Proposed Rulemaking for Greenhouse 

Gas Emissions and Fuel Efficiency 

Standards for Medium- and Heavy-Duty 

Engines and Vehicles–Phase 2

 

Draft Regulatory Impact Analysis 
EPA-420-D-15-900 

June 2015

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency

and

Office of International Policy, Fuel Economy, and Consumer Programs

National Highway Traffic Safety Administration 

U.S. Department of Transportation

Proposed Rulemaking for Greenhouse Gas 

Emissions and Fuel Efficiency 

Standards for Medium- and Heavy-Duty 

Engines and Vehicles–Phase 2 

 

Draft Regulatory Impact Analysis 
TABLE OF CONTENTS

EXECUTIVE SUMMARY ES-10

CHAPTER 1:INDUSTRY CHARACTERIZATION

1.1 Introduction 1-1 

1.2 Trailers 1-9

CHAPTER 2:TECHNOLOGY AND COST

2.1 Overview of Technologies 2-1

2.2 Technology Principles – SI Engines 2-2

2.3 Technology Principles – CI Engines 2-7

2.4 Technology Principles - Vehicles 2-15

2.5 Technology Application and Estimated Costs – HD Pickups and Vans 2-48

2.6 Technology Application – SI Engines 2-64

2.7 Technology Application and Estimated Costs – CI Engines 2-70

2.8 Technology Application and Estimated Costs – Tractors 2-80

2.9 Technology Application and Estimated Costs – Vocational Vehicles 2-109

2.10 Technology Application and Estimated Costs – Trailers 2-152

2.11 Natural Gas 2-190

2.12 Technology Costs 2-191

2.13 Package Costs 2-272

CHAPTER 3:TEST PROCEDURES

3.1 Heavy-Duty Engine Test Procedure 3-1

3.2 Aerodynamic Assessment 3-4

3.3 Tire Rolling Resistance 3-55

3.4 Duty Cycle 3-57

3.5 Tare Weights and Payload 3-63

3.6 Powertrain Test Procedures 3-66

3.7 Hybrid Powertrain Test Procedures 3-73

3.8 Rear Axle Efficiency Test 3-76
3.9 HD Pickup Truck and Van Chassis Test Procedure 3-76

3.10 Alternative Certification Approach 3-79

CHAPTER 4:VEHICLE SIMULATION MODEL

4.1 Purpose and Scope 4-1

4.2 Model Code Description 4-2

4.3 Validation of Phase 2 GEM Simulations 4-11

4.4 EPA and NHTSA HD Vehicle Compliance Model 4-22

4.5 Technology Improvements that Are Recognized in GEM without Simulation 4-34

CHAPTER 5:IMPACTS ON EMISSIONS AND FUEL CONSUMPTION

5.1 Executive Summary 5-1

5.2 Introduction 5-8

5.3 Program Analysis and Modeling Methods 5-9

5.4 Greenhouse Gas Emission and Fuel Consumption Impacts 5-28

5.5 Non-Greenhouse Gas Emission Impacts 5-48

CHAPTER 6:HEALTH AND ENVIRONMENTAL IMPACTS

6.1 Health and Environmental Effects of Non-GHG Pollutants 6-1

6.2 Air Quality Impacts of Non-GHG Pollutants 6-32

6.3 Changes in Atmospheric CO2 Concentrations, Global Mean Temperature, Sea Level 

Rise, and Ocean pH Associated with the Program’s GHG Emissions Reductions 6-40

CHAPTER 7:VEHICLE-RELATED COSTS, FUEL SAVINGS &

MAINTENANCE COSTS

7.1 Vehicle Costs, Fuel Savings and Maintenance Costs vs. the Dynamic Baseline and Using 

Method A 7-1

7.2 Vehicle Costs, Fuel Savings and Maintenance Costs vs. the Less Dynamic Baseline and 

Using Method B 7-18

7.3 Key Parameters Used in the Estimation of Costs and Fuel Savings 7-49
CHAPTER 8.ECONOMIC AND OTHER IMPACTS

8.1 Framework for Benefits and Costs 8-1

8.2 Conceptual Framework for Evaluating Impacts 8-3

8.3 Analysis of the Rebound Effect 8-10

8.4 Impact on Class Shifting, Fleet Turnover, and Sales 8-30

8.5 Monetized GHG Impacts 8-34

8.6 Quantified and Monetized Non-GHG Health and Environmental Impacts 8-48

8.7 Additional Impacts 8-60

8.8 The Effect of Safety Standards and Voluntary Safety Improvements on Vehicle Weight

 8-68

8.9 Petroleum, Energy and National Security Impact 8-72

8.10 Summary of Benefits and Costs 8-87

8.11 Employment Impacts 8-94

8.12 Oil Price Sensitivity Analysis 8-105

CHAPTER 9.SAFETY IMPACTS

9.1 Summary of Supporting HD Vehicle Safety Research 9-1 

9.2 National Academy of Sciences HD Phase 1 and Phase 2 Reports 9-1 

9.3 DOT CAFE Model HD Pickup and Van Safety Analysis 9-2 

9.4 Volpe Research on MD/HD Fuel Efficiency Technologies 9-3 

9.5 Oak Ridge National Laboratory (ORNL) Research on Low Rolling Resistance Truck 

Tires 9-9 

9.6 Additional Safety Consideration 9-9 

9.7 The Agencies’ Assessment of Potential Safety Impacts 9-10 

CHAPTER 10: CAFE MODEL

10.1 HD Pickup and Van Fleet 10-1

10.2 CAFE Model Analysis of Regulatory Alternatives for HD Pickups and Vans 10-12
CHAPTER 11: RESULTS OF THE PREFERRED AND ALTERNATIVE

STANDARDS

11.1 What Are the Alternatives that the Agencies Considered? 11-1

11.2 How Do These Alternatives Compare in Overall GHG Emissions Reductions and Fuel 

Efficiency and Cost? 11-20

11.3 Detailed Technology Projections for Each Category 11-36 

11.4 Numerical Standards Corresponding to Alternative Technology Scenarios 11-73

CHAPTER 12: INITIAL REGULATORY FLEXIBILITY ANALYSIS

12.1 Overview of the Regulatory Flexibility Act 12-1

12.2 Need for Rulemaking and Rulemaking Objectives 12-2

12.3 Definition and Description of Small Businesses 12-2

12.4 Summary of Small Entities to which the Rulemaking will Apply 12-3

12.5 Related Federal Rules 12-4

12.6 Projected Reporting, Recordkeeping, and Other Compliance Requirements 12-4

12.7 Regulatory Flexibilities 12-4

12.8 Projected Economic Effects of the Proposed Rulemaking 12-9

CHAPTER 13: NATURAL GAS VEHICLES AND ENGINES

13.1 Detailed Life-Cycle Analysis 13-1

13.2 Projecting Natural Gas use in HD Trucks 13-23

13.3 Natural Gas Emission Control Measures 13-35
This Page Intentionally Left Blank
Table of Contents, Acronym List, and Executive Summary

ES-1 

List of Acronyms 

μg Microgram

μm Micrometers

2002$ U.S. Dollars in calendar year 2002

2009$ U.S. Dollars in calendar year 2009

A/C Air Conditioning

ABS Antilock Brake Systems

ABT Averaging, Banking and Trading

AC Alternating Current

ACES Advanced Collaborative Emission Study

ALVW Adjusted Loaded Vehicle Weight

AEO Annual Energy Outlook

AES Automatic Engine Shutdown

AHS American Housing Survey

AMOC Atlantic Meridional Overturning Circulation

AMT Automated Manual Transmission

ANL Argonne National Laboratory

APU Auxiliary Power Unit

AQ Air Quality 

AQCD Air Quality Criteria Document

AR4 Fourth Assessment Report

ARB California Air Resources Board 

ASL Aggressive Shift Logic

ASPEN Assessment System for Population Exposure Nationwide 

AT Automatic Transmissions

ATA American Trucking Association

ATIS Automated Tire Inflation System

ATRI Alliance for Transportation Research Institute

ATSDR Agency for Toxic Substances and Disease Registry

ATUS American Time Use Survey

Avg Average

BAC Battery Air Conditioning

BenMAP Benefits Mapping and Analysis Program

bhp Brake Horsepower

bhp-hrs Brake Horsepower Hours

BLS Bureau of Labor Statistics

BSFC Brake Specific Fuel Consumption

BTS Bureau of Transportation Statistics

BTS Bureau of Labor Statistics

BTU British Thermal Unit

CAA Clean Air Act
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CAAA Clean Air Act Amendments

CAD/CAE Computer Aided Design And Engineering

CAE Computer Aided Engineering

CAFE Corporate Average Fuel Economy

CARB California Air Resources Board 

CBI Confidential Business Information

CCP Coupled Cam Phasing

CCSP Climate Change Science Program

Cd Coefficient of Drag

CdA Drag Area

CDC Centers for Disease Control

CFD Computational Fluid Dynamics

CFR Code of Federal Regulations

CH4 Methane

CILCC Combined International Local and Commuter Cycle

CITT Chemical Industry Institute of Toxicology

CMAQ Community Multiscale Air Quality

CO Carbon Monoxide

CO2 Carbon Dioxide

CO2eq CO2 Equivalent

COFC Container-on-Flatcar

COI Cost of Illness

COPD Chronic Obstructive Pulmonary Disease

CoV Coefficient of Variation

CPS Cam Profile Switching

CRC Coordinating Research Council

CRGNSA Columbia River Gorge National Scenic Area

CRR Rolling Resistance Coefficient

CS Climate Sensitivity

CSI Cambridge Systematics Inc.

CSS Coastal Sage Scrub

CSV Comma-separated Values

CVD Cardiovascular Disease

CVT Continuously-Variable Transmission

CW Curb Weight

D/UAF Downward and Upward Adjustment Factor

DCP Dual Cam Phasing

DCT Dual Clutch Transmission

DE Diesel Exhaust

DEAC Cylinder Deactivation

DEER Diesel Engine-Efficiency and Emissions Research

DEF Diesel Exhaust Fluid

DHHS U.S. Department of Health and Human Services
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Diesel HAD Diesel Health Assessment Document

DMC Direct Manufacturing Costs

DO Dissolved Oxygen

DOC Diesel Oxidation Catalyst

DOD Department of Defense

DOE Department of Energy

DOHC Dual Overhead Camshaft Engines

DOT Department of Transportation

DPF Diesel Particulate Filter

DPM Diesel Particulate Matter

DR Discount Rate

DRIA Draft Regulatory Impact Analysis

DVVL Discrete Variable Valve Lift

EC European Commission

EC Elemental Carbon

ECU Electronic Control Unit

ED Emergency Department

EERA Energy and Environmental Research Associates

EFR Engine Friction Reduction

EGR Exhaust Gas Recirculation

EHPS Electrohydraulic Power Steering

EIA

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

Energy)

EISA Energy Independence and Security Act

EMS-HAP Emissions Modeling System for Hazardous Air Pollution

EO Executive Order

EPA Environmental Protection Agency

EPS Electric Power Steering

ERG Eastern Research Group

ESC Electronic Stability Control

EV Electric Vehicle

F Frequency

FEL Family Emission Limit

FET Federal Excise Tax

FEV1 Functional Expiratory Volume

FHWA Federal Highway Administration

FIA Forest Inventory and Analysis

FMCSA Federal Motor Carrier Safety Administration

FOH Fuel Operated Heater

FR Federal Register

FTP Federal Test Procedure

FVC Forced Vital Capacity

g Gram 
ES-4

g/s Gram-per-second

g/ton-mile Grams emitted to move one ton (2000 pounds) of freight over one mile

gal Gallon

gal/1000 tonmileGallons of fuel used to move one ton of payload (2,000 pounds) over 

