pleasure craft (which are included in this proposed rule) 

were taken directly from the estimates used for the recent Clean Air Interstate Rule 

(CAIR) and are based on the 2004 version of the EPA NONROAD model. 

The updated nonroad inventory for the proposed rule is based on the recently 

released 2005 version of the EPA NONROAD model. This newer nonroad model 

includes many changes from the 2004 version, but the ones that most significantly 

affect the estimated inventories are as follows: 

•	 Addition of new evaporative categories for tank permeation, hose permeation, hot 

soak, and running loss emissions. 

•	 Revised methodology for calculating diurnal emissions 

•	 Incorporated the effects of evaporative emission standards for recreational 

vehicles and large spark ignition engines. 

•	 Updated allocations from the national to the state and county level. 

•	 Updated the power range distributions and technology fractions for spark-ignition 

recreational marine engines. 

•	 Updated emission factors, deterioration factors, and technology mix for phase-2 

Class 1 small gasoline engines (≤ 25 hp). 

The net effect of these changes is a 55% increase in VOC from these sources 

(increase of 793,000 tons in 2020 and 820,000 tons in 2030). The corresponding 

change in NOx is a small decrease of 13,000 tons (1.4%) in 2020 and 40,000 tons 

(5%) in 2030. These changes do not affect the estimated ton reductions from this 

proposed rule, but they do affect the total emission inventory in both base and control 

cases. This is shown in Table 0-97 through Table 0-102 in combination with the 

onroad inventory changes described above in section 3.7.2. 

3.7.4 Locomotive Inventory Changes 

The locomotive emission inventory estimates used in the air quality analysis 

were calculated by EPA using a new national inventory estimation spreadsheet model 

developed for this purpose. However, since the air quality analysis, changes have 

been made in the emission rate estimates used in the model and the rate of turnover 

for the locomotive switcher fleet. These changes affect the emission inventory 

estimates for all pollutants and in all calendar years. 

In addition to the changes in the model, the inventory benefits of the proposed 

rule were affected by a change in the assumptions for the effects of the rule. The NOx 

112
 
Chapter 3: Inventory 

emissions of all Tier 0 engines were originally assumed to be affected by the rule in 

the locomotive emission inventory estimates used in the air quality analysis. The 

updated inventories assume that only 1994 and later model year Tier 0 engines are 

affected by the rule. 

The last change to note is that the air quality inventory for locomotives treated 

the calculated HC inventory as if it were VOC. In the updated inventory the HC 

value is properly treated as Total Hydrocarbons (THC), and VOC is reported as 1.053 

* THC. 

The net effect of these updates is a change in tons reduced from locomotives 

ranging from -8 percent to +5 percent, depending on pollutant and year. 

3.7.5 Commercial Marine Vessel Inventory Changes 

The commercial marine vessel (Category 1 and Category 2) emission 

inventory estimates used in the air quality analysis were calculated by EPA using new 

national inventory estimation spreadsheet models developed for this purpose. 

However, since the air quality analysis, changes have been made in some of the 

assumptions used in the model, including the load factors, the sulfur content of the 

diesel certification fuel used for pleasure craft, and the sulfur content of diesel fuel 

used by commercial marine vessels. These changes did not affect the projected ton 

reductions for marine diesel engines, since the baseline and control cases were 

equally affected. These reductions are 5,000 tons VOC and 139,000 tons NOx in 

2020, and 11,000 tons VOC and 346,000 tons NOx in 2030. 

Table 0-97 50-State Annual VOC Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2001* 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 48,115 50,665 5.3% 49,039 52,633 7.3% 47,606 51,813 8.8% 

MARINE DIESEL 14,176 18,768 32.4% 13,677 18,724 36.9% 14,588 20,477 40.4% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

16,978,113 17,868,970 5.2% 11,736,377 13,777,876 17.4% 11,804,110 14,267,387 20.9% 

TOTAL MAN-MADE 

SOURCES 

17,040,404 17,938,403 5.3% 11,799,094 13,849,233 17.4% 11,866,304 14,339,677 20.8% 

* LOCOMOTIVE AND MARINE DIESEL VALUES IN THE "2001" COLUMN ARE ACTUALLY 2002 ESTIMATES. 

113
 
Draft Regulatory Impact Analysis 

Table 0-98 50-State Annual NOx Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2001* 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 1,118,786 1,118,786 0.0% 844,932 860,474 1.8% 835,059 854,226 2.3% 

MARINE DIESEL 711,656 874,462 22.9% 606,021 721,632 19.1% 608,761 728,127 19.6% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

19,854,001 19,786,232 -0.3% 10,006,926 9,518,521 -4.9% 9,570,157 9,020,273 -5.7% 

TOTAL MAN-MADE 

SOURCES 

21,684,444 21,779,480 0.4% 11,457,878 11,100,627 -3.1% 11,013,977 10,602,626 -3.7% 

* LOCOMOTIVE AND MARINE DIESEL VALUES IN THE "2001" COLUMN ARE ACTUALLY 2002 ESTIMATES. 

Table 0-99 50-State Annual PM2.5 Baseline Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2001* 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % 

DIFF 

LOCOMOTIVE 29,660 29,660 0.0% 25,843 26,301 1.8% 24,334 25,109 3.2% 

MARINE DIESEL 23,627 29,827 26.2% 20,087 23,242 15.7% 21,852 24,900 13.9% 

ALL OTHER SOURCES 

(MOBILE & 

STATIONARY) 

2,393,848 2,369,947 -1.0% 2,044,184 2,022,009 -1.1% 2,041,701 2,018,649 -1.1% 

TOTAL MAN-MADE 

SOURCES 

2,447,136 2,429,434 -0.7% 2,090,114 2,071,552 -0.9% 2,087,886 2,068,658 -0.9% 

* LOCOMOTIVE AND MARINE DIESEL VALUES IN THE "2001" COLUMN ARE ACTUALLY 2002 ESTIMATES. 

Table 0-100 50-State Annual VOC Control Case Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 26,790 30,135 12.5% 17,394 20,383 17.2% 

MARINE DIESEL 8,890 13,991 57.4% 3,969 9,801 146.9% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

11,736,377 13,777,876 17.4% 11,804,110 14,267,387 20.9% 

TOTAL MAN-MADE 

SOURCES 

11,772,057 13,822,002 17.4% 11,825,474 14,297,571 20.9% 

* AQ MODELING FOR LOCOMOTIVES USED THC AS VOC, INSTEAD OF USING ACTUAL VOC =1.053 * 

THC. 

114
 
Chapter 3: Inventory 

Table 0-101 50-State Annual NOx Control Case Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 690,885 712,492 3.1% 452,453 453,651 0.3% 

MARINE DIESEL 467,327 576,706 23.4% 262,345 362,927 38.3% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

10,006,926 9,518,521 -4.9% 9,570,157 9,020,273 -5.7% 

TOTAL MAN-MADE 11,165,138 10,807,720 -3.2% 10,284,956 9,836,851 -4.4% 

SOURCES 

Table 0-102 50-State Annual PM2.5 Control Case Emission Levels for Mobile and Other Source 
 

Categories 
 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 15,318 16,368 6.9% 9,617 10,512 9.3% 

MARINE DIESEL 15,367 18,399 19.7% 8,893 11,778 32.4% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

2,044,184 2,022,009 -1.1% 2,041,701 2,018,649 -1.1% 

TOTAL MAN-MADE 2,074,870 2,056,776 -0.9% 2,060,211 2,040,939 -0.9% 

SOURCES 

Table 0-103 50-State Annual VOC Ton Reductions for Mobile and Other Source Categories 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 22,249 22,498 1.1% 30,211 31,430 4.0% 

MARINE DIESEL 4,787 4,734 -1.1% 10,619 10,676 0.5% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 27,036 27,231 0.7% 40,830 42,106 3.1% 

SOURCES 

115
 
Draft Regulatory Impact Analysis 

Table 0-104 50-State Annual NOx Ton Reductions for Mobile and Other Source Categories 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 154,047 147,982 -3.9% 382,606 400,575 4.7% 

MARINE DIESEL 138,694 144,925 4.5% 346,416 365,199 5.4% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 292,741 292,907 0.1% 729,022 765,775 5.0% 

SOURCES 

Table 0-105 50-State Annual PM2.5 Ton Reductions for Mobile and Other Source Categories 

CATEGORY 2020 2030 

AQ 

MODELING 

NPRM % DIFF AQ 

MODELING 

NPRM % DIFF 

LOCOMOTIVE 10,525 9,933 -5.6% 14,717 14,597 -0.8% 

MARINE DIESEL 4,720 4,843 2.6% 12,959 13,122 1.3% 

ALL OTHER SOURCES 

(MOBILE & STATIONARY) 

0 0 0.0% 0 0 0.0% 

TOTAL MAN-MADE 15,245 14,776 -3.1% 27,675 27,719 0.2% 

SOURCES 

116
 
Chapter 3: Inventory 

APPENDIX 3A 

Locomotive and Diesel Marine Contributions to County-Specific Mobile 

Source Emissions in Non-attainment Areas 

117
 
Draft Regulatory Impact Analysis 

Table 0-106 2002 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel 
 

Mobile Sources 
 

FIPS MSA County ST 

2002 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

118
 
Chapter 3: Inventory 

FIPS MSA County ST 

2002 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

119
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2002 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

120
 
Chapter 3: Inventory 

FIPS MSA County ST 

2002 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

121
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2002 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

122
 
Chapter 3: Inventory 

Table 0-107 2020 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel 
 

Mobile Sources 
 

FIPS MSA County ST 

2020 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

123
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2020 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

124
 
Chapter 3: Inventory 

FIPS MSA County ST 

2020 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

125
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2020 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

126
 
Chapter 3: Inventory 

FIPS MSA County ST 

2020 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

127
 
Draft Regulatory Impact Analysis 

Table 0-108 2030 Locomotive and Diesel Marine PM2.5 Tons/Year and Percent of Total Diesel 
 

Mobile Sources 
 

FIPS MSA County ST 

2030 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

128
 
Chapter 3: Inventory 

FIPS MSA County ST 

2030 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

129
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2030 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

130
 
Chapter 3: Inventory 

FIPS MSA County ST 

2030 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

131
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2030 PM2.5 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Diesel 

LM 

Percent 

Mobile 

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% 

132
 
Chapter 3: Inventory 

Table 0-109 2002 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile 
 

Sources 
 

FIPS MSA County ST 

2002 NOx 

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% 

133
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2002 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47065 Chattanooga Hamilton TN 1,569.2 909.5 14,329 17.3% 

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% 

134
 
Chapter 3: Inventory 

FIPS MSA County ST 

2002 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21019 Huntington Boyd KY 430.5 559.8 3,171 31.2% 

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% 

135
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2002 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34017 New York Hudson NJ 777.7 1,486.3 16,558 13.7% 

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% 

136
 
Chapter 3: Inventory 

FIPS MSA County ST 

2002 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53035 Seattle Kitsap WA 0.0 45.6 6,933 0.7% 

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% 

137
 
Draft Regulatory Impact Analysis 

Table 0-110 2020 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile 
 

Sources 
 

FIPS MSA County ST 

2020 NOx 

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% 

138
 
Chapter 3: Inventory 

FIPS MSA County ST 

2020 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47065 Chattanooga Hamilton TN 1,326.0 749.8 5,500 37.7% 

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% 

139
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2020 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21019 Huntington Boyd KY 361.2 454.4 1,599 51.0% 

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% 

140
 
Chapter 3: Inventory 

FIPS MSA County ST 

2020 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34017 New York Hudson NJ 620.9 1,217.1 8,314 22.1% 

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% 

141
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2020 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53035 Seattle Kitsap WA 0.0 54.6 2,268 2.4% 

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% 

142
 
Chapter 3: Inventory 

Table 0-111 2030 Locomotive and Diesel Marine NOx Tons/Year and Percent of Total Mobile 
 

Sources 
 

FIPS MSA County ST 

2030 NOx 

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% 

143
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2030 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

47065 Chattanooga Hamilton TN 1,305.7 757.2 5,151 40.1% 

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% 

144
 
Chapter 3: Inventory 

FIPS MSA County ST 

2030 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

21019 Huntington Boyd KY 355.3 457.1 1,606 50.6% 

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% 

145
 
Draft Regulatory Impact Analysis 

FIPS MSA County ST 

2030 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

34017 New York Hudson NJ 596.4 1,227.0 11,022 16.5% 

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% 

146
 
Chapter 3: Inventory 

FIPS MSA County ST 

2030 NOx 

Diesel 

Locomotive 

Diesel 

Marine 

Total 

Mobile 

LM 

Percent 

53035 Seattle Kitsap WA 0.0 59.2 1,921 3.1% 

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% 

147
 
10 

Draft Regulatory Impact Analysis 

References 

1

 "Calculation of Age Distributions in the Nonroad Model: Growth and Scrappage," EPA420-R-05-

018, December 2005. 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-P-04-009, April 2004. The report is available online at 

http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf 

3

 “Conversion Factors for Hydrocarbon Emission Components,” EPA420-R-05-015, December 2005. 

The report is available online at http://epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05015.pdf 

4

 "Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines," EPA420-R-04-

007, May 2004. Docket EPA-HQ-OAR-2003-0012. The RIA is also available online at 

http://epa.gov/nonroad-diesel/2004fr/420r04007.pdf 

5

 "National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; Technical Support 

Document," EPA-454/R-06-002, January 2006. The report is available online at 

http://www.epa.gov/otaq/regs/toxics/454r06002.pdf 

6

 "Final Regulatory Impact Analysis: Control of Emissions from Marine Diesel Engines," EPA-420-R99-026, November 1999. Docket A-97-50. The report is also available online at 

http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/ria.pdf 

7

 Telephone conversation with Doug Scheffler, American Waterways Operators, May 4, 2006. 

8

 "Annual Energy Outlook 2006," Energy Information Administration, Report #:DOE/EIA0383(2006), February 2006, Table A7. The report is available online at 

http://www.eia.doe.gov/oiaf/archive/aeo06/pdf/0383(2006).pdf 

9

 Swedish Methodology for Environmental Data (SMED), “Methodology for calculating emissions 

from ships: 1. Update of emission factors,” November 4, 2004. 

 Eastern Research Group, Inc. (ERG). [insert final report date] 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. 

11 “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. The 

report is also available online at http://www.epa.gov/otaq/regs/nonroad/marine/ci/fr/r98020.pdf 

12 EPA, “Control of Emissions of Air Pollution From Nonroad Diesel Engines,” 63 FR 56967, October 

23, 1998. Docket A-96-40. The Federal Register notice is also available online at 

http://www.epa.gov/fedrgstr/EPA-AIR/1998/October/Day-23/a24836.htm 

13 "NONROAD2005 CI Marine NPRM," U.S. EPA. 

14 "Nonroad Engine Growth Estimates," NR-008c, EPA420-P-04-008, April 2004. The report is 

available online at http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04008.pdf 

148
 
Chapter 3: Inventory 

15 EPA, 2002 National Emissions Inventory (NEI). County-level fractions of locomotive and 

commercial marine diesel emissions. NEI documentation is available online at 

http://www.epa.gov/ttn/chief/net/2002inventory.html 

16 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 

149
 
Chapter 4: Technological Feasibility 

LOCOMOTIVE AND MARINE TECHNOLOGICAL FEASIBILITY 

4.1 Overview of Emissions Standards and Emissions Control Technologies........ 2 
 

4.2 Emissions 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 Emissions Control ........................................................................ 13 
 

4.3 Feasibility of Tier 4 Locomotive and Marine Standards ................................ 24 
 

4.3.1 Selective Catalytic Reduction (SCR) NOx Control Technology.................. 25 
 

4.3.2 PM and HC Exhaust Aftertreatment Technology ........................................ 32 
 

4.3.3 SCR and CDPF Packaging Feasibility........................................................... 40 
 

4.3.4 Stakeholder Concerns Regarding Locomotive NOx Standard Feasibility. 41 
 

4.4 Feasibility of Marine NTE Standards............................................................... 49 
 

4.5 Conclusions.......................................................................................................... 51 
 

4-1 
 
Draft Regulatory Impact Analysis 

CHAPTER 4: Locomotive and Marine Technological 

Feasibility 

In this chapter we describe in detail the emissions control technologies we 

believe may be used to meet the standards we are proposing. Because of the range of 

engines and applications we cover in this proposal, our proposed standards span a 

range of emissions levels. Correspondingly, we have identified a number of different 

emissions control technologies we expect may be used to meet the proposed 

standards. These technologies range from incremental improvements to existing 

engine components to highly advanced catalytic exhaust treatment systems. 

In this chapter we first summarize our current locomotive and marine diesel 

engine standards and provide an overview of existing and future emissions control 

technologies. We believe that further improvements in existing technologies may be 

used to meet the standards we are proposing for existing engines that are 

remanufactured as new (i.e., Tier 0, Tier 1, Tier 2). 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 

may be applied to meet our proposed interim standards for new engines (i.e., Tier 3). 

We conclude this section with a discussion of catalytic exhaust treatment 

technologies that we believe may be used to meet our proposed 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 would be 

implemented specifically into locomotive and marine applications. Note that much of 

this chapter’s content is based upon the performance of currently available emissions 

control technologies and results from testing that has already been completed. In 

most cases the already-published results show that currently available emissions 

control technologies can be implemented without further improvements to meet the 

standards we are proposing. 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 we are proposing. 

4.1 Overview of Emissions Standards and Emissions 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 

4-2 
 
Chapter 4: Technological Feasibility 

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 will supersede these MARPOL-equivalent standards. For a highspeed marine diesel engine, NOx will be 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/bhp-hr 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 kW2,3. 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 an 

unregulated level 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. 

A Tier 2 PM emission standards are dependent on an engine's volumetric displacement-per-cylinder. 

4-3 
 
Draft Regulatory Impact Analysis 

Selective catalytic reduction (SCR) is a commonly-used aftertreatment device 

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. 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 diesel trucks are required to meet the NOx limits of the 2007 heavyduty Highway Rule, several heavy-duty truck engine manufacturers have indicated 

that they will use SCR technology to meet these standards.4,5 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 emissions control technologies refer to the Regulatory Impact Analysis from our 

Clean Air Nonroad diesel rule.6 

The most effective exhaust aftertreatment used for diesel PM emissions 

control is the diesel particulate filter (DPF). More than a million light diesel vehicles 

that are OEM-equipped with DPF systems have been sold in Europe, and over 

200,000 DPF retrofits to diesel engines have been conducted worldwide.7 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 we are proposing. 

4.2 Emissions Control Technologies for Remanufactured Engine 

Standards and for Tier 3 New Engine Interim Standards 

To meet our proposed locomotive remanufactured engine standards, our 

potential marine remanufactured engine standards, and our proposed Tier 3 

locomotive and marine standards, we believe engine manufacturers will utilize 

incremental improvements to 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 onhighway 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 may be used meet the remanufactured engine and Tier 3 new engine interim 

4-4 
 
Chapter 4: Technological Feasibility 

standards for switch locomotives, line-haul locomotives and marine engines, 

respectively. 

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, new engine calibration, 

new 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 turbocharger 

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 turbocharger 

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 

turbocharger 

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 

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Draft 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 

pre-2014, 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 

turbocharger 

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 proposed 

locomotive remanufactured engine standards, potential marine remanufactured engine 

standards and the Tier 3 locomotive and marine 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 4-1 provides an 

4-6 
 
Chapter 4: Technological Feasibility 

overview and brief comparison of the two-stroke 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 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 reactionzone where combustion is taking place as shown in Figure 4-3. 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 

8 process. 

4-7 
 
Draft Regulatory Impact Analysis 

Figure 4-1: 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 twostroke diesel engines are used for Category 2 marine and locomotive applications. 

4-8 
 
Chapter 4: Technological Feasibility 

Figure 4-2: An idealized example of the net apparent rate of combustion heat release (derived 

from high-speed cylinder pressure measurements) for a direct injection diesel engine with 

indication of the major events and phases of combustion. 

Figure 4-3: An idealized physical schematic of the diesel combustion process. 

4-9 
 
Draft 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 diffusioncontrolled 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.8,9 

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 fuel-rich side of the reaction zone can be pyrolized to form 

elemental carbon particles10. Most of the elemental carbon formed by fuel pyrolysis 

(80% to 98%) is oxidized during later stages of combustion.11,12 The remaining 

elemental carbon agglomerates into complex chain-aggregate soot particles and 

leaves the engine as a component of PM emissions. 

4-10 
 
Chapter 4: Technological Feasibility 

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 

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 4-4). 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 postcombustion catalytic treatment 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 0.03 to 0.04 g/bhp-hr over the line-haul 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 
 
Draft Regulatory Impact Analysis 

Figure 4-4: 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-cylindercontent 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, fuel-related HC 

can be emitted as a concentrated, condensed aerosol ("white smoke"). 

4-12 
 
Chapter 4: Technological Feasibility 

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-cylindercontent basis, is 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 Emissions Control 

Control of diesel emissions via modification of combustion processes is often 

characterized by trade-offs in NOx emissions 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 fuel (<15 ppm S) will 

reduce sulfate PM emissions from locomotive and marine engines by approximately 

0.03 to 0.04 g/bhp-hr, as compared to PM emissions when ~3000 ppm S fuel is used. 

The use of ultra low sulfur 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, ULSD is also necessary to 

enable the use of advanced 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 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., 

4-13 
 
Draft Regulatory Impact Analysis 

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 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 our proposed locomotive 

remanufactured engine standards and the Tier 3 locomotive and marine standards will 

be accomplished in part via lowering charge air cooling temperature. This was one of 

the primary methods of 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. 13 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 “consist” 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 

4-14 
 
Chapter 4: Technological Feasibility 

locomotive and marine engine standards, Tier 3 nonroad standards, and the 1998 and 

later heavy-duty highway standards. We expect similar systems to be used to comply 

with our proposed remanufactured engine standards and will continue to be used to 

comply with our proposed 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.14,15,16,17 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 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 our 

proposed remanufactured engine standards and our proposed Tier 3 locomotive and 

marine 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 the use of EGR will not be 

needed to meet the Proposed Tier 3 locomotive and marine standards or 

remanufactured engine standards, 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 may also use EGR for their marine or 

switch locomotive applications of these engines to provide additional engine 

calibration flexibility. 

4-15 
 
Draft Regulatory Impact Analysis 

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.18 

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.9 

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.19 

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 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 ultra low sulfur diesel 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 our proposed 

remanufacturing standards or our proposed Tier 3 locomotive and marine standards, 

we do believe that EGR is an effective emissions control strategy that could be 

selected by an engine manufacturer as a means to control NOx emissions. EGR may 

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: 

4-16 
 
Chapter 4: Technological Feasibility 

•	 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 may be done either 

mechanically or electronically. Rate shaping has been shown to reduce NOx 

emissions by up to 20 percent.20 Multiple injection/split injection have also been 

shown to significantly reduce particulate emissions, most notably in cases that use 

retarded injection timing or a combination of injection timing retard and EGR to 

control NOx.

21,22,23,24 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 4-5: 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). 

4-17 
 
Draft Regulatory Impact Analysis 

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 mediumspeed 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.25 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. 

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.9 

4-18 
 
Chapter 4: Technological Feasibility 

Figure 4-6: 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 remanufactured engine standards and potential 

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, high-speed 

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 closedcrankcase 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 heavyduty truck engine designs that incorporate these technologies have significantly 

reduced engine-out organic carbon PM emissions. 

4-19 
 
Draft Regulatory Impact Analysis 

 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 our proposed remanufactured locomotive 

engine and Tier 3 locomotive and marine 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 highefficiency 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 phosphorouscontaining oil additives. 

4.2.2.8 Application Specific Differences in Emissions and Emissions 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 

emissions 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 air-supply 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 4-7 illustrates the speed and 

power ranges over which typical locomotives and marine engines operate. 

4-20 
 
Chapter 4: Technological Feasibility 

Figure 4-7: 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 propcurve. The specific example shown is for two similar versions of the EMD two-stroke mediumspeed 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 linehaul 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 latecycle 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 

4-21 
 
Draft Regulatory Impact Analysis 

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 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. This reduced 

elemental carbon PM, coupled with still moderately high lubricating oil consumption, 

results in a PM composition of medium-speed diesel engines that is substantially 

different than that of on-highway diesel engines and many nonroad diesel engines. 

PM emissions from medium-speed diesel engines are dominated by organic carbon 

PM emissions, with the relative contributions of organic carbon and elemental carbon 

PM to total PM approximately reversed from those of 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 tests of Tier 0, Tier 1 and Tier 2 

locomotives. 

Another difference is that 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 disctype 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 4-9). 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. 

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 topoff 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 mediumspeed 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. 

4-22 
 
Chapter 4: Technological Feasibility 

Figure 4-8: Emissions for 6 locomotives tested using 3000 ppm sulfur nonroad diesel fuel. Line-Haul BS-PM Emissions (g/bhp-hr) 

0.340 

0.320 

0.300 

0.280 

0.260 

0.240 

0.220 

0.200 

0.180 

0.160 

0.140 

0.120 

0.100 

0.080 

0.060 

0.040 

0.020 

0.000 

Tier 0: 0.60 g/bhp-hr Organic (mostly lube oil) Tier 1: 0.45 g/bhp-hr 

Sulfate (mostly sulfuric acid + water) 

Elemental Carbon (soot) 

Sulfate 

PM 

0.20 g/bhp-hr (Tier 2) 

Sulfate 

PM 

EC PM 

(Soot) 

Sulfate 

Organic Organic Organic Organic Organic Organic 

PM PM PM PM PM PM

PM 

Sulfate 

PM 

Sulfate 

PM 

Sulfate 

PM 

EC PM EC PM EC PM 

(Soot) (Soot) (Soot) (Soot) 

EC PM 

Tier 0 Tier 0 Tier 1 Tier 1 Tier 2 Tier 2 

Locomotive Locomotive Locomotive Locomotive Locomotive Locomotive 

4-23 
 
Draft Regulatory Impact Analysis 

 Figure 4-9: 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 emissions control technologies that we believe 

may be used to meet our proposed Tier 4 locomotive and marine diesel engine 

standards. In general, these technologies involve the use of catalytic exhaust 

treatment 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 ultra-low sulfur 

diesel fuel in engines that will be designed to meet our proposed 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 that we are proposing. 

4-24 
 
Chapter 4: Technological Feasibility 

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.26, 27, 28 As shown in Figure 4-10, 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, cost-effective 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.29 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.)30 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. 

4-25 
 
Draft Regulatory Impact Analysis 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

NOx Conversion Efficiency (%)

Low-T Base Metal 

Vanadium 

High-T Base Metal 

Figure 4-10: SCR Catalyst NOx Reduction versus Exhaust Gas Temperature Using an 

Ammonia-to-NOx Ratio of 1:131,32,B 

100 150 200 250 300 350 400 450 

Exhaust Gas T @ Catalyst Inlet (°C) 

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 high-temperature exhaust gasses (>200 °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 accomplish high NOx control (which provides 

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. 

4-26 
 

500 
Chapter 4: Technological Feasibility 

adequate NH3 for NOx reduction). Any excess NH3 (ammonia slip) that results from 

such optimization is converted to N2 and water in the slip catalyst. 

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 lowtemperature 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.33 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 (low-SAPS) 

and through reduced oil consumption (as discussed in 4.2.2.7). Low-SAPS oil will 

improve the performance 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 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." The durability of other exhaust 

aftertreatment devices, namely the DOC and DPF, will also benefit from the use of 

ULSD fuel and low-SAPS engine oil - less sulfur and phosphorous will improve 

DOC effectiveness and less ash will increase the DPF 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 

4-27 
 
Draft Regulatory Impact Analysis 

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 proposed 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 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 our 

proposed Tier 4 locomotive and marine standards. Given the steady-state operating 

characteristics of 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 our proposed standards. 

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).34 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 would 

allow seven years to expand and develop this framework to 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.35 The additional operating cost incurred 

by the rail industry will also be dependent on the volume of urea dispensed at each 

4-28 
 
Chapter 4: Technological Feasibility 

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-tolocomotive facilities.36 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 heavyduty 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.37 

If 100% of the 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.35 It is estimated that 190 million gallons of urea solution would require 0.28 

million tons of dry urea (1 ton dry urea is needed to produce 667 gallons of 32.5% 

urea-water solution).35 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.38 

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 for the marine industry, with a yearly diesel fuel 

consumption of 2.2 billion gallons per year, would not significantly impact the urea 

demand-to-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 2017 timeframe of 

the proposed Tier 4 standards. 

4.3.1.2 Establishing the Tier 4 NOx Standard 

The basis for the proposed 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 

4-29 
 
Draft Regulatory Impact Analysis 

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 4-11 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, 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). 

4-30 
 
Chapter 4: Technological Feasibility 

Figure 4-11: 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.39 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 heavyduty, 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.40 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. 

4-31 
 
Draft Regulatory Impact Analysis 

If no improvements were made to technologies which exists 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 proposed in this rulemaking. 

4.3.2 PM and HC Exhaust Aftertreatment Technology 

The most effective exhaust aftertreatment used for diesel PM emissions 

control is the diesel particulate filter (DPF). More than a million light diesel vehicles 

that are OEM-equipped with DPF systems have been sold in Europe, and over 

200,000 DPF retrofits to diesel engines have been conducted worldwide.7 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 passiveregeneration retrofit technologies for diesel engines designed to achieve 

approximately 50 to 60% control of PM emissions.41 Recent data has shown that 

metal-CDPF trapping efficiency for elemental carbon PM can exceed 70% for 

engines with inherently low elemental carbon emissions.42 Data from locomotive 

testing (Figure 4-12) confirms a relatively low elemental carbon fraction and 

relatively high organic fraction for PM emissions from medium-speed Tier 2 

locomotive engines.43 The use of a highly oxidizing PGM catalyst coated directly to 

the CPDF combined with a highly oxidizing DOC mounted upstream of the CDPF 

would provide 95% or greater removal of HC, including the semi-volatile organic 

compounds that contribute to PM. 

A functional schematic of a metal-CDPF is shown in Figure 4-13. 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.44 The metal-CDPF will 

most likely be used in combination with an upstream diesel oxidation catalyst (DOC). 

A diesel oxidation catalyst mounted 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 would 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 location of the metalCDPF 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, 

4-32 
 
Chapter 4: Technological Feasibility 

immediately downstream of the exhaust ports and upstream of the turbocharger's 

exhaust turbine (Figure 4-14) 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 that is similar to 

what is already in common use with dry exhaust manifold configurations in Category 

2 marine applications can be used to increase exhaust and catalyst temperatures 

(Figure 4-15). 

Figure 4-16 shows the expected line-haul locomotive PM reductions for: 

•	 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 4-17 shows the expected PM reductions over the E3 General Marine Duty 

Cycle for: 

•	 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 

Due to the relatively high organic carbon fraction and low elemental carbon 

fraction in the PM emissions, the difference in PM emissions between the metalE

 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 G3CT2 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. 

4-33 
 
Draft Regulatory Impact Analysis 

CDPF and the wall-flow-CDPF is less than 0.01 g/bhp-hr (approximately 0.005 

g/bhp-hr). The 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 would result in PM emissions of approximately 

0.02 g/bhp-hr over the line-haul cycle. The results from a ceramic wall-flow trap 

would be nearly identical at 0.015 g/bhp-hr. This will provide sufficient compliance 

margin to meet the 0.03 g/bhp-hr Tier 3 line-haul locomotive standard. Because PM 

emissions concentrations downstream of a PM trap are characteristically flat or 

relatively constant, we expect very similar PM reductions from marine engines that 

utilize similar PM trap technology. 

Figure 4-18 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. 

4-34 
 
Chapter 4: Technological Feasibility 

Figure 4-12: Brake-specific PM emissions speciated into soluble organic, soluble sulfate, and 

insoluble elemental carbon over the Federal Line-Haul duty cycle. 

0.000 

0.020 

0.040 

0.060 

0.080 

0.100 

0.120 

0.140 

0.160 

0.180 

0.200 

0.220 

0.240 

0.260 

0.280 

0.300 

0.320 

0.340 

Line-Haul BS-PM Emissions (g/bhp-hr) 

Organic (mostly lube oil) 

Sulfate (mostly sulfuric acid + water) 

Elemental Carbon (soot) 

0.20 g/bhp-hr 

(Tier 2 PM Standard) 

Tier 0 PM Standard: 0.60 g/bhp-hr 

Tier 1 PM Standard: 0.45 g/bhp-hr 

Tier 0 Tier 0 Tier 1 Tier 1 Tier 2 Tier 2
 

Locomotive Locomotive Locomotive Locomotive Locomotive Locomotive
 

4-35 
 
Draft Regulatory Impact Analysis 

Figure 4-13: 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 at with engines having relatively low elemental carbon PM 

emissions. 

4-36 
 
Chapter 4: Technological Feasibility 

Figure 4-14: 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 4-15: A two-stroke medium-speed Category 2 marine diesel engine with an insulated 

exhaust manifold and exhaust turbine in use in New York Harbor. 

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Draft Regulatory Impact Analysis 

Figure 4-16: 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. 

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Chapter 4: Technological Feasibility 

Figure 4-17: Brake-specific PM emissions over the E3 General Marine Duty Cycle for a Tier 2 

medium-speed Category 2 diesel engineE and the expected reductions in PM emissions due to 

reduced fuel sulfur levels and application of PM emissions controls. 

4-39 
 
Draft Regulatory Impact Analysis 

Figure 4-18: 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 because they are in the gas-phase in the raw exhaust. Thus 

(organic) PM removal is limited by the kinetically-limited HC oxidation rates over the precious 

metal catalyst applied to the DOC and the CDPF. 

4.3.3 SCR and CDPF Packaging Feasibility 

We expect that locomotive and marine manufacturers may need to repackage/re-design the exhaust system, turbocharger, and intake air aftercooling 

components 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.45 

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 

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Chapter 4: Technological Feasibility 

required for an ammonia-slip-catalyst 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 the engine's peak exhaust flow (in volume units-per-hour) to the volume 

to 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-toengine 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 will need to be approximately 1.7 times the engine displacement, which 

would give 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 . 

4.3.4 Stakeholder Concerns Regarding Locomotive NOx Standard 

Feasibility 

One stakeholder has expressed a number of concerns regarding the feasibility 

of the proposed 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 

4.3.4.1 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 

F

 Space Velocity =300,000 L/min * 60 min/hr/450 L, Catalyst-to-Engine Displacement = 450 L/200 L. 

4-41 
 
Draft Regulatory Impact Analysis 

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 to clean up any ammonia released from the SCR 

provides an additional level of robustness to the closed-loop urea dosing system. 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 would 

not be necessary to dose urea at an NH3-to-NOx ratio (α) of 1:1 (which would allow at 

least 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.46 As shown in Figure 4-19, 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. 

Figure 4-19: Effect of dosing ratio on NOx conversion efficiency and NH3 slip.46 

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 

4-42 
 
Chapter 4: Technological Feasibility 

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 4-20 illustrates a closed-loop urea-SCR control system 

proposed for onroad diesel applications.47 Figure 4-21 illustrates a urea-SCR system 

concept developed by Volkswagen to meet U.S Tier 2, Bin 5 passenger car emission 

standards.48 

Figure 4-20: Adapted from “SCR Technology for NOx Reduction: Series Experience and State 

of Development”.47 

4-43 
 
Draft Regulatory Impact Analysis 

Figure 4-21: Adapted from “LNT or Urea SCR Technology: Which is the right technology for 

TIER 2 BIN 5 passenger vehicles?”48 

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 NOx-sensor-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. 

Ammonia emissions, which are already minimized through the use of closedloop 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. 

Catalyst durability is affected by sulfur and other chemicals that can be present in 

some diesel fuel and lubricating oil. These chemicals have been significantly reduced 

in other applications by the use of ultra-low sulfur diesel fuel and low-SAPS (sulfated 

ash, phosphorous, and sulfur) lubricating oil. Locomotive and marine operators 

already will be using ultra low sulfur diesel fuel by the time urea NOx SCR systems 

would be needed, and low SAPS oil can be used in locomotive and marine engines. 

Thermal and mechanical vibration durability of catalysts has been addressed through 

4-44 
 
Chapter 4: Technological Feasibility 

the selection of proper materials and the design of support and mounting structures 

that are capable of withstanding the shock and vibration levels present in locomotive 

and marine applications. More details on catalyst durability are available in the 

remainder of this section. 

4.3.4.2 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.32,49 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.49 

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 at for some Fe-zeolite catalysts when operated 

at catalyst space velocities much higher than those that would be used for locomotive 

applications (see Figure 4-22). 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 4-22 to a similar, aged FeZeolite system at a lower space velocity (Figure 4-23), the NOx conversion efficiency 

increases to approximately 80% to 90% over the exhaust temperature range for a linehaul 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 4-22 and the results in Figure 4-23: 

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 

4-22 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. 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 

4-45 
 
Draft Regulatory Impact Analysis 

catalyst with fuel-hydrocarbons. The graph in Figure 4-24 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 4-22: 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”.49 

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Chapter 4: Technological Feasibility 

Figure 4-23: NOx conversion efficiency for an Fe-Zeolite urea-SCR catalyst system while varying 

NO2 as a percentage of NOx.

50 Note that the black line represents the case of NOx that is 100% 

NO (0% NO2). 

Figure 4-24: 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”. 32 

4-47 
 
Draft Regulatory Impact Analysis 

Figure 4-25 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 would be encountered during the useful life of a 

locomotive.G 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 4-23 at comparable NO2 as a percentage 

of NOx. 

Figure 4-25: 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 is 

approximately equivalent to full-life thermal aging for a line-haul locomotive taking into account 

that the highest temperatures encountered will be during tunnel operation as part of a consist. 

Adapted from “Urea SCR and DPF System for Tier 2 Diesel Light-Duty Truck”. 32 

G The typical maximum exhaust temperature for a locomotive is 450 °C. During tunnel operation in a 

consist, this temperature can reach 700 °C. However, not all locomotives operate in tunnels, and only 

select locomotives will ever experience this type of operation. Discussions with locomotive 

manufacturers indicate that the typical, yearly accumulated time for units used in tunnel operation 2 

hours. If the locomotive life is 10 years, 20-hours will be the maximum time that an SCR will be 

exposed to elevated exhaust gas temperature conditions. 

4-48 
 
Chapter 4: Technological Feasibility 

4.3.4.3 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 our proposal would require 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. 

4.4 Feasibility of Marine NTE Standards 

We are proposing certain 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 proposing. 

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 are 

proposing to 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 

emissions control, while at the same time help avoid disproportionately stringent 

4-49 
 
Draft Regulatory Impact Analysis 

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 highspeed and high-load. This also would 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 would help clarify when an engine is operating within a 

marine NTE zone. We are also proposing for the first time 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 are proposing to adopt the same NTE 

provisions for auxiliary marine engines as we have already adopted for constantspeed 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 our proposed changes 

to 40 CFR Part 1042, Appendix III, for illustrations of the changes we are proposing. 

We are also proposing changes to the NTE multipliers. We have analyzed 

how our proposed Tier 3 and Tier 4 emissions standards would 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 

proposed NTE zones, slightly higher multipliers would be necessary because of the 

way that our more stringent proposed Tier 3 and Tier 4 emissions standards would 

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 are proposing, the new 

multipliers would range from 1.2 to 1.9 times the standard. Refer to the figures in our 

proposed changes to 40 CFR Part 1042, Appendix III, for illustrations of the changes 

we are proposing. 

We are also proposing to adopt other NTE provisions for marine engines that 

are similar to our existing heavy-duty on-highway and nonroad diesel NTE standards. 

We are proposing these particular changes to account for the implementation of 

catalytic exhaust treatment 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 

4-50 
 
Chapter 4: Technological Feasibility 

technology for on-highway 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 emissions control. This technology also becomes active and efficient 

at temperatures at or above 250 °C. Therefore, in our on-highway 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 identified similar hydrocarbon aftertreatment 

emissions control technology (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. That is why we are proposing that the NTE standards for NOx would 

apply at exhaust temperatures equal to or greater than 150 °C, as measured within 12 

inches of the last NOx aftertreatment device's outlet. For hydrocarbon aftertreatment 

systems, this minimum temperature limit would be 250 °C, which is the same as our 

on-highway and nonroad NTE standards. 

4.5 Conclusions 

Even though our proposal covers a wide range of engines and thus requires 

the implementation of a range of emissions controls technologies, we believe we have 

identified a range of technologically feasible emissions control technologies that 

likely would be used to meet our proposed 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 treatment 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, and its impact on the 

infrastructure of the rail and marine industries. 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 treatment technology are 

technologically feasible for locomotive and marine applications, and thus may be 

used to meet our proposed emissions standards. 

4-51 
 
Draft Regulatory Impact Analysis 

REFERENCES 
 

1

 Title 40, U.S. Code of Federal Regulations, Part 86, §86.007-11 “Emission standards and 

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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

 "Review of SCR Technologies for Diesel Emission Control: European Experience and Worldwide
 

Perspectives," presented by Dr. Emmanuel Joubert, 10th DEER Conference, July 2004. 

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 Lambert, C., “Technical Advantages of Urea SCR for Light-Duty and Heavy-Duty Diesel Vehicle 

Applications,” SAE 2004-01-1292, 2004. 

6

 Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines, U.S. EPA 

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 “Diesel Particulate Filter Maintenance: Current Practices and Experience”, Manufacturers of 

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 Flynn, P., et al, “Minimum Engine Flame Temperature Impacts on Diesel and Spark-Ignition Engine 

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9

 Heywood, John B., “Internal Combustion Engine Fundamentals”, McGraw Hill 1988. 
 

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imaging diagnostics”, SAE 950456, 1995. 

11 Kittelson, et al, “Particle concentrations in a diesel cylinder: comparison of theory and experiment”, 
 

SAE 861569, 1986. 
 

12 Foster, D.E. and D.R. Tree, “Optical measurements of soot particle size, number density and 
 

temperature in a direct injection diesel engine as a function of speed and load”, SAE 940270, 1994. 

13 Dickey, D., Matheaus,A., Ryan, T., “NOx Control in Heavy-Duty Diesel Engines - What is the 
 

Limit?”, SAE 980174, 1998. 
 

14 Herzog, P., et al, "NOx Reduction Strategies for DI Diesel Engines," SAE 920470, 1992. 
 

15 Uyehara, O., "Factors that Affect NOx and Particulates in Diesel Engine Exhaust," SAE 920695, 
 

1992. 

16 Durnholz, M., G. Eifler, and H. Endres, "Exhaust-Gas Recirculation - A Measure to Reduce Exhaust 

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17 Bazari, Z. and B. French, "Performance and Emissions Trade-Offs for a HSDI Diesel Engine - An 

Optimization Study," SAE 930592, 1993 

4-52 
 
Chapter 4: Technological Feasibility 

18 Röpke, S., G.W. Schweimer, and T.S. Strauss, "NOx Formation in Diesel Engines for Various Fuels 

and Intake Gases" SAE 950213, 1995. 

19 Kreso, A.M., et al, “A Study of the Effects of Exhaust Gas Recirculation on Heavy-Duty Diesel 

Engine Emissions” SAE 981422, 1998. 

20 Ghaffarpour, M. and R. Baranescu, “NOx Reduction Using Injection Rate Shaping and Intercooling 

in Diesel Engines,” SAE 960845, 1996. 

21 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. 

22 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 

23 Ricart, L.M. and R.D. Reitz, “Visualization and Modeling of Pilot Injection and Combustion in 

Diesel Engines”, SAE 960833, 1996. 

24 Mather, D.K. and R.D. Reitz, “Modeling the Influence of Fuel Injection Parameters on Diesel 

Engine Emissions”, SAE 980789, 1998. 

25 Bazari, Z. and B. French, "Performance and Emissions Trade-Offs for a HSDI Diesel Engine - An 

Optimization Study", SAE 930592, 1993. 

26 Walker, A.P. et al., “The Development and In-Field Demonstration of Highly Durable SCR Catalyst 

Systems,” SAE 2004-01-1289. 

27 Conway, R. et al., “Combined SCR and DPF Technology for Heavy Duty Diesel Retrofit,” SAE 

2005-01-1862, 2005. 

28 “The Development and On-Road Performance and Durability of the Four-Way Emission Control 

SCRTTM System,” presented by Andy Walker, 9th DEER Conference, August, 2003. 

29 Telephone conversation with Gary Keefe, Argillon, June 7, 2006. 

30 M.J. Bradley & Associates, "Alice Austen Vessel SCR Demonstration Project - Final Report," 

August 2006, www.mjbradley.com/documents/Austen_Alice_Report_Final_31Aug06.pdf. 

31 "SCRT® Technology for Retrofit of Heavy Duty Diesel Applications," presented by Ray Conway, 

11th DEER Conference, August, 2005. 

32 “Urea SCR and DPF System for Tier 2 Diesel Light-Duty Trucks,” presented by Christine Lambert, 

12th DEER Conference, August 2006. 

33 Conway, R. et al., "NOx and PM Reduction Using Combined SCR and DPF Technology in Heavy 

Duty Diesel Applications," SAE 2005-01-3548, 2005. 

34 Miller, W. et al., "The Development of Urea-SCR Technology for US Heavy Duty Trucks," SAE 

2000-01-0190, 2000. 

35 "Viability of Urea Infrastructure for SCR Systems," presented by M.D. Jackson, U.S. EPA Clean 

Diesel Engine Implementation Workshop, August 6, 2003. 

4-53 
 
Draft Regulatory Impact Analysis 

36 Email message from Mike Rush, Association of American Railroads, to Jeff Herzog, U.S. 

Environmental Protection Agency, July, 15, 2002. 

37 "National Transportation Statistics - 2004," Table 4-5, U.S. Bureau of Transportation Statistics. 

38 "Mineral Commodity Summaries 2006," page 118, U.S. Geological Survey, 

www.minerals.usgs.gov/minerals/pubs/mcs/mcs2006.pdf. 

39 “Diesel Particulate Filter Technology for Low-Temperature and Low-NOx/PM Applications”, 

presented by Sougato Chatterjee, 10th DEER Conference, July 2004. 

40 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. 

41 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. 

42 Jacob, E., Lämmerman, R., Pappenheimer, A., Rothe, D. "Exhaust Gas Aftertreatment System for 

Euro 4 Heavy-duty Engines", MTZ, June, 2005. 

43 Smith, B., Sneed, W., Fritz, S. "AAR Locomotive Emissions Testing 2005 Final Report". 

44 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. 

45 Telephone conversation between Brian King, Elliot Bay Design Group, and Brian Nelson, EPA, July 

24, 2006. 

46 Ming, C. et al., “Modelling and Optimization of SCR-Exhaust Aftertreatment Systems,” SAE 2005­

01-0969, 2005. 

47 “SCR Technology for NOx Reduction: Series Experience and State of Development,” presented by 

Manuel Hesser, 11th DEER Conference, August 2005. 

48 “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. 

49 “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. 

50 Data provided to the U.S. EPA by Johnson Matthey Catalytic Systems Division, November 6, 2006. 

4-54 
 
Chapter 5: Engineering Cost Estimates 

CHAPTER 5: ENGINEERING COST ESTIMATES............................................. 3
 

5.1 Methodology for Estimating Engine and Equipment Engineering Costs ....... 4
 

5.2 Engine-Related Engineering Costs for New Engines......................................... 7
 

5.2.1 New Engine Fixed Engineering Costs ......................................................... 7
 

5.2.2 New Engine Variable Engineering Costs .................................................. 27
 

5.3 Equipment-Related Engineering Costs for New Pieces of Equipment .......... 56
 

5.3.1 New Equipment Fixed Engineering Costs ................................................ 56
 

5.3.2 New Equipment Variable Engineering Costs........................................... 60
 

5.4 Operating Costs for New and Remanufactured Engines ................................ 63
 

5.4.1 Increased Operating Costs Associated with Urea Use............................. 63
 

5.4.2 Increased Operating Costs Associated with DPF Maintenance ............. 64
 

5.4.3 Increased Operating Costs Associated with Fuel Consumption Impacts
 

................................................................................................................................ 65 
 

5.4.4 Total Increased Operating Costs ............................................................... 66
 

5.5 Engineering Hardware Costs Associated with the Locomotive 
 

Remanufacturing Program...................................................................................... 73
 

5.6 Summary of Proposed Program Engineering Costs........................................ 79
 

5.6.1 New Engine Engineering Costs.................................................................. 79
 

5.6.2 New Equipment Engineering Costs........................................................... 80
 

5.6.3 Operating Costs for New and Remanufactured Engines ........................ 81
 

5.6.4 Remanufacturing Program Engineering Hardware Costs ..................... 82
 

5.6.5 Total Engineering Costs Associated with the Proposed Program.......... 82
 

5.7 Engineering Costs Associated with a Possible Marine Remanufacturing 
 

Program ..................................................................................................................... 84
 

5.8 Engineering Costs and Savings Associated with Idle Reduction Technology86 

5.9 Analysis of Energy Effects.................................................................................. 93
 

5.10 Cost Effectiveness.............................................................................................. 94
 

5-1 
 
Draft Locomotive and Marine RIA 

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 proposed program and section 5.5 presents 

costs associated with the locomotive remanufacturing program. Section 5.6 

summarizes these costs and presents the total program costs. Section 5.7 presents 

costs associated with a possible marine remanufacturing program, although this 

program is not being proposed. 

To maintain consistency in the way our emission reductions, costs, and costeffectiveness estimates are calculated, our cost methodology relies on the same 

projections of new 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 proposed 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 proposed 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 

a We use the term “engineering costs” to differentiate from “social costs.” Social costs are 

discussed in Chapter 7 of this draft 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. 

5-2 
 
Chapter 5: Engineering Cost Estimates 

relates to regulatory requirements that are part of the proposed rule for Tiers 3 and 4 

emissions standards for locomotive and marine engines. Unless noted otherwise, all 

costs are in 2005 dollars ($2005). 

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 proposed 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 heavyduty 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 

5-3 
 
Draft Locomotive and Marine RIA 

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 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. 

5-4 
 
Chapter 5: Engineering Cost Estimates 

•	 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 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. 

5-5 
 
Draft Locomotive and Marine RIA 

5.2 Engine-Related Engineering Costs for New Engines 

5.2.1 New Engine 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 proposed 

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 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 proposed 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 

5-6 
 
Chapter 5: Engineering Cost Estimates 

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/marineonly 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 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 re-optimization 

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. 

5-7 
 
Draft Locomotive and Marine RIA 

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 proposed 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 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 70 percent of 

the expenditures incurred by nonroad-only manufacturers for the NRT4 rule. 

Therefore, we have assumed that locomotive/marine-only manufacturers will incur 49 

percent of that spent by highway manufacturers in their highway efforts. This lower 

number—roughly $19 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 $19 million estimates represent our estimate of the average 

corporate research expenditures for engine manufacturers. Each manufacturer may 

incur more or less than these average figures. 

These corporate research estimates are outlined in Table 5-1. 

5-8 
 
Chapter 5: Engineering Cost Estimates 

Table 5-1 Estimated Corporate Research Expenditures by Type of Engine Manufacturer 
 

Totals per Manufacturer over Five Years 
 

($Million) 
 

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 $19 

% allocated to PM n/a 33% 

% allocated to NOx+NMHC n/a 67% 

Note: 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. 

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 proposed to meet the Tier 4 standards (i.e., 

proposed 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 $19 million conducting research and 

nine will spend $4 million, for a total corporate research expenditure of just over $92 

million. 

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 (for DPF research to support locomotive engines), 

a portion during the five years prior to 2016 (for SCR and DPF research to support 

marine engines) and the remaining portion during the five years prior to 2017 (for 

SCR research to support locomotive 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 (for SCR and DPF research to support marine engines). The remaining 

manufacturer sold only into the locomotive market and will spend a portion of its 

corporate research dollars during the five years prior to 2015 (for DPF research) and 

the remaining portion during the five years prior to 2017 (for SCR research). 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 

5-9 
 
Draft Locomotive and Marine RIA 

shown in Table 5-2.c 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. 

Table 5-2 Estimated Corporate Research Expenditures Allocated by Market Segment ($Million) 

Market Segment Total Corporate Research 

Expenditure 

PM NOx+NMHC 

Locomotive Switcher/Passenger $ 10.4 $ 3.4 $ 7.0 

Locomotive Line-Haul $ 19.1 $ 6.3 $ 12.8 

Marine C1 $ 37.3 $ 12.3 $ 25.0 

Marine C2 $ 25.6 $ 8.4 $ 17.1 

Total Industry Expenditure $ 92.3 $ 30.5 $ 61.8 

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 emissioncontrol systems, we have attributed two-thirds of the corporate research expenditures to NOx+NMHC 

control and one-third to PM control. Marine C1 includes recreational marine > 2000 kW. 

c

 Note that, throughout this discussion of costs, recreational marine engines over 2000 kW 

are included in the C1 marine category unless otherwise noted. As such, when referring to the 

recreational marine category, we mean recreational marine engines less than 2000 kW unless otherwise 

noted. 

5-10 
 
Chapter 5: Engine

Table 5-3 Estimated Corporate Research Expenditures by Year ($Millions) 

Calendar 

Year 

Locomotive Switchers 

PM NOx+ 

NMHC 

Subtotal 

Locomotive Line-Haul 

PM NOx+ 

NMHC 

Subtotal PM 

Marine C1 

NOx+ 

NMHC 

Subtotal PM 

Marine C2 

NOx+ 

NMHC 

Subtotal 

2006 2007 2008 2009 2010 2011 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 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$0.7 $ - $0.7 

$0.7 $ - $0.7 $0.7 $1.4 $2.1 $0.7 $1.4 $2.1 $0.7 $1.4 $2.1 

$ - $1.4 $1.4 

$ - $1.4 $1.4 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$1.3 $ - $1.3 

$1.3 $ - $1.3 

$1.3 $2.6 $3.8 $1.3 $2.6 $3.8 $1.3 $2.6 $3.8 

$ - $2.6 $2.6 

$ - $2.6 $2.6 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ -$ -$ -$ -

$ -

$2.5 

$2.5 $2.5 $2.5 

$2.5 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$5.0 

$5.0 $5.0 $5.0 

$5.0 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$7.5 

$7.5 $7.5 $7.5 

$7.5 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$1.7 

$1.7 $1.7 $1.7 

$1.7 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$3.4 

$3.4 $3.4 $3.4 

$3.4 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$5.1 

$5.1 $5.1 $5.1 

$5.1 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

Total 

NPV at 7% NPV at 3% 

$3.4 $7.0 $10.4 $2.1 $3.8 $5.9 $2.8 $5.3 $8.1 

$6.3 $12.8 $19.1 $3.9 $7.0 $10.9 $5.1 $9.8 $14.9 

$12.3 

$7.2 $9.7 

$25.0 $14.6 $19.7 

$37.3 $21.8 $29.4 

$8.4 $4.9 $6.7 

$17.1 $10.0 $13.5 

$25.6 $15.0 $20.2 

5-11 
Draft Locomotive and Marine RIA 

As shown in Table 5-3, the net present value of the corporate research is 

estimated at $73 million using a three percent discount rate, and $54 million using a 

seven percent discount rate.d

 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 

Estimated Cost Allocation 

($Millions) 

Estimated Sales from 

2006 to 2040 

$/engine 

Locomotive 

Switcher/Passenger 

$ 8.1 3,212 $ 2,530 

Locomotive Line Haul $ 14.9 19,258 $ 780 

Marine C1 >600 kW $ 29.4 25,597 $ 1,150 

Marine C2 $ 20.2 6,647 $ 3,040 

Total $ 72.7 54,715 $ 1,330 

Note: Marine C1 >600 kW includes recreational marine > 2000 kW. 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 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 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 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 

d

 Throughout Chapter 5 of this draft RIA, net present value (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 engineering costs than by calculating the NPV over 

2007-2040 (3% smaller for 3% NPV and 7% smaller for 7% NPV). 

5-12 
 
Chapter 5: Engineering Cost Estimates 

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 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 and $6.5 million per line for 

engines over 600 kW range. For engine line research associated with the Tier 3 

standards, we have estimated the expenditure per engine line at $1.6 million. This 

value is 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) 

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 

% 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. 

5-13 
 
Draft Locomotive and Marine RIA 

Table 5-6 Tier 3 Engine Line Research Expenditures by Market Segment ($Million) 

Segment Engine Lines 

<600 kW 

Engine Lines 

>600 kW 

Tier 3 

$/line 

Total 

Small Marine 

Recreational Marine 65 $ 1.6 $ 104 

Marine C1 

Marine C2 0 13 $ 1.6 $ 20.8 

Locomotive 

Switcher/Passenger 

6* 2 $ 1.6 $ 12.8 

Locomotive Line Haul 0 2 $ 1.6 $ 3.2 

Total 63 25 $ 140.8 

* 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) 

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 6* 0 $ 3.2 $ 19.2 

Marine C2 0 11 $ 6.5 $ 71.5 

Marine-C2/Loco-LineHaul 0 2 $ 6.5 $ 13.0 

Locomotive Line Haul 0 0 $ 6.5 $ 0 

Total 6 25 $ 181.7 

* 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 proposed program (i.e., Tiers 3 and 4). e 

e

 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 

5-14 
 
Chapter 5: Engineering Cost Estimates 
Draft Locomotive and Marine RIA 

Table 5-8 Estimated Tier 3 Engine Line Research Expenditures by Year ($Millions) 

Calendar 

Year 

Locomotive Switchers 

PM NOx+ 

NMHC 

Subtotal 

Locomotive Line Haul 

PM NOx+ 

NMHC 

Subtotal 

Marine C1; Rec; small 

PM NOx+ 

NMHC 

Subtotal PM 

Marine C2 

NOx+ 

NMHC 

Subtotal 

2006 2007 2008 2009 2010 2011 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 

$ - $ - $ -

$0.8 $1.7 $2.6 $0.8 $1.7 $2.6 $0.8 $1.7 $2.6 $0.8 $1.7 $2.6 $0.8 $1.7 $2.6 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -

$0.2 $0.4 $0.6 $0.2 $0.4 $0.6 $0.2 $0.4 $0.6 $0.2 $0.4 $0.6 $0.2 $0.4 $0.6 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -

$6.9 $13.9 $20.8 $6.9 $13.9 $20.8 $6.9 $13.9 $20.8 $6.9 $13.9 $20.8 $6.9 $13.9 $20.8 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ -

$1.4 $1.4 $1.4 $1.4 $1.4 

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -

$2.8 $2.8 $2.8 $2.8 $2.8 

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -

$4.2 $4.2 $4.2 $4.2 $4.2 

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

Total 

NPV at 7% NPV at 3% 

$4.2 $8.6 $12.8 $3.2 $6.6 $9.8 $3.8 $7.6 $11.4 

$1.1 $2.1 $3.2 $0.8 $1.6 $2.5 $0.9 $1.9 $2.8 

$34.3 $69.7 $104.0 $26.3 $53.4 $79.7 $30.5 $62.0 $92.5 

$6.9 

$5.3 $6.1 

$13.9 

$10.7 $12.4 

$20.8 

$15.9 $18.5 

5-16 
Chapter 5: Engine

Table 5-9 Estimated Tier 4 Engine Line Research Expenditures by Year ($Millions) 

Calendar 

Year 

Locomotive Switchers 

PM NOx+ 

NMHC 

Subtotal 

Locomotive Line Haul 

PM NOx+ 

NMHC 

Subtotal 

Marine C1 > 600 kW 

PM NOx+ 

NMHC 

Subtotal PM 

Marine C2 

NOx+ 

NMHC 

Subtotal 

2006 2007 2008 2009 2010 2011 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 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$1.7 $ - $1.7 

$1.7 $ - $1.7 $1.7 $3.4 $5.1 $1.7 $3.4 $5.1 $1.7 $3.4 $5.1 

$ - $3.4 $3.4 

$ - $3.4 $3.4 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$0.4 $ - $0.4 

$0.4 $ - $0.4 

$0.4 $0.9 $1.3 $0.4 $0.9 $1.3 $0.4 $0.9 $1.3 

$ - $0.9 $0.9 

$ - $0.9 $0.9 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -

$4.7 $9.6 $14.3 

$4.7 $9.6 $14.3 $4.7 $9.6 $14.3 $4.7 $9.6 $14.3 

$4.7 $9.6 $14.3 

$ - $ - $ -

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ -$ -$ -$ -

$ -

$5.1 

$5.1 $5.1 $5.1 

$5.1 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$10.5 

$10.5 $10.5 $10.5 

$10.5 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -$ -$ -$ -

$ -

$15.6 

$15.6 $15.6 $15.6 

$15.6 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

Total 

NPV at 7% NPV at 3% 

$8.5 $17.2 $25.7 $5.3 $9.4 $14.7 $6.9 $13.2 $20.1 

$2.1 $4.4 $6.5 $1.3 $2.4 $3.7 $1.7 $3.3 $5.1 

$23.6 $47.9 $71.5 $13.8 $28.0 $41.8 $18.6 $37.8 $56.5 

$25.7 

$15.0 $20.3 

$52.3 

$30.6 $41.3 

$78.0 

$45.6 $61.6 

5-17 
Draft Locomotive and Marine RIA 

Table 5-10 Estimated Tier 3 & Tier 4 Engine Line Research Expenditures by Year ($Millions) 

Calendar 

Year 

Locomotive Switchers 

PM NOx+ 

NMHC 

Subtotal 

Locomotive Line Haul 

PM NOx+ 

NMHC 

Subtotal 

Marine C1; Rec; small 

PM NOx+ 

NMHC 

Subtotal PM 

Marine C2 

NOx+ 

NMHC 

Subtotal 

2006 2007 2008 2009 2010 2011 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 

$ - $ - $ -

$0.8 $1.7 $2.6 $0.8 $1.7 $2.6 $0.8 $1.7 $2.6 $2.5 $1.7 $4.3 $2.5 $1.7 $4.3 $1.7 $3.4 $5.1 $1.7 $3.4 $5.1 $1.7 $3.4 $5.1 

$ - $3.4 $3.4 

$ - $3.4 $3.4 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -

$0.2 $0.4 $0.6 $0.2 $0.4 $0.6 $0.2 $0.4 $0.6 $0.6 $0.4 $1.1 $0.6 $0.4 $1.1 $0.4 $0.9 $1.3 $0.4 $0.9 $1.3 $0.4 $0.9 $1.3 

$ - $0.9 $0.9 

$ - $0.9 $0.9 

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ - $ - $ -

$6.9 $13.9 $20.8 $6.9 $13.9 $20.8 $6.9 $13.9 $20.8 $6.9 $13.9 $20.8 

$11.6 $23.5 $35.1 

$4.7 $9.6 $14.3 $4.7 $9.6 $14.3 $4.7 $9.6 $14.3 

$4.7 $9.6 $14.3 

$ - $ - $ -

$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -$ - $ - $ -

$ -

$1.4 $1.4 $1.4 $1.4 $6.5 $5.1 $5.1 $5.1 

$5.1 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -

$2.8 $2.8 $2.8 $2.8 

$13.2 $10.5 $10.5 $10.5 

$10.5 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

$ -

$4.2 $4.2 $4.2 $4.2 

$19.8 $15.6 $15.6 $15.6 

$15.6 

$ -

$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -$ -

Total 

NPV at 7% NPV at 3% 

$12.7 $25.8 $38.5 

$8.5 $16.0 $24.5 

$10.7 $20.8 $31.5 

$3.2 $6.5 $9.7 $2.2 $4.0 $6.2 $2.7 $5.2 $7.9 

$57.9 $117.6 $175.5 $40.1 $81.4 $121.5 $49.2 $99.8 $149.0 

$32.6 $20.3 $26.4 

$66.2 $41.2 $53.7 

$98.8 $61.5 $80.1 

5-18 
Chapter 5: Engineering Cost Estimates 

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 $269 million using a three percent discount rate and $214 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 

Estimated Cost Allocation 

($Millions) 

Estimated Sales from 

2006 to 2040 

$/engine 

Locomotive Switcher/Passenger $ 31.5 3,212 $ 9,800 

Locomotive Line Haul $ 7.9 19,258 $ 410 

Small Marine $ 7.1 324,403 $ 20 

Recreational Marine $ 23.8 432,523 $ 60 

Marine C1 <600 kW $ 44.5 303,024 $ 150 

Marine C1 >600 kW $ 73.6 25,597 $ 2,870 

Marine C2 $ 80.1 6,647 $12,050 

Total $ 268.5 1,114,666 $ 240 

Note: Marine C1 >600 kW includes recreational marine > 2000 kW. 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 new 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 new 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. 

5-19 
 
Draft Locomotive and Marine RIA 

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 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. 

5-20 
 
Chapter 5: Engineering Cost Estimates 

Table 5-12 Estimated Tooling Expenditures by Type of Engine Manufacturer 
 

Totals per Engine Line ($Million) 
 

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 arbitrarily 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. 

Table 5-13 Estimated Engine Tooling Expenditures by Market Segment and Tier ($Million) 

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 

Marine Recreational $ 4.2 $ 0 $ 4.2 

Marine Small $ 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 

Note: Marine C1 >600 kW includes recreational marine > 2000 kW. 

5-21 
 
Draft Locomotive and Marine RIA 

Table 5-14 Estimated Tier 3 and Tier 4 Engine Tooling Expenditures by Year ($Millions) 

Calendar Year Switchers 

Locomotive 

Line-Haul Subtotal Marine C1 Marine C2 

Marine 

Small Subtotal Total Spe

2006 2007 

$ -$ -

$ -$ -

$ -$ -

$ -$ - Recreational $ - $ - $ - $ - $ - $ - $ - $ - $ 

$ 

2008 $ - $ - $ - $ - $ - $ - $ - $ - $ 

2009 $ - $ - $ - $ - $ - $ - $ - $ - $ 

2010 $ - $ - $ - $ - $ - $ - $ - $ - $ 

2011 $1.0 $0.6 $1.6 $9.8 $2.6 $4.2 $1.2 $17.8 $1

2012 $ - $ - $ - $ - $ - $ - $ - $ - $ 

2013 $ - $ - $ - $ - $ - $ - $ - $ - $ 

2014 $1.6 $1.1 $2.7 $ - $ - $ - $ - $ - $

2015 $ - $ - $ - $7.8 $8.9 $ - $ - $16.7 $1

2016 $1.6 $1.1 $2.7 $ - $ - $ - $ - $ - $

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 $4.1 $2.8 $6.9 $17.6 $11.5 $4.2 $1.2 $34.5 $4

NPV at 7% $2.3 $1.5 $3.8 $10.5 $6.2 $2.8 $0.8 $20.3 $2

NPV at 3% $3.2 $2.1 $5.3 $14.0 $8.8 $3.5 $1.0 $27.3 $3

5-22 
Chapter 5: Engineering Cost Estimates 

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 

Estimated Cost Allocation 

($Millions) 

Estimated Sales from 

2006 to 2040 

$/engine 

Locomotive Switcher/Passenger $ 3.2 3,212 $ 980 

Locomotive Line Haul $ 2.1 19,258 $ 110 

Small Marine $ 1.0 324,403 $ 3 

Recreational Marine $ 3.5 432,523 $ 10 

Marine C1 <600 kW $ 8.2 303,024 $ 30 

Marine C1 >600 kW $ 5.8 25,597 $ 230 

Marine C2 $ 8.8 6,647 $ 1,320 

Total $ 32.6 1,114,666 $ 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.f 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. 

f

 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. 

5-23 
 
Draft Locomotive and Marine RIA 

Table 5-16 Certification Costs per Engine Family 

$/engine family # of engine families 

Locomotive $ 42,000 46 

Small 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, including those recreational marine engines over 2000 kW, would incur 

certification costs since those C1 engines under 600 kW and the remaining 

recreational marine engines will not be meeting the Tier 4 standards. For the small 

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 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 $4.5 million. Despite fewer engines being certified in 

the Tier 4 timeframe, the costs are roughly equal to the Tier 3 costs because, for the 

Tier 4 standards, we have estimated that locomotive engines are certified twice, once 

for the new PM standard and a second time two years later for the new NOx standard. 

The total certification expenditures are estimated at $9.3 million, or $7.3 

million at a three percent discount rate and $5.5 million at a seven percent discount 

rate. The table also makes clear what portion of costs are allocated to NOx+NMHC 

and PM, with a 50/50 allocation associated with the Tier 3 standards and the marine 

Tier 4 standards. The locomotive Tier 4 certification cost allocations align with the 

Tier 4 standards (PM costs first and NOx+NMHC costs two years later). 

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. 

5-24 
 
Chapter 5: Engineering Cost Estimates 

Table 5-17 Estimated Engine Certification Costs by Year ($Millions) 

Calendar 

Year 

Locomotive 

Switchers LineHaul 

Marine 

C2 

Marine 

Marine 

C1 Recreational Small Total Spent 

Totals 

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 $ - $ - $ - $ - $ - $ - $ - $ - $ -

2013 $ - $ - $ - $ - $ - $ - $ - $ - $ -

2014 $1.0 $1.0 $ - $ - $ - $ - $1.9 $1.9 $ -

2015 $ - $ - $0.3 $0.4 $ - $ - $0.7 $0.4 $0.4 

2016 $1.0 $1.0 $ - $ - $ - $ - $1.9 $ - $1.9 

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 $2.9 $2.9 $0.5 $1.3 $0.9 $0.8 $9.3 $4.6 $4.6 

NPV at 7% $1.6 $1.6 $0.3 $0.8 $0.6 $0.5 $5.5 $2.8 $2.7 

NPV at 3% $2.2 $2.2 $0.4 $1.1 $0.7 $0.6 $7.3 $3.7 $3.6 

Table 5-18 Estimated Engine Certification Costs per Engine 

Estimated Total Cost 

Allocation ($Millions) 

Estimated Sales from 

2006 to 2040 

$/engine 

Locomotive 

Switcher/Passenger 

$ 2.2 3,212 $ 700 

Locomotive Line Haul $ 2.2 19,258 $ 120 

Small Marine $ 0.6 324,403 $ 2 

Recreational Marine $ 0.7 432,523 $ 2 

Marine C1 $ 1.1 328,621 $ 3 

Marine C2 $ 0.4 6,647 $ 60 

Total $ 7.3 1,114,666 $ 10 

Note: Net present values of sales are calculated using zero as the sales figure for 2006. 

5-25 
Draft Locomotive and Marine RIA 

Note that these certification costs may overestimate actual costs because they 

assume all engines would be certified as a result of the proposed new emission 

standards. However, some engines would have been scheduled for new certification 

independent of the proposed 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 New Engine 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 proposed 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 draft RIA.g 

g

 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. 

5-26 
 
Chapter 5: Engineering Cost Estimates 

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 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.h 

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 

h

 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). 

5-27 
 
Draft Locomotive and Marine RIA 

value of 80 percent, i.e., each doubling of cumulative production reduces the former 

cost level by 20 percent. 

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 

Progress Ratio 

97 99 101 103 105 107 

From 22 field studies (n = 108). 

0 

5 

10 

15 

Frequency 

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 new 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 

5-28 
 
Chapter 5: Engineering Cost Estimates 

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 new 

parts and new assemblies will involve new manufacturing operations. As 

manufacturers gain experience with these new systems, comparable learning is 

expected to occur with respect to unit labor and 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. 

5-29 
 
Draft Locomotive and Marine RIA 

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 engineout 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 basic components of the SCR system are well known and 

include the following material elements: 

• a ceramic substrate upon which a NOx catalyst washcoating is applied; 

• a can to hold and support the substrate; 

• a urea dosing unit (urea injector and control computer); 

• a urea storage tank and associated brakets; and, 

• 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 emissioncontrol 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. 

5-30 
 
Chapter 5: Engineering Cost Estimates 

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 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. 

5-31 
 
Draft Locomotive and Marine RIA 

Table 5-19 SCR System Costs (costs shown are costs per SCR system for the given engine power/displacem

Typical Engine Power (kW) 7 25 57 187 37Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18Material and component costs  Catalyst Volume (Liter) 1.0 3.8 9.8 19.1 45Substrate $29 $113 $294 $573 $1,35Washcoating and Canning $423 $517 $721 $1,035 $1,91Platinum $0 $0 $0 $0 $

Catalyst Can Housing $12 $12 $13 $15 $2

 Urea Dosing Unit (Injection Assembly w/ ECU) $500 $527 $585 $674 $92 Urea Solution Tank & Brackets $2 $8 $18 $60 $12NO x sensor (1 sensor/engine) $200 $200 $200 $200 $20

DOC for cleanup $233 $245 $271 $312 $42

Direct Labor Costs Estimated Labor hours 

4 4 4 4 

 Labor Rate ($/hr) $18 $18 $18 $18 $1 Labor Cost $72 $72 $72 $72 $7 Labor Overhead @ 40% $29 $29 $29 $29 $2Total Direct Costs to Mfr. $1,501 $1,723 $2,204 $2,971 $5,04 Warranty Cost (3% claim rate) $111 $128 $164 $221 $37 Mfr. Carrying Cost - Near term $60 $69 $88 $119 $20 Total Cost to Dealer - Near term $1,672 $1,919 $2,456 $3,311 $5,62 Dealer Carrying Cost - Near term $50 $58 $74 $99 $16 Baseline Cost to Buyer - Near term $1,722 $1,977 $2,530 $3,410 $5,79Loco/Marine Cost to Buyer (includes highway learning) - Near term $1,377 $1,581 $2,024 $2,728 $4,63 Warranty Cost (1% claim rate) $37 $43 $55 $74 $12 Mfr. Carrying Cost - Long term $60 $69 $88 $119 $20 Total Cost to Dealer - Long term $1,598 $1,834 $2,347 $3,163 $5,37 Dealer Carrying Cost - Long term $48 $55 $70 $95 $16 Baseline Cost to Buyer - Long term $1,646 $1,889 $2,418 $3,258 $5,53 Baseline Cost to Buyer (includes Highway Learning) - Long term $1,317 $1,511 $1,934 $2,606 $4,43Loco/Marine Cost to Buyer (includes Loco/Marine learning) - Long term $1,053 $1,209 $1,547 $2,085 $3,54

5-32 
Chapter 5: Engineering Cost Estimates 

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 proposal, 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 draft 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 

5-33 
 
Draft Locomotive and Marine RIA 

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 

Urea Dosing Unit 

The costs for the urea dosing unit 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. 

Urea Solution Tank and Brackets 

 The estimated costs for the urea solution tank and brackets is based on 

industry input that fuel tank size is roughly one gallon per engine horsepower and 

urea dosing rate is roughly four percent of the fueling rate. We also estimated that a 

urea tank would cost $60 per 10 gallons of volume. Using these estimates, the 

needed urea tank size and associated cost can be estimated. 