1000 miles

GCAM Global Change Assessment Model

GCW Gross Combined Weight

GDP Gross Domestic Product

GEM Greenhouse gas Emissions Model

GEOS Goddard Earth Observing System

GHG Greenhouse Gases

GIFT Geospatial Intermodal Freight Transportation

GREET

Greenhouse Gases, Regulated Emissions, and Energy Use in 

Transportation

GSF1 Generic Speed Form one

GUI Graphical User Interface

GVWR Gross Vehicle Weight Rating

GWP Global Warming Potential

HABs Harmful Algal Blooms

HAD Diesel Health Assessment Document

HC Hydrocarbon

HD Heavy-Duty

HDUDDS Heavy Duty Urban Dynamometer Driving Cycle

HEG High Efficiency Gearbox

HEI Health Effects Institute 

HES Health Effects Subcommittee 

HEV Hybrid Electric Vehicle

HFC Hydrofluorocarbon

HFET Highway Fuel Economy Dynamometer Procedure

HHD Heavy Heavy-Duty

HHDDT Highway Heavy-Duty Diesel Transient

hp Horsepower

hrs Hours

HRV Heart Rate Variability

HSC High Speed Cruise Duty Cycle

HTUF Hybrid Truck User Forum

hz Hertz

IARC International Agency for Research on Cancer

IATC Improved Automatic Transmission Control

IC Indirect Costs

ICCT International Council on Clean Transport

ICD International Classification of Diseases

ICF ICF International
ES-5

ICM Indirect Cost Multiplier

ICP Intake Cam Phasing

IMAC Improved Mobile Air Conditioning

IMPROVE Interagency Monitoring of Protected Visual Environments

IPCC Intergovernmental Panel on Climate Change

IRFA Initial Regulatory Flexibility Analysis

IRIS Integrated Risk Information System

ISA Integrated Science Assessment

JAMA Journal of the American Medical Association

k Thousand

kg Kilogram

KI kinetic intensity

km Kilometer

km/h Kilometers per Hour

kW Kilowatt

L Liter

lb Pound

LD Light-Duty

LHD Light Heavy-Duty

LLNL Lawrence Livermore National Laboratory’s

LRR Lower Rolling Resistance

LSC Low Speed Cruise Duty Cycle

LT Light Trucks

LTCCS Large Truck Crash Causation Study

LUB Low Friction Lubes

LUC Land Use Change

m2 Square Meters

m3 Cubic Meters

MAGICC Model for the Assessment of Greenhouse-gas Induced Climate Change

MCF Mixed Conifer Forest

MD Medium-Duty

MDPV Medium-Duty Passenger Vehicles

mg Milligram

MHD Medium Heavy-Duty

MHEV Mild Hybrid

mi mile

min Minute

MM Million

MMBD Million Barrels per Day

MMT Million Metric Tons

MOVES Motor Vehicle Emissions Simulator

mpg Miles per Gallon

mph Miles per Hour
ES-6

MSAT Mobile Source Air Toxic

MRL Minimal Risk Level

MT Manual Transmission

MY Model Year

N2O Nitrous Oxide

NA Not Applicable

NAAQS National Ambient Air Quality Standards

NAFA National Association of Fleet Administrators

NAICS North American Industry Classification System 

NAS National Academy of Sciences

NATA National Air Toxic Assessment 

NCAR National Center for Atmospheric Research

NCI National Cancer Institute

NCLAN National Crop Loss Assessment Network

NEC Net Energy Change Tolerance

NEI National Emissions Inventory

NEMS National Energy Modeling System

NEPA National Environmental Policy Act

NESCAUM Northeastern States for Coordinated Air Use Management

NESCCAF Northeast States Center for a Clean Air Future

NESHAP National Emissions Standards for Hazardous Air Pollutants

NHS National Highway System

NHTSA National Highway Traffic Safety Administration

NiMH Nickel Metal-Hydride

NIOSH National Institute of Occupational Safety and Health

Nm Newton-meters

NMHC Nonmethane Hydrocarbons

NMMAPS National Morbidity, Mortality, and Air Pollution Study

NOX Nitrogen Oxide

NO2 Nitrogen Dioxide

NOAA National Oceanic and Atmospheric Administration 

NOx Oxides of Nitrogen

NPRM Notice of Proposed Rulemaking

NPV Net Present Value

NRC National Research Council

NRC-CAN National Research Council of Canada

NREL National Renewable Energy Laboratory

NTP National Toxicology Program

NVH Noise Vibration and Harshness

O&M Operating and maintenance

O3 Ozone

OAQPS Office of Air Quality Planning and Standards

OC Organic Carbon
ES-7

OE Original Equipment

OEHHA Office of Environmental Health Hazard Assessment

OEM Original Equipment Manufacturer

OHV Overhead Valve

OMB Office of Management and Budget

OPEC Organization of Petroleum Exporting Countries

ORD EPA's Office of Research and Development

ORNL Oak Ridge National Laboratory

OTAQ Office of Transportation and Air Quality

Pa Pascal

PAH Polycyclic Aromatic Hydrocarbons

PEF Peak Expiratory Flow

PEMS Portable Emissions Monitoring System

PGM Platinum Group Metal

PHEV Plug-in Hybrid Electric Vehicles

PM Particulate Matter

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

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

POM Polycyclic Organic Matter

Ppb Parts per Billion

Ppm Parts per Million

Psi Pounds per Square Inch

PTO Power Take Off

R&D Research and Development

RBM Resisting Bending Moment

REL Reference Exposure Level

RESS Rechargeable Energy Storage System

RFA Regulatory Flexibility Act

RfC Reference Concentration

RFS2 Renewable Fuel Standard 2

RIA Regulatory Impact Analysis

RPE Retail Price Equivalent

Rpm Revolutions per Minute

RSWT Reduced-Scale Wind Tunnel

S Second

SAB Science Advisory Board

SAB-HES Science Advisory Board - Health Effects Subcommittee

SAE Society of Automotive Engineers

SAR Second Assessment Report

SAV Submerged Aquatic Vegetation

SBA Small Business Administration

SBAR Small Business Advocacy Review

SBREFA Small Business Regulatory Enforcement Fairness Act
ES-8

SCC Social Cost of Carbon

SCR Selective Catalyst Reduction

SER Small Entity Representation

SET Supplemental Emission Test

SGDI Stoichiometric Gasoline Direct Injection

SHEV Strong Hybrid Vehicles

SI Spark-Ignition

SIDI Spark Ignition Direct Injection

SO2 Sulfur Dioxide

SOx Sulfur Oxides

SOA Secondary Organic Aerosol 

SOC State of Charge

SOHC Single Overhead Cam

SOX Oxides of Sulfur

SPR Strategic Petroleum Reserve

STB Surface Transportation Board

Std. Standard

STP Scaled Tractive Power

SUV Sport Utility Vehicle

SVOC Semi-Volatile Organic Compound

SwRI Southwest Research Institute

TAR Technical Assessment Report

TC Total Costs

TCp Total Cost package

TDS Turbocharging And Downsizing

THC Total Hydrocarbon

TIAX TIAX LLC

TMC Technology & Maintenance Council

TOFC Trailer-on-Flatcar

Ton-mile One ton (2000 pounds) of payload over one mile

TPM Tire Pressure Monitoring

TRBDS Turbocharging and Downsizing

TRU Trailer Refrigeration Unit

TSD Technical Support Document

TSS Thermal Storage

TTMA Truck Trailer Manufacturers Association

TW Test Weight

U/DAF Upward and Downward Adjustment Factor

UCT Urban Creep and Transient Duty Cycle

UFP Ultra Fine Particles

URE Unit Risk Estimate

USDA United States Department of Agriculture

USGCRP United States Global Change Research Program
ES-9

UV Ultraviolet

UV-b Ultraviolet-b

VHHD Vocational Heavy Heavy-Duty

VIN Vehicle Identification Number

VIUS Vehicle Inventory Use Survey

VLHD Vocational Light Heavy-Duty

VMHD Vocational Medium Heavy-Duty

VMT Vehicle Miles Traveled

VOC Volatile Organic Compound

VSL Vehicle Speed Limiter

VTRIS Vehicle Travel Information System

VVL Variable Valve Lift

VVT Variable Valve Timing

WACAP Western Airborne Contaminants Assessment Project

WBS Wide Base Singles

WHR Waste Heat Recovery

WHTC World Harmonized Transient Cycle

WHVC World Harmonized Vehicle Cycle

WRF Weather Research Forecasting

WTP Willingness-to-Pay

WTVC World Wide Transient Vehicle Cycle

WVU West Virginia University
ES-10

Executive Summary

The Environmental Protection Agency (EPA) and the National Highway Traffic Safety 

Administration (NHTSA), on behalf of the Department of Transportation, are each proposing 

changes to our comprehensive Heavy-Duty National Program that would further reduce 

greenhouse gas emissions (GHG) and increase fuel efficiency for on-road heavy-duty vehicles, 

responding to the President’s directive on February 18, 2014, to take coordinated steps toward 

the production of even cleaner vehicles. NHTSA’s fuel consumption standards and EPA’s 

carbon dioxide (CO2) emissions standards would be tailored to each of the three current 

regulatory categories of heavy-duty vehicles: (1) Combination Tractors; (2) Heavy-duty Pickup 

Trucks and Vans; and (3) Vocational Vehicles, as well as gasoline and diesel heavy-duty 

engines. In addition, the agencies would be adding new standards for combination trailers. 

EPA’s hydrofluorocarbon emissions standards that currently apply to air conditioning systems in 

tractors, pickup trucks, and vans, would also be applied to vocational vehicles. 

Table 1 and Table 2 present the rule-related fuel savings, costs, benefits and net benefits 

in both present value terms and in annualized terms as calculated by NHTSA and EPA, 

respectively. Table 3 presents the proposed rule’s fully phased-in (MY 2027) numeric standards 

by vehicle (and engine) subcategory, along with the agencies’ projected per vehicle incremental 

cost and incremental improvement in fuel efficiency and CO2 emissions. 

For HD pickups and vans, the agencies are proposing performance-based standards under 

which, as for Phase 1, the average fuel consumption and CO2 emission rates required of a 

manufacturer depend on the mix of vehicles produced by the manufacturer for sale in the U.S. 

For each vehicle, the agencies are again proposing to define the work factor as the sum of (a) 

75% of the vehicle’s maximum payload, 25% of the vehicles maximum towing capacity, and (c) 

375 lbs. if the vehicle has four-wheel drive. The agencies are further proposing that fuel 

consumption and CO2 emission rate targets will apply to each vehicle based on the vehicle’s 

work factor and fuel type, and that the average fuel consumption and CO2 emission rates 

required of the manufacturer will be defined as the production-weighted average of these targets. 

The proposed fuel consumption targets are linear functions defined by the slopes and intercepts 

shown below in Figure 1, Figure 2, and Table 4.
ES-11

Table 1 NHTSA’s Estimated 2018-2029 Model Year Lifetime Discounted Costs, 

Benefits, and Net Benefits using Method A and Relative to the More Dynamic Baseline and Assuming the 3% 

Discount Rate SCC Valuea

(Billions of 2012 Dollars)

Lifetime Present Value – 3% Discount Rate

Vehicle Program -$25.2

Maintenance -$1.1

Fuel Savings $170.1

Benefits $93.8

Net Benefits $238

Annualized Value – 3% Discount Rate

Vehicle Program -$1.0

Maintenance -$0.04

Fuel Savings $6.7

Benefits $3.7

Net Benefits $9.4

Lifetime Present Value - 7% Discount Rate

Vehicle Program -$17

Maintenance -$0.6

Fuel Savings $91.7

Benefits $66.1

Net Benefits $140

Annualized Value – 7% Discount Rate

Vehicle Program -$1.2

Maintenance -$0.04

Fuel Savings $6.7

Benefits $4.8

Net Benefits $10.2

Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

Table 2 EPA’s Estimated 2018-2029 Model Year Lifetime Discounted Costs, 

Benefits, and Net Benefits using Method B and Relative to the Less Dynamic Baseline and Assuming the 3% 

Discount Rate SCC Valuea

(Billions of 2012 Dollars)

Lifetime Present Valuec – 3% Discount Rate

Vehicle Program -$25

Maintenance -$1.1

Fuel Savings $171

Benefits b $97
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Net Benefitsd $242

Annualized Valuee – 3% Discount Rate

Vehicle Program -$1.3

Maintenance -$0.1

Fuel Savings $8.7

Benefits b $4.9

Net Benefitsd $12.3

Lifetime Present Valuec - 7% Discount Rate

Vehicle Program -$17

Maintenance -$0.6

Fuel Savings $90

Benefits b $65

Net Benefitsd $138

Annualized Valuee – 7% Discount Rate

Vehicle Program -$1.3

Maintenance $0.0

Fuel Savings $7.3

Benefits b $4.2

Net Benefitsd $10.1

Notes:

a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 b EPA estimated the benefits associated with four different values of a one ton CO2 reduction 

(model average at 2.5% discount rate, 3%, and 5%; 95th percentile at 3%), which each 

increase over time. For the purposes of this overview presentation of estimated costs and 

benefits, however, the benefits shown here use the marginal value deemed to be central by 

the interagency working group on this topic: the model average at 3% discount rate, in 2012 

dollars. Chapter 8.5 provides a complete list of values for the 4 estimates. Note that net 

present value of reduced CO2 emissions is calculated differently than other benefits. The 

same discount rate used to discount the value of damages from future emissions (SCC at 5, 

3, and 2.5 percent) is used to calculate net present value of SCC for internal consistency. 

Refer to Section Chapter 8.5 for more detail.

c Present value is the total, aggregated amount that a series of monetized costs or benefits 

that occur over time is worth now (in year 2012 dollar terms), discounting future values to 

the present over the lifetime of each model year vehicle. d Net benefits reflect the fuel savings plus benefits minus costs.

e The annualized value is the constant annual value through a 30 year lifetime whose

summed present value equals the present value from which it was derived. Annualized SCC 

values are calculated using the same rate as that used to determine the SCC value, while all 

other costs and benefits are annualized at either 3% or 7%.
ES-13

TABLE 3 SUMMARY OF 

PROPOSED 2027 

STANDARDS INCLUDING 

AVERAGE PER VEHICLE 

COSTS AND PROJECTED 

IMPROVEMENTREGULATORY 

SUBCATEGORY

CO2 GRAMS 

PER TON-MILE

(FOR ENGINES, 

CO2 GRAMS 

PER BRAKE 

HORSEPOWERHOUR; FOR HD 

PUV, GRAMS 

PER MILE) 

FUEL 

CONSUMPTION 

GALLON PER 

1,000 TON-MILE 

(FOR ENGINES, 

GALLONS PER 

100 BRAKE 

HORSEPOWERHOUR; FOR HD 

PUV, GALLONS 

PER 100 MILES)

AVERAGE 

INCREMENTAL 

COST PER 

VEHICLE OR 

ENGINE 

RELATIVE TO 

PHASE 1 COSTS 

IN MODEL 

YEAR 2027 a

AVERAGE 

PERCENT FUEL 

CONSUMPTION 

AND CO2

IMPROVEMENT 

IN MY 2027 

RELATIVE TO 

MY 2017 

Tractors

Class 7 Low Roof Day Cab 87 8.5462 $10,140 19%

Class 7 Mid Roof Day Cab 96 9.4303 $10,140 19%

Class 7 High Roof Day Cab 96 9.4303 $10,099 21%

Class 8 Low Roof Day Cab 70 6.8762 $10,204 19%

Class 8 Mid Roof Day Cab 76 7.4656 $10,204 18%

Class 8 High Roof Day Cab 76 7.4656 $10,209 20%

Class 8 Low Roof Sleeper Cab 62 6.0904 $12,744 22%

Class 8 Mid Roof Sleeper Cab 69 6.7780 $12,744 21%

Class 8 High Roof Sleeper Cab 67 6.5815 $12,842 24%

Trailers

Long Dry Box Trailer 77 7.5639 $1,409 8%

Short Dry Box Trailer 140 13.7525 $1,280 7% 

Long Refrigerated Box Trailer 80 7.8585 $1,253 5%

Short Refrigerated Box Trailer 144 14.1454 $1,253 5%

Notes: a

 Engine costs are included in average vehicle costs. 
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Table 3 (cont.) Summary of Proposed 2027 Standards Including Average Per Vehicle Costs and Projected 

Improvement

REGULATORY 

SUBCATEGORY

CO2 GRAMS PER 

TON-MILE (FOR 

ENGINES, CO2

GRAMS PER 

BRAKE 

HORSEPOWERHOUR; FOR HD 

PUV, GRAMS PER 

MILE) 

FUEL 

CONSUMPTION 

GALLON PER 1,000 

TON-MILE (FOR 

ENGINES, 

GALLONS PER 100 

BRAKE 

HORSEPOWERHOUR; FOR HD 

PUV, GALLONS 

PER 100 MILES)

AVERAGE 

INCREMENTAL 

COST PER 

VEHICLE OR 

ENGINE 

RELATIVE TO 

PHASE 1 COSTS IN 

MODEL YEAR 

2027 a

AVERAGE 

PERCENT FUEL 

CONSUMPTION 

AND CO2

IMPROVEMENT IN 

MY 2027 RELATIVE 

TO MY 2017 

Vocational Diesel

LHD Urban 272 26.7191 $3,489 16%

LHD Multi-Purpose 280 27.5049 $3,490 16%

LHD Regional 292 28.6837 $1,407 16%

MHD Urban 172 16.8959 $4,696 16%

MHD Multi-Purpose 174 17.0923 $4,696 16%

MHD Regional 170 16.6994 $1,395 16%

HHD Urban 182 17.8782 $7,422 16%

HHD Multi-Purpose 183 17.9764 $7,422 16%

HHD Regional 174 17.0923 $4,682 16%

Vocational Gasoline

LHD Urban 299 33.6446 $3,086 12%

LHD Multi-Purpose 308 34.6574 $3,087 12%

LHD Regional 321 36.1202 $1,004 12%

MHD Urban 189 21.2670 $4,327 13%

MHD Multi-Purpose 191 21.4921 $4,327 13%

MHD Regional 187 21.0420 $1,026 13%

HHD Urban 196 22.0547 $7,053 12%

HHD Multi-Purpose 198 22.2797 $7,053 12%

HHD Regional 188 21.1545 $4,313 12%

Diesel Enginesa

LHD Vocational 553 5.4322 $471 4%

MHD Vocational 553 5.4322 $437 4%

HHD Vocational 533 5.2358 $437 4%

MHD Tractor 466 4.5776 $1,698 4%

HHD Tractor 441 4.3320 $1,698 4%

Class 2b and 3 HD Pickups and Vansb

HD Pickup and Van 458 4.8608 $1,357 18%

Notes: a Engine costs are included in average vehicle costs. Costs shown for diesel engines are not additive to vehicle 

costs.

b For HD pickups and vans, Table 3 shows results for MY2029, assuming continuation of proposed MY2027 

standard.
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Figure 1 EPA Proposed CO2 Target Standards and NHTSA Proposed Fuel Consumption Target Standards 

for Diesel HD Pickups and Vans

Figure 2 EPA Proposed CO2 Target Standards and NHTSA Proposed Fuel Consumption Target Standards 

for Gasoline HD Pickups and Vans

3.44

3.94

4.44

4.94

5.44

5.94

6.44

6.94

350

400

450

500

550

600

650

700

750

3000 3500 4000 4500 5000 5500 6000 6500 7000

Work Factor

Diesel Standards

CO2 (grams per mile)

3.94

4.44

4.94

5.44

5.94

6.44

6.94

7.44

7.94

8.44

350

400

450

500

550

600

650

700

750

3000 3500 4000 4500 5000 5500 6000 6500 7000

Work Factor

Gasoline Standards

FuelConsumption (gallons per 100 miles)

CO2(grams per mile)
ES-16

Described mathematically, EPA’s and NHTSA’s proposed target standards are defined 

by the following formulas:

EPA CO2 Target (g/mile) = [a x WF] + b

NHTSA Fuel Consumption Target (gallons/100 miles) = [c x WF] + d 

Where:

WF = Work Factor = [0.75 x (Payload Capacity + xwd)] + [0.25 x Towing Capacity] 

Payload Capacity = GVWR (lb) – Curb Weight (lb) 

xwd = 500 lb if the vehicle is equipped with 4wd, otherwise equals 0 lb. 

Towing Capacity = GCWR (lb) – GVWR (lb)

Coefficients a, b, c, and d are taken from Table 1. 

Table 4. Proposed Phase 2 Coefficients for HD Pickup and Van Target Standards 

DIESEL VEHICLES

Model Year a b c d

2018-2020 a 0.0416 320 0.0004086 3.143

2021 0.0406 312 0.0003988 3.065

2022 0.0395 304 0.0003880 2.986

2023 0.0386 297 0.0003792 2.917

2024 0.0376 289 0.0003694 2.839

2025 0.0367 282 0.0003605 2.770

2026 0.0357 275 0.0003507 2.701

2027 and later 0.0348 268 0.0003418 2.633

Gasoline Vehicles

Model Year a b c d

2018-2020 a 0.044 339 0.0004951 3.815

2021 0.0429 331 0.0004827 3.725

2022 0.0418 322 0.0004703 3.623

2023 0.0408 314 0.0004591 3.533

2024 0.0398 306 0.0004478 3.443

2025 0.0388 299 0.0004366 3.364

2026 0.0378 291 0.0004253 3.274

2027 and later 0.0369 284 0.0004152 3.196

Note: 

a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only.
ES-17

This Draft Regulatory Impact Analysis (RIA) provides detailed supporting 

documentation to EPA and NHTSA joint proposal under each of their respective statutory 

authorities. Because there are slightly different requirements and flexibilities in the two 

authorizing statutes, this Draft RIA provides documentation for the primary joint provisions as 

well as for provisions specific to each agency. 

This RIA is generally organized to provide overall background information, 

methodologies, and data inputs, followed by results of the various technical and economic 

analyses. A summary of each chapter of the RIA follows. 

Chapter 1: Industry Characterization. In order to assess the impacts of greenhouse gas 

(GHG) and fuel consumption regulations upon the affected industries, it is important to 

understand the nature of the industries impacted by the regulations. The heavy-duty vehicle

industries include the manufacturers of Class 2b through Class 8 trucks, engines, trailers and 

some other equipment. Of these categories, trailers are the only industry that would be newly 

regulated under the proposed standards. This chapter provides market information for the trailer

industry, as well as the variety of ownership patterns, for background purposes. 

Chapter 2: Technology and Cost. This chapter presents details of the vehicle and 

engine technologies and technology packages for reducing greenhouse gas emissions and fuel 

consumption. These technologies and technology packages represent potential ways that the 

industry could meet the CO2 and fuel consumption stringency levels, and they provide the basis 

for the technology costs and effectiveness analyses.

Chapter 3: Test Procedures. Laboratory procedures to physically test engines, vehicles, 

and components are a crucial aspect of the heavy-duty vehicle GHG and fuel consumption 

program. The rulemaking would establish some new test procedures for both engine and vehicle 

compliance and would revise existing procedures. This chapter describes the relevant test 

procedures, including methodologies for assessing engine emission performance, the effects of 

aerodynamics and tire rolling resistance, as well as procedures for chassis dynamometer testing 

and their associated drive cycles.

Chapter 4: Vehicle Simulation Model. An important aspect of a regulatory program is 

its ability to accurately estimate the potential environmental benefits of heavy-duty truck 

technologies through testing and analysis. Most large truck manufacturers employ various 

computer simulation methods to estimate truck efficiency for purposes of developing and 

refining their products. Each method has advantages and disadvantages. This section will focus 

on the use of a type truck simulation modeling that the agencies have developed specifically for 

assessing tailpipe GHG emissions and fuel consumption for purposes of this rulemaking. The 

agencies are proposing to revise the existing simulation model -- the “Greenhouse gas Emissions 

Model (GEM)” -- as the primary tool to certify vocational vehicles, combination tractor, and 

combination trailers, Class 2b through Class 8 heavy-duty vehicles that are not heavy-duty 

pickups or vans) and discuss the model in this chapter. 

Chapter 5: Impacts on Emissions and Fuel Consumption. This program estimates 

anticipated impacts from the CO2 emission and fuel efficiency standards. The agencies quantify 

fuel use and emissions from the GHGs carbon dioxide (CO2), methane (CH4), nitrous oxide 
ES-18

(N2O) and hydrofluorocarbons (HFCs). In addition to reducing the emissions of greenhouse 

gases and fuel consumption, this program would also influence the emissions of “criteria” air 

pollutants, including carbon monoxide (CO), fine particulate matter (PM2.5) and sulfur dioxide 

(SOX) and the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOX); and several

air toxics (including benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein), as 

described further in Chapter 5. 

The agencies used EPA’s Motor Vehicle Emission Simulator (MOVES2014) to estimate 

downstream (tailpipe) emission impacts for combination tractors and vocational vehicles, and a 

spreadsheet model based on emission factors the “GREET” model to estimate upstream (fuel 

production and distribution) emission changes resulting from the decreased fuel. For HD 

pickups and vans, the agencies used DOT’s CAFE model to estimate manufacturer responses to 

the proposed standards. NHTSA used the CAFE model to estimate emission impacts, and EPA 

used the CAFE model technology penetration outputs as an input to MOVES to calculate 

emission impacts. Based on these analyses, the agencies estimate that this program would lead 

to 183.4 million metric tons (MMT) of CO2 equivalent (CO2EQ) of annual GHG reduction and 

13.4 billion gallons of fuel savings in the year 2050, as discussed in more detail in Chapter 5. 

Chapter 6: Health and Environmental Impacts. This chapter discusses the health effects 

associated with non-GHG pollutants, specifically: particulate matter, ozone, nitrogen oxides 

(NOX), sulfur oxides (SOX), carbon monoxide and air toxics. These pollutants would not be 

directly regulated by the standards, but the standards would affect emissions of these pollutants 

and precursors. Reductions in these pollutants are the co-benefits of the rulemaking (that is, 

benefits in addition to the benefits of reduced GHGs). This chapter also discusses GHG-related 

impacts, such as changes in atmospheric CO2 concentrations, global mean temperature, sea level 

rise, and ocean pH associated with the program’s GHG emissions reductions. 

Chapter 7: Vehicle-Related Costs of the Program. In this chapter, the agencies present 

our estimate of the costs associated with the proposed program. The presentation summarizes 

the costs associated with new technology expected to be added to meet the GHG and fuel 

consumption standards, including hardware costs to comply with the air conditioning (A/C) 

leakage program. The analysis discussed in Chapter 7 provides our best estimates of incremental 

costs on a per truck basis and on an annual total basis. We also present the fuel savings and 

maintenance costs in this chapter, along with a detailed payback analysis for various vehicle 

segments.

Chapter 8: EPA’s Economic and Other Impacts Analysis. This chapter provides EPA’s 

description of the net benefits of the proposed HD National Program. To reach these 

conclusions, the chapter discusses each of the following aspects of the analyses of benefits:

Rebound Effect: The VMT rebound effect refers to the fraction of fuel savings expected 

to result from an increase in fuel efficiency that is offset by additional vehicle use. 

Energy Security Impacts: A reduction of U.S. petroleum imports reduces both financial 

and strategic risks associated with a potential disruption in supply or a spike in cost of a 

particular energy source. This reduction in risk is a measure of improved U.S. energy security. 
ES-19

Monetized CO2 Impacts: The agencies estimate the monetized benefits of GHG 

reductions by assigning a dollar value to reductions in CO2 emissions using recent estimates of 

the social cost of carbon (SCC). The SCC is an estimate of the monetized damages associated 

with an incremental increase in carbon emissions in a given year. 

Other Impacts: There are other impacts associated with the GHG emissions and fuel 

efficiency standards. Lower fuel consumption would, presumably, result in fewer gallons being 

refilled and, thus, less time spent refueling. The increase in vehicle-miles driven due to a 

positive rebound effect may also increase the societal costs associated with traffic congestion, 

crashes, and noise. However, if drivers drive those additional rebound miles, there must be a 

value to them which we estimate as the value of increased travel. The agencies also discuss the 

impacts of safety standards and voluntary safety improvements on vehicle weight. 

Chapter 8 also presents a summary of the total costs, total benefits, and net benefits 

expected under the program. 

Chapter 9: NHTSA and EPA considered the potential safety impact of technologies that 

improve HD vehicle fuel efficiency and GHG emissions as part of the assessment of regulatory 

alternatives. This chapter discusses the literature and research considered by the agencies, which 

included two National Academies of Science reports, an analysis of safety effects of HD pickups 

and vans using estimates from the DOT report on the effect of mass reduction and vehicle size 

on safety, and agency-sponsored safety testing and research. 

Chapter 10: NHTSA CAFE Model. This chapter describes NHTSA’s CAFE modeling 

system. The agencies used DOT’s CAFE model to estimate manufacturer responses to the 

proposed standards for HD pickups and vans, and NHTSA also used the CAFE model to 

estimate emission impacts for this sector. 

Chapter 11: Results of Preferred and Alternative Standards. The heavy-duty truck 

segment is very complex. The sector consists of a diverse group of impacted parties, including 

engine manufacturers, chassis manufacturers, truck manufacturers, trailer manufacturers, truck 

fleet owners and the public. The agencies have largely designed this program to maximize the 

environmental and fuel savings benefits, taking into account the unique and varied nature of the 

regulated industries. In developing this program, we considered a number of alternatives that 

could have resulted in fewer or potentially greater GHG and fuel consumption reductions than 

the program we are proposing. Chapter 9 section summarizes the alternatives we considered. 

Chapter 12: Small Business Flexibility Analysis. This chapter describes the agencies’

analysis of the small business impacts due to the joint program. 

Chapter 13: Natural Gas Vehicles and Engines. This chapter describes EPA’s lifecycle 

analysis for natural gas used by the heavy-duty truck sector. 
1 of 9 

Chapter 1: Industry Characterization 

1.1 Introduction 

The proposed fuel consumption and CO2 emissions standards described in the 

preamble of this NPRM would be applicable to three currently-regulated categories of heavyduty vehicles: (1) Combination Tractors; (2) Heavy-duty Pickup Trucks and Vans; and (3) 

Vocational Vehicles, as well as gasoline and diesel heavy-duty engines. The industry 

characterization for these sectors can be found in the RIA for the HD Phase 1 rulemaking.1

 

With this proposed rulemaking, the agencies would be setting standards for combination 

trailers for the first time. The characterization laid out in this chapter focuses solely on 

trailers as this subcategory would be the only newly-regulated industry. 

1.2 Trailers 

A trailer is a vehicle designed to haul cargo while being pulled by another powered 

motor vehicle. The most common configuration of large freight trucks consists of a Class 7 or 

8 tractor hauling one or more trailers. Vehicles in these configurations are called 

“combination tractor-trailers” or simply “tractor-trailers”. A trailer may be constructed to rest 

upon the tractor that tows it, or be constructed so part of its weight rests on an auxiliary front 

axle called a “converter dolly” between two or more trailers. Trailers are attached to tractors 

by a coupling pin (or king pin) on the front of the trailer and a horseshoe-shaped coupling 

device called a fifth wheel on the rear of the towing vehicle or on the converter dolly. A 

tractor can also pull international shipping or domestic containers mounted on open-frame 

chassis, which when driven together on the road function as trailers. 

The Truck Trailer Manufacturers Association, an industry trade group primarily for 

manufacturers of Class 7 and 8 truck trailers, offers publications of recommended practices, 

technical bulletins and manuals that cover many aspects of trailer manufacture, and serves as 

a liaison between the industry and government agencies.2

 To date, federal regulations for the 

trailer industry are limited to those issued by the Department of Transportation (See 49 CFR). 

These regulations govern trailer dimensions and weight, as well as trailer safety requirements 

(e.g., lights, reflective materials, bumpers, etc.). In addition, DOT requires that each trailer, 

like other on-road vehicles, must have a Vehicle Identification Number (VIN)3

. The VIN is 

displayed on a label that is permanently-affixed to the trailer. It is required to contain the 

manufacturer identification, make and type of vehicle, model year, type of trailer, body type, 

length, and axle configuration. Trailer manufactures are responsible for reporting each 

trailer’s VIN information to NHTSA prior to the sale of the trailer. 

1.2.1 Trailer Types 

Class 7 and 8 tractors haul a diverse range of trailer types. The most common trailer 

type is the box trailer, which is enclosed and can haul most types of mixed freight. The 

general rectangular shape of these trailers allows operators to maximize freight volume within 

the regulated dimensional limits, since the majority of freight shipped by truck cubes-out (is 

volume-limited) before it grosses-out (is weight-limited). Despite considerable improvements 
2 of 9 

in suspension, material, safety, durability, and other advancements, the basic shape of the box 

trailer has not changed much over the past decades, although its dimensions have increased 

incrementally from what used to be the industry’s standard length of 40’ to today’s standard 

53’ long van trailer. Today, box vans are commonly found in lengths of 28’, 48’, and 53’and 

widths of 102” or 96”. The 28’ vans (“pups”) are often driven in tandem and connected by a 

dolly. Current length restrictions for the total combination tractor-trailer vehicle limit tandem 

operation to 28’ trailers. However, some members of the trucking industry are pushing to 

increase the length limits to allow trailers as long as 33’ to be pulled in tandem, and arguing 

that these “less than truckload” (LTL) operations could increase capacity per truckload, 

reduce the number of trucks on the road, reduce the fuel consumption and emissions of these 

tractor-trailers, and remain within the current weight limits.4,5

Trailers are often highly customized for each order. The general structure of the box 

trailer type is common and consists of vertical support posts in the interior of the trailer 

covered by a smooth exterior surface. However the exterior of the trailer may be constructed 

of aluminum or a range of composite materials. Historically, floors were constructed of 

wood, however many trailer customers are requesting aluminum floors to reduce weight. 

Semi-trailer axles are commonly a dual tandem configuration, but can also be single, spread 

tandem (i.e., two axles separated to maximize axle loads), tridem (i.e., three axles equally 

spaced), tri axles (i.e., three axles consisting of a tandem and a third axle that may be liftable), 

or multi-axles to distribute very heavy loads. Axles can be fixed in place, or allowed to slide 

to adjust weight distribution. Doors are commonly located at the rear of the trailer. The most 

common door is the side-by-side configuration, in which each door opens outward. Roll-up 

doors, which are more costly, allow truck drivers to pull up to loading docks without first 

stopping to open the doors. Roll-up doors are common on trailers with temperature-sensitive 

freight. Additional variations in trailers include side-access doors, or use the underside of the 

trailer for belly boxes or to store on-demand items such as ladders or spare tires. 

The most common box trailer is the standard dry van, which transports cargo that does 

not require special environmental conditions. In addition to the standard rectangular shape, 

dry vans come in several specialty variants, such as drop floor, expandable, and curtain-side. 

Another type of specialty box trailer is the refrigerated van trailer (reefer). This is an 

enclosed, insulated trailer that hauls temperature sensitive freight, with a transportation 

refrigeration unit (TRU) or heating unit mounted in the front of the trailer powered by a small 

(9-36 hp) diesel engine. Figure 1-1 shows an example of the standard dry and refrigerated 

vans. 
3 of 9 

Figure 1-1 Examples of dry and refrigerated van

Many other trailer types are uniquely designed to transport a specific type of freight. 