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, 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 0.5:1 because 

the low emissions conversion demand placed on the DOC is not expected to require a 

larger device. 

5-34 
 
Chapter 5: Engine

Table 5-20 Diesel Oxidation Costs (costs shown are costs per SCR system for the given engine power/displace

Typical Engine Power (kW) 7 25 57 187 37 Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18 Material and component costs  Catalyst Volume (liter) 0.2 0.8 2.0 3.8 9Substrate 

$0 $0 $0 $0 $

Washcoating and Canning 

$187 $195 $212 $238 $31

Platinum 

$1 $4 $10 $19 $4

 Catalyst Can Housing $0 $0 $0 $0 $Direct Labor Costs Estimated Labor hours 

0.5 0.5 0.5 0.5 0

 Labor Rate ($/hr) $18 $18 $18 $18 $1 Labor Cost $9 $9 $9 $9 $ Labor Overhead @ 40% $4 $4 $4 $4 $Total Direct Costs to Mfr. $201 $212 $235 $270 $36 Warranty Cost - Near Term (3% claim rate) $17 $18 $20 $22 $3 Mfr. Carrying Cost - Near Term $8 $8 $9 $11 $1 Total Cost to Dealer - Near Term $226 $238 $264 $303 $41 Dealer Carrying Cost - Near Term $7 $7 $8 $9 $1Loco/Marine Cost to Buyer $233 $245 $271 $312 $42

5-35 
Draft Locomotive and Marine RIA 

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+$2+$200+$233)(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. 

5-36 
 
Chapter 5: Engineering Cost Estimates 

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. 

Table 5-21 SCR System Costs as a Function of Engine Displacement, x, in Liters 

Linear Regression R2 

Line haul locomotive; marine Near-term cost function $185(x) + $1,323 0.999 

Long-term cost function $142(x) + $1,012 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 proposed 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 draft 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; 

5-37 
 
Draft Locomotive and Marine RIA 

• 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 

stage of the supply chain based on industry input.18 

5-38 
 
Chapter 5: Engine

Table 5-22 DPF System Costs (costs shown are costs per DPF system for the given engine power/displacem

Typical Engine Power (kW) 7 25 57 187 37 Typical Engine Displacement (Liter) 0.4 1.5 3.9 7.6 18 Material and component costs  Filter Volume (Liter) 0.7 2.6 6.7 13.0 30Filter Trap 

$46 $176 $461 $898 $2,11

Washcoating and Canning 

$96 $111 $143 $192 $32

Platinum 

$41 $156 $408 $796 $1,87

 Filter Can Housing $9 $10 $11 $12 $1 Differential Pressure Sensor $52 $52 $52 $52 $5Direct Labor Costs Estimated Labor hours 

4 4 4 4 

 Labor Rate ($/hr) $18 $18 $18 $18 $1 Labor Cost $72 $72 $72 $72 $7 Labor Overhead @ 40% $29 $29 $29 $29 $2Total Direct Costs to Mfr. $345 $606 $1,175 $2,051 $4,48 Warranty Cost -- Near Term (3% claim rate) $21 $41 $84 $149 $33 Mfr. Carrying Cost -- Near Term $14 $24 $47 $82 $18 Total Cost to Dealer -- Near Term $380 $671 $1,306 $2,282 $4,99 Dealer Carrying Cost -- Near Term $11 $20 $39 $68 $15 Savings by removing silencer ($52) ($52) ($52) ($52) ($5 Baseline Cost to Buyer -- Near Term $340 $640 $1,293 $2,298 $5,09Loco/Marine Cost to Buyer (includes highway learning) - Near term $272 $512 $1,035 $1,839 $4,07 Warranty Cost -- Long Term (1% claim rate) $7 $14 $28 $50 $11 Mfr. Carrying Cost -- Long Term $14 $24 $47 $82 $18 Total Cost to Dealer -- Long Term $366 $644 $1,250 $2,182 $4,77 Dealer Carrying Cost -- Long Term $11 $19 $38 $65 $14 Savings by removing muffler ($52) ($52) ($52) ($52) ($5 Baseline Cost to Buyer -- Long Term $325 $611 $1,236 $2,196 $4,87 Baseline Cost to Buyer (includes Highway Learning) - Long term $260 $489 $989 $1,757 $3,89Loco/Marine Cost to Buyer (includes Loco/Marine learning) - Long term $208 $391 $791 $1,405 $3,11

5-39 
Draft Locomotive and Marine RIA 

DPF Volume 

During development of this proposal, 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 draft 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 draft 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 

5-40 
 
Chapter 5: Engineering Cost Estimates 

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 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 

5-41 
 
Draft Locomotive and Marine RIA 

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 fleets to determine the total or per engine costs for DPF 

system hardware. The functions calculated for DPF system costs for locomotive linehaul 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 

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 

5-42 
 
Chapter 5: Engineering Cost Estimates 

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 proposed 

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 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. 

5-43 
 
Draft Locomotive and Marine RIA 

Table 5-24 SCR System Marinization Costs 

Typical Engine Power (kW) 64 93 183 620 968 1425 1902 38

Typical Engine Displacement (L) 4.2 7 10.5 27 34.5 51.8 111 2

SCR Catalyst Marinization Hardware Cost $23 $28 $29 $65 $77 $91 $173 $2

Assembly $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 $3

Total SCR Catalyst Marinization Costs - Long term $27 $33 $34 $72 $84 $98 $182 $3

Table 5-25 DPF System Marinization Costs 

Typical Engine Power (kW) 64 93 183 620 968 1425 1902 38

Typical Engine Displacement (L) 4.2 7 10.5 27 34.5 51.8 111 2

DPF Marinization Hardware Cost $15 $22 $29 $52 $61 $75 $112 $2

Assembly $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 $2

Total DPF Marinization Costs - Long term $20 $27 $34 $58 $67 $81 $120 $2

5-44 
Chapter 5: Engineering Cost Estimates 

Table 5-26 Marinization Costs as a function of Engine Displacement, x, in Liters 

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. 

5-45 
 
Draft Locomotive and Marine RIA 

Table 5-27 Summary of Cost Equations for Engine Variable Costs (x represents the dependent 

variable) 

Engine Technology Time Frame Cost Equation Dependent 

Variable 

How Used 

SCR System Costs Near term 

Long term 

$185(x) + $1,323 

$142(x) + $1,012 

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 

5-46 
 
0.0 0.0 0.0 0.0 0.0 

0.0 0.0 0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 

0.0 

0.0 0.0 0.0 0.0 0.0 

0.0 0.0 0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 0.0 

0.0 0.0 

0.0 

Chapter 5: Engineering Cost Estimates 

segments and power ranges are shown in Table 5-28.i For example, the sales 

weighted engine in the marine C1 segment, power range 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 LocoLineHaul 

LocoSwitcher Marine C1 Marine C2 Marine 

Recreational 

Small 

Marine 

Sales Weighted Displacement (Liters) 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 174.2 

2.7 2.5 2.6 

5.8 5.5 5.0 

7.7 10.5 4.9 

18.9 17.6 8.8 

51.8 33.4 93.0 28.9 

69.0 62.5 176.4 48.7 

0.6 

1.6 

Sales Weighted Horsepower 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 4895.2 

67.0 58.2 61.1 

157.7 149.6 159.1 

227.3 301.1 269.7 

660.0 553.2 457.2 

1500.0 1266.3 1508.6 1226.1 

2000.0 2529.4 4014.5 2345.2 

15.8 

36.0 

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 proposed 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 

i

 Note that the Marine C1 entries in the table include recreational marine engines over 2000 

kW. 

5-47 
 
Draft Locomotive and Marine RIA 

segment, those for “<600 kW” or “>600 kW”—for our total cost calculations 

presented in section 5.6. 

5-48 
 
Chapter 5: Engineering Cost Estimates 

Table 5-29 Piece Costs for Engine Hardware by Market Segment and Power Range 

Power Range LineHaul Switchers Marine 

C1 

Marine 

C2 Power Range LineHaul Switchers Marine 

C1 

Marine 

C2 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

SCR System Costs - Near term 

$460 

$778 

$979 

$2,140 

$5,532 $7,514 $18,554 

$33,591 $7,315 $12,904 $34,012 

$33,591 $1,639 $30,502 

$852 

$6,449 $9,431 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

00<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

SCR System Costs - Long term 

$381 

$635 

$796 

$1,723 

$4,431 $5,743 $14,180 

$25,672 $5,855 $9,862 $25,993 

$25,672 $1,323 $23,311 

$695 

$5,163 $7,209 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

SCR Marinization Costs - Near term 

$91 $178 

$133 $300 

$272 

$106 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

SCR Marinization Costs - Long term 

$73 $143 

$107 $242 

$219 

$85 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

DPF System Costs - Near term 

$467 

$918 

$1,203 

$2,847 

$7,650 $7,437 $20,344 

$37,924 $10,175 $13,738 $38,416 

$37,924 $2,137 $34,312 

$1,023 

$8,949 $9,679 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

DPF System Costs - Long term 

$357 

$702 

$920 

$2,177 

$5,850 $5,684 $15,547 

$28,982 $7,781 $10,499 $29,358 

$28,982 $1,634 $26,222 

$782 

$6,843 $7,397 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

DPF Marinization Costs - Near term 

$71 $135 

$102 $225 

$205 

$82 

0<hp<25 

25<=hp<50 

50<=hp<75 

75<=hp<200 

200<=hp<400 

400<=hp<800 

800<=hp<2000 

2000+ 

All engines 

<800 hp only 

>800 hp only 

DPF Marinization Costs - Long term 

$57 $108 

$82 $180 

$163 

$66 

5-49 
 
Draft Locomotive and Marine RIA 

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: Urea 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; New Tier 4 Engines Only 

($Millions) 

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 $ - $30.9 $30.9 $ -

2016 854 $32.4 $ - $32.4 $32.4 $ -

2017 877 $25.4 $29.4 $54.8 $25.4 $29.4 

2018 894 $25.9 $30.0 $55.9 $25.9 $30.0 

2019 917 $26.6 $23.6 $50.1 $26.6 $23.6 

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 $25.9 $55.0 $29.2 $25.9 

2023 1034 $30.0 $26.6 $56.5 $30.0 $26.6 

2024 1048 $30.4 $26.9 $57.3 $30.4 $26.9 

2025 1078 $31.2 $27.7 $58.9 $31.2 $27.7 

2026 1096 $31.8 $28.1 $59.9 $31.8 $28.1 

2027 1119 $32.4 $28.7 $61.2 $32.4 $28.7 

2028 1136 $32.9 $29.2 $62.1 $32.9 $29.2 

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 $30.6 $65.0 $34.5 $30.6 

2033 1209 $35.0 $31.0 $66.1 $35.0 $31.0 

2034 1223 $35.5 $31.4 $66.9 $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 $29.4 $62.5 $33.2 $29.4 

2039 1112 $32.2 $28.6 $60.8 $32.2 $28.6 

2040 1078 $31.2 $27.7 $58.9 $31.2 $27.7 

NPV at 7% $196.5 $152.5 $349.0 $196.5 $152.5 

NPV at 3% $426.6 $346.4 $773.0 $426.6 $346.4 

5-50 
 
Chapter 5: Engineering Cost Estimates 

Table 5-31 Annual Locomotive Switcher & Passenger Engine Variable Costs; New Tier 4 
 

Engines Only ($Millions) 
 

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 $ - $0.9 $0.9 $ -

2016 94 $1.0 $ - $1.0 $1.0 $ -

2017 94 $0.7 $0.7 $1.4 $0.7 $0.7 

2018 94 $0.7 $0.7 $1.4 $0.7 $0.7 

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 $5.9 $14.5 $8.6 $5.9 

NPV at 3% $20.4 $14.5 $35.0 $20.4 $14.5 

5-51 
 
Draft Locomotive and Marine RIA 

Table 5-32 Annual C2 Marine Engine Variable Costs; New Tier 4 Engines Only ($Millions) 

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 $16.2 $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 $7.8 $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 $8.0 

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 $17.4 $9.2 $8.2 

2030 351 $9.2 $8.2 $0.1 $17.5 $9.3 $8.2 

2031 354 $9.3 $8.3 $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 $18.2 $9.6 $8.5 

2035 367 $9.6 $8.6 $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 $19.2 $10.1 $9.0 

NPV at 7% $54.4 $48.3 $0.8 $103.5 $54.7 $48.7 

NPV at 3% $119.3 $106.1 $1.7 $227.1 $120.2 $106.9 

5-52 
 
Chapter 5: Engineering Cost Estimates 

Table 5-33 Annual C1 Marine (>600 kW/805 hp) Engine Variable Costs including Recreational 
 

Marine >2000 kW; New Tier 4 Engines Only ($Millions) 
 

Calendar 

Year Sales DPF SCR Marinization Total PM NOx+ 

NMHC 

2006 $ - $ - $ - $ - $ - $ -

2007 $ - $ - $ - $ - $ - $ -

2008 $ - $ - $ - $ - $ - $ -

2009 $ - $ - $ - $ - $ - $ -

2010 $ - $ - $ - $ - $ - $ -

2011 $ - $ - $ - $ - $ - $ -

2012 1127 $ - $ - $ - $ - $ - $ -

2013 1140 $ - $ - $ - $ - $ - $ -

2014 1154 $ - $ - $ - $ - $ - $ -

2015 1167 $ - $ - $ - $ - $ - $ -

2016 1180 $11.4 $11.1 $0.2 $22.8 $11.5 $11.2 

2017 1194 $11.6 $11.3 $0.2 $23.0 $11.7 $11.4 

2018 1207 $8.9 $8.7 $0.2 $17.8 $9.0 $8.8 

2019 1221 $9.0 $8.8 $0.2 $18.0 $9.1 $8.9 

2020 1234 $9.1 $8.9 $0.2 $18.2 $9.2 $9.0 

2021 1248 $9.2 $9.0 $0.2 $18.4 $9.3 $9.1 

2022 1262 $9.3 $9.1 $0.2 $18.6 $9.4 $9.2 

2023 1276 $9.4 $9.2 $0.2 $18.8 $9.5 $9.3 

2024 1290 $9.5 $9.3 $0.2 $19.0 $9.6 $9.4 

2025 1304 $9.6 $9.4 $0.2 $19.2 $9.7 $9.5 

2026 1318 $9.7 $9.5 $0.2 $19.4 $9.8 $9.6 

2027 1332 $9.9 $9.6 $0.2 $19.7 $10.0 $9.7 

2028 1346 $10.0 $9.7 $0.2 $19.9 $10.1 $9.8 

2029 1361 $10.1 $9.8 $0.2 $20.1 $10.2 $9.9 

2030 1375 $10.2 $9.9 $0.2 $20.3 $10.3 $10.0 

2031 1390 $10.3 $10.0 $0.2 $20.5 $10.4 $10.1 

2032 1404 $10.4 $10.1 $0.2 $20.7 $10.5 $10.2 

2033 1419 $10.5 $10.2 $0.2 $20.9 $10.6 $10.3 

2034 1434 $10.6 $10.3 $0.2 $21.2 $10.7 $10.4 

2035 1449 $10.7 $10.4 $0.2 $21.4 $10.8 $10.6 

2036 1464 $10.8 $10.6 $0.2 $21.6 $10.9 $10.7 

2037 1479 $10.9 $10.7 $0.2 $21.8 $11.1 $10.8 

2038 1494 $11.1 $10.8 $0.2 $22.0 $11.2 $10.9 

2039 1509 $11.2 $10.9 $0.2 $22.3 $11.3 $11.0 

2040 1525 $11.3 $11.0 $0.2 $22.5 $11.4 $11.1 

NPV at 7% $59.5 $58.0 $1.2 $118.6 $60.1 $58.6 

NPV at 3% $131.0 $127.7 $2.7 $261.4 $132.3 $129.0 

5-53 
 
Draft Locomotive and Marine RIA 

Table 5-34 Total Annual Engine Variable Costs; New Tier 4 Engines Only ($Millions) 

Calendar 

Year Locomotive Marine C1 

Marine 

C2 

Recreational 

Marine 

Small 

Marine Total PM NOx+ 

NMHC 

2006 $ - $ - $ - $ - $ - $ - $ - $ -

2007 $ - $ - $ - $ - $ - $ - $ - $ -

2008 $ - $ - $ - $ - $ - $ - $ - $ -

2009 $ - $ - $ - $ - $ - $ - $ - $ -

2010 $ - $ - $ - $ - $ - $ - $ - $ -

2011 $ - $ - $ - $ - $ - $ - $ - $ -

2012 $ - $ - $ - $ - $ - $ - $ - $ -

2013 $ - $ - $ - $ - $ - $ - $ - $ -

2014 $ - $ - $ - $ - $ - $ - $ - $ -

2015 $31.9 $ - $ - $ - $ - $31.9 $31.9 $ -

2016 $33.3 $22.8 $20.2 $ - $ - $76.3 $55.6 $20.8 

2017 $56.3 $23.0 $20.4 $ - $ - $99.7 $48.6 $51.1 

2018 $57.4 $17.8 $15.7 $ - $ - $90.9 $44.0 $46.9 

2019 $51.4 $18.0 $15.9 $ - $ - $85.3 $44.8 $40.5 

2020 $53.1 $18.2 $16.0 $ - $ - $87.3 $45.9 $41.4 

2021 $54.8 $18.4 $16.2 $ - $ - $89.4 $47.0 $42.4 

2022 $56.3 $18.6 $16.3 $ - $ - $91.2 $48.0 $43.3 

2023 $58.7 $18.8 $16.4 $ - $ - $94.0 $49.5 $44.5 

2024 $59.8 $19.0 $16.6 $ - $ - $95.4 $50.2 $45.2 

2025 $61.6 $19.2 $16.7 $ - $ - $97.6 $51.4 $46.2 

2026 $62.8 $19.4 $16.9 $ - $ - $99.2 $52.2 $46.9 

2027 $64.3 $19.7 $17.0 $ - $ - $101.0 $53.2 $47.8 

2028 $65.3 $19.9 $17.2 $ - $ - $102.4 $53.9 $48.5 

2029 $66.2 $20.1 $17.4 $ - $ - $103.6 $54.6 $49.0 

2030 $66.9 $20.3 $17.5 $ - $ - $104.7 $55.2 $49.5 

2031 $68.0 $20.5 $17.7 $ - $ - $106.1 $55.9 $50.2 

2032 $69.0 $20.7 $17.8 $ - $ - $107.6 $56.7 $50.9 

2033 $70.1 $20.9 $18.0 $ - $ - $109.0 $57.5 $51.6 

2034 $71.0 $21.2 $18.2 $ - $ - $110.3 $58.2 $52.2 

2035 $71.4 $21.4 $18.3 $ - $ - $111.1 $58.5 $52.5 

2036 $69.4 $21.6 $18.5 $ - $ - $109.5 $57.7 $51.8 

2037 $67.9 $21.8 $18.6 $ - $ - $108.4 $57.1 $51.3 

2038 $66.3 $22.0 $18.8 $ - $ - $107.2 $56.4 $50.7 

2039 $64.5 $22.3 $19.0 $ - $ - $105.7 $55.6 $50.1 

2040 $62.5 $22.5 $19.2 $ - $ - $104.2 $54.8 $49.3 

NPV at 7% $363.5 $118.6 $103.5 $ - $ - $585.6 $319.9 $265.7 

NPV at 3% $808.0 $261.4 $227.1 $ - $ - $1,296.5 $699.6 $596.9 

Note: Marine C1 costs include recreational marine >2000 kW. 

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 with the primary difference between the two being the two year delay in Tier 4 

NOx standards for locomotive engines. 

5-54 
 
Chapter 5: Engineering Cost Estimates 

5.3 Equipment-Related Engineering Costs for New Pieces of 

Equipment 

In this section, we present our estimated costs associated with the piece of 

equipment into which the new 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). 

5.3.1 New Equipment Fixed Engineering Costs 

5.3.1.1 New 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. Unfortunately, we do not have good data regarding 

vessel sales. We do have good data regarding engine sales using the PSR database 

for 2002. 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), 

including those recreational marine engines >2000 kW, 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 new vessel will have 

at least one propulsion engine while having anywhere from zero to many auxiliary 

engines. In the year 2015—one year before vessels would be adding engines 

equipped with aftertreatment devices and, hence, being redesigned—this leaves us 

with 993 marine C1 propulsion engines >600 kw and 147 marine C2 propulsion 

engines. 

We know that most vessels in the larger C1 and the C2 categories are fitted 

with more than one engine. To remain conservative, we estimated that, on average, 

each new vessel is fitted with 1.5 new propulsion engines. This results in an 

estimated 660 marine C1 and 100 marine C2 vessels sold. 

5-55 
 
Draft Locomotive and Marine RIA 

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 one-design/five-vessel or even ten or more-vessel effort. To be 

conservative, we have estimated that a redesign effort will accommodate two new 

vessels. That is, on average, a fleet of 100 new C2 vessels would require 50 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. 

Table 5-35 Estimated Vessel Redesigns in Year One and Costs per Redesign 

Hp 

Range 

Propulsion 

Engines in 

2015 

Engines / 

Vessel Vessels Vessels / Redesign Redesigns $/Redesign 

Marine-C1 

propulsion >800hp 993 1.5 660 2 330 $50,000 

Marine-C2 

propulsion All 147 1.5 100 2 50 $100,000 

Total 1140 760 380 

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 C1 

and C2 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 new vehicle design is typically carriedover 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. 

5-56 
 
Chapter 5: Engine

Table 5-36 Estimated Total Number of Vessel Redesigns and the Associated Annual Costs; New Tier 4 Equipment only (mo

$Millions) 

Calendar Year C1 Redesigns C2 Redesigns Annual Total Redesigns Cumulative Redesigns C1 Redesign Costs C2 Redesign Costs Annual Total Costs 

2006 - - - - $ - $ - $ - 

2007 - - - - $ - $ - $ - 

2008 - - - - $ - $ - $ - 

2009 - - - - $ - $ - $ - 

2010 - - - - $ - $ - $ - 

2011 - - - - $ - $ - $ - 

2012 - - - - $ - $ - $ - 

2013 - - - - $ - $ - $ - 

2014 - - - - $ - $ - $ - 

2015 330 50 380 380 $16.5 $5.0 $21.5 

2016 170 30 200 580 $8.5 $3.0 $11.5 

2017 90 20 110 690 $4.5 $2.0 $6.5 2018 50 10 60 750 $2.5 $1.0 $3.5 

2019 50 10 60 810 $2.5 $1.0 $3.5 

2020 50 10 60 870 $2.5 $1.0 $3.5 

2021 50 10 60 930 $2.5 $1.0 $3.5 

2022 50 10 60 990 $2.5 $1.0 $3.5 

2023 50 10 60 1050 $2.5 $1.0 $3.5 

2024 - - - - $ - $ - $ - 2025 - - - - $ - $ - $ - 2026 - - - - $ - $ - $ - 2027 - - - - $ - $ - $ - 2028 - - - - $ - $ - $ - 2029 - - - - $ - $ - $ - 2030 - - - - $ - $ - $ - 2031 - - - - $ - $ - $ - 

2032 - - - - $ - $ - $ - 

2033 - - - - $ - $ - $ - 

2034 - - - - $ - $ - $ - 

2035 - - - - $ - $ - $ - 

2036 - - - - $ - $ - $ - 

2037 - - - - $ - $ - $ - 

2038 - - - - $ - $ - $ - 

2039 - - - - $ - $ - $ - 

2040 - - - - $ - $ - $ - 

Total $16.0 $60.5 

NPV at 7% $7.0 $26.7 

NPV at 3% $11.1 $42.2 

$44.5 

$19.7 $31.1 

5-57 
Draft Locomotive and Marine RIA 

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 Estimated Total Number of Locomotive Redesigns and the Associated Annual Costs; 

New Tier 4 Equipment only (monetary entries are in $Millions) 

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 

5-58 
 
Chapter 5: Engineering Cost Estimates 

The net present value of the vessel redesign costs are estimated at $42 million 

using a three percent discount rate and at $27 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 $43 million using a three percent discount rate and at $27 million using a 

seven percent discount rate. These equipment redesign costs are arbitrarily split 

evenly between NOx+NMHC and PM control. 

5.3.2 New Equipment 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 new locomotive or a new 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. 

Table 5-38 Estimated Variable Costs per Piece of New Equipment 

 $/new equipment 

Locomotive Line-haul $4,000 

Locomotive Switcher $4,000 

Marine C1 (600-1492 kW; 805-2000 hp) $2,000 

Marine C1 (>2000 kW) $4,000 

Marine C1 (sales weighted) $2,700 

Marine C2 (600-1492 kW; 805-2000 hp) $2,000 

Marine C2 (>2000 kW) $4,000 

Marine C2 (sales weighted) $3,500 

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 5-39, in 

which we have used the sales weighted costs shown in Table 5-38 for marine vessels. 

As shown, we estimate the net present value of annual equipment variable costs at 

$99 million using a three percent discount rate and $44 million using a seven percent 

5-59 
Draft Locomotive and Marine RIA 

discount rate. These costs are arbitrarily split evenly between NOx+NMHC and PM 

control. 

5-60 
 
Chapter 5: Engine

Table 5-39 Annual Equipment Variable Costs; New Tier 4 Equipment Only ($Millions) 

Calendar 

Year 

Line Haul 

Sales Costs 

Switchers 

Sales Costs 

Locomotive 

Subtotal 

Marine C1 

Vessels Costs 

Marine C2 

Vessels Costs 

Marine 

Subtotal To

2006 $ - $ - $ - $ - $ - $ - 

2007 $ - $ - $ - $ - $ - $ - 

2008 $ - $ - $ - $ - $ - $ - 

2009 $ - $ - $ - $ - $ - $ - 

2010 $ - $ - $ - $ - $ - $ - 

2011 $ - $ - $ - $ - $ - $ - 

2012 767 $ - 92 $ - $ - $ - $ - $ - 

2013 765 $ - 92 $ - $ - $ - $ - $ - 

2014 780 $ - 93 $ - $ - $ - $ - $ - 

2015 816 $3.3 93 $0.4 $3.6 $ - $ - $ - 

2016 854 $3.4 94 $0.4 $3.8 666 $1.8 101 $0.4 $2.2 2017 877 $3.5 94 $0.4 $3.9 672 $1.8 102 $0.4 $2.2 2018 894 $3.6 94 $0.4 $4.0 678 $1.8 103 $0.4 $2.2 

2019 917 $3.7 94 $0.4 $4.0 684 $1.9 104 $0.4 $2.2 

2020 948 $3.8 94 $0.4 $4.2 690 $1.9 105 $0.4 $2.2 

2021 979 $3.9 94 $0.4 $4.3 696 $1.9 106 $0.4 $2.3 

2022 1007 $4.0 95 $0.4 $4.4 703 $1.9 106 $0.4 $2.3 

2023 1034 $4.1 160 $0.6 $4.8 709 $1.9 107 $0.4 $2.3 

2024 1048 $4.2 183 $0.7 $4.9 715 $1.9 108 $0.4 $2.3 

2025 1078 $4.3 201 $0.8 $5.1 722 $2.0 109 $0.4 $2.3 

2026 1096 $4.4 212 $0.8 $5.2 728 $2.0 110 $0.4 $2.4 

2027 1119 $4.5 227 $0.9 $5.4 735 $2.0 111 $0.4 $2.4 

2028 1136 $4.5 239 $1.0 $5.5 742 $2.0 112 $0.4 $2.4 

2029 1150 $4.6 247 $1.0 $5.6 748 $2.0 113 $0.4 $2.4 

2030 1158 $4.6 263 $1.1 $5.7 755 $2.0 114 $0.4 $2.5 

2031 1173 $4.7 281 $1.1 $5.8 762 $2.1 115 $0.4 $2.5 

2032 1190 $4.8 292 $1.2 $5.9 769 $2.1 116 $0.4 $2.5 

2033 1209 $4.8 296 $1.2 $6.0 776 $2.1 118 $0.4 $2.5 

2034 1223 $4.9 305 $1.2 $6.1 782 $2.1 119 $0.4 $2.5 

2035 1231 $4.9 302 $1.2 $6.1 790 $2.1 120 $0.4 $2.6 

2036 1197 $4.8 294 $1.2 $6.0 797 $2.2 121 $0.4 $2.6 

2037 1172 $4.7 287 $1.1 $5.8 804 $2.2 122 $0.4 $2.6 

2038 1144 $4.6 278 $1.1 $5.7 811 $2.2 123 $0.4 $2.6 

2039 1112 $4.4 269 $1.1 $5.5 818 $2.2 124 $0.4 $2.7 

2040 1078 $4.3 263 $1.1 $5.4 826 $2.2 125 $0.4 $2.7 

NPV at 7% $26.1 $4.3 $30.4 $11.6 $2.3 $13.9 $

NPV at 3% $57.4 $10.3 $67.7 $25.8 $5.1 $30.9 $

5-61 
Draft Locomotive and Marine RIA 

5.4 Operating Costs for New and Remanufactured 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 new 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 due to our expectation that the tighter NOx 

standard may in part be met using retarded fuel injection timing. 

5.4.1 Increased Operating Costs Associated with Urea Use 

New Tier 4 engines are expected to be equipped with urea SCR systems. The 

costs associated with the urea SCR system, including the urea tank and urea dosing 

system, are discussed in section 5.2.2.1 of this chapter. To estimate the costs 

associated with urea use, we first considered the urea dosage rate. For this analysis, 

we have used a urea dosing rate of four percent urea to every gallon of fuel burned. 

Using our marine and locomotive emissions analysis work (see Chapter 3 of this draft 

RIA), we can determine the gallons of fuel burned every year by SCR equipped 

pieces of equipment. The amount of urea used each year is then four percent of those 

gallons. 

The cost per gallon of urea would be dependent on the volume of urea 

dispensed at each facility, with smaller refueling sites experiencing higher costs. 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 and 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 percent urea-water solution. 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.25 With the implementation of SCR for 

the on-highway truck fleet in 2010, the economic factors for each urea supply option 

will be well-known prior to implementation of the 2016 and 2017 NOx standards for 

marine engines and locomotives, respectively. To remain conservative, for this 

analysis we have used a urea cost of $1.00/gallon. This cost should cover the costs 

associated with distributing urea to the necessary point of transfer to locomotive 

5-62 
 
Chapter 5: Engineering Cost Estimates 

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 draft 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 “pass-through” DPF technologies as 

discussed in Chapter 4 of this draft 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 proposed 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 emissioncontrol 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 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 

5-63 
 
Draft Locomotive and Marine RIA 

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 pass 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 new Tier 4 engines. 

We have also estimated an incremental operating cost associated with the 

locomotive remanufacturing program (see section 5.5 of this chapter for our analysis 

of other costs associated with the locomotive remanufacturing program). 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. 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. 

Using the gallons burned in new DPF equipped engines and, for line-haul and 

passenger locomotives, the gallons burned in remanufactured Tier 0 engines, along 

with an estimated diesel fuel price less taxes of $1.28/gallon, the costs associated with 

a fuel consumption impact can be calculated.j

 These costs are presented in section 

j

 To estimate the diesel fuel price, we started with the annual average nationwide price for 2004 for 

high sulfur diesel fuel (excluding taxes) sold to commercial consumers from Table 41 of the Energy 

Information Administration (EIA) Petroleum Marketing Annual 2004. We adjusted this 2004 price of 

$1.24/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 2004 as reported in Table 12 of the Annual Energy 

Outlook (AEO) 2006. Note that the Petroleum Marketing Annual 2005 shows a corresponding 

5-64 
 
Chapter 5: Engineering Cost Estimates 

5.4.4 of this chapter. The costs associated with the fuel consumption impact are split 

evenly between NOx and PM control. 

5.4.4 Total Increased Operating Costs 

The increased annual operating costs for each applicable market segment— 

locomotive line haul; switcher/passenger; non-recreational marine C1>600 kW; 

marine C2—are presented in Table 5-40, Table 5-41, Table 5-42, and Table 5-43, 

respectively. These costs are summarized to give the total increased operating costs 

in Table 5-44. Table 5-45 shows the increased operating costs by cost element— 

urea, DPF maintenance, and fuel consumption impact. 

Note that operating costs are attributed as follows: costs associated with urea 

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. 

nationwide average price for 2005 of $1.80/gallon versus $1.24/gallon. However, the AEO 2007 was 

not released in time to update our estimated 2012 price using on the $1.80/gallon number. Were we to 

simply use the $1.80/gallon number, it would increase our 2030 costs from $605 million to $646 

million, or roughly seven percent. For the final rule, we will update the fuel price to ensure that we are 

using the most recent data available. 

5-65 
Draft Locomotive and Marine RIA 

Table 5-40 Estimated Increased Operating Costs for Line Haul Locomotives; New Tier 4 and Remanufactured Tier 0 an

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Urea 

Usage 

(MM gal) 

Annual 

Urea Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Reman Tier 

0 Fuel 

Usage 

(MM gal) 

New Tier 

4 Fuel 

Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

2006 2007 2008 2009 2010 2011 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 

0 0 0 0 0 0 0 0 0 0 0 

217 438 665 899 

1141 1390 1853 2314 2573 2832 3093 3354 3614 3871 4127 4383 4639 4893 5144 5383 5614 5837 6050 6253 

0 0 0 0 0 0 0 0 0 0 0 9 

18 27 36 46 56 74 93 

103 113 124 134 145 155 165 175 186 196 206 215 225 233 242 250 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $8.7 

$17.5 $26.6 $36.0 $45.7 $55.6 $74.1 $92.6 

$102.9 $113.3 $123.7 $134.2 $144.6 $154.8 $165.1 $175.3 $185.5 $195.7 $205.7 $215.3 $224.6 $233.5 $242.0 $250.1 

0 0 0 0 0 0 0 0 0 

202 413 630 851 

1078 1312 1554 1803 2059 2314 2573 2832 3093 3354 3614 3871 4127 4383 4639 4893 5144 5383 5614 5837 6050 6253 

0 0 0 0 0 0 0 0 0 

1009 2065 3149 4255 5389 6561 7771 9017 

10296 11572 12866 14162 15467 16770 18069 19355 20637 21916 23193 24464 25718 26917 28072 29184 30250 31267 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

$0.2 $0.4 $0.6 $0.9 $1.1 $1.3 $1.6 $1.8 $2.1 $2.3 $2.6 $2.8 $3.1 $3.4 $3.6 $3.9 $4.1 $4.4 $4.6 $4.9 $5.1 $5.4 $5.6 $5.8 $6.0 $6.3 

0 0 

147 145 375 778 945 

1174 1227 

1232 1400 1553 1511 1444 1335 1219 1108 1002 

901 804 710 622 539 462 393 330 273 221 168 124 

88 58 33 16 

5 

0 0 0 0 0 0 0 0 0 

202 413 630 851 

1078 1312 1554 1803 2059 2314 2573 2832 3093 3354 3614 3871 4127 4383 4639 4893 5144 5383 5614 5837 6050 6253 

0 0 1 1 4 8 9 

12 12 

14 18 22 24 25 26 28 29 31 32 34 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 

NPV at 7% NPV at 3% 

$546.3 

$1,455.8 

$15.0 $38.6 

5-66 
Chapter 5: Engine

Table 5-41Estimated Increased Operating Costs for New Tier 4 Switcher & Passenger Locomotives and Remanufactured Tier 

Locomotives 

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Urea 

Usage 

(MM gal) 

Annual 

Urea Cost 

($MM) 

DPF 

Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

Reman Tier 

0 

Passenger 

Fuel Usage 

(MM gal) 

New Tier 4 

Passenger 

Fuel Usage 

(MM gal) 

Increased 

Passenger 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

2006 2007 2008 2009 2010 2011 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 

0 0 0 0 0 0 0 0 0 0 0 

17 27 38 49 60 78 

101 119 138 159 180 203 227 253 281 310 340 371 403 434 464 494 524 553 

0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 3 4 5 6 6 7 8 9 

10 11 12 14 15 16 17 19 20 21 22 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.7 $1.1 $1.5 $2.0 $2.4 $3.1 $4.0 $4.8 $5.5 $6.3 $7.2 $8.1 $9.1 

$10.1 $11.2 $12.4 $13.6 $14.8 $16.1 $17.4 $18.6 $19.8 $20.9 $22.1 

0 0 0 0 0 0 0 0 0 

10 21 31 42 53 63 74 85 

101 119 138 159 180 203 227 253 281 310 340 371 403 434 464 494 524 553 

0 0 0 0 0 0 0 0 0 

51 

104 156 209 263 317 371 426 505 594 691 793 902 

1016 1137 1265 1404 1549 1699 1856 2014 2170 2322 2472 2619 2764 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.2 $0.2 $0.2 $0.2 $0.3 $0.3 $0.3 $0.3 $0.4 $0.4 $0.4 $0.5 $0.5 $0.5 $0.6 

0 0 5 

17 29 40 44 48 51 47 49 49 44 38 33 28 22 17 14 10 

7 4 3 1 0 0 0 0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 0 7 

14 22 29 36 44 51 58 66 73 80 88 95 

102 109 116 123 131 138 146 154 161 168 173 178 183 

0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 

NPV at 7% NPV at 3% 

$37.1 

$102.4 

$1.0 

$2.6 

5-67 
Draft Locomotive and Marine RIA 

Table 5-42 Estimated Increased Operating Costs for Marine C1 Engines >600 kW, including Recreational Marine >2000 kW

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Urea Usage 

(MM gal) 

Annual Urea 

Cost 

($MM) 

DPF Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

New Tier 4 

Fuel Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

An

C

2006 2007 2008 2009 2010 2011 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 

0 0 0 0 0 0 0 0 0 0 

55 

150 283 414 545 676 805 933 

1059 1183 1305 1424 1539 1639 1719 1782 1835 1882 1925 1964 1999 2030 2060 2087 2113 

0 0 0 0 0 0 0 0 0 0 2 6 

11 17 22 27 32 37 42 47 52 57 62 66 69 71 73 75 77 79 80 81 82 83 85 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $2.2 $6.0 

$11.3 $16.6 $21.8 $27.0 $32.2 $37.3 $42.4 $47.3 $52.2 $57.0 $61.5 $65.6 $68.8 $71.3 $73.4 $75.3 $77.0 $78.6 $79.9 $81.2 $82.4 $83.5 $84.5 

0 0 0 0 0 0 0 0 0 0 

55 

150 283 414 545 676 805 933 

1059 1183 1305 1424 1539 1639 1719 1782 1835 1882 1925 1964 1999 2030 2060 2087 2113 

0 0 0 0 0 0 0 0 0 0 

275 752 

1413 2072 2727 3378 4024 4664 5295 5917 6527 7121 7693 8196 8597 8912 9174 9412 9627 9819 9993 

10152 10300 10437 10566 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

$0.1 $0.2 

$0.3 $0.4 $0.5 $0.7 $0.8 $0.9 $1.1 $1.2 $1.3 $1.4 $1.5 $1.6 $1.7 $1.8 $1.8 $1.9 $1.9 $2.0 $2.0 $2.0 $2.1 $2.1 $2.1 

0 0 0 0 0 0 0 0 0 0 

55 

150 

283 414 545 676 805 933 

1059 1183 1305 1424 1539 1639 1719 1782 1835 1882 1925 1964 1999 2030 2060 2087 2113 

0 0 0 0 0 0 0 0 0 0 

1 

2 

3 4 5 7 8 9 

11 12 13 14 15 16 17 18 18 19 19 20 20 20 21 21 21 

NPV at 7% NPV at 3% 

$241.9 $620.4 

$6.0 

$15.5 

5-68 
Chapter 5: Engine

Table 5-43 Estimated Increased Operating Costs for Marine C2 Engines; New Tier 4 Engines 

Calendar 

Year 

SCR 

Equipped 

Fuel Usage 

(MM gal) 

Urea Usage 

(MM gal) 

Annual Urea 

Cost 

($MM) 

DPF Equipped 

Fuel Usage 

(MM gal) 

# of DPF 

Maintenance 

Events/Year 

Annual DPF 

Maintenance 

Cost 

($MM) 

New Tier 4 

Fuel Usage 

(MM gal) 

Increased 

Fuel 

Consumption 

at 1 percent 

(MM gal) 

An

C

2006 2007 2008 2009 2010 2011 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 

0 0 0 0 0 0 0 0 

55 

110 213 317 422 526 631 735 840 944 

1048 1151 1255 1358 1461 1564 1666 1767 1868 1967 2064 2160 2253 2335 2407 2468 2520 

0 0 0 0 0 0 0 0 2 4 9 

13 17 21 25 29 34 38 42 46 50 54 58 63 67 71 75 79 83 86 90 93 96 99 

101 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $2.2 $4.4 $8.5 

$12.7 $16.9 $21.1 $25.2 $29.4 $33.6 $37.8 $41.9 $46.0 $50.2 $54.3 $58.5 $62.6 $66.6 $70.7 $74.7 $78.7 $82.6 $86.4 $90.1 $93.4 $96.3 $98.7 

$100.8 

0 0 0 0 0 0 0 0 

55 

110 213 317 422 526 631 735 840 944 

1048 1151 1255 1358 1461 1564 1666 1767 1868 1967 2064 2160 2253 2335 2407 2468 2520 

0 0 0 0 0 0 0 0 

276 552 

1063 1585 2108 2632 3155 3677 4199 4719 5238 5756 6274 6791 7307 7820 8331 8836 9339 9833 

10321 10800 11263 11675 12033 12339 12598 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

$0.1 $0.1 $0.2 $0.3 $0.4 $0.5 $0.6 $0.7 $0.8 $0.9 $1.0 $1.2 $1.3 $1.4 $1.5 $1.6 $1.7 $1.8 $1.9 $2.0 $2.1 $2.2 $2.3 $2.3 $2.4 $2.5 $2.5 

0 0 0 0 0 0 0 0 

55 

110 213 317 422 526 631 735 840 944 

1048 1151 1255 1358 1461 1564 1666 1767 1868 1967 2064 2160 2253 2335 2407 2468 2520 

0 0 0 0 0 0 0 0 

1 

1 2 3 4 5 6 7 8 9 

10 12 13 14 15 16 17 18 19 20 21 22 23 23 24 25 25 

NPV at 7% NPV at 3% 

$264.4 $671.4 

$6.6 

$16.8 

5-69 
Draft Locomotive and Marine RIA 

Table 5-44 Estimated Increased Operating Costs by Market Segment Associated with the 
 

Proposal ($Millions) 
 

Calendar 

Year 

Locomotive 

Line-haul 

Locomotive 

Switcher & 

Passenger 

Marine C1 

>600kW 

Marine 

C2 Total PM NOx+ 

NMHC 

2006 $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 

2008 $1.9 $0.1 $0.0 $0.0 $1.9 $1.0 $1.0 

2009 $1.9 $0.2 $0.0 $0.0 $2.1 $1.0 $1.0 

2010 $4.8 $0.4 $0.0 $0.0 $5.2 $2.6 $2.6 

2011 $10.0 $0.5 $0.0 $0.0 $10.5 $5.2 $5.2 

2012 $12.1 $0.6 $0.0 $0.0 $12.7 $6.3 $6.3 

2013 $15.0 $0.6 $0.0 $0.0 $15.6 $7.8 $7.8 

2014 $15.7 $0.7 $0.0 $3.0 $19.3 $8.6 $10.7 

2015 $18.6 $0.7 $0.0 $5.9 $25.2 $10.6 $14.6 

2016 $23.6 $0.8 $3.0 $11.4 $38.9 $14.4 $24.4 

2017 $37.2 $1.6 $8.1 $17.0 $64.0 $18.5 $45.4 

2018 $48.6 $2.1 $15.2 $22.7 $88.6 $21.7 $66.9 

2019 $59.9 $2.5 $22.3 $28.3 $113.1 $24.7 $88.4 

2020 $71.2 $3.0 $29.3 $33.9 $137.4 $27.5 $109.9 

2021 $82.7 $3.5 $36.3 $39.6 $162.1 $30.3 $131.8 

2022 $94.7 $4.2 $43.3 $45.2 $187.4 $33.2 $154.2 

2023 $115.3 $5.2 $50.2 $50.8 $221.5 $36.2 $185.3 

2024 $136.0 $6.0 $57.0 $56.4 $255.4 $39.2 $216.2 

2025 $148.7 $6.8 $63.7 $61.9 $281.1 $42.2 $239.0 

2026 $161.5 $7.7 $70.2 $67.5 $306.9 $45.2 $261.7 

2027 $174.4 $8.7 $76.6 $73.1 $332.7 $48.3 $284.5 

2028 $187.3 $9.7 $82.8 $78.6 $358.4 $51.3 $307.1 

2029 $200.3 $10.7 $88.2 $84.1 $383.4 $54.3 $329.1 

2030 $213.3 $11.9 $92.5 $89.6 $407.3 $57.2 $350.1 

2031 $226.3 $13.1 $95.9 $95.1 $430.3 $60.0 $370.4 

2032 $239.3 $14.4 $98.7 $100.5 $452.9 $62.7 $390.2 

2033 $252.4 $15.7 $101.3 $105.8 $475.2 $65.4 $409.7 

2034 $265.4 $17.1 $103.6 $111.1 $497.1 $68.1 $429.0 

2035 $278.3 $18.5 $105.7 $116.2 $518.7 $70.8 $447.9 

2036 $290.7 $19.9 $107.5 $121.2 $539.3 $73.3 $466.0 

2037 $302.8 $21.2 $109.2 $125.6 $558.8 $75.8 $483.1 

2038 $314.5 $22.5 $110.8 $129.5 $577.2 $78.1 $499.2 

2039 $325.7 $23.8 $112.3 $132.8 $594.5 $80.2 $514.3 

2040 $336.5 $25.0 $113.7 $135.6 $610.7 $82.3 $528.4 

NPV at 7% $866.7 $47.7 $325.4 $355.7 $1,595.4 $267.2 $1,328.3 

NPV at 3% $2,176.2 $125.7 $834.4 $903.0 $4,039.3 $631.4 $3,407.9 

5-70 
 
Chapter 5: Engineering Cost Estimates 

Table 5-45 Estimated Increased Operating Costs by Cost Element Associated with the Proposal 

($Millions) 

Calendar 

Year Urea Use DPF Maintenance 

Fuel 

Impact Total PM NOx+ 

NMHC 

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 $1.9 $1.9 $1.0 $1.0 

2009 $0.0 $0.0 $2.1 $2.1 $1.0 $1.0 

2010 $0.0 $0.0 $5.2 $5.2 $2.6 $2.6 

2011 $0.0 $0.0 $10.5 $10.5 $5.2 $5.2 

2012 $0.0 $0.0 $12.7 $12.7 $6.3 $6.3 

2013 $0.0 $0.0 $15.6 $15.6 $7.8 $7.8 

2014 $2.2 $0.1 $17.1 $19.3 $8.6 $10.7 

2015 $4.4 $0.3 $20.5 $25.2 $10.6 $14.6 

2016 $10.7 $0.7 $27.4 $38.9 $14.4 $24.4 

2017 $28.0 $1.1 $34.8 $64.0 $18.5 $45.4 

2018 $46.8 $1.6 $40.2 $88.6 $21.7 $66.9 

2019 $65.7 $2.1 $45.3 $113.1 $24.7 $88.4 

2020 $85.0 $2.6 $49.9 $137.4 $27.5 $109.9 

2021 $104.5 $3.0 $54.6 $162.1 $30.3 $131.8 

2022 $124.5 $3.5 $59.4 $187.4 $33.2 $154.2 

2023 $153.2 $4.0 $64.3 $221.5 $36.2 $185.3 

2024 $181.6 $4.5 $69.2 $255.4 $39.2 $216.2 

2025 $201.8 $5.0 $74.3 $281.1 $42.2 $239.0 

2026 $222.0 $5.6 $79.3 $306.9 $45.2 $261.7 

2027 $242.2 $6.1 $84.4 $332.7 $48.3 $284.5 

2028 $262.3 $6.6 $89.6 $358.4 $51.3 $307.1 

2029 $281.8 $7.0 $94.6 $383.4 $54.3 $329.1 

2030 $300.4 $7.5 $99.4 $407.3 $57.2 $350.1 

2031 $318.3 $8.0 $104.1 $430.3 $60.0 $370.4 

2032 $335.8 $8.4 $108.7 $452.9 $62.7 $390.2 

2033 $353.1 $8.8 $113.2 $475.2 $65.4 $409.7 

2034 $370.1 $9.3 $117.7 $497.1 $68.1 $429.0 

2035 $386.8 $9.7 $122.2 $518.7 $70.8 $447.9 

2036 $402.7 $10.1 $126.5 $539.3 $73.3 $466.0 

2037 $417.8 $10.4 $130.6 $558.8 $75.8 $483.1 

2038 $431.9 $10.8 $134.5 $577.2 $78.1 $499.2 

2039 $445.2 $11.1 $138.2 $594.5 $80.2 $514.3 

2040 $457.6 $11.4 $141.7 $610.7 $82.3 $528.4 

NPV at 7% $1,089.7 $28.6 $477.1 $1,595.4 $267.2 $1,328.3 

NPV at 3% $2,850.0 $73.5 $1,115.7 $4,039.3 $631.4 $3,407.9 

As shown in Table 5-45, the net present value of the annual operating costs is 

estimated at $4 billion at a three percent discount rate or $1.6 billion at a seven 

percent discount rate. The primary increased operating cost is associated with urea 

use which accounts for nearly three quarters of the estimated costs. Since urea use is 

meant for NOx+NMHC control, most of the increased operating costs are attributed to 

NOx+NMHC control. Of note in these operating cost tables is the annual reduction of 

gallons consumed by remanufactured Tier 0 locomotives. This is a result of older 

Tier 0 locomotives slowly being retired from duty and being replaced by new Tier 4 

locomotives. This also explains the corresponding increase in gallons consumed by 

5-71 
 
Draft Locomotive and Marine RIA 

new Tier 4 locomotives. Not shown in the locomotive operating cost tables are 

gallons consumed by remanufactured Tier 1, 2 and 3 locomotives because we expect 

no increased operating cost for those locomotives as a result of this proposal (no new 

aftertreatment devices so no urea nor DPF maintenance costs and no fuel 

consumption impact). Also, in Table 5-41 where fuel consumption impacts are 

calculated, we have considered gallons burned by remanufactured and new Tier 4 

passenger locomotives but have not included switchers. We have not included 

switchers because those locomotives are expected to be powered by nonroad Tier 4 

engines having better fuel economy than the switcher engines they replace. 

5.5 Engineering Hardware Costs Associated with the Locomotive 

Remanufacturing Program 

Our proposal also contains requirements that remanufactured locomotives 

meet more stringent standards than those to which they were designed originally. 

Because the standards for those engines are more stringent, they cannot necessarily be 

remanufactured to 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 proposal. The remanufacturing process is not a low cost 

endeavor. However, it is much less costly than purchasing a perfectly new engine. 

The costs we have estimated for the remanufacturing program are meant to capture 

the incremental costs associated with remanufacturing as a result of the proposed 

program. 

To summarize the proposed 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 newly built 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. 

To meet the proposed 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 new locomotives to meet our current new locomotive standards. To 

meet the lower NOx standard proposed for Tier 0 locomotives, we expect possible 

improvements in the fuel system, the turbo charger, and the engine calibration. Such 

changes are expected to impact fuel consumption as was discussed in section 5.4.3 of 

5-72 
 
Chapter 5: Engineering Cost Estimates 

this draft RIA. We have estimated the incremental 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 proposed 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 costs to be roughly 

equivalent to the 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. 

To meet the more stringent proposed 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 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 locomotives have larger engines and, 

hence, larger fuel rails and pumps, etc. We have not estimated an incremental 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 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 proposed 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 new 

Tier 3 products and for application to their existing Tier 2 products at the time of 

remanufacturing. In the case of the new 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 proposed standards. However, we have accounted for these 

5-73 
 
Draft Locomotive and Marine RIA 

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 

proposed 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 94 percent, respectively, relative to the original cost. 

The 60/94 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 costs already mentioned above associated with costs for Tier 2 

remanufacturing (i.e., $11,750 or $8,700). 

These estimated incremental remanufacturing costs are summarized in Table 

5-46. 

Table 5-46 Estimated Incremental Costs Associated with the Locomotive Remanufacturing 
 

Program ($/remanufacture) 
 

Segment Tier 1st Remanufacture 2nd Remanufacture 

Locomotive Line-haul Tier 0 $33,800 $22,300 

Tier 1 $33,800 $22,300 

Tier 2 $11,750 $0 

Tier 3 $0 $0 

Tier 4 $66,000 $66,000 

Locomotive Switcher/Passenger Tier 0 $33,800 $22,300 

Tier 1 $33,800 $22,300 

Tier 2 $8,700 $0 

Tier 3 $0 $0 

Tier 4 $21,700 $21,700 

Using these per remanufacture costs, we can calculate the total costs 

associated with the proposed remanufacturing program. These costs are presented in 

Table 5-47 for line haul and Table 5-48 for switchers and passenger locomotives. See 

Chapter 3 of this draft RIA for how we determined the rate at which locomotives are 

remanufactured. The number remanufactured and the calendar years in which they 

occur are also shown in the tables. As shown, the net present value of the annual 

5-74 
Chapter 5: Engineering Cost Estimates 

remanufacturing costs is estimated at $1.2 billion and $0.6 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 $150 million and $85 million at a three and seven percent discount 

rate, respectively. In total, the proposed remanufacturing program would have a net 

present value cost of $1.4 billion at a three percent discount rate and $0.7 billion at a 

seven percent discount rate. Note that, while not shown in Table 5-47 and Table 

5-48, the costs associated with the proposed locomotive remanufacturing program are 

arbitrarily split evenly between NOx+NMHC and PM control. This split is shown in 

Table 5-54. 

5-75 
 
Draft Locomotive and Marine RIA 

Table 5-47 Estimated Annual Costs Associated with the Remanufacturing Program for Line Haul Locomo

Calendar 

Year 

Tier 0 

Remans $/reman Subtotal ($MM) 

Tier 1 

Remans $/reman Subtotal ($MM) 

Tier 2 

Remans $/reman Subtotal ($MM) Remans 

Tier 3 

$/reman Subtotal ($MM) 

Remans 

2006 2007 2008 2009 2010 2011 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 

-

-

661 $33,800 

- $33,800 

1220 $33,800 

2078 $33,800 

972 $33,800 

1310 $33,800 

618 $33,800 390 $33,800 

1174 $33,800 1164 $22,300 1271 $22,300 

231 $22,300 370 $22,300 

- $22,300 

579 $22,300 

1103 $22,300 

501 $22,300 646 $22,300 

- $22,300 - $22,300 

622 $22,300 610 $22,300 505 $22,300 

- $22,300 - $22,300 - $22,300 

- $22,300 - $22,300 - $22,300 - $22,300 - $22,300 - $22,300 - $22,300 

$0.0 

$0.0 

$22.3 

$0.0 

$41.2 

$70.2 $32.8 $44.3 $20.9 $13.2 $39.7 $26.0 $28.4 

$5.2 

$8.2 

$0.0 

$12.9 $24.6 $11.2 $14.4 

$0.0 $0.0 

$13.9 $13.6 $11.3 

$0.0 $0.0 

$0.0 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

-

-

-

803 $33,800 

- $33,800 

489 $33,800 931 $33,800 

- $33,800 - $33,800 - $33,800 - $33,800 - $22,300 - $22,300 

803 $22,300 

- $22,300 

489 $22,300 

931 $22,300 

- $22,300 - $22,300 - $22,300 

- $22,300 - $22,300 

- $22,300 - $22,300 - $22,300 

442 $22,300 

- $22,300 

- $22,300 

220 $22,300 419 $22,300 

- $22,300 - $22,300 - $22,300 - $22,300 - $22,300 

$0.0 

$0.0 

$0.0 

$27.1 

$0.0 

$16.5 $31.5 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

$17.9 

$0.0 

$10.9 

$20.8 

$0.0 $0.0 $0.0 

$0.0 $0.0 

$0.0 $0.0 $0.0 

$9.8 

$0.0 

$0.0 $4.9 $9.3 

$0.0 $0.0 $0.0 $0.0 $0.0 

-

-

-

-

---

719 $11,749 826 $11,749 646 $11,749 666 $11,749 693 $11,749 729 $11,749 

751 $11,749 

- $0 

- $0 

- $0 

719 $0 826 $0 646 $0 

666 $0 693 $0 

729 $0 751 $0 

- $0 

- $0 

- $0 

- $0 

- $0 - $0 

324 $0 372 $0 291 $0 300 $0 312 $0 

$0.0 

$0.0 

$0.0 

$0.0 

$0.0 

$0.0 $0.0 

$8.4 $9.7 $7.6 $7.8 $8.1 $8.6 $8.8 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

$0.0 $0.0 

$0.0 $0.0 

$0.0 

$0.0 

$0.0 

$0.0 

$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

-

-

-

-

-

--

------

-

767 765 780 

---

--

--

767 

765 

780 

-

-------

$0 $0 

$0 

$0 $0 $0 

$0 $0 

$0 $0 $0 

$0 

$0 

$0 

$0 $0 $0 $0 $0 $0 $0 

$0 

$0 

$0 

$0 

$0 

$0 $0 

$0 $0 $0 $0 $0 $0 $0 $0 $0 

$0 

$0 $0 $0 

$0 $0 

$0 $0 $0 

$0 

$0 

$0 

$0 $0 $0 $0 $0 $0 $0 

-

-

-

-

-

--

------

--

-

-

816 854 877 

894 917 

948 979 

1007 

1034 

1048 

1894 

1950 1996 2030 2067 2106 2152 2197 

NPV at 

7% 

NPV at 

3% 

$231.7 $332.9 

$72.1 

$105.2 

$28.4 $42.8 

$0 $0 

5-76 
Chapter 5: Engine

Table 5-48 Estimated Annual Costs Associated with the Remanufacturing Program for Switcher and Passenger L

Calendar 

Year Remans 

Tier 0/1 

$/reman Subtotal ($MM) Remans 

Tier 2 

$/reman Subtotal ($MM) Remans 

Tier 3 

$/reman Subtotal ($MM) Remans 

Tier 

$/rem

2006 2007 2008 2009 2010 2011 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 

--

31 78314 312 309 307 307 

269 271 273 274 276 278 279 281 318 315 311 266 260 253 245 236 226 190 179 166 154 142 132 123 

$33,800 $33,800 

$33,800 $33,800 $33,800 $33,800 $33,800 

$33,800 $33,800 $22,300 $22,300 $22,300 $22,300 $22,300 $22,300 $22,300 $22,300 

$22,300 

$22,300 $22,300 $22,300 $22,300 $22,300 

$22,300 

$22,300 $22,300 $22,300 

$22,300 

$22,300 $22,300 $22,300 $22,300 $22,300 

$0.0 $0.0 

$1.1 $2.6 

$10.6 $10.5 $10.5 $10.4 $10.4 

$9.1 $9.2 $6.1 $6.1 $6.2 $6.2 $6.2 $6.3 $7.1 $7.0 

$6.9 

$5.9 $5.8 $5.6 $5.5 $5.3 

$5.0 

$4.2 $4.0 $3.7 

$3.4 

$3.2 $2.9 $2.7 $2.5 $2.3 

--

--

-----

112 154 88 89 90 91 

----

57169 

86 88 89 90 

45 

---

57114 

46 46 

$8,728 $8,728 

$8,728 $8,728 $8,728 $8,728 

$0 $0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

$0 

$0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

$0.0 $0.0 

$0.0 $0.0 

$0.0 $0.0 $0.0 $0.0 $0.0 

$1.0 $1.3 

$0.8 $0.8 $0.8 $0.8 

$0.0 $0.0 $0.0 $0.0 

$0.0 

$0.0 

$0.0 $0.0 $0.0 $0.0 

$0.0 

$0.0 $0.0 $0.0 

$0.0 

$0.0 

$0.0 $0.0 $0.0 $0.0 

--

--

-----

--

----

46 46 92 92 

46 

-

----

46 

46 92 92 

46 

-

----

$0 $0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

$0 

$0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

$0 $0 

$0 $0 

$0 $0 $0 $0 $0 

$0 $0 

$0 $0 $0 $0 $0 $0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

$0 

$0 $0 $0 

$0 

$0 

$0 $0 $0 $0 

--

--

-----

--

--------

47 

93 

94 94 94 94 

94 

94160 183 

201 

212 

230 238 

$21

$21

$21$21$21$21

$21

$21

$21$21

$21

$21

$21$21$21$21

NPV at 7% NPV at 3% 

114 105 

$75.0 

$123.9 46 46 $2.4 $3.8 $0 $0 246 263 

5-77 
Draft Locomotive and Marine RIA 

5.6 Summary of Proposed 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 proposed program. Section 5.6.2 summarizes 

the equipment-related costs associated with the proposed program. Section 5.6.3 

summarizes the operating costs associated with the proposed program for both new 

and remanufactured engines. Section 5.6.4 summarizes the hardware costs associated 

with the locomotive remanufacturing program. Section 5.6.5 summarizes all these 

costs and presents the total estimated costs for the proposed 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 New Engine Engineering Costs 

5.6.1.1 New Engine 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 5-49. The table also includes net present values using 

both a three percent and a seven percent discount rate. 

Table 5-49 Summary of Engine-Related Fixed Costs for the Proposed Program ($Millions) 

Costs Incurred 2006-2040 NPV at 

3% 

2006-2040 NPV at 

7% 

Engine and Emission Control 

Research 

$ 415 $ 341 $ 267 

Engine Tooling $ 41 $ 33 $ 24 

Engine Certification $ 9 $ 7 $ 6 

Total Engine Fixed Costs $ 466 $ 381 $ 297 

Total Allocated to PM $ 162 $ 133 $ 104 

Total Allocated to NOx+NMHC $ 303 $ 248 $ 193 

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 New Engine 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 

5-78 
 
Chapter 5: Engineering Cost Estimates 

of engine displacement (see Table 5-27). Using these equations, we have calculated 

the hardware costs for new engines meeting the proposed standards for each year 

through 2040. We present those annual engine variable costs in Section 5.2.2. Table 

5-50 shows the net present value of those annual costs using a three percent discount 

rate and a seven percent discount rate. 

Table 5-50 Summary of Engine-Related Variable Costs for the Proposed Program ($Millions) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Locomotive $ 808 $ 364 

C1 Marine & Recreational Marine >2000 kW $ 261 $ 119 

C2 Marine $ 227 $ 104 

Recreational Marine <2000 kW $ 0 $ 0 

Small Marine $ 0 $ 0 

Total Engine Variable Costs $ 1,297 $ 586 

Total Allocated to PM $ 700 $ 320 

Total Allocated to NOx+NMHC $ 597 $ 266 

Note: The PM/NOx+NMHC cost allocations for engine variable costs are as follows: Urea 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 New Equipment Engineering Costs 

5.6.2.1 New Equipment Fixed Engineering Costs 

Equipment fixed costs are discussed in detail in Section 5.3.1. Table 5-51 

shows the estimated equipment fixed costs—for redesign efforts—associated with the 

proposed program. The table also includes net present values of the annual costs 

using both a three percent and a seven percent discount rate. 

Table 5-51 Summary of Equipment-Related Fixed Costs for the Proposed Program ($Millions) 

Costs 

Incurred 

2006-2040 NPV at 

3% 

2006-2040 NPV at 

7% 

Locomotive $ 0.7 $ 0.5 $ 0.4 

C1 Marine & Recreational Marine 

>2000 kW 

$ 45 $ 31 $ 20 

C2 Marine $ 16 $ 11 $ 7 

Recreational Marine <2000 kW $ 0 $ 0 $ 0 

Small Marine $ 0 $ 0 $ 0 

Total Equipment Fixed Costs $ 61 $ 43 $ 27 

Total Allocated to PM $ 31 $ 21 $ 14 

Total Allocated to NOx+NMHC $ 31 $ 21 $ 14 

5-79 
 
Draft Locomotive and Marine RIA 

Note: Equipment fixed costs are arbitrarily split evenly between NOx+NMHC and PM control. 

5.6.2.2 New Equipment 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 proposed standards for each year 

through 2040. We present those annual equipment variable costs in Section 5.3.2. 

Table 5-52 shows the net present value of those annual costs using a three percent and 

a seven percent discount rate. 

Table 5-52 Summary of Equipment-Related Variable Costs for the Proposed Program 

($Millions) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Locomotive $ 68 $ 30 

C1 Marine & Recreational Marine >2000 kW $ 26 $ 12 

C2 Marine $ 5 $ 2 

Recreational Marine <2000 kW $ 0 $ 0 

Small Marine $ 0 $ 0 

Total Equipment Variable Costs $ 99 $ 44 

Total Allocated to PM $ 50 $ 22 

Total Allocated to NOx+NMHC $ 50 $ 22 

Note: Equipment variable costs are arbitrarily split evenly between NOx+NMHC and PM control. 

5.6.3 Operating Costs for New and Remanufactured 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 5-53 shows the net present value of those annual operating costs using 

a three percent and a seven percent discount rate. 

Table 5-53 Summary of Operating Costs for the Proposed Program ($Millions) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Urea DPF 

Maint. 

Fuel Total Urea DPF 

Maint. 

Fuel Total 

Locomotive $1,558 $41 $702 $2,302 $583 $16 $315 $914 

C1 Marine $620 $16 $199 $834 $242 $6 $77 $325 

C2 Marine $671 $17 $215 $903 $264 $7 $85 $356 

Recreational 

Marine 

$ 0 $0 $ 0 $ 0 $ 0 $ 0 $ 0 $ 0 

Small Marine $ 0 $ 0 $ 0 $ 0 $ 0 $ 0 $ 0 $ 0 

Total Operating 

Costs 

$2,850 $74 $1,116 $4,039 $1,090 $29 $477 $1,595 

5-80 
 
Chapter 5: Engineering Cost Estimates 

Total Allocated to 

PM 

$ 0 $74 $558 $631 $ 0 $29 $239 $267 

Total Allocated to 

NOx+NMHC 

$2,850 $ 0 $558 $3,408 $1,090 $ 0 $239 $1,328 

Note: Operating costs are attributed as follows: costs associated with urea 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 Remanufacturing Program Engineering Hardware Costs 

Costs associated with the locomotive remanufacturing program 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. Table 5-54 shows the net present value of those annual remanufacturing 

costs using a three percent and a seven percent discount rate. 

Table 5-54 Summary of Locomotive Remanufacturing Program Hardware Costs ($Millions) 

2006-2040 NPV at 3% 2006-2040 NPV at 7% 

Line Haul $ 1,224 $ 597 

Switcher & Passenger $ 150 $ 85 

Total Remanufacturing Costs $ 1,374 $ 682 

Total Allocated to PM $ 687 $ 341 

Total Allocated to NOx+NMHC $ 687 $ 341 

Note: Costs associated with the proposed locomotive remanufacturing program are arbitrarily split 

evenly between NOx+NMHC and PM control. 

5.6.5 Total Engineering Costs Associated with the Proposed Program 

Table 5-55 shows the total annual costs for each market segment—locomotive 

line haul, C2 marine, etc—for the proposed program. Table 5-56 shows the total 

annual costs for each cost element—engine, equipment, operating, etc.—on an annual 

basis for the proposed program. As shown, the net present value of the annual costs 

is estimated at $7.2 billion at a three percent discount rate and $3.2 billion at a seven 

percent discount rate. In the year 2030, the annual costs are estimated at $605 

million. 

Note that costs throughout this 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 

5-81 
 
Draft Locomotive and Marine RIA 

equally except where new standards are implemented in different years (e.g., for Tier 

4 locomotive standards); urea SCR systems including marinization costs on marine 

applications are attributed 100% to NOx+NMHC control; DPF systems including 

marinization costs on marine applications are attributed 100% to PM control; 

equipment fixed and variable costs are arbitrarily split evenly between NOx+NMHC 

and PM control; costs associated with urea 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 arbitrarily split evenly between NOx+NMHC and PM 

control. 

Table 5-55 Estimated Annual Engineering Costs by Market Segment for the Proposed Program 

($Millions) 

Calendar 

Year 

Locomotive 

Line Haul Switcher & 

Passenger 

C2 

Marine 

C1 

Marine 

>600kW 

Marine 

C1 

Marine 

<600kW 

Recreational 

Marine 

Small 

Marine 

Total 

2006 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2007 $0.6 $2.6 $4.2 $2.3 $10.9 $5.8 $1.7 $28.2 

2008 $24.9 $3.7 $4.2 $2.3 $10.9 $5.8 $1.7 $53.5 

2009 $29.6 $5.4 $4.2 $2.3 $10.9 $5.8 $1.7 $60.0 

2010 $48.4 $15.9 $4.2 $2.3 $10.9 $5.8 $1.7 $89.3 

2011 $100.6 $18.0 $27.7 $29.4 $16.2 $10.9 $3.7 $206.5 

2012 $81.5 $18.2 $20.7 $21.8 $0.0 $0.0 $0.0 $142.2 

2013 $72.9 $18.2 $20.7 $21.8 $0.0 $0.0 $0.0 $133.5 

2014 $53.7 $21.2 $23.7 $21.8 $0.0 $0.0 $0.0 $120.4 

2015 $77.0 $16.9 $40.8 $46.5 $0.0 $0.0 $0.0 $181.2 

2016 $112.5 $20.0 $35.0 $36.0 $0.0 $0.0 $0.0 $203.5 

2017 $129.7 $10.2 $39.8 $37.4 $0.0 $0.0 $0.0 $217.2 

2018 $145.0 $10.8 $39.8 $37.4 $0.0 $0.0 $0.0 $232.9 

2019 $145.7 $11.1 $45.6 $44.7 $0.0 $0.0 $0.0 $247.0 

2020 $135.0 $11.6 $51.3 $51.9 $0.0 $0.0 $0.0 $249.9 

2021 $151.0 $11.4 $57.1 $59.2 $0.0 $0.0 $0.0 $278.6 

2022 $187.4 $12.2 $62.9 $66.3 $0.0 $0.0 $0.0 $328.8 

2023 $254.5 $15.1 $68.6 $73.4 $0.0 $0.0 $0.0 $411.6 

2024 $265.0 $16.2 $73.3 $77.9 $0.0 $0.0 $0.0 $432.6 

2025 $284.2 $18.3 $79.1 $84.9 $0.0 $0.0 $0.0 $466.4 

2026 $284.8 $19.4 $84.8 $91.6 $0.0 $0.0 $0.0 $480.7 

2027 $300.6 $20.5 $90.5 $98.3 $0.0 $0.0 $0.0 $509.9 

2028 $330.4 $21.6 $96.2 $104.7 $0.0 $0.0 $0.0 $552.9 

2029 $346.0 $22.6 $101.9 $110.3 $0.0 $0.0 $0.0 $580.8 

2030 $359.0 $23.8 $107.6 $114.8 $0.0 $0.0 $0.0 $605.2 

2031 $373.2 $25.1 $113.2 $118.5 $0.0 $0.0 $0.0 $630.0 

2032 $378.3 $25.8 $118.7 $121.5 $0.0 $0.0 $0.0 $644.3 

2033 $448.3 $28.4 $124.2 $124.3 $0.0 $0.0 $0.0 $725.2 

2034 $470.8 $30.1 $129.6 $126.9 $0.0 $0.0 $0.0 $757.5 

2035 $491.6 $31.6 $134.9 $129.2 $0.0 $0.0 $0.0 $787.3 

2036 $495.0 $32.8 $140.1 $131.3 $0.0 $0.0 $0.0 $799.1 

2037 $508.0 $34.2 $144.7 $133.2 $0.0 $0.0 $0.0 $820.1 

2038 $520.6 $35.3 $148.7 $135.1 $0.0 $0.0 $0.0 $839.7 

2039 $533.0 $36.4 $152.2 $136.8 $0.0 $0.0 $0.0 $858.4 

2040 $544.8 $37.7 $155.2 $138.4 $0.0 $0.0 $0.0 $876.0 

NPV at 

7% 

NPV at 

3% 

$1,859.0 $186.0 

$4,258.3 $366.4 

$551.4 

$1,255.8 

$555.6 

$1,259.5 

$45.3 

$52.9 

$25.7 

$30.2 

$8.0 

$9.4 

$3,231.1 

$7,232.5 

5-82 
 
Chapter 5: Engineering Cost Estimates 

Table 5-56 Estimated Annual Engineering Costs by Cost Element for the Proposed Program 

($Millions) 

Calendar 

Year 

Engine 

Costs 

Equipment 

Costs 

Loco 

Reman 

Costs 

Operating 

Costs Total PM NOx+NMHC 

2006 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

2007 $28.2 $0.0 $0.0 $0.0 $28.2 $9.3 $18.9 

2008 $28.2 $0.0 $23.4 $1.9 $53.5 $22.0 $31.5 

2009 $28.2 $0.0 $29.8 $2.1 $60.0 $25.2 $34.8 

2010 $32.2 $0.0 $51.8 $5.2 $89.3 $41.9 $47.4 

2011 $98.8 $0.0 $97.3 $10.5 $206.5 $93.3 $113.2 

2012 $54.8 $0.0 $74.8 $12.7 $142.2 $61.8 $80.4 

2013 $54.8 $0.0 $63.1 $15.6 $133.5 $57.5 $76.1 

2014 $59.4 $0.7 $41.0 $19.3 $120.4 $52.1 $68.3 

2015 $100.0 $25.1 $30.8 $25.2 $181.2 $93.2 $88.1 

2016 $89.2 $17.5 $58.0 $38.9 $203.5 $107.7 $95.8 

2017 $99.7 $12.6 $40.9 $64.0 $217.2 $93.9 $123.3 

2018 $90.9 $9.7 $43.8 $88.6 $232.9 $92.4 $140.5 

2019 $85.3 $9.8 $38.8 $113.1 $247.0 $93.9 $153.2 

2020 $87.3 $9.9 $15.2 $137.4 $249.9 $86.0 $163.9 

2021 $89.4 $10.1 $17.1 $162.1 $278.6 $90.9 $187.7 

2022 $91.2 $10.2 $39.9 $187.4 $328.8 $106.2 $222.5 

2023 $94.0 $10.6 $85.6 $221.5 $411.6 $133.7 $277.9 

2024 $95.4 $7.2 $74.6 $255.4 $432.6 $130.3 $302.3 

2025 $97.6 $7.5 $80.2 $281.1 $466.4 $137.4 $329.0 

2026 $99.2 $7.6 $67.0 $306.9 $480.7 $134.7 $345.9 

2027 $101.0 $7.8 $68.4 $332.7 $509.9 $139.5 $370.3 

2028 $102.4 $7.9 $84.1 $358.4 $552.9 $151.3 $401.6 

2029 $103.6 $8.0 $85.7 $383.4 $580.8 $155.8 $425.0 

2030 $104.7 $8.1 $85.1 $407.3 $605.2 $159.0 $446.2 

2031 $106.1 $8.3 $85.2 $430.3 $630.0 $162.7 $467.3 

2032 $107.6 $8.4 $75.5 $452.9 $644.3 $161.4 $483.0 

2033 $109.0 $8.5 $132.5 $475.2 $725.2 $193.4 $531.8 

2034 $110.3 $8.7 $141.3 $497.1 $757.5 $201.3 $556.2 

2035 $111.1 $8.7 $148.9 $518.7 $787.3 $208.1 $579.2 

2036 $109.5 $8.5 $141.8 $539.3 $799.1 $206.2 $593.0 

2037 $108.4 $8.4 $144.4 $558.8 $820.1 $209.3 $610.8 

2038 $107.2 $8.3 $147.0 $577.2 $839.7 $212.2 $627.6 

2039 $105.7 $8.2 $150.0 $594.5 $858.4 $215.0 $643.4 

2040 $104.2 $8.0 $153.1 $610.7 $876.0 $217.7 $658.3 

NPV at 7% $882.6 $71.4 $681.7 $1,595.4 $3,231.1 $1,067.9 $2,163.2 

NPV at 3% $1,677.7 $141.3 $1,374.4 $4,039.3 $7,232.5 $2,222.1 $5,010.5 

5.7 Engineering Costs Associated with a Possible Marine 

Remanufacturing Program 

We are requesting comment on the possibility of requiring a remanufacturing 

program for commercial marine propulsion engines over 600 kW (805 hp), including 

those recreational marine engines over 2000 kW. While such a program is not being 

proposed, we believe it is important to estimate costs associated with such a program 

so as to better inform commenters. We have estimated these costs in a manner 

similar to those generated for the proposed locomotive remanufacture program. We 

5-83 
 
Draft Locomotive and Marine RIA 

have estimated the number of remanufactured engines as being equal to our estimate 

of sales of marine propulsion engines >600 kW, but shifted by nine years to represent 

the time passage between original sale and remanufacture. We then multiplied those 

estimated remanufactured engines by the same Tier 0/1 and Tier 2 costs per 

remanufacture estimated for locomotives since we would expect a similar or identical 

remanufacturing kit to be used on marine as locomotive engines. 