Platform trailers carry cargo that may not be easily contained within or loaded and unloaded 

into a box trailer, such as large, nonuniform equipment or machine components. Platforms 

come in different configurations including standard flatbed, gooseneck, and drop deck. Tank 

trailers are pressure-tight enclosures designed to carry liquids, gases or bulk, dry solids and 

semi-solids. Tank trailers are generally constructed of steel or aluminum. The plumbing for 

intake and discharge of the contents could be located below the tank or at the rear. There are 

also a number of other specialized trailers such as grain (with and without hoppers), dump 

(frameless, framed, bottom dump, demolition), automobile hauler (open or enclosed), 

livestock trailers (belly or straight), construction and heavy-hauling trailers (tilt bed, 

hydraulic). 

A sizable fraction of U.S. freight is transported in large, steel containers both 

internationally via ocean-going vessels and domestically via rail cars. Containers are 

constructed with steel sidewalls and external support beams, which results in a corrugated 

exterior. These containers haul mixed freight and are designed with similar dimensions to 

box trailers. Ocean-going international shipping containers are typically 20-feet or 40-feet in 

length. Domestic containers, which often travel by rail, are 53-feet in length. Transport of 

these containers from ports or rail to their final destination requires the container to be loaded 

on a specialty piece of equipment called a chassis. The chassis, which is attached to the fifth 

wheel of a Class 7 or 8 tractor, consists of a frame, axles, suspension, brakes and wheel 

assemblies, as well as lamps, bumpers and other required safety components. Fixed chassis 

vary in length according to the type of container that will be attached, though some chassis 

adjust to accommodate different sizes. When the chassis and container are assembled the unit 

serves the same function as a road trailer.6 However, under customs regulations, the container 

itself is not considered part of a road vehicle.7

 

ACT Research compiles factory shipment information from a Trailer Industry Control 

Group that represents 80 percent of the U.S. trailer industry. Figure 1-2 shows the 

distribution of trailers sold in the U.S. based on ACT Research’s 2013 factory shipment data. 

The most common type of trailer in use today is the dry van trailer, followed by the 
4 of 9 

refrigerated van. Together, these box vans make up greater than 70 percent of the industry. 

Trailer Body Builders’ annual trailer output report estimates there were over 240,000 trailers 

sold in North America 2013. 

Figure 1-2 ACT Research’s 2013 U.S. factory shipments 

 

1.2.2 Trailer Manufacturers 

Trailer Body Builders’ annual trailer output report estimates there were over 240,000 

trailers sold in North America in 2013. The diverse van, platform, tank and specialty trailers 

are produced by a large number of trailer manufacturers. EPA estimates there are 114 trailer 

manufacturers. Trailers are far less mechanically complex than the tractors that haul them, 

and much of trailer manufacturing is done by hand. This relatively low barrier to entry for 

trailer manufacturing accounts, in part, for the large number of trailer manufacturers. Figure 

1-3 shows that over 70 percent of the manufacturing output of the industry comes from just 

five manufacturers. 

56%

15%

9%

2%

3%

2%

3%

1%

4% 4% 1%

ACT Research 2013 Factory Shipments

Dry Van

Refrigerated Van

Platform

HeavyLowbed

MediumLowbed

Dump

Tank Liquid

Tank Bulk

Grain

Other Trailer

Chassis
5 of 9 

Figure 1-3 2013 Trailer Output Report from Trailer Body Builders 

Table 1-1 Illustrates the varying revenue among trailer manufacturers and further 

distinguishes the very different roles in that market played by small and large manufacturers. 

The revenue numbers were obtained from Hoovers online company database.8 Over 80 

percent of trailer manufacturers meet the Small Business Administration’s (SBA) definition of 

a small business (i.e., less than 500 employees), yet these manufacturers make up less than 25 

percent of the overall revenue from the industry. In fact, a majority of the small business 

trailer manufacturers make less than $10 million in revenue per year. 

Table 1-1 Summary of 2013 Trailer Industry Revenue by Business Size 

Revenue Range Business Size 

All Sizes Large Smalla

> 1000M 1 1 0 

$500M - $999M 0 0 0 

$400M - $499M 1 1 0 

$300M - $399M 0 0 0 

$200M - $299M 3 3 0 

$100M - $199M 3 3 0 

$50M - $99M 13 7 6 

$40M - $49M 14 2 12 

$15M - $19M 9 1 8 

$10M - $14M 3 0 3 

$5M - $9M 26 0 26 

< $5M 41 1 40 

Total Companies 114 19 95 

Total Revenue ($M) 4965 3799 1166 

Average Revenue ($M) 44 200 12 
6 of 9 

Box Trailer Mfrs 14 9 5 

Non-Box Trailer Mfrs 109 17 92 Note: 

a The Small Business Administration (SBA) defines a trailer 

manufacturer as a “small business” if it has fewer than 500 employees 

The trailer industry was particularly hard hit by the recent recession. Trailer 

manufacturers saw deep declines in new trailer sales of 46 percent in 2009; some trailer 

manufacturers saw sales drop as much as 71 percent. This followed overall trailer industry 

declines of over 30 percent in 2008. The 30 largest trailer manufacturers saw sales decline 72 

percent from 282,750 in 2006, to only 78,526 in 2009. Several trailer manufacturers shut 

down entire production facilities and a few went out of business altogether. Trailer 

production has steadily grown across the industry since 2010 and, although historic 

production peaks have not been repeated to date, it has now returned to levels close to those 

seen in the mid-2000s. Figure 1-4 shows the ACT Research’s annual factory shipments, 

which illustrates the unsteady production over the past 17 years. 

Figure 1-4 Annual Factory Shipments Tracked by ACT Research 

1.2.3 Trailer Use 

In order to determine the appropriate tractor type for each trailer, the agencies 

investigated “primary trip length” results from the Vehicle Inventory and Use Survey 

database to determine the distribution of trailers in short- and long-haul applications.9

 Using a 

primary trip length of 500 miles or less to represent short-haul use, the agencies found that, of 

the reported vehicles, over 50 percent of the 53-feet and longer dry vans were used in longhaul and over 80 percent of the shorter vans were used in short-haul applications. Over 70 

percent of the reported 53-feet and longer refrigerated vans were long-haul trailers, with 65 

percent of the shorter refrigerated vans used in short-haul applications. The survey found that 

0

50000

100000

150000

200000

250000

300000

350000

400000

199619971998199920002001200220032004200520062007200820092010

2011

20122013

Total Factory Shipments 

(All Trailer Types)

ACT Research Annual Factory Shipments
7 of 9 

non-box trailers are most frequently used for short-haul. Figure 1-5 summarizes these 

findings. 

Figure 1-5 2002 Vehicle Inventory and Use Survey Considering Primary Trip Length for TractorTrailers 

Truck drivers and trucking fleets frequently do not control all or even any of the 

trailers that they haul. Trailers can be owned by freight customers, large equipment leasing 

companies, third party logistics companies, and even other trucking companies. Containers 

on chassis, which function as trailers, are rarely owned by truck operators. Rather, they are 

owned or leased by ocean-going shipping companies, port authorities or others. This 

distinction between who hauls the freight and who owns the equipment in which it is hauled 

means that truck owners and operators have limited ability to be selective about the trailers 

they carry, and very little incentive or ability to take steps to reduce the fuel use of trailers that 

they neither own or control. 

For refrigerated trailers, the story is slightly different. These trailers are used more 

intensely and accumulate more annual miles than other trailers. Over time, refrigerated 

trailers can also develop problems that interfere with their ability to keep freight temperaturecontrolled. For example, the insulating material inside a refrigerated trailer’s walls can 

gradually lose its thermal capabilities due to aging or damage from forklift punctures. The 

door seals on a refrigerated trailer can also become damaged or loose with age, which greatly 

affects the insulation characteristics of the trailer, similar to how the door seal on a home 

refrigerator can reduce the efficiency of that appliance. As a result of age-related problems 

and more intense usage, refrigerated trailers tend to have shorter procurement cycles than dry 

van trailers, which means a faster turnover rate, although still not nearly as fast as for trucks in 

their first use. 
8 of 9 

Tractor-trailers are often used in conjunction with other modes of transportation (e.g., 

shipping and rail) to move goods across the country, known as intermodal shipping. 

Intermodal traffic typically begins with containers carried on ships, and then they are loaded 

onto railcars, and finally transported to their end destination via truck. Trucks that are used in 

intermodal applications are of two primary types. Trailer-on-flatcar (TOFC) involves lifting 

the entire trailer or the container attached to its chassis onto the railcar. In container-onflatcar (COFC) applications, the container is removed from the chassis and placed directly on 

the railcar. The use of TOFCs allows for faster transition from rail to truck, but is more 

difficult to stack on a vessel; therefore the use of COFCs has been increasing steadily. Both 

applications are used throughout the U.S. with the largest usage found on routes between 

West Coast ports and Chicago, and between Chicago and New York. 

1.2.4 Trailer Fleet Size Relative to the Tractor Fleet 

In 2013, over 800,000 trailers were owned by for-hire fleets and almost 300,000 were 

owned by private fleets. Trailers that are purchased by fleets are typically kept much longer 

than are the tractors, so trucks and trailers have different purchasing cycles. Also, many 

trailers are owned by shippers or by leasing companies, not by the trucking fleets. Because of 

the disconnect between owners, the trailer owners may not benefit directly from fuel 

consumption and GHG emission reductions. 

The industry generally recognizes that the ratio of the number of dry van trailers in the fleet 

relative to the number of tractors is typically three-to-one. 10 Typically at any one time, two 

trailers are parked while one is being transported. Certain private fleets may have ratios as 

high as six-to-one and owner-operators may have a single trailer for their tractor. The ratio of 

refrigerated vans to tractors is closer to two-to-one. This is partly due to the fact that it is 

more expensive to purchase and operate refrigerated vans compared to dry vans. Specialty 

trailers, such as tanks and flatbeds are often attached to a single trailer throughout much of 

their life. This characteristic of the trailer fleet impacts the cost effectiveness of trailer 

technologies. The annual savings achieved due to these technologies are proportional to the 

number of miles traveled in a year and the analysis for many of the trailers must account for 

some amount of inactivity, which will reduce the benefits. 

 
9 of 9 

References

1

 U.S. EPA and NHTSA, 2011. Regulatory Impact Analysis for the Final Rulemaking to Establish Greenhouse 

Gas Emissions Standards and Fuel Efficiency Standards for Medium and Heavy-Duty Engines and Vehicles. 

EPA-420-R-11-901. Available at: http://www.epa.gov/otaq/climate/documents/420r11901.pdf 2 http://www.ttmanet.org

3

 49 CFR 565 

4

 “Both Sides of Truck Weight, Size Gear Up for Next Battle”. Szakonyi, Mark. February 25, 2014. Available 

at: http://www.joc.com/regulation-policy/transportation-policy/us-transportation-policy. Accessed: August 18, 

2014. 

5

 “Wabash Shows What a 33-Foot Pup Would Look Like”. Berg, Tom. Heavy-Duty Trucking 

TruckingInfo.com. March 31, 2014. Available at: www.truckinginfo.com/blog/trailertalk/story/2014/03/wabash-shows-what-a-33-foot-pup-would-look-like.aspx. Accessed: September 23, 2014. 6

 Per 46 CFR § 340.2. 

7

 19 CFR 115.3 

8

 Dun & Bradstreet. Hoover’s Inc. Online Company Database. Available at: http://www.hoovers.com. 9

 U.S. Census Bureau. 2002 Economic Census – Vehicle Inventory and Use Survey. 2002. Available at: 

https://www.census.gov/prod/ec02/ec02tv-us.pdf 

10 TIAX. LLC. “Assessment of Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles,” Final 

Report to the National Academy of Sciences, November 19, 2009. Page 4-49. 
2-1 

Chapter 2: Technology and Cost 

2.1 Overview of Technologies 

In discussing the potential for CO2 emission and fuel consumption reductions, it can be 

helpful to think of the work flow through the system. The initial work input is fuel. Each gallon 

of fuel has the potential to produce some amount of work and will produce a set amount of CO2

(about 22 pounds (10 kg) of CO2 per gallon of diesel fuel). The engine converts the chemical 

energy in the fuel to useable work to move the truck. Any reductions in work demanded of the 

engine by the vehicle or improvements in engine fuel conversion efficiency will lead directly to 

CO2 emission and fuel consumption reductions. 

Current diesel engines are around 40 percent efficient over a range of operating 

conditions depending on engine sizes and applications, while gasoline engine efficiency is much 

lower than that of diesel engines. This means that approximately one-third of the fuel’s chemical 

energy is converted to useful work and roughly two-thirds is lost to friction, gas exchange, and 

waste heat in the coolant and exhaust. In turn, the truck uses this work delivered by the engine to 

overcome overall vehicle-related losses such as aerodynamic drag, tire rolling resistance, friction 

in the vehicle driveline, and to provide auxiliary power for components such as air conditioning 

and lights. Lastly, the vehicle’s operation, such as vehicle speed and idle time, affects the 

amount of total energy required to complete its activity. While it may be intuitive to look first to 

the engine for CO2 emission and fuel consumption reductions given that only about one-third of 

the fuel is converted to useable work, it is important to realize that any improvement in vehicle 

efficiency proportionally reduces both the work demanded and the energy wasted. 

Technology is one pathway to improve heavy-duty truck GHG emissions and fuel 

consumption. Near-term solutions exist, such as those being deployed by SmartWay partners in 

heavy-duty truck long haul applications. Other solutions are currently underway in the light-duty 

vehicle segment, especially in the large pickup sector where some of the technologies can apply 

to the heavy-duty pickup trucks covered under this rulemaking. Long-term solutions are 

currently under development to improve efficiencies and cost-effectiveness. While there is not a 

“silver bullet” that will significantly eliminate GHG emissions from heavy-duty trucks like the 

catalytic converter has for criteria pollutant emissions, significant GHG and fuel consumption 

reductions can be achieved through a combination of engine, vehicle system, and operational 

technologies. 

The following sections will discuss technologies in relation to each of the proposed 

regulatory subcategories – Heavy-Duty Pickup Trucks and Vans, Heavy-Duty Engines, Class 7 

and 8 Combination Tractors, Trailers, and Class 2b-8 Vocational Vehicles. In each of these 

sections, information on technological approaches, costs, and percent improvements is provided. 

Depending on the segment, the vehicle-level technologies available for consideration may 

include idle reduction, improved tire rolling resistance, improved transmissions, improved axles, 

weight reduction, improved accessories, and aerodynamic technologies. Depending on the 

segment, the engine-level technologies available for consideration may include friction 

reduction, variable valve timing, cylinder deactivation, turbocharging, downsizing, combustion 

optimization, aftertreatment optimization, and waste heat recovery. The agencies are not 
2-2 

projecting that all of the technologies discussed in these sections would be used for compliance 

with the engine and vehicle standards, for reasons that are also discussed in each section. 

Nevertheless, the potential for there to be technologies other than those which form the basis for 

the compliance pathway set forth by the agencies, or which can be used in different combinations 

or penetration rates than that projected compliance pathway, is an important consideration in 

assessing the feasibility of the proposed standards. Summaries of all of the technologies, along 

with the corresponding costs, fuel consumption and GHG emissions improvement percentages 

are provided in this chapter. A summary of engine and vehicle technologies, effectiveness, and 

costs for HD pickup trucks and vans is provided in Chapter 2.5. Summaries of engine 

technologies, effectiveness, and costs are provided in Chapters 2.6 and 2.7. A summary of 

technologies, effectiveness, and costs for tractors is provided in Chapter 2.8. A summary of 

technologies, effectiveness, and costs for vocational vehicles is provided in Chapter 2.9. A 

summary of technologies, effectiveness, and costs for trailers is provided in Chapter 2.10. A 

detailed analysis of technology costs is found in Chapters 0 and 2.13. 

EPA and NHTSA collected information on the cost and effectiveness of fuel 

consumption and CO2 emission reducing technologies from several sources. The primary 

sources of information were the 2010 National Academy of Sciences report on Technologies and 

Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles (NAS)1, 

TIAX’s assessment of technologies to support the NAS panel report (TIAX)2, EPA’s HeavyDuty Lumped Parameter Model3

, the analysis conducted by NESCCAF, ICCT, Southwest 

Research Institute and TIAX for reducing fuel consumption of heavy-duty long haul combination 

tractors (NESCCAF/ICCT)4, and the technology cost analysis conducted by ICF for EPA (ICF).5 

In addition, the agencies relied on NHTSA’s technology assessment report under contract with 

SwRI and Tetra Tech.6,7,8 In addition, the agencies used the vehicle simulation model (the 

Greenhouse gas Emissions Model or GEM) to quantify the effectiveness of various technologies 

on CO2 emission and fuel consumption reductions in terms of vehicle performance as they are 

evaluated in determining compliance with the HD program. The simulation tool is described in 

draft RIA Chapter 4 in more detail. 

2.2 Technology Principles – SI Engines 

The engine technology principles described in this chapter for SI and CI engines are 

typically described as applying for gasoline and diesel-fueled engines, respectively. Even so, 

these technology principles generally also apply for engines powered by other fuels, including 

natural gas. In Section II of the preamble to these rules, the agencies describe regulatory 

provisions that differ between SI and CI engines. Technologies related to closed crankcases for 

natural gas engines are described below in Chapter 2.11 and in the Preamble Section II. The 

agencies describe technologies and test procedures related to minimizing evaporative emissions 

from natural gas fuel systems in Chapter 2.11 as well as in Section XI of the preamble to these 

rules. The agencies’ approach in this document is to first describe the principles of how 

technologies can work for an engine, without specifying the type of vehicle into which it will be 

installed, or the test cycle over which it will be certified. Later, in Chapter 2.5, the agencies 

describe a subset of these technologies as they apply specifically to complete HD pickup trucks 

and vans. In Chapter 2.6, the agencies describe a subset of these technologies as they apply to SI 

engines intended for vocational vehicles. 
2-3 

2.2.1 Engine Friction Reduction 

In addition to low friction lubricants, manufacturers can reduce friction and improve fuel 

consumption by improving the design of engine components and subsystems. Examples include 

improvements in low-tension piston rings, piston skirt design, roller cam followers, improved 

crankshaft design and bearings, material coatings, material substitution, more optimal thermal 

management, and piston and cylinder surface treatments. The 2010 NAS Report, NESCCAF9

and EEA10 reports as well as confidential manufacturer data used in the light-duty vehicle 

rulemaking suggested a range of effectiveness for engine friction reduction to be between 1 to 3 

percent. Reduced friction in bearings, valve trains, and the piston-to-liner interface would 

improve efficiency. Any friction reduction must be carefully developed to avoid issues with 

durability or performance capability. 

2.2.2 Variable Valve Timing 

Variable valve timing (VVT) classifies a family of valve-train designs that alter the 

timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, increase 

specific power, and control the level of residual gases in the cylinder. VVT reduces pumping 

losses when the engine is lightly loaded by controlling valve timing closer to the optimum 

needed to sustain horsepower and torque. VVT can also improve volumetric efficiency at higher 

engine speeds and loads. Additionally, VVT can be used to alter (and optimize) the effective 

compression ratio where it is advantageous for certain engine operating modes (e.g., in the 

Atkinson Cycle). 

VVT has now become a widely adopted technology in the light duty fleet: in MY 2014, 

most of all new cars and light trucks had engines with some method of variable valve timing.11

Manufacturers are currently using many different types of variable valve timing, which have a 

variety of different names and methods. Therefore, the degree of further improvement across the 

fleet is limited by the level of valvetrain technology already implemented on the vehicles. The 

three major types of VVT are listed below. 

Each implementation of VVT uses a cam phaser to adjust the camshaft angular position 

relative to the crankshaft position, referred to as “camshaft phasing.” This phase adjustment 

results in changes to the pumping work required by the engine to accomplish the gas exchange 

process. The majority of current cam phaser applications use hydraulically-actuated units, 

powered by engine oil pressure and managed by a solenoid that controls the oil pressure supplied 

to the phaser. 

2.2.2.1 Coupled Cam Phasing for Overhead Valve (OHV) and Single Overhead 

Camshaft (SOHC) Engines 

Valvetrains with coupled (or coordinated) cam phasing (CCP) can modify the timing of 

both the inlet valves and the exhaust valves an equal amount by varying the phasing of the 

camshaft across an engine’s range of operating speeds; also known as VVT. For engines 

configured as an overhead valve (OHV) or as a single overhead camshaft (SOHC) only one cam 

phaser is required per camshaft to achieve CCP. 
2-4 

Based on the heavy-duty Phase 1 vehicle rulemaking, 2015 NHTSA Technology Study, 

and previously-received confidential manufacturer data, the agencies estimate the fuel 

consumption reduction effectiveness of this technology to be between 1 and 3 percent over 

average driving patterns. 

2.2.2.2 Intake Cam Phasing (ICP) for Dual Overhead Camshaft Engines (DOHC) 

Valvetrains with ICP, which is the simplest of the cam phasing technologies, can modify 

the timing of the inlet valves by phasing the intake camshaft while the exhaust valve timing 

remains fixed. This requires the addition of a cam phaser on each bank of intake valves on the 

engine. An in-line 4-cylinder engine has one bank of intake valves, while V-configured engines 

have two banks of intake valves. 

Some newer Class 2b and 3 market entries are offering dual overhead camshaft (DOHC) 

engine designs where two camshafts are used to operate the intake and exhaust valves 

independently. Consistent with the heavy-duty 2014-2018 MY vehicle rulemaking and the SwRI 

report, the agencies agree with the effectiveness values of 1 to 2 percent reduction in fuel 

consumption over average driving patterns, for this technology. 

2.2.2.3 Dual Cam Phasing (DCP) for Dual Overhead Camshaft Engines (DOHC) 

The most flexible VVT design is dual (independent) cam phasing, where the intake and 

exhaust valve opening and closing events are controlled independently. This option allows the 

option of controlling valve overlap, which can be used as an internal EGR strategy. At low 

engine loads, DCP creates a reduction in pumping losses, resulting in improved fuel 

consumption. Increased internal EGR also results in lower engine-out NOX emissions. The 

amount by which fuel consumption is improved depends on the residual tolerance of the 

combustion system. Additional improvements are observed at idle, where low valve overlap 

may result in improved combustion stability, potentially reducing idle fuel consumption. DCP 

requires two cam phasers on each bank of the engine. 

Some newer Class 2b and 3 market entries are offering dual overhead camshaft (DOHC) 

engine designs where two camshafts are used to operate the intake and exhaust valves 

independently. Consistent with the light-duty 2012-2016 MY vehicle rulemaking and the SwRI 

report, the agencies agree with the effectiveness values of 1 to 3 percent reduction in fuel 

consumption over average driving patterns, for this technology. 

2.2.2.4 Variable Valve Lift (VVL) 

Controlling the lift of the valves provides a potential for further efficiency improvements. 

By optimizing the valve-lift profile for specific engine operating regions, the pumping losses can 

be reduced by reducing the amount of throttling required to produce the desired engine power 

output. By moving the throttling losses further downstream of the throttle valve, the heat 

transfer losses that occur from the throttling process are directed into the fresh charge-air mixture 

just prior to compression, delaying the onset of knock-limited combustion processes. Variable 

valve lift control can also be used to induce in-cylinder mixture motion, which improves fuel-air 

mixing and can result in improved thermodynamic efficiency. Variable valve lift control can 
2-5 

also potentially reduce overall valvetrain friction. At the same time, such systems may also incur 

increased parasitic losses associated with their actuation mechanisms. A number of 

manufacturers have already implemented VVL into their fleets (Toyota, Honda, and BMW), but 

overall this technology is still available for most of the fleet. There are two major classifications 

of variable valve lift, described below: 

2.2.2.5 Discrete Variable Valve Lift (DVVL) 

Discrete variable valve lift (DVVL) systems allow the selection between two or three 

discrete cam profiles by means of a hydraulically-actuated mechanical system. By optimizing 

the cam profile for specific engine operating regions, the pumping losses can be reduced by 

reducing the amount of throttling required to produce the desired engine power output. This 

increases the efficiency of the engine. These cam profiles consist of a low and a high-lift lobe, 

and may include an inert or blank lobe to incorporate cylinder deactivation (in the case of a 3-

step DVVL system). DVVL is normally applied together with VVT control. DVVL is also 

known as Cam Profile Switching (CPS). DVVL is a mature technology in LD applications with 

low technical risk. 

Based on the light-duty MY 2017-2025 final rule, previously-received confidential 

manufacturer data, 2015 NHTSA Technology Study, and report from the Northeast States Center 

for a Clean Air Future (NESCCAF), the agencies estimate the fuel consumption reduction 

effectiveness of this technology to be between 1 and 3 percent over average driving patterns. 

2.2.3 Cylinder Deactivation 

In conventional spark-ignited engines throttling the airflow controls engine torque output. 

At partial loads, efficiency can be improved by using cylinder deactivation instead of throttling. 

Cylinder deactivation can improve engine efficiency by disabling or deactivating (usually) half 

of the cylinders when the load is less than half of the engine’s total torque capability – the valves 

are kept closed, and no fuel is injected – as a result, the trapped air within the deactivated 

cylinders is simply compressed and expanded as an air spring, with reduced friction and heat 

losses. The active cylinders combust at almost double the load required if all of the cylinders 

were operating. Pumping losses are significantly reduced as long as the engine is operated in this 

“part cylinder” mode. Effectiveness improvements scale roughly with engine displacement-tovehicle weight ratio: the higher displacement-to-weight vehicles, operating at lower relative 

loads for normal driving, have the potential to operate in part-cylinder mode more frequently. 

Cylinder deactivation is less effective on heavily-loaded vehicles because they require more 

power and spend less time in areas of operation where only partial power is required. The 

technology also requires proper integration into the vehicles which is difficult in the vocational 

vehicle segment where often the engine is sold to a chassis manufacturer or body builder without 

knowing the type of transmission or axle used in the vehicle or the precise duty cycle of the 

vehicle. The cylinder deactivation requires fine tuning of the calibration as the engine moves 

into and out of deactivation mode to achieve acceptable NVH. Additionally, cylinder 

deactivation would be difficult to apply to vehicles with a manual transmission because it 

requires careful gear change control. NHTSA and EPA adjusted the 2017-2025 MY light-duty 

rule estimates using updated power to weight ratings of heavy-duty trucks and confidential 

business information and downwardly adjusted the effectiveness to 0 to 3 percent over average 
2-6 

driving patterns for these vehicles to reflect the differences in drive cycle and operational 

opportunities compared to light-duty vehicles. 

2.2.4 Stoichiometric Gasoline Direct Injection (SGDI) 

Stoichiometric gasoline direct injection (SGDI) engines inject fuel at high pressure 

directly into the combustion chamber (rather than into the intake port in port fuel injection). 

SGDI requires changes to the injector design, an additional high pressure fuel pump, new fuel 

rails to handle the higher fuel pressures, and changes to the cylinder head and piston crown 

design. Direct injection of the fuel into the cylinder improves cooling of the air/fuel charge 

within the cylinder, which allows for higher compression ratios and increased thermodynamic 

efficiency without the onset of combustion knock. Recent injector design advances, improved 

electronic engine management systems and the introduction of multiple injection events per 

cylinder firing cycle promote better mixing of the air and fuel, enhance combustion rates, 

increase residual exhaust gas tolerance, and improve cold start emissions. SGDI engines achieve 

higher power density and match well with other technologies, such as boosting and variable 

valvetrain designs. 

Several manufacturers have recently introduced vehicles with SGDI engines, including 

GM and Ford, who have announced their plans to increase dramatically the number of SGDI 

engines in their vehicle portfolios. 

Based on the heavy-duty 2014-2018 MY vehicle rulemaking, 2015 NHTSA Technology 

Study, and previously-received confidential manufacturer data, the agencies estimate the fuel 

consumption reduction effectiveness of SGDI to be between 1 and 2 percent over average 

driving patterns. 

2.2.5 Turbocharging and Downsizing (TRBDS) 

The specific power of a naturally aspirated engine is primarily limited by the rate at 

which the engine is able to draw air into the combustion chambers. Turbocharging and 

supercharging (grouped together here as boosting) are two methods to increase the intake 

manifold pressure and cylinder charge-air mass above naturally aspirated levels. Boosting 

increases the airflow into the engine, thus increasing the specific power level, and with it the 

ability to reduce engine displacement while maintaining performance. This effectively reduces 

the pumping losses at lighter loads in comparison to a larger, naturally aspirated engine. 

Almost every major manufacturer currently markets a vehicle with some form of 

boosting. While boosting has been a common practice for increasing performance for several 

decades, turbocharging has considerable potential to improve fuel economy and reduce CO2

emissions when the engine displacement is also reduced. Specific power levels for a boosted 

engine often exceed 100 hp/L, compared to average naturally aspirated engine power densities of 

roughly 70 hp/L. As a result, engines can be downsized roughly 30 percent or higher while 

maintaining similar peak output levels. In the last decade, improvements to turbocharger turbine 

and compressor design have improved their reliability and performance across the entire engine 

operating range. New variable geometry turbines and ball-bearing center cartridges allow faster 

turbocharger spool-up (virtually eliminating the once-common “turbo lag”) while maintaining 
2-7 

high flow rates for increased boost at high engine speeds. Low speed torque output has been 

dramatically improved for modern turbocharged engines. However, even with turbocharger 

improvements, maximum engine torque at very low engine speed conditions, for example launch 

from standstill, is increased less than at mid and high engine speed conditions. The potential to 

downsize engines may be less on vehicles with low displacement to vehicle mass ratios for 

example a very small displacement engine in a vehicle with significant curb weight, in order to 

provide adequate acceleration from standstill, particularly up grades or at high altitudes. 

Use of gasoline direct injection (GDI) systems with turbocharged engines and charge air 

cooling also reduces the fuel octane requirements for knock limited combustion and allows the 

use of higher compression ratios. Ford’s “EcoBoost” downsized, turbocharged GDI engines 

introduced on MY 2010 vehicles allow the replacement of V8 engines with V6 engines with 

improved in 0-60 mph acceleration and with fuel economy improvements of up to 12 percent as 

documented in their technical paper.12

Recently published data with advanced spray-guided injection systems and more 

aggressive engine downsizing targeted towards reduced fuel consumption and CO2 emissions 

reductions indicate that the potential for reducing CO2 emissions for turbocharged, downsized 

GDI engines may be as much as 15 to 30 percent relative to port-fuel-injected engines. 

Confidential manufacturer data suggest an incremental range of fuel consumption and CO2

emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other publiclyavailable sources suggest a fuel consumption and CO2 emission reduction of 8 to 13 percent 

compared to current-production naturally-aspirated engines without friction reduction or other 

fuel economy technologies: a joint technical paper by Bosch and Ricardo suggesting fuel 

economy gain of 8 to 10 percent for downsizing from a 5.7 liter port injection V8 to a 3.6 liter 

V6 with direct injection using a wall-guided direct injection system;13 a Renault report 

suggesting a 11.9 percent NEDC fuel consumption gain for downsizing from a 1.4 liter port 

injection in-line 4-cylinder engine to a 1.0 liter in-line 4-cylinder engine, also with wall-guided 

direct injection;14 and a Robert Bosch paper suggesting a 13 percent NEDC gain for downsizing 

to a turbocharged DI engine, again with wall-guided injection.15 These reported fuel economy 

benefits show a wide range depending on the SGDI technology employed. 