The estimated annual costs of a possible marine remanufacturing program are 

presented in Table 5-57. As shown, we have estimated the net present value of the 

annual costs at $413 million and $275 million at a three percent and seven percent 

discount rate, respectively. Including a marine remanufacturing program would 

increase the net present value of the annual costs associated with the proposal from 

$7.2 billion to $7.6 billion using a three percent discount rate and from $3.2 billion to 

$3.5 billion using a seven percent discount rate. On an annual basis, including a 

marine remanufacturing program would increase the costs of the proposed program in 

2030 from $605 million to $618 million. 

5-84 
 
Chapter 5: Engineering Cost Estimates 

Table 5-57 Estimated Annual Costs Associated with a Possible Remanufacturing Program for 
 

Marine Engines >600 kW 
 

Calendar 

Year 

Tier 0/1 

Remans $/reman Subtotal 

($MM) 

Tier 2 

Remans $/reman Subtotal 

($MM) 

Total 

($MM) 

2006 - $0.0 - $0.0 $0.0 

2007 - $0.0 - $0.0 $0.0 

2008 866 $33,800 $29.3 - $0.0 $29.3 

2009 902 $33,800 $30.5 - $0.0 $30.5 

2010 939 $33,800 $31.7 - $0.0 $31.7 

2011 976 $33,800 $33.0 - $0.0 $33.0 

2012 1013 $33,800 $34.2 - $0.0 $34.2 

2013 1025 $33,800 $34.7 - $0.0 $34.7 

2014 1038 $33,800 $35.1 - $0.0 $35.1 

2015 1050 $33,800 $35.5 - $0.0 $35.5 

2016 - $33,800 $0.0 1063 $11,749 $12.5 $12.5 

2017 829 $22,300 $18.5 1076 $11,749 $12.6 $31.1 

2018 866 $22,300 $19.3 1088 $11,749 $12.8 $32.1 

2019 902 $22,300 $20.1 1101 $11,749 $12.9 $33.1 

2020 939 $22,300 $20.9 1114 $11,749 $13.1 $34.0 

2021 976 $22,300 $21.8 - $11,749 $0.0 $21.8 

2022 1013 $22,300 $22.6 - $11,749 $0.0 $22.6 

2023 1025 $22,300 $22.9 - $11,749 $0.0 $22.9 

2024 1038 $22,300 $23.1 - $11,749 $0.0 $23.1 

2025 1050 $22,300 $23.4 - $11,749 $0.0 $23.4 

2026 - $0 $0.0 1063 $11,749 $12.5 $12.5 

2027 - $0 $0.0 1076 $11,749 $12.6 $12.6 

2028 - $0 $0.0 1088 $11,749 $12.8 $12.8 

2029 - $0 $0.0 1101 $11,749 $12.9 $12.9 

2030 - $0 $0.0 1114 $11,749 $13.1 $13.1 

2031 - $0 $0.0 - $11,749 $0.0 $0.0 

2032 - $0 $0.0 - $11,749 $0.0 $0.0 

2033 - $0 $0.0 - $11,749 $0.0 $0.0 

2034 - $0 $0.0 - $11,749 $0.0 $0.0 

2035 - $0 $0.0 - $11,749 $0.0 $0.0 

2036 - $0 $0.0 - $11,749 $0.0 $0.0 

2037 - $0 $0.0 - $11,749 $0.0 $0.0 

2038 - $0 $0.0 - $11,749 $0.0 $0.0 

2039 - $0 $0.0 - $11,749 $0.0 $0.0 

2040 - $0 $0.0 - $11,749 $0.0 $0.0 

NPV at 7% $235.7 $40.1 $275.9 

NPV at 3% $337.0 $75.9 $413.0 

5.8 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, not 

antifreeze to cool their engines because water is much more efficient at removing 

heat, and therefore, one of the primary reasons they idle is to keep the water 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, 

5-85 
 
Draft Locomotive and Marine RIA 

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, including heat and air conditioning. 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 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 heat and circulate the water and oil. They range in price from $4,000 ­

$14,000 depending on the options installed.k

 These systems are most widely used on 

passenger trains that return to the same location at night, but are not 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. An electronic timer controls the switching, and no operator 

intervention is required. The cost of the system is approximately $4000, and it can be 

installed in just two hours.l Third, an Auxiliary Power Unit (APU) is an idle 

reduction technology that reduces unnecessary idling by using a small diesel engine 

(less than 50 hp) to provide power to run cab accessories, heat and circulate water and 

oil, and charge the locomotive batteries 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 primarily on the region in which the locomotive it is installed on operates. 

The cost of an APU is approximately $25,000 - $35,000.m Fourth, a more complex 

solution is being demonstrated in 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.n

 Fifth, locomotive engines 

can be replaced with two or three smaller on-highway engines. The on-highway 

k Linda Gaines, “Reduction of Impacts from Locomotive Idling”, Center for Transportation Research, 

Argonne National Laboratory 

l

 www.energyconversions.com/lei1.htm 

m Case Study: Chicago Locomotive Idle Reduction Project, EPA420-R-04-003 

n 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 

5-86 
 
Chapter 5: Engineering Cost Estimates 

engines are referred to as gensetso which allow one smaller engine to idle while the 

others are used when more power is needed. Sixth, a hybrid-electric system has been 

designed for switch yard purposes only (known as the GreenGoat.)p 

Finally, one of the most cost effective onboard solutions that can provide idle 

reduction benefits to both line-haul and switcher locomotives nearly everywhere 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. 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 is 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 are 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.q

 An AESS alone can provide some fuel savings during the cold 

winter, but when combined with an APU will achieve considerable fuel savings. 

Under the proposed program, AESS systems will be required on all newly-built Tier 3 

and Tier 4 locomotives, and on all existing locomotives when they are first 

remanufactured under the revised remanufacturing program (see section III.C.(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.k,r This cost estimate includes not only labor costs for installation, but also 

o www.northeastdiesel.org, "Multi-Engine GenSet Ultra Low Emissions Road-Switcher Locomotive" 

presentation by National Railway Equipment Co., Jan, 2006. 

p

 www.railpower.com 

q

 See “Case Study: Chicago Locomotive Idle Reduction Project” (EPA420-R-04-003) (March, 2004), 

available at http://www.epa.gov/smartway/documents/420r04003.pdf 

r

 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 

5-87 
 
Draft Locomotive and Marine RIA 

the hardware costs for a basic AESS microprocessor system and monitoring 

equipment (systems including GPS or satellite uplink optional features are more 

expensive). 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). The regulatory duty cycle (see 40CFR 92.132 for 

more details) indicates that a line-haul idles for 38% of its operating time, and that a 

switcher locomotive idles for 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. 

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 a 50% reduction in low idling, although additional reductions in both low 

and normal idling may be possible. Using this reduction value, we have estimated 

that an AESS will reduce unnecessary idling by over 410 hours a year on a line-haul, 

and approximately 660 hours a year on a switcher. This means that over the useful 

life a line-haul locomotive, we expect at least 2,900 hours of idling at a 3% net 

present value (2,500 at 7% 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 from Tier 2 Certification data, and a 

price of $1.28 for one gallon of diesel fuel and the yearly amount of idle hours 

avoided, we can estimate that this technology will pay for itself in just under four 

years on a switcher locomotive, and over one useful life on a line-haul locomotive 

will return all but $500.00 of the initial investment. 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 remanufacturing the locomotive and therefore, it should 

continue to provide savings throughout the additional useful lives of the locomotives. 

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 5-58 shows the different 

payback times associated with the different savings estimates. Data from locomotives 

in the field indicate that payback time may be just slightly over one year, and that 

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Chapter 5: Engineering Cost Estimates 

figure comes from an average of both line-haul and switcher locomotives that have 

been collected over many years of operation in many different geographical regions 

of the country. 

Table 5-58 Estimates of Typical AESS Payback Time by other Sources 

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 

EPA - Ann Arbor 2,650 665 3d 2,000 $1.28 $2,600 3.8 years 

DOE 5,300 2,650 4.5 12,000 $1.28 $15,400 8 months 

EPA - NE 4,000 1,000 3-11 5,700 $1.28 $7,300 1.4 years 

SmartStart Reports 3,840a 2,050 4.5 9,200 $1.28 $11,800 10 months a This average value comes from data accumulated over at least three years on both line-haul and switcher 

locomotives 

b

 The $1.28 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 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 newly 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 here, these reductions are considered as part of the emission 

reductions from the proposed standards. Under the current and proposed regulations, 

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. 

AESS targets ‘low idle’ operation which occurs when the locomotive is not: 

• Maintaining critical system parameters (such as air brake cylinder pressure) 

• Propelling the locomotive 

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Draft Locomotive and Marine RIA 

• Protecting the engine from freezing 

• Providing cabin comfort to its crew. 

The AESS is designed to eliminate unnecessary idling which is primarily 

composed of low idle, and it is estimated that at least half of this low idle can be 

eliminated using an AESS.s,t,u This conservative estimate shows that on a perlocomotive basis, idling of a line-haul locomotive can be reduced by over 400 hours 

annually or at least 3,500 hours over its useful life using an AESS. For switchers, 

which spend considerably more time idling because of their function, AESS could 

reduce idling by over 660 hours annually or by at least 15,000 hours over the useful 

life of the locomotive. 

This reduced idling time means less fuel consumed. Tier 2 certification data 

indicates that modern locomotives typically burn 3 gallons of fuel an hour during 

low-idle. We calculated the cost savings of using an AESS based on an estimated 

diesel fuel price less taxes of $1.28/gallon (see 5.4.3 for more details). For a line-haul 

locomotive, use of an AESS is estimated to provide fuel cost savings of almost 

$1,600 annually. Over the useful life, this would mean a net present value savings of 

nearly $11,000 at a three percent discount rate ($9,500 at a seven percent discount 

rate). For a switcher locomotive, an AESS could provide fuel savings of nearly 

$2,500 annually or, over its useful life, a net present value savings of approximately 

$41,000 at a three percent discount rate ($28,000 at a seven percent discount rate). 

Idle reduction would also result in emissions reductions. 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 5-59 shows the annual fuel savings, the associated cost savings, and the 

emissions reductions we estimate would result from the proposed AESS 

s

 David E. Brann, “Locomotive Idling Reduction” 

http://www1.eere.energy.gov/vehiclesandfuels/pdfs/idling_2004/brann.pdf 

t

 http://www.arb.ca.gov/railyard/ryagreement/aess_electromotive.pdf u

 Draft Maryland Locomotive Idle Reduction Program Demonstration Project – DE-FG36-02GO12022 

http://www.osti.gov/bridge/servlets/purl/838872-D6MxUD/838872.PDF 

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Chapter 5: Engineering Cost Estimates 

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 -5-60 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 at idle, and further, we expect PM control to be 

reduced due to poor oxidation efficiency at idle. The ability of aftertreatment 

technologies to control emissions during idle operation is discussed in more detail in 

Chapter 4 of this draft RIA. Because of the limitations of the aftertreatment 

technology at idle, idle reduction via an AESS system is the best method to ensure 

control of emissions at idle. 

Table 5-59 Annual Effects of Using AESS on Line-Haul and Switcher Locomotives 

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 1,584 0.005 0.27 

Switcher 2,650 663 1,988 2,544 0.007 0.44 

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 $1.28/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)
 

Table -5-60 NPV 3% & 7% Effects of Using AESS Over the First Useful Life on Line-Haul and 

Switcher Locomotives 

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($) 

Net 

Savings 

($) 

PM 

Emission 

Reductionsf 

(tons) 

NOx 

Emission 

Reductionsg 

(tons) 

Line-Haul NPV 3% 12,000 2,900 8,700 11,000 10,000 1,000 0.032 1.9 

NPV 7% 9,900 2,500 7,400 9,500 10,000 -500 0.027 1.6 

Switcher NPV 3% 42,000 11,000 32,000 41,000 10,000 29,000 0.12 7.0 

NPV 7% 29,000 7,400 22,000 28,000 10,000 16,000 0.08 4.9 

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 

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Draft Locomotive and Marine RIA 

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 $1.28/gallon (see section 5.4.3) 
 

f Using PM estimate of 10g/hr emitted during low idle (estimated from Tier 2 Certification Data) 
 

g 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 -5-60, a net savings is likely 

which would, in effect, reduce the overall cost of our proposed 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. 

5.9 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 per day, or increase energy usage in excess of 

either of those thresholds. The proposed locomotive and marine program is projected 

to have an impact on fuel usage in excess of one of these thresholds. 

Section 5.4.3 of this draft RIA presents 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 engines. Table 5-40 through Table 5-43 show the 

increased number of gallons we have estimated would be consumed as a result of the 

proposed program. 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 5-61. As shown, in the year 2026, our proposed 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 5-61 do not reflect the potential fuel 

savings associated with automatic engine stop/start (AESS) systems or other idle 

reduction technologies. As discussed in section 5.8, such technologies can provide 

significant fuel savings which could offset the increased fuel consumption estimates 

shown in Table 5-61. 

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Chapter 5: Engineering Cost Estimates 

Table 5-61 Estimated Increase in Fuel Consumed in Million Gallons per Year and Average 
 

Barrels per Day 
 

Increase in Fuel Consumed 

(Million gallons per year) Calendar 

Year 

Locomotive Marine Total 

Barrels/day 

2006 0 0 0 0 

2007 0 0 0 0 

2008 2 0 2 99 

2009 2 0 2 106 

2010 4 0 4 264 

2011 8 0 8 534 

2012 10 0 10 646 

2013 12 0 12 797 

2014 13 1 13 870 

2015 15 1 16 1043 

2016 19 3 21 1399 

2017 23 5 27 1775 

2018 24 7 31 2047 

2019 26 9 35 2307 

2020 27 12 39 2544 

2021 29 14 43 2781 

2022 30 16 46 3025 

2023 31 19 50 3275 

2024 33 21 54 3528 

2025 35 23 58 3785 

2026 36 26 62 4043 

2027 38 28 66 4303 

2028 40 30 70 4564 

2029 42 32 74 4820 

2030 44 34 78 5066 

2031 46 35 81 5304 

2032 48 37 85 5538 

2033 50 38 88 5771 

2034 52 40 92 5999 

2035 54 41 95 6227 

2036 56 43 99 6447 

2037 58 44 102 6657 

2038 60 45 105 6856 

2039 62 46 108 7044 

2040 64 46 111 7224 

5.10 Cost Effectiveness 

One tool that can be used to assess the value of the proposed program is the 

costs incurred per ton of emissions reduced. This analysis involves a comparison of 

our proposed program to other measures that have been or could be implemented. 

We have calculated the cost per ton of our proposed program based on the net present 

value of all costs incurred and all emission reductions generated from the current year 

2006 through the year 2040. This approach captures all of the costs and emissions 

reductions from our proposed program including those costs incurred and emissions 

reductions generated by the locomotive remanufacturing program. The baseline case 

for this evaluation is the existing set of engine standards for locomotive and marine 

diesel engines and the existing locomotive remanufacturing requirements. The 

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Draft Locomotive and Marine RIA 

analysis timeframe is meant to capture both the early period of the program when 

very few new engines that meet the proposed standards would be in the fleet, and the 

later period when essentially all engines would meet the new standards. 

Table 5-62 shows the emissions reductions associated with the proposed 

locomotive and marine program. These reductions are discussed in more detail in 

Chapter 3 of this draft RIA. 

Table 5-62 Estimated Emissions Reductions Associated with the Proposed Locomotive and 
 

Marine Standards (Short tons) 
 

Year PM2.5 PM10 a

 NOx NMHC 

2015 7,000 7,000 84,000 14,000 

2020 15,000 15,000 293,000 25,000 

2030 28,000 29,000 765,000 39,000 

2040 38,000 40,000 1,123,000 50,000 

NPV at 3% 315,000 325,000 7,869,000 480,000 

NPV at 7% 136,000 140,000 3,188,000 216,000 

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. 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. 

Using the costs associated with PM and NOx control shown in Table 5-56 and 

the emission reductions shown in Table 5-62, we can calculate the $/ton associated 

with the proposed program. These are shown in Table 5-63. 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 draft RIA. 

Table 5-63 Proposed Program Aggregate Cost per Ton and Long-Term Annual Cost per Ton 

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 $600 $630 $550 

PM $6,840 $7,640 $5,560 

The costs per ton shown in Table 5-63 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 year 2030 using the annual costs and emissions 

reductions in that year alone. These numbers are also shown in Table 5-63 and 

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Chapter 5: Engineering Cost Estimates 

represent the long-term annual costs per ton of emissions reduced.v All of the costs 

per ton include costs and emission reductions that will occur from the locomotive 

remanufacturing program. 

We can also look at the costs, emissions reductions, and cost per ton 

associated with each of the proposed program elements: the locomotive 

remanufacturing program; the Tier 3 program; and, the Tier 4 program. We have 

done this simply by breaking out the costs we have allocated to each of these program 

elements and the emissions reductions we have allocated to each of these program 

elements. In other words, we have not done a true incremental analysis that would 

look at the costs and emissions reductions of, say, the Tier 3 program were it to go on 

forever, or the Tier 4 program were it done absent of the Tier 3 program. We have 

looked at program alternatives that would approximate such an analysis and have 

summarized our findings in Chapter 8 of this draft RIA. There, we look at 

alternatives that consist of a Tier 3 program that lasts forever but also includes a 

locomotive remanufacturing program. We have also looked at a Tier 4 program 

absent any Tier 3 standards but, again, that alternative includes a locomotive 

remanufacturing program and a different Tier 4 start year. Here, we look simply at 

the costs and emissions reductions we have allocated to each of our program elements 

within the context of the entire program. The results are shown in Table 5-64. The 

table shows costs, reductions, and costs per ton in the year 2030 and as net present 

values using a three percent discount rate. The results show that the Tier 3 program is 

the most cost efficient of the program elements, and that all three elements are very 

cost efficient. 

v “Long-term” cost here refers to the ongoing cost of the program where only operating and 

variable costs remain (no more fixed costs). We have chosen 2030 to represent those costs here. 

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Draft Locomotive and Marine RIA 

Table 5-64 Costs, Emissions Reductions, and Cost per Ton Associated with the Proposed Program 

Present Values @ 3% 

Costs ($Millions) Locomotive Marine Loco & Marine Total Locomotive MProgram Element PM NOx PM NOx PM NOx PM NOx PM Reman Program (T0,T1,T2) $401 $401 n/a n/a $401 $401 $802 $11 $11 n/a Tier 3 $6 $11 $45 $83 $51 $94 $145 $0 $0 $0 Tier 4 $1,180 $2,626 $590 $1,890 $1,770 $4,515 $6,285 $102 $259 $46 Total Cost of Proposal $1,587 $3,038 $635 $1,973 $2,222 $5,010 $7,233 $113 $270 $46 

Present Values @ 3% 2

Reductions (Tons) Locomotive Marine Loco & Marine Locomotive MProgram Element PM2.5 NOx PM2.5 NOx PM2.5 NOx PM2.5 NOx PM2.5 Reman Program (T0,T1,T2) 64,020 694,410 n/a n/a 64,020 694,410 3,010 24,440 n/a Tier 3 

62,420 78,260 82,040 2,135,320 144,460 2,213,580 5,860 7,110 7,960 

Tier 4 57,200 3,480,330 49,300 1,489,400 106,500 4,969,730 5,730 369,030 5,170 Total Reductions from Proposal 183,630 4,252,990 131,350 3,624,720 314,980 7,877,710 14,600 400,580 13,120 

Present Values @ 3% 2

Cost Effectiveness ($/ton) Locomotive Marine Loco & Marine Locomotive MProgram Element PM10 NOx PM10 NOx PM10 NOx PM10 NOx PM10 Reman Program (T0,T1,T2) $6,080 $580 n/a n/a $6,080 $580 $3,480 $440 n/a Tier 3 $90 $140 $540 $40 $350 $40 $0 $0 $0 Tier 4 $20,010 $750 $11,600 $1,270 $16,120 $910 $17,340 $700 $8,600 $/ton of Proposal $8,380 $714 $4,690 $540 $6,840 $640 $7,520 $670 $3,390 Note: Table 5-63 shows $/ton NOx; there is a slight difference compared to tables showing $/ton NOx+NMHC. 

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Chapter 5: Engineering Cost Estimates 

References for Chapter 5 

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. A-2001-28, 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.
 

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). 

5-97 
 
21 

Draft Locomotive and Marine RIA 

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.

 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 “Viability of Urea Infrastructure for SCR Systems,” presented by M.D. Jackson, at the U.S. EPA 
 

Clean Diesel Implementation Workshop, August 6, 2003. 
 

5-98 
 
Chapter 6: Cost-Benefit Analysis 

CHAPTER 6: COST-BENEFIT ANALYSIS 

6.1 Overview .................................................................................................................................. 2 

6.2 Air Quality Impacts for Benefits Analysis.......................................................................... 10 

6.2.1 Converting CMAQ Outputs to Full-Season Profiles for Benefits Analysis............. 11 

6.2.2 PM2.5 Air Quality Results ............................................................................................. 11 

6.3 Benefits Analysis – Data and Methods................................................................................ 12 

6.3.1 Valuation Concepts ....................................................................................................... 13 

6.3.2 Growth in WTP Reflecting National Income Growth Over Time ........................... 15 

6.3.3 Demographic Projections ............................................................................................. 18 

6.3.4 Methods for Describing Uncertainty........................................................................... 19 

6.3.4.1 Sources of Uncertainty.......................................................................................... 20 

6.3.4.2 Uncertainties Associated with Health Impact Functions based on Reported 

Effect Estimates from the Epidemiological Literature.................................................. 21 

6.3.5 Health Benefits Assessment Methods.......................................................................... 25 

6.3.5.1 Selection of Health Endpoints .............................................................................. 26 

6.3.5.2 Sources of Information for Effect Estimates ...................................................... 26 

6.3.5.3 Information Used in Quantifying C-R Functions............................................... 28 

6.3.5.4 Treatment of Potential Thresholds in Health Impact Functions...................... 48 

6.3.5.5 Baseline Health Effect Incidence Rates............................................................... 50 

6.3.5.6 Selecting Unit Values for Monetizing Health Endpoints................................... 53 

6.3.6 Human Welfare Impact Assessment ........................................................................... 64 

6.3.6.1 Visibility Benefits................................................................................................... 65 

6.3.6.2 Agricultural, Forestry, and Other Vegetation-Related Benefits....................... 69 

6.3.6.3 Benefits from Reductions in Materials Damage................................................. 70 

6.3.6.4 Benefits from Reduced Ecosystem Damage........................................................ 71 

6.4 Benefits Analysis Results for the Proposed Standards...................................................... 71 

6.5 Comparison of Costs and Benefits....................................................................................... 83 

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Draft Regulatory Impact Analysis 

CHAPTER 6: Cost-Benefit Analysis 

6.1 Overview 

This chapter presents our analysis of the health and environmental benefits that can be 

expected to occur as a result of the proposed locomotive and marine engine standards throughout 

the period from initial implementation through 2030. Nationwide, the engines subject to the 

proposed emission standards in this rule are a significant source of mobile source air pollution. 

The proposed standards would 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 proposed 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. 

All of the benefit estimates for the proposed control options in this analysis are based on 

an analytical structure and sequence similar to that used in the final PM NAAQS analysis.1 The 

benefits analysis relies on three major components: 

• Calculation of the impact of the proposed 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 shorter-term (acute) 

exposures (e.g., respiratory and cardiovascular symptoms resulting in hospital admissions, 

asthma exacerbations, and acute and chronic bronchitis). Welfare effects 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. 

EPA typically quantifies PM- and ozone-related benefits in its regulatory impact analyses 

(RIAs) when possible. In the analysis of past air quality regulations, ozone-related benefits have 

6-2
Chapter 6: Cost-Benefit Analysis 

included morbidity endpoints and welfare effects such as damage to commercial crops. EPA has 

not recently included a separate and additive mortality effect for ozone, independent of the effect 

associated with fine particulate matter. For a number of reasons, including 1) advice from the 

Science Advisory Board (SAB) Health and Ecological Effects Subcommittee (HEES) that EPA 

consider the plausibility and viability of including an estimate of premature mortality associated 

with short-term ozone exposure in its benefits analyses and 2) conclusions regarding the 

scientific support for such relationships in EPA’s 2006 Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (the CD), EPA is in the process of determining how to 

appropriately characterize ozone-related mortality benefits within the context of benefits 

analyses for air quality regulations. As part of this process, we are seeking advice from the 

National Academy of Sciences (NAS) regarding how the ozone-mortality literature should be 

used to quantify the reduction in premature mortality due to diminished exposure to ozone, the 

amount of life expectancy to be added and the monetary value of this increased life expectancy 

in the context of health benefits analyses associated with regulatory assessments. In addition, the 

Agency has sought advice on characterizing and communicating the uncertainty associated with 

each of these aspects in health benefit analyses. 

Since the NAS effort is not expected to conclude until 2008, the agency is currently 

deliberating how best to characterize ozone-related mortality benefits in its rulemaking analyses 

in the interim. For the analysis of the proposed locomotive and marine standards, we do not 

quantify an ozone mortality benefit. So that we do not provide an incomplete picture of all of the 

benefits associated with reductions in emissions of ozone precursors, we have chosen not to 

include an estimate of total ozone benefits in the proposed RIA. By omitting ozone benefits in 

this proposal, we acknowledge that this analysis underestimates the benefits associated with the 

proposed standards. Our analysis, however, indicates that the rule's monetized PM2.5 benefits 

alone substantially exceed our estimate of the costs. 

Table 6.1-1 summarizes the annual monetized health and welfare benefits associated with 

the proposed standards for two years, 2020 and 2030. There are a number of items to note about 

these benefits: 

• 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 emission control program being proposed. The differences reflect 

further refinements of the regulatory program since we performed the air quality modeling 

for this rule. Section 3.6 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 proposed regulatory scenario. 

• Consistent with the approach used in the recent RIA for the PM NAAQS, rather than 

presenting both a “primary” estimate of the benefits and a separate characterization of the 

uncertainty associated with that estimate, the current analysis follows the recommendation of 

the National Research Council’s (NRC) 2002 report “Estimating the Public Health Benefits 

of Proposed Air Pollution Regulations” to begin moving the assessment of uncertainties from 

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Draft Regulatory Impact Analysis 

its ancillary analyses into its main benefits presentation through the conduct of probabilistic 

analyses. 

• Since the publication of CAIR, we have completed a full-scale expert elicitation designed to 

more fully characterize the state of our understanding of the concentration-response function 

for PM-related premature mortality. Consistent with the approach used in the recent RIA for 

the PM NAAQS, the elicitation results form a major component of the current effort to use 

probabilistic assessment techniques to integrate uncertainty into the main benefits analysis. 

• Since the publication of CAIR, a follow-up to the Harvard Six-Cities study on premature 

mortality was published (Laden et al., 2006 based on Dockery et al., 1993),2,3 which both 

confirmed the effect size from the first study and provided additional confirmation that 

reductions in PM2.5 directly result in reductions in the risk of premature death. Consistent 

with the approach used in the recent RIA for the PM NAAQS, we further characterize 

uncertainty by presenting a range of PM-related premature mortality estimates derived from 

both the American Cancer Society (ACS) cohort study (Pope et al.,2002),4

 used as the 

primary estimate of PM-related mortality in previous Regulatory Impact Analyses (RIAs), 

and the Six-Cities study (Laden et al., 2006). 

• Consistent with the approach used in the recent RIA for the PM NAAQS, we have updated 

our projections of mortality incidence rates to be consistent with the U.S. Census population 

projections that form the basis of our future population estimates. Compared to the 

methodology used in the CAIR analysis, this change will result in a reduction in mortality 

impacts in future years, as overall mortality rates are projected to decline for most age 

groups. 

• Consistent with the approach used in the recent RIA for the PM NAAQS, we provide 

additional characterizations of the impacts of assuming alternative thresholds in the 

concentration-response functions derived from the epidemiology literature. Unless 

specifically noted, our base PM-related premature mortality benefits estimates are based on 

an assumed cutpoint in the long-term mortality concentration-response function at 10 μg/m3, 

and an assumed cutpoint in the short-term morbidity concentration-response functions at 10 

μg/m3. We also show the results of a sensitivity analysis for PM-related premature mortality, 

with 4 alternative cutpoints, at 3 μg/m3, 7.5 μg/m3, 12 μg/m3, and 14 μg/m3. 

6-4
Chapter 6: Cost-Benefit Analysis 

Table 6.1-1. Estimated Monetized PM-Related Health Benefits of the Proposed Locomotive and Marine

Engine Standards 

Total Benefitsa,b,c,d (billions 2005$) 

2020 2030 

PM mortality derived from the ACS cohort study;Morbidity functions from epidemiology literature 

Using a 3% discount rate 

 Confidence Intervals (5th - 95th %ile) 

Using a 7% discount rate 

 Confidence Intervals (5th - 95th %ile) 

$4.4+B 

($1.0 - $10) 

$4.0+B 

($1.0 - $9.2) 

$12+B

($2.1 - $27) 

$11+B

($1.8 - $25) 

PM mortality derived from lower bound and upper bound expert-based result;e

 Morbidity functions from 

epidemiology literature 

Using a 3% discount rate 

 Confidence Intervals (5th - 95th %ile) 

Using a 7% discount rate 

 Confidence Intervals (5th - 95th %ile) 

$1.7+B - $12+B 

($0.2 - $8.5) – ($2.0 - $27) 

$1.6+B - $11+B 

($0.2 - $7.8) – ($1.8 - $24) 

$4.6+B - $33+B

($1.0 - $23) – ($5.4 - $72) 

$4.3+B - $30+B

($1.0 - $21) – ($4.9 - $65) a

 Benefits include avoided cases of mortality, chronic illness, and other morbidity health endpoints. b

 PM-related mortality benefits estimated using an assumed PM threshold of 10 μg/m3. There is uncertainty about 

which threshold to use and this may impact the magnitude of the total benefits estimate. For a more detailed 

discussion of this issue, please refer to Section 6.6.1.3 of the RIA. 

c

 For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of additional monetary 

benefits and disbenefits. A detailed listing of unquantified health and welfare effects is provided in Table 6.1-2. d

 Results reflect the use of two different discount rates: 3 and 7 percent, which are recommended by EPA’s 

Guidelines for Preparing Economic Analyses5

 and OMB Circular A-4. Results are rounded to two significant digits 

for ease of presentation and computation. 

e The effect estimates of nine of the twelve experts included in the elicitation panel fall within the empiricallyderived range provided by ACS and Six-Cities studies. One of the experts fall below this range and two 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. Likewise the 5th and 95th percentiles for these highest and lowest judgments of the effect 

estimate do not imply any particular distribution within those bounds. The distribution of benefits estimates 

associated with each of the twelve expert responses can be found in tables 6.4-3 and 6.4-4. 

Table 6.1-2 lists the full complement of human health and welfare effects associated with 

PM, ozone and air toxics, and identifies those effects that are quantified for the primary estimate 

and those that remain unquantified because of current limitations in methods or available data. 

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Draft Regulatory Impact Analysis 

Table 6.1-2. Human Health and Welfare Effects of Pollutants Affected by the Proposed 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 

Cardiovascular emergency room visits 

Acute respiratory symptoms 

Chronic respiratory damage 

Premature aging of the lungs 

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 

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 

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Chapter 6: Cost-Benefit Analysis 

Pollutant/Effect 

Quantified and Monetized in Base 

Estimatesa Unquantified Effects - Changes in: 

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 

Healthg 

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 

Welfareg 

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 proposed 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).6 

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. See Section 6.5.3 for more details. 

f

 In addition to primary economic endpoints, there are a number of biological responses that have been associated 

with ozone health including increased airway responsiveness to stimuli, inflammation in the lung, acute 

inflammation and respiratory cell damage, and increased susceptibility to respiratory infection. The public health 

impact of these biological responses may be partly represented by our quantified endpoints. 

g

 The categorization of unquantified toxic health and welfare effects is not exhaustive. 

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Draft Regulatory Impact Analysis 

The general benefits analysis framework is as follows: 

• Given baseline and post-control emissions inventories for the emission species expected to 

affect ambient air quality, we use sophisticated photochemical air quality models to estimate 

baseline and post-control ambient concentrations of PM and visibility for each year. 

• The estimated changes in ambient concentrations are then combined with monitoring data to 

estimate population-level potential exposures to changes in ambient concentrations for use in 

estimating health effects. Modeled changes in ambient data are also used to estimate changes 

in visibility. 

• Changes in population exposure to ambient air pollution are then input to impact functionsA 

to generate changes in the incidence of health effects, or changes in other exposure metrics 

are input to dose-response functions to generate changes in welfare effects. Because these 

estimates contain uncertainty, we characterize the benefits estimates probabilistically when 

appropriate information is available. 

• The resulting effects changes are then assigned monetary values, taking into account 

adjustments to values for growth in real income out to the year of analysis (values for health 

and welfare effects are in general positively related to real income levels). 

• Finally, values for individual health and welfare effects are summed to obtain an estimate of 

the total monetary value of the benefits resulting from the changes in emissions. 

EPA is currently developing a comprehensive integrated strategy for characterizing the 

impact of uncertainty in key elements of the benefits modeling process (e.g., emissions 

modeling, air quality modeling, health effects incidence estimation, valuation) on the benefits 

estimates. A recently completed component of this effort is an expert elicitation designed to 

characterize more fully our understanding of PM-related mortality resulting from both short-term 

and long-term exposure.B

 We include the results of the formal expert elicitation among the 

sources of information used in developing health impact functions for this benefits analysis. The 

results of the ‘pilot’ for this expert elicitation were presented in RIAs for both the Nonroad 

Diesel and Clean Air Interstate Rules.7,8 The results of these elicitation projects, including peer 

review comments, are available on EPA’s Web site, at http://www.epa.gov/ttn/ecas/. In addition, 

A The term “impact function” as used here refers to the combination of a) an effect estimate obtained from the 

epidemiological literature, b) the baseline incidence estimate for the health effect of interest in the modeled 

population, c) the size of that modeled population, and d) the change in the ambient air pollution metric of interest. 

These elements are combined in the impact function to generate estimates of changes in incidence of the health 

effect. The impact function is distinct from the C-R function, which strictly refers to the estimated equation from 

the epidemiological study relating incidence of the health effect and ambient pollution. We refer to the specific 

value of the relative risk or estimated coefficients in the epidemiological study as the “effect estimate.” In 

referencing the functions used to generate changes in incidence of health effects for this RIA, we use the term 

“impact function” rather than C-R function because “impact function” includes all key input parameters used in the 

incidence calculation. 

B

 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002). 

6-8
Chapter 6: Cost-Benefit Analysis 

similar to our approach in the Nonroad Diesel and CAIR RIAs, we present a distribution of 

benefits estimates based on a more limited set of uncertainties, those characterized by the 

sampling error and variability in the underlying health and economic valuation studies used in 

the benefits modeling framework. We note that incorporating only the uncertainty from random 

sampling error omits important sources of uncertainty (e.g., in the functional form of the model, 

as discussed below). 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 that are summarized later in this chapter. 

The benefits estimates generated for the proposed standards are subject to a number of 

assumptions and uncertainties, which are discussed throughout this document. For example, key 

assumptions underlying the data-derived concentration-response functions for the PM2.5-related 

mortality category include the following: 

1. Inhalation of fine particles is causally associated with premature death at 

concentrations near those experienced by most Americans on a daily basis. Although 

biological mechanisms for this effect have not yet been specifically identified, the 

weight of the available epidemiological, toxicological, and experimental evidence 

supports an assumption of causality. The impacts of including a probabilistic 

representation of causality are explored using the results of the expert elicitation. 

2. All fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality. This is an important assumption, because the 

composition of PM produced via transported precursors emitted from locomotive and 

marine engines may differ significantly from direct PM released from electric 

generating units (EGUs) and other industrial sources.C In accordance with advice 

from the CASAC, EPA has determined that no clear scientific grounds exist for 

supporting differential effects estimates by particle type, based on information in the 

most recent Criteria Document. We provide a decomposition of benefits by PM 

component species to provide additional insights into the makeup of the benefits 

associated with reductions in overall PM2.5 mass (See Tables 5-32 and 5-33). 

3. The C-R function for fine particles is approximately linear within the range of 

ambient concentrations under consideration (above the assumed threshold of 10 

μg/m3

). Thus, we assume that the CR functions are applicable to estimates of health 

benefits associated with reducing fine particles in areas with varied concentrations of 

PM, including both regions that are in attainment with PM2.5 standards and those that 

do not meet the standards. However, we examine the impact of this assumption by 

looking at alternative thresholds in a sensitivity analysis. 

The first and third of these assumptions are directly addressed in the expert elicitation, 

providing probabilistic characterizations of the likelihood of causality and the shape of the 

concentration-response function. The second of these is not directly addressed by the expert 

C

 Even within certain components such as directly emitted PM, there may be significant differences in toxicity of 

component particles such as trace metals and specific carbonaceous species. 

6-9
Draft Regulatory Impact Analysis 

elicitation, and remains a significant source of uncertainty in the state of knowledge about the 

health benefits associated with various emission reduction strategies. 

In addition, a key assumption underlying the entire analysis is that the forecasts for future 

emissions and associated air quality modeling are valid. Because we are projecting emissions 

and air quality out to 2030, there are inherent uncertainties in all of the factors that underlie the 

future state of emissions and air quality levels. While it is important to keep in mind the 

difficulties, assumptions, and inherent uncertainties in the overall enterprise, these analyses are 

based on peer-reviewed scientific literature and up-to-date assessment tools, and we believe the 

results are highly useful in assessing the impacts of this rule. 

In addition to the quantified and monetized benefits summarized above, a number of 

additional categories associated with ozone and PM2.5 and its precursor emissions are not 

currently amenable to quantification or valuation. These include 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. 

Additionally, 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). As a result, 

monetized benefits generated for the primary estimate may underestimate the total benefits 

attributable to attainment of alternative standards. 

Benefits estimated for this analysis were generated using the Environmental Benefits 

Mapping and Analysis Program (BenMAP). BenMAP is a computer program developed by EPA 

that integrates a number of the modeling elements used in previous RIA’s (e.g., interpolation 

functions, population projections, health impact functions, valuation functions, analysis and 

pooling methods) to translate modeled air concentration estimates into health effect incidence 

estimates and monetized benefit estimates. Interested parties may wish to consult the webpage 

http://www.epa.gov/ttn/ecas/benmodels.html for more information. 

This chapter is organized as follows. In Section 6.2, we provide an overview of the air 

quality impacts modeled for the proposed standards that are used as inputs to the benefits 

analysis. In Section 6.3, we document the key methods and inputs used in the benefits analysis. 

In Section 6.4, we report the results of the analysis for human health and welfare effects. In 

Section 6.5, we present a comparison of the costs and benefits associated with the proposed 

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 proposed 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 PM2.5 related to emission reductions estimated to occur due to the 

proposed 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 proposed rule, contains detailed information 

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Chapter 6: Cost-Benefit Analysis 

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 emission control program being proposed. 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 3.6 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 proposed regulatory scenario. 

6.2.1 Converting CMAQ Outputs to Full-Season Profiles for Benefits Analysis 

This analysis extracted hourly, surface-layer PM concentrations for each grid cell from 

the standard CMAQ output files. To estimate PM-related health and welfare effects for the 

contiguous United States, we use 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 36 km grid.D 

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 

proposed emissions controls. Full documentation of the revised SMAT methodology is 

contained in the Air Quality Modeling TSD. 

6.2.2 PM2.5 Air Quality Results 

This section provides a summary of the predicted ambient PM2.5 concentrations from the 

CMAQ model for the 2020 and 2030 base cases and changes associated with the proposed rule. 

Table 6.2-1 provides those 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, 

therefore, better reflects the potential benefits of these predicted changes through exposure 

changes to these populations. 

DThe 36-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

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Draft Regulatory Impact Analysis 

Table 6.2-1. Summary of CMAQ-Derived Population-Weighted PM2.5 Air Quality Metrics for Health

Benefits Endpoints Due to the Locomotive and Marine Engine Proposed Standards

 2020 2030 

Statistica

 Changeb Changeb 

PM2.5 Metrics: National Population-Weighted Average (ug/m3) 

Annual Average Concentration 0.05 0.10 a

 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.

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. 

6.3 Benefits Analysis – Data and Methods 

Given changes in environmental quality (ambient air quality, visibility, nitrogen, and 

sulfate deposition), the next step is to determine the economic value of those changes. We 

follow a “damage-function” approach in calculating total benefits of the modeled changes in 

environmental quality. This approach estimates changes in individual health and welfare 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

to those changes assuming independence of the individual values. Total benefits are calculated 

simply as the sum of the values for all nonoverlapping health and welfare endpoints. This 

imposes no overall preference structure and does not account for potential income or substitution 

effects (i.e., adding a new endpoint will not reduce the value of changes in other endpoints). The 

“damage-function” approach is the standard approach for most benefit-cost analyses of 

environmental quality programs and has been used in several recent published analyses 

(Banzhaf, Burtraw, and Palmer, 2002; Hubbell et al., 2004; Levy et al., 2001; Levy et al., 1999; 

Ostro and Chestnut, 1998). 

To assess economic value in a damage-function framework, the changes in environmental 

quality must be translated into effects on people or on the things that people value. In some 

cases, the changes in environmental quality can be directly valued, as is the case for changes in 

visibility. In other cases, such as for changes in PM, a health and welfare impact analysis must 

first be conducted to convert air quality changes into effects that can be assigned dollar values. 

Inherent in each of these steps is a high degree of uncertainty, due both to the randomness of 

environmental factors such as meteorology, and the difficulty in measuring and predicting model 

inputs such as pollutant emissions. As such, where possible, we incorporate probabilistic 

representations of model inputs and outputs. However, in many cases, probabilistic 

representations are not available. In these cases, we use the best available science and models, 

and characterize uncertainty using sensitivity analyses. 

For the purposes of this RIA, the health impacts analysis is limited to those health effects 

that are directly linked to ambient levels of air pollution and specifically to those linked to PM2.5. 

These impacts may be positive or negative, but in general, for the proposed standards, they are 

expected to be small relative to the direct air pollution-related impacts. 

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The welfare impacts analysis is limited to changes in the environment that have a direct 

impact on human welfare. For this analysis, we are limited by the available data to examine 

impacts of changes in visibility. We also provide qualitative discussions of the impact of 

changes in other environmental and ecological effects, for example, changes in yields for 

commercial forests and noncommercial crops and changes in deposition of nitrogen and sulfur to 

terrestrial and aquatic ecosystems, but we are unable to place an economic value on these 

changes. 

We note at the outset that EPA rarely has the time or resources to perform extensive new 

research to measure either the health outcomes or their values for this analysis. Thus, similar to 

Kunzli et al. (2000) and other recent health impact analyses, our estimates are based on the best 

available methods of benefits transfer. Benefits transfer is the science and art of adapting 

primary research from similar contexts to obtain the most accurate measure of benefits for the 

environmental quality change under analysis. Where appropriate, adjustments are made for the 

level of environmental quality change, the sociodemographic and economic characteristics of the 

affected population, and other factors to improve the accuracy and robustness of benefits 

estimates. 

6.3.1 Valuation Concepts 

In valuing health impacts, we note that reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a fairly small amount for a 

large population. The appropriate economic measure is willingness to payE (WTP) for changes 

in risk prior to the regulation (Freeman, 2003).F

 Adoption of WTP as the measure of value 

implies that the value of environmental quality improvements depends on the individual 

preferences of the affected population and that the existing distribution of income (ability to pay) 

is appropriate. For some health effects, such as hospital admissions, WTP estimates are 

generally not available. In these cases, we use the cost of treating or mitigating the effect as the 

measure of benefits. These cost of illness (COI) estimates generally (although not in every case) 

understate the true value of reductions in risk of a health effect, because they do not include the 

value of avoided pain and suffering from the health effect (Harrington and Portney, 1987; Berger 

et al., 1987). 

E

 For many goods, WTP can be observed by examining actual market transactions. For example, if a gallon of 

bottled drinking water sells for $1, it can be observed that at least some people are willing to pay $1 for such water. 

For goods not exchanged in the market, such as most environmental “goods,” valuation is not as straightforward. 

Nevertheless, a value may be inferred from observed behavior, such as sales and prices of products that result in 

similar effects or risk reductions (e.g., nontoxic cleaners or bike helmets). Alternatively, surveys can be used in an 

attempt to directly elicit WTP for an environmental improvement. 

F

 In general, economists tend to view an individual’s WTP for an improvement in environmental quality as the 

appropriate measure of the value of a risk reduction. An individual’s willingness to accept (WTA) compensation for 

not receiving the improvement is also a valid measure. However, WTP is generally considered to be a more readily 

available and conservative measure of benefits. In some cases, such as the value of fatal risk reductions, we use 

WTA measures due to the difficulty in obtaining WTP estimates. For cases where the changes in the good are small 

WTP and WTA are approximately equal. 

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Draft Regulatory Impact Analysis 

One distinction in environmental benefits estimation is between use values and nonuse 

values. Although no general agreement exists among economists on a precise distinction 

between the two (see Freeman [2003]), the general nature of the difference is clear. Use values 

are those aspects of environmental quality that affect an individual’s welfare directly. These 

effects include changes in product prices, quality, and availability; changes in the quality of 

outdoor recreation and outdoor aesthetics; changes in health or life expectancy; and the costs of 

actions taken to avoid negative effects of environmental quality changes. 

Nonuse values are those for which an individual is willing to pay for reasons that do not 

relate to the direct use or enjoyment of any environmental benefit but might relate to existence 

values and bequest values. Nonuse values are not traded, directly or indirectly, in markets. For 

this reason, measuring nonuse values has proven to be significantly more difficult than 

measuring use values. The air quality changes produced by the proposed standards would cause 

changes in both use and nonuse values, but the monetary benefits estimates are almost 

exclusively for use values. 

More frequently than not, the economic benefits from environmental quality changes are 

not traded in markets, so direct measurement techniques cannot be used. There are three main 

nonmarket valuation methods used to develop values for endpoints considered in this analysis: 

stated preference (including contingent valuation [CV]), indirect market (e.g., hedonic wage), 

and avoided cost methods. 

The stated preference method values endpoints by using carefully structured surveys to 

ask a sample of people what amount of compensation is equivalent to an improvement in 

environmental quality. There is an extensive scientific literature and body of practice on both the 

theory and technique of stated preference-based valuation. Well-designed and well-executed 

stated preference studies are valid for estimating the benefits of air quality regulations.G Stated 

preference valuation studies form the complete or partial basis for valuing a number of health 

and welfare endpoints, including the value of mortality risk reductions, chronic bronchitis (CB) 

risk reductions, minor illness risk reductions, and visibility improvements. 

Indirect market methods can also be used to infer the benefits of pollution reduction. The 

most important application of this technique for our analysis is the calculation of the VSL for use 

in estimating benefits from mortality risk reductions. No market exists where changes in the 

probability of death are directly exchanged. However, people make decisions about occupation, 

precautionary behavior, and other activities associated with changes in the risk of death. By 

examining these risk changes and the other characteristics of people’s choices, it is possible to 

G Concerns about the reliability of value estimates from CV studies arose because research has shown that bias can 

be introduced easily into these studies if they are not carefully conducted. Accurately measuring WTP for avoided 

health and welfare losses depends on the reliability and validity of the data collected. There are several issues to 

consider when evaluating study quality, including but not limited to 1) whether the sample estimates of WTP are 

representative of the population WTP; 2) whether the good to be valued is understood and accepted by the 

respondent; 3) whether the elicitation format is designed to minimize strategic responses; 4) whether WTP is 

sensitive to respondent familiarity with the good, to the size of the change in the good, and to income; 5) whether the 

estimates of WTP are broadly consistent with other estimates of WTP for similar goods; and 6) the extent to which 

WTP responses are consistent with established economic principles. 

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infer information about the monetary values associated with changes in mortality risk (see 

Section 6.3.5). 

Avoided cost methods are ways to estimate the costs of pollution by using the 

expenditures made necessary by pollution damage. For example, if buildings must be cleaned or 

painted more frequently as levels of PM increase, then the appropriately calculated increment of 

these costs is a reasonable lower-bound estimate (under most, although not all, conditions) of 

true economic benefits when PM levels are reduced. Avoided costs methods are also used to 

estimate some of the health-related benefits related to morbidity, such as hospital admissions (see 

Section 6.3.5). In general, avoided cost methods should be used only if there is no information 

available using other valuation methods (OMB Circular A-4 offers some additional caution on 

the use of avoided cost methods). 

6.3.2 Growth in WTP Reflecting National Income Growth Over Time 

Our analysis accounts for expected growth in real income over time. Economic theory 

argues that WTP for most goods (such as environmental protection) will increase if real incomes 

increase. There is substantial empirical evidence that the income elasticityH of WTP for health 

risk reductions is positive, although there is uncertainty about its exact value. Thus, as real 

income increases, the WTP for environmental improvements also increases. Although many 

analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income 

level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that 

income elasticity is substantially less than one and thus relatively inelastic. As real income rises, 

the WTP value also rises but at a slower rate than real income. 

The effects of real income changes on WTP estimates can influence benefits estimates in 

two different ways: through real income growth between the year a WTP study was conducted 

and the year for which benefits are estimated, and through differences in income between study 

populations and the affected populations at a particular time. Empirical evidence of the effect of 

real income on WTP gathered to date is based on studies examining the former. The 

Environmental Economics Advisory Committee (EEAC) of the Science Advisory Board (SAB) 

advised EPA to adjust WTP for increases in real income over time but not to adjust WTP to 

account for cross-sectional income differences “because of the sensitivity of making such 

distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 2000a). 

A recent advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis, has provided conflicting advice. While agreeing with “the 

general principle that the willingness to pay to reduce mortality risks is likely to increase with 

growth in real income (U.S. EPA-SAB, 2004a, p. 52)” and that “The same increase should be 

assumed for the WTP for serious nonfatal health effects (U.S. EPA-SAB, 2004a, p. 52),” they 

note that “given the limitations and uncertainties in the available empirical evidence, the Council 

does not support the use of the proposed adjustments for aggregate income growth as part of the 

primary analysis (U.S. EPA-SAB, 2004a, p. 53).” Until these conflicting advisories have been 

H Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income. 

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Draft Regulatory Impact Analysis 

reconciled, EPA will continue to adjust valuation estimates to reflect income growth using the 

methods described below, while providing sensitivity analyses for alternative income growth 

adjustment factors. 

Based on a review of the available income elasticity literature, we adjusted the valuation 

of human health benefits upward to account for projected growth in real U.S. income. Faced 

with a dearth of estimates of income elasticities derived from time-series studies, we applied 

estimates derived from cross-sectional studies in our analysis. Details of the procedure can be 

found in Kleckner and Neumann (1999). An abbreviated description of the procedure we used to 

account for WTP for real income growth between 1990 and 2020 is presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and 

chronic health effects, and premature mortality. Note that because of the variety of empirical 

sources used in deriving the income elasticities, there may appear to be inconsistencies in the 

magnitudes of the income elasticities relative to the severity of the effects (a priori one might 

expect that more severe outcomes would show less income elasticity of WTP). We have not 

imposed any additional restrictions on the empirical estimates of income elasticity. One 

explanation for the seeming inconsistency is the difference in timing of conditions. WTP for 

minor illnesses is often expressed as a short term payment to avoid a single epidsode. WTP for 

major illnesses and mortality risk reductions are based on longer term measures of payment 

(such as wages or annual income). Economic theory suggests that relationships become more 

elastic as the length of time grows, reflecting the ability to adjust spending over a longer time 

period. Based on this theory, it would be expected that WTP for reducing long term risks would 

be more elastic than WTP for reducing short term risks. We also expect that the WTP for 

improved visibility in Class I areas would increase with growth in real income. The relative 

magnitude of the income elasticity of WTP for visibility compared with those for health effects 

suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic with 

respect to income. The elasticity values used to adjust estimates of benefits in 2020 and 2030 are 

presented in Table 6.3-1. 

Table 6.3-1. Elasticity Values Used to Account for Projected Real Income Growtha 

Benefit Category Central Elasticity Estimate 

Minor Health Effect 0.14 

Severe and Chronic Health Effects 0.45 

Premature Mortality 0.40 

Visibility 0.90 a Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates 

are assigned an adjustment factor of 1.0. 

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 1990 to 2020 and 2030 are needed to adjust benefits to reflect real per capita 

income growth. For consistency with the emissions and benefits modeling, we used national 

population estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates 

(Hollman, Mulder, and Kallan, 2000). These population estimates are based on application of a 

cohort-component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 

2000). For the years between 2000 and 2030, we applied growth rates based on the U.S. Census 

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Chapter 6: Cost-Benefit Analysis 

Bureau projections to the U.S. Census estimate of national population in 2000. We used 

projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010.I 

We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s 

(2000) for the years 2010 to 2024.J

 We were unable to find reliable projections of GDP past 

2024. As such, we assume that per capita GDP remains constant between 2024 and 2030. 

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 6.3-1. Benefits for each of the categories (minor health effects, severe 

and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. Table 6.3-2 lists the estimated 

adjustment factors. Note that, for premature mortality, we applied the income adjustment factor 

to the present discounted value of the stream of avoided mortalities occurring over the lag period. 

Also note that because of a lack of data on the dependence of COI and income, and a lack of data 

on projected growth in average wages, no adjustments are made to benefits based on the COI 

approach or to work loss days and worker productivity. This assumption leads us to underpredict 

benefits in future years because it is likely that increases in real U.S. income would also result in 

increased COI (due, for example, to increases in wages paid to medical workers) and increased 

cost of work loss days and lost worker productivity (reflecting that if worker incomes are higher, 

the losses resulting from reduced worker production would also be higher). 

Table 6.3-2. Adjustment Factors Used to Account for Projected Real Income Growtha 

Benefit Category 2020 2030 

Minor Health Effect 1.066 1.076 

Severe and Chronic Health 

Effects 

1.229 1.266 

Premature Mortality 1.201 1.233 

Visibility 1.517 1.613 a Based on elasticity values reported in Table 6.3-1, U.S. Census population projections, and projections of real 

GDP per capita. 

6.3.3 Demographic Projections 

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use projections based 

on economic forecasting models developed by Woods and Poole, Inc. The Woods and Poole 

(WP) database contains county-level projections of population by age, sex, and race out to 2025. 

Projections in each county are determined simultaneously with every other county in the United 

States to take into account patterns of economic growth and migration. The sum of growth in 

I

 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/ 

tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 2007 

to 2010 are based on average GDP growth rates between 1999 and 2007. 

J

 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections. 

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Draft Regulatory Impact Analysis 

county-level populations is constrained to equal a previously determined national population 

growth, based on Bureau of Census estimates (Hollman, Mulder, and Kallan, 2000). According 

to WP, linking county-level growth projections together and constraining to a national-level total 

growth avoids potential errors introduced by forecasting each county independently. County 

projections are developed in a four-stage process. First, national-level variables such as income, 

employment, and populations are forecasted. Second, employment projections are made for 172 

economic areas defined by the Bureau of Economic Analysis, using an “export-base” approach, 

which relies on linking industrial-sector production of non-locally consumed production items, 

such as outputs from mining, agriculture, and manufacturing with the national economy. The 

export-based approach requires estimation of demand equations or calculation of historical 

growth rates for output and employment by sector. Third, population is projected for each 

economic area based on net migration rates derived from employment opportunities and 

following a cohort-component method based on fertility and mortality in each area. Fourth, 

employment and population projections are repeated for counties, using the economic region 

totals as bounds. The age, sex, and race distributions for each region or county are determined 

by aging the population by single year of age by sex and race for each year through 2030 based 

on historical rates of mortality, fertility, and migration. 

The WP projections of county-level population are based on historical population data 

from 1969 through 1999 and do not include the 2000 Census results. Given the availability of 

detailed 2000 Census data, we constructed adjusted county-level population projections for each 

future year using a two-stage process. First, we constructed ratios of the projected WP 

populations in a future year to the projected WP population in 2000 for each future year by age, 

sex, and race. Second, we multiplied the block-level 2000 Census population data by the 

appropriate age-, sex-, and race-specific WP ratio for the county containing the census block for 

each future year. This results in a set of future population projections that is consistent with the 

most recent detailed Census data. 

As noted above, values for environmental quality improvements are expected to increase 

with growth in real per capita income. Accounting for real income growth over time requires 

projections of both real GDP and total U.S. populations. For consistency with the emissions and 

benefits modeling, we used national population estimates based on the U.S. Census Bureau 

projections. 

6.3.4 Methods for Describing Uncertainty 

The NRC (2002) highlighted the need for EPA to conduct rigorous quantitative analysis 

of uncertainty in its benefits estimates as well as the need for presenting these estimates to 

decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In 

response to these comments, EPA has initiated the development of a comprehensive 

methodology for characterizing the aggregate impact of uncertainty in key modeling elements on 

both health incidence and benefits estimates 

In the current analysis, consistent with the approach used in the RIA for the recent PM 

NAAQS, EPA continues to move forward on one of the key recommendations of the NRC – 

moving the assessment of uncertainties from its ancillary analyses into its main benefits 

presentation through the conduct of probabilistic analyses. In this proposed rule, EPA addressed 

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key sources of uncertainty by Monte Carlo propagation of uncertainty in the concentrationresponse (C-R) functions and economic valuation functions through its base estimates as well as 

by continuing its practice of conducting a series of ancillary sensitivity analyses examining the 

impact of alternate assumptions on the benefits estimates. It should be noted that the Monte 

Carlo-generated distributions of benefits reflect only some of the uncertainties in the input 

parameters. Uncertainties associated with emissions, air quality modeling, populations, and 

baseline health effect incidence rates are not represented in the distributions of benefits of 

attaining alternative standards. Issues such as correlation between input parameters and the 

identification of reasonable upper and lower bounds for input distributions characterizing 

uncertainty in additional model elements will be addressed in future versions of the uncertainty 

framework. 

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, in 

characterizing the uncertainty related to the estimates of total benefits it is particularly important 

to attempt to characterize the uncertainties associated with this endpoint. As such the analysis for 

this rule incorporates the results of our recent expert elicitation to characterize uncertainty in the 

effect estimates used to estimate premature mortality resulting from exposures to PM into the 

main analysis. In collaboration with OMB, EPA completed a pilot expert elicitation in 2004, 

which was used to characterize uncertainty in the PM mortality C-R function in the Nonroad 

Diesel and CAIR RIAs. EPA has recently completed a full-scale expert elicitation that 

incorporated peer-review comments on the pilot application, and that provides a more robust 

characterization of the uncertainty in the premature mortality function. This expert elicitation 

was designed to evaluate uncertainty in the underlying causal relationship, the form of the 

mortality impact function (e.g., threshold versus linear models) and the fit of a specific model to 

the data (e.g., confidence bounds for specific percentiles of the mortality effect estimates). 

Additional issues, such as the ability of long-term cohort studies to capture premature mortality 

resulting from short-term peak PM exposures, were also addressed in the expert elicitation. 

For this proposal, consistent with the approach used in the RIA for the recent PM 

NAAQS, EPA addressed key sources of uncertainty through Monte Carlo propagation of 

uncertainty in the C-R functions and economic valuation functions and through a series of 

sensitivity analyses examining the impact of alternate assumptions on the benefits estimates that 

are generated. It should be noted that the Monte Carlo-generated distributions of benefits reflect 

only some of the uncertainties in the input parameters. Uncertainties associated with emissions, 

air quality modeling, populations, and baseline health effect incidence rates are not represented 

in the distributions of benefits of attaining alternative standards. 

Our distributions of total benefits do not completely represent full uncertainty because of 

the uncertainty in model elements discussed below (see Table 6.3-3). Uncertainty about specific 

aspects of the health and welfare estimation models is discussed in greater detail in the following 

sections. The estimated distributions of total benefits may not completely capture the shape and 

location of the actual distribution of total benefits. 

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Draft Regulatory Impact Analysis 

6.3.4.1 Sources of Uncertainty 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined 

both in this and preceding chapters, many inputs were used to derive the final estimate of 

benefits, including emission inventories, air quality models (with their associated parameters and 

inputs), epidemiological health effect estimates, estimates of values (both from WTP and COI 

studies), population estimates, income estimates, and estimates of the future state of the world 

(i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and, 

depending on its role in the benefits analysis, may have a disproportionately large impact on final 

estimates of total benefits. For example, emissions estimates are used in the first stage of the 

analysis. As such, any uncertainty in emissions estimates will be propagated through the entire 

analysis. When compounded with uncertainty in later stages, small uncertainties in emission 

levels can lead to large impacts on total benefits. 

Some key sources of uncertainty in each stage of the benefits analysis are the following: 

• gaps in scientific data and inquiry; 

• variability in estimated relationships, such as epidemiological effect estimates, introduced 

through differences in study design and statistical modeling; 

• errors in measurement and projection for variables such as population growth rates; 

• errors due to misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; and 

• biases due to omissions or other research limitations. 

Some of the key uncertainties in the benefits analysis are presented in Table 6.3-3. 

More specifically, there are key uncertainties in many aspects of the health impact 

functions used in our analyses. These are discussed in detail in the following section. 

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Chapter 6: Cost-Benefit Analysis 

Table 6.3-3. Primary Sources of Uncertainty in the Benefits Analysis 

1. Uncertainties Associated with Impact Functions 

● The value of the PM effect estimate in each impact function. 

● Application of a single impact function to pollutant changes and populations in all locations. 

● Similarity of future-year impact functions to current impact functions. 

● Correct functional form of each impact function. 

● Extrapolation of effect estimates beyond the range of PM concentrations observed in the source 

epidemiological study. 

● Application of some impact functions only to those subpopulations matching the original study

population.

2. Uncertainties Associated with PM Concentrations 

● Responsiveness of the models to changes in precursor emissions resulting from the control policy. 

● Projections of future levels of precursor emissions, especially organic carbonaceous particle emissions. 

● Model chemistry for the formation of ambient nitrate concentrations. 

● Lack of speciation monitors in some areas requires extrapolation of observed speciation data. 

● CMAQ model performance in the Western U.S., especially California indicates significant 

underprediction of PM2.5. 

3. Uncertainties Associated with PM Mortality Risk 

● Differential toxicity of specific component species within the complex mixture of PM has not been

determined. 

● The extent to which adverse health effects are associated with low-level exposures that occur many times 

in the year versus peak exposures. 

● The extent to which effects reported in the long-term exposure studies are associated with historically 

higher levels of PM rather than the levels occurring during the period of study. 

● Reliability of the limited ambient PM2.5 monitoring data in reflecting actual PM2.5 exposures. 

4. Uncertainties Associated with Possible Lagged Effects 

● The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM 

levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent 

years. 

5. Uncertainties Associated with Baseline Incidence Rates 

● Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may

not accurately represent the actual location-specific rates. 

● Current baseline incidence rates may not approximate well baseline incidence rates in 2020 and 2030. 

● Projected population and demographics may not represent well future-year population and demographics. 

6. Uncertainties Associated with Economic Valuation 

● Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and 

therefore have uncertainty surrounding them. 

● Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because 

of differences in income or other factors. 

7. Uncertainties Associated with Aggregation of Monetized Benefits 

● Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or 

unmonetized benefits are not included. 

6.3.4.2 Uncertainties Associated with Health Impact Functions based on Reported Effect 

Estimates from the Epidemiological Literature 

Within-Study Variation. Within-study variation refers to the precision with which a given 

study estimates the relationship between air quality changes and health effects. Health effects 

studies provide both a “best estimate” of this relationship plus a measure of the statistical 

uncertainty of the relationship. The size of this uncertainty depends on factors such as the 

number of subjects studied and the size of the effect being measured. The results of even the 

most well-designed epidemiological studies are characterized by this type of uncertainty, though 

6-21
Draft Regulatory Impact Analysis 

well-designed studies typically report narrower uncertainty bounds around the best estimate than 

do studies of lesser quality. In selecting health endpoints, we generally focus on endpoints 

where a statistically significant relationship has been observed in at least some studies, although 

we may pool together results from studies with both statistically significant and insignificant 

estimates to avoid selection bias. 

Across-Study Variation. Across-study variation refers to the fact that different published 

studies of the same pollutant/health effect relationship typically do not report identical findings; 

in some instances the differences are substantial. These differences can exist even between 

equally well designed and executed studies and may result in health effect estimates that vary 

considerably. Across-study variation can result from a variety of possible causes. Such 

differences might simply be associated with different measurement techniques. Sources of 

variation can be introduced by the air quality monitoring technique, measurement averaging 

times, health endpoint data sources (differences in the way medical records are kept at different 

institutions or questionnaire wording). One possibility is that estimates of the single true 

relationship between a given pollutant and a health effect differ across studies because of 

differences in study design, random chance, or other factors. For example, a hypothetical study 

conducted in New York and one conducted in Seattle may report different C-R functions for the 

relationship between PM and mortality, in part because of differences between these two study 

populations (e.g., demographics, activity patterns). Alternatively, study results may differ 

because these two studies are in fact estimating different relationships; that is, the same reduction 

in PM in New York and Seattle may result in different reductions in premature mortality. This 

may result differences in the relative sensitivity of these two populations to PM pollution and 

differences in the composition of PM in these two locations, as well as other factors. In either 

case, where we identified multiple studies that are appropriate for estimating a given health 

effect, we generated a pooled estimate of results from each of those studies. 

Application of C-R Relationship Nationwide. Regardless of the use of impact functions based 

on effect estimates from a single epidemiological study or multiple studies, each impact function 

was applied uniformly throughout the United States to generate health benefit estimates. 

However, to the extent that pollutant/health effect relationships are region specific, applying a 

location-specific impact function at all locations in the United States may result in overestimates 

of health effect changes in some locations and underestimates of health effect changes in other 

locations. It is not possible, however, to know the extent or direction of the overall effect on 

health benefit estimates introduced by applying a single impact function to the entire United 

States. This may be a significant uncertainty in the analysis, but the current state of the scientific 

literature does not allow for a region-specific estimation of health benefits for most health 

outcomes.K 

Extrapolation of Impact Functions Across Populations. Epidemiological studies often focus 

on specific age ranges, either due to data availability limitations (e.g., most hospital admission 

data come from Medicare records, which are limited to populations 65 and older), or to simplify 

K Although we are not able to use region-specific effect estimates, we use region-specific baseline incidence rates 

where available. This allows us to take into account regional differences in health status, which can have a 

significant impact on estimated health benefits. 

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Chapter 6: Cost-Benefit Analysis 

data collection (e.g., some asthma symptom studies focus on children at summer camps, which 

usually have a limited age range). We have assumed for the primary analysis that most impact 

functions should be applied only to those populations with ages that strictly match the 

populations in the underlying epidemiological studies. However, in many cases, there is no 

biological reason why the observed health effect would not also occur in other populations 

within a reasonable range of the studied population. For example, Dockery et al. (1996) 

examined acute bronchitis in children aged 8 to 12. There is no biological reason to expect a 

very different response in children aged 6 or 14. By excluding populations outside the range in 

the studies, we may be underestimating the health impact in the overall population. In response 

to recommendations from the SAB-HES, where there appears to be a reasonable physiological 

basis for expanding the age group associated with a specific effect estimate beyond the study 

population to cover the full age group (e.g., expanding from a study population of 7 to 11 year 

olds to the full 6- to 18-year child age group), we have done so and used those expanded 

incidence estimates in the primary analysis. 

Uncertainties in Concentration-Response Functions. The following uncertainties exist in 

almost all concentration-response functions for PM-related health effects. For expository 

purposes, and because of the importance of mortality, we focus the discussion on how these 

uncertainties affect the PM mortality concentration-response functions. 

Causality: Epidemiological studies are not designed to definitively prove causation. For the 

analysis of the proposed standards, we assumed a causal relationship between exposure to 

elevated PM and premature mortality, based on the consistent evidence of a correlation between 

PM and mortality reported in the substantial body of published scientific literature (CASAC, 

2005). As with all health effects included in our analysis, a weight of evidence process is used to 

evaluate endpoints before including them in the analysis. 

Other Pollutants: PM concentrations are correlated with the concentrations of other criteria 

pollutants, such as ozone and CO. To the extent that there is correlation, this analysis may be 

assigning mortality effects to PM exposure that are actually the result of exposure to other 

pollutants. Recent studies suggest that ozone may have mortality effects independent of PM. 

Shape of the C-R Function: The shape of the true PM mortality C-R function is uncertain, but 

this analysis assumes the C-R function has a non-threshold log-linear form throughout the 

relevant range of exposures. If this is not the correct form of the C-R function, or if certain 

scenarios predict concentrations well above the range of values for which the C-R function was 

fitted, avoided mortality may be misestimated. 

In addition, there is ongoing debate as to whether there exists a threshold below which 

there would be no benefit to further reductions in PM2.5. Some researchers have hypothesized 

the presence of a threshold relationship. The nature of the hypothesized relationship is the 

possibility that there exists a PM concentration level below which further reductions no longer 

yield premature mortality reduction benefits. EPA’s most recent PM2.5 Criteria Document 

concludes that “the available evidence does not either support or refute the existence of 

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Draft Regulatory Impact Analysis 

thresholds for the effects of PM on mortality across the range of concentrations in the studies” 

(U.S. EPA, 2004b, p. 9-44). EPA’s Science Advisory Board (SAB) that provides advice on 

benefits analysis methodsL

 has recommended modeling premature mortality associated with PM 

exposure as a non-threshold effect, that is, with harmful effects to exposed populations 

regardless of the absolute level of ambient PM concentrations. 

Regional Differences: As discussed above, significant variability exists in the results of different 

PM/mortality studies. This variability may reflect regionally specific C-R functions resulting 

from regional differences in factors such as the physical and chemical composition of PM. If 

true regional differences exist, applying the PM-mortality C-R function to regions outside the 

study location could result in misestimation of effects in these regions. 

Relative Toxicity of PM Component Species: In this analysis, all fine particles, regardless of 

their chemical composition, are assumed to be equally potent in causing premature mortality. 

This is an important assumption, because there may be significant differences between PM 

produced via transported precursors, direct PM released from automotive engines, and direct PM 

from other industrial sources. The analysis also assumes that all components of fine particles 

have equal toxicity (because the available epidemiological effect estimates are based on total 

PM2.5 mass rather than the mass of individual component species). While it is reasonable to 

expect that the potency of components may vary across the numerous effect categories 

associated with particulate matter, EPA’s interpretation of scientific information considered to 

date is that such information does not yet provide a basis for quantification beyond using fine 

particle mass. However, to provide information that may be useful as additional studies become 

available, we are providing estimates of the proportions of benefits that are attributable to 

specific components of PM2.5, e.g., ammonium sulfate, ammonium nitrate, elemental carbon, 

organic carbon, and crustal material (which includes metals). This apportionment does not make 

any assumptions about the relative toxicity of the different species; rather, it divides total 

benefits based on the contribution of reductions in individual component species to the overall 

reduction in PM2.5 mass. 

Lag Time Between Change in Exposure and Health Impact: There is a time lag between changes 

in PM exposures and the total realization of changes in health effects. Within the context of 

benefits analyses, this term is often referred to as “cessation lag.” For the chronic PM/mortality 

relationship, the length of the cessation lag is unknown. The existence of such a lag is important 

for the valuation of premature mortality incidence because economic theory suggests that 

benefits occurring in the future should be discounted. There is no specific scientific evidence of 

the existence or structure of a health effects cessation lag for reductions in exposures to fine PM. 

L

 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 2004b) is characterized by the following: “For the studies 

of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work on this issue. 

They report that the associations between PM2.5 and both all-cause and cardiopulmonary mortality were near linear 

within the relevant ranges, with no apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, 

Figure 3, and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. Therefore, it 

is reasonable for EPA to assume a no threshold model down to, at least, the low end of the concentrations reported 

in the studies.” 

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Chapter 6: Cost-Benefit Analysis 

Information about latency (the amount of time between exposure and onset of a health effect) 

may inform our understanding of cessation lags. 

Scientific literature on adverse health effects similar to those associated with PM (e.g., 

smoking-related disease) and the difference in the effect size between chronic exposure studies 

and daily mortality studies suggests that all incidences of premature mortality reduction 

associated with a given incremental change in PM exposure probably would not occur in the 

same year as the exposure reduction. The smoking-related literature also implies that lags of up 

to a few years or longer are plausible, although it is worth noting here that in the case of ambient 

air pollution we are predicting the effects of reduced exposure rather than complete cessation. 

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. Diseases with longer 

progressions should be characterized by long-term lag structures, while impacts occurring in 

populations with existing disease may be characterized by short-term lags. 

A key question is the distribution of causes of death within the relatively broad categories 

analyzed in the cohort studies used. While we may be more certain about the appropriate length 

of cessation lag for lung cancer deaths, it is not clear what the appropriate lag structure should be 

for different types of 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 the use of disease-specific lag models, 

and the construction of a segmented lag distribution to combine differential lags across causes of 

death. The SAB-HES recommended that until additional research has been completed, EPA 

should assume a segmented lag structure characterized by 30% of mortality reductions occurring 

in the first year, 50% occurring evenly over years 2 to 5 after the reduction in PM2.5, and 20% 

occurring evenly over the years 6 to 20 after the reduction in PM2.5 (EPA-COUNCIL-LTR-05­

001, 2004). 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. For future 

analyses, the specific distribution of deaths over time will need to be determined through 

research on causes of death and progression of diseases associated with air pollution. It is 

important to keep in mind that changes in the lag assumptions do not change the total number of 

estimated deaths but rather the timing of those deaths. 

Cumulative Effects: We attribute the PM-mortality relationship in the underlying 

epidemiological studies to cumulative exposure to PM. However, the relative roles of PM 

exposure duration and PM exposure level in inducing premature mortality are still uncertain at 

this time. 

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Draft Regulatory Impact Analysis 

6.3.5 Health Benefits Assessment Methods 

The largest monetized benefits of reducing ambient concentrations of PM and ozone are 

attributable to reductions in health risks associated with air pollution. EPA’s Criteria Documents 

for ozone and PM list numerous health effects known to be linked to ambient concentrations of 

these pollutants (EPA, 2006; 2006). As discussed above, quantification of health impacts 

requires several inputs, including epidemiological effect estimates (concentration-response 

functions), baseline incidence and prevalence rates, potentially affected populations, and 

estimates of changes in ambient concentrations of air pollution. Previous sections have 

described the population and air quality inputs. This section describes the effect estimates and 

baseline incidence and prevalence inputs and the methods used to quantify and monetize changes 

in the expected number of incidences of various health effects. These include premature 

mortality, nonfatal heart attacks, chronic bronchitis, acute bronchitis, hospital admissions, 

emergency room visits for asthma, upper and lower respiratory symptoms, asthma exacerbations, 

minor restricted activity days and days of work lost. 

As discussed above, we have chosen to not include estimates of ozone-related health 

effects in this analysis, though the proposed standards are expected to reduce ambient levels of 

ozone. Some health effects are excluded from this analysis for three reasons: the possibility of 

double-counting, uncertainties in applying effect relationships based on clinical studies to the 

affected population, or a lack of an established relationship between the health effect and 

pollutant in the published epidemiological literature. Unquantifed effects are listed in Table 6.1­

2. An improvement in ambient PM2.5 and ozone air quality may reduce the number of incidences 

within each unquantified effect category that the U.S. population would experience. Although 

these health effects are believed to be PM and ozone induced, effect estimates are not available 

for quantifying the benefits associated with reducing these effects. The inability to quantify 

these effects lends a downward bias to the monetized benefits presented in this analysis. 

6.3.5.1 Selection of Health Endpoints 

We base our selection of health endpoints on consistency with EPA Criteria Documents 

and Staff Papers, with input and advice from the EPA Science Advisory Board Health Effects 

Subcommittee, a scientific review panel specifically established to provide advice on the use of 

the scientific literature in developing benefits analyses for air pollution regulations 

(http://www.epa.gov/sab/). In general, we follow a weight of evidence approach, based on the 

biological plausibility of effects, availability of concentration-response functions from wellconducted peer-reviewed epidemiological studies, cohesiveness of results across studies, and a 

focus on endpoints reflecting public health impacts (like hospital admissions) rather than 

physiological responses (such as changes in clinical measures like Forced Expiratory Volume 

(FEV1)). 

6.3.5.2 Sources of Information for Effect Estimates 

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

6-26
Chapter 6: Cost-Benefit Analysis 

the weight of evidence surrounding a particular health impact, however, only epidemiology 

studies provide direct concentration-response relationships which can be used to evaluate 

population-level impacts of reductions in ambient pollution levels. 

However, standard environmental epidemiology studies provide only a limited 

representation of the uncertainty associated with a specific C-R function, measuring only the 

statistical error in the estimates, and usually relating more to the power of the underlying study 

(driven largely by population size and the frequency of the outcome measure). There are many 

other sources of uncertainty in the relationships between ambient pollution and population level 

health outcomes, including many sources of model uncertainty, such as model specification, 

potential confounding between factors that are both correlated with the health outcome and each 

other, and many other factors. As such, in recent years, EPA has begun investigating how expert 

elicitation methods can be used to integrate across various sources of data in developing C-R 

functions for regulatory benefits analyses. 

Expert elicitation is useful in integrating the many sources of information about 

uncertainty in the C-R function, because it allows experts to synthesize these data sources using 

their own mental models, and provide a probabilistic representation of their synthesis of the data 

in the form of a probability distribution of the C-R function. Figure 6.3-1 shows how expert 

elicitation builds on both the direct empirical data on C-R relationships and other less direct 

evidence to develop probabilistic distributions of C-R functions. EPA has used expert elicitation 

to inform the regulatory process in the past (see for example the previous staff paper for the lead 

NAAQS, U.S. EPA, 1990). In the current analysis, we have only used expert elicitation to 

characterize the C-R function for the relationship between fine PM and premature mortality. 

However, similar methods could be used to characterize C-R functions for other health 

outcomes. 

6-27
Epidemiology Toxicology Laboratory/

Clinical

Direct

C-R functions +

Standard Errors

Expert

Elicitation

Probabalistic C-R functions

Sources of Scientific Data

Epidemiology Toxicology Laboratory/

Clinical

Direct

C-R functions +

Standard Errors

Expert

Elicitation

Probabalistic C-R functions

Draft Regulatory Impact Analysis 

Figure 6.3-1. Sources and Integration of Scientific Data in Informing Development of Health Impact

Functions 

Epidemiology Toxicology Laboratory/ 

Clinical 

Direct 

C-R functions + 

Standard Errors 

Expert 

Elicitation 

Probabalistic C-R functions 

Sources of Scientific Data 

6.3.5.3 Information Used in Quantifying C-R Functions 

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between PM and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 6.3-4. These criteria include 

consideration of whether the study was peer-reviewed, the match between the pollutant studied 

and the pollutant of interest, the study design and location, and characteristics of the study 

population, among other considerations. The selection of C-R functions for the benefits analysis 

is guided by the goal of achieving a balance between comprehensiveness and scientific 

defensibility. 

In general, the use of results from more than a single study can provide a more robust 

estimate of the relationship between a pollutant and a given health effect. However, there are 

often differences between studies examining the same endpoint, making it difficult to pool the 

results in a consistent manner. For example, studies may examine different pollutants or 

different age groups. For this reason, we consider very carefully the set of studies available 

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Chapter 6: Cost-Benefit Analysis 

examining each endpoint and select a consistent subset that provides a good balance of 

population coverage and match with the pollutant of interest. In many cases, either because of a 

lack of multiple studies, consistency problems, or clear superiority in the quality or 

comprehensiveness of one study over others, a single published study is selected as the basis of 

the effect estimate. 

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Technical Appendices provides details of the procedures used to 

combine multiple impact functions (Abt Associates, 2005). In general, we used fixed or random 

effects models to pool estimates from different studies of the same endpoint. Fixed effects 

pooling simply weights each study’s estimate by the inverse variance, giving more weight to 

studies with greater statistical power (lower variance). Random effects pooling accounts for both 

within-study variance and between-study variability, due, for example, to differences in 

population susceptibility. We used the fixed effects model as our null hypothesis and then 

determined whether the data suggest that we should reject this null hypothesis, in which case we 

would use the random effects model.M Pooled impact functions are used to estimate hospital 

admissions and asthma exacerbations. For more details on methods used to pool incidence 

estimates, see the BenMAP Technical Appendices (Abt Associates, 2005), which are available 

with the BenMAP software at http://www.epa.gov/ttn/ecas/benmodels.html. 

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available. 

The specific studies from which effect estimates for the primary analysis are drawn are 

included in Table 6.3-5. In all cases where effect estimates are drawn directly from 

epidemiological studies, standard errors are used as a partial representation of the uncertainty in 

the size of the effect estimate. Detailed information about the form and parameters of each 

impact function used in this analysis can be found in the BenMAP users manual.N For those 

functions not included in the BenMAP users manual, we include a technical memo to the docket 

for this rule. Below we provide the basis for selecting these studies. 

M In this analysis, the fixed effects model assumes that there is only one pollutant coefficient for the entire modeled 

area. The random effects model assumes that studies conducted in different locations are estimating different 

parameters; therefore, there may be a number of different underlying pollutant coefficients. N Interested parties may wish to consult the webpage http://www.epa.gov/ttn/ecas/benmodels.html to download the 

BenMAP users manual. The users manual is also included in the docket for this rule. 

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Draft Regulatory Impact Analysis 

Table 6.3-4 Summary of Considerations Used in Selecting C-R Functions 

Consideration Comments 

Peer-Reviewed 

Research 

Peer-reviewed research is preferred to research that has not undergone the peer-review 

process. 

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective 

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. More recent 

studies are also preferred because of possible changes in pollution mixes, medical care, 

and lifestyle over time. However, when there are only a few studies available, studies 

from all years will be included. 

Population 

Attributes 

The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity across age 

or other relevant demographic factors. In the absence of effect estimates specific to age, 

sex, preexisting condition status, or other relevant factors, it may be appropriate to select 

effect estimates that cover the broadest population to match with the desired outcome of 

the analysis, which is total national-level health impacts. When available, multi-city 

studies are preferred to single city studies because they provide a more generalizable 

representation of the C-R function. 

Study Size Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, either through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children). 

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, 

and lifestyle. 

Pollutants Included 

in Model 

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it 

is important to use properly specified impact functions that include both pollutants. 

Using single-pollutant models in cases where both pollutants are expected to affect a 

health outcome can lead to double-counting when pollutants are correlated. 

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10 

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically 

Valuable Health 

Effects 

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. These health effects are not 

quantified in this analysis. 

Nonoverlapping 

Endpoints 

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 

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Chapter 6: Cost-Benefit Analysis 

Table 6.3-5. Endpoints and Studies Used to Calculate Total Monetized Health Benefits 

Endpoint Pollutant Study 

Study 

Population 

Premature Mortality 

Premature mortality — 

cohort study, all-cause 

Premature mortality, total 

exposures 

Premature mortality — 

all-cause 

PM2.5 

PM2.5 

PM2.5 

Pope et al. (2002)9 

Laden et al. (2006)10 

Expert Elicitation (IEc, 2006)11

Woodruff et al. (1997)12

>29 years 

>25 years 

 >24 years 

 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis 

Nonfatal heart attacks 

PM2.5 

PM2.5 

Abbey et al. (1995)13 

Peters et al. (2001)14 

>26 years 

Adults 

Hospital Admissions 

Respiratory 

Cardiovascular 

Asthma-related ER visits 

PM2.5 

PM2.5 

PM2.5 

PM2.5 

PM2.5 

PM2.5 

PM2.5 

Pooled estimate: 

Moolgavkar (2003)—ICD 490-496 (COPD)15 

Ito (2003)—ICD 490-496 (COPD)16 

Moolgavkar (2000)—ICD 490-496 (COPD)17 

Ito (2003)—ICD 480-486 (pneumonia) 

Sheppard (2003)—ICD 493 (asthma)18 

Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic heart 

disease, dysrhythmia, heart failure) 

Moolgavkar (2000)—ICD 390-429 (all cardiovascular) 

Norris et al. (1999)19 

>64 years 

20–64 years 

>64 years 

<65 years 

>64 years 

20–64 years 

0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)20 8–12 years 

Upper respiratory 

symptoms 

Lower respiratory 

symptoms 

Asthma exacerbations 

Work loss days 

MRADs 

PM2.5 

PM2.5 

PM2.5 

PM2.5

PM2.5 

Pope et al. (1991)21 

Schwartz and Neas (2000)22 

Pooled estimate: 

Ostro et al. (2001)23 (cough, wheeze and shortness of 

breath) 

Vedal et al. (1998)24 (cough) 

 Ostro (1987)25 

Ostro and Rothschild (1989)26 

Asthmatics, 9– 

11 years 

7–14 years 

6–18 yearsa 

18–65 years 

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 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. 

PM2.5-Related Adult Premature Mortality – Epidemiological Basis. 

Both long- and short-term exposures to ambient levels of air pollution have been 

associated with increased risk of premature mortality. The size of the mortality risk estimates 

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Draft Regulatory Impact Analysis 

from epidemiological studies, the serious nature of the effect itself, and the high monetary value 

ascribed to prolonging life make mortality risk reduction the most significant health endpoint 

quantified in this analysis. 

Although a number of uncertainties remain to be addressed by continued research (NRC, 

1998), a substantial body of published scientific literature documents the correlation between 

elevated PM concentrations and increased mortality rates (US EPA, 2004). Time-series methods 

have been used to relate short-term (often day-to-day) changes in PM concentrations and 

changes in daily mortality rates up to several days after a period of elevated PM concentrations. 

Cohort methods have been used to examine the potential relationship between community-level 

PM exposures over multiple years (i.e., long-term exposures) and community-level annual 

mortality rates. Researchers have found statistically significant associations between PM and 

premature mortality using both types of studies. In general, the risk estimates based on the 

cohort studies are larger than those derived from time-series studies. Cohort analyses are 

thought to better capture the full public health impact of exposure to air pollution over time, 

because they capture the effects of long-term exposures and possibly some component of shortterm exposures (Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues 

surrounding the estimation of premature mortality. To demonstrate the sensitivity of the benefits 

estimates to the specific sources of information regarding the impact of PM2.5 exposures on the 

risk of premature death, we are providing estimates in our results tables based on studies derived 

from the epidemiological literature and from the recent EPA sponsored expert elicitation. The 

epidemiological studies from which these estimates are drawn are described below. The expert 

elicitation project and the derivation of effect estimates from the expert elicitation results are 

described in the next section. 

Over a dozen studies have found significant associations between various measures of 

long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and 

Seskin (1977). Most of the published studies found positive (but not always statistically 

significant) associations with available PM indices such as total suspended particles (TSP). 

However, exploration of alternative model specifications sometimes raised questions about 

causal relationships (e.g., Lipfert, Morris, and Wyzga [1989]). These early “ecological crosssectional” studies (e.g., Lave and Seskin [1977]; Ozkaynak and Thurston [1987]) were criticized 

for a number of methodological limitations, particularly for inadequate control at the individual 

level for variables that are potentially important in causing mortality, such as wealth, smoking, 

and diet. Over the last 10 years, several studies using “prospective cohort” designs have been 

published that appear to be consistent with the earlier body of literature. These new “prospective 

cohort” studies reflect a significant improvement over the earlier work because they include 

individual-level information with respect to health status and residence. The most extensive 

analyses have been based on data from two prospective cohort groups, often referred to as the 

Harvard “Six-Cities Study” (Dockery et al., 1993; Laden et al, 2006) and the “American Cancer 

Society or ACS study” (Pope et al., 1995; Pope et al, 2002; Pope et al, 2004); these studies have 

found consistent relationships between fine particle indicators and premature mortality across 

multiple locations in the United States. A third major data set comes from the California-based 

7th Day Adventist Study (e.g., Abbey et al., 1999), which reported associations between longterm PM exposure and mortality in men. Results from this cohort, however, have been 

inconsistent, and the air quality results are not geographically representative of most of the 

United States, and the lifestyle of the population is not reflective of much of the U.S. population. 

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Chapter 6: Cost-Benefit Analysis 

Analysis is also available for a cohort of adult male veterans diagnosed with hypertension has 

been examined (Lipfert et al., 2000; Lipfert et al, 2003, 2006). The characteristics of this group 

differ from the cohorts in the Six-Cities, ACS, and 7th Day Adventist studies with respect to 

income, race, health status, and smoking status. Unlike previous long-term analyses, this study 

found some associations between mortality and ozone but found inconsistent results for PM 

indicators. Because of the selective nature of the population in the veteran’s cohort, we have 

chosen not to include any effect estimates from the Lipfert et al. (2000) study in our benefits 

assessment.O 

Given their consistent results and broad geographic coverage, and importance in 

informing the NAAQS development process, the Six-Cities and ACS data have been particularly 

important in benefits analyses. The credibility of these two studies is further enhanced by the 

fact that the initial published studies (Pope et al, 1995 and Dockery et al 1993) were subject to 

extensive reexamination and reanalysis by an independent team of scientific experts 

commissioned by HEI (Krewski et al., 2000). The final results of the reanalysis were then 

independently peer reviewed by a Special Panel of the HEI Health Review Committee. The 

results of these reanalyses confirmed and expanded those of the original investigators. While the 

HEI reexamination lends credibility to the original studies, it also highlights sensitivities 

concerning the relative impact of various pollutants, such as SO2, the potential role of education 

in mediating the association between pollution and mortality, and the influence of spatial 

correlation modeling. 

Further confirmation and extension of the findings of the 1993 Six City Study and the 

1995 ACS study were recently completed using more recent air quality and a longer follow-up 

period for the ACS cohort was recently published (Pope et al, 2002, 2004; Laden et al, 2006). 

The follow up to the Harvard Six City Study both confirmed the effect size from the first 

analysis and provided additional confirmation that reductions in PM2.5 are likely to result in 

reductions in the risk of premature death. This additional evidence stems from the observed 

reductions in PM2.5 in each city during the extended follow-up period. Laden et al. (2006) found 

that mortality rates consistently went down at a rate proportionate to the observed reductions in 

PM2.5. 

The extended analyses of the ACS cohort data (Pope et al., 2002, 2004) provides 

additional refinements to the analysis of PM-related mortality by a) extending the follow-up 

period for the ACS study subjects to 16 years, which triples the size of the mortality data set; b) 

O EPA recognizes that the ACS cohort also is not representative of the demographic mix in the general population. 

The ACS cohort is almost entirely white and has higher income and education levels relative to the general 

population. EPA’s approach to this problem is to match populations based on the potential for demographic 

characteristics to modify the effect of air pollution on mortality risk. Thus, for the various ACS-based models, we 

are careful to apply the effect estimate only to ages matching those in the original studies, because age has a 

potentially large modifying impact on the effect estimate, especially when younger individuals are excluded from 

the study population. For the Lipfert analysis, the applied population should be limited to that matching the sample 

used in the analysis. This sample was all male, veterans, and diagnosed hypertensive. There are also a number of 

differences between the composition of the sample and the general population, including a higher percentage of 

African Americans (35%) and a much higher percentage of smokers (81% former smokers, 57% current smokers) 

than the general population (12% African American, 24% current smokers). 

6-33
Draft Regulatory Impact Analysis 

substantially increasing exposure data, including additional measurement of cohort exposure to 

PM2.5 following implementation of the PM2.5 standard in 1999; c) controlling for a variety of 

personal risk factors including occupational exposure and diet; and d) using advanced statistical 

methods to evaluate specific issues that can adversely affect risk estimates including the 

possibility of spatial autocorrelation of survival times in communities located near each other. 

The NRC (2002) also recommended that EPA review the database of cohort studies and 

consider developing a weighted mean estimate based on selected studies. Because of the 

differences in the study designs and populations considered in the ACS and Harvard Six-cities 

studies, we have elected to not pool the results of the studies, instead presenting a range of 

estimates reflecting the different sources of impact estimates. 

In developing and improving the methods for estimating and valuing the potential 

reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel 

recommended using long-term prospective cohort studies in estimating mortality risk reduction 

(U.S. EPA,1999b). This recommendation has been confirmed by a recent report from the 

National Research Council, which stated that “it is essential to use the cohort studies in benefits 

analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). More 

specifically, the SAB recommended emphasis on the ACS study because it includes a much 

larger sample size and longer exposure interval and covers more locations (e.g., 50 cities 

compared to the Six-Cities Study) than other studies of its kind. Because of the refinements in 

the extended follow-up analysis, the SAB-HES recommends using the Pope et al. (2002) study as 

the basis for the primary mortality estimate for adults and suggests that alternate estimates of 

mortality generated using other cohort and time-series studies could be included as part of the 

sensitivity analysis (U.S. EPA-SAB, 2004b). 

The SAB-HES also recommended using the specific estimated relative risks from the 

Pope et al. (2002) study based on the average exposure to PM2.5, measured by the average of two 

PM2.5 measurements, over the periods 1979–1983 and 1999–2000. In addition to relative risks 

for all-cause mortality, the Pope et al. (2002) study provides relative risks for cardiopulmonary, 

lung cancer, and all-other cause mortality. Because of concerns regarding the statistical 

reliability of the all-other cause mortality relative risk estimates, we calculated mortality impacts 

for the primary analysis based on the all-cause relative risk. Based on our most recently 

available SAB guidance, we provide mortality impacts based on the ACS study as the best 

estimate for comparing across the current and previous RIAs. This provides historical continuity 

with past analyses and serves as one point of reference in interpreting the results of the expert 

elicitation (see discussion below). 

The RIAs for the CAIR and Clean Air Nonroad Diesel rules included an estimate of 

mortality impacts based on application of the C-R function derived from the Harvard Six-cities 

study. In those analyses, the Six-cities estimate was included as a sensitivity analysis in an 

appendix to the RIA. Following the NAS advice to begin moving sensitivity and uncertainty 

analyses into the main body of the RIA, we are including a separate estimate based on the Sixcities study to complement the estimate based on the ACS study. This also reflects the weight 

that was placed on both the ACS and Harvard Six-city studies by experts participating in the 

PM2.5 mortality expert elicitation. 

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Chapter 6: Cost-Benefit Analysis 

As noted above, since the most recent SAB review, an extended follow-up of the Harvard 

Six-cities study has been published (Laden et al., 2006). We use this specific estimate to 

represent the Six-cities study because it reflects the most up-to-date science and because it was 

cited by many of the experts in their elicitation responses. We note that because of the recent 

publication date of the Laden et al (2006) study, it has not undergone the CASAC and SAB-HES 

review received by the Pope et al (2002) and earlier Six-cities publications (see Dockery et al, 

1993). However, it is clear from the expert elicitation that the results published in Laden et al 

(2006) are potentially influential, and in fact, the expert elicitation results encompass within their 

range the estimates from both the Pope et al (2002) and Laden et al (2006) studies. As part of 

the NAAQS review process, EPA conducted a provisional assessment of “new” science 

published since the closing date for the PM Criteria Document. The provisional assessment 

found that “new” studies generally strengthen the evidence that acute and chronic exposures to 

fine particles are associated with health effects. The provisional assessment found that the 

results reported in the studies do not dramatically diverge from previous findings, and, taken in 

context with the findings of the Criteria Document, the new information and findings do not 

materially change any of the broad scientific conclusions regarding the health effects of PM 

exposure made in the Criteria Document. The Laden et al (2006) study was included in this 

provisional assessment and therefore can be considered to be covered under the broad findings of 

the provisional assessment. 

A number of additional analyses have been conducted on the ACS cohort data (Jarrett et 

al., 2005; Krewski et al., 2005; Pope et al., 2004). These studies have continued to find a strong 

significant relationship between PM2.5 and mortality outcomes. Specifically, much of the recent 

research has suggested a stronger relationship between cardiovascular mortality and lung cancer 

mortality with PM2.5, and a less significant relationship between respiratory-related mortality and 

PM2.5. 

EPA’s is committed to seeking the advice of its Science Advisory Board to review 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. 

PM2.5-Related Adult Premature Mortality – Expert Elicitation Study 

Among the recommendations made by the National Research Council (NRC) in its 2002 

review of EPA's method for assessing health benefits of air pollution regulations was a 

recommendation for EPA to consider the use of formally elicited expert judgments as a means of 

characterizing uncertainty in inputs to health benefits analyses. As part of its efforts to improve 

the characterization of uncertainties in its benefits estimates, EPA has conducted a study of the 

concentration-response (C-R) relationship between changes in PM2.5 exposures and mortality 

using formally elicited expert judgments. The goal of the study was to elicit from a sample of 

health experts probabilistic distributions describing uncertainty in estimates of the reduction in 

mortality among the adult U.S. population resulting from reductions in ambient annual average 

6-35
Draft Regulatory Impact Analysis 

PM2.5 levels. These distributions were obtained through a formal interview protocol using 

methods designed to elicit subjective expert judgments. 

In 2003 and 2004, EPA conducted a pilot-scale elicitation study with five experts to 

explore the effectiveness of expert judgment techniques for characterizing uncertainty and to 

explore the use of the expert judgment results in the context of economic benefits analysis 

(Industrial Economics, 2004). EPA previously applied the results of the pilot-scale study as part 

of its uncertainty analysis in the regulatory analysis accompanying the Clean Air Interstate Rule 

(CAIR) (U.S. EPA, 2005). EPA has recently completed a full-scale expert elicitation analysis of 

the PM2.5-mortality relationship that included numerous refinements based on insights from 

conducting the pilot study and on comments from peer reviewers of the pilot (Industrial 

Economics, 2006). This analysis applies the results of the full-scale study. 

The full-scale study involved personal interviews with twelve health experts who have 

conducted research on the relationship between PM2.5 exposures and mortality. These experts 

were selected through a peer-nomination process and included experts in epidemiology, 

toxicology, and medicine. The elicitation interview consisted of a protocol of carefully 

structured questions, both qualitative and quantitative, about the nature of the PM2.5-mortality 

relationship.P The questions requiring qualitative responses probed experts' beliefs concerning 

key evidence and critical sources of uncertainty and enabled them to establish a conceptual basis 

supporting their quantitative judgments. Questions covered topics such as potential biological 

mechanisms linking PM2.5 exposures with mortality; the role of study design in capturing 

PM/mortality effects; key scientific evidence on the magnitude of the PM/mortality relationship; 

sources of potential error or bias in epidemiological results; the likelihood of a causal 

relationship between PM2.5 and mortality, and the shape of the C-R function. The main 

quantitative question in the protocol asked experts to provide the 5th, 25th, 50th, 75th, and 95th 

percentiles of a probabilistic distribution for the percent change in U.S. annual, adult all-cause 

mortality resulting from a 1 μg/m3

 change in annual average PM2.5 exposure, assuming a range of 

baseline PM2.5 levels between 4 and 30 μg/m3. This quantitative question was designed to yield 

results appropriate for application in EPA's quantitative health benefit analyses. 

P In addition to the elicitation interviews, the twelve experts participated in pre- and post-elicitation workshops. The 

pre-elicitation workshop was designed to prepare the experts by familiarizing them with the protocol, providing 

them information about probabilistic judgments, and allowing them to discuss key issues and relevant evidence. At 

this workshop, the experts were also provided with “briefing book” materials, including a CD containing relevant 

studies and background information pages with data on air quality in the US, population demographics, health 

status, summaries of published effect estimates, and data on other factors potentially useful to experts in developing 

their judgments (air conditioning use, housing stock, PM composition, educational attainment). The post-elicitation 

workshop was designed to anonymously share and discuss results of the expert interviews; discuss key areas where 

expert opinion varied; and clarify any questions that may have arisen during the interviews. Experts were given the 

opportunity to revise their judgments in response to discussions at this workshop; however, experts were not 

encouraged to reach a consensus opinion. 

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Chapter 6: Cost-Benefit Analysis 

The results of the full-scale study consist of twelve individual distributions for the 

coefficient or slope of the C-R function relating changes in annual average PM2.5 exposures to 

annual, adult all-cause mortality. The results have not been combined in order to preserve the 

breadth and diversity of opinion on the expert panel. In applying these results in a benefits 

analysis context, EPA incorporates information about each expert's judgments concerning the 

shape of the C-R function (including the potential for a population threshold PM2.5 concentration 

below which there is no effect on mortality), the distribution of the slope of the C-R function, 

and the likelihood that the PM2.5-mortality relationship is or is not causal (unless the expert 

incorporated this last element directly in his slope distribution - see Industrial Economics, 2006). 

Consistent with the approach used in the RIA for the recent PM NAAQS, we constructed 

a corresponding set of 12 health impact functions for premature mortality based on the responses 

of the 12 experts (designated A through L). For those experts providing log-linear non-threshold 

functions, construction of a health impact function was straightforward, and directly matched the 

construction of health impact functions based on the epidemiology literature.Q In these cases, the 

expert’s function can be translated into a health impact function of the form: 

β ⋅ΔPM Δy = y0 ⋅(e −1), 

Where y0 is the baseline incidence, equal to the baseline incidence rate time the potentially 

affected population, β is the effect estimate provided by the expert, and ΔPM is the change in 

PM2.5. 

Some experts specified a piecewise log-linear function, in which case we developed 

health impact functions that incorporate ambient concentration levels. For example, Expert B 

specified a piecewise function with two segments, representing the concentration-response 

function for ambient concentrations between 4 and 10 μg/m3

 and between 10 and 30 μg/m3. In 

this case, the expert’s function can be translated into a health impact function of the form: 

⎧y01 ⎪ ⋅( − )if Q0 eβ1 ⋅ΔPM 1 < 10 

2 

Δy =

⎩

⎨

⎪y02 ⋅(eβ ⋅ΔPM −1)if Q0 ≥ 10 , 

Where Q0 is the baseline concentration of PM2.5, y01 is the baseline incidence for populations 

living in areas with baseline concentrations of PM2.5 less than 10 μg/m3

, y02 is the baseline 

incidence for populations living in areas with baseline concentrations of PM2.5 greater than or 

equal to10 μg/m3

, and β1 and β2 are the effect estimates corresponding to the segments of the 

C-R function relating to ambient concentrations between 4 and 10 μg/m3

 and 10 and 30 μg/m3, 

respectively. 

Q Note that in the expert elicitation protocol, we specified the relevant range of exposure as between 4 and 30 μg/m3

. 

As such, when applying the expert elicitation based functions, benefits are only estimated for starting concentrations 

greater than 4 μg/m3

. 

6-37
Draft Regulatory Impact Analysis 

A third form specified by one expert (Expert K) included both a piecewise log-linear 

function and a probabilistic threshold. Expert K did not provide a full set of information about 

the shape of the distribution of the threshold, providing only the probability that a threshold 

existed between 0 and 5 μg/m3

 (equal to 0.4) and the probability that a threshold existed between 

5 and 10 μg/m3

 (equal to 0.1). The probability that a threshold above 10 existed was set to zero, 

and the probability that there was no threshold was specified as 0.50. We assumed that the 

probability distribution across the range 0 to 5 was uniform, such that the probability of a 

threshold between 0 and 1, 1 and 2, etc. was equal. Likewise, we assumed that the probability 

distribution across the range 5 to 10 was uniform. Expert K also provided a two segment 

piecewise log-linear function, with the segments defined over the ranges 4 to 16 μg/m3

, and 16 to 

30 μg/m3

. Using this information, we translated Expert K’s responses into the following three 

conditional health impact functions: 

(K1) Δy = ⎨

⎪⎧y01 ⋅(eβ1 ⋅ΔPM −1)if Q0 < 16 

⎩ 02 

β 2 ⋅ΔPM 

0 ⎪y ⋅(e −1)if Q ≥ 16 

β1 ⋅ΔPM ⎧y ⋅(e −1)×0.0if 0 ≤ Q < 1 ⎪

y 

01 

⋅(eβ1 ⋅ΔPM −1)×0.2 if 1 ≤ Q 

0 

⎪ < 2 01 0 

⎪ β ⋅ΔPM

y ⋅(e 1 −1)×0.4 if 2 ≤ Q < 3 ⎪ 01 0

(K2) Δy = ⎪

⎨y01 ⋅(eβ1 ⋅ΔPM −1)×0.6 if 3 ≤ Q0 < 4

⎪y ⋅(eβ1 ⋅ΔPM −1)×0.8if 4 ≤ Q < 5 ⎪ 01 0 

β1 ⎪ ⋅ΔPM

y ⋅(e −1) if 5 ≤ Q < 16 ⎪ 01 0 

β 2 ⋅ΔPM ⎪y ⋅(e −1) if Q ≥ 16 ⎩ 02 0 

β1 ⋅ΔPM ⎧y01 ⋅(e −1)×0.0if 0 ≤ Q0 < 6 ⎪ β1 ⋅ΔPM ⎪y01 ⋅(e −1)×0.2 if 6 ≤ Q0 < 7 

⎪y01 ⋅(eβ1 ⋅ΔPM −1)×0.4 if 7 ≤ Q0 < 8 

(K3) Δy = ⎪

⎨

⎪

y01 ⋅(eβ1 ⋅ΔPM −1)×0.6 if 8 ≤ Q0 < 9 

⎪y01 ⋅(eβ1 ⋅ΔPM −1)×0.8if 9 ≤ Q0 < 10 ⎪

⎪ β1 ⋅ΔPM

y01 ⋅(e −1)×1.0if 10 ≤ Q0 < 16 ⎪ β 2 ⋅ΔPM ⎪ 1 ⎩y02 ⋅(e − )if Q0 ≥ 16 

Function K1 is associated with a no threshold segmented log-linear specification with a knot at 

16 μg/m3

. Function K2 represents the segmented log-linear function with a threshold between 0 

and 5 μg/m3

, with the cumulative probability of a threshold at or below the initial concentration 

Q0 increasing as Q0 decreases (this will result in a declining expected value of the impact at 

lower initial concentrations). Likewise, function K3 represented the segmented log-linear 

function with a threshold between 5 and 10 μg/m3

. The results of applying the three conditional 

functions are then combined using Monte Carlo analysis with weights of 0.5, 0.4, and 0.1 

assigned to conditional functions K1, K2, and K3, respectively. 

6-38
Chapter 6: Cost-Benefit Analysis 

In addition to specifying a function form, each expert provided a representation of the 

distribution (or distributions for those who specified piecewise functions) of the effect size (in 

terms of the percent change in premature mortality associated with a one microgram change in 

annual mean PM2.5). Six of the experts simply chose a normal distribution, which is completely 

specified with two parameters, the mean and standard deviation (see Figure 6.3-2 for example). 

In one case, the expert specified a triangular distribution, which is represented by a minimum, 

maximum, and most likely value (see Figure 6.3-3). In another case, the expert specified a 

Weibull distribution, which has three parameters representing scale, location, and shape (see 

Figure 6.3-4). Four of the experts did not choose a parametric distribution, preferring instead to 

provide only effect estimates at particular percentiles of their distributions. In these cases, we 

constructed custom distributions to represent their percentiles. For these custom distributions, 

we assume a continuous and smooth transition of the distribution between the reported 

percentiles (see Figure 6.3-5 for example). 

Figure 6.3-2. Example Normal Distribution for Expert A 

Figure 6.3-3. Example Triangular Distribution for Expert D 

6-39
Draft Regulatory Impact Analysis 

Figure 6.3-4. Example Weibull Distribution for Expert J 

Figure 6.3-5. Example Custom Distribution for Expert B 

In one special case, Expert E provided a normal distribution that implied a negative tail at 

the 2.5th percentile (the lower bound of a typical 95 percent confidence interval), but also 

specified a minimum value at zero. In this case, we treated the distribution as a truncated 

normal. In the case, the mean of the resulting incidence distribution will be shifted upwards 

relative to a full normal, to adjust for the mass of the distribution that would have been below 

zero (see Figure 6.3-6). Note that in the figure, the mean of the normal distribution specified by 

Expert C is 1.2, while the mean of the implied truncated normal will be 1.34. 

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Chapter 6: Cost-Benefit Analysis 

Figure 6.3-6. Truncated Normal Distribution for Expert C 

In some cases, experts included in their reported distributions the likelihood that the 

relationship between PM2.5 and mortality was not causal, e.g., that reducing PM2.5 would not 

actually reduce the risk of premature death. In these cases, the distributions are unconditional, 

and included zero with some probability to reflect views on less than certain causality. In most 

cases, the experts chose to specify a conditional distribution, such that the distribution of the 

effect estimate is conditional on there being a causal relationship. In these cases, the final 

estimated distribution of avoided incidence of premature mortality will be the expected value of 

the unconditional distribution. In practice, we implement this by estimating each expert’s 

conditional distribution and then, using Monte Carlo sampling, construct an unconditional 

distribution using the expert’s reported probability of a causal relationship. To illustrate how 

these various components of an expert’s results are combined to produce an estimate of the 

distribution of reduced mortality associated with a reduction in ambient PM2.5, we provide an 

example calculation for Expert K. This example calculation is graphically displayed in Figure 

6.3-7. In Figure 6.3-7, the initial application of Expert K’s conditional concentration-response 

functions provides 3 distributions associated with reductions in PM2.5 concentrations in the range 

of starting concentrations from 4 to 16 μg/m3

. These distributions are assigned weights based on 

the expert’s judgments about the likelihood of a threshold existing in the ranges 0 to 5, 5 to 10, 

or not at all. These weights are used to develop a new distribution for the change in mortality for 

starting concentrations between 4 and 16. These are then added to the distribution of the change 

in mortality associated with reductions in PM2.5 in the range of starting concentrations from 16 to 

30 μg/m3

. This gives an overall distribution of reductions in mortality for the full range of 

starting concentrations, conditional on the existence of a causal relationship. This conditional 

distribution is then combined with the expert’s judgment about causality (35 percent likelihood 

of a causal relationship), to derive the unconditional distribution of changes in mortality, which, 

as can be seen in the figure, is composed of a mass of probability at zero (reflecting the 

likelihood of no causal relationship), and a probability density function (PDF) over the remaining 

35 percent of probability characterized by the conditional distribution. As expected, the 

unconditional 

6-41
.000 

Draft Regulatory Impact Analysis Figure 6.3-7. Example Calculations Expert K 

Initial Distributions: 

Range 4 to 16, Threshold 0 to 5 Range 4 to 16, Threshold 5 to 10 Range 4 to 16, No Thresh

Mean = 249.07 Mean = 234.45 Mean = 435

-76.40 86.34 249.07 411.81 574.54 -71.91 81.27 234.45 387.63 540.81 -223.54 105.95 435.44 

p=0.1 p=0.5 p=0.4 

Forecast: Threshold Adjusted Distribution for rang Range 16 to 30, No Threshold 

15,000 Trials Frequency Chart 193 Outliers 

Mean = 252.55 

Frequency C

.000 .006 .012 .018 .025 

39.32 347.23 655.15 

15,000 Trials 

Forecast: Total Range I

0 

-182.69 80.86 344.40 607.94 871.49 

.028 .021 .014 .007 

422 316.5 211 

105.5 

+ =

-129.71 61.42 252.55 443.68 634.81 

p

p=0.65 

Final Distribution of Avoided Incidence of Pre

0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 1.20% 1.40% 1.60% 1.80% 2.00% 

0 631251872483103724344955576196807428048669279

Avoided Premature Mor

Relative Frequency 

Relative Frequency at 0 is 65% 

M

Causality Probability 

.650 .488 .325 .163 

Mean = 0.35 

.000 

0.00 0.25 0.50 0.75 1.00 

42
Chapter 6: Cost-Benefit Analysis 

distribution has a mean change in mortality that is 35 percent of the mean of the conditional 

distribution. 

PM2.5-Related Infant Mortality. Recently published studies have strengthened the case for an 

association between PM exposure and respiratory inflammation and infection leading to 

premature mortality in children under 5 years of age. Specifically, the SAB-HES noted the 

release of the WHO Global Burden of Disease Study focusing on ambient air, which cites several 

recently published time-series studies relating daily PM exposure to mortality in children (U.S. 

EPA-SAB, 2004b). The SAB-HES also cites the study by Belanger et al. (2003) as 

corroborating findings linking PM exposure to increased respiratory inflammation and infections 

in children. Recently, a study by Chay and Greenstone (2003) found that reductions in TSP 

caused by the recession of 1981–1982 were related to reductions in infant mortality at the county 

level. With regard to the cohort study conducted by Woodruff et al. (1997), the SAB-HES notes 

several strengths of the study, including the use of a larger cohort drawn from a large number of 

metropolitan areas and efforts to control for a variety of individual risk factors in infants (e.g., 

maternal educational level, maternal ethnicity, parental marital status, and maternal smoking 

status). Based on these findings, the SAB-HES recommends that EPA incorporate infant 

mortality into the primary benefits estimate and that infant mortality be evaluated using an 

impact function developed from the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004b). A 

more recent study by Woodruff et al. (2006) continues to find associations between PM2.5 and 

infant mortality. The study also found the most significant relationships with respiratory-related 

causes of death. We have not yet sought comment from the SAB on this more recent study and 

as such continue to rely on the earlier 1997 analysis. 

Chronic Bronchitis. CB is characterized by mucus in the lungs and a persistent wet cough for 

at least 3 months a year for several years in a row. CB affects an estimated 5% of the U.S. 

population (American Lung Association, 1999). A limited number of studies have estimated the 

impact of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) 

provide evidence that long-term PM exposure gives rise to the development of CB in the United 

States. Because the proposed standards are expected to reduce primarily PM2.5, this analysis uses 

only the Abbey et al. (1995) study, because it is the only study focusing on the relationship 

between PM2.5 and new incidences of CB. 

Nonfatal Myocardial Infarctions (heart attacks). Nonfatal heart attacks have been linked with 

short-term exposures to PM2.5 in the United States (Peters et al., 2001) and other countries 

(Poloniecki et al., 1997). We used a recent study by Peters et al. (2001) as the basis for the 

impact function estimating the relationship between PM2.5 and nonfatal heart attacks. A more 

recent study by Zanobetti and Schwartz (2005) used a similar method to Peters et al. (2001), but 

focused on adults 65 and older, and used PM10 as the PM indicator. They found a significant 

relationship between nonfatal heart attacks and PM10, although the magnitude of the effect was 

much lower than Peters et al. This may reflect the use of PM10, the more limited age range, or 

the less precise diagnosis of heart attack used in defining the outcome measure. Other studies, 

such as Domenici et al. (2006), Samet et al. (2000), and Moolgavkar (2000), show a consistent 

relationship between all cardiovascular hospital admissions, including those for nonfatal heart 

attacks, and PM. Given the lasting impact of a heart attack on long-term health costs and 

earnings, we provide a separate estimate for nonfatal heart attacks. The estimate used in the 

analysis of the proposed standards is based on the single available U.S. PM2.5 effect estimate 

6-43 
Draft Regulatory Impact Analysis 

from Peters et al. (2001). The finding of a specific impact on heart attacks is consistent with 

hospital admission and other studies showing relationships between fine particles and 

cardiovascular effects both within and outside the United States. Several epidemiologic studies 

(Liao et al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate variability 

(an indicator of how much the heart is able to speed up or slow down in response to momentary 

stresses) is negatively related to PM levels. Heart rate variability is a risk factor for heart attacks 

and other coronary heart diseases (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; 

Tsuji et al., 1996). As such, significant impacts of PM on heart rate variability are consistent 

with an increased risk of heart attacks. 

Hospital and Emergency Room Admissions. Because of the availability of detailed hospital 

admission and discharge records, there is an extensive body of literature examining the 

relationship between hospital admissions and air pollution. Because of this, many of the hospital 

admission endpoints use pooled impact functions based on the results of a number of studies. In 

addition, some studies have examined the relationship between air pollution and emergency 

room visits. Since most emergency room visits do not result in an admission to the hospital (the 

majority of people going to the emergency room are treated and return home), we treat hospital 

admissions and emergency room visits separately, taking account of the fraction of emergency 

room visits that are admitted to the hospital. 

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much evidence linking PM with other 

types of hospital admissions. The only type of emergency room visits that have been 

consistently linked to PM in the United States are asthma-related visits. 

To estimate avoided incidences of PM2.5 related cardiovascular hospital admissions in 

populations aged 65 and older, we use effect estimates from studies by Moolgavkar (2003) and 

Ito (2003). However, only Moolgavkar (2000) provided a separate effect estimate for 

populations 20 to 64.R

 Total cardiovascular hospital admissions are thus the sum of the pooled 

estimates from Moolgavkar (2003) and Ito (2003) for populations over 65 and the Moolgavkar 

(2000) based impacts for populations aged 20 to 64. Cardiovascular hospital admissions include 

admissions for myocardial infarctions. To avoid double-counting benefits from reductions in 

myocardial infarctions when applying the impact function for cardiovascular hospital 

admissions, we first adjusted the baseline cardiovascular hospital admissions to remove 

admissions for myocardial infarctions. 

To estimate total avoided incidences of respiratory hospital admissions, we used impact 

functions for several respiratory causes, including chronic obstructive pulmonary disease 

R

 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence 

criteria. However, given that no other estimates are available for this age group, we chose to use the existing study. 

Updates have been provided for the 65 and older population, and showed little difference. Given the very small 

(<5%) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions between the 

original and reanalyzed results, we do not expect the difference in the effect estimates for the 20 to 64 population to 

differ significantly. As such, the choice to use the earlier, uncorrected analysis will likely not introduce much bias. 

6-44
Chapter 6: Cost-Benefit Analysis 

(COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published 

studies show a statistically significant relationship between PM10 and respiratory hospital 

admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and Ito (2003) 

provide effect estimates for COPD in populations over 65, allowing us to pool the impact 

functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for 

populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate 

for populations over 65 and the single study estimate for populations 20 to 64. Only Ito (2003) 

estimated pneumonia and only for the population 65 and older. In addition, Sheppard (2003) 

provided an effect estimate for asthma hospital admissions for populations under age 65. Total 

avoided incidence of PM-related respiratory-related hospital admissions is the sum of COPD, 

pneumonia, and asthma admissions. 

To estimate the effects of PM air pollution reductions on asthma-related ER visits, we use 

the effect estimate from a study of children 18 and under by Norris et al. (1999). As noted 

earlier, there is another study by Schwartz examining a broader age group (less than 65), but the 

Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al. (1999) effect 

estimate because it better matched the pollutant of interest. Because children tend to have higher 

rates of hospitalization for asthma relative to adults under 65, we will likely capture the majority 

of the impact of PM2.5 on asthma emergency room visits in populations under 65, although there 

may still be significant impacts in the adult population under 65. 

Acute Health Events and Work Loss Days. As indicated in Table 6.1-2, in addition to 

mortality, chronic illness, and hospital admissions, a number of acute health effects not requiring 

hospitalization are associated with exposure to ambient levels of PM. The sources for the effect 

estimates used to quantify these effects are described below. 

Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes of 

acute bronchitis annually (American Lung Association, 2002c). Acute bronchitis is 

characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a 

number of days. According to the MedlinePlus medical encyclopedia,S

 with the exception of 

cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute 

bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate 

developed from Dockery et al. (1996). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 

7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000). 

Because asthmatics have greater sensitivity to stimuli (including air pollution), children 

with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or 

stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air 

pollution on upper respiratory symptoms has thus focused on effects in asthmatics. Incidences of 

upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect 

estimate developed from Pope et al. (1991). 

S

 See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 

6-45 
Draft Regulatory Impact Analysis 

Health effects from air pollution can also result in missed days of work (either from 

personal symptoms or from caring for a sick family member). Days of work lost due to PM2.5 

were estimated using an effect estimate developed from Ostro (1987). 

MRADs result when individuals reduce most usual daily activities and replace them with 

less strenuous activities or rest, yet not to the point of missing work or school. For example, a 

mechanic who would usually be doing physical work most of the day will instead spend the day 

at a desk doing paper and phone work because of difficulty breathing or chest pain. The effect of 

PM2.5 and ozone on MRAD was estimated using an effect estimate derived from Ostro and 

Rothschild (1989). 

In analyzing the proposed standards, we have followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the primary estimate. To prevent doublecounting, we focused the estimation on asthma exacerbations occurring in children and excluded 

adults from the calculation.T

 Asthma exacerbations occurring in adults are assumed to be 

captured in the general population endpoints such as work loss days and MRADs. Consequently, 

if we had included an adult-specific asthma exacerbation estimate, we would likely double-count 

incidence for this endpoint. However, because the general population endpoints do not cover 

children (with regard to asthmatic effects), an analysis focused specifically on asthma 

exacerbations for children (6 to 18 years of age) could be conducted without concern for doublecounting. 

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 

2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed 

a group of 138 African-American children in Los Angeles for 13 weeks, recording daily 

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

T

 Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about doublecounting of benefits. Concerns over double-counting stem from the fact that studies of the general population also 

include asthmatics, so estimates based solely on the asthmatic population cannot be directly added to the general 

population numbers without double-counting. In one specific case (upper respiratory symptoms in children), the 

only study available is limited to asthmatic children, so this endpoint can be readily included in the calculation of 

total benefits. However, other endpoints, such as lower respiratory symptoms and MRADs, are estimated for the 

total population that includes asthmatics. Therefore, to simply add predictions of asthma-related symptoms 

generated for the population of asthmatics to these total population-based estimates could result in double-counting, 

especially if they evaluate similar endpoints. The SAB-HES, in commenting on the analytical blueprint for 812, 

acknowledged these challenges in evaluating asthmatic symptoms and appropriately adding them into the primary 

analysis (U.S. EPA-SAB, 2004b). However, despite these challenges, the SAB-HES recommends the addition of 

asthma-related symptoms (i.e., asthma exacerbations) to the primary analysis, provided that the studies use the panel 

study approach and that they have comparable design and baseline frequencies in both asthma prevalence and 

exacerbation rates. Note also, that the SAB-HES, while supporting the incorporation of asthma exacerbation 

estimates, does not believe that the association between ambient air pollution, including ozone and PM, and the new 

onset of asthma is sufficiently strong to support inclusion of this asthma-related endpoint in the primary estimate. 

6-46
Chapter 6: Cost-Benefit Analysis 

endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see 

below). Vedal et al. (1998) followed a group of elementary school children, including 74 

asthmatics, located on the west coast of Vancouver Island for 18 months including measurements 

of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, 

phlegm, wheeze, chest tightness) through the use of daily diaries. Association between PM10 and 

respiratory symptoms for the asthmatic population was only reported for two endpoints: cough 

and PEF. Because it is difficult to translate PEF measures into clearly defined health endpoints 

that can be monetized, we only included the cough-related effect estimate from this study in 

quantifying asthma exacerbations. We employed the following pooling approach in combining 

estimates generated using effect estimates from the two studies to produce a single asthma 

exacerbation incidence estimate. First, we pooled the separate incidence estimates for shortness 

of breath, wheeze, and cough generated using effect estimates from the Ostro et al. study, 

because each of these endpoints is aimed at capturing the same overall endpoint (asthma 

exacerbations) and there could be overlap in their predictions. The pooled estimate from the 

Ostro et al. study is then pooled with the cough-related estimate generated using the Vedal study. 

The rationale for this second pooling step is similar to the first; both studies are attempting to 

quantify the same overall endpoint (asthma exacerbations). 

Additional epidemiological studies are available for characterizing asthma-related health 

endpoints (the full list of epidemiological studies considered for modeling asthma-related 

incidence is presented in Table 6.3-6). However, based on recommendations from the SABHES, we decided not to use these additional studies in generating the primary estimate. In 

particular, the Yu et al. (2000) estimates show a much higher baseline incidence rate than other 

studies, which may lead to an overstatement of the expected impacts in the overall asthmatic 

population. The Whittemore and Korn (1980) study did not use a well-defined endpoint, instead 

focusing on a respondent-defined “asthma attack.” Other studies looked at respiratory symptoms 

in asthmatics but did not focus on specific exacerbations of asthma. 

6.3.5.4 Treatment of Potential Thresholds in Health Impact Functions 

Unless specifically noted, our premature mortality benefits estimates are based on an 

assumed cutpoint in the premature mortality concentration-response function at 10 μg/m3

, and an 

assumed cutpoint of 10 μg/m3 for the concentration-response functions for morbidity associated 

with short term exposure to PM2.5. The 10 μg/m3 threshold reflects comments from CASAC 

(U.S. EPA Science Advisory Board, 2005). To consider the impact of a threshold in the 

response function for the chronic mortality endpoint on the primary benefits estimates, we also 

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 adjusted 

the mortality function slopes accordingly.U This sensitivity analysis allows us to determine the 

change (reduction) in avoided mortality cases and associated monetary benefits associated with 

U Note, that the adjustment to the mortality slopes was only done for the 10 μg/m3

 , 12 μg/m3 , and 14 μg/m3 

cutpoints since the 7.5 μg/m3 and background cutpoints are at or below the lowest measured exposure levels 

reported in the ACS cohort study, for the combined exposure dataset. See Appendix H for a complete discussion of 

the slope adjustment procedure. 

6-47 
Draft Regulatory Impact Analysis 

alternative cutpoints. Five cutpoints (including the base case assumption) were included in this 

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), (d) 7.5 μg/m3 (reflects 

recommendations from SAB-HES to consider estimating mortality benefits down to the lowest 

exposure levels considered in the ACS cohort study used as the basis for modeling chronic 

mortality) and (e) background or 3 μg/m3

 (reflects NRC recommendation to consider effects all 

the way to background). 

Table 6.3-6. Studies Examining Health Impacts in the Asthmatic Population Evaluated for Use in the 

Benefits Analysis 

Endpoint Definition Pollutant Study Study Population 

Asthma Attack Indicators 

Shortness of breath Prevalence of shortness of 

breath; incidence of 

shortness of breath 

PM2.5 Ostro et al. (2001) African-American 

asthmatics, 8–13 

Cough Prevalence of cough; 

incidence of cough 

PM2.5 Ostro et al. (2001) African-American 

asthmatics, 8–13 

Wheeze Prevalence of wheeze; 

incidence of wheeze 

PM2.5 Ostro et al. (2001) African-American 

asthmatics, 8–13 

Asthma 

exacerbation 

>= 1 mild asthma symptom: 

wheeze, cough, chest 

tightness, shortness of 

breath 

PM10, 

PM1.0 

Yu et al. (2000) Asthmatics, 5–13 

Cough Prevalence of cough PM10 Vedal et al. (1998) Asthmatics, 6–13 

Other Symptoms/Illness Endpoints 

Upper respiratory 

symptoms 

>= 1 of the following: 

runny or stuffy nose; wet 

cough; burning, aching, or 

red eyes 

PM10 Pope et al. (1991) Asthmatics, 9–11 

Moderate or worse 

asthma 

Probability of moderate (or 

worse) rating of overall 

asthma status 

PM2.5 Ostro et al. (1991) Asthmatics, all ages 

Acute bronchitis >= 1 episodes of bronchitis 

in the past 12 months 

PM2.5 McConnell et al. 

(1999) 

Asthmatics, 9–15 

Phlegm “Other than with colds, 

does this child usually seem 

congested in the chest or 

bring up phlegm?” 

PM2.5 McConnell et al. 

(1999) 

Asthmatics, 9–15 

Asthma attacks Respondent-defined asthma 

attack 

PM2.5 Whittemore and 

Korn (1980) 

Asthmatics, all ages 

6.3.5.5 Baseline Health Effect Incidence Rates 

The 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 an estimate of the absolute number of avoided cases. 

For example, a typical result might be that a 10 μg/m3

 decrease in daily PM2.5 levels might 

decrease hospital admissions by 3%. To then to convert this relative change into a number of 

cases, the baseline incidence of the health effect is necessary. The baseline incidence rate 

provides an estimate of the incidence rate (number of cases of the health effect per year, usually 

6-48
Chapter 6: Cost-Benefit Analysis 

per 10,000 or 100,000 general population) in the assessment location corresponding 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 (e.g., if the baseline incidence rate is number 

of cases per year per 100,000 population, it must be multiplied by the number of 100,000s in the 

population). 

Some epidemiological studies examine the association between pollution levels and 

adverse health effects in a specific subpopulation, such as asthmatics or diabetics. In these cases, 

it is necessary to develop not only baseline incidence rates, but also prevalence rates for the 

defining condition (e.g., asthma). For both baseline incidence and prevalence data, we use agespecific rates where available. Impact functions are applied to individual age groups and then 

summed over the relevant age range to provide an estimate of total population benefits. 

In most cases, because of a lack of data or methods, we have not attempted to project 

incidence rates to future years, instead assuming that the most recent data on incidence rates is 

the best prediction of future incidence rates. In recent years, better data on trends in incidence 

and prevalence rates for some endpoints, such as asthma, have become available. We are 

working to develop methods to use these data to project future incidence rates. However, for our 

primary benefits analysis, we continue to use current incidence rates. The one exception is in the 

case of premature mortality. In this case, we have projected mortality rates such that future 

mortality rates are consistent with our projections of population growth (Abt Associates, 2005). 

Compared with previous analyses, this will result in a reduction in the mortality related impacts 

of air pollution in future years. 

Table 6.3-7 summarizes the baseline incidence data and sources used in the benefits 

analysis. We use the most geographically disaggregated data available. For premature mortality, 

county-level data are available. For hospital admissions, regional rates are available. However, 

for all other endpoints, a single national incidence rate is used, due to a lack of more spatially 

disaggregated data. In these cases, we used national incidence rates whenever possible, because 

these data are most applicable to a national assessment of benefits. However, for some studies, 

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. 

6-49 
Draft Regulatory Impact Analysis 

Table 6.3-7: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General 

Population 

Endpoint Parameter Rates 

Value Sourcea 

Mortality Daily or annual mortality 

rate 

Age-, cause-, and 

county-specific rate 

CDC Wonder (1996–1998) 

Hospitalizations Daily hospitalization rate Age-, region-, and 

cause-specific rate 

1999 NHDS public use data filesb 

Asthma ER Visits Daily asthma ER visit rate Age- and regionspecific visit rate 2000 NHAMCS public use data 

filesc

; 1999 NHDS public use data 

filesb 

Chronic Bronchitis Annual prevalence rate per 

person 

- Aged 18–44 

- Aged 45–64 

- Aged 65 and older 

0.0367 

0.0505 

0.0587 

1999 NHIS (American Lung 

Association, 2002b, Table 4) 

Annual incidence rate per 

person 

0.00378 Abbey et al. (1993, Table 3) 

Nonfatal 

Myocardial 

Infarction (heart 

attacks) 

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+ 

- Northeast 

- Midwest 

- South 

- West 

0.0000159 

0.0000135 

0.0000111 

0.0000100 

1999 NHDS public use data filesb

; 

adjusted by 0.93 for probability of 

surviving after 28 days (Rosamond 

et al., 1999) 

Asthma 

Exacerbations 

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) 

Ostro et al. (2001) 

Prevalence among asthmatic 

children 

- daily wheeze 

- daily cough 

- daily dyspnea 

0.038 

0.086 

0.045 

Vedal et al. (1998) 

Acute Bronchitis Annual bronchitis incidence 

rate, children 

0.043 American Lung Association (2002c, 

Table 11) 

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

childrend 

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD incidence rate 

per person (18–65) 

- Aged 18–24 

- Aged 25–44 

- Aged 45–64 

0.00540 

0.00678 

0.00492 

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41); U.S. 

Bureau of the Census (2000) 

Minor RestrictedActivity Days Daily MRAD incidence rate 

per person 

0.02137 Ostro and Rothschild (1989, p. 243) 

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. 

6-50 
c

Chapter 6: Cost-Benefit Analysis 

b

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/. 

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/. 

d Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze. 

Baseline age, cause, and county-specific mortality rates were obtained from the U.S. 

Centers for Disease Control and Prevention (CDC) for the years 1996 through 1998. CDC 

maintains an online data repository of health statistics, CDC Wonder, accessible at 

http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death records and 

U.S. Census Bureau postcensal population estimates. Mortality rates were averaged across 3 

years (1996 through 1998) to provide more stable estimates. When estimating rates for age 

groups that differed from the CDC Wonder groupings, we assumed that rates were uniform 

across all ages in the reported age group. For example, to estimate mortality rates for individuals 

ages 30 and up, we scaled the 25- to 34-year-old death count and population by one-half and then 

generated a population-weighted mortality rate using data for the older age groups. 

To estimate age- and county-specific mortality rates in years 2000 through 2020, we 

calculated adjustment factors, based on a series of Census Bureau projected national mortality 

rates, to adjust the CDC Wonder age- and county-specific mortality rates in 1996-1998 to 

corresponding rates for each future year. For the analysis year 2020, these adjustment factors 

ranged across age categories from 0.76 to 0.86 

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 6.3-7 lists the baseline incidence rates and their sources for asthma symptom 

endpoints. Table 6.3-8 lists the prevalence rates used to determine the applicable population for 

asthma symptom endpoints. Note that these reflect current asthma prevalence and assume no 

change in prevalence rates in future years. As noted above, we are investigating methods for 

projecting asthma prevalence rates in future years. However, it should be noted that current 

trends in asthma prevalence do not lead us to expect that asthma prevalence rates will be more 

than 4% overall in 2020, or that large changes will occur in asthma prevalence rates for 

individual age categories (Mansfield et al., 2005). 

Table 6.3-8 Asthma Prevalence Rates Used to Estimate Asthmatic Populations in Impact Functions 

Population Group Asthma Prevalence Rates 

Value Source 

All Ages 0.0386 American Lung Association (2002a, Table 7)—based on 1999 HIS 

< 18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS 

5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS 

18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS 

45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS 

65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS 

Male, 27+ 0.021 2000 HIS public use data filesa 

African American, 5 to 17 0.0726 American Lung Association (2002a, Table 9)—based on 1999 HIS 

African American, <18 0.0735 American Lung Association (2002a, Table 9)—based on 1999 HIS a

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. 

6-51 
Draft Regulatory Impact Analysis 

6.3.5.6 Selecting Unit Values for Monetizing Health Endpoints 

The appropriate economic value for a change in a health effect depends on whether the 

health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has 

occurred). Reductions in ambient concentrations of air pollution generally lower the risk of 

future adverse health affects by a small amount for a large population. The appropriate 

economic measure is therefore ex ante WTP for changes in risk. However, epidemiological 

studies generally provide estimates of the relative risks of a particular health effect avoided due 

to a reduction in air pollution. A convenient way to use this data in a consistent framework is to 

convert probabilities to units of avoided statistical incidences. This measure is calculated by 

dividing individual WTP for a risk reduction by the related observed change in risk. For 

example, suppose a measure 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 mortality amounts to $1 million ($100/0.0001 

change in risk). Using this approach, the size of the affected population is automatically taken 

into account by the number of incidences predicted by epidemiological studies applied to the 

relevant population. The same type of calculation can produce values for statistical incidences of 

other health endpoints. 

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the cost of treating or mitigating the effect as a primary 

estimate. For example, for the valuation of hospital admissions we use the avoided medical costs 

as an estimate of the value of avoiding the health effects causing the admission. These COI 

estimates generally (although not in every case) understate the true value of reductions in risk of 

a health effect. They tend to reflect the direct expenditures related to treatment but not the value 

of avoided pain and suffering from the health effect. Table 6.3-9 summarizes the value estimates 

per health effect that we used in this analysis. Values are presented both for a 1990 base income 

level and adjusted for income growth out to 2020 and 2030. Note that the unit values for 

hospital admissions are the weighted averages of the ICD-9 code-specific values for the group of 

ICD-9 codes included in the hospital admission categories. A discussion of the valuation 

methods for premature mortality and CB is provided here because of the relative importance of 

these effects. Discussions of the methods used to value nonfatal myocardial infarctions (heart 

attacks) and school absence days are provided because these endpoints have only recently been 

added to the analysis and the valuation methods are still under development. In the following 

discussions, unit values are presented at 1990 levels of income for consistency with previous 

analyses. Equivalent future-year values can be obtained from Table 6.3-9. COI estimates are 

converted to constant 1999 dollar equivalents using the medical CPI. 

Valuing Reductions in Premature Mortality Risk. Following the advice of the EEAC of the 

SAB, EPA currently uses the VSL approach when calculating mortality benefits, because we 

believe this calculation provides the most reasonable single estimate of an individual’s 

willingness to trade off money for reductions in mortality risk (EPA, 2000a). The VSL approach 

is a summary measure for the value of small changes in mortality risk experienced by a large 

number of people. The mean value of avoiding one statistical death is assumed to be $5.5 

million in 1999 dollars. This represents a central value consistent with the range of values 

suggested by recent meta-analyses of the wage-risk VSL literature. The distribution of VSL is 

characterized by a confidence interval from $1 to $10 million, based on two meta-analyses of the 

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Chapter 6: Cost-Benefit Analysis 

wage-risk VSL literature. The $1 million lower confidence limit represents the lower end of the 

interquartile range from the Mrozek and Taylor (2002) meta-analysis. The $10 million upper 

confidence limit represents the upper end of the interquartile range from the Viscusi and Aldy 

(2003) meta-analysis. The mean estimate of $5.5 million is consistent with the mean VSL of 

$5.4 million estimated in the Kochi et al. (2006) meta-analysis. Because the majority of the 

studies in these meta-analyses are based on datasets from the early 1990s or previous decades, 

we continue to assume that the VSL estimates provided by those meta-analyses are in 1990 

income equivalents. Future research might provide income-adjusted VSL values for individual 

studies that can be incorporated into the meta-analyses. This would allow for a more reliable 

base-year estimate for use in adjusting VSL for aggregate changes in income over time. 

As indicated in the previous section on quantification of premature mortality benefits, we 

assumed for this analysis that some of the incidences of premature mortality related to PM 

exposures occur in a distributed fashion over the 20 years following exposure. To take this into 

account in the valuation of reductions in premature mortality, we applied an annual 3% discount 

rate to the value of premature mortality occurring in future years.V 

V The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. EPA adopted a 3% discount rate for its base estimate in this case to reflect reliance on a “social 

rate of time preference” discounting concept. We have also calculated benefits and costs using a 7% rate consistent 

with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet regulatory 

requirements. In this case, the benefit and cost estimates were not significantly affected by the choice of discount 

rate. Further discussion of this topic appears in EPA’s Guidelines for Preparing Economic Analyses (EPA, 2000b). 

6-53 
Draft Regulatory Impact Analysis 

Table 6.3-9. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

Derivation of Estimates 

1990 Income 

Level 

2020 Income 

Levelb 

2030 Income 

Levelb 

Premature Mortality (Value of a 

Statistical Life): PM2.5-related 

$5,500,000 $6,600,000 $6,800,000 Point estimate is the mean of a normal distribu

confidence interval between $1 and $10 milliois based on two meta-analyses of the wage-rismillion represents the lower end of the interquMrozek and Taylor (2002)27 meta-analysis and 

represents the upper end of the interquartile raand Aldy (2003)28 meta-analysis. The VSL re

small change in mortality risk aggregated ovepopulation. 

Chronic Bronchitis (CB) $340,000 $420,000 $430,000 Point estimate is the mean of a generated distr

avoid a case of pollution-related CB. WTP to pollution-related CB is derived by adjusting WViscusi et al., [1991]29) to avoid a severe case 

difference in severity and taking into account with 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 lost 

medical costs over a 5-year period following a 

earnings estimates are based on Cropper and K

Direct medical costs are based on simple averRussell et al. (1998)31 and Wittels et al. (1990

Lost earnings: Cropper and Krupnick (1990). Present discou

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 discou2. Russell et al. (1998), 5-year period ($22,33

$21,113 at 7% discount rate)

 (continued) 

54 
Chapter 6: C

Table 6.3-9. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

Derivation of Estimates 

1990 Income 

Level 

2020 Income 

Levelb 

2030 Income 

Levelb 

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 direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000)33 (www.ahrq.gov). 

Pneumonia (ICD codes 480-487) 

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted pneumonia category illnesses) reported in AResearch and Quality (2000) (www.ahrq.gov

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000) (www.ahrq.gov). 

All Cardiovascular (ICD codes 390-429) 

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted cardiovascular category illnesses) reported in Research and Quality (2000) (www.ahrq.gov

Emergency Room Visits for 

Asthma 

$286 $286 $286 Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997)34 and 

(2) $260.67, from Stanford et al. (1999).35

 (continued) 
Draft Regulatory Impact Analysis 

Table 6.3-9. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

Derivation of Estimates 

1990 Income 

Level 

2020 Income 

Levelb 

2030 Income 

Levelb 

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms 

(URS) 

$25 $27 $27 Combinations of the three symptoms for whi

available that closely match those listed by Pdifferent “symptom clusters,” each describin

dollar value was derived for each type of URestimates of WTP (IEc, 1994)36 to avoid eac

and assuming additivity of WTPs. The dollaaverage of the dollar values for the seven dif

Lower Respiratory Symptoms 

(LRS) 

$16 $17 $17 Combinations of the four symptoms for whic

available that closely match those listed by S11 different “symptom clusters,” each descridollar value was derived for each type of LRestimates of WTP (IEc, 1994) to avoid each and assuming additivity of WTPs. The dollaaverage of the dollar values for the 11 differ

Asthma Exacerbations $42 $45 $45 Asthma exacerbations are valued at $42 per 

mean of average WTP estimates for the four “bad asthma day,” described in Rowe and Ch

study surveyed asthmatics to estimate WTP asthma day,” as defined by the subjects. For an asthma attack is assumed to be equivalent asthma is moderate or worse as reported in th(1986) study. 

Acute Bronchitis $360 $380 $390 Assumes a 6-day episode, with daily value e

low and high values for related respiratory syin Neumann et al. (1994).38

 (continued) 

56 
Chapter 6: C

Table 6.3-9. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

Derivation of Estimates 

1990 Income 

Level 

2020 Income 

Levelb 

2030 Income 

Levelb 

Restricted Activity and Work/School Loss Days Work Loss Days (WLDs) Variable 

(national median = ) 

County-specific median annual wages divid

of vacation) and then by 5—to get median dCensus, compiled by Geolytics, Inc. 

Minor Restricted Activity Days 

(MRADs) 

$51 $54 $55 Median WTP estimate to avoid one MRAD 

a Although the unit values presented in this table are in year 2000 dollars, all monetized annual benefit estimates associated with the pro

been inflated to reflect values in year 2005 dollars. We use the Consumer Price Indexes to adjust both WTP- and COI-based benefits e

from 2000 dollars.40 For WTP-based estimates, we use an inflation factor of 1.13 based on the CPI-U for “all items.” For COI-based e

inflation factor of 1.24 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 envi

increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment income growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to Chapter 9 of impact analysis (EPA, 2004). Note that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the s

of the future year of analysis. 
Draft Regulatory Impact Analysis 

The economics literature concerning the appropriate method for valuing reductions in premature 

mortality risk is still developing. The adoption of a value for the projected reduction in the risk 

of premature mortality is the subject of continuing discussion within the economics and public 

policy analysis community. EPA strives to use the best economic science in its analyses. Given 

the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and 

population characteristics, we use a single VSL for all reductions in mortality risk. 

Although there are several differences between the labor market studies EPA uses to 

derive a VSL estimate and the PM air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward adjustments. 

Table 6.3-10 lists some of these differences and the expected effect on the VSL estimate for air 

pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment 

factors, EPA believes it is reasonable to continue to use the $5.5 million value while 

acknowledging the significant limitations and uncertainties in the available literature. 

Table 6.3-10. Expected Impact on Estimated Benefits of Premature Mortality Reductions of Differences 

Between Factors Used in Developing Applied VSL and Theoretically Appropriate VSL 

Attribute Expected Direction of Bias 

Age Uncertain, perhaps overestimate 

Life Expectancy/Health Status Uncertain, perhaps overestimate 

Attitudes Toward Risk Underestimate 

Income Uncertain 

Voluntary vs. Involuntary Uncertain, perhaps underestimate 

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate 

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised EPA to “continue to use a wage-risk-based VSL 

as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of 

these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be 

made is the timing of the risk” (U.S. EPA, 2000a). In developing our primary estimate of the 

benefits of premature mortality reductions, we have followed this advice and discounted over the 

lag period between exposure and premature mortality. 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits associated 

with PM2.5-related premature mortality are the largest category of monetized benefits associated 

with the proposed standards. In addition, in prior analyses, EPA has identified valuation of 

mortality benefits as the largest contributor to the range of uncertainty in monetized benefits (see 

U.S. EPA, 1999).W Because of the uncertainty in estimates of the value of premature mortality 

W This conclusion was based on a assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM 

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Chapter 6: Cost-Benefit Analysis 

avoidance, it is important to adequately characterize and understand the various types of 

economic approaches available for mortality valuation. Such an assessment also requires an 

understanding of how alternative valuation approaches reflect that some individuals may be more 

susceptible to air pollution-induced mortality or reflect differences in the nature of the risk 

presented by air pollution relative to the risks studied in the relevant economics literature. 

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival plus WTP to improve other individuals’ survival rates. 

The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

Each individual’s survival curve, or the probability of surviving beyond a given age, should shift 

as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

such characteristics as age, health state, and the current age to which the individual is likely to 

survive. 

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

study we value avoided premature mortality risk using the VSL approach. 

Other uncertainties specific to premature mortality valuation include the following: 

• Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL saved by air pollution 

reduction. Although there is considerable variation in the analytical designs and data 

used in the existing literature, the majority of the studies involve the value of risks to a 

middle-aged working population. Most of the studies examine differences in wages of 

risky occupations, using a hedonic wage approach. Certain characteristics of both the 

mortality expert elicitation may result in different conclusions about the relative contribution of sources of 

uncertainty. 

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Draft Regulatory Impact Analysis 

population affected and the mortality risk facing that population are believed to affect the 

average WTP to reduce the risk. The appropriateness of a distribution of WTP based on 

the current VSL literature for valuing the mortality-related benefits of reductions in air 

pollution concentrations therefore depends not only on the quality of the studies (i.e., how 

well they measure what they are trying to measure), but also on the extent to which the 

risks being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations. 

• Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the proposed standards rests on the assumption that, within a 

reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For 

example, suppose a study provides a result that the average WTP for a reduction in 

mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting 

from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is 

linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP 

of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in 

the study). Under the assumption of linearity, the estimate of the VSL does not depend 

on the particular amount of risk reduction being valued. This assumption has been shown 

to be reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998). 

• Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air 

pollution-related risks are incurred involuntarily. Some evidence suggests that people 

will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. If 

this is the case, WTP estimates based on wage-risk studies may understate WTP to 

reduce involuntarily incurred air pollution-related mortality risks. 

• Sudden versus protracted death: A final important difference related to the nature of the 

risk may be that some workplace mortality risks tend to involve sudden, catastrophic 

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that 

the mortality risks addressed in this assessment are associated with longer periods of 

illness or greater pain and suffering than are the risks addressed in the valuation 

literature, the WTP measurements employed in the present analysis would reflect a 

downward bias. 

• Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

6-60 
Chapter 6: Cost-Benefit Analysis 

value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed in 

hedonic studies reflects the preferences of the marginal worker (i.e., that worker who 

demands the highest compensation for his risk reduction). This worker must have either 

higher risk, lower risk tolerance, or both. However, the risk estimate used in hedonic 

studies is generally based on average risk, so the VSL may be upwardly biased because 

the wage differential and risk measures do not match. 

• Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age. 