The agencies reviewed estimates from the 2017-2025 final light-duty rule, the TSD, and 

existing public literature. The previous estimate from the MYs 2017-2025 suggested a 12 to 14 

percent effectiveness improvement, which included low friction lubricant (level one), engine 

friction reduction (level one), DCP, DVVL and SGDI, over baseline fixed-valve engines, similar 

to the estimate for Ford’s EcoBoost engine, which is already in LD production. Additionally, the 

agencies analyzed Ricardo vehicle simulation data and the 2015 NHTSA Technology Study for 

various turbocharged engine packages. 

2.3 Technology Principles – CI Engines 

2.3.1 Low Temperature Exhaust Gas Recirculation 

Most LHDD, MHDD, and HHDD engines sold in the U.S. market today use cooled EGR, 

in which part of the exhaust gas is routed through a cooler (rejecting energy to the engine 

coolant) before being returned to the engine intake manifold. EGR is a technology employed to 
2-8 

reduce peak combustion temperatures and thus NOX. Low-temperature EGR uses a larger or 

secondary EGR cooler to achieve lower intake charge temperatures, which tend to further reduce 

NOX formation. For a given NOX requirement, low-temperature EGR can allow changes such as 

more advanced injection timing that would increase engine efficiency slightly more than one 

percent. Because low-temperature EGR reduces the engine’s exhaust temperature, it has not 

been considered as part of a technology package that also includes exhaust energy recovery 

systems such as turbocompounding or a bottoming cycle. 

2.3.2 Combustion System Optimization 

Improvements on the fuel injection system that allow more flexible fuel injection 

capability with higher injection pressure can improve engine fuel efficiency, while maintaining 

the same emission level. Combustion system optimization, featuring piston bowl, injector tip 

and the number of holes, in conjunction with the advanced fuel injection system, is able to 

further improve engine performance and fuel efficiency. Manufacturers have been working to 

improve engines these areas for some time. At this point, all engine manufacturers have 

substantial development efforts underway that we project would be translated into production in 

the near future. Some examples include the combustion development programs conducted by 

Cummins16, Detroit Diesel17, and Navistar18 funded by Department of Energy as part of 

Supertruck program. These manufacturers found the improvement due to combustion alone 

during this program was 1 to 2 percent. While their findings are still more towards research 

environment, specifically targeting one optimal operating point, the results of these research 

programs do support the possibility that some of technologies they are developing could be 

applied to production in the time frame of 2027. The agencies have determined that it is feasible 

that fuel consumption and CO2 emissions could be reduced by as much as 1.0 percent in the 

agencies’ certification cycles in the 2027 time frame through the use of these technologies. 

Some technologies were evaluated but not included in the agencies’ technical feasibility 

analysis for the Phase 2 regulation since the agencies do not anticipate these technologies will be 

commercially available by 2027. For example, alternative combustion processes such as 

homogeneous charge compression ignition (HCCI), premixed charge compression ignition 

(PCCI), low-temperature combustion (LTI), and reactivity controlled compression ignition 

(RCCI) technologies were not included in the feasibility analysis for Phase 2. While these 

technologies show good indicated thermal efficiency, fuel savings over the entire range of engine 

operation is still a major challenge. At the current level of development it is not clear that the 

technologies will be in commercial production by 2027. This, however, does not preclude the 

use of these technologies for compliance should manufacturers develop and commercialize these 

alternative combustion or other approaches. 

2.3.3 Model Based Control 

Another important area of potential improvement is advanced engine control 

incorporating model based calibration to reduce losses of control during transient operation. 

Improvements in computing power and speed would make it possible to use much more 

sophisticated algorithms that are more predictive than today’s controls. Because such controls 

are only beneficial during transient operation, they would reduce emissions over the Federal Test 

Procedure (FTP) cycle, but not over the Supplemental Emission Test (SET) cycle. Detroit Diesel 
2-9 

introduced the next generation model based control concept, achieving 4 percent thermal 

efficiency improvement while simultaneously reducing emissions in transient operations in their 

earlier report.19 More recently, this model based control technology was put into their one of 

vehicles for final demonstration under DOE’s Supertruck program.20 Their model based concept 

features a series of real time optimizers with multiple inputs and multiple outputs. This 

controller contains many physical based models for engine and aftertreatment. It produces fully 

transient engine performance and emissions predictions in a real-time manner. Although we do 

not project that this control concept would be in 2017 production, it would be a realistic estimate 

that this type of real time model control could be in production during the Phase 2 time frame, 

thus significantly improving engine fuel economy. 

2.3.4 Turbocharging System 

Many advanced turbocharger technologies can be potentially added into production in the 

time frame between 2021 and 2027 and some of them are already in production, such as 

mechanical or electric turbocompound, more efficiency variable geometry turbine, and Detroit 

Diesel patented asymmetric turbocharger. A turbocompound system extracts energy from the 

exhaust to provide additional power. Mechanical turbocompounding includes a power turbine 

located downstream of the turbine which in turn is connected to the crankshaft to supply 

additional power. On-highway demonstrations of this technology began in the early 1980s. It 

has been first used in heavy duty production by Detroit Diesel for their DD15 and DD16 engines 

and they claim a 3 to 5 percent fuel consumption reduction due to the system.21 Results are duty 

cycle dependent, and require significant time at high load to see a fuel efficiency improvement. 

Light load factor vehicles can expect little or no benefit. Volvo reports two to four percent fuel 

consumption improvement in line haul applications, which would be likely in production even 

before 2020.22 

Electric turbo-compound is another potential area that can improve engine brake 

efficiency. These are attained through better vehicle integration and lower backpressure impacts. 

Since the electric power turbine speed is no longer linked to crankshaft speed, this allows more 

efficient operation of the turbine. Navistar reports on the order of a 1 to 1.5 percent efficiency 

improvement over mechanical turbocompound systems at 0.5 to 0.7 gm/hp-hr engine-out NOX

levels, but dropping at lower engine-out NOX.

23 However, this concept may not work well with 

lower engine out NOX as indicated in this report, showing zero benefit is reported at 0.3 to 0.4 

gm/hp-hr engine-out NOX, due to lower available temperature. Navistar reports a 1.6 percent 

fuel efficiency improvement, again as compared to a mechanical turbocompound system.24

Two-stage turbocharger technology has been used in production by Navistar and other 

manufacturers. Ford’s new developed 6.7L diesel engine features a twin-compressor 

turbocharger. Higher boost with wider range of operations and higher efficiency can further 

enhance engine performance, thus fuel economy. It is expected that this type of technology will 

continue to be improved by better matching with system and developing higher compressor and 

turbine efficiency. 

Furthermore, improved turbocharger efficiency when combined with turbocompounding 

was shown in the SwRI study to reduce fuel consumption while maintaining criteria emissions 

limits. Findings show that there is limited scope for improved turbocharger efficiency on 
2-10 

engines which do not use turbocompound, because an increase in turbocharger efficiency would 

result in reduced or eliminated EGR flow which in turn would cause the engine to exceed NOx 

emissions requirements. 

2.3.5 Engine Breathing System 

Various high efficiency air handling (air and exhaust transport) processes could be 

produced in the 2020 and 2024 time frame. To maximize the efficiency of such processes, 

induction systems may be improved by manufacturing more efficiently designed flow paths 

(including those associated with air cleaners, chambers, conduit, mass air flow sensors and intake 

manifolds) and by designing such systems for improved thermal control. Improved 

turbocharging and air handling systems must include higher efficiency EGR systems and 

intercoolers that reduce frictional pressure loss while maximizing the ability to thermally control 

induction air and EGR. EGR systems that often rely upon an adverse pressure gradient (exhaust 

manifold pressures greater than intake manifold pressures) must be reconsidered and their 

adverse pressure gradients minimized. “Hybrid” EGR strategies which rely upon pressure 

gradients and EGR pumps may provide pathways for improvement. Other components that offer 

opportunities for improved flow efficiency include cylinder heads, ports and exhaust manifolds 

to further reduce pumping losses. Cummins reports 1.4 percent through optimization.25 Detroit 

Diesel projects a two percent fuel efficiency improvement through air handling system 

development.26 Navistar predicts almost four percent through a combination of variable intake 

valve closing timing (IVC), turbocharger efficiency and match improvements.24 A few plots in 

this reference show another four percent, but these are not explained. 

Variable air breathing systems such as variable valve actuation may provide additional 

gains at different loads and speeds. The primary gain in diesel engines is achieved by varying 

the EVO event versus engine speed and load, in conjunction with turbocharger optimization to 

minimize blowdown losses. Navistar reports a 1.25 percent fuel consumption improvement.23 

Again, all these reference points are referred to a single optimal point conducted at DOE 

Supertruck program. 

2.3.6 Engine Parasitic and Friction Reduction 

Engine parasitic and friction reduction is another key technical area that can be further 

improved in production moving to the 2020 and 2027 time frame. Reduced friction in bearings, 

valve trains, and the piston-to-liner interface can improve efficiency. Friction reduction 

opportunities in the engine valve train and at its roller/tappet interfaces exist for several 

production engines. The piston at its skirt/cylinder wall interface, wrist pin and oil ring/cylinder 

wall interface offers opportunities for friction reduction. Use of more advanced oil lubricant that 

could be available for production in the future can also play a key role in reducing friction. Any 

friction reduction must be carefully developed to avoid issues with durability or performance 

capability. Lube pump as well water pump are another areas that improve efficiency. Navistar 

identifies a combined improvement of up to two percent through reduced bearing friction, 

reduced piston and ring friction, and unspecified lube pump improvements.27 In his 2012 paper 

he reports 5.5 percent through a combination of friction reduction and both lube and cooling 

system improvements.23 Later in the same presentation he specifies 0.45 percent demonstrated 

through water pump improvements and 0.3 percent through lube pump improvements. The total 
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number of 5.5 percent seems optimistic, even for a single optimal point. Cummins reports a 

combined number of 3 percent.25. Detroit Diesel reports a combined number of two percent, 

with 0.5 percent coming from improved water pump efficiency. 26 Navistar shows a 0.9 percent 

benefit for a variable speed water pump and variable displacement oil pump; piston/ring/liner 

friction reduction as 0.5 percent; bearing friction reduction as 0.6 percent.24 In addition, FederalMogul recently announced new piston ring coatings that can lead to a 20 percent reduction in 

engine friction and looking to the future sees an opportunity to reduce friction by an additional 

30 percent, equivalent to 1.2 percent reduction in brake specific fuel consumption at road load 

conditions.28 It should be noted that water pump improvements include both pump efficiency 

improvement and variable speed or on/off controls. Lube pump improvements are primarily 

achieved using variable displacement pumps and may also include efficiency improvement. All 

of these results shown in this paragraph are demonstrated through DOE Supertruck program 

under single optimal point. 

2.3.7 Integrated Aftertreatment System 

All manufacturers now use diesel particulate filter (DPF) to reduce particulate matter 

(PM) and use SCR to reduce NOX emissions, and these types of technologies are likely to be 

deployed for many years to come. Three areas are considered to improve integrated 

aftertreament systems. The first is to have better combustion system optimization through 

increased aftertreatment efficiency. The second is to reduce backpressure through further 

development of the devices themselves. The third is to reduce ammonia slip out of SCR during 

transient operation, thus reducing the urea consumption. This is in turn translated into reduced 

fuel consumption. Navistar reports a seven to eight percent improvement projected through a 

combination of higher cylinder pressure, injection optimization, and engine/aftertreatment 

optimization.23 Cummins reports a 0.5 percent improvement through improved aftertreatment 

flow (catalyst size optimization and improved NOX surface utilization).25 Detroit Diesel projects 

a two percent fuel efficiency improvement through reduced EGR (thinner wall DPF, improved 

SCR cell density, and catalyst material optimization).26

2.3.8 Engine Downsizing 

Engine downsizing can be more effective if it is combined with the down speeding. This 

is due to increased vehicle efficiency resulting in lower power demand. This lower power 

demand shifts the vehicle operating points to lower load zones, which moves the engine away 

from some of the optimum operation points. In order to compensate for this loss, down speeding 

allows the engine to move back into the optimum operating points resulting in reduced fuel 

consumption. Increasing power density by reducing the engine size allow the vehicle operating 

points move back to the optimum operating points, thus further improving fuel economy. Both 

Detroit Diesel and Volvo show the same methodology of how downsizing should be properly 

used.29,30 Detroit Diesel also shows that engine downsizing can reduce the friction due to smaller 

surface area as opposed to bigger bore engine.26 

2.3.9 Waste Heat Recovery 

Organic Rankine Cycle waste heat recovery (WHR) systems have been studied for many 

years. The agencies’ overall assessment of WHR as a fuel saving technology is that it offers 
2-12 

great promise in the long term. However, it would take several years to develop, and initially, it 

would be viable primarily in line-haul applications. The agencies recognize the many challenges 

that would need to be overcome, but believe with enough time and development effort, this can 

be done. The discussion below highlights these challenges to show why the agencies currently 

believe WHR would not achieve a substantial market penetration until 2027. 

The basic approach of these systems is to use engine exhaust waste heat from multiple 

sources to evaporate a working fluid through a heat exchanger, which is then passed through a 

turbine or equivalent expander to create mechanical or electrical power. The working fluid is 

then condensed back to the fluid tank, and pulled back to the flow circuit through a pump to 

continue the cycle. With support of the Department of Energy, three major engine and vehicle 

manufacturers have developed the WHR technology under the Supertruck program. Cummins’ 

WHR system is based on an organic Rankine cycle using refrigerant as the working fluid.31,32

The system recovers heat from the EGR cooler, as well as from the exhaust gas downstream of 

the aftertreatment system. It converts that heat to power through a mechanical gear train coupled 

to the engine output shaft. Some iterations of the system also sought gains from low-temperature 

coolant and lubricant heat rejection via a parallel loop. The system includes a recuperator that 

transfers post-turbine energy back into the working fluid loop prior to the condenser. This 

recuperator reduces condenser heat rejection requirements and improves overall system 

efficiency. Volvo has developed a similar system to Cummins’ with variations in terms of 

hardware components, but uses ethanol as the working fluid instead of a refrigerant.33 Daimler, 

on the other hand, has developed a different system to recover heat from the exhaust gas using an 

electrical generator to provide power to charge a high-voltage battery, where this battery system 

is primarily used to drive a hybrid system. Daimler uses ethanol as the working fluid, similar to 

Volvo’s system. 

Pre-prototype WHR systems have proven to be very efficient under right conditions. In 

demonstrations where operation occurred at a single optimal engine operating point, Cummins 

reports potential efficiency gains from WHR on the order of 2.8 percent points from the engine 

without WHR31. Volvo reports around 2.5 percent points33. Daimler reports 2.3 percent 

points.29 All of these manufacturers use the type of WHR just described (including both 

mechanisms of mechanical work transfer to crankshaft and electric generator) in vehicle 

demonstrations for the DOE Supertruck program. It is important to note that all of these WHR 

systems are still in the pre-prototype stage of research and development. Despite the promising 

performance of pre-prototype WHR systems, the cost and complexity of these packages from 

Cummins, Volvo and Daimler remain high. The agencies believe manufacturers will improve 

these systems over time just as they have for other advanced technologies that initially had high 

cost and complexity at a comparable stage of development. 

The technology also poses issues relating to package size and transient response 

challenges. Thus, the agencies believe that WHR would be less effective in urban traffic and 

would most likely be applied to line haul vehicles, consistent with the technology path on which 

the proposed standards are premised. 