Valuing Reductions in the Risk of Chronic Bronchitis. The best available estimate of WTP to 

avoid a case of CB comes from Viscusi, Magat, and Huber (1991). The Viscusi, Magat, and 

Huber study, however, describes a severe case of CB to the survey respondents. We therefore 

employ an estimate of WTP to avoid a pollution-related case of CB, based on adjusting the 

Viscusi, Magat, and Huber (1991) estimate of the WTP to avoid a severe case. This is done to 

account for the likelihood that an average case of pollution-related CB is not as severe. The 

adjustment is made by applying the elasticity of WTP with respect to severity reported in the 

Krupnick and Cropper (1992) study. Details of this adjustment procedure are provided in the 

Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003). 

We use the mean of a distribution of WTP estimates as the central tendency estimate of 

WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates 

uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, 

Magat, and Huber; the severity level of an average pollution-related case of CB (relative to that 

of the case described by Viscusi, Magat, and Huber); and the elasticity of WTP with respect to 

severity of the illness. Based on assumptions about the distributions of each of these three 

uncertain components, we derive a distribution of WTP to avoid a pollution-related case of CB 

by statistical uncertainty analysis techniques. The expected value (i.e., mean) of this distribution, 

which is about $331,000 (2000$), is taken as the central tendency estimate of WTP to avoid a 

PM-related case of CB. 

Valuing Reductions in Nonfatal Myocardial Infarctions (Heart Attacks). The Agency has 

recently incorporated into its analyses the impact of air pollution on the expected number of 

nonfatal heart attacks, although it has examined the impact of reductions in other related 

cardiovascular endpoints. We were not able to identify a suitable WTP value for reductions in 

the risk of nonfatal heart attacks. Instead, we use a COI unit value with two components: the 

direct medical costs and the opportunity cost (lost earnings) associated with the illness event. 

Because the costs associated with a myocardial infarction extend beyond the initial event itself, 

6-61 
Draft Regulatory Impact Analysis 

we consider costs incurred over several years. Using age-specific annual lost earnings estimated 

by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a present discounted 

value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,774 for 

someone between the ages of 25 and 44, $12,932 for someone between the ages of 45 and 54, 

and $74,746 for someone between the ages of 55 and 65. The corresponding age-specific 

estimates of lost earnings (in 2000$) using a 7% discount rate are $7,855, $11,578, and $66,920, 

respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for 

populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates 

for these age groups. 

We found three possible sources in the literature of estimates of the direct medical costs 

of myocardial infarction: 

• Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,211 (in 1986$) for people who were admitted to the hospital and 

survived hospitalization. (There does not appear to be any discounting used.) Wittels et 

al. was used to value coronary heart disease in the 812 Retrospective Analysis of the 

Clean Air Act. Using the CPI-U for medical care, the Wittels estimate is $109,474 in 

year 2000$. This estimated cost is based on a medical cost model, which incorporated 

therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” 

as consultants). The model used medical data and medical decision algorithms to 

estimate the probabilities of certain events and/or medical procedures being used. The 

authors note that the average length of hospitalization for acute myocardial infarction has 

decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 

1983). Wittels et al. used 10 days as the average in their study. It is unclear how much 

further the length of stay for myocardial infarction may have decreased from 1983 to the 

present. The average length of stay for ICD code 410 (myocardial infarction) in the year2000 Agency for Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days. 

However, this may include patients who died in the hospital (not included among our 

nonfatal myocardial infarction cases), whose length of stay was therefore substantially 

shorter than it would be if they had not died. 

• Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $49,651 in 2000$ 

for myocardial infarction patients, using statistical prediction (regression) models to 

estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were 

included. 

• Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal 

myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. Converting 

to year 2000$, that would be $23,353 for a 5-year period (without discounting) or 

$29,568 for a 10-year period. 

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Chapter 6: Cost-Benefit Analysis 

In summary, the three different studies provided significantly different values (see Table 

6.3-11). 

Table 6.3-11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks 

Study Direct Medical Costs (2000$) Over an x-Year Period, for x = 

Wittels et al. (1990) $109,474a

 5 

Russell et al. (1998) $22,331b

 5 

Eisenstein et al. (2001) $49,651b

 10 

Russell et al. (1998) $27,242b

 10 a Wittels et al. (1990) did not appear to discount costs incurred in future years. b Using a 3% discount rate. Discounted values as reported in the study. 

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from Wittels 

et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

$65,902, and added it to the 5-year opportunity cost estimate. The resulting estimates are given 

in Table 6.3-12. 

Table 6.3-12. Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial Infarction 

Age Group Opportunity Cost Medical Costa

 Total Cost 

0–24 $0 $65,902 $65,902 

25–44 $8,774b

 $65,902 $74,676 

45–54 $12,253b

 $65,902 $78,834 

55–65 $70,619b

 $65,902 $140,649 

> 65 $0 $65,902 $65,902 a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). b From Cropper and Krupnick (1990), using a 3% discount rate. 

6.3.6 Human Welfare Impact Assessment 

Ozone, PM and their precursor emissions have numerous documented effects on 

environmental quality that affect human welfare. These welfare effects include direct damages 

to property, either through impacts on material structures or by soiling of surfaces, direct 

economic damages in the form of lost productivity of crops and trees, indirect damages through 

alteration of ecosystem functions, and indirect economic damages through the loss in value of 

recreational experiences or the existence value of important resources. EPA’s Criteria 

Documents for ozone, PM, NOx, and SO2 list numerous physical and ecological effects known to 

be linked to ambient concentrations of these pollutants (U.S. EPA, 2005; 1993). This section 

describes individual effects and how we quantify and monetize them. These effects include 

changes in nitrogen and sulfate deposition, and visibility. 

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Draft Regulatory Impact Analysis 

6.3.6.1 Visibility Benefits 

Changes in the level of ambient PM2.5 caused by the reduction in emissions associated 

with the proposed standards will change the level of visibility throughout the United States. 

Visibility directly affects people’s enjoyment of a variety of daily activities. Individuals value 

visibility both in the places they live and work, in the places they travel to for recreational 

purposes, and at sites of unique public value, such as the Great Smoky Mountains National Park. 

This section discusses the measurement of the economic benefits of improved visibility. 

It is difficult to quantitatively define a visibility endpoint that can be used for valuation. 

Increases in PM concentrations cause increases in light extinction, a measure of how much the 

components of the atmosphere absorb light. More light absorption means that the clarity of 

visual images and visual range is reduced, ceteris paribus. Light absorption is a variable that can 

be accurately measured. Sisler (1996) created a unitless measure of visibility, the deciview, 

based directly on the degree of measured light absorption. Deciviews are standardized for a 

reference distance in such a way that one deciview corresponds to a change of about 10% in 

available light. Sisler characterized a change in light extinction of one deciview as “a small but 

perceptible scenic change under many circumstances.” Air quality models were used to predict 

the change in visibility, measured in deciviews, of the areas affected by the control options.X 

EPA considers benefits from two categories of visibility changes: residential visibility 

and recreational visibility. In both cases economic benefits are believed to consist of use values 

and nonuse values. Use values include the aesthetic benefits of better visibility, improved road 

and air safety, and enhanced recreation in activities like hunting and birdwatching. Nonuse 

values are based on people’s beliefs that the environment ought to exist free of human-induced 

haze. Nonuse values may be more important for recreational areas, particularly national parks 

and monuments. 

Residential visibility benefits are those that occur from visibility changes in urban, 

suburban, and rural areas and also in recreational areas not listed as federal Class I areas.Y For 

the purposes of this analysis, recreational visibility improvements are defined as those that occur 

specifically in federal Class I areas. A key distinction between recreational and residential 

benefits is that only those people living in residential areas are assumed to receive benefits from 

X A change of less than 10% in the light extinction budget represents a measurable improvement in visibility but 

may not be perceptible to the eye in many cases. Some of the average regional changes in visibility are less than one 

deciview (i.e., less than 10% of the light extinction budget) and thus less than perceptible. However, this does not 

mean that these changes are not real or significant. Our assumption is then that individuals can place values on 

changes in visibility that may not be perceptible. This is quite plausible if individuals are aware that many 

regulations lead to small improvements in visibility that, when considered together, amount to perceptible changes 

in visibility. 

Y The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection. 

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Chapter 6: Cost-Benefit Analysis 

residential visibility, while all households in the United States are assumed to derive some 

benefit from improvements in Class I areas. Values are assumed to be higher if the Class I area 

is located close to their home.Z 

Only two existing studies provide defensible monetary estimates of the value of visibility 

changes. One is a study on residential visibility conducted in 1990 (McClelland et al., 1993) and 

the other is a 1988 survey on recreational visibility value (Chestnut and Rowe, 1990a; 1990b). 

Although there are a number of other studies in the literature, they were conducted in the early 

1980s and did not use methods that are considered defensible by current standards. Both the 

Chestnut and Rowe and McClelland et al. studies use the CV method. There has been a great 

deal of controversy and significant development of both theoretical and empirical knowledge 

about how to conduct CV surveys in the past decade. In EPA’s judgment, the Chestnut and 

Rowe study contains many of the elements of a valid CV study and is sufficiently reliable to 

serve as the basis for monetary estimates of the benefits of visibility changes in recreational 

areas.AA This study serves as an essential input to our estimates of the benefits of recreational 

visibility improvements in the primary benefits estimates. Consistent with SAB advice, EPA has 

designated the McClelland et al. study as significantly less reliable for regulatory benefit-cost 

analysis, although it does provide useful estimates on the order of magnitude of residential 

visibility benefits (U.S. EPA-SAB, 1999). Residential visibility benefits are not calculated for 

this analysis. 

The Chestnut and Rowe study measured the demand for visibility in Class I areas 

managed by the National Park Service (NPS) in three broad regions of the country: California, 

the Southwest, and the Southeast. Respondents in five states were asked about their WTP to 

protect national parks or NPS-managed wilderness areas within a particular region. The survey 

used photographs reflecting different visibility levels in the specified recreational areas. The 

visibility levels in these photographs were later converted to deciviews for the current analysis. 

The survey data collected were used to estimate a WTP equation for improved visibility. In 

addition to the visibility change variable, the estimating equation also included household 

income as an explanatory variable. 

The Chestnut and Rowe study did not measure values for visibility improvement in Class 

I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the United 

States. We can infer the value of visibility changes in the other Class I areas by transferring 

Z

 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the Nonroad 

Diesel rulemaking (Abt Associates, 2003). 

AA An SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe study is 

the best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did not formally 

approve use of these estimates because of concerns about the peer-reviewed status of the study. EPA believes the 

study has received adequate review and has been cited in numerous peer-reviewed publications (Chestnut and 

Dennis, 1997). 

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Draft Regulatory Impact Analysis 

values of visibility changes at Class I areas in the study regions. A complete description of the 

benefits transfer method used to infer values for visibility changes in Class I areas outside the 

study regions is provided in the Benefits TSD for the Nonroad Diesel rulemaking (Abt 

Associates, 2003). 

The Chestnut and Rowe study, although representing the best available estimates, has a 

number of limitations. These include the following: 

• The age of the study (late 1980s) will increase the uncertainty about the correspondence 

of the estimated values to those that might be provided by current or future populations. 

• The survey focused only on populations in five states, so the application of the estimated 

values to populations outside those states requires that preferences of populations in the 

five surveyed states be similar to those of nonsurveyed states. 

• There is an inherent difficulty in separating values expressed for visibility improvements 

from an overall value for improved air quality. The Chestnut and Rowe study attempted 

to control for this by informing respondents that “other households are being asked about 

visibility, human health, and vegetation protections in urban areas and at national parks in 

other regions.” However, most of the respondents did not feel that they were able to 

segregate visibility at national parks entirely from residential visibility and health effects. 

• It is not clear exactly what visibility improvements the respondents to the Chestnut and 

Rowe survey were valuing. For the purpose of the benefits analysis for this rule, EPA 

assumed that respondents provided values for changes in annual average visibility. 

Because most policies will result in a shift in the distribution of visibility (usually 

affecting the worst days more than the best days), the annual average may not be the most 

relevant metric for policy analysis. 

• The WTP question asked about changes in average visibility. However, the survey 

respondents were shown photographs of only summertime conditions, when visibility is 

generally at its worst. It is possible that the respondents believed those visibility 

conditions held year-round, in which case they would have been valuing much larger 

overall improvements in visibility than what otherwise would be the case. 

• The survey did not include reminders of possible substitutes (e.g., visibility at other 

parks) or budget constraints. These reminders are considered to be best practice for 

stated preference surveys. 

• The Chestnut and Rowe survey focused on visibility improvements in and around 

national parks and wilderness areas. The survey also focused on visibility improvements 

of national parks in the southwest United States. Given that national parks and 

wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate 

obtained from Chestnut and Rowe can be transferred to other national parks and 

wilderness areas, without introducing additional uncertainty. 

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Chapter 6: Cost-Benefit Analysis 

In general, the survey design and implementation reflect the period in which the survey 

was conducted. Since that time, many improvements to the stated preference methodology have 

been developed. As future survey efforts are completed, EPA will incorporate values for 

visibility improvements reflecting the improved survey designs. 

The estimated relationship from the Chestnut and Rowe study is only directly applicable 

to the populations represented by survey respondents. EPA used benefits transfer methodology 

to extrapolate these results to the population affected by the reductions in precursor emissions 

associated with the proposed standards. A general WTP equation for improved visibility 

(measured in deciviews) was developed as a function of the baseline level of visibility, the 

magnitude of the visibility improvement, and household income. The behavioral parameters of 

this equation were taken from analysis of the Chestnut and Rowe data. These parameters were 

used to calibrate WTP for the visibility changes resulting from the proposed standards. The 

method for developing calibrated WTP functions is based on the approach developed by Smith et 

al. (2002). Available evidence indicates that households are willing to pay more for a given 

visibility improvement as their income increases (Chestnut, 1997). The benefits estimates here 

incorporate Chestnut’s estimate that a 1% increase in income is associated with a 0.9% increase 

in WTP for a given change in visibility. 

Using the methodology outlined above, EPA estimates that the total WTP for the 

visibility improvements in California, Southwestern, and Southeastern Class I areas associated 

with the proposed standards would be $150 million in 2020 and $400 million in 2030. These 

values includes the value to households living in the same states as the Class I areas as well as 

values for all households in the United States living outside the states containing the Class I 

areas, and the value accounts for growth in real income. 

One major source of uncertainty for the visibility benefits estimate is the benefits transfer 

process used. Judgments used to choose the functional form and key parameters of the 

estimating equation for WTP for the affected population could have significant effects on the 

size of the estimates. Assumptions about how individuals respond to changes in visibility that 

are either very small or outside the range covered in the Chestnut and Rowe study could also 

affect the results. 

6.3.6.2 Agricultural, Forestry, and Other Vegetation-Related Benefits 

The Ozone Criteria Document notes that “ozone affects vegetation throughout the United 

States, impairing crops, native vegetation, and ecosystems more than any other air pollutant” 

(EPA, 2006).41 Changes in ground-level ozone are expected to improve crop and forest yields 

throughout the country as a result of the proposed standards. 

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 agricultural products’ supply and demand. The resulting welfare 

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Draft Regulatory Impact Analysis 

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 forestry benefits estimates for the proposed 

standards. 

Laboratory and field experiments have 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 typical of those 

found in the United States.”42 In addition, economic studies have shown a relationship between 

observed ozone levels and crop yields.43 

Ozone also has been shown conclusively to cause discernible injury to forest trees (EPA, 

1996; Fox and Mickler, 1996).68,44 In our previous analysis of the HD Engine/Diesel Fuel rule, 

we were able to quantify the effects of changes in ozone concentrations on tree growth for a 

limited set of species. Because of resource limitations, we were not able to quantify such 

impacts for this analysis.

 NOx emission reductions will 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 oversaturation due to an abundance of on-farm nitrogen production, primarily from 

animal manure. In these areas, reductions in atmospheric deposition of nitrogen 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 other nutrients, 

confound assessments of marginal changes in any one stressor or nutrient in forest ecosystems. 

However, reductions in the deposition of nitrogen could have negative effects on forest and 

vegetation growth in ecosystems where nitrogen is a limiting factor (EPA, 1993). 

On the other hand, there is evidence that forest ecosystems in some areas of the United 

States are nitrogen saturated (EPA, 1993). Once saturation is reached, adverse effects of 

additional nitrogen begin to occur such as soil acidification, which can lead to leaching of 

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Chapter 6: Cost-Benefit Analysis 

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. 

6.3.6.3 Benefits from Reductions in Materials Damage 

The proposed standards are expected to produce economic benefits in the form of 

reduced materials damage. There are two important categories of these benefits. Household 

soiling refers to the accumulation of dirt, dust, and ash on exposed surfaces. Particulate matter 

also has corrosive effects on commercial/industrial buildings and structures of cultural and 

historical significance. The effects on historic buildings and outdoor works of art are of 

particular concern because of the uniqueness and irreplaceability of many of these objects. 

Previous EPA benefits analyses have been able to provide quantitative estimates of 

household soiling damage. Consistent with SAB advice, we determined that the existing data 

(based on consumer expenditures from the early 1970s) are too out of date to provide a reliable 

estimate of current household soiling damages (U.S. EPA, 1998). 

EPA is unable to estimate any benefits to commercial and industrial entities from reduced 

materials damage. Nor is EPA able to estimate the benefits of reductions in PM-related damage 

to historic buildings and outdoor works of art. Existing studies of damage to this latter category 

in Sweden (Grosclaude and Soguel, 1994) indicate that these benefits could be an order of 

magnitude larger than household soiling benefits. 

6.3.6.4 Benefits from Reduced Ecosystem Damage 

The effects of air pollution on the health and stability of ecosystems are potentially very 

important but are at present poorly understood and difficult to measure. 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 light-filtering effect of thick algal mats, and 

fundamental shifts in phytoplankton community structure (Bricker et al., 1999). 

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. 

If better models of ecological effects can be defined, EPA believes that progress can be 

made in estimating WTP measures for ecosystem functions. These estimates would be superior 

to avoided cost estimates in placing economic values on the welfare changes associated with air 

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Draft Regulatory Impact Analysis 

pollution damage to ecosystem health. For example, if nitrogen or sulfate loadings can be linked 

to measurable and definable changes in fish populations or definable indexes of biodiversity, 

then stated preference studies can be designed to elicit individuals’ WTP for changes in these 

effects. This is an important area for further research and analysis and will require close 

collaboration among air quality modelers, natural scientists, and economists. 

6.4 Benefits Analysis Results for the Proposed Standards 

Applying the impact and valuation functions described previously in this chapter to the 

estimated changes in PM2.5 associated with the proposed standards results in estimates of the 

changes in health effects (e.g., premature mortalities, cases, admissions) and the associated 

monetary values for those changes. Estimates of physical health impacts are presented in Table 

6.4-1. Monetized values for those health endpoints are presented in Table 6.4-2, along with total 

aggregate monetized benefits. All of the monetary benefits are in constant-year 2005 dollars. 

For each endpoint and total benefits, we provide both the mean estimate and the 95% confidence 

interval. 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5-related health and welfare effects could be quantified or monetized. The 

monetized value of all of these unquantified effects is represented by adding an unknown “B” to 

the aggregate total. The estimate of total monetized health benefits of the proposed standards is 

thus equal to the subset of monetized PM2.5- related health benefits plus B, the sum of the 

nonmonetized health and welfare benefits. 

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 evaluation 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 proposed standards would 

result in between 570 and 1,300 cases of avoided PM2.5-related premature deaths annually in 

2020 and between 1,500 and 3,400 avoided premature deaths annually in 2030. Note that in the 

case of the premature mortality estimates derived from the expert elicitation, we report the 95% 

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Chapter 6: Cost-Benefit Analysis 

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. 

As noted above, we provide two approaches to estimating avoided premature mortality 

associated with PM2.5 exposure. Our estimate of total monetized benefits in 2020 for the 

proposed standards, using the ACS and Six-Cities PM mortality studies, is between $4.4 billion 

and $9.2 billion, assuming a 3 percent discount rate (or $4.0 and $8.3 billion assuming a 7 

percent discount rate). In 2030, the monetized benefits are estimated to be between $12 billion 

and $25 billion (or $11 and $23 billion assuming a 7 percent discount rate). The monetized 

benefit associated with reductions in the risk of PM2.5-related premature mortality is over 90 

percent of total monetized health benefits, in part because we are unable to quantify a number 

benefits categories (see Table 6.1-2). These unquantified benefits may be substantial, although 

their magnitude is highly uncertain. Our estimate of total monetized benefits based on the expert 

elicitation is between $1.7 billion and $12 billion, assuming a 3 percent discount rate (or $1.6 

and $11 billion assuming a 7 percent discount rate). In 2030, the monetized benefits are 

estimated to be between $4.6 billion and $33 billion (or $4.3 and $30 billion assuming a 7 

percent discount rate). 

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 6.4-2. 

Following these tables, we also provide a more comprehensive graphical presentation of 

the distributions of incidence generated using the available information from empirical studies 

and expert elicitation. Figures 6.4-1 and 6.4-2 present box plots of 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 distributions are depicted as box plots with the diamond symbol (—) showing the mean, the 

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Draft Regulatory Impact Analysis 

dash (–) showing the median (50th percentile), the box defining the interquartile range (bounded 

by the 25th and 75th percentiles), and the whiskers defining the 90% confidence interval 

(bounded by the 5th and 95th percentiles of the distribution). The mean and 90% confidence 

interval for each separate estimate of mortality is also provided in Tables 6.4-3 and 6.4-4. 

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.BB 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) 45, (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) 46 and (e) background or 3 μg/m3 

(reflects NRC recommendation to consider effects all the way to background).47 We repeat this 

sensitivity analysis for the RIA of the proposed standards, the results of which can be found in 

Table 6.4-5. 

A sensitivity analysis such as this can be difficult to interpret, because when a threshold 

above the lowest observed level of PM2.5 in the underlying epidemiology study (Pope et al., 

2002) is assumed, the slope of the concentration-response function above that level must be 

adjusted upwards to account for the assumed threshold.CC Depending on the amount of slope 

adjustment and the proportion of the population exposed above the assumed threshold, the 

estimated mortality impact can either be lower (if most of the exposures occur below the 

threshold) or higher (if most of the exposures occur above the threshold). To demonstrate this 

possibility, we present an example from the proposed PM NAAQS RIA. In its examination of 

the benefits of attaining alternative PM NAAQS in Chicago,DD the analysis found that, because 

annual mean levels are generally higher in Chicago, there was a two-part pattern to the 

relationship between assumed threshold and mortality impacts. As the threshold increased from 

background to 7.5 μg/m3

, the mortality impact fell (because there is no slope adjustment). 

However, at an assumed threshold of 10 μg/m3

, estimated mortality impacts actually increased, 

because the populations exposed above 10 μg/m3 were assumed to have a larger response to 

particulate matter reductions (due to the increased slope above the assumed threshold). And 

finally, mortality impacts again fell to zero if a 15 μg/m3

 threshold was assumed, because these 

impacts were measured incremental to attainment of the current standard. 

BB 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. 

CC See NAS (2002)87 and CASAC (2005)85 for discussions of this issue. DD See the proposed PM NAAQS RIA (2005),67 Appendix A, pp. A63-A64.

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Chapter 6: Cost-Benefit Analysis 

Our analysis of the proposed standards also demonstrates this possibility. In Table 6.4-5, 

we can see that there is a two-part pattern to the relationship between assumed threshold and 

mortality impacts. As the threshold increases from background to 7.5 μg/m3, we see no 

difference in mortality impact (because all changes in PM appear to occur above a 7.5 μg/m3 

threshold and there is no slope adjustment). At a threshold of 10 μg/m3, however, estimated 

mortality impacts actually increase, because the populations exposed above 10 μg/m3 are 

assumed to have a larger response to particulate matter reductions (due to the increased slope 

above the assumed threshold). Finally, like the PM NAAQS example, mortality impacts again 

fall as the threshold is increased. 

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Draft Regulatory Impact Analysis 

Table 6.4-1. Estimated Reduction in Incidence of Adverse Health Effects Related to the Proposed Standardsa

 2020 2030 

Health Effect Mean Incidence Reduction 

(5th – 95th %ile) 

PM-Related Endpoints 

Premature Mortality – Derived from Epidemiology Studiesb,c 

Adult, age 30+ - Range based on Pope et al. 

2002 and Laden et al.2006, Respectively 

Infant, age <1 year – Woodruff et al. 1997 

570 - 1,300 

(220-920)–(710-1,900) 

1 

(1-2) 

1,500 - 3,400 

(590-2,400)-(1,900-5,000) 

2 

(1-4) 

Premature Mortality – Derived from Expert Elicitationc,d 

Adult, age 25+ - Lower and Upper Bound EE Results, 

Respectively 

180 - 1,700 

(0-830) – (870-2,600) 

460 - 4,600 

(0-2,200) – (2,300-6,900) 

Chronic bronchitis (adult, age 26 and over) 370 

(68 – 670) 

940 

(170 – 1,700) 

Acute myocardial infarction (adults, age 18 and older) 1,200 

(640 – 1,700) 

3,300 

(1,800 – 4,800) 

Hospital admissions—respiratory (all ages)e 130 

(65 – 200) 

350 

(170 – 510) 

Hospital admissions—cardiovascular (adults, age >18)f 270 

(170 – 380) 

770 

(490 – 1,100) 

Emergency room visits for asthma (age 18 years and younger) 460 

(270 – 650) 

1,000 

(620 – 1,500) 

Acute bronchitis (children, age 8–12) 1,000 

(0 – 2,100) 

2,600 

(0 – 5,300) 

Lower respiratory symptoms (children, age 7–14) 11,000 

(5,400 – 17,000) 

28,000 

(14,000 – 43,000) 

Upper respiratory symptoms (asthmatic children, age 9–18) 8,300 

(2,600 – 14,000) 

21,000 

(6,600 – 35,000) 

Asthma exacerbation (asthmatic children, age 6–18) 10,000 

(1,100 – 29,000) 

26,000 

(2,800 – 74,000) 

Work loss days (adults, age 18–65) 71,000 

(62,000 – 81,000) 

170,000 

(150,000 – 190,000) 

Minor restricted-activity days (adults, age 18–65) 420,000 

(360,000 – 490,000) 

1,000,000 

(850,000 – 1,200,000) a

 Incidence is rounded to two significant digits. PM estimates represent benefits from the proposed standards 

nationwide. 

b

 Based on application of the effect estimate derived from the Pope et al (2002) cohort study and the Laden et al 

(2006) study.48,49 Note that these two estimates are not additive; instead, they provide a range of mortality 

incidence derived from the epidemiology literature. Infant premature mortality based upon studies by Woodruff, et 

al. 1997.50 

 PM-related mortality benefits estimated using an assumed PM threshold at 10 μg/m3. There is uncertainty about 

which threshold to use and this may impact the magnitude of the total benefits estimate. 

d

 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).51 The lower bound result reflects 

the function derived from the expert with the most conservative effect estimate. The upper bound result reflects the 

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c
Chapter 6: Cost-Benefit Analysis 

function derived from the expert with the least conservative effect estimate. It should be noted, however, that the 

weight of expert-based opinion on the risk of premature death is skewed towards the range reflected by the 

published scientific studies. The effect estimates of nine of the twelve experts included in the elicitation panel falls 

within the scientific study-based range provided by Pope and Laden. One of the experts fall below this range and 

two of the experts are above this range. 

e

 Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma. 

f

 Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure. 

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Table 6.4-2. 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) 

Premature mortality – Derived from

Epidemiology Studiesc,d,e

Adult, age 30+ - ACS study (Pope et

al. 2002)

3% discount rate

7% discount rate

Adult, age 30+ - Six-Cities study

(Laden et al. 2006) 

3% discount rate

7% discount rate

$3,900

($500 - $8,800)

$3,700

($500 - $7,900)

$8,700

($1,400 - $18,000)

$7,800

($1,300 - $17,000)

$10,000 

($1,500 - $24,000)

$9,400

($1,300 - $21,000)

$24,000 

($3,800 - $50,000)

$21,000 

($3,400 - $45,000)

Infant Mortality,<1 year – Woodruff 

et al. 1997 

3% discount rate

7% discount rate

$8 

($1 - $18) 

$7 

($1 - $16) 

$17 

($3 - $37) 

$15 

($2 - $$33) 

Premature mortality – Derived from

Expert Elicitationc,d,e,f

Adult, age 25+ - Lower bound EE 

result 

3% discount rate

7% discount rate

Adult, age 25+ - Upper bound EE 

result 

3% discount rate

7% discount rate

$1,200

($0 - $7,200) 

$1,100

($0 - $6,500) 

$12,000 

($1,800 - $25,000)

$11,000 

($1,600 - $23,000)

$3,300

($0 - $20,000)

$3,000

($0 - $18,000)

$31,000 

($4,800 - $68,000)

$28,000 

($4,400 - $62,000)

Chronic bronchitis (adults, 26 and 

over) 

$200

($10 - $800)

$500

($26 - $2,100) 

Non-fatal acute myocardial 

infarctions

3% discount rate

7% discount rate

$123

($32 - $270)

$119

($30 - $270)

$330

($80 - $730)

$320

($76 - $720)

Hospital admissions for respiratory 

causes 

$2.7 

($1.3 - $4.0) 

$7.2 

($3.6 - $11) 

Hospital admissions for 

cardiovascular causes 

$7.3 

($4.6 - $10) 

$21 

($13 - $28)

Emergency room visits for asthma $0.16 $0.37 

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($0.09 - $0.26) ($0.20 - $0.60)

Acute bronchitis (children, age 8–12) $0.44

($0 - $1.2) 

$1.1 

($0 - $3.1) 

Lower respiratory symptoms

(children, 7–14)

$0.21 

($0.07 - $0.43)

$0.53 

($0.18 - $1.1)

Upper respiratory symptoms (asthma, 

9–11)

$0.24 

($0.05 - $0.59)

$0.62 

($0.14 - $1.5)

Asthma exacerbations $0.53 

($0.04 - $2.0)

$1.4 

($0.10 - $5.1)

Work loss days $11 

($9.6 - $12) 

$27 

($23 - $30)

Minor restricted-activity days

(MRADs) 

$12 

($0.61 – $25) 

$29 

($1.5 - $60) 

Recreational Visibility, 86 Class I 

areas 

$150

(na)f

$400

(na) 

Monetized Total – PM-Mortality 

Derived from Epi. Studies; Morbidity 

Functions 

3% discount rate

7% discount rate

$4.4 - $9.2 Billion

($1.0 - $10) – ($1.6 - $20)

$4.0 - $8.3 Billion

($1.0 - $9.2) – ($1.5 - $18) 

$12 - $25 Billion

($2.1 - $27) – ($4.4 - $53)

$11 - $23 Billion

($1.8 - $25) – ($3.9 - $48)

Monetized Total – PM-Mortality 

Derived from Expert Elicitationg

; 

Morbidity Functions

3% discount rate

7% discount rate

$1.7 - $12 Billion($0.2 - $8.5) – ($2.0 

- $27)$1.6 - $11 Billion($0.2 - $7.8) –

($1.8 - $24) 

$4.6 - $33 Billion($1.0 - $23) 

– ($5.4 - $72)$4.3 - $30 

Billion($1.0 - $21) – ($4.9 - 

$65)

a

 Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM

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

 PM-related mortality benefits estimated using an assumed PM threshold of 10 μg/m3. There is

uncertainty about which threshold to use and this may impact the magnitude of the total benefits estimate. 

d 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).i,ii

e

 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. 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. 

g

 It should be noted that the effect estimates of nine of the twelve experts included in the elicitation panel

falls within the scientific study-based range provided by Pope and Laden. One of the experts fall below

this range and two of the experts are above this range. 

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Chapter 6: Cost-Benefit Analysis 

6-77 

Figure 6.4-1. Results of Application of Expert Elicitation: Annual Reductions in Premature 

Mortality in 2020 Associated with the Proposed Standards 

804

183

839

1,038

780

612

956

1,730

731

1,049 1,072

1,297 1,389

574

0

500

1,000

1,500

2,000

2,500

3,000

Pope et

al.

(2002)

Laden et

al.

(2006)

Expert A Expert B Expert C Expert D Expert E Expert F Expert G Expert H Expert I Expert J Expert K Expert L

Reduction in Premature Mortality in 2020

Distributions

Derived from

Epidemiology

Studies

Distributions Based on Expert Elicitation

Note: Distributions labeled Expert A – Expert L are based on individual expert responses. The distributions labeled Pope et al. (2002) and 

Laden et al. (2006) are based on the means and standard errors of the C-R functions from the studies. The dotted lines enclose a range bounded 

by the means of the two data-derived distributions. 
Pope et

al.

(2002)

Laden et

al.

(2006)

Expert A Expert B Expert C Expert D Expert E Expert F Expert G Expert H Expert I Expert J Expert K Expert L

Draft Regulatory Impact Analysis 

 Figure 6.4-2. Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 

2030 Associated with the Proposed Standards 

2,114 

484 

2,206 

2,728 

2,050 

1,609 

2,514 

1,921 

4,542 

2,757 2,818 

3,650 3,408 

1,508 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

Reduction in Premature Mortality in 2030 

Distributions 

Derived 

from 

Epidemiology 

Studies 

Distributions Based on Expert Elicitation 

Note: Distributions labeled Expert A – Expert L are based on individual expert responses. The distributions labeled Pope et al. (2002) 

and Laden et al. (2006) are based on the means and standard errors of the C-R functions from the studies. The dotted lines enclose a 

range bounded by the means of the two data-derived distributions. 

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Chapter 6: Cost-Benefit Analysis 

Table 6.4-3. Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2020 

Associated with the Proposed Standards 

Source of Mortality 

Estimate 

2020 Primary Option 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 220 570 920 

Laden et al. (2006) 710 1,300 1,900 

Expert A 250 1,400 2,500 

Expert B 170 1,100 2,300 

Expert C 190 1,000 2,300 

Expert D 150 730 1,200 

Expert E 870 1,700 2,600 

Expert F 670 960 1,400 

Expert G 0 610 1,100 

Expert H 3 780 1,800 

Expert I 170 1,000 1,900 

Expert J 250 840 1,900 

Expert K 0 180 830 

Expert L 170 800 1,400 

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Draft Regulatory Impact Analysis 

Table 6.4-4. Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 2030 

Associated with the Proposed Standards 

Source of Mortality 

Estimate 

2020 Primary Option 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 590 1,500 2,400 

Laden et al. (2006) 1,900 3,400 5,000 

Expert A 670 3,700 6,600 

Expert B 450 2,800 5,900 

Expert C 500 2,800 5,900 

Expert D 400 1,900 3,200 

Expert E 2,300 4,500 6,900 

Expert F 1,800 2,500 3,600 

Expert G 0 1,600 3,000 

Expert H 8 2,100 4,700 

Expert I 440 2,700 4,900 

Expert J 660 2,200 4,900 

Expert K 0 480 2,200 

Expert L 450 2,100 3,700 

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Chapter 6: Cost-Benefit Analysis 

Table 6.4-5. PM-Related Mortality Benefits of the Proposed Standards: Cutpoint Sensitivity Analysis Using 

the ACS Study (Pope et al., 2002)a 

Certainty that Benefits 

are At Least Specified 

Value 

Level of 

Assumed 

Threshold 

Discount 

Rate 

PM Mortality Benefits (Billion 2005$) 

2020 2030 

More Certain that 

Benefits Are at Least 

as Large 

14 μg/m3 b 3% 

7% 

$1.8 

$1.6 

$5.4 

$4.8 

12 μg/m3 

3% 

7% 

$2.5 

$2.3 

$7.2 

$6.5 

10 μg/m3 c 3% 

7% 

$3.9 

$3.5 

$10.4 

$9.4 

Less Certain that 

Benefits Are at Least 

as Large 

7.5 μg/m3 d 3% 

7% 

$3.4 

$3.1 

$9.2 

$8.3 

3 μg/m3 e 3% 

7% 

$3.4 

$3.1 

$9.2 

$8.3 a

 Note that this table only presents the effects of a cutpoint on PM-related mortality incidence and valuation 

estimates. 

b

 Alternative annual PM NAAQS. 

c CASAC (2005)85 

d

 SAB-HES (2004)86 

e

 NAS (2002)87 

6.5 Comparison of Costs and Benefits 

In estimating the net benefits of the proposed 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 6.5-1 

contains the estimates of monetized benefits and estimated social welfare costs for the proposed 

rule and each of the proposed control programs. The annual social welfare costs of all provisions 

of this proposed rule are described more fully in Chapter 7 of this RIA.EE 

EE The estimated 2030 social welfare cost of 267.3 million is based on an earlier version of the engineering costs of 

the rule which estimated $568.3 million engineering costs in 2030 (see table 5-17). The current engineering cost 

estimate for 2030 is $605 million. See Section V.C.5 for an explanation of the difference. The estimated social 

costs of the program will be updated for the final rule. 

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Draft Regulatory Impact Analysis 

The results in Table 6.5-1 suggest that the 2020 monetized benefits of the proposed 

standards are greater than the expected social welfare costs. Specifically, the annual benefits of 

the total program would be between $4.4 + B billion and $9.2 + B billion annually in 2020 using 

a three percent discount rate (or $4.0 and $8.3 billion assuming a 7 percent discount rate), 

compared to estimated social costs of approximately $250 million in that same year. These 

benefits are expected to increase to between $12 + B billion and $25 + B billion annually in 2030 

using a three percent discount rate (or $11 and $23 billion assuming a 7 percent discount rate), 

while the social costs are estimated to be approximately $600 million. Though there are a 

number of health and environmental effects associated with the proposed standards that we are 

unable to quantify or monetize (represented by “+B”; see Table 6.1-2), the benefits of the 

proposed standards far outweigh the projected costs. When we examine the benefit-to-cost 

comparison for the rule standards separately, we also find that the benefits of the specific engine 

class standards far outweigh their projected costs. 

Table 6.5-1. Summary of Annual Benefits and Costs of the Proposed Standardsa 

(Millions of 2005 dollars) 

Description 

2020 

(Millions of 2005 dollars) 

2030 

(Millions of 2005 

dollars) 

Estimated Social Costsb

 Locomotive 

Marine 

Total Social Costs 

$147 

$103 

$250 

$383 

$222 

$605 

Estimated Health Benefits of the Proposed Standardsc,d

 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 

$2,300+B - $4,800+B 

$2,100+B - $4,400+B 

$2,100+B - $4,400+B 

$1,900+B - $3,900+B 

$4,400+B - $9,200+B 

$4,000+B - $8,300+B 

$4,700+B - $9,800+B 

$4,300+B - $8,900+B 

$7,100+B - $15,000+B 

$6,400+B - $14,000+B 

$12,000+B - $25,000+B 

$11,000+B - $23,000+B 

Annual Net Benefits (Total Benefits – Total Costs) 

 3 percent discount rate

 7 percent discount rate 

$4,150+B - $8,950+B 

$3,750+B - $8,050+B 

$11,400+B - $24,400+B 

$10,400+B - $22,400+B 

a

 All estimates are rounded to three significant digits and 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 

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Chapter 6: Cost-Benefit Analysis 

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. FF 

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. 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). Note that the benefits in this table reflect PM mortality derived from the 

ACS (Pope et al., 2002) and Six-Cities (Laden et al., 2006) studies. Valuation of nonfatal myocardial infarctions 

(MI) assumes discounting over a 5-year period, reflecting lost earnings and direct medical costs following a nonfatal 

MI. Note that we do not calculate a net present value of benefits associated with the proposed standards. d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and monetized are 

listed in Table 6.1-2. 

FFU.S. Environmental Protection Agency, 2000. Guidelines for Preparing Economic Analyses. 

www.yosemite1.epa.gov/ee/epa/eed/hsf/pages/Guideline.html. 

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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Draft Regulatory Impact Analysis 

Appendix 6.A 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: nonhealth 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 proposed 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).54 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 cost6-84
Chapter 6: Cost-Benefit Analysis 

effectiveness of reductions in ambient PM2.5 in achieving improvements in 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 proposed 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 this chapter shows that the proposed standards would achieve substantial health 

benefits whose monetized value far exceeds costs (net benefits are between $12 and $28 billion 

in 2030, based on empirically derived estimates of PM mortality and using a 3 percent discount 

rate). Despite the risk of oversimplifying benefits, cautiously-interpreted cost-effectiveness 

calculations may provide further evidence of whether the costs associated with the proposed 

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 

proposed 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: 

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Draft Regulatory Impact Analysis 

• In 2020, the proposed standards will result in: 

• 570 (95% CI: 180 – 960) premature PM-related deaths avoided, or 

• 5,900 (95% CI: 4,100 – 7,600) PM-related life years gained (discounted at 3 

percent), or 

• 11,000 (95% CI: 7,900 – 18,000) MILYs gained (discounted at 3 percent). 

• In 2030, the proposed standards will result in: 

• 1,500 (95% CI: 590 – 2,400) premature PM-related deaths avoided, or 

• 15,000 (95% CI: 10,000 – 20,000) PM-related life years gained (discounted at 3 

percent), or 

• 23,000 (95% CI: 16,000 – 34,000) MILYs gained (discounted at 3 percent). 

• Using a 7 percent discount rate, mean discounted life years gained are 3,700 for the 

proposed standards in 2020 and 9,500 in 2030; mean MILYs gained are 7,300 in 2020 

and 15,000 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 $180 million in 2020 and $550 million in 2030 based on 

a 3 percent discount rate, or $120 million in 2020 and $440 million in 2030 based on a 7 

percent discount rate. 

• Other health and visibility benefits are valued at $200 million in 2020 and $510 million 

in 2030. 

Direct private compliance costs for the proposed standards are $240 million in 2020 and $600 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 proposed standards result in cost savings. As such, traditional costeffectiveness 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 proposed 

standards would need to exceed $920 million (95% CI: $700 million – $1,400 million) in 

2020 and $2.2 billion (95% CI: $1.6 billion – $3.0 billion) in 2030 to have a cost per MILY 

that exceeds a benchmark of $50,000, based on a 3 percent discount rate. 

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Chapter 6: Cost-Benefit Analysis 

• Annual costs of the proposed standards would need to exceed $1.5 billion (95% CI: $1.1 

billion – $2.3 billion) in 2020 and $3.4 billion (95% CI: $2.4 billion – $4.7 billion) in 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 proposed standards would need to exceed 

$680 million in 2020 and $1.7 billion in 2030 to have a cost per MILY that exceeds a 

benchmark of $50,000, and would need to exceed $1.0 billion in 2020 and $2.5 billion in 

2030 to have a cost per MILY that exceeds a benchmark of $100,000. 

Given costs of $240 million and $600 million in 2020 and 2030, respectively, the 

proposed 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 healthbased CEA of the proposed 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 healthbased CEA. 

Table 6.A-1: Estimated Reduction in Incidence of All-cause Premature Mortality Associated with the 

Proposed Standards in 2020 and 2030 

Reduction in All-Cause Premature Mortality 

(95% CI) 

Age Interval 2020 2030 

30 – 34 5 

(2-9) 

11 

(3-18) 

35 – 44 15 

(5-26) 

35 

(11-59) 

45 – 54 31 

(10-52) 

64 

(20-110) 

55 – 64 78 

(25-130) 

150 

(49-260) 

65 – 74 130 

(40-210) 

340 

(110-570) 

75 – 84 140 

(46-240) 

460 

(150-780) 

85+ 180 

(56-300) 

450 

(140-750) 

Total 570 

(180-960) 

1,510 

(480-2,500) 

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Draft Regulatory Impact Analysis 

Table 6.A-2: Estimated Life Years Gained from All-cause Premature Mortality Risk Reductions Associated 

with the Proposed Standards in 2020 and 2030 

Life Years Gained from Mortality Risk Reduction, 3% Discount Rate 

(95% CI) 

Age Interval 2020 2030 

25 – 34 120 

(27-210) 

250 

(77-450) 

35 – 44 350 

(120-560) 

800 

(250-1,300) 

45 – 54 610 

(190-1,000) 

1,300 

(420-2,100) 

55 – 64 1,300 

(420-2,200) 

2,500 

(800-4,200) 

65 – 74 1,600 

(500-2,700) 

4,300 

(1,300-7,200) 

75 – 84 1,300 

(400-2,100) 

4,100 

(1,400-6,800) 

85+ 630 

(200-1,100) 

1,600 

(520-2,700) 

Total 5,900 

(4,100-7,600) 

15,000 

(10,000-20,000) 

Table 6.A-3: Estimated Reduction in Incidence of Chronic Bronchitis Associated with the Proposed 

Standards in 2020 and 2030 

Reduction in Incidence (95% Confidence Interval) 

Age Interval 2020 2030 

25 – 34 88 

(8-170) 

200 

(18-380) 

35 – 44 99 

(9-190) 

250 

(22-480) 

45 – 54 91 

(8-170) 

210 

(19-400) 

55 – 64 93 

(8-180) 

200 

(18-380) 

65 – 74 66 

(6-130) 

190 

(17-360) 

75 – 84 32 

(3-61) 

110 

(10-210) 

85+ 13 

(1-26) 

37 

(3-70) 

Total 480 

(43-920) 

1,200 

(110-2,300) 

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Chapter 6: Cost-Benefit Analysis 

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 12.21 6.56 

35 44 9.84 5.90 

45 54 7.54 5.06 

55 64 5.36 4.03 

65 74 3.41 2.85 

75 84 2.15 1.93 

85+ 0.79 0.76 

Table 6.A-5: Estimated Reduction in Nonfatal Acute Myocardial Infarctions Associated with the Proposed

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 4 

(2-5) 

8 

(4-11) 

35 – 44 38 

(20-55) 

97 

(52-140) 

45 – 54 121 

(65-177) 

280 

(150-410) 

55 – 64 290 

(160-420) 

630 

(340-920) 

65 – 74 340 

(190-500) 

1,000 

(550-1,500) 

75 – 84 250 

(140-370) 

870 

(470-1,300) 

85+ 130 

(70-190) 

360 

(190-520) 

Total 1,200 

(640-1,700) 

3,270 

(1,800-4,800) 

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Draft Regulatory Impact Analysis 

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 4.04 2.10 

25 34 3.38 1.95 

35 44 2.73 1.74 

45 54 2.08 1.48 

55 64 1.44 1.12 

65 74 0.95 0.81 

75 84 0.57 0.52 

85+ 0.31 0.30 

Table 6.A-7. Estimated Gains in 3 Percent Discounted MILYs Associated with the Proposed 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 - - 3 

(0-5) 

3 

(0-5) 

25–34 120 

(27-210) 

560 

(38-1,400) 

15 

(4-32) 

710 

(160-1,500) 

35–44 350 

(120-560) 

590 

(42-1,400) 

170 

(73-600) 

1,100 

(550-2,100) 

45–54 610 

(190-1,000) 

460 

(34-1,100) 

420 

(200-1,600) 

1,500 

(960-2,900) 

55–64 1,300 

(420-2,200) 

380 

(31-890) 

710 

(340-2,900) 

2,400 

(1,600-4,900) 

65–74 1,600 

(500-2.700) 

190 

(12-440) 

820 

(300-2,600) 

2,600 

(1,500-4,700) 

75–84 1,300 

(400-2,100) 

62 

(4-150) 

460 

(130-1,000) 

1,800 

(870-2,800) 

85+ 630 

(200-1,100) 

10 

(1-23) 

110 

(43-300) 

750 

(340-1,300) 

Total 5,900 

(4,100-7,600) 

2,300 

(173-5,300) 

2,700 

(1,100-9,100) 

11,000 

(7,900-18,000) a Note that all estimates have been rounded to two significant digits. 

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Chapter 6: Cost-Benefit Analysis 

Table 6.A-8: Estimated Gains in 3 Percent Discounted MILYs Associated with the Proposed 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 

(0-5) 

3 

(0-5) 

25–34 250 

(77-450) 

1,300 

(79-3,100) 

15 

(4-32) 

1,600 

(340-3,400) 

35–44 800 

(250-1,300) 

1,500 

(85-3,500) 

170 

(76-590) 

2,400 

(1,000-4,600) 

45–54 1,300 

(420-2,100) 

1,100 

(75-2,500) 

420 

(200-1,600) 

2,700 

(1,600-4,900) 

55–64 2,500 

(800-4,200) 

790 

(61-1,800) 

710 

(360-2,900) 

4,000 

(2,500-7,100) 

65–74 4,300 

(1,300-7,200) 

530 

(38-1,200) 

820 

(310-2,500) 

5,600 

(2,900-9,400) 

75–84 4,100 

(1,400-6,800) 

210 

(14-500) 

460 

(140-1,000) 

4,800 

(2,100-7,600) 

85+ 1,600 

(520-2,700) 

28 

(2-66) 

110 

(44-300) 

1,700 

(700-2,900) 

Total 15,000 

(10,000-20,000) 

5,400 

(390-13,000) 

2,700 

(1,100-9,000) 

23,000 

(16,000-34,000) a Note that all estimates have been rounded to two significant digits. 

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Draft Regulatory Impact Analysis 

Table 6.A-9: Summary of Health-Based Cost Effectiveness Results for the Proposed Standards in 2020 

and 2030a 

Result Using 3% Discount Rate (95% Confidence Interval)

 2020 2030 

Life years gained from mortality 

risk reductions 

5,900 

(4,100-7,600) 

15,000 

(10,000-20,000) 

QALY gained from reductions in 

chronic bronchitis 

2,300 

(173-5,300) 

5,400 

(390-13,000) 

QALY gained from reductions in 

acute myocardial infarctions 

2,700 

(1,100-9,100) 

2,700 

(1,100-9,000) 

Total gain in MILYs 11,000 

(7,900-18,000) 

23,000 

(16,000-34,000) 

Avoided cost of illness 

 Chronic bronchitis $57 Million 

($37 - $89 Million) 

$130 Million 

($86 - $210 Million)

 Nonfatal AMI $120 Million 

($67 - $200 Million) 

$420 Million 

($170 - $550 Million) 

Other benefits (based on COI and 

WTP estimates) 

$200 Million 

($180 - $210 Million) 

$510 Million 

($480 – $540 Million) 

Implementation strategy costsb $240 Million $600 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. 

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Chapter 6: Cost-Benefit Analysis 

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 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).55 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 (EPA-SAB-COUNCILADV-04-002, 2004). They suggest that diseases with longer progressions should be 

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Draft Regulatory Impact Analysis 

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).56 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, 

(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. 

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Chapter 6: Cost-Benefit Analysis 

(1995)57 and Dockery et al. (1993)58 studies, respectively.GG 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 (scaled from the CAND analysis) 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-1. 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. 

GG 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. 

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Draft Regulatory Impact Analysis 

Table 6B-1. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Lag Assumptions 

(Relative to Primary Benefits Estimates of the Proposed Standards) 

Avoided Incidencesa Value (million 2005$)b 

Description of Sensitivity Analysis 2020 2030 2020 2030 

Alternative Lag Structures for PM-Related Premature Mortality 

None Incidences all occur in the first year 570 1,500 $4,300 $11,500 

8-year Incidences all occur in the 8th year 

 3% Discount Rate 570 1,500 $3,500 $9,300

 7% Discount Rate 570 1,500 $2,600 $7,100 

15-year Incidences all occur in the 15th year 

 3% Discount Rate 570 1,500 $2,800 $7,600

 7% Discount Rate 570 1,500 $1,600 $4,400

20 percent of incidences occur in 1st

Alternative year, 50 percent in years 2 to 5, and 

Segmented 30 percent in years 6 to 20

 3% Discount Rate 570 1,500 $3,700 $10,100 

 7% Discount Rate 570 1,500 $3,200 $8,700 

50 percent of incidences occur in 

5-Year years 1 and 2 and 50 percent in years 

Distributed 2 to 5 

 3% Discount Rate 570 1,500 $4,000 $10,900 

 7% Discount Rate 570 1,500 $3,800 $10,200 a

 Incidences rounded to two significant digits. 

b Dollar values rounded to two significant digits. Note that dollar values reflect the use of a 3 percent discount rate

in the primary lag adjustment for valuation of alternative mortality C-R functions. 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. For example, the overall impact of moving from the previous 5-year distributed 

lag to the segmented lag recommended by the SAB-HES in 2004 in the 2030 primary estimate is 

relatively modest, reducing PM-related mortality benefits by approximately 5 percent when a 3 

percent discount rate is used and approximately 10 percent when a 7 percent discount rate is 

used. If no lag is assumed, benefits increase by around 10 percent relative to the segmented lag 

with a 3 percent discount rate and 23 percent with a 7 percent discount rate. 

6.B.2 Visibility Benefits in Additional Class I Areas 

The Chestnut and Rowe (1990) 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 parks in one region is 

6-96
c 

Chapter 6: Cost-Benefit Analysis 

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-2. 

Table 6.B-2: Monetary Benefits Associated with Improvements in Visibility in Additional Federal Class I 

Areas in 2020 and 2030 (in millions of 2005$)a

Year Northwestb

 Centralc Northeastd Total 

2020 $11 $55 $10 $75 

2030 $30 $130 $20 $180 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. 

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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Draft Regulatory Impact Analysis 

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Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American 

Medical Association 287:1132-1141. 

10 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and 

Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667-672. 

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Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health 

Perspectives 105(6):608-612. 

13 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. 1995. “Estimated Long-Term Ambient 

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Environmental Health 50(2): 139-152. 

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14 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. 2001. “Increased Particulate Air Pollution and the 

Triggering of Myocardial Infarction.” Circulation 103:2810-2815. 

15 Moolgavkar, S.H. 2003. “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook 

Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, MA: 

Health Effects Institute. 

16 Ito, K. 2003. “Associations of Particulate Matter Components with Daily Mortality and Morbidity in Detroit, 

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17 Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three 

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18 Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 

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Health Effects Institute. 

19 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. 1999. “An Association 

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Perspectives 107(6):489-493. 

20 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, 

and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American Children-Respiratory Symptoms.” 

Environmental Health Perspectives 104(5):500-505. 

21 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “Respiratory Health and PM10 

Pollution: A Daily Time Series Analysis.” American Review of Respiratory Diseases 144:668-674. 

22 Schwartz, J., and L.M. Neas. 2000. “Fine Particles are More Strongly Associated than Coarse Particles with 

Acute Respiratory Health Effects in Schoolchildren.” Epidemiology 11:6-10. 

23 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. “Air Pollution and Exacerbation of 

Asthma in African-American Children in Los Angeles.” Epidemiology 12(2):200-208. 

24 Vedal, S., J. Petkau, R. White, and J. Blair. 1998. “Acute Effects of Ambient Inhalable Particles in Asthmatic 

and Nonasthmatic Children.” American Journal of Respiratory and Critical Care Medicine 157(4):1034-1043. 

25 Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of Environmental 

Economics Management 14:87-98. 

26 Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An Observational Study 

of Multiple Pollutants.” Environmental Research 50:238-247. 

27 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of 

Policy Analysis and Management 21(2):253-270. 

28 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates 

Throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 

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29 Viscusi, W.K., W.A. Magat, and J. Huber. 1991. “Pricing Environmental Health Risks: Survey Assessments of 

Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis.” Journal of Environmental Economics and 

Management 21:32-51. 

30 Cropper, M.L., and A.J. Krupnick. 1990. “The Social Costs of Chronic Heart and Lung Disease.” Resources for 

the Future. Washington, DC. Discussion Paper QE 89-16-REV. 

31 Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of Coronary 

Artery Disease in the United States.” American Journal of Cardiology 81(9):1110-1115. 

32 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in the United 

States.” American Journal of Cardiology 65(7):432-440. 

33 Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project. 

Rockville, MD. http://www.ahrq.gov/HCUPnet/. 

34 Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A National 

Estimate of the Economic Costs of Asthma.” American Journal of Respiratory and Critical Care Medicine 156(3 Pt 

1):787-793. 

35 Stanford, R., T. McLaughlin, and L.J. Okamoto. 1999. “The Cost of Asthma in the Emergency Department and 

Hospital.” American Journal of Respiratory and Critical Care Medicine 160(1):211-215. 

36 Industrial Economics, Incorporated (IEc). March 31, 1994. Memorandum to Jim DeMocker, Office of Air and 

Radiation, Office of Policy Analysis and Review, U.S. Environmental Protection Agency. 

37 Rowe, R.D., and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits Analysis— 

Executive Summary.” Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental 

Protection Agency, Office of Policy Analysis. EPA-230-09-86-018. Washington, DC. 

38 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. March 31, 1994. “Linkage Between Health Effects Estimation 

and Morbidity Valuation in the Section 812 Analysis—Draft Valuation Document.” Industrial Economics 

Incorporated (IEc) Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and 

Radiation, Office of Policy Analysis and Review. 

39 Tolley, G.S. et al. January 1986. Valuation of Reductions in Human Health Symptoms and Risks. University of 

Chicago. Final Report for the U.S. Environmental Protection Agency. 

40 Council of Economic Advisors. 2005. The Annual Report of the Council of Economic Advisors. In: Economic 

Report of the President. Table B-60. U.S. Government Printing Office: Washington, DC. 

41 U.S. Environmental Protection Agency (2006) Air quality criteria for ozone and related photochemical oxidants 

(second external review draft) Research Triangle Park, NC: National Center for Environmental Assessment; report 

no. EPA/600R-05/004aB-cB, 3v.Available: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 

2006] 

42 Lee, E.H. and W.E. Hogsett. 1996. Methodology for Calculating Inputs for Ozone Secondary Standard Benefits 

Analysis: Part II. Prepared for U.S. EPA, Office of Air Quality Planning and Standards. March. 

43 Garcia, P., B. Dixon, and J. Mjelde. 1986. “Measuring the Benefits of Envrionmental Change Using a Duality 

Approach: The Case of Ozone and Illinois Cash Grain Farms.” Journal of Environmental Economics and 

Management. 13: 69-80. 

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44 Fox, S., and R.A. Mickler. 1996. “Impact of Air Pollutants on Southern Pine Forests.” Ecological Studies 118. 

New York: Springer Verlag. 

45 Clean Air Science Advisory Committee. June 2005. EPA’s Review of the National Ambient Air Quality 

Standards for Particulate Matter (Second Draft PM Staff Paper, January 2005). A Review by the PM Review Panel 

of the EPA Clean Air Science Advisory Committee. EPASAB-CASAC-05-007. 

46 EPA-SAB-COUNCIL_ADV_04-002. March 2004. Advisory on 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: 

Advisory by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. 

47 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. 

48 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung 

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141. 

 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and 

Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667-672. 

50 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health 

Perspectives 105(6):608-612. 

51 Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse 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. 

52 U.S. Environmental Protection Agency, 2000. Guidelines for Preparing Economic Analyses. 

www.yosemite1.epa.gov/ee/epa/eed/hsf/pages/Guideline.html. 

53 Office of Management and Budget, The Executive Office of the President, 2003. Circular A-4. 

http://www.whitehouse.gov/omb/circulars. 

54 Miller W, Robinson LA, Lawrence RS, eds. Valuing Health: Cost Effectiveness Analysis for Regulation. 

Committee to Evaluate Measures of Health Benefits for Environmental, Health, and Safety Regulation (Lawrence 

RS, chair), Board on Health Care Services, Institute of Medicine, National Academy Press, Washington D.C., 2006. 

55 EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments (CAAA) Section 812 

Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ecological Effects Subcommittee on 

Initial Assessments of Health and Ecological Effects. Part 2. 

56 EPA-COUNCIL-LTR-05-001. December 2004. Advisory Council on Clean Air Compliance Analysis Response 

to Agency Request on Cessation Lag. 

57 Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 

1995. “Particulate Air Pollution as a Predictor of Mortality in a Prospective Study of U.S. Adults.” American 

Journal of Respiratory Critical Care Medicine 151:669-674. 

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58 Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. Speizer. 1993. 

“An Association between Air Pollution and Mortality in Six U.S. Cities.” New England Journal of Medicine 

329(24):1753-1759. 

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Chapter 7: Economic Impact Analysis 

CHAPTER 7: ECONOMIC IMPACT ANALYSIS ............................................. 7-3
 

7.1 Overview and Results .............................................................................................. 7-4
 

7.1.1 What is an Economic Impact Analysis?......................................................... 7-4
 

7.1.2 What Methodology Did EPA Use in this Economic Impact Analysis? ....... 7-4
 

7.1.3 What Economic Sectors are Included in the Economic Impact Model? .... 7-5
 

7.1.4 Summary of Results ....................................................................................... 7-10
 

7.2 Economic Methodology ......................................................................................... 7-21
 

7.2.1 Behavioral Economic Models........................................................................ 7-21
 

7.2.2 What is the Economic Theory Underlying the EIM? ................................. 7-22
 

7.2.3 How Is the EIM Used to Estimate Economic Impacts?.............................. 7-30
 

7.3 EIM Data Inputs and Model Solution.................................................................. 7-42
 

7.3.1 Market Equilibrium Conditions................................................................... 7-43
 

7.3.2 Compliance Costs........................................................................................... 7-49
 

7.3.3 Behavioral Parameters .................................................................................. 7-62
 

7.3.4 Economic Impact Model Structure .............................................................. 7-66
 

7.3.5 Estimating Impacts ........................................................................................ 7-69
 

7.4 Methods for Describing Uncertainty.................................................................... 7-69
 

APPENDIX 7A: IMPACTS ON MARINE ENGINE MARKETS.................... 7-77
 

APPENDIX 7B: IMPACTS ON THE EQUIPMENT MARKETS ................... 7-86
 

APPENDIX 7C: IMPACTS ON TRANSPORTATION SERVICE MARKETS7-114
 

APPENDIX 7D: TIME SERIES OF SOCIAL COSTS.................................... 7-118
 

APPENDIX 7E: MODEL EQUATIONS........................................................... 7-123
 

7E.1 Economic Model Equations.............................................................................. 7-123
 

7E.1.1 Supply Equations....................................................................................... 7-123
 

7E.1.2 Demand Equations ................................................................................... 7-124
 

7E.1.3 Market Equilibrium Conditions ............................................................. 7-125
 

7E.2 Computing With-Regulation Equilibrium Conditions within the Spreadsheet 7­
 

7E.3 Social Costs: Consumer and Producer Economic Welfare Calculations..... 7-127
 

APPENDIX 7F: ELASTICITY PARAMETERS FOR ECONOMIC IMPACT
 

MODELING......................................................................................................... 7-129
 

7-1 
 

125 
135 

Draft Regulatory Impact Analysis 

APPENDIX 13G: INITIAL MARKET EQUILIBRIUM - PRICE FORECASTS7­

APPENDIX 7H: SENSITIVITY ANALYSIS ................................................... 7-137
 

7H.1 Model Elasticity Parameters............................................................................ 7-137
 

7H.2 Fixed Cost Shift Scenario................................................................................. 7-142
 

7H.3 Marine Operating Cost Scenario..................................................................... 7-146
 

7-2 
 
Chapter 7: Economic Impact Analysis 

CHAPTER 7: Economic Impact Analysis 

We prepared an Economic Impact Analysis (EIA) to estimate the economic 

impacts of the proposed 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 proposed program to be approximately $600 

million in 2030.1, 2 The impact of these costs on society are expected to be minimal, with 

the prices of rail and marine transportation services estimated to increase by less about 

0.4 percent for locomotive transportation services and about 0.6 percent for marine 

transportation services. The rail sector is expected to bear about 64 percent of the social 

costs of the program in 2030, and the marine sector is expected to bear about 36 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 (63.3 and 33.2 

percent, respectively). The remaining 3.5 percent is expected to be borne by locomotive, 

marine engine, and marine vessel manufacturers and fishing and recreational vessel users. 

With regard to market-level impacts in 2030, the average price of a locomotive is 

expected to increase about 2.6 percent ($49,100 per unit), but sales are not expected to 

decrease. In the marine markets, 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, 

the average price of an engine is expected to increase by about 8.4 percent for C1 engines 

and 18.7 percent for C2 engines ($13,300 and $48,700, respectively). However, the 

expected impact of these increased prices on the average price of vessels that use these 

engines is smaller, at about 1.1 percent and 3.6 percent respectively ($16,200 and 

$141,600). The decrease in engine and vessel production is expected to be negligible, at 

less than 10 units. For engines less than 800 hp and the vessels that use them, the 

expected price increase and quantity decrease are expected to be negligible, less than 0.1 

percent. Finally, even with the increases in the prices of locomotives and large marine 

diesel engines, the expected impacts on prices in the locomotive and marine 

transportation service markets are small, at 0.4 and 0.6 percent, respectively. 

1

 All estimates presented in this section are in 2005$. 

2

 The estimated 2030 social welfare cost of 267.3 million is based on an earlier version of the engineering 

costs of the rule which estimated $568.3 million engineering costs in 2030 (see table 5-17). The current 

engineering cost estimate for 2030 is $605 million. See 7.1.4 for an explanation of the difference. The 

estimated social costs of the program will be updated for the final rule. 

7-3 
 
Draft Regulatory Impact Analysis 

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 proposed 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. 

7.1.2 What Methodology Did EPA Use in this Economic Impact Analysis? 

The EIM is the behavioral model we developed to estimate price and quantity 

changes and total social costs associated with the emission controls under consideration. 

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 proposed 

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 proposed program 

by simulating economic behavior. This is done by creating a model of the initial, precontrol market for a product, shocking it 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. 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. 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 

7-4 
 
Chapter 7: Economic Impact Analysis 

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 demand in 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 and equipment components of each model 

and simulate how compliance costs can be expected to ripple through the affected market. 

7.1.3 What Economic Sectors are Included in the Economic Impact Model? 

In this EIA we estimate the impacts of the proposed 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 

7-5 
 
Draft Regulatory Impact Analysis 

Model Dimension Rail Sector Marine Sector 

Description of Markets: 

Supply 

Locomotive: locomotive 

manufacturers (integrated 

manufacturers); 3 categories 

 Line Haul 

Passenger

 Switcher 

Rail Transportation Services: Entities 

that provide rail transportation 

services (railroads, primarily Class I) 

Engines: Marine Engine Manufacturers; 8 

categories 

 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: Marine vessel 

manufacturers; 7 categories 

 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 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 

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 

7-6 
 
Chapter 7: Economic Impact Analysis 

Model Dimension Rail Sector Marine Sector 

Supply Elasticity Locomotives (all): 2.7 (elastic) 

Rail Transportation Market: 0.6 

(inelastic) 

Engines: 3.8 (elastic) 

Vessels: 

2.7 Commercial (elastic) 

1.6 Recreational and Fishing (elastic) 

Marine Transportation Market: 0.6 

(inelastic) 

Demand Elasticity Locomotives (all): Derived 

Rail Transportation Market: -0.5 

(inelastic) 

Engines: Derived 

Vessels: 

Commercial: Derived 

Recreational and Fishing : -1.4

 (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 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 in any given year are for line haul 

applications; a small number of passenger locomotives are built every year, 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. 

7-7 
 
Draft Regulatory Impact Analysis 

Recently, a new switcher market is emerging in which manufacturers are expected 

to be less integrated, and the manufacturer of the engine is expected to be separate from 

the manufacturer of the switcher.3 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 (integrated manufacturers; same behavioral 

parameters), but uses separate baseline equilibrium prices and quantities. The 

compliance costs used for switchers reflect the expected design characteristics for these 

locomotives and their lower total power. Consistent with the cost analysis, the passenger 

market is combined with the switcher market in this EIA because we do not have separate 

compliance costs estimates for each of those two market segments. 

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 proposed 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 

3

 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. 

7-8 
 
Chapter 7: Economic Impact Analysis 

propulsion, and auxiliary. Engines are broken out into eight categories based on 

horsepower and displacement. 

• Small marine diesel engines 

• <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 

For the purpose of the EIA, the C1/C2 threshold is 5 l/cyl displacement, even 

though the new C1/C2 threshold is proposed to be 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: 

• Recreational 

• Fishing 

• Tow/tug/push 

• Ferry 

• Supply/crew 

• Cargo 

• Other commercial 

For fishing and recreational vessels, the purchasers of those vessels are the end 

users, and so the EIM is a two-level model for those two markets. For the fishing market, 

this approach is appropriate because demand for fishing vessels comes directly from the 

fishing industry; fishing vessels are a fixed capital input for that industry. For the 

recreational 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 is derived from the transportation services they provide, and so 

demand is from the transportation service market and the providers of those services 

more specifically. Therefore it is necessary to include the marine transportation services 

market in the model. 

7-9 
 
Draft Regulatory Impact Analysis 

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 individual levels of the rail and marine components of the EIM are linked to 

provide feedback between consumers and producers in the relevant 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 

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. 

7-10 
 
Chapter 7: Economic Impact Analysis 

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. In this section we present 

summarized results for selected years. 

Due to the structure of the program (see Section 7.3.3), the estimated market and 

social costs impacts of the program in the early years are small and are primarily due to 

the locomotive remanufacturing program. By 2016, the impacts of the program are more 

significant due to the operational costs associated with the Tier 4 standards (urea usage). 

Consequently, a large share of the social costs of the program after the Tier 4 standards to 

into effect fall on the marine and rail transportation service sectors. These operational 

costs are incurred by the providers of these services, but they are expected to pass along 

some of these costs to their customers. 

The results of the economic impact analysis presented in this Chapter are based 

on an earlier version of the engineering costs developed for this rule. The engineering 

costs for 2030 presented in Chapter 5 are estimated to be $605 million, which is $37 

million more than the compliance costs used in this EIA. Over the period from 2007 

through 2040, the net present value of the engineering costs in Chapter 5 is $7.2 billion 

while the NPV of the estimated social costs over that period based on the compliance 

costs used in his chapter is $6.9 billion (3 percent discount rate). The differences are 

primarily in the form of operating costs ($22 million for the rail sector, $10 million for 

the marine sector). The variable costs for locomotives are slightly smaller ($4.0 million) 

and for marine are somewhat higher ($5.0 million). The difference for marine engines 

occurs in part because the engineering costs in Chapter 3 include Tier 4 costs for 

recreational marine engines over 2,000 kW. There are also small differences for the 

estimated operating costs. As a result of these differences, the amount of the social costs 

imposed on producers and consumers of rail and marine transportation services as a result 

of the proposed program would be larger than estimated in this section, while the impacts 

on the prices and quantities of locomotives would be slightly less. In addition, there 

would be larger social costs for the recreational marine sector. Nevertheless, the 

estimated market impacts and the distribution of the social costs among stakeholders 

would be about the same as those presented below. 

7.1.4.1 Market Analysis Results 

In the market analysis, we estimate how prices and quantities of goods affected by 

the proposed 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 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. 

7-11 
 
Draft Regulatory Impact Analysis 

A summary of the market analysis results is presented in Table 7-2 for 2011, 

2016, and 2030. These years were chosen because 2011 is the first year of the Tier 3 

standards, 2016 is when the Tier 4 standards begin for most engines, and 2030 illustrates 

the long-term impacts of the program. Results for all years can be found in the 

appendices to this Chapter. 

The estimated market impacts are designed to provide a broad overview of the 

expected market impacts that is useful when considering the impacts of the rule. 

Absolute price changes and relative price/quantity changes reflect production-weighted 

averages of the individual market-level estimates generated by the model for each group 

of engine/equipment markets. For example, the estimated marine diesel engine price 

changes are production-weighted averages of the estimated results for all of the marine 

diesel engine markets included in the group.4

 The absolute change in quantity is the sum 

of the decrease in units produced across sub-markets within each engine/equipment 

group. For example, the estimated marine diesel engine quantity changes reflect the total 

decline in marine diesel engines produced. 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. 

Locomotive Sector Impacts. On the locomotive side, the proposed program is 

expected to have a negligible impact on locomotive prices and quantities. In 2011, 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). 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 $425, or about 0.02 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 1.9 percent 

($35,900), on average and the quantity produced is expected to decrease by about 0.1 

percent, on average (less than 1 locomotive). Locomotive transportation service prices 

4

 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. 

7-12 
 
Chapter 7: Economic Impact Analysis 

are expected to decrease by about 0.1 percent). By 2030, the price of new locomotives is 

expected to increase by about 2.6 percent ($49,000), on average, and the quantity 

expected to decrease by about 0.2 percent (less than 1 locomotive). The price of rail 

transportation services is expected to increase by about 0.4 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, the proposed program does not begin to affect market prices or 

quantities until the Tier 4 standards go into effect, which is in 2016 for most engines. For 

these engines, the price of a new engines in 2016 is expected to increase between 11.0 

and 24.6 percent, on average ($17,300 for C1 engines above 800 hp and $64,100 for C2 

engines above 800 hp), depending on the type of engine, and sales are expected to 

decrease less than 2.0 percent, on average. The price of vessels that use them is expected 

to increase between 1.7 and 1.0 percent ($20,900 for vessels that use C1 engines above 

800 hp and $188,600 for vessels that use C2 engines above 800 hp) and sales are 

expected to decrease less than 2.0 percent. The percent change in price in the marine 

transportation sector is expected to be about 0.1 percent. By 2030, the price of these 

engines is expected to increase between 8.4 and 18.7 percent, on average ($13,300 for C1 

engines above 800 hp and $48,700 for C2 engine above 800 hp), depending on the type 

of engine, and sales are expected to decrease by less than 2 percent, on average. The 

price of vessels that use them is expected to increase between 1 and 3.6 percent ($16,200 

for vessels that use C1 engines above 800 hp and $141,600 for vessels that use C2 

engines above 800 hp) and sales are expected to decrease by less than 2 percent. The 

percent change in price in the marine transportation is expected to be about 0.6 percent. 

With regard to engines below 800 hp, the market impacts of the program are 

expected to be negligible.5

 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 example, the large vessels 

5

 The market results for engines and vessels below 800 hp are provided in a Technical Support 

Document that can be found in the docket for this rule. 