WHR may offer the benefit of replacing the EGR cooler and decrease cooling system 

heat rejection requirements by converting some heat into work. To the extent that WHR systems 

use exhaust heat, they may increase the overall cooling system heat rejection requirement, thus 
2-13 

increasing radiator size, which can have negative impacts on cooling fan power needs, as well as 

on vehicle aerodynamics. Significant challenges could arise if the space under the vehicle hood 

happens to be tight, leaving little or no room for increased radiator size, which would necessitate 

a redesign of the vehicle front face, sacrificing potential aerodynamic improvements. This issue 

becomes more challenging for those truck cooling systems that are already at cooling capacity 

design limits. 

Current pre-prototype systems are heavy, estimated to be on the order of 300-500 lbs 

depending on system design. Without time to optimize designs, any efforts to reduce weight by 

simply reducing the size of the key components, such as boilers and condensers, would likely 

negatively reduce the system efficiency. However, with enough lead time, the agencies believe 

manufacturers may be able to improve materials and designs to reduce overall system weight 

without compromising efficiency. 

Manufacturers have not yet arrived at a consensus on which working fluid(s) to be used 

in WHR systems to balance concerns regarding performance, global warming potential (GWP), 

and safety. Current working fluids have a high GWP (conventional refrigerant), are expensive 

(low GWP refrigerant), are hazardous (ammonia, etc.), are flammable (ethanol/methanol), or can 

freeze (water). One of the challenges is determining how to seal the working fluid properly 

under the vacuum condition with high temperature to avoid safety issues for 

flammable/hazardous working fluids. Addressing leaks would also be an important issue with 

respect to greenhouse gas emission for a high GWP working fluid. Because of these challenges, 

choosing a working fluid will be an important factor for system safety, efficiency, and overall 

production viability. 

Other key challenging issues in the WHR system are its reliability, durability, and market 

acceptance. Durability concerns that have been raised include: boiler fouling and cracking issues 

associated with high thermal gradients, thermal shock, condenser fouling, and various sensor and 

actuator durability under harsh temperature and pressure conditions. It can be reasonably 

estimated that of the current WHR systems under development by the major manufacturers 

consists of at least two hundred parts including all major components, such as expanders, boilers, 

condensers, and fluid pumps, together with many fasteners, wiring cables, sensors, actuators, and 

piping. Determining overall system efficacy and reliability involves rigorous testing in support 

of comprehensive Failure Modes and Effects Analysis (FEMA). These parts, as well as the 

entire WHR system as a whole, must undergo severe winter and summer tests. Multiple trucks 

equipped with the same WHR system must be run on the road, accumulating multiple millions of 

miles. During these tests, all failures must be recorded, which are associated with specific failure 

modes or error codes. Root causes must be determined. Warranty costs for each failure mode 

based on the part cost and labor must be assigned. Due to the large number of components of 

WHR, some of the failure modes might not be identified during the road tests even with multiple 

extreme weather tests. It would be a high risk for any manufacturers to put their new technology 

into the market without careful WHR system validations and proof of on-the-road tests. 

Similarly, purchasers might be unlikely to risk early adoption of such complex technology if 

deployed prematurely (without substantial testing) due to significant concerns and costs related 

to potential down time. 
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Based on the literature and preceding discussion, WHR technology can be characterized 

as being in the technology demonstration stage for purposes such as the DOE Supertruck 

program. Even though a few trucks with WHR technology have been tested on the road,33,34,35 

many of the components used in the trucks and product-acceptable packaging are still years away 

from production. Figure 2-1 shows a generic form of product process flow. As can be seen from 

this figure, it would take 5-15 years from applied research/development to a prototype depending 

on the complexity of the technology. During the prototype stage, all prototype components must 

be available and extensive engine and vehicle tests with WHR must be conducted. The 

production start-up phase would follow. After that, significant efforts must be made from a 

prototype to a commercial product, which typically takes about five years for a complex system 

like WHR. During this approximate five-year period, multiple vehicles should go through all 

weather condition tests, which would help to detect possible failure modes and determine 

warranty cost associated with them. In addition, long lead-time parts and tools should be 

identified; market launch and initial results on operating stability should be completed. 

Furthermore, designs should be released to production, and all product components should be 

available. Finally, production parts on customer fleets and all weather road testing should be 

verified before finally launching production, and distributing parts to the vehicle service network 

for maintenance and repair should be ready. 

Figure 2-1 Product Process Flow 

The standards the agencies are proposing can provide an effective incentive for 

manufacturers to reach commercial product stage earlier than would otherwise occur. It can 

motivate manufacturers to shorten the period for advancing from a complicated prototype system 

to a commercial product. It can also help to ensure the market penetration after launching a 

product. Nevertheless, in order for WHR to be produced commercially, several things are 

needed. First, it is critical to optimize the WHR package volume, cooling capability, and aero 

drag at typical cruise speeds on highway since the most significant benefits of WHR technology 

would be in line-haul applications. Removal of the exhaust heat exchangers located at the 

exhaust pipe could reduce the total system volume and weight. Working fluids could be selected 

with reasonable low GWP and high performance potential. One approach could be to put a few 

hundred trucks into fleets for trial in the next several years, so that a comprehensive FEMA can 

be thoroughly identified and warranty cost analyses can be more precisely conducted before 
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launching into full volume production. Manufacturers have shown in the past that a robust 

FEMA process can address most problems before a technology is more widely introduced. 

Therefore, the lead time appears to be one of the most noticeable constraints. We believe that all 

the issues and hurdles discussed could be resolved with adequate lead time. 

2.4 Technology Principles - Vehicles 

2.4.1 Aerodynamics 

The aerodynamic efficiency of heavy-duty vehicles has gained increasing interest in 

recent years as fuel prices, competitive freight markets, and overall environmental awareness has 

focused owners and operators on getting as much useful work out of every gallon of diesel fuel 

as possible. Up to 25 percent of the fuel consumed by a line-haul tractor traveling at highway 

speeds is used to overcome aerodynamic drag forces, making aerodynamic drag a significant 

contributor to a Class 7 or 8 tractor’s GHG emissions and fuel consumption.36 Because 

aerodynamic drag varies by the square of the vehicle speed, small changes in the tractor 

aerodynamics can have significant impacts on GHG emissions and fuel efficiency of that vehicle. 

With much of their driving at highway speed, the benefits of reduced aerodynamic drag for Class 

7 or 8 tractors can be significant.37

The common measure of aerodynamic efficiency is the coefficient of drag (Cd). The 

aerodynamic drag force (i.e., the force the vehicle must overcome due to air) is a function of the 

Cd, the area presented to the wind (i.e., the projected area perpendicular to the direction of travel 

or frontal area), and the square of the vehicle speed. Cd values for today’s line-haul fleet 

typically range from greater than 0.80 for a classic body tractor to approximately 0.58 for 

tractors that incorporate a full package of widely, commercially available aerodynamic features 

on both the tractor and trailer. 

While designers of heavy-duty vehicles and aftermarket products try to aerodynamically 

streamline heavy-duty vehicles, there are some challenges. Aerodynamic design must meet 

practical and safety needs such as providing for physical access and visual inspections of vehicle 

equipment. Since weight added to the vehicle can impact its overall fuel efficiency, GHG 

emissions and, in limited cases, the amount of freight the vehicle can carry, aerodynamic design 

and devices must balance the aerodynamic benefit with the contribution to the vehicle weight. In 

addition, aerodynamic designs and devices must balance being as light and streamlined as 

possible with in-use application durability to withstand the rigors a working freight vehicle 

encounters while traveling or loading and unloading. 

However, there are some macro and micro scale techniques that can be employed to 

reduce vehicle drag such as reducing vehicle size, especially, the frontal area; smoothing the 

shape to make it more aerodynamically efficient, thus reducing the Cd; and/or re-directing air to 

prevent entry into areas of high drag (e.g., wheel wells) or to maintain smooth air flow in certain 

areas of the vehicle. Reducing the size of the vehicle can reduce the frontal area; which reduces 

the pressure building up on the lateral surface area exposed to the airflow. Improving the vehicle 

shape may include revising the fore components of the vehicle such as rearward canting/raking 

or smoothing/rounding the edges of the front end components (e.g., bumper, headlights, 

windshield, hood, cab, mirrors) or integrating the components at key interfaces (e.g., 
2-16 

windshield/glass to sheet metal) to alleviate fore vehicle drag. Finally, redirecting the air to 

prevent areas of low pressure and slow moving air; thus eliminating areas where air builds 

creating turbulent vortices and increasing drag. Techniques such as blocking gaps in the sheet 

metal, ducting of components, shaping or extending sheet metal to reduce flow separation and 

turbulence are methods being considered to direct air from areas of high drag (e.g., underbody, 

tractor-trailer gap, underbody and/or rear of trailer). 

The issue for heavy-duty vehicles is that the cab and/or passenger compartment is 

designed for a specific purpose such as accommodating an inline cylinder engine or allowing for 

clear visibility given the size of the vehicle. Consequently, a reduction in vehicle size and/or 

frontal area may not be realistic for some applications. This also may necessitate an expensive, 

ground-up vehicle redesign and, with a tractor model lifecycle of up to 10 years, may mean that a 

mid-cycle tractor design is not feasible. In addition, the frontal area is also defined by the shape 

behind the cab so reducing just the cab frontal area/size reduction may not be effective. Thus, 

this approach is something that may occur in a long-term timeframe of 10-15 years from today. 

Instead, most heavy-duty tractor manufacturers have explored, or are exploring, the latter 

two techniques in the short-term. Compared to previous generation tractors, every high roof 

tractor today has a roof fairing directing air over the top of the cab, fuel tank/chassis fairings that 

prevent side air from flowing underneath the vehicle, and cab side extenders that prevent flow 

from being trapped in the tractor-trailer gap. As a compliance strategy for HD Phase 1, many 

manufacturers refined the aerodynamic shape of their front end components and other 

components (e.g., curving or further extending side extenders) resulting in efficiency difference 

between pre- and post-HD Phase 1, model year tractor aerodynamic performance. Further, 

manufacturers have developed new tractor designs that are taking advantage of sealing gaps in 

sheet metal to redirect the flow and introducing some hard edges to induce turbulent flow on 

certain surfaces to prevent premature flow separation and downstream turbulent flow. For HD 

Phase 2, we anticipate manufacturers would continue to apply these techniques across their 

models and continue to explore refinements and re-designs in other areas of the tractor. 

In addition to tractor improvements, there has been growth in the market for trailer 

aerodynamic devices encouraged by our successful SmartWay Partnership and Technology 

Verification Program. These devices function similar to components on the tractor by preventing 

air intrusion into areas of the trailer prone to high aerodynamic drag including the tractor-trailer 

gap, the trailer underbody, and the rear of the trailer as shown in Figure 2-2 and Figure 2-3 

below. 
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Figure 2-2 Progression of total drag along a typical line-haul tractor-trailer vehicle 

Figure 2-3 Low pressure regions contributing to aerodynamic drag along a typical line-haul tractor-trailer 

vehicle

To address this, trailer front/nose devices are being used to round the front end and edges 

of the trailer while also reducing the tractor-trailer gap; skirts on the side of the trailer prevent air 

entering the underside of the trailer and becoming turbulent on the various underbody structure 

components; and trailer aft/rear treatments reduce separation of air flow of the rear edge of the 

trailer to reduce the large wake of turbulent air behind the trailer. Based on current SmartWay 

Technology Verification, these devices can reduce fuel consumption from one to nine percent, 

depending on the technology, and if it is employed individually or in combination. 

As a result, we believe there is an opportunity within HD Phase 2 to promote continual 

improvement of tractor aerodynamics and capitalize on the potential improvement that 

aerodynamic trailer devices can provide for trailers, and overall combination tractor-trailer 

efficiency. 
2-18 

2.4.2 Advanced Aerodynamic Concepts 

For HD Phase 2, we are proposing standards that would be fully phased in by the 2027 

model year. This represents a significant amount of time from today’s action. As such, it is 

possible that by the time the Phase 2 standards are implemented, the state of heavy-duty 

aerodynamic technology and performance may have significantly advanced. Thus, there may be 

a need to have standards to adequately address future tractor-trailer aerodynamic advances. 

Accordingly, we are considering aerodynamic concepts that can achieve aerodynamic 

performance beyond that of the HD Phase 2 aerodynamic standards. There are many approaches 

applicable to today’s tractors and trailers that are not considered in the HD Phase 2 aerodynamic 

standards and advanced research aimed at creating a completely new design paradigm for 

tractor-trailer combinations. 

The advanced aerodynamic standards would not be required but would rather serve as a 

marker for future aerodynamic concepts and/or as a metric for HD Phase 2 advanced/innovative 

aerodynamic technologies. 

2.4.2.1 Aerodynamic Improvements to Current Tractor-Trailer Combinations Based 

on Existing Technology 

2.4.2.1.1 Manufacturer Commercial Initiatives 

In order to anticipate technology advancement, it is important to benchmark current 

technology improvements based on today’s tractors and trailers. A number of Class 8 tractor 

OEM’s have incorporated the technologies requested by their customers to improve fuel 

economy and to meet the HD Phase 1 standards. These technologies include side skirts, boat 

tails and roof fairings as well as some driver monitoring tools. Recently Jack Roberts released 

an article on the internet titled: “Photo, video: Western Star introduces re-designed on-highway 

tractor.” 38

Figure 2-4 Pictures of the Western Star Class 8 Tractors 

In addition to providing photos and videos of Western Star’s redesigned on-highway 

tractor, the article describes a multitude of new features that define the new tractor. These 

features include: 
2-19 

 A new sweptback four piece bumper with an under bumper valance that contributes 

to aerodynamic efficiency. 

 New halogen headlights that are optimized for aerodynamic performance and 

excellent visibility. 

 A state-of-the-art visor specifically engineered to work with the impressive slope in 

the hood’s rear air ramp to direct airflow over the cab without an aerodynamic 

penalty. 

 Roof and cab fairings that sweep back for tighter trailer gap and help direct air flow 

over and around the trailer. 

 Optional chassis side fairings that reduce drag by up to 6 percent while still providing 

easy access to batteries and DEF tank. 

 The Western Star Twin Force dual air intake, which feeds a massive centrally 

mounted air filter to improve efficiency. 

This example demonstrates that manufacturers are continuing to find ways to improve 

tractors and are continually exploring concepts, such as those in used in the SuperTruck 

initiative, to improve commercially-available products. 

2.4.2.1.2 Supplier Research: SABIC Roof Fairing Technology and 

Manufacturing 

Developments in aerodynamics have long been assumed to yield advancements in vehicle 

fuel efficiency. SABIC Innovative Plastics US LLC (SABIC) evaluated a variety of injection 

moldable thermoplastic roof fairing designs for a heavy tractor day cab to quantify efficiencies 

that could be obtained through advanced aerodynamics. Computational Fluid Dynamic (CFD) 

modeling was performed by Exa Corporation, an industry recognized leader in CFD. Multiple 

designs exhibited significant reductions in drag compared to a baseline roof fairing (Figure 1 of 

Figure 2-5). The baseline represented a top performing roof fairing on the market today. The 

best performing SABIC concept (Figure 2) achieved a 5.8 percent reduction in drag and fuel use 

compared to the baseline. Under the well-established 2:1 relationship between delta drag and 

fuel use, the fuel efficiency improved by nearly 3 percent from the baseline design. 

The design concept optimized the shape to manage the airflow over the vehicle and 

enable reduced drag and increased fuel economy. Air channels – developed for injection 

molding processes – limit the air stagnation on the front of the trailer as well as accelerate and 

control the direction of the air flow. This innovative concept has been validated using state of 

the art CFD methods. On vehicle tests are suggested to validate findings from these studies. 