7-13 
 
Draft Regulatory Impact Analysis 

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 

2011 

Rail Sector 

Locomotives $0 -$425 -0.02% 0 -0.1% 

Transportation Services NA NAa 0.1% NAa -0.1% 

Marine Sector 

Engines 

 C1>800 hp $0 $0 0.00% 0 0.0%

 C2>800 hp $0 $0 0.00% 0 0.0%

 Other marine $0 $0 0.00% 0 0.0% 

Vessels

 C1>800 hp $0 $0 0.00% 0 0.0%

 C2>800 hp $0 $0 0.00% 0 0.0%

 Other marine $0 $0 0.00% 0 0.0% 

Transportation Services NA NAa 0.00% NAa 0.0% 

2016 

Rail Sector 

Locomotives $36,363 $35,929 1.9% 0 -0.1% 

Transportation Services NA NAa 0.1% NAa -0.1% 

Marine Sector a 

Engines 

 C1>800 hp $18,105 $17,330 11.0% -7 -1.7%

 C2>800 hp $64,735 $64,073 24.6% -1 -0.9%

 Other marine $0 $0 0.00% 0 0.0% 

Vessels

 C1>800 hp $2,980 $20,898 1.5% -9 -1.7%

 C2>800 hp $6,515 $188,559 4.8% -1 -0.9%

 Other marine $0 -$1 0.00% -0 0.0% 

Transportation Services NA NAa 0.1% NAa -0.1% 

2030 

Rail Sector 

Locomotives $50,291 $49,087 2.6% 0 -0.2% 

Transportation Services NA NAa 0.4% NAa -0.2% 

7-14 
 
Chapter 7: Economic Impact Analysis 

Market Average Change in Price Change in Quantity 

Variable 

Engineering Absolute Percent Absolute Percent 

Cost Per Unit 

Marine Sector 

Engines 

 C1>800 hp $13,885 $13,261 8.4% -6 -1.4%

 C2>800 hp $49,360 $48,692 18.7% -1 -0.9%

 Other marine $0 $0 0.0% 0 0.0% 

Vessels

 C1>800 hp $2,979 $16,155 1.1% -8 -1.5%

 C2>800 hp $6,516 $141,563 3.6% -1 -0.9%

 Other marine $0 -$4 0.0% -2 0.0% 

Transportation Services NA NAa 0.6% NAa -0.3% 

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 quanitity; see 

7.3.1.5. 

7.1.4.2 Economic Welfare Analysis 

In the economic welfare analysis we look at the costs to society of the proposed 

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 proposed program (fixed, variable, 

operating, and remanufacturing costs). Detailed economic welfare results for the 

proposed 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 propose 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. 

Table 7-3 Estimated Annual Engineering and Social Costs Through 2040 (2005$, $million) 

Year Engineering Costs Total 

Social 

Costs 

Marine 

operating 

costs 

Marine 

engine 

and vessel 

costs 

Rail 

operating 

costs 

Rail 

remanuf. 

costs 

Rail 

new locomotive 

costs 

Total 

2007 $0.0 $25.0 $0.0 $0.0 $3.2 $28.2 $28.2 

7-15 
 
Draft Regulatory Impact Analysis 

Engineering Costs 

2008 $0.0 $25.0 $1.3 $56.7 $3.2 $86.1 $86.1 

2009 $0.0 $25.0 $1.4 $33.2 $3.2 $62.7 $62.7 

2010 $0.0 $25.0 $3.8 $51.5 $7.3 $87.5 $87.5 

2011 $0.0 $86.0 $7.9 $96.9 $10.8 $201.6 $201.5 

2012 $0.0 $41.2 $9.7 $74.3 $12.3 $137.5 $137.5 

2013 $0.0 $41.2 $12.0 $62.4 $12.3 $127.9 $127.9 

2014 $2.8 $41.2 $12.6 $40.0 $16.9 $113.5 $113.5 

2015 $5.6 $74.1 $14.9 $29.1 $48.8 $172.5 $172.5 

2016 $14.8 $48.6 $19.0 $55.5 $55.3 $193.1 $192.6 

2017 $23.9 $44.9 $32.7 $39.3 $66.5 $207.3 $206.7 

2018 $36.0 $33.9 $44.6 $41.9 $67.9 $224.3 $223.9 

2019 $48.0 $34.2 $56.5 $36.7 $61.9 $237.4 $236.9 

2020 $60.0 $34.5 $68.5 $12.9 $64.0 $239.9 $239.5 

2021 $72.0 $34.8 $80.8 $14.9 $66.2 $268.7 $268.2 

2022 $83.9 $35.1 $93.6 $37.4 $68.1 $318.1 $317.6 

2023 $95.7 $35.4 $106.7 $83.2 $69.8 $390.8 $390.2 

2024 $107.5 $35.7 $120.1 $72.0 $70.8 $406.0 $405.4 

2025 $119.1 $35.9 $133.8 $76.5 $72.5 $437.9 $437.2 

2026 $130.6 $36.2 $147.7 $63.2 $73.5 $451.2 $450.4 

2027 $141.9 $33.6 $161.5 $64.6 $74.7 $476.3 $475.5 

2028 $153.0 $33.9 $175.5 $80.3 $75.6 $518.2 $517.3 

2029 $163.3 $34.2 $189.4 $81.8 $76.3 $544.9 $544.0 

2030 $172.6 $34.5 $203.3 $81.2 $76.8 $568.3 $567.3 

2031 $181.2 $34.8 $217.1 $81.4 $77.6 $592.1 $591.1 

2032 $189.0 $35.1 $231.1 $77.2 $78.5 $610.9 $609.8 

2033 $196.4 $35.4 $244.9 $133.5 $78.9 $689.2 $688.0 

2034 $203.6 $35.7 $258.7 $142.6 $79.6 $720.1 $718.8 

2035 $210.4 $36.0 $272.4 $150.1 $79.8 $748.8 $747.4 

2036 $216.9 $36.4 $285.8 $143.2 $77.5 $759.7 $758.3 

2037 $222.7 $36.7 $299.2 $145.9 $75.8 $780.3 $778.8 

2038 $227.9 $37.0 $312.0 $148.8 $73.9 $799.6 $798.1 

2039 $232.4 $37.3 $324.4 $152.0 $71.8 $818.0 $816.4 

2040 $236.3 $37.7 $336.3 $155.0 $69.5 $834.7 $833.2 

2040 NPV at 3%a,b $6,907.8 $6,896.8 

2040 NPV at 7% a,b $3,107.7 $3,103.1 

2030 NPV at 3% a,b $3,938.7 $3,932.6 

2030 NPV at 7% a,b $2,175.5 $2,172.5 

a EPA EPA presents the present value of cost and benefits estimates using both a three percent and a 

seven percent social discount rate. According to OMB Circular A-4, “the 3 percent discount rate 

represents the ‘social rate of time preference’... [which] means the rate at which ‘society’ discounts 

future consumption flows to their present value”; “the seven percent rate is an estimate of the average 

before-tax rate of return to private capital in the U.S. economy ... [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 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). 

7-16 
 
Chapter 7: Economic Impact Analysis 

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 57.3 percent in 2011 to 67.3 percent 

in 2016. Producers and consumers of locomotive transportation services are expected to 

bear most of those costs, ranging from 51.9 percent in 2011 to 63.3 percent in 2030. As 

explained above, these results assume the railroads absorb all remanufacture kit 

compliance costs (the remanufacture kit manufacturers pass all costs of the new standards 

to the railroads). The marine sector is expected to bear the remaining social costs, 

Figure 7-1. Estimated Annual Social Costs, 2007-2040 (2005$, $million) 

$900 

$800 

$700 

$600 

$500 

$400 

$300 

$200 

$100 

$0 

2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 

ranging from 42.7 percent in 2011 to 32.7 percent in 2016. Producers of marine diesel 

engines are expected to bear more of the program costs in the early years (42.7 percent in 

2011), but by 2020 producers and consumers in the marine transportation services market 

are expected to bear a larger share of the social costs, 31.5 percent. 

7-17 
 
Draft Regulatory Impact Analysis 

Table 7-4. Summary of Estimated Net Social Costs for 2011, 2016, 2020, 2030 (2005$, $million) 

Stakeholder Group 2011 2016 

 Surplus 

Change 

Percent Surplus 

Change 

Percent 

Locomotives 

Locomotive producers -$11.1 5.5% -$13.4 7.0% 

Rail transportation service providers -$47.5 23.6% -$52.9 27.5% 

Rail transportation service consumers -$57.0 28.3% -$63.5 33.0% 

Total locomotive sector -$115.6 57.3% -$129.7 67.3% 

Marine 

Marine engine producers -$86.0 42.7% -$0.9 0.5%

 C1 > 800 hp -$22.8 -$0.7 

 C2 > 800 hp -$27.8 -$0.2 

 Other marine -$35.4 -$0.0 

Marine vessel producers -$0 0.0% -$18.0 9.3%

 C1 > 800 hp -$0 -$13.6 

 C2 > 800 hp -$0 -$4.4 

 Other marine -$0 -$0.0 

 Recreational and fishing vessel consumers -$0 0.0% -$9.6 5.0% 

Marine transportation service providers -$0 0.0% -$15.6 8.1% 

Marine transportation service consumers -$0 0.0% -$18.7 9.7% 

Total marine sector -$86.0 42.7% -$62.9 32.7% 

TOTAL PROGRAM -$201.5 -$192.6 

Stakeholder Group 2020 2030 

 Surplus 

Change 

Percent Surplus 

Change 

Percent 

Locomotives 

Locomotive producers -$0.7 0.3% -$1.8 0.3% 

Rail transportation service providers -$65.8 27.5% -$163.2 28.8% 

Rail transportation service consumers -$78.9 32.9% -$195.9 34.5% 

Total locomotive sector -$145.3 60.7% -$360.9 63.6% 

Marine 

Marine engine producers -$0.8 0.3% -$0.9 0.2%

 C1 > 800 hp -$0.6 -$0.7 

 C2 > 800 hp -$0.2 -$0.2 

 Other marine -$0.0 -$0.0 

Marine vessel producers -$10.1 4.2% -$8.2 1.4%

 C1 > 800 hp -$7.8 -$6.4 

 C2 > 800 hp -$2.3 -$1.6 

 Other marine -$0.1 -$0.1 

 Recreational and fishing vessel consumers -$7.8 3.3% -$8.5 1.5% 

Marine transportation service providers -$34.3 14.3% -$85.8 15.1% 

Marine transportation service consumers -$41.2 17.2% -$103.0 18.2% 

Total marine sector -$94.1 39.3% -$206.5 36.4% 

TOTAL PROGRAM -$239.5 100.0% -$567.3 100.0% 

7-18 
 
Chapter 7: Economic Impact Analysis 

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 $29.3 –$0.8 

Vessel Producers $5.2 –$10.1 

Engine price changes –$8.1 

Equipment cost changes –$2.0 

Recreational and Fishing Consumers -$7.8 

Engine price changes -$6.2 

Equipment cost changes -$1.6 

Transportation Service Providers $60.0 –$34.3 

Increased price vessels –$6.9 

Operating costs –$27.4 

Users of Transportation Service –$41.2 

Increased price vessels –$8.2 

Operating costs –$32.9 

Rail Markets 

Locomotive Producers $64.0 –$0.7 

Rail Service Providers $81.4 –$65.8 

Increased price new locomotives –$28.8 

Remanufacturing costs $9.5 –$8.1 

Operating costs $63.6 –$28.9 

Users of Rail Transportation Service –$78.9 

Increased price new locomotives –$34.6 

Remanufacturing costs –$9.7 

Operating costs –$34.7 

TOTAL $239.9 $239.6 

7-19 
 
Draft Regulatory Impact Analysis 

The present value of net social costs of the proposed standards through 2040, shown 

in Table 7-3, is estimated to be is estimated to be $6.9 billion (2005$).6 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.7

 Using that discount rate, the present value of the net social costs 

through 2040 is estimated to be $3.1 billion (2005$). 

Error! Reference source not found. shows the distribution of total surplus losses 

for the program from 2006 through 2040. This table shows that the rail sector is expected 

to bear about 65 percent of the total program social costs through 2040, and that most of 

the costs are expected to be borne by the rail transportation service producers and 

consumers. On the marine side, most of the marine sector costs are expected to be borne 

by the marine transportation service providers and 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 

NPV 3% 

Percent of 

Total Surplus 

Surplus Change 

NPV 7% 

Percent of 

Total Surplus 

Locomotives 

Locomotive producers $92.8 1.3% $63.5 2.0% 

Rail transportation service 

providers $1,988.8 28.8% $878.1 28.3% 

Rail transportation service 

consumers $2,386.4 34.6% $1,053.7 33.9% 

Total locomotive sector $4,468.1 64.8% $1,995.4 64.4% 

Marine 

Marine engine producers $313.3 4.5% $242.3 7.8%

 C1 > 800 hp $102.1 $73.9 

 C2 > 800 hp $112.4 $84.4 

 Other marine $98.7 $84.0 

6

 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). 

7

 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. 

7-20 
 
Chapter 7: Economic Impact Analysis 

Marine vessel producers $143.8 2.1% $71.3 2.3%

 C1 > 800 hp $110.1 $54.3 

 C2 > 800 hp $32.4 $16.5 

 Other marine $1.3 $0.5 

 Recreational and fishing 

vessel consumers $110.0 1.6% $51.0 1.6% 

Marine transportation service 

providers $846.2 12.3% $338.2 10.9% 

Marine transportation service 

consumers $1,015.4 14.7% $405.9 13.1% 

Total marine sector $2,428.7 35.2% $1,107.7 35.7% 

TOTAL PROGRAM $6,896.8 $3,103.1 

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 

markets (prices and quantities) and stakeholder groups (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.

3 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 

7-21 
 
Draft Regulatory Impact Analysis 

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 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.4 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. 

7-22 
 
Chapter 7: Economic Impact 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 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."5 

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 proposed 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 proposed 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 on electricity produced using coal transported by rail, or on 

manufactured productions that use that electricity. Similarly, while we look at the 

impacts of the control program on the price of fishing vessels, we do not look at the 

impacts on the prices of food products that use fish as an input. This is because these 

inputs (trail 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 locomotive or 

marine transportation services or marine vessels as an input. 

7-23 
 
Draft Regulatory Impact Analysis 

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. 

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.8

 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 

8

 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. 

7-24 
 
Chapter 7: Economic Impact Analysis 

unit. Thus, social welfare would be increased by inducing the monopolist to increase 

production. Social cost estimates associated with a proposed regulation are 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 12866.6 

Perfect competition is widely accepted for this type of analysis and only in rare 

cases are other approaches used.7

 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. 

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.8

 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 

7-25 
 
Draft Regulatory Impact Analysis 

of competing railroads with parallel lines. These mergers increase competition by 

creating more efficient, lower cost railway networks.9 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.10 

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.

11 

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 

7-26 
 
Chapter 7: Economic Impact Analysis 

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 average costs of production are constant with respect to 

output.9 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. 

9

 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. 

7-27 
 
Draft Regulatory Impact Analysis 

Figure 7-3 Long-Run: Full-Cost Pass-Through 

$ d 

b S1

: With Regulation 

Q 1 Q 0 

a P0 

c P1 Price 

Increase } } Unit Cost Increase 

S0

: Without Regulation 

D 

Output 

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 pass-through” and is illustrated in Error! Reference 

source not found.. 

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 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. 

7-28 
 
Chapter 7: Economic Impact Analysis 

Consequently a time horizon that falls between the very short-run/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 Intermediat 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. 

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 

7-29 
 
Draft Regulatory Impact Analysis 

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. 

7-30 
 
Chapter 7: Economic Impact Analysis 

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 proposed rule, 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) declines 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 proposed 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 

7-31 
 
Draft Regulatory Impact Analysis 

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 to change in demand and are illustrated 

as shifts in the position of the demand curve.10 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 proposed 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 proposed 

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 

proposed program 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 proposed program. 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 proposed 

regulatory program is expected to affect vessel producers in two ways. First, these 

10 An accessible detailed discussion of these concepts can be found in Chapters 5-7 of Nicholson’s (1998) 

intermediate microeconomics textbook. 

7-32 
 
Chapter 7: Economic Impact Analysis 

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. 

In the marine market case, the demand for engines is directly linked to the 

production of vessels that uses those engines.11 For this reason, it is reasonable to assume 

that the input-output 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. 

11 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. 

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Draft Regulatory Impact Analysis 

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 

7-34 
 
Chapter 7: Economic Impact Analysis 

market. All of the market supply and demand curves and the elasticity parameters are 

described in Appendix 7F. 

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). 

7-35 
 
Draft Regulatory Impact Analysis 

Figure 7-7. Economic Welfare Calculations: Changes in Consumer, Producer, and Total Surplus 

P1 

P2 

S 

S′ 

D 

$/Q 

A 

Q2 Q1 Q/t 

(a) Change in Consumer Surplus with 

Regulation 

$/Q 

P2 

P1 

S 

S′ 

D 

B 

C 

Q2 Q1 Q/t 

(b) Change in Producer Surplus with 

Regulation 

P1 

P2 

S 

S′ 

D 

$/Q 

D 

Q2 Q1 Q/t 

(c) Net Change in Economic Welfare with 

Regulation 

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.” 

7-36 
 
Chapter 7: Economic Impact Analysis 

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. 

In 

7-37 
 
Draft Regulatory Impact Analysis 

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 

7-38 
 
Chapter 7: Economic Impact Analysis 

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 

7-39 
 
Draft Regulatory Impact Analysis 

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). 

7-40 
 
Chapter 7: Economic Impact Analysis 

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. 

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 

7-41 
 
Draft Regulatory Impact Analysis 

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. 

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. 

7-42 
 
Chapter 7: Economic Impact Analysis 

•	 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. 

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 

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 Error! Reference source not found.. 

Note that to be consistent with the engineering costs analysis, passenger locomotives are 

included with the switcher locomotives. 

7-43 
 
Draft Regulatory Impact Analysis 

Table 7-7 Locomotive Sales (2007 through 2040) 

Year Line Haul Sales Switcher/Passenger 

Sales 

2007 646 112 

2008 666 192 

2009 693 128 

2010 729 130 

2011 751 133 

2012 767 138 

2013 765 251 

2014 780 278 

2015 816 299 

2016 854 311 

2017 877 332 

2018 894 344 

2019 917 352 

2020 948 369 

2021 979 387 

2022 1,007 398 

2023 1,034 399 

2024 1,048 407 

2025 1,078 401 

2026 1,096 394 

2027 1,119 384 

2028 1,136 378 

2029 1,150 370 

2030 1,158 368 

2031 1,173 362 

2032 1,190 358 

2033 1,209 316 

2034 1,223 303 

2035 1,231 291 

2036 1,197 279 

2037 1,172 267 

2038 1,144 255 

2039 1,112 248 

2040 1,078 234 

7.3.1.2 Locomotive Initial Equilibrium Prices 

The price used for new line-haul locomotives used in the EIM is $2 million 

(2005$). The 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 the 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 

7-44 
 
Chapter 7: Economic Impact Analysis 

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 

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. The sales for 2002 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 

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. 

The vessel sales data for 2002 were derived by apportioning the commercial 

propulsion engine sales in Table 7-8 to vessel types based on current vessel populations.12 

The vessel sales 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 58 0 1 0 0 1 3,924 3,983 

50–200 2,293 247 40 41 13 31 6,294 8,959 

200–400 601 65 10 11 3 8 1,332 2,031 

400–800 703 76 12 13 4 10 2,110 2,927 

C1 800–2,000 203 22 4 4 1 3 299 535 

C1 >2,000 84 9 1 2 0 1 89 187 

7-45 
 
Draft Regulatory Impact Analysis 

C2 800–2,000 0 1 0 1 0 0 0 3 

C2 >2,000 9 27 3 15 4 1 0 58 

Total 3,951 447 71 86 26 54 14,047 18,683 

The marine diesel engine sales used in the EIM for 2007 through 2040 were 

projected by 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. 

The marine vessel sales used in the EIM for 2007 through 20404 were projected 

by creating a ratio of engines to vessels using the 2002 data and applying that to future 

years engine sales. The ratios used for commercial vessels are contained in Table 7-10. 

Ratios were not estimated for recreational vessels because the Tier 3 standards do not 

require vessel modifications.12 

Table 7-10 Ratio of Vessels to Engines 

fishing tow ferries supply cargo other Total 

<50 0.97 0.00 0.02 0.00 0.00 0.01 1.00 

50-200 0.86 0.09 0.01 0.02 0.00 0.01 1.00 

200-400 0.86 0.09 0.01 0.02 0.00 0.01 1.00 

400-800 0.86 0.09 0.01 0.02 0.00 0.01 1.00 

800-2000 0.86 0.09 0.01 0.02 0.00 0.01 1.00 

>2000 0.86 0.09 0.01 0.02 0.00 0.01 1.00 

800-2000 0.15 0.46 0.04 0.26 0.07 0.01 1.00 

>2000 0.15 0.46 0.04 0.26 0.07 0.01 1.00 

7.3.1.4 Marine Engine and Vessel Initial Equilibrium Prices 

The EIM uses baseline equilibrium engine prices for C1 commercial propulsion 

engines were obtained from an internet search of engine prices.13 These prices are 

contained in Table 7-11. The C2 propulsion engine prices were obtained by multiplying 

the C1 commercial propulsion engines by about 1.5. This reflects the larger cylinder 

displacement of these engines and the fact that they are built for longer hours of use. The 

auxiliary engine prices were derived by dividing the propulsion engine prices by 2. This 

is because auxiliary marine diesel engines are often more similar to land-based engines 

and don’t require the same types of modifications for use in the marine environment. 

12 This EIA was based on an earlier version of the cost analysis that did not include compliance 

costs for recreational vessels >2000 kW. 

7-46 
 
Chapter 7: Economic Impact Analysis 

They are also designed to operate at constant load and don’t see the transients 

experienced by propulsion engines. The recreational engine prices were derived by 

multiplying the propulsion engines by 1.25, reflecting 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. 

Table 7-11. Per Unit Marine Diesel Engine Prices (2005$) 

Marine Diesel Engine 

Categories (by hp) 

Commercial 

Propulsion 

Recreational Auxiliary 

< 50 hp $7,000 $8,750 $3,500 

50-200 hp $16,000 $20,000 $8,000 

200-400 hp $21,000 $26,250 $10,500 

400-800 hp $50,000 $62,500 $25,000 

C1 800-2,000 hp $155,000 $193,750 $77,500 

C1 > 2,000 hp $300,000 $375,000 $150,000 

C2 800-2,000 hp $230,000 NA $115,000 

C2 > 2,000 hp $450,000 NA $225,000 

The baseline equilibrium marine vessel prices used in the EIM were derived from 

the engine prices by applying an assumed ratio of the price of the vessel to the price of 

the propulsion engines onboard. Table 7-12 sets out the ratios used to estimate the vessel 

prices, and Table 7-13 sets out the vessel prices used in the EIA. 

Table 7-12. 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 7-13. 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 

7-47 
 
Draft Regulatory Impact Analysis 

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 

proposed program, although it does not allow estimation of the absolute price and 

absolute quantity change. 

Baseline data for the EIM’s railroad and marine service revenues are reported in 

Table 7-13. Revenue data for the rail transportation services freight revenue comes from 

the Association of American Railroads Freight Railroad Statistics, Condensed Income 

Statement, revenue for freight and passenger services.14 Revenue data for the marine 

transportation services sector comes from the U.S. Census reports revenues for the 

marine service sector for 2002.13 Revenue data for 2002 was obtained for the following 

NAICS codes: 483113 (coastal & great lakes freight), 483114 (coastal & great lakes 

passenger), 4832 (inland water transportation), 4872(Scenic & sightseeing transportation, 

water), plus a portion of 4883 (support activity for water transportation). The 2002 

revenue data was adjusted for 2005 using the GDP deflator index. 

Table 7-14. Railroad and Marine Service Markets Baseline Revenue Data ($billions) 

13 We adjusted marine transportation service revenue to reflect 2005 dollars using the latest GDP deflator. 

7-48 
 
Chapter 7: Economic Impact Analysis 

Transportation Service Market 2002 Annual Growth Rate 2005 

Railroad Services Market NR 0.9% $44.5 

Marine Services Market $13.8 0.9% $14.2 

To estimate production 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) 

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 2004.15 The marine sector accounted for about 1,047.1 million 

tons in that year.16 So, 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 proposed standards are estimated by shocking the initial 

market equilibrium conditions by the amount of the compliance costs. The EIM uses an 

earlier version of the engineering costs developed for this rule (see Section 7.1.4 above). 

Table 7-15 summarizes how the compliance costs are applied to each component 

of the EIM to simulate the effect of the emission control program. There are no 

compliance costs for the demand side of these markets. This is because the program does 

not regulate consumers or impose direct compliance costs on them (see also Section 

7.2.3.1). 

Table 7-15. Summary of Types of Compliance Costs 

Market Category Supply Shift Demand Shift 

Entity Direct Costs Indirect Costs 

Rail Locomotive Loco Mfr Variable costs N/A No demand 

7-49 
 
Draft Regulatory Impact Analysis 

Transportation 

Services 

Railroad Urea, Fuel, 

remanufacture kit 

Higher 

locomotive 

prices 

shift; see 

7.2.3.1 

Marine <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 

Transportation 

Services 

Vessel 

Owner 

Urea, fuel Higher engine 

and vessel prices 

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 with a couple of exceptions. First, the EIA 

contains costs for closed crankcase ventilation systems which were subsequently 

removed from the cost analysis presented in Chapter 5. Second, the engine-related 

hardware costs here in the EIA do not include costs associated with urea SCR tanks and 

brackets which, we decided, should be considered vessel related hardware costs for the 

EIA. For marine diesel engine fixed costs in the EIA, we simply divided the annual 

engine fixed costs by the projected sales for the given year, rather than using the present 

value per engine costs presented in several tables throughout section 5.2.1. 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 (only a few years of sales). 

On the vessel side, we used the vessel hardware costs shown in Table 5-38, and 

added to that the costs for urea SCR tanks and brackets. Importantly, the costs associated 

with the urea tank and brackets are incurred for every engine (auxiliary and propulsion), 

while the vessel hardware costs shown in Table 5-38 are incurred for every vessel. To 

arrive at a per vessel cost for the EIA, we multiplied the urea tank and bracket costs by 

the projected number of engines (auxiliary and propulsion) and then divided by the 

projected number of vessels, then added the vessel hardware costs shown in Table 5-38. 

In the end, the vessel hardware costs presented here look different than those presented in 

Chapter 5 due to different accounting, but the total costs are not affected by that 

accounting difference. The vessel fixed costs are the annual redesign costs divided by the 

projected number of vessel sales during the given years. Note that the annual fixed costs 

have been allocated to power ranges based on the percentage of engines within the 

appropriate power range. Also note that the per-unit cost estimates are based on an 

average of 1.5 propulsion engines per vessel. 

For locomotives, we used essentially the same methodology. The variable costs 

are taken from Tables 5-29 and 5-38, with the same difference associated with closed 

crankcase ventilation system costs noted above. Annual fixed costs are simply divided 

by the sales for the given year making them, once again, appear rather large on a per 

7-50 
 
Chapter 7: Economic Impact Analysis 

locomotive basis here in the EIA. 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 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 locomotives and marine diesel engines, this means a staggered set of 

fixed costs, as described in Table 7-16, with the compliance costs for the different Tiers 

overlapping in some years. It should be remembered that the EIA is based on an earlier 

version of the cost analysis and may not reflect changes to the way in which costs are 

allocated for the proposed program as described in Chapter 5. For marine vessels, there 

are no compliance costs associated with the Tier 3 standards since they are engine-based 

controls and will not affect the footprint of the engine. The marine vessel compliance 

costs for Tier 4 begin in 2015 and are incurred over a 15-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 vessel costs are allocated; note that the final costs in Chapter 5 reflect these costs 

distributed over a shorter period). 

Table 7-16 Locomotive and Marine Engine Compliance Costs Schedule 

Loco T3 Loco T4 PM Loco T4 NOx Marine T3 Marine T4 

2007 9 9

2008 9 9

2009 9 9

2010 9 9 9

2011 9 9 9 9

2012 Effective Date 9 9 Effective Date 9

2013 9 9 9

2014 9 9 9

2015 Effective Date 9 9

2016 9 Effective Date 

2017 Effective Date 

7.3.2.1 Locomotive Compliance Costs 

The estimated per unit compliance costs for new locomotives used in the EIM are 

summarized in Table 7-17. 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. There is some overlap in 

these two programs, with the Tier 3 fixed costs applying in 2007 through 2011, and the 

Tier 4 fixed costs applying in 2010 through 2016. The latter period represents 5 years of 

7-51 
 
Draft Regulatory Impact Analysis 

fixed costs for the PM aftertreatment standards (2010 through 2014) and 5 years of fixed 

costs for the NOx aftertreatment standards (2012 through 2016). 

Table 7-17 Estimated Per Unit Compliance Costs – New Locomotives (2005$) 

Line Haul Locomotive Switcher, Passenger Locomotive 

Year Variable Fixed Total Variable Fixed Total 

2007 $0 $991 $991 $0 $22,767 $22,767 

2008 $0 $991 $991 $0 $13,304 $13,304 

2009 $0 $923 $923 $0 $19,938 $19,983 

2010 $0 $3,197 $3,197 $0 $37,929 $37,929 

2011 $0 $5,134 $5,134 $0 $51,419 $51,914 

2012 $0 $6,678 $6,678 $0 $52,200 $52,200 

2013 $0 $6,694 $5,694 $0 $28,777 $28,777 

2014 $0 $9,329 $9,239 $0 $35,031 $35,031 

2015 $44,390 $4,204 $48,594 $14,353 $16,179 $30,531 

2016 $44,390 $6,465 $50,855 $14,353 $23,603 $37,956 

2017 $68,544 $0 $68,544 $19,230 $0 $19,230 

2018 $68,544 $0 $68,544 $19,230 $0 $19,230 

2019+ $60,624 $0 $60,624 $17,770 $0 $19,230 

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 7-18 (C2 engines), Table 7-19 (C1 engines), Table 7-20 

(recreational engines), and Table 7-21 (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. 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.14 Because this EIA is uses an earlier version of the 

compliance costs estimates that did not include Tier 4 standards for recreational engines 

above 2,000 kW, the costs for those engines and vessels are not included in these tables. 

14 It should be noted that there is an inconsistency in the cost analysis, which applies the operational costs 

for these C2 engines in 2014 but does not include the compliance costs for engines or vessels until later 

years. While this affects the individual year results for early years, the differences disappear by 2016 by 

which year all marine diesel engines above 800 hp have aftertreatment standards. 

7-52 
 
Chapter 7: Economic Impact Analysis 

Table 7-18 Estimated Per Unit Compliance Costs - C2 Commercial 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 $93,647 $69,382 

Total $14,571 $14,441 $14,312 $14,184 $93,647 $69,382 

>2,000 Variable $0 $0 $0 $0 $0 $0 

Fixed $14,571 $14,441 $14,312 $14,184 $93,647 $69,382 

Total $14,571 $14,441 $14,312 $14,184 $93,647 $69,382 

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

800-2,000 Variable $0 $0 $0 $39,059 39,059 29,827 

Fixed $68,763 $68,150 $97,398 $0 $0 $0 

Total $68,763 $68,150 $97,398 $39,059 39,059 29,827 

>2,000 Variable $0 $0 $0 $72,301 $72,301 $55,121 

Fixed $68,763 $68,150 $97,398 $0 $0 $0 

Total $68,763 $68,150 $97,398 $72,301 $72,301 $55,121 

Table 7-19 Estimated Per Unit Compliance Costs – C1 Commercial 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 $1,475 $0 

Total $836 $829 $822 $814 $1,475 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 

Fixed $836 $829 $822 $814 $1,475 $0 

Total $836 $829 $822 $814 $1,475 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 

Fixed $836 $829 $822 $814 $1,475 $0 

Total $836 $829 $822 $814 $1,475 $0 

800-2,000 Variable $0 $0 $0 $0 $0 $0 

Fixed $2,033 $2,015 $1,997 $1,979 $25,553 $22,720 

Total $2,033 $2,015 $1,997 $1,979 $25,553 $22,720 

>2,000 Variable $0 $0 $0 $0 $0 $0 

Fixed $2,033 $2,015 $1,997 $1,979 $25,553 $22,720 

Total $2,033 $2,015 $1,997 $1,979 $25,553 $22,720 

Hp 

Category 

Cost Type 2013 2014 2015 2016 2017 2018+ 

50-200 Variable $0 $0 $0 $0 $0 $0 

7-53 
 
Draft Regulatory Impact Analysis 

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 $15,319 $15,319 $11,763 

Fixed $22,517 $22,316 $28,928 $0 $0 $0 

Total $22,517 $22,316 $28,928 $15,319 $15,319 $11,763 

>2,000 Variable $0 $0 $0 $26,296 $26,926 $20,116 

Fixed $22,517 $22,316 $28,928 $0 $0 $0 

Total $22,517 $22,316 $28,928 $26,296 $26,926 $20,116 

Table 7-20 Estimated Per Unit Compliance Costs – Recreational Engines (2005$) 

Hp 

Category 

Cost Type 2007 2008 2009 2010 2011 2012 

50-200 Variable $0 $0 $0 $0 $0 $0 

Fixed $403 $393 $384 $375 $684 $0 

Total $403 $393 $384 $375 $684 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 

Fixed $403 $393 $384 $375 $684 $0 

Total $403 $393 $384 $375 $684 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 

Fixed $403 $393 $384 $375 $684 $0 

Total $403 $393 $384 $375 $684 $0 

800-2,000 Variable $0 $0 $0 $0 $0 $0 

Fixed $403 $393 $384 $375 $684 $0 

Total $403 $393 $384 $375 $684 $0 

>2,000 Variable $0 $0 $0 $0 $0 $0 

Fixed $403 $393 $384 $375 $684 $0 

Total $403 $393 $384 $375 $684 $0 

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 

7-54 
 
Chapter 7: Economic Impact Analysis 

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 7-21 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 $126 $124 $123 $122 $245 $0 

Total $126 $124 $123 $122 $245 $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 

The estimated per unit compliance costs for marine vessels used in the EIM are 

summarized in Table 7-22 (C2 vessels, i.e., vessels with a C2 main propulsion engine), 

Table 7-23 (C1 vessels, i.e., vessels with a C1 main propulsion engine), Error! 

Reference source not found. (recreational vessels), and Error! Reference source not 

found. (small marine vessels). There are no vessel compliance costs associated with the 

Tier 3 standards. This means there are no vessel compliance costs at all for recreational 

vessels or Small vessels (those with a propulsion engine below 50 hp). This is because 

the Tier 3 engine footprint is not expected to be modified from the Tier 2 configuration. 

The sole vessel compliance costs are those associated with the Tier 4 aftertreatment 

standards. These begin in 2015, with the fixed costs, which continue through 2027.15 

Variable costs begin in 2016 and continue for all years of the analysis. Because this EIA 

is uses an earlier version of the compliance costs estimates that did not include Tier 4 

15 It should be noted that there is an inconsistency in the cost analysis, which applies the operational costs 

for these C2 engines in 2014 but does not include the compliance costs for engines or vessels until later. 

years. While this affects the individual year results for early years, the differences disappear by 2016 by 

which year all marine diesel engines above 800 hp have aftertreatment standards. 

7-55 
 
Draft Regulatory Impact Analysis 

standards for recreational engines above 2,000 kW, the costs for those engines and 

vessels are not included in these tables. 

Table 7-22 Per Unit Compliance Costs – C2 Vessels (2005$) 

Hp 

Category 

Cost 

Type 

2015 2016 2017 2018 2019 2020 2021 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

800-2,000 Variable $0 $3,964 $3,964 $3,964 $3,964 $3,964 $3,964 

Fixed $50,000 $29,732 $19,645 $9,735 $9,648 $9,562 $9,477 

Total $50,000 $33,697 $23,609 $13,699 $13,612 $13,526 $13,441 

>2,000 Variable $0 $7,155 $7,155 $7,155 $7,155 $7,155 $7,155 

Fixed $50,000 $29,732 $19,645 $9,735 $9,648 $9,562 $9,477 

Total $50,000 $36,887 $26,799 $16,889 $16,803 $16,716 $16,631 

Hp 

Category 

Cost 

Type 

2022 2023 2024 2025 2027 2028 2029+ 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

800-2,000 Variable $3,964 $3,964 $3,964 $3,964 $3,964 $3,964 $3,964 

Fixed $9,392 $9,308 $9,225 $9,143 $9,061 $0 $0 

Total $13,356 $13,273 $13,190 $13,107 $13,026 $3,964 $3,964 

>2,000 Variable $7,155 $7,155 $7,155 $7,155 $7,155 $7,155 $7,155 

Fixed $9,392 $9,308 $9,225 $9,143 $9,061 $0 $0 

Total $16,547 $16,463 $16,380 $16,298 $16,216 $7,155 $7,155 

7-56 
 
Chapter 7: Economic Impact Analysis 

Table 7-23 C1 Per Unit Compliance Costs – C1 Vessels (2005$) 

Hp 

Category 

Cost 

Type 

2015 2016 2017 2018 2019 2020 2021 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

800-2,000 Variable $0 $2,385 $2,385 $2,385 $2,385 $2,385 $2,385 

Fixed $25,000 $12,884 $6,876 $3,894 $3,859 $3,825 $3,791 

Total $25,000 $15,269 $9,261 $6,279 $6,244 $6,210 $6,176 

>2,000 Variable $0 $4,672 $4,672 $4,672 $4,672 $4,672 $4,672 

Fixed $25,000 $12,884 $6,876 $3,894 $3,859 $3,825 $3,791 

Total $25,000 $17,556 $11,547 $8,565 $8,531 $8,496 $8,462 

Hp 

Category 

Cost 

Type 

2022 2023 2024 2025 2027 2028 2029+ 

50-200 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

200-400 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

400-800 Variable $0 $0 $0 $0 $0 $0 $0 

Fixed $0 $0 $0 $0 $0 $0 $0 

Total $0 $0 $0 $0 $0 $0 $0 

800-2,000 Variable $2,385 $2,385 $2,385 $2,385 $2,385 $2,385 $2,385 

Fixed $3,757 $3,723 $3,690 $3,657 $3,625 $0 $0 

Total $6,142 $6,108 $6,075 $6,042 $6,010 $2,385 $2,385 

>2,000 Variable $4,672 $4,672 $4,672 $4,672 $4,672 $4,672 $4,672 

Fixed $3,757 $3,723 $3,690 $3,657 $3,625 $0 $0 

Total $8,428 $8,395 $8,362 $8,329 $8,296 $4,672 $4,672 

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 remanufacture program. 

7-57 
Draft Regulatory Impact Analysis 

Figure 7-9. Estimated TotalCompliance Costs by Type, 2007-2040 

$-

$100,000,000 

$200,000,000 

$300,000,000 

$400,000,000 

$500,000,000 

$600,000,000 

$700,000,000 

$800,000,000 

$900,000,000 

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040 

Year 

Dollars

Marine Engines, Vessels 

Locomotives 

Operating Costs 

Total Costs 

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: urea use, DPF 

maintenance, fuel consumption. The costs associated with urea use would affect only 

those locomotives or vessels equipped with a urea SCR engine. Maintenance costs 

associated with the DPF (for periodic cleaning of accumulated ash resulting from 

unburned material that acculuates 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. The 

fuel consumption impact is expected to occur more broadly, for both Tier 4 locomotives 

and engines and for remanufactured Tier 0 locomotives. 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 to the rail and marine transportation 

services markets, 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 

7-58 
 
Chapter 7: Economic Impact Analysis 

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. This approach was taken because the operating costs (fuel and urea 

consumption) were estimated based on fuel consumption and we believe that 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 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. As a 

result of this assumption, the impacts on the marine transportation service market may be 

somewhat over-estimated. 

Table 7-24 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 $1,221,312 $40,179 $1,261,491 

2009 $0 $0 $1,210,900 $160,835 $1,371,735 

2010 $0 $0 $3,515,299 $280,687 $3,795,986 

2011 $0 $0 $7,551,076 $392,336 $7,943,411 

2012 $0 $0 $9,225,485 $435,933 $9,661,419 

2013 $0 $0 $11,514,508 $472,292 $11,986,800 

2014 $0 $2,811,138a $12,052,336 $501,894 $15,365,368 

2015 $0 $5,629,325a $14,325,890 $559,055 $20,514,270 

2016 $3,748,711 $11,007,676 $18,337,278 $670,590 $33,764,256 

2017 $7,493,511 $16,385,211 $30,926,013 $1,737,068 $56,541,803 

2018 $14,199,915 $21,762,262 $41,788,824 $2,767,721 $80,518,723 

2019 $20,882,573 $27,139,987 $52,700,319 $3,820,549 $104,543,428 

2020 $27,527,317 $32,504,807 $63,571,104 $4,915,367 $128,518,594 

2021 $34,133,758 $37,868,806 $74,765,580 $6,052,613 $152,820,757 

2022 $40,682,747 $43,220,176 $86,363,005 $7,215,420 $177,481,347 

2023 $47,173,642 $48,558,846 $98,350,736 $8,390,480 $202,473,704 

2024 $53,573,572 $53,885,232 $110,512,199 $9,592,182 $227,563,184 

2025 $59,881,593 $59,200,774 $123,059,778 $10,783,930 $252,926,074 

2026 $66,065,578 $64,518,680 $135,694,126 $11,963,396 $278,241,780 

2027 $72,088,258 $69,826,073 $148,437,564 $13,108,658 $303,460,553 

2028 $77,882,344 $75,125,645 $161,217,797 $14,247,139 $328,472,925 

7-59 
 
Draft Regulatory Impact Analysis 

Marine 

C1>800Hp 

Marine C2 Loco-Line haul Loco-Switcher 

& Passenger 

Total 

2029 $82,861,888 $80,389,769 $174,009,828 $15,376,721 $352,638,206 

2030 $86,995,449 $85,630,509 $186,753,267 $16,509,822 $375,889,047 

2031 $90,362,827 $90,821,351 $199,510,957 $17,637,122 $398,332,257 

2032 $93,020,024 $95,974,911 $212,291,194 $18,764,807 $420,050,936 

2033 $95,380,145 $101,051,358 $225,111,725 $19,796,404 $441,339,632 

2034 $97,498,765 $106,062,320 $237,920,399 $20,801,857 $462,283,341 

2035 $99,411,516 $110,968,576 $250,602,975 $21,784,330 $482,767,397 

2036 $101,151,649 $115,727,740 $263,081,520 $22,741,774 $502,702,683 

2037 $102,751,192 $119,955,873 $275,486,662 $23,684,289 $521,878,016 

2038 $104,242,148 $123,625,344 $287,475,707 $24,563,660 $539,906,858 

2039 $105,628,120 $126,752,201 $299,021,332 $25,393,609 $556,795,262 

2040 $106,922,034 $129,386,543 $310,117,313 $26,160,790 $572,586,680 a It should be noted that there is an inconsistency in the cost analysis, which applies the operational costs for 

these C2 engines in 2014 but does not include the compliance costs for engines or vessels until later years. 

While this affects the individual year results for early years, the differences disappear by 2016 by which year 

all marine diesel engines above 800 hp have aftertreatment standards. 

Remanufacturing Costs. Railroads are also subject to costs associated with the 

periodic remanufacturing of their locomotives. They 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). In the EIM, 

these remanufacture costs are treated as operating costs and applied to the railroads along 

with their urea costs. This approach was chosen because these costs are periodic and 

recurring. Specifically, they apply to every engine, but only at five to seven year 

intervals. An important consequence of this modeling approach is that it assumes that the 

locomotive owner bears the full cost of the remanufacturing kit and the kit provider does 

not bear any of the cost. However, we believe this simplifying assumption is appropriate. 

The mandatory nature of the requirement would result in a price elasticity of demand that 

is close to zero (inelastic) because 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. The cost of 

a remanufacture kit would have to be very high before the option of pulling the 

locomotive out of service or purchasing a new 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 

7-60 
 
Chapter 7: Economic Impact Analysis 

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. 

There is no corresponding remanufacture requirement for marine diesel engines 

(see Chapter 8 for a discussion of a programmatic alternative that would set such a 

requirement in place for marine diesel engines above 800 hp). 

Table 7-25 Per Unit Locomotive Remanufacture Costs – Line Haul 

Tier 0 Tier 1 Tier 2 Tier 3 Tier 4 

2007 $0 $0 $0 $0 $0 

2008 $33,800 $0 $0 $0 $0 

2009 $33,800 $33,800 $0 $0 $0 

2010 $33,800 $33,800 $0 $0 $0 

2011 $33,800 $33,800 $0 $0 $0 

2012 $33,800 $33,800 $0 $0 $0 

2013 $33,800 $33,800 $11,749 $0 $0 

2014 $33,800 $33,800 $11,749 $0 $0 

2015 $33,800 $33,800 $11,749 $0 $0 

2016 $33,800 $33,800 $11,749 $0 $0 

2017 $22,300 $22,300 $11,749 $0 $0 

2018 $22,300 $22,300 $11,749 $0 $0 

2019 $22,300 $22,300 $11,749 $0 $0 

2020 $22,300 $22,300 $0 $0 $0 

2021 $22,300 $22,300 $0 $0 $0 

2022 $22,300 $22,300 $0 $0 $0 

2023+ $22,300 $22,300 $0 $0 $66,421 

Table 7-26 Per Unit Locomotive Remanufacture Costs – Switcher and Passenger 

Tier 0 Tier 1 Tier 2 Tier 3 Tier 4 

2007 $0 $0 $0 $0 $0 

2008 $33,800 $33,800 $0 $0 $0 

2009 $33,800 $33,800 $0 $0 $0 

2010 $33,800 $33,800 $0 $0 $0 

2011 $33,800 $33,800 $0 $0 $0 

2012 $33,800 $33,800 $0 $0 $0 

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Draft Regulatory Impact Analysis 

Tier 0 Tier 1 Tier 2 Tier 3 Tier 4 

2013 $33,800 $33,800 $0 $0 $0 

2014 $33,800 $33,800 $0 $0 $0 

2015 $33,800 $33,800 $8,728 $0 $0 

2016 $33,800 $33,800 $8,728 $0 $0 

2017 $22,300 $22,300 $8,728 $0 $0 

2018 $22,300 $22,300 $8,728 $0 $0 

2019 $22,300 $22,300 $8,728 $0 $0 

2020 $22,300 $22,300 $8,728 $0 $0 

2021 $22,300 $22,300 $0 $0 $0 

2022 $22,300 $22,300 $0 $0 $0 

2023 $22,300 $22,300 $0 $0 $0 

2024 $22,300 $22,300 $0 $0 $0 

2025+ $22,300 $22,300 $0 $0 $21,872 

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 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. 

 Table 7-27 and Table 7-28 provide a summary of the demand and supply 

elasticities used to estimate the economic impact of the proposed standards. Elasticities 

from peer-reviewed literature were used when possible. Otherwise, the elasticities were 

estimated using accepted empirical methods (i.e. econometrically; see Appendix 7F) or 

are derived internally by the EIM. It should be noted that the elasticities in these tables 

reflect intermediate run behavioral changes. In the long run, supply and demand are 

expected to be more elastic since more changes can be made to production processes. 

7.3.3.1 Demand Elasticities 

The EIM requires demand elasticities for the rail and marine transportation 

markets and the recreational and fishing vessel markets. The demand elasticities for the 

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Chapter 7: Economic Impact Analysis 

locomotive, commercial vessels, and marine diesel engine markets are derived in the 

model. This is another behavioral feature of the model that allows linkages between the 

different components of the model. 

The elasticity for rail transportation services demand is from the peer-reviewed 

literature and is inelastic (-0.5).17 This means that the quantity demanded is not expected 

to be sensitive to price. 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 the demand elasticity for the marine transportation sector 

in the peer-reviewed literature. Due to difficulties in gathering the appropriate data to 

estimate this elasticity, we decided instead to 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. Cargo, ferries, supply/crew 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 a price elasticity of demand that was 

estimated in 1987 for the National Marine Manufacturers Association.18 At -1.4, this 

demand elasticity is elastic, meaning that consumers are expected to be sensitive to a 

change in price. This is reasonable because recreational marine vessels are a 

discretionary purchase and consumers have other recreational alternatives. 

There were no previously estimated demand elasticities available for the fishing 

vessel market. Because the demand elasticity for commercial vessels is intenally derived 

in the EIM, it was not possible to use the commercial vessel market as a proxy. 

Therefore, we used the estimated demand elasticity for recreationals vessel to 

approximate the demand elasticity for fishing vessels. The results would be a 

conservative case, as we would not expect the fishing vessel market to be so elastic since 

the vessel is an important input to fishing production. 

7.3.3.2 Supply Elasticities 

Unlike the demand elasticities, it is necessary to estimate a supply elasticity for 

each of the affected markets. 

For the rail transportation service market we use the supply elasticity from our 

previous economic impact analysis for the Clean Air Nonroad Diesel (Nonroad Tier 4) 

rule (EPA420-R-04-007). That supply elasticity, from the peer-reviewed literature, is 

0.6. This supply elasticity is in elastic, meaning that rail service providers are expected to 

be insensitive to a price change. 

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Draft Regulatory Impact Analysis 

For the line-haul locomotives, we used a calibration method approach to estimate 

the supply elasticity (see Appendix 7F). At 2.7, this elasticity is elastic, meaning that 

producers are expected to be sensitive to changes in price. The EIM uses the same 

supply elasticity for switcher/passenger locomotives. This approach was taken because 

the market for switchers is currently not very developed. Even if data were available to 

estimate this supply elasticity, the switcher/passenger locomotive market is expected to 

change (see Chapter 1 and the discussion earlier in this chapter). Because it is not 

possible to know how this market will develop, we determined that our best estimate 

would be the line haul supply elasticity. 

We were unable to find published supply elasticity estimates for marine 

transportation services and therefore the EIM uses the same supply elasticity as for rail 

transportation services. Again, this is reasonable because the marine transportation 

service sector provides a similar service, although with different geographic constraints. 

For commercial marine vessels, we use the same approach as for line haul 

locomotives and used the calibration method to estimate the supply elasticity. At 2.3, this 

elasticity is elastic, meaning that producers are expected to be sensitive to changes in 

price. 

For recreational marine vessels, we used the supply elasticity we estimated in our 

2002 recreational vehicle rule.19 At 1.6, this supply elasticity is elastic, meaning that 

producers are sensitive to changes in price. They are less sensitive to price changes than 

commercial vessel manufacturers, however. 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. 

There are no prior estimates of the supply elasticity of fishing vessels. The EIM 

uses the same supply elasticity as recreational vessels. This is reasonable because fishing 

vessels often have many of the same characteristics as recreational vessels (high-speed 

planning vessels with fiberglass hulls) and so their production techniques would be 

similar. At the high end of the market, however, this market may behave more like the 

commercial vessel market. 

The supply elasticity for marine diesel engines is taken from our 2004 Clean Air 

Nonroad Diesel rule.20 This is reasonable because the vast majority of marine diesel 

engines affected by this rule are derived from land-based marine or highway diesel 

engines. At 3.8, this supply elasticity is elastic, meaning that engine producers are 

expected to be sensitive to price increases. 

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Chapter 7: Economic Impact Analysis 

Because the demand and supply elasticity estimates are key inputs to the model, a 

sensitivity analysis was performed to consider the uncertainty that is associated with the 

estimation process. The results are presented in Appendix 7H. 

Table 7-27. 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 

Marine 

Transp. Svcs 

-0.5 Literature 

Estimate 

Assumed 

value 

Uses the same elasticity as the locomotive 

transportation services sector. 

Vessels— 

Commercial 

Derived 

Vessels— 

Fishing 

-1.4 Econometric 

Estimate 

Assumed 

value 

Uses the same elasticity as the recreation 

vessels sector. 

Vessels— 

Recreational 

-1.4 Econometric 

Estimate 

Previous 

EPA 

Economic 

Analysis 

U.S. Environmental Protection Agency 

(EPA). 2002. Final Regulatory Support 

Document: Control of Emissions from 

Unregulated Nonroad Engines. EPA420­

R-02-022. Available at <http://www.epa. 

gov/otaq/regs/nonroad/2002/r02022.pdf>. 

Engines Derived 

Table 7-28. Supply Elasticities Used in EIM 

Market Estimate Source Method Input Data Source 

Rail 

Rail Transp. 

Svcs 

0.6 Literature 

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>. 

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Draft Regulatory Impact Analysis 

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 

0.6 Assumed 

value Uses the same elasticity as the rail transportation 

services sector. 

Vessels— 

Commercial 

2.3 EPA 

Estimate 

Calibration 

Method 

U.S. Bureau of the Census. 2004a. “Railroad 

Rolling Stock Manufacturing: 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— 

Fishing 

1.6 Assumed 

value 

Assumed 

value Uses the same elasticity as the recreation vessels 

sector. 

Vessels— 

Recreational 

1.6 Econometric 

Estimate 

Previous 

EPA 

Economic 

Analysis 

U.S. Environmental Protection Agency (EPA). 

2002. Final Regulatory Support Document: 

Control of Emissions from Unregulated Nonroad 

Engines. EPA420-R-02-022. Available at 

<http://www.epa.gov/otaq/regs/nonroad/2002/r02 

022.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>. 

7.3.4 Economic Impact Model Structure 

7.3.4.1 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 

7-66 
Chapter 7: Economic Impact Analysis 

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 withregulation 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 with-regulation 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. 

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Draft Regulatory Impact Analysis 

Figure 7-10 Estimating With-Regulation Equilibrium 

7.3.4.2 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. 

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Chapter 7: Economic Impact Analysis 

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 (1986). 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: 

P • i 1 + = P1

⎛

⎜

⎜

⎝

z 

qd ⎞

⎟

⎟

⎠ qs 
 

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 marketclearing 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.21 

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 

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Draft Regulatory Impact Analysis 

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 proposed program are listed and described in 

more detail in Table 7-29. 

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 proposed 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 anlaysis, we use a published 

demand elasticity for recreational marine (Raboy) and for rail trainsportation services 

(Boyer). On the supply side, we were able to find published elasticities for only the rail 

transportation sector (Ivaldi and McCollough). 

When published elasticities of supply or demand are not available, it is necessary 

to estimate these values econometrically. In this analysis, we used estimated values for 

the price elasticity of supply for engines and recreational vessels (see Appendix 7F). 

These estimates, which were performed for earlier rulemakings (2004 NRT4 rule; 2002 

recretional vehicle rule), 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 plant-level 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 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. 

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Chapter 7: Economic Impact Analysis 

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Draft Regulatory Impact Analysis 

Table 7-29 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 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 proposed rule. 

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 

72 
Chapter 7: Economic Impact Analysis 

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 4­digit 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 

73 
Draft Regulatory Impact Analysis 

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 

74 
Chapter 7: Economic Impact Analysis 

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, alternative methods to shock to the market 

equilibrium (fixed and variable costs) and alternative methods to incorporate 

operational costs (across a larger group of marine vessels). The results of these 

analyses are contained in Appendix 7H. A summary of the results are presented in 

Table 7-30. 

Table 7-30. Results of Sensitivity Analysis 

Parameter Year Change in 

Value 

Impact 

Price 

Elasticity of 

Supply 

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 

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 

Price 

Elasticity of 

Demand 

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 

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 

2011, 

2015 

Include 

fixed and 

variable 

costs 

2011: Price increase larger than primary case but decrease in 

quantity produced remains small, less than 2.5 percent (less than 15 

units) for commercial marine engines and vessels and less than 1 

percent (about 200 engines and vessels) for recreational marine 

engines and vessels. Negligible change in locomotive markets. 

Distribution of social costs shifts from manufacturers to user groups. 

2015: Price increase larger than primary case, but decrease in 

quantity produced remains small, less than 2.0 percent (less than 10 

units) for commercial marine engines and vessels and less than 0.1 

percent (less than 15 engines and vessels) for recreational marine 

engines and vessels. Negligible change in locomotive markets. 

Distribution of social costs shifts from manufacturers to user groups. 

Operating 

Costs 

2020 Alternate 

distribution 

Negligible change in results; increase in social cost burden for 

recreational and fishing vessel consumers 

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Chapter 7: Economic Impact Analysis 

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. Table 7A-1 through Table 

7A-8 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.16 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 

sensitivity analysis in Appendix 7H includes a case that applies both fixed and 

variable costs to the relevant markets. 

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). 

16 This version of the EIA is based on an earlier version of the marine emission control program that 

did not apply Tier 4 standards to any recreational marine diesel engines. 

7-77 
 
Table 7A-1. Impact on C1 Commercial Auxiliary Engine Market: 800–2000 hp (Average Price per Engine = $77,500)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.3 -$0.3 2008 $0 $0 0.0% 0.0% $0.3 -$0.3 2009 $0 $0 0.0% 0.0% $0.3 -$0.3 2010 $0 $0 0.0% 0.0% $0.3 -$0.3 2011 $0 $0 0.0% 0.0% $3.9 -$3.9 2012 $0 $0 0.0% 0.0% $3.5 -$3.5 2013 $0 $0 0.0% 0.0% $3.5 -$3.5 2014 $0 $0 0.0% 0.0% $3.5 -$3.5 2015 $0 $0 0.0% 0.0% $4.6 -$4.6 2016 $15,319 $14,958 19.3% -1.8% $2.5 -$0.1 2017 $15,319 $14,958 19.3% -1.8% $2.5 -$0.1 2018 $11,763 $11,478 14.8% -1.4% $1.9 Loss less than $0.1 2019 $11,763 $11,478 14.8% -1.4% $1.9 Loss less than $0.1 2020 $11,763 $11,478 14.8% -1.4% $2.0 Loss less than $0.1 2021 $11,763 $11,477 14.8% -1.4% $2.0 Loss less than $0.1 2022 $11,763 $11,477 14.8% -1.4% $2.0 Loss less than $0.1 2023 $11,763 $11,476 14.8% -1.4% $2.0 Loss less than $0.1 2024 $11,763 $11,476 14.8% -1.4% $2.0 Loss less than $0.1 2025 $11,763 $11,475 14.8% -1.4% $2.1 Loss less than $0.1 2026 $11,763 $11,475 14.8% -1.4% $2.1 -$0.1 2027 $11,763 $11,474 14.8% -1.4% $2.1 -$0.1 2028 $11,763 $11,474 14.8% -1.4% $2.1 -$0.1 2029 $11,763 $11,473 14.8% -1.4% $2.1 -$0.1 2030 $11,763 $11,473 14.8% -1.4% $2.1 -$0.1 2031 $11,763 $11,473 14.8% -1.4% $2.2 -$0.1 2032 $11,763 $11,473 14.8% -1.4% $2.2 -$0.1 2033 $11,763 $11,473 14.8% -1.4% $2.2 -$0.1 2034 $11,763 $11,472 14.8% -1.4% $2.2 -$0.1 2035 $11,763 $11,472 14.8% -1.4% $2.2 -$0.1 2036 $11,763 $11,472 14.8% -1.4% $2.3 -$0.1 2037 $11,763 $11,472 14.8% -1.4% $2.3 -$0.1 2038 $11,763 $11,472 14.8% -1.4% $2.3 -$0.1 2039 $11,763 $11,471 14.8% -1.4% $2.3 -$0.1 2040 $11,763 $11,472 14.8% -1.4% $2.3 -$0.1 NPV at 3% $44.0 -$16.9 NPV at 7% $24.8 -$12.4 

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. 

7-78 
Table 7A-2 Impact on C1 Commercial Auxiliary Engine Market: >2000 hp (Average Price per Engine = $150,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 2011 $0 $0 0.0% 0.0% $0.4 –$0.4 2012 $0 $0 0.0% 0.0% $0.3 –$0.3 2013 $0 $0 0.0% 0.0% $0.3 –$0.3 2014 $0 –$3 0.0% 0.0% $0.3 –$0.3 2015 $0 –$6 0.0% 0.0% $0.4 –$0.4 2016 $26,294 $26,194 17.4% –0.5% $0.4 Loss less than $0.1 

2017 $26,295 $26,185 17.4% –0.5% $0.4 Loss less than $0.1 

2018 $20,115 $20,012 13.3% –0.5% $0.3 Loss less than $0.1 

2019 $20,114 $19,999 13.3% –0.6% $0.3 Loss less than $0.1 

2020 $20,118 $19,991 13.2% –0.6% $0.3 Loss less than $0.1 

2021 $20,113 $19,975 13.2% –0.7% $0.3 Loss less than $0.1 

2022 $20,114 $19,964 13.2% –0.7% $0.3 Loss less than $0.1 

2023 $20,113 $19,952 13.2% –0.8% $0.3 Loss less than $0.1 

2024 $20,117 $19,945 13.2% –0.8% $0.3 Loss less than $0.1 

2025 $20,113 $19,931 13.2% –0.9% $0.3 Loss less than $0.1 

2026 $20,113 $19,921 13.2% –0.9% $0.3 Loss less than $0.1 

2027 $20,118 $19,917 13.2% –1.0% $0.3 Loss less than $0.1 

2028 $20,116 $19,905 13.1% –1.0% $0.3 Loss less than $0.1 

2029 $20,117 $19,898 13.1% –1.1% $0.3 Loss less than $0.1 

2030 $20,117 $19,891 13.1% –1.1% $0.3 Loss less than $0.1 

2031 $20,116 $19,883 13.1% –1.1% $0.3 Loss less than $0.1 

2032 $20,113 $19,874 13.1% –1.2% $0.3 Loss less than $0.1 

2033 $20,114 $19,870 13.1% –1.2% $0.3 Loss less than $0.1 

2034 $20,114 $19,865 13.1% –1.2% $0.3 Loss less than $0.1 

2035 $20,117 $19,865 13.1% –1.2% $0.4 Loss less than $0.1 

2036 $20,114 $19,857 13.1% –1.3% $0.4 Loss less than $0.1 

2037 $20,114 $19,854 13.1% –1.3% $0.4 Loss less than $0.1 

2038 $20,113 $19,850 13.1% –1.3% $0.4 Loss less than $0.1 

2039 $20,116 $19,851 13.1% –1.3% $0.4 Loss less than $0.1 

2040 $20,117 $19,850 13.1% –1.3% $0.4 Loss less than $0.1 

NPV at 3% $5.8 –$1.5 

NPV at 7% $3.1 –$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. 

7-79 
Table 7A-3 Impact on C2 Commercial Auxiliary Engine Market: 800–2,000 hp (Average Price per Engine = $115,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.9 -$0.9 2008 $0 $0 0.0% 0.0% $0.9 -$0.9 2009 $0 $0 0.0% 0.0% $0.9 -$0.9 2010 $0 $0 0.0% 0.0% $0.9 -$0.9 2011 $0 $0 0.0% 0.0% $5.7 -$5.7 2012 $0 $0 0.0% 0.0% $4.2 -$4.2 2013 $0 $0 0.0% 0.0% $4.2 -$4.2 2014 $0 -$1 0.0% 0.0% $4.2 -$4.2 2015 $0 -$2 0.0% 0.0% $6.1 -$6.1 2016 $39,059 $38,759 33.7% -1.0% $2.5 Loss less than $0.1 

2017 $39,058 $38,755 33.7% -1.0% $2.5 Loss less than $0.1 

2018 $29,827 $29,587 25.7% -0.8% $1.9 Loss less than $0.1 

2019 $29,827 $29,581 25.7% -0.8% $1.9 Loss less than $0.1 

2020 $29,827 $29,577 25.7% -0.8% $2.0 Loss less than $0.1 

2021 $29,827 $29,573 25.7% -0.8% $2.0 Loss less than $0.1 

2022 $29,827 $29,568 25.7% -0.9% $2.0 Loss less than $0.1 

2023 $29,827 $29,564 25.7% -0.9% $2.0 Loss less than $0.1 

2024 $29,826 $29,560 25.7% -0.9% $2.0 Loss less than $0.1 

2025 $29,826 $29,555 25.7% -0.9% $2.1 Loss less than $0.1 

2026 $29,827 $29,552 25.7% -0.9% $2.1 Loss less than $0.1 

2027 $29,827 $29,546 25.7% -0.9% $2.1 Loss less than $0.1 

2028 $29,827 $29,543 25.7% -0.9% $2.1 Loss less than $0.1 

2029 $29,826 $29,539 25.7% -1.0% $2.1 Loss less than $0.1 

2030 $29,827 $29,537 25.7% -1.0% $2.1 Loss less than $0.1 

2031 $29,827 $29,534 25.7% -1.0% $2.2 Loss less than $0.1 

2032 $29,827 $29,532 25.7% -1.0% $2.2 Loss less than $0.1 

2033 $29,827 $29,530 25.7% -1.0% $2.2 Loss less than $0.1 

2034 $29,827 $29,528 25.7% -1.0% $2.2 Loss less than $0.1 

2035 $29,826 $29,526 25.7% -1.0% $2.2 Loss less than $0.1 

2036 $29,826 $29,525 25.7% -1.0% $2.3 Loss less than $0.1 

2037 $29,827 $29,524 25.7% -1.0% $2.3 Loss less than $0.1 

2038 $29,827 $29,523 25.7% -1.0% $2.3 Loss less than $0.1 

2039 $29,827 $29,523 25.7% -1.0% $2.3 Loss less than $0.1 

2040 $29,827 $29,522 25.7% -1.0% $2.3 Loss less than $0.1 

NPV at 3% $50.3 -$22.7 NPV at 7% $29.7 -$17.1 

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. 

7-80 
Table 7A-4 Impact on C2 Commercial Auxiliary Engine Market: >2,000 hp (Average Price per Engine = $225,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $1.3 -$1.3 2008 $0 $0 0.0% 0.0% $1.3 -$1.3 2009 $0 $0 0.0% 0.0% $1.3 -$1.3 2010 $0 $0 0.0% 0.0% $1.3 -$1.3 2011 $0 $0 0.0% 0.0% $8.7 -$8.7 2012 $0 $0 0.0% 0.0% $6.5 -$6.5 2013 $0 $0 0.0% 0.0% $6.5 -$6.5 2014 $0 -$2 0.0% 0.0% $6.5 -$6.5 2015 $0 -$5 0.0% 0.0% $9.4 -$9.4 2016 $72,301 $71,824 31.9% -0.8% $7.0 Loss less than $0.1 2017 $72,301 $71,816 31.9% -0.8% $7.1 Loss less than $0.1 2018 $55,120 $54,733 24.3% -0.7% $5.5 Loss less than $0.1 2019 $55,121 $54,724 24.3% -0.7% $5.5 Loss less than $0.1 2020 $55,121 $54,715 24.3% -0.7% $5.6 Loss less than $0.1 2021 $55,121 $54,706 24.3% -0.7% $5.6 Loss less than $0.1 2022 $55,121 $54,697 24.3% -0.7% $5.7 Loss less than $0.1 2023 $55,120 $54,688 24.3% -0.7% $5.7 Loss less than $0.1 2024 $55,120 $54,680 24.3% -0.7% $5.8 Loss less than $0.1 2025 $55,121 $54,672 24.3% -0.8% $5.8 Loss less than $0.1 2026 $55,121 $54,664 24.3% -0.8% $5.9 Loss less than $0.1 2027 $55,121 $54,656 24.3% -0.8% $5.9 Loss less than $0.1 2028 $55,121 $54,649 24.3% -0.8% $6.0 -$0.1 2029 $55,120 $54,642 24.3% -0.8% $6.0 -$0.1 2030 $55,121 $54,637 24.3% -0.8% $6.1 -$0.1 2031 $55,121 $54,632 24.3% -0.8% $6.1 -$0.1 2032 $55,120 $54,627 24.3% -0.8% $6.2 -$0.1 2033 $55,120 $54,623 24.3% -0.8% $6.3 -$0.1 2034 $55,121 $54,620 24.3% -0.8% $6.3 -$0.1 2035 $55,121 $54,616 24.3% -0.9% $6.4 -$0.1 2036 $55,120 $54,613 24.3% -0.9% $6.4 -$0.1 2037 $55,121 $54,610 24.3% -0.9% $6.5 -$0.1 2038 $55,121 $54,608 24.3% -0.9% $6.5 -$0.1 2039 $55,121 $54,607 24.3% -0.9% $6.6 -$0.1 2040 $55,121 $54,605 24.3% -0.9% $6.7 -$0.1 NPV at 3% $113.5 -$35.1 

NPV at 7% $62.2 -$26.4 

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. 

7-81 
Table 7A-5 Impact on C1 Commercial Propulsion Engine Market: 800–2,000 hp (Average Price per Engine = $155,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $1.0 -$1.0 2008 $0 $0 0.0% 0.0% $1.0 -$1.0 2009 $0 $0 0.0% 0.0% $1.0 -$1.0 2010 $0 $0 0.0% 0.0% $1.0 -$1.0 2011 $0 $0 0.0% 0.0% $13.1 -$13.1 2012 $0 $0 0.0% 0.0% $11.7 -$11.7 2013 $0 $0 0.0% 0.0% $11.7 -$11.7 2014 $0 $0 0.0% 0.0% $11.7 -$11.7 2015 $0 -$1 0.0% 0.0% $15.3 -$15.3 2016 $15,319 $14,597 9.4% -1.8% $8.2 -$0.4 2017 $15,319 $14,596 9.4% -1.8% $8.3 -$0.4 2018 $11,763 $11,194 7.2% -1.4% $6.4 -$0.3 2019 $11,763 $11,193 7.2% -1.4% $6.5 -$0.3 2020 $11,763 $11,192 7.2% -1.4% $6.5 -$0.3 2021 $11,763 $11,191 7.2% -1.4% $6.6 -$0.3 2022 $11,763 $11,190 7.2% -1.4% $6.6 -$0.3 2023 $11,763 $11,189 7.2% -1.4% $6.7 -$0.3 2024 $11,763 $11,188 7.2% -1.4% $6.8 -$0.3 2025 $11,763 $11,187 7.2% -1.4% $6.8 -$0.3 2026 $11,763 $11,186 7.2% -1.4% $6.9 -$0.3 2027 $11,763 $11,185 7.2% -1.4% $6.9 -$0.3 2028 $11,763 $11,185 7.2% -1.4% $7.0 -$0.3 2029 $11,763 $11,184 7.2% -1.4% $7.1 -$0.3 2030 $11,763 $11,183 7.2% -1.4% $7.1 -$0.3 2031 $11,763 $11,183 7.2% -1.4% $7.2 -$0.4 2032 $11,763 $11,182 7.2% -1.4% $7.3 -$0.4 2033 $11,763 $11,182 7.2% -1.4% $7.3 -$0.4 2034 $11,763 $11,181 7.2% -1.4% $7.4 -$0.4 2035 $11,763 $11,181 7.2% -1.4% $7.5 -$0.4 2036 $11,763 $11,181 7.2% -1.4% $7.5 -$0.4 2037 $11,763 $11,180 7.2% -1.4% $7.6 -$0.4 2038 $11,763 $11,180 7.2% -1.4% $7.7 -$0.4 2039 $11,763 $11,180 7.2% -1.4% $7.7 -$0.4 2040 $11,763 $11,180 7.2% -1.4% $7.8 -$0.4 NPV at 3% $146.3 -$58.3 

NPV at 7% $82.3 -$42.2 

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. 

7-82 
Table 7A-6 Impact on C1 Commercial Propulsion Engine Market: >2,000 hp (Average Price per Engine = $300,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.4 -$0.4 2008 $0 $0 0.0% 0.0% $0.4 -$0.4 2009 $0 $0 0.0% 0.0% $0.4 -$0.4 2010 $0 $0 0.0% 0.0% $0.4 -$0.4 2011 $0 $0 0.0% 0.0% $5.4 -$5.4 2012 $0 $0 0.0% 0.0% $4.9 -$4.9 2013 $0 $0 0.0% 0.0% $4.9 -$4.9 2014 $0 $0 0.0% 0.0% $4.9 -$4.9 2015 $0 -$1 0.0% 0.0% $6.4 -$6.4 2016 $26,296 $25,082 8.4% -1.5% $5.8 -$0.3 2017 $26,296 $25,081 8.4% -1.5% $5.9 -$0.3 2018 $20,115 $19,157 6.4% -1.2% $4.5 -$0.2 2019 $20,115 $19,155 6.4% -1.2% $4.6 -$0.2 2020 $20,116 $19,154 6.4% -1.2% $4.6 -$0.2 2021 $20,116 $19,152 6.4% -1.2% $4.7 -$0.2 2022 $20,115 $19,150 6.4% -1.2% $4.7 -$0.2 2023 $20,116 $19,146 6.4% -1.2% $4.8 -$0.2 2024 $20,115 $19,144 6.4% -1.2% $4.8 -$0.2 2025 $20,115 $19,143 6.4% -1.2% $4.8 -$0.2 2026 $20,116 $19,142 6.4% -1.2% $4.9 -$0.2 2027 $20,116 $19,141 6.4% -1.2% $4.9 -$0.2 2028 $20,115 $19,140 6.4% -1.2% $5.0 -$0.2 2029 $20,116 $19,136 6.4% -1.2% $5.0 -$0.2 2030 $20,115 $19,136 6.4% -1.2% $5.1 -$0.2 2031 $20,115 $19,135 6.4% -1.2% $5.1 -$0.2 2032 $20,116 $19,136 6.4% -1.2% $5.1 -$0.2 2033 $20,115 $19,133 6.4% -1.2% $5.2 -$0.3 2034 $20,116 $19,133 6.4% -1.2% $5.3 -$0.3 2035 $20,116 $19,133 6.4% -1.2% $5.3 -$0.3 2036 $20,115 $19,131 6.4% -1.2% $5.4 -$0.3 2037 $20,116 $19,131 6.4% -1.2% $5.4 -$0.3 2038 $20,115 $19,129 6.4% -1.2% $5.5 -$0.3 2039 $20,115 $19,130 6.4% -1.2% $5.5 -$0.3 2040 $20,116 $19,131 6.4% -1.2% $5.5 -$0.3 NPV at 3% $88.0 -$25.4 NPV at 7% $46.6 -$18.1 

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. 

7-83 
Table 7A-7 Impact on C2 Commercial Propulsion Engine Market: 800–2,000 hp (Average Price per Engine = $232,500)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.1 -$0.1 2008 $0 $0 0.0% 0.0% $0.1 -$0.1 2009 $0 $0 0.0% 0.0% $0.1 -$0.1 2010 $0 $0 0.0% 0.0% $0.1 -$0.1 2011 $0 $0 0.0% 0.0% $0.7 -$0.7 2012 $0 $0 0.0% 0.0% $0.5 -$0.5 2013 $0 $0 0.0% 0.0% $0.5 -$0.5 2014 $0 -$2 0.0% 0.0% $0.5 -$0.5 2015 $0 -$5 0.0% 0.0% $0.7 -$0.7 2016 $39,057 $38,458 16.7% -1.0% $0.3 Loss less than $0.1 2017 $39,057 $38,451 16.7% -1.0% $0.3 Loss less than $0.1 2018 $29,829 $29,347 12.8% -0.8% $0.2 Loss less than $0.1 2019 $29,829 $29,338 12.8% -0.8% $0.2 Loss less than $0.1 2020 $29,829 $29,329 12.8% -0.8% $0.2 Loss less than $0.1 2021 $29,829 $29,320 12.7% -0.8% $0.2 Loss less than $0.1 2022 $29,829 $29,312 12.7% -0.9% $0.2 Loss less than $0.1 2023 $29,829 $29,303 12.7% -0.9% $0.2 Loss less than $0.1 2024 $29,829 $29,295 12.7% -0.9% $0.2 Loss less than $0.1 2025 $29,829 $29,287 12.7% -0.9% $0.2 Loss less than $0.1 2026 $29,829 $29,279 12.7% -0.9% $0.2 Loss less than $0.1 2027 $29,825 $29,263 12.7% -0.9% $0.2 Loss less than $0.1 2028 $29,825 $29,256 12.7% -0.9% $0.2 Loss less than $0.1 2029 $29,825 $29,250 12.7% -1.0% $0.2 Loss less than $0.1 2030 $29,825 $29,244 12.7% -1.0% $0.2 Loss less than $0.1 2031 $29,825 $29,239 12.7% -1.0% $0.2 Loss less than $0.1 2032 $29,825 $29,235 12.7% -1.0% $0.2 Loss less than $0.1 2033 $29,825 $29,231 12.7% -1.0% $0.2 Loss less than $0.1 2034 $29,825 $29,228 12.7% -1.0% $0.2 Loss less than $0.1 2035 $29,825 $29,225 12.7% -1.0% $0.2 Loss less than $0.1 2036 $29,825 $29,222 12.7% -1.0% $0.2 Loss less than $0.1 2037 $29,825 $29,219 12.7% -1.0% $0.2 Loss less than $0.1 2038 $29,825 $29,217 12.7% -1.0% $0.2 Loss less than $0.1 2039 $29,825 $29,216 12.7% -1.0% $0.2 Loss less than $0.1 2040 $29,825 $29,215 12.7% -1.0% $0.2 Loss less than $0.1 NPV at 3% $5.5 -$2.6 

NPV at 7% $3.3 -$1.9 

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. 

7-84 
Table 7A-8 Impact on C2 Commercial Propulsion Engine Market: >2,000 hp (Average Price per Engine = $450,000)a,b 

Variable Absolute Change Change Change Total Change in Engine 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $1.9 -$1.9 2008 $0 $0 0.0% 0.0% $1.9 -$1.9 2009 $0 $0 0.0% 0.0% $1.9 -$1.9 2010 $0 $0 0.0% 0.0% $1.9 -$1.9 2011 $0 $0 0.0% 0.0% $12.7 -$12.7 2012 $0 $0 0.0% 0.0% $9.5 -$9.5 2013 $0 $0 0.0% 0.0% $9.5 -$9.5 2014 $0 -$5 0.0% 0.0% $9.5 -$9.5 2015 $0 -$9 0.0% 0.0% $13.7 -$13.7 2016 $72,301 $71,346 15.9% -0.8% $10.2 -$0.1 2017 $72,301 $71,332 15.9% -0.8% $10.3 -$0.1 2018 $55,121 $54,346 12.1% -0.7% $8.0 -$0.1 2019 $55,121 $54,327 12.1% -0.7% $8.0 -$0.1 2020 $55,121 $54,309 12.1% -0.7% $8.1 -$0.1 2021 $55,121 $54,291 12.1% -0.7% $8.2 -$0.1 2022 $55,121 $54,273 12.1% -0.7% $8.2 -$0.1 2023 $55,121 $54,256 12.1% -0.7% $8.3 -$0.1 2024 $55,121 $54,239 12.1% -0.7% $8.4 -$0.1 2025 $55,120 $54,223 12.0% -0.8% $8.5 -$0.1 2026 $55,120 $54,207 12.0% -0.8% $8.5 -$0.1 2027 $55,120 $54,192 12.0% -0.8% $8.6 -$0.1 2028 $55,121 $54,178 12.0% -0.8% $8.7 -$0.1 2029 $55,121 $54,165 12.0% -0.8% $8.8 -$0.2 2030 $55,120 $54,153 12.0% -0.8% $8.9 -$0.2 2031 $55,120 $54,143 12.0% -0.8% $8.9 -$0.2 2032 $55,121 $54,134 12.0% -0.8% $9.0 -$0.2 2033 $55,121 $54,126 12.0% -0.8% $9.1 -$0.2 2034 $55,120 $54,118 12.0% -0.8% $9.2 -$0.2 2035 $55,120 $54,111 12.0% -0.9% $9.3 -$0.2 2036 $55,121 $54,105 12.0% -0.9% $9.3 -$0.2 2037 $55,121 $54,100 12.0% -0.9% $9.4 -$0.2 2038 $55,120 $54,095 12.0% -0.9% $9.5 -$0.2 2039 $55,121 $54,093 12.0% -0.9% $9.6 -$0.2 2040 $55,120 $54,090 12.0% -0.9% $9.7 -$0.2 NPV at 3% $165.0 -$52.0 

NPV at 7% $90.4 -$38.9 

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. 

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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). Results are presented for 26 

separate equipment markets: 2 locomotive markets (line-haul and switchers) and 24 

vessel markets. Table 7B-1 through Table 7B-26 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 consumer surplus (recreational and fishing markets only) 

• changes in total surplus (recreational and 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.17 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 sensitivity analysis in Appendix 7H includes a case that 

applies both fixed and variable costs to the relevant markets. 

17 This version of the EIA is based on an earlier version of the marine emission control program that 

did not apply Tier 4 standards to any recreational marine diesel engines. 

7-86 
 
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). 

/149 

7-87 
Table 7B-1. Impact on Locomotive Market: Line-Haul (Average Price per Locomotive = $2,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.6 -$0.6 2008 $0 -$255 0.0% 0.0% $0.6 -$0.8 2009 $0 -$151 0.0% 0.0% $0.6 -$0.7 2010 $0 -$239 0.0% 0.0% $2.3 -$2.5 2011 $0 -$449 0.0% -0.1% $3.9 -$4.2 2012 $0 -$357 0.0% 0.0% $5.1 -$5.4 2013 $0 -$313 0.0% 0.0% $5.1 -$5.4 2014 $0 -$219 0.0% 0.0% $7.2 -$7.4 2015 $44,390 $44,041 2.2% 0.0% $39.7 -$3.7 2016 $44,390 $43,911 2.2% -0.1% $43.4 -$5.9 2017 $68,544 $67,982 3.4% -0.1% $60.1 -$0.5 2018 $68,544 $67,923 3.4% -0.1% $61.3 -$0.6 2019 $60,624 $60,006 3.0% -0.1% $55.6 -$0.6 2020 $60,624 $60,050 3.0% -0.1% $57.5 -$0.5 2021 $60,624 $59,991 3.0% -0.1% $59.3 -$0.6 2022 $60,624 $59,852 3.0% -0.1% $61.0 -$0.8 2023 $60,624 $59,626 3.0% -0.1% $62.7 -$1.0 2024 $60,624 $59,623 3.0% -0.1% $63.5 -$1.0 2025 $60,624 $59,557 3.0% -0.1% $65.3 -$1.1 2026 $60,624 $59,561 3.0% -0.1% $66.5 -$1.2 2027 $60,624 $59,509 3.0% -0.2% $67.8 -$1.2 2028 $60,624 $59,407 3.0% -0.2% $68.9 -$1.4 2029 $60,624 $59,359 3.0% -0.2% $69.7 -$1.5 2030 $60,624 $59,321 3.0% -0.2% $70.2 -$1.5 2031 $60,624 $59,279 3.0% -0.2% $71.1 -$1.6 2032 $60,624 $59,253 3.0% -0.2% $72.1 -$1.6 2033 $60,624 $59,017 3.0% -0.2% $73.3 -$1.9 2034 $60,624 $58,950 2.9% -0.2% $74.2 -$2.0 2035 $60,624 $58,891 2.9% -0.2% $74.6 -$2.1 2036 $60,624 $58,892 2.9% -0.2% $72.5 -$2.1 2037 $60,624 $58,858 2.9% -0.2% $71.0 -$2.1 2038 $60,624 $58,827 2.9% -0.2% $69.4 -$2.1 2039 $60,624 $58,798 2.9% -0.2% $67.4 -$2.0 2040 $60,624 $58,772 2.9% -0.3% $65.4 -$2.0 NPV at 3% $886.5 -$44.5 

NPV at 7% $408.7 -$27.7 

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-2. Impact on Locomotive Market: Switchers (Average Price per Locomotive = $1,300,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $2.6 -$2.6 2008 $0 -$166 0.0% 0.0% $2.6 -$2.6 2009 $0 -$98 0.0% 0.0% $2.6 -$2.6 2010 $0 -$155 0.0% 0.0% $4.9 -$5.0 2011 $0 -$292 0.0% -0.1% $6.9 -$7.0 2012 $0 -$232 0.0% 0.0% $7.2 -$7.2 2013 $0 -$203 0.0% 0.0% $7.2 -$7.3 2014 $0 -$142 0.0% 0.0% $9.7 -$9.8 2015 $14,353 $14,126 1.1% 0.0% $9.1 -$4.9 2016 $14,353 $14,042 1.1% -0.1% $11.8 -$7.4 2017 $19,230 $18,865 1.5% -0.1% $6.4 -$0.1 2018 $19,230 $18,827 1.4% -0.1% $6.6 -$0.1 2019 $17,770 $17,368 1.3% -0.1% $6.3 -$0.1 2020 $17,770 $17,396 1.3% -0.1% $6.6 -$0.1 2021 $17,770 $17,358 1.3% -0.1% $6.9 -$0.2 2022 $17,770 $17,268 1.3% -0.1% $7.1 -$0.2 2023 $17,770 $17,121 1.3% -0.1% $7.1 -$0.3 2024 $17,770 $17,119 1.3% -0.1% $7.2 -$0.3 2025 $17,770 $17,076 1.3% -0.1% $7.1 -$0.3 2026 $17,770 $17,078 1.3% -0.1% $7.0 -$0.3 2027 $17,770 $17,045 1.3% -0.2% $6.8 -$0.3 2028 $17,770 $16,978 1.3% -0.2% $6.7 -$0.3 2029 $17,770 $16,947 1.3% -0.2% $6.6 -$0.3 2030 $17,770 $16,922 1.3% -0.2% $6.5 -$0.3 2031 $17,770 $16,895 1.3% -0.2% $6.4 -$0.3 2032 $17,770 $16,878 1.3% -0.2% $6.4 -$0.3 2033 $17,770 $16,725 1.3% -0.2% $5.6 -$0.3 2034 $17,770 $16,681 1.3% -0.2% $5.4 -$0.3 2035 $17,770 $16,643 1.3% -0.2% $5.2 -$0.3 2036 $17,770 $16,644 1.3% -0.2% $4.9 -$0.3 2037 $17,770 $16,622 1.3% -0.2% $4.7 -$0.3 2038 $17,770 $16,602 1.3% -0.2% $4.5 -$0.3 2039 $17,770 $16,582 1.3% -0.2% $4.4 -$0.3 2040 $17,770 $16,566 1.3% -0.3% $4.2 -$0.3 NPV at 3% $128.0 -$48.4 

NPV at 7% $73.6 -$35.8 

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. 

7-89 
Table 7B-3. Impact on C1 Tow/Tug/Push Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,550,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2014 $0 -$27 0.0% 0.0% $0.0 Loss less than $0.1 

2015 $0 -$55 0.0% 0.0% $0.9 -$0.9 2016 $2,384 $29,430 1.9% -0.1% $0.5 -$0.5 2017 $2,386 $29,346 1.9% -0.1% $0.3 -$0.3 2018 $2,385 $22,900 1.5% -0.1% $0.2 -$0.2 2019 $2,386 $22,791 1.5% -0.1% $0.2 -$0.2 2020 $2,384 $22,682 1.5% -0.1% $0.2 -$0.2 2021 $2,385 $22,578 1.5% -0.1% $0.2 -$0.2 2022 $2,385 $22,477 1.5% -0.2% $0.2 -$0.2 2023 $2,386 $22,377 1.4% -0.2% $0.2 -$0.2 2024 $2,386 $22,280 1.4% -0.2% $0.2 -$0.2 2025 $2,386 $22,186 1.4% -0.2% $0.2 -$0.2 2026 $2,386 $22,094 1.4% -0.2% $0.2 -$0.2 2027 $2,386 $22,005 1.4% -0.2% $0.1 -$0.1 2028 $2,385 $21,920 1.4% -0.3% $0.1 -$0.1 2029 $2,384 $21,843 1.4% -0.3% $0.1 -$0.1 2030 $2,386 $21,778 1.4% -0.3% $0.1 -$0.1 2031 $2,385 $21,718 1.4% -0.3% $0.1 -$0.1 2032 $2,386 $21,668 1.4% -0.3% $0.1 -$0.1 2033 $2,385 $21,619 1.4% -0.3% $0.1 -$0.1 2034 $2,386 $21,576 1.4% -0.3% $0.1 -$0.1 2035 $2,386 $21,537 1.4% -0.3% $0.1 -$0.1 2036 $2,384 $21,498 1.4% -0.3% $0.1 -$0.1 2037 $2,384 $21,467 1.4% -0.3% $0.1 -$0.1 2038 $2,384 $21,443 1.4% -0.3% $0.1 -$0.1 2039 $2,384 $21,423 1.4% -0.3% $0.1 -$0.1 2040 $2,384 $21,409 1.4% -0.3% $0.1 -$0.1 NPV at 3% $3.1 -$2.5 NPV at 7% $1.7 -$1.4 

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. 

7-90 
Table 7B-4. Impact on C1 Tow/Tug/Push Vessel Market: >2,000 hp (Average Price per Vessel = $3,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2014 $0 -$53 0.0% 0.0% $0.0 Loss less than $0.1 

2015 $0 -$106 0.0% 0.0% $0.3 -$0.3 2016 $4,675 $49,380 1.6% -0.1% $0.2 -$0.2 2017 $4,674 $49,214 1.6% -0.1% $0.1 -$0.1 2018 $4,673 $38,375 1.3% -0.1% $0.1 -$0.1 2019 $4,671 $38,155 1.3% -0.1% $0.1 -$0.1 2020 $4,669 $37,940 1.3% -0.1% $0.1 -$0.1 2021 $4,674 $37,732 1.3% -0.1% $0.1 -$0.1 2022 $4,672 $37,519 1.3% -0.2% $0.1 -$0.1 2023 $4,669 $37,440 1.2% -0.2% $0.1 -$0.1 2024 $4,673 $37,239 1.2% -0.2% $0.1 -$0.1 2025 $4,669 $37,032 1.2% -0.2% $0.1 -$0.1 2026 $4,672 $36,837 1.2% -0.2% $0.1 -$0.1 2027 $4,668 $36,638 1.2% -0.2% $0.1 $0.0 2028 $4,671 $36,450 1.2% -0.3% $0.1 $0.0 2029 $4,673 $36,403 1.2% -0.3% $0.1 $0.0 2030 $4,675 $36,244 1.2% -0.3% $0.1 $0.0 2031 $4,669 $36,091 1.2% -0.3% $0.1 $0.0 2032 $4,670 $35,958 1.2% -0.3% $0.1 $0.0 2033 $4,671 $35,951 1.2% -0.3% $0.1 $0.0 2034 $4,671 $35,827 1.2% -0.3% $0.1 $0.0 2035 $4,671 $35,708 1.2% -0.3% $0.1 -$0.1 2036 $4,670 $35,709 1.2% -0.3% $0.1 -$0.1 2037 $4,670 $35,604 1.2% -0.3% $0.1 -$0.1 2038 $4,668 $35,621 1.2% -0.3% $0.1 -$0.1 2039 $4,673 $35,539 1.2% -0.3% $0.1 -$0.1 2040 $4,672 $35,459 1.2% -0.3% $0.1 -$0.1 NPV at 3% $1.5 -$1.1 NPV at 7% $0.8 -$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. 

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Table 7B-5. Impact on C2 Tow/Tug/Push Vessel Market: 800–2,000 hp (Average Price per Vessel = $2,325,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2014 $0 -$45 0.0% 0.0% $0.0 Loss less than $0.1 

2015 $0 -$89 0.0% 0.0% $0.5 -$0.5 2016 $3,960 $138,748 6.0% -0.1% $0.3 -$0.3 2017 $3,967 $138,497 6.0% -0.1% $0.2 -$0.2 2018 $3,963 $106,330 4.6% -0.1% $0.1 -$0.1 2019 $3,969 $106,067 4.6% -0.1% $0.1 -$0.1 2020 $3,965 $105,799 4.6% -0.1% $0.1 -$0.1 2021 $3,960 $105,539 4.6% -0.1% $0.1 -$0.1 2022 $3,965 $105,290 4.6% -0.2% $0.1 -$0.1 2023 $3,960 $105,038 4.6% -0.2% $0.1 -$0.1 2024 $3,964 $104,798 4.6% -0.2% $0.1 -$0.1 2025 $3,968 $104,564 4.5% -0.2% $0.1 -$0.1 2026 $3,962 $104,327 4.5% -0.2% $0.1 -$0.1 2027 $3,966 $105,410 4.6% -0.2% $0.0 Loss less than $0.1 

2028 $3,969 $105,181 4.6% -0.3% $0.0 Loss less than $0.1 

2029 $3,962 $104,955 4.6% -0.3% $0.0 Loss less than $0.1 

2030 $3,964 $104,759 4.6% -0.3% $0.0 Loss less than $0.1 

2031 $3,966 $104,574 4.5% -0.3% $0.0 Loss less than $0.1 

2032 $3,968 $104,403 4.5% -0.3% $0.0 Loss less than $0.1 

2033 $3,960 $104,230 4.5% -0.3% $0.0 Loss less than $0.1 

2034 $3,961 $104,070 4.5% -0.3% $0.0 Loss less than $0.1 

2035 $3,961 $103,917 4.5% -0.3% $0.0 Loss less than $0.1 

2036 $3,962 $103,770 4.5% -0.3% $0.0 Loss less than $0.1 

2037 $3,962 $103,635 4.5% -0.3% $0.0 Loss less than $0.1 

2038 $3,962 $103,510 4.5% -0.3% $0.0 Loss less than $0.1 

2039 $3,961 $103,396 4.5% -0.3% $0.0 Loss less than $0.1 

2040 $3,960 $103,287 4.5% -0.3% $0.0 Loss less than $0.1 

NPV at 3% $1.7 -$1.4 NPV at 7% $1.0 -$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. 

7-92 
Table 7B-6. Impact on C2 Tow/Tug/Push Vessel Market: >2,000 hp (Average Price per Vessel = $4,500,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2014 $0 -$90 0.0% 0.0% $0.0 Loss less than $0.1 

2015 $0 -$178 0.0% 0.0% $1.9 -$1.9 2016 $7,154 $218,206 4.8% -0.1% $1.4 -$1.1 2017 $7,154 $217,926 4.8% -0.1% $1.0 -$0.8 2018 $7,156 $167,280 3.7% -0.1% $0.6 -$0.4 2019 $7,154 $166,921 3.7% -0.1% $0.6 -$0.4 2020 $7,155 $166,572 3.7% -0.1% $0.6 -$0.4 2021 $7,155 $166,229 3.7% -0.1% $0.7 -$0.5 2022 $7,154 $165,895 3.7% -0.2% $0.7 -$0.5 2023 $7,156 $165,570 3.7% -0.2% $0.7 -$0.5 2024 $7,154 $165,251 3.7% -0.2% $0.7 -$0.5 2025 $7,154 $164,943 3.7% -0.2% $0.7 -$0.5 2026 $7,154 $164,644 3.7% -0.2% $0.7 -$0.5 2027 $7,155 $164,356 3.7% -0.2% $0.3 -$0.2 2028 $7,154 $164,079 3.6% -0.3% $0.3 -$0.2 2029 $7,154 $163,832 3.6% -0.3% $0.3 -$0.2 2030 $7,154 $163,613 3.6% -0.3% $0.3 -$0.2 2031 $7,155 $163,422 3.6% -0.3% $0.3 -$0.2 2032 $7,154 $163,254 3.6% -0.3% $0.3 -$0.2 2033 $7,154 $163,099 3.6% -0.3% $0.3 -$0.2 2034 $7,154 $162,956 3.6% -0.3% $0.3 -$0.2 2035 $7,155 $162,826 3.6% -0.3% $0.3 -$0.2 2036 $7,154 $162,704 3.6% -0.3% $0.3 -$0.2 2037 $7,154 $162,604 3.6% -0.3% $0.3 -$0.2 2038 $7,154 $162,522 3.6% -0.3% $0.3 -$0.3 2039 $7,155 $162,462 3.6% -0.3% $0.3 -$0.3 2040 $7,154 $162,413 3.6% -0.3% $0.3 -$0.3 NPV at 3% $8.6 -$6.7 NPV at 7% $4.6 -$3.6 

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. 

7-93 
Table 7B-7. Impact on C1 Ferries Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,550,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% $0.0 Loss less than $0.1 

2014 $0 -$27 0.0% 0.0% $0.0 Loss less than $0.1 

2015 $0 -$55 0.0% 0.0% $0.1 -$0.1 2016 $2,391 $29,437 1.9% -0.1% $0.1 -$0.1 2017 $2,388 $29,348 1.9% -0.1% $0.1 Loss less than $0.1 

2018 $2,384 $22,899 1.5% -0.1% $0.0 Loss less than $0.1 

2019 $2,380 $22,786 1.5% -0.1% $0.0 Loss less than $0.1 

2020 $2,376 $22,675 1.5% -0.1% $0.0 Loss less than $0.1 

2021 $2,390 $22,583 1.5% -0.1% $0.0 Loss less than $0.1 

2022 $2,385 $22,476 1.5% -0.2% $0.0 Loss less than $0.1 

2023 $2,381 $22,372 1.4% -0.2% $0.0 Loss less than $0.1 

2024 $2,393 $22,287 1.4% -0.2% $0.0 Loss less than $0.1 

2025 $2,388 $22,188 1.4% -0.2% $0.0 Loss less than $0.1 

2026 $2,384 $22,092 1.4% -0.2% $0.0 Loss less than $0.1 

2027 $2,379 $21,998 1.4% -0.2% $0.0 Loss less than $0.1 

2028 $2,390 $21,925 1.4% -0.3% $0.0 Loss less than $0.1 

2029 $2,384 $21,843 1.4% -0.3% $0.0 Loss less than $0.1 

2030 $2,379 $21,771 1.4% -0.3% $0.0 Loss less than $0.1 

2031 $2,389 $21,723 1.4% -0.3% $0.0 Loss less than $0.1 

2032 $2,383 $21,665 1.4% -0.3% $0.0 Loss less than $0.1 

2033 $2,378 $21,612 1.4% -0.3% $0.0 Loss less than $0.1 

2034 $2,387 $21,578 1.4% -0.3% $0.0 Loss less than $0.1 

2035 $2,381 $21,531 1.4% -0.3% $0.0 Loss less than $0.1 

2036 $2,390 $21,503 1.4% -0.3% $0.0 Loss less than $0.1 

2037 $2,383 $21,466 1.4% -0.3% $0.0 Loss less than $0.1 

2038 $2,392 $21,450 1.4% -0.3% $0.0 Loss less than $0.1 

2039 $2,385 $21,423 1.4% -0.3% $0.0 Loss less than $0.1 

2040 $2,378 $21,403 1.4% -0.3% $0.0 Loss less than $0.1 

NPV at 3% $0.5 -$0.4 NPV at 7% $0.3 -$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. 

7-94 
Table 7B-8. Impact on C1 Ferries Vessel Market: >2,000 hp (Average Price per Vessel = $3,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$53 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$106 0.0% 0.0% <$0.1 Loss less than $0.1 

2016 $4,657 $49,362 1.6% -0.1% <$0.1 Loss less than $0.1 

2017 $4,666 $49,206 1.6% -0.1% <$0.1 Loss less than $0.1 

2018 $4,674 $38,376 1.3% -0.1% <$0.1 Loss less than $0.1 

2019 $4,682 $38,167 1.3% -0.1% <$0.1 Loss less than $0.1 

2020 $4,690 $37,961 1.3% -0.1% <$0.1 Loss less than $0.1 

2021 $4,648 $37,706 1.3% -0.1% <$0.1 Loss less than $0.1 

2022 $4,655 $37,503 1.3% -0.2% <$0.1 Loss less than $0.1 

2023 $4,662 $37,433 1.2% -0.2% <$0.1 Loss less than $0.1 

2024 $4,668 $37,234 1.2% -0.2% <$0.1 Loss less than $0.1 

2025 $4,673 $37,037 1.2% -0.2% <$0.1 Loss less than $0.1 

2026 $4,679 $36,843 1.2% -0.2% <$0.1 Loss less than $0.1 

2027 $4,683 $36,653 1.2% -0.2% <$0.1 Loss less than $0.1 

2028 $4,687 $36,467 1.2% -0.3% <$0.1 Loss less than $0.1 

2029 $4,691 $36,421 1.2% -0.3% <$0.1 Loss less than $0.1 

2030 $4,649 $36,219 1.2% -0.3% <$0.1 Loss less than $0.1 

2031 $4,653 $36,075 1.2% -0.3% <$0.1 Loss less than $0.1 

2032 $4,655 $35,944 1.2% -0.3% <$0.1 Loss less than $0.1 

2033 $4,658 $35,938 1.2% -0.3% <$0.1 Loss less than $0.1 

2034 $4,660 $35,816 1.2% -0.3% <$0.1 Loss less than $0.1 

2035 $4,661 $35,699 1.2% -0.3% <$0.1 Loss less than $0.1 

2036 $4,663 $35,701 1.2% -0.3% <$0.1 Loss less than $0.1 

2037 $4,663 $35,597 1.2% -0.3% <$0.1 Loss less than $0.1 

2038 $4,664 $35,617 1.2% -0.3% <$0.1 Loss less than $0.1 

2039 $4,664 $35,530 1.2% -0.3% <$0.1 Loss less than $0.1 

2040 $4,664 $35,451 1.2% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.2 -$0.2 NPV at 7% $0.1 -$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-9. Impact on C2 Ferries Vessel Market: 800–2,000 hp (Average Price per Vessel = $2,325,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$45 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$89 0.0% 0.0% <$0.1 Loss less than $0.1 2016 $3,946 $138,733 6.0% -0.1% <$0.1 Loss less than $0.1 

2017 $3,910 $138,440 6.0% -0.1% <$0.1 Loss less than $0.1 

2018 $3,986 $106,353 4.6% -0.1% <$0.1 Loss less than $0.1 

2019 $3,951 $106,048 4.6% -0.1% <$0.1 Loss less than $0.1 

2020 $3,915 $105,750 4.6% -0.1% <$0.1 Loss less than $0.1 

2021 $3,988 $105,567 4.6% -0.1% <$0.1 Loss less than $0.1 

2022 $3,953 $105,277 4.6% -0.2% <$0.1 Loss less than $0.1 

2023 $3,917 $104,995 4.6% -0.2% <$0.1 Loss less than $0.1 

2024 $3,987 $104,821 4.6% -0.2% <$0.1 Loss less than $0.1 

2025 $3,952 $104,547 4.5% -0.2% <$0.1 Loss less than $0.1 

2026 $3,917 $104,281 4.5% -0.2% <$0.1 Loss less than $0.1 

2027 $3,984 $105,428 4.6% -0.2% <$0.1 Loss less than $0.1 

2028 $3,948 $105,161 4.6% -0.3% <$0.1 Loss less than $0.1 

2029 $4,013 $105,007 4.6% -0.3% <$0.1 Loss less than $0.1 

2030 $3,978 $104,773 4.6% -0.3% <$0.1 Loss less than $0.1 

2031 $3,942 $104,550 4.5% -0.3% <$0.1 Loss less than $0.1 

2032 $4,005 $104,440 4.5% -0.3% <$0.1 Loss less than $0.1 

2033 $3,969 $104,239 4.5% -0.3% <$0.1 Loss less than $0.1 

2034 $3,934 $104,043 4.5% -0.3% <$0.1 Loss less than $0.1 

2035 $3,994 $103,949 4.5% -0.3% <$0.1 Loss less than $0.1 

2036 $3,958 $103,766 4.5% -0.3% <$0.1 Loss less than $0.1 

2037 $3,923 $103,596 4.5% -0.3% <$0.1 Loss less than $0.1 

2038 $3,980 $103,529 4.5% -0.3% <$0.1 Loss less than $0.1 

2039 $3,945 $103,380 4.5% -0.3% <$0.1 Loss less than $0.1 

2040 $4,000 $103,327 4.5% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.2 -$0.1 NPV at 7% $0.1 -$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. 

7-96 
Table 7B-10. Impact on C2 Ferries Vessel Market: >2,000 hp (Average Price per Vessel = $4,500,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$90 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$178 0.0% 0.0% $0.2 -$0.2 2016 $7,158 $218,210 4.8% -0.1% $0.1 -$0.1 2017 $7,150 $217,923 4.8% -0.1% $0.1 -$0.1 2018 $7,142 $167,266 3.7% -0.1% $0.1 Loss less than $0.1 

2019 $7,161 $166,927 3.7% -0.1% $0.1 Loss less than $0.1 

2020 $7,151 $166,568 3.7% -0.1% $0.1 Loss less than $0.1 

2021 $7,141 $166,216 3.7% -0.1% $0.1 Loss less than $0.1 

2022 $7,158 $165,898 3.7% -0.2% $0.1 Loss less than $0.1 

2023 $7,147 $165,561 3.7% -0.2% $0.1 Loss less than $0.1 

2024 $7,162 $165,258 3.7% -0.2% $0.1 Loss less than $0.1 

2025 $7,150 $164,939 3.7% -0.2% $0.1 Loss less than $0.1 

2026 $7,163 $164,653 3.7% -0.2% $0.1 Loss less than $0.1 

2027 $7,150 $164,352 3.7% -0.2% <$0.1 Loss less than $0.1 

2028 $7,163 $164,088 3.6% -0.3% <$0.1 Loss less than $0.1 

2029 $7,149 $163,827 3.6% -0.3% <$0.1 Loss less than $0.1 

2030 $7,160 $163,619 3.6% -0.3% <$0.1 Loss less than $0.1 

2031 $7,145 $163,412 3.6% -0.3% <$0.1 Loss less than $0.1 

2032 $7,155 $163,255 3.6% -0.3% <$0.1 Loss less than $0.1 

2033 $7,163 $163,108 3.6% -0.3% <$0.1 Loss less than $0.1 

2034 $7,147 $162,950 3.6% -0.3% <$0.1 Loss less than $0.1 

2035 $7,155 $162,825 3.6% -0.3% <$0.1 Loss less than $0.1 

2036 $7,162 $162,712 3.6% -0.3% <$0.1 Loss less than $0.1 

2037 $7,145 $162,594 3.6% -0.3% <$0.1 Loss less than $0.1 

2038 $7,150 $162,519 3.6% -0.3% <$0.1 Loss less than $0.1 

2039 $7,155 $162,462 3.6% -0.3% <$0.1 Loss less than $0.1 

2040 $7,160 $162,419 3.6% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.8 -$0.6 

NPV at 7% $0.4 -$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-11. Impact on C1 Supply/Crew Vessel Market: 800–2,000 hp (Average Price per Vessel = $3,100,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$55 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$108 0.0% 0.0% $0.1 -$0.1 2016 $2,385 $29,105 0.9% -0.1% $0.1 -$0.1 2017 $2,381 $28,931 0.9% -0.1% $0.1 Loss less than $0.1 

2018 $2,377 $22,414 0.7% -0.1% <$0.1 Loss less than $0.1 

2019 $2,389 $22,209 0.7% -0.1% <$0.1 Loss less than $0.1 

2020 $2,384 $21,991 0.7% -0.1% <$0.1 Loss less than $0.1 

2021 $2,380 $21,778 0.7% -0.1% <$0.1 Loss less than $0.1 

2022 $2,391 $21,587 0.7% -0.2% <$0.1 Loss less than $0.1 

2023 $2,386 $21,384 0.7% -0.2% <$0.1 Loss less than $0.1 

2024 $2,381 $21,186 0.7% -0.2% <$0.1 Loss less than $0.1 

2025 $2,392 $21,009 0.7% -0.2% <$0.1 Loss less than $0.1 

2026 $2,386 $20,822 0.7% -0.2% <$0.1 Loss less than $0.1 

2027 $2,380 $20,640 0.7% -0.2% <$0.1 Loss less than $0.1 

2028 $2,390 $20,482 0.7% -0.3% <$0.1 Loss less than $0.1 

2029 $2,384 $20,326 0.7% -0.3% <$0.1 Loss less than $0.1 

2030 $2,378 $20,187 0.7% -0.3% <$0.1 Loss less than $0.1 

2031 $2,387 $20,079 0.6% -0.3% <$0.1 Loss less than $0.1 

2032 $2,381 $19,971 0.6% -0.3% <$0.1 Loss less than $0.1 

2033 $2,389 $19,886 0.6% -0.3% <$0.1 Loss less than $0.1 

2034 $2,383 $19,792 0.6% -0.3% <$0.1 Loss less than $0.1 

2035 $2,391 $19,720 0.6% -0.3% <$0.1 Loss less than $0.1 

2036 $2,384 $19,640 0.6% -0.3% <$0.1 Loss less than $0.1 

2037 $2,391 $19,586 0.6% -0.3% <$0.1 Loss less than $0.1 

2038 $2,384 $19,529 0.6% -0.3% <$0.1 Loss less than $0.1 

2039 $2,391 $19,498 0.6% -0.3% <$0.1 Loss less than $0.1 

2040 $2,384 $19,463 0.6% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.5 -$0.5 NPV at 7% $0.3 -$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. 

7-98 
Table 7B-12. Impact on C1 Supply/Crew Vessel Market: >2,000 hp (Average Price per Vessel = $6,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

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 –$402 0.0% 0.0% $0.0 $0.0 2015 $0 –$799 0.0% 0.0% $0.8 –$0.8 2016 $4,671 $45,225 0.8% –0.3% $0.6 –$0.6 2017 $4,672 $43,974 0.7% –0.3% $0.4 –$0.5 2018 $4,673 $32,249 0.5% –0.4% $0.3 –$0.4 2019 $4,670 $30,635 0.5% –0.4% $0.3 –$0.4 2020 $4,673 $29,061 0.5% –0.5% $0.3 –$0.5 2021 $4,672 $27,513 0.5% –0.6% $0.3 –$0.6 2022 $4,671 $26,001 0.4% –0.6% $0.3 –$0.6 2023 $4,673 $24,663 0.4% –0.7% $0.3 –$0.7 2024 $4,671 $23,221 0.4% –0.8% $0.3 –$0.7 2025 $4,672 $21,819 0.4% –0.8% $0.3 –$0.8 2026 $4,673 $20,457 0.3% –0.9% $0.3 –$0.8 2027 $4,673 $19,136 0.3% –0.9% $0.2 –$0.7 2028 $4,672 $17,870 0.3% –1.0% $0.2 –$0.8 2029 $4,672 $16,860 0.3% –1.0% $0.2 –$0.8 2030 $4,671 $15,846 0.3% –1.1% $0.2 –$0.9 2031 $4,672 $14,953 0.2% –1.1% $0.2 –$0.9 2032 $4,673 $14,166 0.2% –1.1% $0.2 –$0.9 2033 $4,671 $13,556 0.2% –1.2% $0.2 –$1.0 2034 $4,671 $12,875 0.2% –1.2% $0.2 –$1.0 2035 $4,671 $12,242 0.2% –1.2% $0.2 –$1.0 2036 $4,673 $11,778 0.2% –1.2% $0.2 –$1.1 2037 $4,672 $11,279 0.2% –1.3% $0.2 –$1.1 2038 $4,670 $10,982 0.2% –1.3% $0.2 –$1.1 2039 $4,671 $10,651 0.2% –1.3% $0.2 –$1.1 2040 $4,672 $10,391 0.2% –1.3% $0.2 –$1.2 NPV at 3% $3.9 –$10.5 NPV at 7% $2.1 –$4.6 

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. 

7-99 
Table 7B-13. Impact on C2 Supply/Crew Vessel Market: 800–2,000 hp (Average Price per Vessel = $2,325,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$45 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$89 0.0% 0.0% $0.3 -$0.3 2016 $3,967 $138,755 6.0% -0.1% $0.2 -$0.2 2017 $3,969 $138,498 6.0% -0.1% $0.1 -$0.1 2018 $3,970 $106,337 4.6% -0.1% $0.1 -$0.1 2019 $3,971 $106,068 4.6% -0.1% $0.1 -$0.1 2020 $3,972 $105,806 4.6% -0.1% $0.1 -$0.1 2021 $3,972 $105,551 4.6% -0.1% $0.1 -$0.1 2022 $3,972 $105,297 4.6% -0.2% $0.1 -$0.1 2023 $3,972 $105,050 4.6% -0.2% $0.1 -$0.1 2024 $3,971 $104,804 4.6% -0.2% $0.1 -$0.1 2025 $3,970 $104,566 4.5% -0.2% $0.1 -$0.1 2026 $3,969 $104,334 4.5% -0.2% $0.1 -$0.1 2027 $3,968 $105,412 4.6% -0.2% <$0.1 Loss less than $0.1 

2028 $3,966 $105,178 4.6% -0.3% <$0.1 Loss less than $0.1 

2029 $3,964 $104,957 4.6% -0.3% <$0.1 Loss less than $0.1 

2030 $3,961 $104,757 4.6% -0.3% <$0.1 Loss less than $0.1 

2031 $3,959 $104,567 4.5% -0.3% <$0.1 Loss less than $0.1 

2032 $3,972 $104,407 4.5% -0.3% <$0.1 Loss less than $0.1 

2033 $3,969 $104,239 4.5% -0.3% <$0.1 Loss less than $0.1 

2034 $3,965 $104,075 4.5% -0.3% <$0.1 Loss less than $0.1 

2035 $3,962 $103,917 4.5% -0.3% <$0.1 Loss less than $0.1 

2036 $3,958 $103,766 4.5% -0.3% <$0.1 Loss less than $0.1 

2037 $3,969 $103,642 4.5% -0.3% <$0.1 Loss less than $0.1 

2038 $3,964 $103,513 4.5% -0.3% <$0.1 Loss less than $0.1 

2039 $3,959 $103,394 4.5% -0.3% <$0.1 Loss less than $0.1 

2040 $3,969 $103,296 4.5% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $1.0 -$0.8 NPV at 7% $0.5 -$0.5 

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. 

7-100 
Table 7B-14. Impact on C2 Supply/Crew Vessel Market: >2,000 hp (Average Price per Vessel = $4,500,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$90 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$178 0.0% 0.0% $1.1 -$1.1 2016 $7,153 $218,205 4.8% -0.1% $0.8 -$0.7 2017 $7,154 $217,926 4.8% -0.1% $0.6 -$0.4 2018 $7,154 $167,278 3.7% -0.1% $0.4 -$0.2 2019 $7,154 $166,921 3.7% -0.1% $0.4 -$0.2 2020 $7,154 $166,571 3.7% -0.1% $0.4 -$0.3 2021 $7,152 $166,227 3.7% -0.1% $0.4 -$0.3 2022 $7,155 $165,896 3.7% -0.2% $0.4 -$0.3 2023 $7,153 $165,567 3.7% -0.2% $0.4 -$0.3 2024 $7,154 $165,251 3.7% -0.2% $0.4 -$0.3 2025 $7,155 $164,944 3.7% -0.2% $0.4 -$0.3 2026 $7,156 $164,646 3.7% -0.2% $0.4 -$0.3 2027 $7,155 $164,357 3.7% -0.2% $0.2 -$0.1 2028 $7,155 $164,080 3.6% -0.3% $0.2 -$0.1 2029 $7,153 $163,831 3.6% -0.3% $0.2 -$0.1 2030 $7,155 $163,614 3.6% -0.3% $0.2 -$0.1 2031 $7,153 $163,420 3.6% -0.3% $0.2 -$0.1 2032 $7,154 $163,254 3.6% -0.3% $0.2 -$0.1 2033 $7,154 $163,099 3.6% -0.3% $0.2 -$0.1 2034 $7,154 $162,957 3.6% -0.3% $0.2 -$0.1 2035 $7,154 $162,824 3.6% -0.3% $0.2 -$0.1 2036 $7,156 $162,706 3.6% -0.3% $0.2 -$0.1 2037 $7,155 $162,604 3.6% -0.3% $0.2 -$0.1 2038 $7,156 $162,524 3.6% -0.3% $0.2 -$0.1 2039 $7,153 $162,459 3.6% -0.3% $0.2 -$0.1 2040 $7,153 $162,412 3.6% -0.3% $0.2 -$0.1 NPV at 3% $4.9 -$3.8 NPV at 7% $2.6 -$2.1 

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-15. Impact on C1 Cargo Vessel Market: 800–2,000 hp (Average Price per Vessel = $3,100,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$55 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$108 0.0% 0.0% <$0.1 Loss less than $0.1 

2016 $2,400 $29,119 0.9% -0.1% <$0.1 Loss less than $0.1 

2017 $2,378 $28,928 0.9% -0.1% <$0.1 Loss less than $0.1 

2018 $2,412 $22,449 0.7% -0.1% <$0.1 Loss less than $0.1 

2019 $2,390 $22,210 0.7% -0.1% <$0.1 Loss less than $0.1 

2020 $2,369 $21,976 0.7% -0.1% <$0.1 Loss less than $0.1 

2021 $2,401 $21,800 0.7% -0.1% <$0.1 Loss less than $0.1 

2022 $2,380 $21,576 0.7% -0.2% <$0.1 Loss less than $0.1 

2023 $2,411 $21,409 0.7% -0.2% <$0.1 Loss less than $0.1 

2024 $2,390 $21,194 0.7% -0.2% <$0.1 Loss less than $0.1 

2025 $2,368 $20,986 0.7% -0.2% <$0.1 Loss less than $0.1 

2026 $2,398 $20,834 0.7% -0.2% <$0.1 Loss less than $0.1 

2027 $2,377 $20,637 0.7% -0.2% <$0.1 Loss less than $0.1 

2028 $2,406 $20,498 0.7% -0.3% <$0.1 Loss less than $0.1 

2029 $2,384 $20,326 0.7% -0.3% <$0.1 Loss less than $0.1 

2030 $2,363 $20,172 0.7% -0.3% <$0.1 Loss less than $0.1 

2031 $2,391 $20,083 0.6% -0.3% <$0.1 Loss less than $0.1 

2032 $2,369 $19,959 0.6% -0.3% <$0.1 Loss less than $0.1 

2033 $2,396 $19,892 0.6% -0.3% <$0.1 Loss less than $0.1 

2034 $2,375 $19,784 0.6% -0.3% <$0.1 Loss less than $0.1 

2035 $2,401 $19,730 0.6% -0.3% <$0.1 Loss less than $0.1 

2036 $2,379 $19,635 0.6% -0.3% <$0.1 Loss less than $0.1 

2037 $2,404 $19,599 0.6% -0.3% <$0.1 Loss less than $0.1 

2038 $2,383 $19,528 0.6% -0.3% <$0.1 Loss less than $0.1 

2039 $2,407 $19,514 0.6% -0.3% <$0.1 Loss less than $0.1 

2040 $2,386 $19,465 0.6% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.2 -$0.2 NPV at 7% $0.1 -$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. 

7-102 
Table 7B-16. Impact on C1 Cargo Vessel Market: >2,000 hp (Average Price per Vessel = $6,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$106 0.0% 0.0% <$0.1 Loss less than $0.1 

2015 $0 -$210 0.0% 0.0% <$0.1 Loss less than $0.1 

2016 $4,606 $48,681 0.8% -0.1% <$0.1 Loss less than $0.1 

2017 $4,723 $48,469 0.8% -0.1% <$0.1 Loss less than $0.1 

2018 $4,681 $37,458 0.6% -0.1% <$0.1 Loss less than $0.1 

2019 $4,639 $36,989 0.6% -0.1% <$0.1 Loss less than $0.1 

2020 $4,598 $36,530 0.6% -0.1% <$0.1 Loss less than $0.1 

2021 $4,708 $36,227 0.6% -0.1% <$0.1 Loss less than $0.1 

2022 $4,666 $35,780 0.6% -0.2% <$0.1 Loss less than $0.1 

2023 $4,625 $35,473 0.6% -0.2% <$0.1 Loss less than $0.1 

2024 $4,731 $35,189 0.6% -0.2% <$0.1 Loss less than $0.1 

2025 $4,689 $34,764 0.6% -0.2% <$0.1 Loss less than $0.1 

2026 $4,647 $34,349 0.6% -0.2% <$0.1 Loss less than $0.1 

2027 $4,606 $33,944 0.6% -0.2% <$0.1 Loss less than $0.1 

2028 $4,707 $33,694 0.6% -0.3% <$0.1 Loss less than $0.1 

2029 $4,665 $33,458 0.6% -0.3% <$0.1 Loss less than $0.1 

2030 $4,624 $33,128 0.6% -0.3% <$0.1 Loss less than $0.1 

2031 $4,721 $32,967 0.5% -0.3% <$0.1 Loss less than $0.1 

2032 $4,679 $32,693 0.5% -0.3% <$0.1 Loss less than $0.1 

2033 $4,638 $32,553 0.5% -0.3% <$0.1 Loss less than $0.1 

2034 $4,731 $32,439 0.5% -0.3% <$0.1 Loss less than $0.1 

2035 $4,689 $32,201 0.5% -0.3% <$0.1 Loss less than $0.1 

2036 $4,647 $32,090 0.5% -0.3% <$0.1 Loss less than $0.1 

2037 $4,606 $31,885 0.5% -0.3% <$0.1 Loss less than $0.1 

2038 $4,695 $31,946 0.5% -0.3% <$0.1 Loss less than $0.1 

2039 $4,653 $31,781 0.5% -0.3% <$0.1 Loss less than $0.1 

2040 $4,612 $31,634 0.5% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.1 -$0.1 NPV at 7% <$0.1 Loss less than $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. 

7-103 
Table 7B-17. Impact on C2 Cargo Vessel Market: 800–2,000 hp (Average Price per Vessel = $4,650,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$85 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$169 0.0% 0.0% $0.1 -$0.1 2016 $3,972 $138,276 3.0% -0.1% <$0.1 Loss less than $0.1 

2017 $3,937 $137,857 3.0% -0.1% <$0.1 Loss less than $0.1 

2018 $3,972 $105,630 2.3% -0.1% <$0.1 Loss less than $0.1 

2019 $3,937 $105,165 2.3% -0.1% <$0.1 Loss less than $0.1 

2020 $3,971 $104,780 2.3% -0.1% <$0.1 Loss less than $0.1 

2021 $3,936 $104,335 2.3% -0.1% <$0.1 Loss less than $0.1 

2022 $3,969 $103,965 2.3% -0.2% <$0.1 Loss less than $0.1 

2023 $3,934 $103,537 2.3% -0.2% <$0.1 Loss less than $0.1 

2024 $3,966 $103,183 2.2% -0.2% <$0.1 Loss less than $0.1 

2025 $3,997 $102,838 2.2% -0.2% <$0.1 Loss less than $0.1 

2026 $3,962 $102,438 2.2% -0.2% <$0.1 Loss less than $0.1 

2027 $3,992 $103,418 2.2% -0.2% <$0.1 Loss less than $0.1 

2028 $3,956 $103,027 2.2% -0.3% <$0.1 Loss less than $0.1 

2029 $3,985 $102,727 2.2% -0.3% <$0.1 Loss less than $0.1 

2030 $3,950 $102,395 2.2% -0.3% <$0.1 Loss less than $0.1 

2031 $3,978 $102,150 2.2% -0.3% <$0.1 Loss less than $0.1 

2032 $3,942 $101,866 2.2% -0.3% <$0.1 Loss less than $0.1 

2033 $3,969 $101,659 2.2% -0.3% <$0.1 Loss less than $0.1 

2034 $3,995 $101,460 2.2% -0.3% <$0.1 Loss less than $0.1 

2035 $3,959 $101,212 2.2% -0.3% <$0.1 Loss less than $0.1 

2036 $3,984 $101,036 2.2% -0.3% <$0.1 Loss less than $0.1 

2037 $3,949 $100,820 2.2% -0.3% <$0.1 Loss less than $0.1 

2038 $3,973 $100,684 2.2% -0.3% <$0.1 Loss less than $0.1 

2039 $3,937 $100,506 2.2% -0.3% <$0.1 Loss less than $0.1 

2040 $3,961 $100,400 2.2% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.3 -$0.2 NPV at 7% $0.1 -$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-18. Impact on C2 Cargo Vessel Market: >2,000 hp (Average Price per Vessel = $9,000,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$168 0.0% 0.0% <$0.1 Loss less than $0.1 

2015 $0 -$334 0.0% 0.0% $0.3 -$0.3 2016 $7,150 $217,255 2.4% -0.1% $0.2 -$0.2 2017 $7,157 $216,739 2.4% -0.1% $0.1 -$0.1 2018 $7,147 $165,883 1.8% -0.1% $0.1 -$0.1 2019 $7,153 $165,219 1.8% -0.1% $0.1 -$0.1 2020 $7,159 $164,568 1.8% -0.1% $0.1 -$0.1 2021 $7,147 $163,914 1.8% -0.1% $0.1 -$0.1 2022 $7,152 $163,292 1.8% -0.2% $0.1 -$0.1 2023 $7,156 $162,684 1.8% -0.2% $0.1 -$0.1 2024 $7,159 $162,093 1.8% -0.2% $0.1 -$0.1 2025 $7,162 $161,518 1.8% -0.2% $0.1 -$0.1 2026 $7,148 $160,943 1.8% -0.2% $0.1 -$0.1 2027 $7,149 $160,403 1.8% -0.2% <$0.1 Loss less than $0.1 

2028 $7,150 $159,886 1.8% -0.3% <$0.1 -$0.1 2029 $7,151 $159,422 1.8% -0.3% <$0.1 -$0.1 2030 $7,151 $159,012 1.8% -0.3% <$0.1 -$0.1 2031 $7,150 $158,652 1.8% -0.3% <$0.1 -$0.1 2032 $7,149 $158,336 1.8% -0.3% <$0.1 -$0.1 2033 $7,147 $158,045 1.8% -0.3% <$0.1 -$0.1 2034 $7,160 $157,790 1.8% -0.3% <$0.1 -$0.1 2035 $7,157 $157,539 1.8% -0.3% <$0.1 -$0.1 2036 $7,154 $157,310 1.7% -0.3% <$0.1 -$0.1 2037 $7,150 $157,118 1.7% -0.3% <$0.1 -$0.1 2038 $7,160 $156,975 1.7% -0.3% <$0.1 -$0.1 2039 $7,155 $156,853 1.7% -0.3% <$0.1 -$0.1 2040 $7,149 $156,760 1.7% -0.3% <$0.1 -$0.1 NPV at 3% $1.3 -$1.3 NPV at 7% $0.7 -$0.7 

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. 

7-105 
Table 7B-19. Impact on C1 Other Commercial Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,085,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$19 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$38 0.0% 0.0% $0.1 -$0.1 2016 $2,381 $29,525 2.7% -0.1% $0.1 -$0.1 2017 $2,383 $29,466 2.7% -0.1% <$0.1 Loss less than $0.1 

2018 $2,384 $23,043 2.1% -0.1% <$0.1 Loss less than $0.1 

2019 $2,385 $22,966 2.1% -0.1% <$0.1 Loss less than $0.1 

2020 $2,386 $22,892 2.1% -0.1% <$0.1 Loss less than $0.1 

2021 $2,386 $22,818 2.1% -0.1% <$0.1 Loss less than $0.1 

2022 $2,387 $22,747 2.1% -0.2% <$0.1 Loss less than $0.1 

2023 $2,387 $22,677 2.1% -0.2% <$0.1 Loss less than $0.1 

2024 $2,387 $22,608 2.1% -0.2% <$0.1 Loss less than $0.1 

2025 $2,387 $22,541 2.1% -0.2% <$0.1 Loss less than $0.1 

2026 $2,386 $22,476 2.1% -0.2% <$0.1 Loss less than $0.1 

2027 $2,386 $22,413 2.1% -0.2% <$0.1 Loss less than $0.1 

2028 $2,385 $22,353 2.1% -0.3% <$0.1 Loss less than $0.1 

2029 $2,384 $22,299 2.1% -0.3% <$0.1 Loss less than $0.1 

2030 $2,383 $22,251 2.1% -0.3% <$0.1 Loss less than $0.1 

2031 $2,382 $22,208 2.0% -0.3% <$0.1 Loss less than $0.1 

2032 $2,381 $22,170 2.0% -0.3% <$0.1 Loss less than $0.1 

2033 $2,379 $22,135 2.0% -0.3% <$0.1 Loss less than $0.1 

2034 $2,377 $22,103 2.0% -0.3% <$0.1 Loss less than $0.1 

2035 $2,375 $22,072 2.0% -0.3% <$0.1 Loss less than $0.1 

2036 $2,392 $22,063 2.0% -0.3% <$0.1 Loss less than $0.1 

2037 $2,390 $22,039 2.0% -0.3% <$0.1 Loss less than $0.1 

2038 $2,388 $22,019 2.0% -0.3% <$0.1 Loss less than $0.1 

2039 $2,385 $22,003 2.0% -0.3% <$0.1 Loss less than $0.1 

2040 $2,382 $21,990 2.0% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.4 -$0.3 NPV at 7% $0.2 -$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. 

7-106 
Table 7B-20. Impact on C1 Other Commercial Vessel Market: >2,000 hp (Average Price per Vessel = $2,100,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$38 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$74 0.0% 0.0% <$0.1 Loss less than $0.1 2016 $4,692 $49,587 2.4% -0.1% <$0.1 Loss less than $0.1 

2017 $4,650 $49,428 2.4% -0.1% <$0.1 Loss less than $0.1 

2018 $4,673 $38,652 1.8% -0.1% <$0.1 Loss less than $0.1 

2019 $4,694 $38,519 1.8% -0.1% <$0.1 Loss less than $0.1 

2020 $4,653 $38,325 1.8% -0.1% <$0.1 Loss less than $0.1 

2021 $4,673 $38,192 1.8% -0.1% <$0.1 Loss less than $0.1 

2022 $4,693 $38,061 1.8% -0.2% <$0.1 Loss less than $0.1 

2023 $4,652 $38,000 1.8% -0.2% <$0.1 Loss less than $0.1 

2024 $4,671 $37,870 1.8% -0.2% <$0.1 Loss less than $0.1 

2025 $4,689 $37,739 1.8% -0.2% <$0.1 Loss less than $0.1 

2026 $4,647 $37,551 1.8% -0.2% <$0.1 Loss less than $0.1 

2027 $4,665 $37,424 1.8% -0.2% <$0.1 Loss less than $0.1 

2028 $4,682 $37,299 1.8% -0.3% <$0.1 Loss less than $0.1 

2029 $4,698 $37,310 1.8% -0.3% <$0.1 Loss less than $0.1 

2030 $4,656 $37,145 1.8% -0.3% <$0.1 Loss less than $0.1 

2031 $4,672 $37,047 1.8% -0.3% <$0.1 Loss less than $0.1 

2032 $4,686 $36,957 1.8% -0.3% <$0.1 Loss less than $0.1 

2033 $4,645 $36,934 1.8% -0.3% <$0.1 Loss less than $0.1 

2034 $4,659 $36,850 1.8% -0.3% <$0.1 Loss less than $0.1 

2035 $4,672 $36,767 1.8% -0.3% <$0.1 Loss less than $0.1 

2036 $4,685 $36,803 1.8% -0.3% <$0.1 Loss less than $0.1 

2037 $4,697 $36,727 1.7% -0.3% <$0.1 Loss less than $0.1 

2038 $4,655 $36,719 1.7% -0.3% <$0.1 Loss less than $0.1 

2039 $4,667 $36,654 1.7% -0.3% <$0.1 Loss less than $0.1 

2040 $4,678 $36,595 1.7% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.2 -$0.1 NPV at 7% $0.1 -$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. 

7-107 
Table 7B-21. Impact on C2 Other Commercial Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,627,500)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$33 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$65 0.0% 0.0% <$0.1 Loss less than $0.1 2016 $3,950 $138,883 8.6% -0.1% <$0.1 Loss less than $0.1 

2017 $3,915 $138,627 8.6% -0.1% <$0.1 Loss less than $0.1 

2018 $3,880 $106,460 6.6% -0.1% <$0.1 Loss less than $0.1 

2019 $3,846 $106,204 6.6% -0.1% <$0.1 Loss less than $0.1 

2020 $3,811 $105,954 6.6% -0.1% <$0.1 Loss less than $0.1 

2021 $3,777 $105,710 6.6% -0.1% <$0.1 Loss less than $0.1 

2022 $4,118 $105,841 6.6% -0.2% <$0.1 Loss less than $0.1 

2023 $4,081 $105,601 6.6% -0.2% <$0.1 Loss less than $0.1 

2024 $4,045 $105,363 6.5% -0.2% <$0.1 Loss less than $0.1 

2025 $4,009 $105,131 6.5% -0.2% <$0.1 Loss less than $0.1 

2026 $3,973 $104,904 6.5% -0.2% <$0.1 Loss less than $0.1 

2027 $3,938 $105,987 6.6% -0.2% <$0.1 Loss less than $0.1 

2028 $3,902 $105,757 6.6% -0.3% <$0.1 Loss less than $0.1 

2029 $3,868 $105,537 6.6% -0.3% <$0.1 Loss less than $0.1 

2030 $3,833 $105,334 6.5% -0.3% <$0.1 Loss less than $0.1 

2031 $3,799 $105,138 6.5% -0.3% <$0.1 Loss less than $0.1 

2032 $4,107 $105,296 6.5% -0.3% <$0.1 Loss less than $0.1 

2033 $4,071 $105,115 6.5% -0.3% <$0.1 Loss less than $0.1 

2034 $4,034 $104,937 6.5% -0.3% <$0.1 Loss less than $0.1 

2035 $3,998 $104,764 6.5% -0.3% <$0.1 Loss less than $0.1 

2036 $3,963 $104,598 6.5% -0.3% <$0.1 Loss less than $0.1 

2037 $3,927 $104,441 6.5% -0.3% <$0.1 Loss less than $0.1 

2038 $3,892 $104,293 6.5% -0.3% <$0.1 Loss less than $0.1 

2039 $3,858 $104,153 6.5% -0.3% <$0.1 Loss less than $0.1 

2040 $3,823 $104,016 6.5% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% <$0.1 Loss less than $0.1 

NPV at 7% <$0.1 Loss less than $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. 

7-108 
Table 7B-22. Impact on C2 Other Commercial Vessel Market: >2,000 hp (Average Price per Vessel = $3,150,000)a,b 

Variable Absolute Change Change Change Total Change in Equipment 

Engineering in Price in Price in Quantity Engineering Costs Manufacturers’ Surplus 

Year Cost/Unit ($) (%) (%) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 

2014 $0 -$66 0.0% 0.0% <$0.1 Loss less than $0.1 2015 $0 -$131 0.0% 0.0% $0.1 -$0.1 2016 $7,111 $218,447 6.9% -0.1% <$0.1 Loss less than $0.1 

2017 $7,145 $218,275 6.9% -0.1% <$0.1 Loss less than $0.1 

2018 $7,178 $167,718 5.3% -0.1% <$0.1 Loss less than $0.1 

2019 $7,114 $167,391 5.3% -0.1% <$0.1 Loss less than $0.1 

2020 $7,146 $167,165 5.3% -0.1% <$0.1 Loss less than $0.1 

2021 $7,177 $166,944 5.3% -0.1% <$0.1 Loss less than $0.1 

2022 $7,113 $166,633 5.3% -0.2% <$0.1 Loss less than $0.1 

2023 $7,142 $166,422 5.3% -0.2% <$0.1 Loss less than $0.1 

2024 $7,170 $166,216 5.3% -0.2% <$0.1 Loss less than $0.1 

2025 $7,197 $166,016 5.3% -0.2% <$0.1 Loss less than $0.1 

2026 $7,133 $165,732 5.3% -0.2% <$0.1 Loss less than $0.1 

2027 $7,159 $165,545 5.3% -0.2% <$0.1 Loss less than $0.1 

2028 $7,184 $165,366 5.2% -0.3% <$0.1 Loss less than $0.1 

2029 $7,120 $165,120 5.2% -0.3% <$0.1 Loss less than $0.1 

2030 $7,144 $164,982 5.2% -0.3% <$0.1 Loss less than $0.1 

2031 $7,166 $164,863 5.2% -0.3% <$0.1 Loss less than $0.1 

2032 $7,188 $164,761 5.2% -0.3% <$0.1 Loss less than $0.1 

2033 $7,124 $164,583 5.2% -0.3% <$0.1 Loss less than $0.1 

2034 $7,144 $164,498 5.2% -0.3% <$0.1 Loss less than $0.1 

2035 $7,164 $164,420 5.2% -0.3% <$0.1 Loss less than $0.1 

2036 $7,182 $164,350 5.2% -0.3% <$0.1 Loss less than $0.1 

2037 $7,118 $164,213 5.2% -0.3% <$0.1 Loss less than $0.1 

2038 $7,136 $164,170 5.2% -0.3% <$0.1 Loss less than $0.1 

2039 $7,153 $164,141 5.2% -0.3% <$0.1 Loss less than $0.1 

2040 $7,169 $164,122 5.2% -0.3% <$0.1 Loss less than $0.1 

NPV at 3% $0.2 -$0.2 NPV at 7% $0.1 -$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. 

7-109 
Table 7B-23. Impact on C1 Fishing Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,085,000)a,b 

Variable Absolute Change in Change in Total Change in Equipment Change in Change in Total 

Engineering Change in Price Quantity Engineering Manufacturers’ Consumer Surplus Surplus 

Year Cost/Unit Price ($) (%) (%) Costs (million $) Surplus (million $) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2014 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2015 $0 $0 0.0% 0.0% $8.0 -$8.0 Loss less than $0.1 -$7.96 

2016 $2,385 $15,870 1.5% -2.0% $4.9 -$8.6 -$5.05 -$13.60 2017 $2,385 $15,869 1.5% -2.0% $3.0 -$6.7 -$5.09 -$11.77 2018 $2,385 $12,468 1.1% -1.6% $2.1 -$4.8 -$4.04 -$8.86 2019 $2,385 $12,467 1.1% -1.6% $2.1 -$4.8 -$4.08 -$8.92 2020 $2,385 $12,467 1.1% -1.6% $2.1 -$4.9 -$4.12 -$8.99 2021 $2,385 $12,466 1.1% -1.6% $2.1 -$4.9 -$4.15 -$9.06 2022 $2,385 $12,465 1.1% -1.6% $2.1 -$4.9 -$4.19 -$9.13 2023 $2,385 $12,464 1.1% -1.6% $2.1 -$5.0 -$4.23 -$9.20 2024 $2,385 $12,463 1.1% -1.6% $2.1 -$5.0 -$4.27 -$9.27 2025 $2,385 $12,462 1.1% -1.6% $2.1 -$5.0 -$4.30 -$9.34 2026 $2,385 $12,462 1.1% -1.6% $2.1 -$5.1 -$4.34 -$9.42 2027 $2,385 $12,461 1.1% -1.6% $0.8 -$3.8 -$4.38 -$8.21 2028 $2,385 $12,460 1.1% -1.6% $0.9 -$3.9 -$4.42 -$8.29 2029 $2,385 $12,460 1.1% -1.6% $0.9 -$3.9 -$4.46 -$8.36 2030 $2,385 $12,459 1.1% -1.6% $0.9 -$3.9 -$4.50 -$8.44 2031 $2,385 $12,459 1.1% -1.6% $0.9 -$4.0 -$4.54 -$8.51 2032 $2,385 $12,458 1.1% -1.6% $0.9 -$4.0 -$4.58 -$8.59 2033 $2,385 $12,458 1.1% -1.6% $0.9 -$4.0 -$4.62 -$8.67 2034 $2,385 $12,457 1.1% -1.6% $0.9 -$4.1 -$4.66 -$8.74 2035 $2,385 $12,457 1.1% -1.6% $0.9 -$4.1 -$4.71 -$8.82 2036 $2,385 $12,457 1.1% -1.6% $0.9 -$4.2 -$4.75 -$8.90 2037 $2,385 $12,457 1.1% -1.6% $0.9 -$4.2 -$4.79 -$8.98 2038 $2,385 $12,456 1.1% -1.6% $0.9 -$4.2 -$4.83 -$9.06 2039 $2,385 $12,456 1.1% -1.6% $0.9 -$4.3 -$4.88 -$9.14 2040 $2,385 $12,456 1.1% -1.6% $0.9 -$4.3 -$4.92 -$9.23 NPV at 3% $28.4 -$68.3 -$58.2 -$126.5 

NPV at 7% $15.6 -$33.8 -$26.5 -$60.3 

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. 

7-110 
Table 7B-24. Impact on C1 Fishing Vessel Market: >2,000 hp (Average Price per Vessel = $2,100,000)a,b 

Variable Absolute Change in Change in Total Change in Equipment Change in Change in Total 

Engineering Change in Price Quantity Engineering Manufacturers’ Consumer Surplus Surplus 

Year Cost/Unit Price ($) (%) (%) Costs (million $) Surplus (million $) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2014 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2015 $0 -$1 0.0% 0.0% $2.8 -$2.8 Loss less than $0.1 -$2.80 2016 $4,672 $26,671 1.3% -1.8% $2.0 -$4.1 -$2.98 -$7.05 2017 $4,671 $26,670 1.3% -1.8% $1.3 -$3.4 -$3.01 -$6.43 2018 $4,672 $20,959 1.0% -1.4% $1.0 -$2.5 -$2.39 -$4.93 2019 $4,671 $20,955 1.0% -1.4% $1.0 -$2.6 -$2.41 -$4.97 2020 $4,671 $20,950 1.0% -1.4% $1.0 -$2.6 -$2.43 -$5.01 2021 $4,671 $20,942 1.0% -1.4% $1.0 -$2.6 -$2.45 -$5.05 2022 $4,672 $20,935 1.0% -1.4% $1.0 -$2.6 -$2.48 -$5.09 2023 $4,672 $20,996 1.0% -1.4% $1.0 -$2.6 -$2.51 -$5.14 2024 $4,671 $20,985 1.0% -1.4% $1.0 -$2.7 -$2.53 -$5.18 2025 $4,671 $20,972 1.0% -1.4% $1.0 -$2.7 -$2.55 -$5.22 2026 $4,672 $20,960 1.0% -1.4% $1.0 -$2.7 -$2.57 -$5.26 2027 $4,672 $20,946 1.0% -1.4% $0.6 -$2.3 -$2.59 -$4.86 2028 $4,671 $20,930 1.0% -1.4% $0.6 -$2.3 -$2.61 -$4.90 2029 $4,672 $20,981 1.0% -1.4% $0.6 -$2.3 -$2.64 -$4.95 2030 $4,671 $20,963 1.0% -1.4% $0.6 -$2.3 -$2.66 -$4.99 2031 $4,672 $20,945 1.0% -1.4% $0.6 -$2.3 -$2.68 -$5.03 2032 $4,671 $20,925 1.0% -1.4% $0.6 -$2.4 -$2.71 -$5.07 2033 $4,671 $20,969 1.0% -1.4% $0.6 -$2.4 -$2.74 -$5.13 2034 $4,671 $20,947 1.0% -1.4% $0.6 -$2.4 -$2.76 -$5.17 2035 $4,672 $20,925 1.0% -1.4% $0.6 -$2.4 -$2.78 -$5.21 2036 $4,671 $20,963 1.0% -1.4% $0.6 -$2.5 -$2.81 -$5.27 2037 $4,671 $20,939 1.0% -1.4% $0.6 -$2.5 -$2.83 -$5.31 2038 $4,671 $20,974 1.0% -1.4% $0.6 -$2.5 -$2.86 -$5.37 2039 $4,671 $20,947 1.0% -1.4% $0.6 -$2.5 -$2.88 -$5.41 2040 $4,671 $20,920 1.0% -1.4% $0.7 -$2.5 -$2.91 -$5.45 NPV at 3% $13.6 -$36.2 -$34.4 -$70.6 

NPV at 7% $7.1 -$17.4 -$15.6 -$33.1 

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. 

7-111 
Table 7B-25. Impact on C2 Fishing Vessel Market: 800–2,000 hp (Average Price per Vessel = $1,627,500)a,b 

Variable Absolute Change in Change in Total Change in Equipment Change in Change in Total 

Engineering Change in Price Quantity Engineering Manufacturers’ Consumer Surplus Surplus 

Year Cost/Unit Price ($) (%) (%) Costs (million $) Surplus (million $) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2014 $0 -$2 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2015 $0 -$5 0.0% 0.0% $0.1 -$0.1 Loss less than $0.1 -$0.15 2016 $3,976 $74,266 4.6% -6.5% $0.1 -$0.3 -$0.21 -$0.48 2017 $3,974 $74,193 4.6% -6.5% $0.1 -$0.2 -$0.21 -$0.46 2018 $3,972 $57,092 3.5% -5.0% $0.0 -$0.2 -$0.17 -$0.34 2019 $3,970 $57,033 3.5% -5.0% $0.0 -$0.2 -$0.17 -$0.34 2020 $3,967 $56,975 3.5% -5.0% $0.0 -$0.2 -$0.17 -$0.35 2021 $3,964 $56,919 3.5% -4.9% $0.0 -$0.2 -$0.17 -$0.35 2022 $3,961 $56,861 3.5% -4.9% $0.0 -$0.2 -$0.17 -$0.35 2023 $3,958 $56,806 3.5% -4.9% $0.0 -$0.2 -$0.17 -$0.35 2024 $3,954 $56,749 3.5% -4.9% $0.0 -$0.2 -$0.17 -$0.36 2025 $3,950 $56,694 3.5% -4.9% $0.0 -$0.2 -$0.18 -$0.36 2026 $3,977 $56,656 3.5% -4.9% $0.0 -$0.2 -$0.18 -$0.36 2027 $3,973 $57,298 3.6% -5.0% $0.0 -$0.2 -$0.18 -$0.34 2028 $3,968 $57,238 3.6% -5.0% $0.0 -$0.2 -$0.18 -$0.34 2029 $3,962 $57,178 3.6% -5.0% $0.0 -$0.2 -$0.18 -$0.35 2030 $3,957 $57,121 3.5% -5.0% $0.0 -$0.2 -$0.19 -$0.35 2031 $3,952 $57,064 3.5% -5.0% $0.0 -$0.2 -$0.19 -$0.35 2032 $3,975 $57,025 3.5% -5.0% $0.0 -$0.2 -$0.19 -$0.35 2033 $3,969 $56,970 3.5% -5.0% $0.0 -$0.2 -$0.19 -$0.36 2034 $3,962 $56,915 3.5% -4.9% $0.0 -$0.2 -$0.19 -$0.36 2035 $3,956 $56,861 3.5% -4.9% $0.0 -$0.2 -$0.19 -$0.36 2036 $3,977 $56,823 3.5% -4.9% $0.0 -$0.2 -$0.20 -$0.37 2037 $3,970 $56,771 3.5% -4.9% $0.0 -$0.2 -$0.20 -$0.37 2038 $3,962 $56,720 3.5% -4.9% $0.0 -$0.2 -$0.20 -$0.37 2039 $3,955 $56,670 3.5% -4.9% $0.0 -$0.2 -$0.20 -$0.37 2040 $3,974 $56,634 3.5% -4.9% $0.0 -$0.2 -$0.20 -$0.38 NPV at 3% $0.5 -$2.5 -$2.4 -$4.9 NPV at 7% $0.3 -$1.2 -$1.1 -$2.3 

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. 

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Table 7B-26. Impact on C2 Fishing Vessel Market: >2,000 hp (Average Price per Vessel = $3,150,000)a,b 

Variable Absolute Change in Change in Total Change in Equipment Change in Change in Total 

Engineering Change in Price Quantity Engineering Manufacturers’ Consumer Surplus Surplus 

Year Cost/Unit Price ($) (%) (%) Costs (million $) Surplus (million $) (million $) (million $) 

2007 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2008 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2009 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2010 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2011 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2012 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2013 $0 $0 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2014 $0 -$6 0.0% 0.0% <$0.1 Loss less than $0.1 Loss less than $0.1 Loss less than $0.1 

2015 $0 -$12 0.0% 0.0% $0.6 -$0.6 Loss less than $0.1 -$0.58 

2016 $7,154 $116,881 3.7% -5.2% $0.4 -$1.5 -$1.34 -$2.86 2017 $7,157 $116,864 3.7% -5.2% $0.3 -$1.4 -$1.35 -$2.77 2018 $7,152 $89,954 2.9% -4.0% $0.2 -$1.0 -$1.06 -$2.10 2019 $7,154 $89,932 2.9% -4.0% $0.2 -$1.0 -$1.07 -$2.11 2020 $7,156 $89,910 2.9% -4.0% $0.2 -$1.1 -$1.07 -$2.13 2021 $7,157 $89,888 2.9% -4.0% $0.2 -$1.1 -$1.08 -$2.15 2022 $7,158 $89,866 2.9% -4.0% $0.2 -$1.1 -$1.09 -$2.17 2023 $7,158 $89,844 2.9% -4.0% $0.2 -$1.1 -$1.10 -$2.19 2024 $7,157 $89,822 2.9% -4.0% $0.2 -$1.1 -$1.11 -$2.20 2025 $7,156 $89,802 2.9% -4.0% $0.2 -$1.1 -$1.12 -$2.22 2026 $7,154 $89,781 2.9% -4.0% $0.2 -$1.1 -$1.13 -$2.24 2027 $7,152 $89,760 2.8% -4.0% $0.1 -$1.0 -$1.14 -$2.14 2028 $7,157 $89,745 2.8% -4.0% $0.1 -$1.0 -$1.15 -$2.16 2029 $7,154 $89,727 2.8% -4.0% $0.1 -$1.0 -$1.16 -$2.18 2030 $7,157 $89,714 2.8% -4.0% $0.1 -$1.0 -$1.17 -$2.20 2031 $7,153 $89,699 2.8% -4.0% $0.1 -$1.0 -$1.18 -$2.22 2032 $7,155 $89,689 2.8% -4.0% $0.1 -$1.0 -$1.19 -$2.24 2033 $7,157 $89,680 2.8% -4.0% $0.1 -$1.1 -$1.20 -$2.26 2034 $7,151 $89,667 2.8% -4.0% $0.1 -$1.1 -$1.22 -$2.28 2035 $7,152 $89,659 2.8% -4.0% $0.1 -$1.1 -$1.23 -$2.30 2036 $7,152 $89,651 2.8% -4.0% $0.1 -$1.1 -$1.24 -$2.32 2037 $7,152 $89,644 2.8% -4.0% $0.1 -$1.1 -$1.25 -$2.34 2038 $7,158 $89,642 2.8% -4.0% $0.1 -$1.1 -$1.26 -$2.36 2039 $7,156 $89,637 2.8% -4.0% $0.1 -$1.1 -$1.27 -$2.38 2040 $7,154 $89,633 2.8% -4.0% $0.1 -$1.1 -$1.28 -$2.40 NPV at 3% $2.7 -$14.8 -$15.2 -$30.0 NPV at 7% $1.4 -$7.0 -$6.9 -$13.9 

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. 

7-113 
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). 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). 

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Table 7C-1. Table 7C-1. Impact on Railroad Transportation Services Market 

Change Change Total Change in Service Change in Service Change in 

in Price in Quantity Engineering Costs Consumer Surplus Provider Surplus Total Surplus 

Year (%) (%) (million $) (million $) (million $) (million $) 

2007 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2008 0.1% 0.0% $58.0 -$31.5 -$26.2 -$57.7 2009 0.0% 0.0% $34.5 -$18.8 -$15.6 -$34.4 2010 0.1% 0.0% $55.3 -$30.0 -$25.0 -$55.1 2011 0.1% -0.1% $104.8 -$57.0 -$47.5 -$104.4 2012 0.1% 0.0% $84.0 -$45.6 -$38.0 -$83.6 2013 0.1% 0.0% $74.4 -$40.4 -$33.7 -$74.1 2014 0.1% 0.0% $52.6 -$28.6 -$23.8 -$52.4 2015 0.1% 0.0% $44.0 -$45.9 -$38.2 -$84.1 2016 0.1% -0.1% $74.5 -$63.5 -$52.9 -$116.3 2017 0.2% -0.1% $72.0 -$75.2 -$62.6 -$137.8 2018 0.2% -0.1% $86.5 -$83.8 -$69.8 -$153.6 2019 0.2% -0.1% $93.3 -$84.2 -$70.2 -$154.4 2020 0.2% -0.1% $81.4 -$78.9 -$65.8 -$144.7 2021 0.2% -0.1% $95.7 -$87.9 -$73.2 -$161.1 2022 0.2% -0.1% $131.0 -$108.0 -$90.0 -$198.1 2023 0.3% -0.1% $189.9 -$140.9 -$117.4 -$258.3 2024 0.3% -0.1% $192.1 -$142.6 -$118.8 -$261.4 2025 0.3% -0.1% $210.4 -$153.4 -$127.8 -$281.2 2026 0.3% -0.1% $210.9 -$154.2 -$128.5 -$282.7 2027 0.3% -0.2% $226.1 -$163.1 -$135.9 -$299.0 2028 0.3% -0.2% $255.7 -$179.6 -$149.7 -$329.4 2029 0.3% -0.2% $271.2 -$188.4 -$157.0 -$345.5 2030 0.4% -0.2% $284.5 -$195.9 -$163.2 -$359.1 2031 0.4% -0.2% $298.5 -$203.9 -$169.9 -$373.9 2032 0.4% -0.2% $308.3 -$209.7 -$174.8 -$384.5 2033 0.4% -0.2% $378.4 -$248.0 -$206.6 -$454.6 2034 0.5% -0.2% $401.3 -$260.7 -$217.2 -$477.9 2035 0.5% -0.2% $422.5 -$272.3 -$227.0 -$499.3 2036 0.5% -0.2% $429.0 -$274.6 -$228.9 -$503.5 2037 0.5% -0.2% $445.1 -$282.5 -$235.4 -$517.9 2038 0.5% -0.2% $460.8 -$290.0 -$241.7 -$531.7 2039 0.5% -0.2% $476.4 -$297.4 -$247.8 -$545.2 2040 0.5% -0.3% $491.3 -$304.2 -$253.5 -$557.8 NPV at 3% $3,457.2 -$2,386.5 -$1,988.8 -$4,375.3 

NPV at 7% $1,514.5 -$1,053.7 -$878.1 -$1,931.9 

a Figures are in 2005 dollars. 

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b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 

Table 7C-2. Impact on Marine Transportation Services Market 

Change Change Total Change in Service Change in Service Change in 

in Price in Quantity Engineering Costs Consumer Surplus Provider Surplus Total Surplus 

Year (%) (%) (million $) (million $) (million $) (million $) 

2007 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2008 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2009 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2010 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2011 0.0% 0.0% $0.0 $0.0 $0.0 $0.0 2012 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 2014 0.0% 0.0% $2.8 -$1.5 -$1.3 -$2.8 2015 0.0% 0.0% $5.6 -$3.1 -$2.5 -$5.6 2016 0.1% -0.1% $14.8 -$18.7 -$15.6 -$34.3 2017 0.1% -0.1% $23.9 -$23.8 -$19.8 -$43.6 2018 0.2% -0.1% $36.0 -$27.9 -$23.3 -$51.2 2019 0.2% -0.1% $48.0 -$34.6 -$28.8 -$63.4 2020 0.2% -0.1% $60.0 -$41.2 -$34.3 -$75.4 2021 0.3% -0.1% $72.0 -$47.7 -$39.8 -$87.5 2022 0.3% -0.2% $83.9 -$54.3 -$45.2 -$99.5 2023 0.3% -0.2% $95.7 -$60.7 -$50.6 -$111.4 2024 0.4% -0.2% $107.5 -$67.2 -$56.0 -$123.2 2025 0.4% -0.2% $119.1 -$73.6 -$61.3 -$134.9 2026 0.4% -0.2% $130.6 -$79.9 -$66.6 -$146.4 2027 0.5% -0.2% $141.9 -$86.1 -$71.8 -$157.9 2028 0.5% -0.3% $153.0 -$92.2 -$76.8 -$169.0 2029 0.5% -0.3% $163.3 -$97.8 -$81.5 -$179.4 2030 0.6% -0.3% $172.6 -$103.0 -$85.8 -$188.8 2031 0.6% -0.3% $181.2 -$107.7 -$89.8 -$197.5 2032 0.6% -0.3% $189.0 -$112.0 -$93.4 -$205.4 2033 0.6% -0.3% $196.4 -$116.1 -$96.8 -$212.9 2034 0.6% -0.3% $203.6 -$120.1 -$100.1 -$220.2 2035 0.6% -0.3% $210.4 -$123.9 -$103.2 -$227.1 2036 0.6% -0.3% $216.9 -$127.5 -$106.2 -$233.7 2037 0.7% -0.3% $222.7 -$130.7 -$108.9 -$239.7 2038 0.7% -0.3% $227.9 -$133.6 -$111.3 -$244.9 2039 0.7% -0.3% $232.4 -$136.1 -$113.5 -$249.6 2040 0.7% -0.3% $236.3 -$138.4 -$115.3 -$253.7 NPV at 3% $1,648.3 -$1,015.4 -$846.2 -$1,861.6 

NPV at 7% $646.4 -$405.9 -$338.2 -$744.1 

a Figures are in 2005 dollars. 

7-116 
b Net present values for 2006 are calculated using a social discount rate of 3% and 7% over the 2007 to 2040 time period. 

7-117 
Appendix 7D: Time Series of Social Costs 

This appendix provides a time series of the rule’s estimated social costs from 

2007 through 2040. 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). 

7-118 
 
Table 7D-1. Time Series of Social Costs: 2007 to 2040 (Million $)a,b 

Stakeholder Groups 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 

Locomotives 

Locomotive producers –$3.2 –$3.4 –$3.3 –$7.5 –$11.1 –$12.6 –$12.6 –$17.2 –$8.6 –$13.4 –$0.6 –$0.7 Rail transportation service $0.0 –$26.2 –$15.6 –$25.0 –$47.5 –$38.0 –$33.7 –$23.8 –$38.2 –$52.9 –$62.6 –$69.8 providers Users of rail transportation $0.0 –$31.5 –$18.8 –$30.0 –$57.0 –$45.6 –$40.4 –$28.6 –$45.9 –$63.5 –$75.2 –$83.8 service Total locomotive sector –$3.2 –$61.1 –$37.7 –$62.5 –$115.6 –$96.3 –$86.7 –$69.5 –$92.8 –$129.7 –$138.4 –$154.3 

Marine 

Marine engine producers –$25.0 –$25.0 –$25.0 –$25.0 –$86.0 –$41.2 –$41.2 –$41.2 –$56.6 –$0.9 –$0.9 –$0.8 

C1 >800 hp –$1.7 –$1.7 –$1.8 –$1.8 –$22.8 –$20.4 –$20.4 –$20.4 –$26.7 –$0.7 –$0.7 –$0.6 C2 >800 hp –$4.2 –$4.2 –$4.2 –$4.2 –$27.8 –$20.7 –$20.7 –$20.7 –$29.9 –$0.2 –$0.2 –$0.2 Other marine –$19.1 –$19.1 –$19.1 –$19.1 –$35.4 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine vessel producers $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$17.5 –$18.0 –$14.1 –$9.9 

C1 >800 hp $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$12.5 –$13.6 –$10.6 –$7.7 C2 >800 hp $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$5.0 –$4.4 –$3.5 –$2.2 Other marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Recreational and fishing $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$9.6 –$9.7 –$7.7  vessel consumers Marine transportation $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$1.3 –$2.5 –$15.6 –$19.8 –$23.3 service providers Users of marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$1.5 –$3.1 –$18.7 –$23.8 –$27.9 transportation service Total marine sector –$25.0 –$25.0 –$25.0 –$25.0 –$86.0 –$41.2 –$41.2 –$44.0 –$79.8 –$62.9 –$68.3 –$69.5 Total program –$28.2 –$86.1 –$62.7 –$87.5 –$201.5 –$137.5 –$127.9 –$113.5 –$172.5 –$192.6 –$206.7 –$223.9 

(continued) 

7-119 
Table 7D-1. Time Series of Social Costs: 2007 to 2040 (Million $)a,b (continued) 

Stakeholder Groups 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 

Locomotives 

Locomotive producers –$0.7 –$0.7 –$0.8 –$1.0 –$1.3 –$1.3 –$1.4 –$1.4 –$1.5 –$1.7 –$1.8 –$1.8 Rail transportation service –$70.2 –$65.8 –$73.2 –$90.0 –$117.4 –$118.8 –$127.8 –$128.5 –$135.9 –$149.7 –$157.0 –$163.2 providers Users of rail transportation –$84.2 –$78.9 –$87.9 –$108.0 –$140.9 –$142.6 –$153.4 –$154.2 –$163.1 –$179.6 –$188.4 –$195.9 service Total locomotive sector –$155.1 –$145.3 –$161.9 –$199.0 –$259.6 –$262.7 –$282.6 –$284.1 –$300.6 –$331.0 –$347.2 –$360.9 

Marine 

Marine engine producers –$0.8 –$0.8 –$0.8 –$0.8 –$0.8 –$0.8 –$0.8 –$0.9 –$0.9 –$0.9 –$0.9 –$0.9 

C1 >800 hp –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.6 –$0.7 C2 >800 hp –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 Other marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 

Marine vessel producers –$10.0 –$10.1 –$10.2 –$10.3 –$10.4 –$10.6 –$10.7 –$10.8 –$7.9 –$8.0 –$8.1 –$8.2 

C1 >800 hp –$7.7 –$7.8 –$7.9 –$7.9 –$8.0 –$8.1 –$8.1 –$8.2 –$6.2 –$6.3 –$6.4 –$6.4 C2 >800 hp –$2.2 –$2.3 –$2.3 –$2.3 –$2.4 –$2.4 –$2.4 –$2.5 –$1.5 –$1.6 –$1.6 –$1.6 Other marine $0.0 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 –$0.1 

Recreational and fishing –$7.7 –$7.8 –$7.9 –$7.9 –$8.0 –$8.1 –$8.1 –$8.2 –$8.3 –$8.4 –$8.4 –$8.5  vessel consumers Marine transportation –$28.8 –$34.3 –$39.8 –$45.2 –$50.6 –$56.0 –$61.3 –$66.6 –$71.8 –$76.8 –$81.5 –$85.8 service providers Users of marine –$34.6 –$41.2 –$47.7 –$54.3 –$60.7 –$67.2 –$73.6 –$79.9 –$86.1 –$92.2 –$97.8 –$103.0 transportation service Total marine sector –$81.9 –$94.1 –$106.4 –$118.5 –$130.6 –$142.6 –$154.5 –$166.3 –$174.9 –$186.3 –$196.8 –$206.5 Total program –$236.9 –$239.5 –$268.2 –$317.6 –$390.2 –$405.4 –$437.2 –$450.4 –$475.5 –$517.3 –$544.0 –$567.3 

(continued) 

7-120 
Table 7D-1. Time Series of Social Costs: 2007 to 2040 (Million $)a,b (continued) 

NPV NPV 

Stakeholder Groups 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 (3%) (7%) 

Locomotives 

Locomotive producers –$1.9 –$1.9 –$2.3 –$2.4 –$2.5 –$2.4 –$2.4 –$2.4 –$2.3 –$2.3 –$92.8 –$63.5 Rail transportation service –$169.9 –$174.8 –$206.6 –$217.2 –$227.0 –$228.9 –$235.4 –$241.7 –$247.8 –$253.5 –$1,988.8 –$878.1 providers Users of rail transportation –$203.9 –$209.7 –$248.0 –$260.7 –$272.3 –$274.6 –$282.5 –$290.0 –$297.4 –$304.2 –$2,386.5 –$1,053.7 service Total locomotive sector –$375.8 –$386.4 –$456.9 –$480.3 –$501.8 –$505.9 –$520.3 –$534.1 –$547.6 –$560.1 –$4,468.1 –$1,995.4 

Marine 

Marine engine producers –$0.9 –$0.9 –$1.0 –$1.0 –$1.0 –$1.0 –$1.0 –$1.0 –$1.0 –$1.0 –$313.3 –$242.3 

C1 >800 hp –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$0.7 –$102.1 –$73.9 C2 >800 hp –$0.2 –$0.2 –$0.2 –$0.3 –$0.3 –$0.3 –$0.3 –$0.3 –$0.3 –$0.3 –$112.4 –$84.4 Other marine $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 –$98.7 –$84.0 

Marine vessel producers –$8.3 –$8.4 –$8.5 –$8.6 –$8.7 –$8.8 –$8.9 –$9.0 –$9.1 –$9.1 –$143.8 –$71.3 

C1 >800 hp –$6.5 –$6.6 –$6.6 –$6.7 –$6.8 –$6.8 –$6.9 –$7.0 –$7.0 –$7.1 –$110.1 –$54.3 C2 >800 hp –$1.7 –$1.7 –$1.7 –$1.7 –$1.8 –$1.8 –$1.8 –$1.8 –$1.9 –$1.9 –$32.4 –$16.5 Other marine –$0.1 –$0.1 –$0.1 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$0.2 –$1.3 –$0.5 

Recreational and fishing –$8.6 –$8.7 –$8.7 –$8.8 –$8.9 –$9.0 –$9.1 –$9.1 –$9.2 –$9.3 –$110.0 –$50.1  vessel consumers Marine transportation –$89.8 –$93.4 –$96.8 –$100.1 –$103.2 –$106.2 –$108.9 –$111.3 –$113.5 –$115.3 –$846.2 –$338.2 service providers Users of marine –$107.7 –$112.0 –$116.1 –$120.1 –$123.9 –$127.5 –$130.7 –$133.6 –$136.1 –$138.4 –$1,015.4 –$405.9 transportation service Total marine sector –$215.3 –$223.4 –$231.1 –$238.5 –$245.6 –$252.5 –$258.6 –$264.1 –$268.9 –$273.1 –$2,428.7 –$1,107.7 Total program –$591.1 –$609.8 –$688.0 –$718.8 –$747.4 –$758.3 –$778.8 –$798.1 –$816.4 –$833.2 –$6,896.8 –$3,103.1 

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. 

7-121 
7-122 
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: 

ε = 

dQ

dp

s 

/

/ Q

p 

s 

s . (7E.1)

Next, we can use “hat” notation to transform Eq. (7E.1) to proportional 

changes and rearrange terms: 

Q$

s = ε s p$ (7E.1a) 

where 

Q$

s = percentage change in the quantity of market supply, 

gs = market elasticity of supply, and 

p$ = percentage change in market price. 

As Fullerton and Metcalfe (2002) note, this approach takes the elasticity 

definition and turns it into a linear behavioral equation for each market. 

To introduce the direct impact of the regulatory program, we assume the 

direct per-unit 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: 

$ c c

MC = = . (7E.1b) MCo po 

7-123 
 
The with-regulation supply response to price and cost changes can now be written as: 

Qˆ 

s = εs ( pˆ – MˆC ) (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: 

$ c + α(Δ peng ) 

= 

c + α(Δ peng ) 

MC = (7E.1d) MCo po 

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): 

$ c + α(Δ pequip ) 

= 

c + α(Δ pequip ) 

MC = (7E.1e) MCo po 

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 equipment18 

demand responses to price changes as: 

Q$

d = ηd p$ (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. 

18 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. 

7-124 
 
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$

input = Q$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$

s = Q$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. 

7-125 
 
P 

D 

$/Q

S1: With Regulation 

P* 

A B 

P Price 

Increase 

Unit Cost 

Increase 

S0: Without 

Regulation 

C 

Q/t

Figure 7E-1. Computing With-Regulation Equilibrium 

The economic model uses a similar type of algorithm for determining withregulation 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 (1986). 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 

P • i 1 + = P1

⎛

⎜

⎜

⎝

qd 

qs 

⎞

⎟

⎟

⎠ (7E.5) 
 

4. Recalculate market supply with new prices. 

7-126 
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. 

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 7E2. 

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 

proposed rule. These calculations exclude any environmental benefits associated with 

the proposed rule. 

7-127 
 
) 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 

7-128 
 
Appendix 7F: Elasticity Parameters for Economic Impact
 

Modeling 
 

Elasticities were obtained from peer-reviewed literature or were obtained from 

other sources 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 anlaysis. 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 and this appendix describes the methods 

and data used to identify an acceptable value for the economic impact analysis. 

It should be noted that the methods we used to estimate the price elasticites 

described below have certain limitations. The production function approach that was 

used to estimate several of the supply elasticities was used due to limitations in 

available data. Specifically, firm level or plant level data was unavailable for the 

companies that operate in the affected sectors. As a result, several of the supply 

elasticities were estimated using a production function approach 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 

ESTIMA 

TE SOURCE 

METHO 

D 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 

7-129 
 
MARKET 

ESTIMA 

TE SOURCE 

METHO 

D DATA SOURCE 

Marine 

Marine Transp. 

Svcs 

-0.5 Literature 

Estimate 

Assumed 

value 

Uses the same elasticity as the locomotive 

transportation services sector. 

Vessels— 

Commercial 

Derived 

Vessels— 

Fishing 

-1.4 Econometric 

Estimate 

Assumed 

value 

Uses the same elasticity as the recreation 

vessels sector. 

Vessels— 

Recreational 

-1.4 Econometric 

Estimate 

Previous 

EPA 

Economic 

Analysis 

U.S. Environmental Protection Agency 

(EPA). 2002. Final Regulatory Support 

Document: Control of Emissions from 

Unregulated Nonroad Engines. EPA420-R02-022. Available at <http://www.epa. 

gov/otaq/regs/nonroad/2002/r02022.pdf>. 

Engines Derived 

Table 7F-2. Summary of Supply Elasticities Used in EIM 

MARKE 

T 

ESTIMA 

TE SOURCE 

METHO 

D DATA SOURCE 

Rail 

Rail 

Transp. 

Svcs 

0.6 Literature 

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>. 

Locomoti 

ves 

2.7 EPA 

Estimate 

Calibration 

Method 

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. 

Marine 

Marine 

Transp. 

Svcs 

0.6 Literature 

Estimate 

Assumed 

value 

Uses the same elasticity as the rail 

transportation services sector. 

7-130 
 
Vessels— 

Commerci 

al 

2.3 EPA 

Estimate 

Calibration 

Method 

U.S. Bureau of the Census. 2004. “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— 

Fishing 

1.6 Economet 

ric 

Estimate 

Assumed 

value 

Uses the same elasticity as the recreation 

vessels sector. 

Vessels— 

Recreation 

al 

1.6 Economet 

ric 

Estimate 

Previous 

EPA 

Economic 

Analysis 

U.S. Environmental Protection Agency (EPA). 

2002. Final Regulatory Support Document: 

Control of Emissions from Unregulated 

Nonroad Engines. EPA420-R-02-022. 

Available at 

<http://www.epa.gov/otaq/regs/nonroad/2002/r 

02022.pdf>. 

Engines 3.8 Economet 

ric 

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 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.19 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. 22 The industry-level benchmark data set offers 

advantages over previously used data sets (e.g., National Bureau of Economic 

Research [NBER] Manufacturing Productivity Database) because it relies on the 

latest industrial classification system (North American Industry Classification System 

[NAICS]). Using the latest classification system allows us to select a more precise 

industry code that characterizes locomotive manufacturing. In addition, EPA can use 

the most up-to-date data set available for the analysis. 

19 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). 

7-131 
 
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 intermediaterun 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 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 

7-132 
 
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. 

Table 7F-3. Benchmark Supply Elasticities for NAICS 336510 (Railroad Rolling 

Stock Manufacturing): 1997–2004 ($1,000) 

Value of Value Share 

Year Shipments Value Added Payroll Costs of Capital (θ)

a 

Supply Elasticity 

ε = (1 − θ) × σ/ θ

σ=1 

(Cobb-Douglas) 

2.6 

3.2 

2.1 

2.6 

2.4 

2.5 

2.8 

3.1 

2004 $7,566,129 

2003 $7,404,763 

2002 $7,793,382 

2001 $8,578,053 

2000 $9,722,424 

1999 $10,352,310 

1998 $9,256,810 

1997 $8,263,395 

Parameter Statistics 

$3,216,704 

$2,909,834 

$3,741,703 

$3,824,449 

$4,360,089 

$4,460,735 

$3,848,408 

$3,345,283 

$1,123,054 

$1,156,084 

$1,195,073 

$1,449,784 

$1,480,181 

$1,532,969 

$1,440,110 

$1,319,135 

28% 

24% 

33% 

28% 

30% 

28% 

26% 

25% 

Average 
 

Standard deviation
 

Upper bound (95% confidence interval) 
 

Lower bound (95% confidence interval) 
 

2.7 

0.4 

3.4 

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. 

7-133 
 
Table 7F-4. Benchmark Supply Elasticities for NAICS 336611 (Ship Building & 

Repairing): 1997–2004 ($1,000) 

Year Value of Shipments 

Value 

Added 

Payroll 

Costs 

Value Share 

of Capital (θ)

a 

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 

Supply Elasticity 

ε = (1 − θ) × σ/ θ

σ=1 

(Cobb-Douglas) 

Average 

Standard Deviation
 

Upper Bound (95% Confidence Interval) 
 

Lower Bound (95% Confidence Interval) 
 

2.3 

0.5 

3.2 

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. 

7-134 
 
Appendix 13G: 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. 

Equilibrium price forecasts typically use one of two approaches (EPA 1999, p 

5-25). 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. 

7-135 
 
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 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. 

7-136 
 
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 (modeled by supply and demand 

elasticities) and how the engineering compliance costs are included in the market 

supply function shift. This appendix examines the sensitivity of the results 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. Three model components are examined: 

• Scenario 1: alternative market supply and demand elasticity parameters 

• Scenario 2: alternative ways to treat the market supply shifts 

• Scenario 3: alternative ways to treat marine operating costs 

The results of these sensitivity analyses are presented below. Although 

estimates of total economic welfare changes are similar for many of the scenarios, the 

different assumptions highlight the role the assumptions play in determining the 

distribution of welfare changes among stakeholders. 

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). 

7H.1 Model Elasticity Parameters 

Key model parameters include supply and demand elasticity estimates used by 

the model to characterize behavioral responses of producers and consumers in each 

market. 

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 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. 

7-137 
 
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 25 percent while holding 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 

-1.1 -1.4 -1.8 

Marine engines Derived N/A 

SUPPLY ELASTICITIES 

Rail and marine 

transportation 

services 

Literature 

estimate 

0.45 0.6 0.8 

Locomotives Calibration 

Estimate 

2.0 2.7 3.4 

Commercial 

marine vessels 

Calibration 

Estimate 

2.0 2.3 3.4 

Recreational and 

fishing vessels 

Econometric 

Estimate 

1.2 1.6 2.0 

Marine engines Econometric 

Estimate 

2.9 3.8 4.8 

The results of this analysis for 2020 are presented in Tables 7F-2 and 7F-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 

7-138 
 
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 17 percent to 

21 percent for marine and from 33 percent to 36 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 17 percent to 

19 percent for marine and from 33 percent to 37 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 14 percent 

to 18 percent for marine and from 28 percent to 32 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 14 percent to 16 

percent for marine and from 28 percent to 31 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. 

7-139 
 
Table 7H-2. Sensitivity Analysis for Supply Elasticity Parameters: 2020a 

Market–Level Impacts 

Variable 

Engineering 

Cost Per Unit 

Primary Case Supply Upper Supply Lower 

Change in Price Change in Quantity Change in Price Change in Quantity Change in Price Change in Quantity 

Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Percent 

Locomotives Locomotives $48,610 Transportation services NA Marine Engines C1 >800 hp $13,882 C2 >800 hp $49,379 Other marine $0 Equipment C1 >800 hp $2,979 C2 >800 hp $6,516 Other marine $0 Transportation services NA 

$48,092 2.54% 

NA 0.16% 

$13,269 8.42% 

$48,818 18.7% 

$0 0.00% 

$16,331 1.14% 

$143,933 3.64% 

–$2 0.00% 

NA 0.24% 

0 –0.08% 

NA –0.08% 

–6 –1.35% 

–1 –0.72% 

0 0.00% 

–7 –1.35% 

–1 –0.71% 

–1 0.00% 

NA –0.12% 

$48,177 2.54% 

NA 0.16% 

$13,620 8.63% 

$48,683 18.70% 

–$5 –0.02% 

$22,965 1.14% 

$141,157 3.69% 

–$42 –0.02% 

NA 1.10% 

0 –0.08% 

NA –0.08% 

–3 –0.71% 

–1 –1.10% 

–3 –0.11% 

–4 –0.71% 

–1 –1.10% 

–18 –0.05% 

NA –0.55% 

$48,041 2.53% 

NA 0.13% 

$13,542 8.63% 

$48,475 18.63% 

–$6 –0.03% 

$21,279 1.05% 

$136,129 3.53% 

–$54 –0.02% 

NA 0.86% 

0 –0.06% 

NA –0.06% 

–2 –0.56% 

–1 –0.86% 

–2 –0.09% 

–3 –0.56% 

–1 –0.85% 

–14 –0.04% 

NA –0.43% 

Welfare Impacts (Million $) Surplus Change Share Surplus Change Share Surplus Change Share 

Locomotives Locomotive producers Rail transportation service providers Users of rail transportation service Total locomotive sector Marine Marine engine producers C1 >800 hp C2 >800 hp Other marine Marine vessel producers C1 >800 hp C2 >800 hp Other marine 

Rec/fishing vessel consumers 

Marine transportation service providers Users of marine transportation service Total marine sector 

–$0.7 

 –$65.8 

–$78.9 

–$145.3 

–$0.8 

–$0.6 –$0.2 

$0.0 

–$10.1 

–$7.8 –$2.3 

–$0.1 

–$7.8 

 –$34.3  –$41.2  –$94.1 

0.3% 

27.5% 

32.9% 

60.7% 

0.3% 

0.2% 0.1% 

0.0% 

4.2% 

3.3% 0.9% 0.0% 

3.3% 

14.3% 17.2% 39.3% 

–$0.6 

–$57.9 

–$86.9 

–$145.3 

–$0.7 

–$0.3 –$0.2 

–$0.2 

–$8.5 

–$4.7 –$2.5 –$1.3 

–$2.4 

–$32.6 –$48.8 –$93.0 

0.2% 

24.3% 

36.4% 

61.0% 

0.3% 

0.1% 0.1% 

0.1% 

3.6% 

2.0% 1.1% 0.5% 

1.0% 

13.7% 20.5% 39.0% 

–$0.7 

–$76.1 

–$68.5 

–$145.4 

–$0.9 

–$0.3 –$0.3 

–$0.3 

–$10.1 

–$5.5 –$3.0 

–$1.6 

–$1.9 

–$42.2 –$38.0 –$93.1 

0.3% 

31.9% 

28.7% 

61.0% 

0.4% 

0.1% 0.1% 

0.1% 

4.2% 

2.3% 1.2% 

0.7% 

0.8% 

17.7% 15.9% 39.0% 

Total program –$239.5 100.0% –$238.3 100.0% –$238.4 100.0% 

a Figures are in 2005 dollars. 

7-140 
Table 7H-3. Sensitivity Analysis for Demand Elasticity Parameters: 2020a 

Market–Level Impacts 

Variable Engineering Cost Per Unit 

Primary Case Demand Upper Bound Demand Lower Bound 

Change in Price Change in Quantity Change in Price Change in Quantity Change in Price Change in Quantity 

Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Percent Absolute Percent 

Locomotives Locomotives $48,610 Transportation services NA Marine Engines C1 >800 hp $13,882 C2 >800 hp $49,379 Other marine $0 Equipment C1 >800 hp $2,979 C2 >800 hp $6,516 Other marine $0 Transportation services NA 

$48,092 2.54% 

NA 0.16% 

$13,269 8.42% 

$48,818 18.74% 

$0 0.03% 

$16,331 1.14% 

$143,933 3.64% 

–$2 0.02% 

NA 0.24% 

0 –0.08% 

NA –0.08% 

–6 –1.35% 

–1 –0.72% 

0 0.00% 

–7 –1.35% 

–1 –0.71% 

–1 0.00% 

NA –0.12% 

$48,058 2.53% 

NA 0.13% 

$13,552 8.59% 

$48,501 18.64% 

–$6 –0.03% 

$21,492 1.06% 

$136,763 3.55% 

–$52 –0.02% 

NA 0.89% 

0 –0.08% 

NA –0.08% 

–3 –0.72% 

–1 –1.11% 

–3 –0.11% 

–4 –0.72% 

–1 –1.11% 

–18 –0.05% 

NA –0.55% 

$48,193 2.54% 

NA 0.17% 

$13,629 8.64% 

$48,709 18.71% 

–$4 –0.02% 

$23,174 1.15% 

$141,778 3.71% 

–$40 –0.02% 

NA 1.13% 

0 –0.06% 

NA –0.06% 

–2 –0.55% 

–1 –0.85% 

–2 –0.08% 

–3 –0.55% 

–1 –0.84% 

–14 –0.04% 

NA –0.42% 

Welfare Impacts (Million $) Surplus Change Share Surplus Change Share Surplus Change Share 

Locomotives Locomotive producers Rail transportation service providers Users of rail transportation service Total locomotive sector Marine Marine engine producers C1 >800 hp C2 >800 hp Other marine Marine vessel producers C1 >800 hp C2 >800 hp Other marine 

Rec/fishing vessel consumers 

Marine transportation service providers Users of marine transportation service Total marine sector 

–$0.7 

 –$65.8 

–$78.9 

–$145.3 

–$0.8 

–$0.6 –$0.2 

$0.0 

–$10.1 

–$7.8 –$2.3 

–$0.1 

–$7.8 

 –$34.3  –$41.2  –$94.1 

0.3% 

27.5% 

32.9% 

60.7% 

0.3% 

0.2% 0.1% 

0.0% 

4.2% 

3.3% 0.9% 

0.0% 

3.3% 

14.3% 17.2% 39.3% 

–$0.8 

–$73.8 

–$70.8 

–$145.3 

–$0.9 

–$0.7 –$0.2 

$0.0 

–$10.9 

–$8.5 –$2.4 

–$0.1 

–$6.9 

–$38.5 –$36.9 –$94.1 

0.3% 

30.8% 

29.6% 

60.7% 

0.4% 

0.3% 0.1% 

0.0% 4.6% 

3.5% 1.0% 

0.0% 

2.9% 

16.1% 15.4% 39.3% 

–$0.6 

–$55.7 

–$89.1 

–$145.4 

–$0.7 

–$0.5 –$0.2 

–$0.0 –$9.1 

–$7.0 –$2.1 

$0.0 

–$8.9 

–$29.1 –$46.5 –$94.2 

0.2% 

23.2% 

37.2% 

60.7% 

0.3% 

0.2% 0.1% 

0.0% 3.8% 

2.9% 0.9% 

0.0% 

3.7% 

12.1% 19.4% 39.3% 

Total program –$239.5 100.0% –$239.4 100.0% –$239.5 100.0% 

a Figures are in 2005 dollars. 

7-141 
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 both variable compliance costs and the fixed costs incurred in that year. 

This would allow the manufacturers to cover the fixed costs that occur in that year. 

We present the results of this analysis for 2011 and 2015. In 2011, 

locomotive manufacturers would be incurring fixed costs associated with the Tier 3 

and Tier 4 PM standards; marine engine manufacturers would be incurring costs in 

connection with both the Tier 3 and Tier 4 standards. Therefore, 2011 is a high-cost 

year for the program. In 2015, locomotive manufacturers would be incurring fixed 

costs for Tier 4 NOx standards; marine engine manufacturers would be incurring costs 

for the Tier 4 standards. In addition the vessel redesign costs begin in 2015. Both 

2011 and 2015 costs are also expected to be elevated due to certification costs. 

The results of this analysis are presented in Tables 7H-4 and 7H-5. 

In 2011, the changes in the results are considerable. In the market analysis, 

the expected price change for locomotives increases from -$425 to $11,700, although 

this is a small increase on a percentage basis (less than 1 percent). The prices of 

commercial marine engines and commercial marine vessels change significantly. The 

engine price increases increase from zero percent to 17 percent and 40 percent for 

commercial C1 and C2 engines, and from zero percent to 2.0 and 7.2 percent for 

commercial C1 and C2 vessels. 

With regard to the social welfare analysis in 2011, the share of the surplus loss 

borne by locomotive and marine diesel engine producers decreases, while the share 

borne by rail and marine transportation service providers and users, as well as marine 

vessel suppliers, increases. This is because the fixed costs are passed from the 

producers to the end users. The share of the surplus loss decreases from 5.5 percent 

to 0.2 percent for locomotive producers and from 42.7 percent to 1.5 percent for 

marine engine producers, and increases from zero percent to 12.4 percent for marine 

vessel producers. The share increases from 23.6 percent and 28.3 percent to 26.1 

percent and 31.3 percent for rail transportation service providers and users, and from 

zero percent to 6.5 percent and 7.8 percent for marine transportation service providers 

and users. 

The impacts the 2015 results, for the Tier 4 program, are similar with large 

changes in the expected price increases and a shift from the engine and locomotive 

suppliers to the vessel suppliers and transportation service markets. In this case, 

however, there is a larger shift to the marine transportation service market, with the 

vessel suppliers bearing less of the costs. Specifically, the engine producer share is 

only about one percent in this case, with the marine transportation service providers 

7-142 
 
and users bearing about 10.6 percent and 12.7 percent of the costs. This is still a 

significant increase, compared to the base case of 1.5 percent and 1.8 percent 

respectively (due to the operation costs from urea usage). 

Even with these cost 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. There is 

no decrease in locomotive sales and commercial marine sales are expected to 

decrease by less than 200 units in 2011 and 2015 (less than 4 percent). Rail and 

marine transportation service prices are expected to increase by less than 1 percent. 

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. 

7-143 
 
Table 7H-4. Sensitivity Analysis for Supply Shifts: 2011a 

Variable Cost Only Supply Shift Fixed and Variable Cost Supply Shift 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 

Locomotives $425 -0.02% 0 -0.06% $11,706 0.79% 0 -0.07% 

Transportation services NA -0.12% NA -0.06% NA 0.13% NA -0.07% 

Marine 

Engines 

C1 >800 hp $0 -0.00% 0 -0.00% $24.557 16.82% –9 –2.37% 

C2 >800 hp $0 -0.00% 0 -0.00% $92,754 39.9% –1 –1.28% Other marine $0 -0.00% 0 -0.00% $739 4.99% –45 –0.94% 

Equipment 

C1 >800 hp $0 -0.00% 0 -0.00% $27.112 2.03% –11 –2.37% 

C2 >800 hp $0 -0.00% 0 -0.00% $264,897 7.21% –1 –1.24% Other marine $0 -0.00% 0 -0.00% $603 0.52% –203 –0.64% 

Transportation services NA -0.00% NA -0.00% NA 0.10% NA –0.05% 

Welfare Impacts (Million $) Surplus Change Share Surplus Change Share 

Locomotives 

Locomotive producers –$11.1 5.5% –$0.4 0.2% 

Rail transportation service providers –$47.5 23.6% –$52.3 26.1% Users of rail transportation service –$57.0 28.3% –$62.8 31.3% Total locomotive sector –$115.6 57.3% –$115.6 57.6% 

Marine 

Marine engine producers –$86.0 42.7% –$2.9 1.5% 

C1 >800 hp –$22.8 11.3% –$0.9 0.4% C2 >800 hp –$27.8 13.8% –$0.3 0.1% Other marine $35.4 17.6% –$1.8 0.9% 

Marine vessel producers –$0.00 0.0% –$24.9 12.4% 

C1 >800 hp –$0.00 0.0% –$8.7 4.3% C2 >800 hp –$0.00 0.0% –$1.9 0.9% Other marine –$0.00 0.0% –$14.4 7.2% 

Rec/fishing vessel consumers –$0.00 0.0% –$28.4 14.1% 

Marine transportation svc providers –$0.00 0.0% –$13.0 6.5% Users of marine transportation service –$0.00 0.0% –$15.6 7.8% Total marine sector –$86.0 42.7% –$84.9 42.4% Total program –$201.5 100.0% –$200.5 100.0% a Figures are in 2005 dollars. 

7-144 
Table 7H-5. Sensitivity Analysis for Supply Shifts: 2015a 

Variable Cost Only Supply Shift Fixed and Variable Cost Supply Shift 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 

Locomotives $36,023 1.90% 0 -0.05% $43,405 2.39% 0 -0.05% 

Transportation services NA 0.09% NA -0.05% NA 0.10% NA -0.05% 

Marine 

Engines 

C1 >800 hp –$1 0.00% 0 0.00% $27,274 18.71% –16 –3.94% 

C2 >800 hp –$6 0.00% 0 –0.01% $96,310 41.44% –1 –1.55% Other marine $0 0.00% 0 0.00% $0 0.00% 0 0.00% 

Equipment 

C1 >800 hp –$11 0.00% 0 0.00% $45,071 3.37% –20 –3.92% 

C2 >800 hp –$147 0.00% 0 –0.01% $321,217 8.71% –2 –1.51% Other marine $0 0.00% 0 0.00% -$1 0.00% 0 0.00% 

Transportation services NA 0.02% NA –0.01% NA 0.13% NA –0.07% 

Welfare Impacts (Million $) Surplus Change Share Surplus Change Share 

Locomotives 

Locomotive producers –$8.6 5.0% –$0.4 0.2% 

Rail transportation service providers –$38.2 22.2% –$42.0 24.5% Users of rail transportation service –$45.9 26.6% –$50.4 29.5% Total locomotive sector –$92.8 53.8% –$92.7 54.5% 

Marine 

Marine engine producers –$56.6 32.8% –$1.8 1.1% 

C1 >800 hp –$26.7 15.5% –$1.5 0.9% C2 >800 hp –$29.9 17.3% –$0.3 0.2% Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel producers –$17.5 10.2% –$17.2 10.0% 

C1 >800 hp –$12.5 7.2% –$14.8 8.7% C2 >800 hp –$5.0 2.9% –$2.3 1.4% Other marine $0.0 0.0% $0.0 0.0% 

Rec/fishing vessel consumers $0.0 0.0% –$19.5 11.4% 

Marine transportation svc providers –$2.5 1.5% –$18.1 10.6% Users of marine transportation service –$3.1 1.8% –$21.7 12.7% Total marine sector –$79.8 46.2% –$78.3 45.8% Total program –$172.5 100.0% –$171.0 100.0% a Figures are in 2005 dollars. 

7-145 
7H.3 Marine Operating Cost Scenario 

In the primary case, all operating costs are allocated to the marine 

transportation service providers. This assumption likely overstates the share of the 

operating costs for this sector because it includes operating costs that are associated 

with recreational vessels that have marine diesel engines above 2,700 hp (2,000 kW) 

and with fishing vessels that have marine diesel engines above 800 hp (600 kW). 

In this scenario, we attempt to allocate these extra operating costs to fishing 

and recreational vessels. The difficulty with this scenario is devising a way to 

allocate the costs. Because urea usage is a function of fuel use, it is reasonable to 

allocate the costs as a function of fuel usage. However, there is no publicly available 

data that indicates these fuel usage rates. Therefore, we estimate the fraction of 

operating costs as a function of the share of the total population. This method likely 

overstates the operating costs in the other direction, over-allocating the costs to 

recreational and fishing vessels. This is because this allocation method assumes that 

all vessels consume fuel in the same proportion; this is unlikely to be the case for 

recreational and fishing vessels, since usage of these vessels tends to be seasonal and 

they tend to be used for fewer hours a year. However, this sensitivity analysis will 

provide an indication of how sensitive the results are to differences in the allocation 

of operating costs. 

The results of this analysis are contained in Table 7H-6. The market analysis 

shows a small increase in the price increase and a small decrease in the quantity 

decrease for marine diesel engines and vessels. There is also a small decrease in the 

amount of marine transportation services provided and a smaller increase in the price. 

The main change, not surprisingly, is smaller decreases in share of surplus loss for 

marine engine and vessel producers and a larger share of the surplus loss for 

recreational and fishing vessel consumers, from 3.3 percent ($7.8 million) to 5.4 

percent ($12.9 million). There is a corresponding decrease in the share of surplus loss 

for marine transportation service providers and users, from 14.3 percent to 13.7 

percent, and from 17.2 percent to 16.5 percent, respectively. 

7-146 
 
Table F-4. Sensitivity Analysis for Marine Operating Costs: 2020a 

F-147
 

Primary Case Operating Cost Sensitivity

Change in Price Change in Quantity Change in Price Change in Quantity

Market–Level Impacts Absolute Percent Absolute Percent Absolute Percent Absolute Percent 

Locomotives

Locomotives $48,092 2.54% 0 –0.08% $48,092 2.54% 0 -0.08% 

Transportation services NA 0.16% NA –0.08% NA 0.16% NA -0.08% 

Marine

Engines

C1 >800 hp $13,269 8.42% –6 –1.35% $13,189 8.37% -6 -1.54% 

C2 >800 hp $48,818 18.74% –1 –0.72% $48,792 18.73% -1 -0.75% Other marine $0 0.03% 0 0.00% $0 0.00% 0 0.00% 

Equipment

C1 >800 hp $16,331 1.14% –7 –1.35% $18,537 1.31% -8 -1.54% 

C2 >800 hp $143,933 3.64% –1 –0.71% $152,840 3.87% -1 -0.75% Other marine –$2 0.02% –1 0.00% -$2 0.00% -1 0.00% 

Transportation services NA 0.24% NA –0.12% NA 0.23% NA -0.12% 

Welfare Impacts (Million $) Surplus Change Share Share 

Locomotives

Locomotive producers -$0.7 0.3% -$0.7 0.3% 

Rail transportation service providers -$65.8 27.5% -$65.8 27.5% Users of rail transportation service -$78.9 32.9% -$78.9 32.9% Total locomotive sector -$145.3 60.7% -$145.3 60.7% 

Marine

Marine engine producers -$0.8 0.3% -$0.9 0.4% 

C1 >800 hp -$0.6 0.2% -$0.7 0.3% C2 >800 hp -$0.2 0.1% -$0.2 0.1% Other marine $0.0 0.0% $0.0 0.0% 

Marine vessel producers -$10.1 4.2% -$7.9 3.3% 

C1 >800 hp -$7.8 3.3% -$6.6 2.7% C2 >800 hp -$2.3 0.9% -$1.3 0.6% Other marine -$0.1 0.0% -$0.1 0.0% 

Rec/fishing vessel consumers -$7.8 3.3% -$12.9 5.4% 

Marine transportation service 

providers -$34.3 14.3% -$32.9 13.7% 

Users of marine transportation service -$41.2 17.2% -$39.5 16.5% Total marine sector -$94.1 39.3% -$94.1 39.3% Total program -$239.5 100.0% -$239.4 100.0% a Figures are in 2005 dollars 
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7

 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 

8

 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 

9

 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. 
 

10 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. 

11 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 

12 “Marine Vessel Sales” Memorandum from John Mueller, Office of Transportation and Air Quality, to docket 

EPA-HQ-OAR-2003-0190, February 28, 2007. 

13 “Marine Diesel Engine Prices” Memorandum from John Mueller, Office of Transportation and Air Quality, to 

docket EPA-HQ-OAR-2003-0190, February 28, 2007. 

14 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. 

15 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. 

16 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. 

148 
 
17 Boyer, K.D. 1997 Principles of Transportation Economics. Reading, MA: Addison-Wesley. 

18 Raboy, David G. 1987. Results of an Economic Anlaysis of Proposed Excise Taxes on Boats mimeo. 

Washington DC: Patton, Boggs, and Blow. Preapred for the National Marine Manufacturing Association. Docket 

A-2000-01, Document IV-A-129. 

19 U.S. Environmental Protection Agency (EPA) 2002. Final Regulatory Impact Analysis: Control of Emissions 

from Unregulated Nonroad Engines. EPA420-R-02-022. Available at 

http://www.epa.gov/otaq/regs/nonroad/2002/r02022.pdf 

20 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 

21 See, for example, Harberger, Arnold C. 1974. Taxation and Welfare. Chicago: University of Chicago Press. 

22 Rutherford, T. 1998. “CES Preferences and Technology: A Practical Introduction.” GAMS MPSGE Guide. 

Washington, DC: GAMS Development Corporation. F-149
Draft Locomotive and Marine RIA 

CHAPTER 8: REGULATORY ALTERNATIVES ............................................. 8-1
 

8.1 Alternatives Considered .................................................................................... 8-1
 

8.1.1 Alternative 1: Exclusion of Locomotive Remanufacturing................... 8-2
 

8.1.2 Alternative 2: Tier 4 Advanced One Year ............................................... 8-2
 

8.1.3 Alternative 3: Tier 4 Exclusively in 2013 ................................................. 8-2
 

8.1.4 Alternative 4: Elimination of Tier 4 ......................................................... 8-2
 

8.1.5 Alternative 5: Inclusion of Marine Remanufacturing ............................ 8-2
 

8.2 Emission Inventory Impacts ............................................................................. 8-3
 

8.2.1 Methodology ............................................................................................... 8-3
 

8.2.2 Analysis ....................................................................................................... 8-4
 

8.3 Summary of Results......................................................................................... 8-11
 

8.3.1 Alternative 1: Exclusion of Locomotive Remanufacturing.................. 8-11
 

8.3.2 Alternative 2: Tier 4 Advanced One Year ............................................. 8-11
 

8.3.3 Alternative 3: Tier 4 Exclusively in 2013 ............................................... 8-11
 

8.3.4 Alternative 4: Elimination of Tier 4 ....................................................... 8-12
 

8.3.5 Alternative 5: Inclusion of Marine Remanufacturing .......................... 8-13
 
Chapter 8: Alternatives 

CHAPTER 8: Regulatory Alternatives 

Our proposal consists of a broad and comprehensive program to reduce 

emissions from locomotive and marine diesel engines. As we have developed this 

proposal, we have evaluated a number of alternatives with regard to the scope and 

timing of the proposed standards. We also examined an alternative that would require 

emission reductions from a significant fraction of the existing marine diesel engine 

fleet. This section presents a summary of our analysis of these alternative control 

scenarios. We believe our proposal to be superior to the alternatives considered here 

given the feasibility, cost, and environmental impact of each. In this chapter we 

present and discuss the alternative program options that we evaluated in order to 

make this determination. 

8.1 Alternatives Considered 

Our proposed emission control program consists of a two-step program to 

reduce NOx and PM engine standards. The two steps consist of: (1) near-term 

emission standards that reflect the application of engine-based controls to new diesel 

marine engines and locomotives, and (2) long-term emission standards that reflect the 

application of high efficiency catalytic aftertreatment technology which will be 

enabled by the availability of clean diesel fuel with sulfur content capped at 15 parts 

per million. It also includes a locomotive remanufacturing program that sets new 

more stringent standards for Tier 0,1, and 2 applications. We have developed 

emission inventory impacts, cost estimates and benefit estimates for two types of 

alternatives. The first type looks at the impacts of varying the timing and scope of 

our proposed standards. The second considers a programmatic alternative that would 

set emission standards for existing marine diesel engines. 

Table 8-1 Summary of Alternatives and Standards 

Proposal 

• Locomotive Remanufacturing 

• Tier 3 Near-term program 

• Tier 4 Long-term standards 

Alternative 1: Exclusion of Locomotive 

Remanufacturing 

• Tier 3 Near-term program 

• Tier 4 Long-term standards 

Alternative 2: Tier 4 Advanced One Year 

• Locomotive Remanufacturing 

• Tier 3 Near-term program 

• Tier 4 Long-term standards moved ahead one year 

Alternative 3: Tier 4 Exclusively in 2013 • Tier 4 Long-term standards moved ahead to 2013 

Alternative 4: Elimination of Tier 4 • Locomotive Remanufacturing 

• Tier 3 Near-term program 

Alternative 5: Inclusion of Marine Remanufacturing 

• Locomotive Remanufacturing 

• Tier 3 Near-term program 

• Tier 4 Long-term standards 

• Marine Remanufacturing 

8-1
 
Draft Locomotive and Marine RIA 

8.1.1 Alternative 1: Exclusion of Locomotive Remanufacturing 

Alternative 1 examines the potential impacts of the locomotive 

remanufacturing program by excluding it from the analysis (see section III.C.(1)(a)(i) 

for more details of the locomotive remanufacturing program). It is identical to the 

proposal with the exception of the removal of the locomotive 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 the proposed 

locomotive remanufacturing program. 

8.1.2 Alternative 2: Tier 4 Advanced One Year 

Alternative 2 is the most stringent of our alternatives, and considers the 

possibility of pulling ahead the Tier 4 standards by one year for both the locomotive 

and marine programs, while leaving the rest of the proposed program the same. The 

timing and scope of both Tier 3 and the locomotive remanufacturing program would 

remain unchanged. These results can be compared with the results of the primary 

program to estimate the additional benefits that could occur if compliant engines were 

introduced one year earlier for both tiers of standards. 

8.1.3 Alternative 3: Tier 4 Exclusively in 2013 

Alternative 3 most closely reflects the program we described in our Advanced 

Notice of Proposed Rulemaking, whereby we would set new aftertreatment based 

emission standards as soon as possible. In this case, we believe the earliest that such 

standards could logical be started is in 2013 (3 months after the introduction of 15 

ppm ULSD in this sector). This alternative would eliminate the Tier 3 standards and 

locomotive remanufacturing standards, while pulling the Tier 4 standards ahead to 

2013 for all portions of the Tier 4 program. These results can be compared with the 

results of the primary program to estimate the benefits that would be lost if engine 

manufacturers were not required to develop emission control packages for near-term 

standards but, instead, could focus their efforts on the long-term standards. 

8.1.4 Alternative 4: Elimination of Tier 4 

Alternative 4 would eliminate the Tier 4 standards, retaining the Tier 3 and 

locomotive remanufacturing requirements. The timing and scope of both Tier 3 and 

the locomotive remanufacturing program would remain unchanged. These results can 

be compared with the results of the primary program to estimate the benefits that 

would be foregone if the more technology-forcing standards are not adopted at this 

time. 

8.1.5 Alternative 5: Inclusion of Marine Remanufacturing 

We are considering a fifth programmatic alternative which would impose a 

requirement on existing marine diesel engines similar to the existing remanufacture 

8-2
 
Chapter 8: Alternatives 

program for locomotives (see Section VI.A.2 of the preamble for further details). The 

standards would apply to engines above 800 hp and would consist of a two-part 

program. In the first part, which could begin as early as 2008, vessel owners and 

rebuilders (also called remanufacturers) would be required to use a certified kit when 

the engine is rebuilt (or remanufactured) if such a kit is available. Initially, these kits 

would be expected to be locomotive kits and therefore applicable only to those 

engines derived from similar locomotive engines. Eventually, however, it is expected 

that the large engine manufacturers would also provide kits for their engines. In the 

second part of this program, which could begin in 2013, the remanufacturer/owner of 

a marine diesel engine identified by the EPA as a high-sales volume engine model 

would have to meet specified emission requirements when the engine is 

remanufactured. Specifically, the remanufacturer or owner would be required to use 

a certified system to meet the standard; if no certified system is available, he or she 

would need to either retrofit an emission reduction technology for the engine that 

demonstrates at least a 25 percent reduction and does not exceed 0.22 g/kW-hr PM 

(equivalent to the new Tier 0/1 PM limit) or repower (replace the engine with a new 

one). 

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 inuse emissions factors for each alternative. (Refer to Chapter 3 of this Draft RIA for a 

more complete discussion of how the primary control inventories were generated). 

The results are shown in Table 8-1. 

8.2.1.2 Costs 

We have estimated the costs associated with each alternative using the same 

methods employed for the proposal. 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 proposed locomotive 

remanufacturing program. We have estimated the cost per remanufactured 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 alternatives 2 & 3 

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 cost in the summary table reflecting the same cost estimation we have used 

for our primary case 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 

8-3
 
Draft Locomotive and Marine RIA 

of Tier 4. The details of our estimated remanufacturing program costs can be found 

in Chapter 5 of this draft RIA. 

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 

proposed 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 proposal, 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 proposed 

standards were then generated using the inputs and methods described in Chapter 6 of 

the draft 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. 

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 2030 is 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; 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. 

8-4
 
Chapter 8: Alternatives 

Table 8-2 Inventory, Cost, and Benefits year from 2006-2040 

Primary Case Alternative 1 

Calendar Year 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

Benefitsa,b 

(Billions) 

PM2.5 only 

2030 3% (7%) 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total Costs 

(Millions) 

Benefitsa,b 

(Billions) 

PM2.5 only 

2030 3% (7%) 

2006 0 0 $0 --- 0 0 $0 --- 

2007 0 0 $30 --- 0 0 $30 --­

2008 200 4,500 $50 --- 0 0 $30 --- 

2009 600 4,700 $60 --- 3 0 $30 --- 

2010 1,100 15,000 $90 --- 6 0 $30 --­

2011 2,200 33,000 $210 --- 12 140 $100 --- 

2012 3,500 43,000 $140 --- 350 1,800 $50 --- 

2013 4,500 56,000 $130 --- 840 5,600 $50 --- 

2014 5,600 71,000 $120 --- 1,600 18,000 $60 --- 

2015 6,800 84,000 $180 --- 2,500 31,000 $130 --- 

2016 8,600 110,000 $200 --- 3,700 51,000 $130 --- 

2017 10,000 160,000 $220 --- 5,000 94,000 $160 --- 

2018 12,000 210,000 $230 --- 6,400 140,000 $170 --- 

2019 13,000 250,000 $250 --- 7,800 190,000 $190 --- 

2020 15,000 290,000 $250 $4.4-$9.2 

($4.0-$8.3) 

9,300 230,000 $220 $3.2-$6.7 

($2.9-$6.0) 

2021 16,000 340,000 $280 --- 11,000 280,000 $250 --- 

2022 17,000 380,000 $330 --- 12,000 330,000 $270 --- 

2023 19,000 440,000 $410 --- 14,000 390,000 $370 --- 

2024 20,000 510,000 $430 --- 15,000 460,000 $400 --- 

2025 21,000 550,000 $470 --- 17,000 510,000 $430 --- 

2026 23,000 600,000 $480 --- 19,000 560,000 $470 --- 

2027 24,000 640,000 $510 --- 20,000 600,000 $500 --- 

2028 25,000 680,000 $550 --- 22,000 650,000 $530 --- 

2029 27,000 720,000 $580 --- 23,000 700,000 $560 --- 

2030 28,000 770,000 $610 $12-$25 

($11-$23) 

25,000 740,000 $580 8.8-$19 

($8.0-$17) 

2031 29,000 810,000 $630 --- 26,000 780,000 $610 --- 

2032 30,000 850,000 $640 --- 28,000 830,000 $640 --- 

2033 31,000 880,000 $730 --- 29,000 870,000 $720 --- 

2034 32,000 920,000 $760 --- 30,000 910,000 $750 --- 

2035 34,000 960,000 $790 --- 32,000 950,000 $770 --- 

2036 35,000 1,000,000 $800 --- 33,000 990,000 $790 --- 

2037 36,000 1,030,000 $820 --- 34,000 1,000,000 $820 --- 

2038 37,000 1,060,000 $840 --- 35,000 1,100,000 $840 --- 

2039 38,000 1,090,000 $860 --- 37,000 1,100,000 $860 --- 

2040 38,000 1,120,000 $880 --- 37,000 1,100,000 $870 --- 

2040 NPV 3% 315,000 7,870,000 $7,230 --- 250,000 7,180,000 $6,430 --- 

2040 NPV 7% 135,000 3,180,000 $3,230 --- 100,000 2,780,000 $2,700 --- 

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. 
 

8-5 
Draft Locomotive and Marine RIA 

Alternative 2 Alternative 3 

Calendar Year 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total Costs 

(Millions)a 

Benefitsb,c 

(Billions) 

PM2.5 only 

2030 3% (7%) 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

Benefitsa,b 

(Billions) 

PM2.5 only 

2030 3% (7%) 

2006 0 0 $0 --- 0 0 $0 --- 

2007 0 0 $30 --- 0 0 $0 --- 

2008 200 4,500 $50 --­ 0 0 $50 --­

2009 600 4,700 $80 --­ 0 0 $50 --­

2010 1,100 15,000 $130 --­ -8 0 $50 --­

2011 2,200 33,000 $210 --­ -16 0 $50 --­

2012 3,500 43,000 $140 --- -27 0 $100 --- 

2013 4,700 62,000 $160 --- 1,900 40,000 $130 --- 

2014 6,100 78,000 $180 --- 2,700 81,000 $150 --- 

2015 7,300 100,000 $170 --- 3,600 120,000 $150 --- 

2016 9,300 150,000 $220 --- 4,900 160,000 $180 --- 

2017 11,000 200,000 $230 --- 6,200 210,000 $200 --- 

2018 13,000 240,000 $250 --- 7,500 250,000 $230 --- 

2019 14,000 290,000 $270 --- 8,800 290,000 $260 --- 

2020 16,000 330,000 $270 $4.6-$9.7 

($4.2-$8.7) 

10,000 340,000 $340 $3.6-$7.4 

($3.2-$6.7) 

2021 17,000 370,000 $300 --- 12,000 380,000 $370 --- 

2022 18,000 420,000 $360 --- 13,000 430,000 $400 --- 

2023 19,000 500,000 $440 --- 14,000 470,000 $430 --- 

2024 21,000 540,000 $460 --- 16,000 520,000 $460 --- 

2025 22,000 580,000 $490 --- 17,000 560,000 $490 --- 

2026 23,000 630,000 $500 --- 19,000 600,000 $520 --- 

2027 25,000 670,000 $530 --- 20,000 650,000 $550 --- 

2028 26,000 710,000 $570 --- 22,000 690,000 $580 --- 

2029 27,000 750,000 $600 --- 23,000 730,000 $600 --- 

2030 28,000 790,000 $620 $12-$25 

($11-$23) 

25,000 770,000 $630 $11-$24 

($10-$21) 

2031 30,000 830,000 $650 --- 26,000 810,000 $650 --- 

2032 31,000 870,000 $660 --- 27,000 850,000 $730 --- 

2033 32,000 910,000 $740 --- 29,000 880,000 $760 --- 

2034 33,000 950,000 $770 --- 30,000 920,000 $790 --- 

2035 34,000 980,000 $800 --- 31,000 960,000 $810 --- 

2036 35,000 1,000,000 $810 --- 32,000 990,000 $830 --- 

2037 36,000 1,000,000 $830 --- 33,000 1,000,000 $850 --- 

2038 37,000 1,100,000 $850 --- 34,000 1,100,000 $870 --- 

2039 38,000 1,100,000 $860 --- 35,000 1,100,000 $890 --- 

2040 39,000 1,100,000 $880 --- 36,000 1,100,000 $910 --- 

2040 NPV 3% 324,000 8,290,000 $7590 +C --- 255,000 8,050,000 $7410 +C --- 

2040 NPV 7% 140,000 3,390,000 $3440 +C --- 104,000 3,280,000 $3220 +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 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 

8-6 
Chapter 8: Alternatives 

Alternative 4 Alternative 5 

Calendar Year 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

Benefitsa,b 

(Billions) 

PM2.5 only 

2030 3% (7%) 

PM2.5 

Emissions 

Reductions 

(tons) 

NOx 

Emissions 

Reductions 

(tons) 

Total 

Costs 

(Millions) 

Benefitsa,b 

(Billions) 

PM2.5 only 

2030 3% (7%) 

2006 0 0 $0 --- 0 0 $0 --- 

2007 0 0 $28 --­ 0 0 $30 --- 

2008 200 4,500 $53 --­ 560 13,000 $80 --- 

2009 600 4,700 $60 --­ 1,300 20,000 $90 --- 

2010 1,100 15,000 $85 --­ 2,200 36,000 $120 --- 

2011 2,200 33,000 $160 --- 3,700 58,000 $240 --- 

2012 3,500 43,000 $87 --­ 5,200 70,000 $180 --- 

2013 4,500 56,000 $79 --­ 6,700 87,000 $170 --- 

2014 5,600 71,000 $57 --­ 8,000 110,000 $160 --- 

2015 6,500 84,000 $47 --­ 9,000 120,000 $220 --- 

2016 7,800 110,000 $77 --­ 11,000 140,000 $220 --- 

2017 9,000 130,000 $61 --­ 13,000 190,000 $250 --- 

2018 9,900 140,000 $64 --­ 14,000 230,000 $270 --- 

2019 10,800 150,000 $58 --­ 16,000 270,000 $280 --- 

2020 11,000 160,000 $33 $3.2-$6.7 

($2.9-$6.0) 

17,000 310,000 $280 $5.0-$10 

($4.5-$9.4) 

2021 12,000 160,000 $33 --­ 18,000 350,000 $300 --- 

2022 13,000 170,000 $54 --­ 19,000 390,000 $350 --- 

2023 13,000 180,000 $45 --­ 20,000 450,000 $430 --- 

2024 14,000 190,000 $30 --­ 22,000 520,000 $460 --- 

2025 14,000 200,000 $32 --­ 23,000 560,000 $490 --- 

2026 15,000 210,000 $15 --­ 24,000 600,000 $490 --- 

2027 15,000 210,000 $14 --­ 25,000 650,000 $520 --- 

2028 16,000 220,000 $26 --­ 26,000 690,000 $570 --- 

2029 16,000 230,000 $25 --­ 28,000 730,000 $590 --- 

2030 17,000 240,000 $22 $6.2-$13 

($5.7-$12) 

29,000 770,000 $620 $12-$26 

($11-$23) 

2031 17,000 240,000 $19 --­ 30,000 810,000 $630 --- 

2032 18,000 250,000 $8 --- 31,000 850,000 $640 --- 

2033 18,000 260,000 $7 --- 32,000 890,000 $730 --- 

2034 19,000 270,000 $11 --­ 33,000 930,000 $760 --- 

2035 19,000 270,000 $14 --­ 34,000 960,000 $790 --- 

2036 19,000 280,000 $4 --- 35,000 1,000,000 $800 --- 

2037 20,000 290,000 $4 --- 36,000 1,000,000 $820 --- 

2038 20,000 290,000 $3 --- 37,000 1,100,000 $840 --- 

2039 20,000 300,000 $3 --- 38,000 1,100,000 $860 --- 

2040 21,000 300,000 $2 --- 39,000 1,100,000 $880 --- 

2040 NPV 3% 207,000 2,910,000 $950 --- 342,000 8,190,000 $7,650 --- 

2040 NPV 7% 94,000 1,310,000 $650 --- 151,000 3,400,000 $3,510 --- 

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 
Draft Locomotive and Marine RIA 

Table 8-3 Summary of Total Inventory and Costs Through 2040 NPV 3% 

Program 

PM2.5 Emissions 

Reductions 

(tons) NPV 3% 

NOx Emissions 

Reductions 

(tons) NPV 3% 

Total Costs 

(Millions) 

NPV 3%a 

Primary Case 315,000 7,870,000 $7,230 

Alternative 1: Exclusion of Locomotive 

Remanufacturing 250,000 7,180,000 $6,430 

Alternative 2: Tier 4 Advanced One 

Year 324,000 8,290,000 $7,590+C 

Alternative 3: Tier 4 Exclusively in 2013 255,000 8,050,000 $7,410+C 

Alternative 4: Elimination of Tier 4 207,000 2,910,000 $950 

Alternative 5: Inclusion of Marine 

Remanufacturing 342,000 8,190,000 $7,650 

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 

PM2.5 Emissions 

Reductions 

(tons) NPV 7% 

NOx Emissions 

Reductions 

(tons) NPV 7% 

Total Costs 

(Millions) 

NPV 7%a 

Primary Case 135,000 3,180,000 $3,230 

Alternative 1: Exclusion of Locomotive 

Remanufacturing 100,000 2,780,000 $2,700 

Alternative 2: Tier 4 Advanced One 

Year 140,000 3,390,000 $3,440+C 

Alternative 3: Tier 4 Exclusively in 2013 104,000 3,280,000 $3,220+C 

Alternative 4: Elimination of Tier 4 94,000 1,310,000 $650 

Alternative 5: Inclusion of Marine 

Remanufacturing 151,000 3,400,000 $3,510 

a ‘C’ represents additional costs necessary to accelerate the introduction of Tier 4 technologies that we 

are unable to estimate at this time. 

8-8
 
Chapter 8: Alternatives 

Table 8-5 Summary of Inventory, Costs, and Benefits for 2020 

2020 PM2.5 

Emissions 

Reductions 

(tons) 

2020 NOx 

Emissions 

Reductions 

(tons) 

2020 Total 

Costs 

(Millions) 

2020 Benefits 

(Billions) 

PM2.5 only 

3% (7%) 

Primary Case 15,000 290,000 $250 $4.4-$9.2 

($4.0$8.3) 

Alternative 1 9,300 230,000 $220 $3.2-$6.7 

($2.9-$6.0) 

Alternative 2 16,000 330,000 $270 $4.6-$9.7 

($4.2-$8.7) 

Alternative 3 10,000 340,000 $340 $3.6-$7.4 

($3.2-$6.7) 

Alternative 4 11,000 160,000 $33 $3.2-$6.7 

($2.9-$6.0) 

Alternative 5 17,000 310,000 $280 $5.0-$10 

($4.5-$9.4) 

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. 
 

Table 8-6 Summary of Inventory, Costs, and Benefits for 2030 

2030 PM2.5 

Emissions 

Reductions 

(tons) 

2030 NOx 

Emissions 

Reductions 

(tons) 

2030 Total 

Costs 

(Millions) 

2030 Benefitsa,b 

(Billions) 

PM2.5 only 

3% (7%) 

Primary Case 28,000 770,000 $610 $12-$25 

($11-$23) 

Alternative 1: Exclusion of 

Locomotive Remanufacturing 25,000 740,000 $580 $8.8-$19 

($8.0-$17) 

Alternative 2: Tier 4 

Advanced One Year 28,000 790,000 $620 $12-$25 

($11-$23) 

Alternative 3: Tier 4 

Exclusively in 2013 25,000 770,000 $630 $11-$24 

($10-$21) 

Alternative 4: Elimination of 

Tier 4 17,000 240,000 $22 $6.2-$13 

($5.7-$12) 

Alternative 5: Inclusion of 

Marine Remanufacturing 29,000 770,000 $620 $12-$26 

($11-$23) 

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. 
 

8-9 
Draft Locomotive and Marine RIA 

Figure 8.2-1 PM2.5 Inventories for 2006-2040 

NOx (thousands of tons) PM 2.5 (thousands of tons) 

63 

53 

43 

33 

23 

13 

Primary 

Option 1 

Option 2 

Option 3 

Option 4 

Option 5 

Base 

2006 2011 2016 2021 2026 2031 2036 

Figure 8.2-2 NOx Inventories for 2006-2040 

1,900 

1,725 

1,550 

1,375 

1,200 

1,025 

850 

675 

500 

Primary 

Option 1 

Option 2 

Option 3 

Option 4 

Option 5 

Base 

2006 2011 2016 2021 2026 2031 2036
 

8-10
 
Chapter 8: Alternatives 

8.3 Summary of Results 

8.3.1 Alternative 1: Exclusion of Locomotive Remanufacturing 

Table 8-2 shows that the locomotive remanufacturing program provides 

substantial inventory impacts and benefits for a marginal increase in costs. This 

alternative shows that through 2040 the locomotive remanufacturing program would 

reduce PM2.5 emissions by 65,000 tons NPV 3% (35,000 tons NPV 7%) and NOx 

emissions by nearly 690,000 tons NPV 3% (400,000 tons at NPV 7%) at a cost of 

$870 million (NPV 3%). The monetized health and welfare benefits of the 

locomotive remanufacturing program in 2030 are $2.9-6.3 billion at a 3% discount 

rate (DR) or $2.7-$5.7 at a 7% DR. While this alternative could have the advantage of 

enabling industry to focus its resources on Tier 3 and Tier 4 technology development, 

given its substantial benefits, we have decided to retain the locomotive 

remanufacturing program in our proposal. 

8.3.2 Alternative 2: Tier 4 Advanced One Year 

This alternative is the most environmentally protective alternative we have 

given consideration to. However, our review of the technical challenges to introduce 

the Tier 4 program, especially in the context of the locomotive remanufacturing 

program and the Tier 3 standards which go before it, leads us to conclude that 

introducing Tier 4 a year earlier is not feasible. Our analysis suggests that 

introducing Tier 4 one year earlier than our proposal could reduce PM2.5 emissions by 

9,000 tons NPV 3% (5,000 tons NPV 7%) and NOx emissions by 420,000 tons NPV 

3% (210,000 tons NPV 7%). We are unable to make an accurate estimate of the cost 

for such an approach since we do not believe it to be feasible at this time. However, 

we have reported a cost in the summary table reflecting the same cost estimation 

method we have used for our primary case 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 introduce 

Tier 4 one year earlier. 

8.3.3 Alternative 3: Tier 4 Exclusively in 2013 

Alternative 3 most closely reflects the program we described in our Advanced 

Notice of Proposed Rulemaking, 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 in is 2013 (3 months after the introduction of 15 

ppm ULSD in this sector). Alternative 3 eliminates our proposed Tier 3 standards 

and the locomotive remanufacturing standards, while pulling the Tier 4 standards 

ahead to 2013 for all portions of the Tier 4 program. As with alternative 2, we are 

concerned that it may not be feasible to introduce Tier 4 technologies on locomotive 

8-11
 
Draft Locomotive and Marine RIA 

and marine diesel engines earlier than the proposal specifies. However, eliminating 

the technical work necessary to develop the Tier 3 and locomotive remanufacturing 

programs would certainly go a long way towards making such an approach possible. 

This alternative would actually result in substantially higher PM emissions than our 

primary case while reducing NOx emissions. Through 2040 this alternative loses 

more than 60,000 tons NPV 3% (31,000 tons NPV 7%) of PM2.5 reductions while 

only adding approximately 180,000 tons NPV 3% (100,000 tons NPV 7%) of NOx 

reductions. As a result in 2030 alone, this alternative realizes approximately $0.6­

$1.3 billion less at a 3% DR ($0.5-$1.2 billion less at a 7% DR) in public health and 

welfare benefits than does our proposal. As was the case with alternative 2, we have 

used the same cost estimation approach for this alternative as that of our proposal, 

and have denoted the unestimated costs that are necessary to accelerate the 

development of Tier 4 technologies with a ‘C’ in the summary tables. While 

alternative 3 could have been considered the Agency’s leading option going into this 

rulemaking process, our review of the technical challenges necessary to introduce 

Tier 4 technologies and the substantial additional benefits that a more comprehensive 

solution can provide has lead us to drop this approach in favor of the comprehensive 

proposal we have laid out today. 

8.3.4 Alternative 4: Elimination of Tier 4 

Alternative 4 would eliminate the Tier 4 standards and retain the Tier 3 and 

locomotive remanufacturing requirements. This alternative allows us to consider the 

value of combining the Tier 3 and locomotive remanufacturing standards together as 

one program, and conversely, allows us to see the additional benefits gained when 

combining them with the Tier 4 standards. As a stand alone alternative, the combined 

Tier 3 and locomotive remanufacturing program is very attractive, resulting in large 

emission reductions of 207,000 tons NPV 3% (94,000 tons NPV 7%) of PM2.5 and 

2,910,000 tons NPV 3% (1,310,000 tons NPV 7%) of NOx through 2040 at an 

estimated cost of $950 million NPV 3% ($650 million at NPV 7%) through the same 

time period. In 2030 alone, such a program is projected to realize health and welfare 

benefits of $5.5-$12 billion at a 3% DR ($5.0-$11 billion at a 7% DR). Yet, this 

alternative falls well short of the total benefits that our comprehensive program is 

expected to realize, and also would not take advantage of new aftertreatment 

technologies which have been developed and used on both nonroad and on-highway 

applications. Elimination of Tier 4 would result in the loss of 108,000 tons NPV 3% 

(41,000 tons NPV 7%) of PM2.5 and almost 4,960,000 tons NPV 3% (1,870,000 tons 

NPV 7%) of NOx through 2040. Through the addition of the Tier 4 standards, the 

estimated health and welfare benefits are nearly doubled in 2030. As these 

alternatives show, each element of our comprehensive program: the locomotive 

remanufacturing program, the Tier 3 emission standards, or 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 today’s proposal. 

8-12
 
Chapter 8: Alternatives 

8.3.5 Alternative 5: Inclusion of Marine Remanufacturing 

This alternative would provide additional PM and NOx benefits as shown in 

Figure 8.2-1 and Figure 8.2-2. With regard to benefits, the application of locomotive 

remanufacture kits to similar marine diesel engines would be expected to result in 

similar reductions in PM and NOx emissions. In some cases, this could be as much as 

60 percent reduction for PM and 25 percent reduction for NOx. However, because 

many marine diesel engines start at a cleaner baseline, we would not expect to 

accomplish the same reductions from all engines that would be subject to the 

program. Based on a minimal control case of a 25 percent PM reduction from 

existing marine diesel engines above 800 hp, we estimate about an additional 27,000 

tons NPV 3% (16,000 tons NPV 7%) of PM2.5 reductions, and an additional 320,000 

tons NPV 3% (220,000 tons NPV 7%) of NOx reductions through 2040. In general, 

we estimate that the compliance costs associated with this program to be about $10 

million per year in additional costs in 2030. Using the benefits transfer approach 

from the primary control scenario to estimate the benefits of these inventory 

reductions, the additional monetized benefits would be expected to be about $0.3-$0.7 

billion at a 3% DR ($0.3-$0.6 at a 7% DR) in 2030. 

8-13