(from Matthew D. Marks, Senior Business Manager, Regulatory Automotive and Mass 

Transportation, November 14, 2014). 
2-20 

Figure 2-5 Surface X-Force (dimensionless) on Baseline and SABIC Concept Roof Fairing 

Aerodynamic (surface) force is the force exerted on a body whenever there is a relative 

velocity between the body and the air. These plots represent this force in the direction of the 

vehicle travel at highway speeds. Red indicates a ‘pushing’ of the vehicle rearward, while blue 

indicates a ‘pulling’ of the vehicle forward. 

Figure 2-6 SABIC Concept Roof Fairing Operation

We are currently coordinating with SABIC on future efforts to determine feasibility and 

capability of this concept on additional areas of the tractor (e.g., bumper, hood, fuel tank/chassis 

skirt fairings, cab side extenders). 

2.4.2.1.3 HD Phase 1 Research: External Active Grille Shutter Potential on 

Heavy-Duty Tractors 

During HD Phase 1 aerodynamic assessment, we looked at several trends to understand 

some of the aerodynamic trends such as removal of tractor chassis fairings and side extenders, 

different tractor-trailer gap widths, and different trailer leading edge radii. However, one trend 

of particular relevance to advanced aerodynamic improvements for current tractors is the case of 

open versus closed grille. 
2-21 

We evaluated the open vs. closed grille trend in the full and reduced scale wind tunnel. 

Below in Figure 3-7 is picture of a 1/8th scale tractor model in the reduced scale wind tunnel with 

the grille covered with aluminum tape to simulate a fully closed grille. 

Figure 2-7 Photo of 1/8th scale model of a tractor with the front, external grille 

covered with aluminum tape to simulate a closed grill configuration. 

Below in Table 2-1 and Table 2-2 are the results of our open versus closed grille 

evaluations in the full and reduced scale wind tunnel separately. The tables provide the deltas 

for an open grille CD minus the closed grille CD; where the CDs have been corrected for 

blockage, in the case of the full scale wind tunnel, and normalized for differences in measured 

frontal area between the full and reduced scale wind tunnels using a nominal frontal area of 10.4 

m2 (111.95 in2). For the full scale wind tunnel, only one tractor OEM was tested. In contrast, for 

the reduced scale wind tunnel, three tractor OEMs were tested. 

Table 2-1 Full Scale Wind Tunnel Results for Open verses Close Grille Configurations 

TRACTOR 

MODEL

DELTA WACD 

@55MPH

% DELTA CD VS. OPEN GRILLE 

CD

1 0.003 0.60%

Table 2-2 Reduced Scale Wind Tunnel Results for Open versus Close Grille Configurations 

TRACTOR 

MODEL

DELTA WACD 

@55MPH

% DELTA CD VS. OPEN GRILLE 

CD

A 0.010 1.69%

B 0.012 1.89%

C 0.009 1.45%

Based on the data in these tables, there is a potential wind-average drag improvement of 

0.6 percent to 1.45 percent from closing off the external, front grille of the tractor. This indicates 

the potential of active grille shutter systems on heavy-duty tractors. These systems are currently 

being applied on light duty vehicles behind the external grille to improve aerodynamics. 
2-22 

However, a recent SAE paper determined that the optimal position for active grille shutter 

systems was the external grille flush with the vehicle sheet metal.39 This technique could be 

implemented on the external grille designs for current-design, heavy-duty tractors as well. 

2.4.2.1.4 National Research Council of Canada Historical Research on 

Improving Heavy-Duty Tractors 

The National Research Council of Canada (NRC-Can) performed an assessment of the 

drag benefit or detriment of various tractor components40 and found the following in Table 2-3. 

Table 2-3 Reduced Scale Wind Tunnel Results for Open versus Close Grille Configurations 

COMPONENT DELTA 

WACD

OEM Side Mirrors -0.0156

OEM Fender Mirrors -0.0098

Wheel Covers (Tractor and Trailer) 0.0020

Tractor Drive Axle Wrap-Around Splash Guards 0.0049

Roof Fairing Rear-Edge Filler 0.0137

Based on this table, there is the potential to improve tractor aerodynamics by 206 counts 

(0.0206 WACD) with the addition of wheel covers, drive axle wrap around splash guards, and 

roof fairing rear edge filler, and up to 460 counts (0.0460) if the OEM side and fender mirrors 

are replaced with a camera system, as suggested by the study, and combined with the wheel 

covers, drive axle wrap around splash guards, and roof fairing rear edge filler. Therefore, 

considering the current wind-average drag performance of current heavy-duty tractors, this study 

demonstrates the possibility to improve tractors an additional ~1 percent with some simple 

changes. 

2.4.2.2 Aerodynamic Improvements to Current Tractor-Trailer Combinations Based 

on Complete Vehicle Redesign 

This section contains summaries of ongoing work from various DOE efforts as well as 

individual efforts such as Airflow Truck Company to develop improved aerodynamic Class 8 

vehicles. In addition to aerodynamics, there are other technologies such as driver awareness and 

ability to drive for maximum fuel economy with increased aerodynamics. Overall it is expected 

that the research being performed over the next year or two will reveal drastic improvements in 

CdA and fuel economy. DOE’s Lawrence Livermore National Laboratory is also looking at the 

aerodynamics of tankers.

2.4.2.2.1 Collaborative, Government-Industry Advanced Aerodynamic 

Research: SuperTruck Program 

DOE’s SuperTruck project is one of several initiatives that are part of the 21st Century Truck 

Partnership. The partnership is a public-private initiative to stimulate innovation in the trucking 

industry through sponsorship from government agencies, companies, national laboratories and 

universities. DOE’s Vehicle Technologies Program provided matching funds to the program. 
2-23 

Cummins, Peterbilt and their program partners invested $38.8 million in private funds for the 

first four years of the project with additional funding provided for future research beyond 2014. 

The goal of the SuperTruck Initiative was to develop a tractor that could meet or exceed 

10 mpg – where tractors at this point are averaging between 5.5 and 6.5 mpg. Advances in 

engines, aerodynamics and more helped the tractor project increase its fuel economy. The 

SuperTruck objectives included development and demonstration of a highly efficient and clean 

diesel engine, an advanced waste heat recovery system, an aerodynamic tractor and trailer 

combination and a lithium ion battery auxiliary power unit, to reduce engine idling. Eaton Corp, 

also part of the Cummins-Peterbilt SuperTruck project team, contributed technologies including 

the design, development and prototyping of an advanced automated transmission that facilitated 

reduced engine-operating speeds. Cummins and Eaton jointly designed shift schedules and other 

features to yield further improved fuel efficiency. 

Details of the SuperTruck that achieved 10.7 mpg are in video on the todaystrucking.com 

website and are presented in four videos.41 Aerodynamic features of the tractor include the 

following: airflow into the engine compartment (through the front bumper, through the radiator 

and under the vehicle), less clearance between the road and the bottom of the tractor (rubber skirt 

under steps), close gaps on tractor/trailer (between hood and bumper, etc.), minimized gap 

between the trailer and tractor with a ball and socket design, full trailer skirt, roof fairing, smaller 

mirrors, minimized gap between wheels and wheel wells, wheel covers, boat tail, air foil on rear 

bumper design, single wide tires, and perforated mud flaps that allow air to bypass through them 

and reduce drag. A picture of this truck based on a Peterbilt tractor is shown in Figure 2-7 

below. 

Even with the addition of these aerodynamic features, overall the tractor mass was 

reduced by over 1,300 lbs. The article states that the CFD analysis of the tractor showed a 50 

percent reduction in drag and with a 2:1 drag reduction the aero improvements resulted in a 25 

percent improvement in fuel economy. In the 300 mile test course shown on the video, it was 

stated that the tractor achieved 10.7-11.1 mpg. 

Figure 2-7 Peterbilt SuperTruck Concept (Picture from: http://www.peterbilt.com/about/media/2014/396/) 

This effort represents the first step in the evolution of improving the aerodynamic 

efficiency of tractor-trailer by radically redesigning today’s tractor-trailer combination, as a 

wholly integrated system rather each component, tractor and trailer, independently. 
2-24 

2.4.2.2.2 Government Sponsored Advanced Aerodynamic Research: 

Lawrence Livermore National Laboratory 

Lawrence Livermore National Laboratory’s (LLNL) Kambiz Salari presented 

information at the 2014 DOE Annual Merit Review on “DOE’s Effort to Improve Heavy Duty 

Vehicle Fuel Efficiency through Improved Aerodynamics”. A joint project with Wabash, 

Navistar, Michelin, Safeway, Frito Lay, Praxair, Freight Wing Inc, ATDynamics, Kentucky 

Trailer and Spirit with funding for work in 2013 and 2014, the objective was to develop a new 

integrated tractor-trailer design from ground up by: first, designing the first generation of an 

integrated tractor-trailer geometry called Generic Speed Form one (GSF1); and second 

performing wind tunnel tests of selected aero devices for tractor-trailers and tankers to improve 

fuel efficiency. The goal was to reduce aerodynamic drag of Class 8 tractor-trailers by 

approximately 25 percent leading to a 10-15 percent increase in fuel efficiency at 65 mph. In 

addition, the group developed an aerodynamic tractor-trailer prototype designed to achieve 50 

percent reduced aerodynamic drag as shown in Figure 2-8. This effort represents the next 

generation of tractors and trailers: a completely redesigned, fully integrated, optimized shape for 

the tractor-trailer combination. 

Figure 2-8 Pictures showing future heavy-duty tractor trailer concept to achieve >50% aerodynamic 

improvement for Class 8 line haul heavy-duty vehicles 

2.4.2.2.3 Independent Advanced Aerodynamic Research: Airflow Truck 

Company Bullet Truck Concept 

In addition to the work being performed by the OEMs and consortiums mentioned above, 

there are also independent commercial initiatives underway to radically redesign the tractortrailer combination similar to the concept by Lawrence Livermore National Laboratories 

discussed above. 

The Class 8 tractor and trailer modifications in Figure 2-9 were designed, built, and tested 

in 2012 by Mr. Robert Sliwa of the Airflow Truck Company. Mr. Sliwa built his first 

aerodynamic tractor in the 1983 when he was an owner-operator. After that, Mr. Sliwa became 
2-25 

interested in computers and used his computer background along with his truck driver and race 

car driver experience to create the Bullet Truck. His current design is described at 

www.airflowtruck.com and his tractor design modifications are similar in appearance to the 

bullet looking trains used in Europe. The tractor uses a 1999 engine and the test was conducted 

in the manner in which the tractor was driven at 55 mph by an experienced driver throughout its 

test while loaded at 65,000 lbs from Newington, Connecticut to Tracy, California. 

The website shows that the vehicle achieved 13.4 mpg during this trip and included 

traveling through the Rocky Mountains. CFD analyses of the design after the vehicle was built 

found a modest decrease in CdA, thus giving credence to the design work under the hood (most 

of which are outlined at airflowtruck.com) and driving techniques. Several new technologies 

were developed during this work which included retractable tractor steps, all electric air 

conditioning, crankshaft mounted cooling fan, computer-controlled fan hub, waterless engine 

coolant, reduced engine parasitic losses, full tractor and trailer side skirts, 4 axle ATIS, and an 

engine feedback information display. 

Figure 2-9 Figure of the Bullet Truck by Airflow Truck Company9

Another prototype is in development and it will include further aerodynamic optimization 

of the tractor and trailer, and the combination thereof, as well as the addition of a boat tail. 

These technologies and more are expected to result in higher mpg under similar test conditions. 

Other changes in the vehicle makeup and test would include a more modern engine and testing at 

higher speeds (>55 mpg (60-65mpg)) may influence the end results. 
2-26 

Figure 2-10 Figure of a Highly Aerodynamic Concept Class 8 Tractor

Although it is difficult to separate the aerodynamic factors from the engine and 

operational factors that lead to the claimed 13.4 mpg, the designs being explored and built by 

Mr. Sliwa are indicative of the type of tractor-trailer combinations we anticipate will be built in 

the future. 

2.4.3 Tires 

2.4.3.1 Improved Rolling Resistance 

Research indicates that a tire’s contribution to overall vehicle fuel efficiency is 

approximately proportional to the vehicle weight on it.42 Energy loss associated with tires is 

mainly due to deformation of the tires under the load of the vehicle, known as hysteresis, but 

smaller losses result from aerodynamic drag, and other friction forces between the tire and road 

surface and the tire and wheel rim. Collectively the forces that result in energy loss from the 

tires are referred to as rolling resistance. Tires with higher rolling resistance lose more energy, 

thus using more fuel and producing more CO2 emissions in operation, while tires with lower 

rolling resistance lose less energy, and use less fuel and produce less CO2 emissions in operation. 

A tire’s rolling resistance is a factor considered in the design of the tire, and is affected by 

the tread and casing compound materials, the architecture of the casing, tread design, and the tire 

manufacturing process. It is estimated that 35 to 50 percent of a tire’s rolling resistance is from 

the tread and the other 50 to 65 percent is from the casing.42 Tire inflation can also impact 

rolling resistance in that under-inflated tires can result in increased deformation and contact with 

the road surface. In addition to the effect on CO2 emissions and fuel consumption, these design 

and use characteristics of tires also influence durability, traction (both wet and dry grip), vehicle 

handling, ride comfort, and noise. Tires that have higher rolling resistance are likely designed to 

address one or more of these other tire attributes. 

EPA’s SmartWay program identified test methods and established criteria to designate 

certain tires as having lower rolling resistance (LRR) for use in the program’s emissions tracking 

system, verification program, and SmartWay vehicle specifications. To measure a tire’s 

efficiency, the vertical load supported by the tire must be considered, because rolling resistance 

is a function of the load on a tire. EPA uses a tire’s rolling resistance coefficient (CRR) to 

characterize LRR tires. CRR is measured using the ISO 28850 test method (see 40 CFR 

1037.520(c),) and reported as the rolling resistance force over vertical load (kg/metric ton). 

Differences in rolling resistance of up to 50 percent have been identified for tires designed to 

equip the same vehicle.43 
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LRR tires are commercially available from most tire manufacturers and can be applied to 

vehicles in all MD/HD classes. According to an energy audit conducted by Argonne National 

Lab, tires were shown to be the second largest contributor to energy losses for a Class 6 delivery 

truck at 50 percent load and speeds up to 35 mph (a typical average speed of urban delivery 

vehicles).44 For Class 8 tractor-trailers, the share of vehicle energy required to overcome rolling 

resistance is estimated at nearly 13 percent.45 

NHTSA, EPA, and ARB met with stakeholders from the tire industry (Bridgestone, 

Continental, Cooper, Goodyear, and Michelin) in 2014 to discuss the next generation of LRR 

tires for the Phase 2 timeframe for all segments of Class 2b-8 vehicles, including trailers. 

Manufacturers discussed forecasts for rolling resistance levels and production availability in the 

Phase 2 timeframe, as well as their plans for improving rolling resistance performance while 

maintaining other performance parameters such as traction, handling, wear, mass reduction, 

retreadability, and structural durability. 

The meetings included specific discussions of the impacts of the current generation of 

LRR tires on vehicle stopping distance and handling. Manufacturers indicated no known safety 

disbenefit in the current on-road fleet from use of LRR tires. While the next generation of tires 

may require some tradeoffs in wear performance and costs over the next 10 years to achieve 

better tire rolling resistance performance, manufacturers said they will not trade off safety for 

performance. They also emphasized that keeping tires inflated (through proper maintenance or 

automatic systems) was the best way to assure long term fuel efficiency and safety during 

vehicle operation. 

2.4.3.2 Wide Base Singles 

Low rolling resistance tires can be offered for dual assembly tires and as wide base 

singles (WBS). Wide base singles are primarily intended for combination tractor-trailers, but 

some vocational vehicles are able to accommodate them. In the early years of this technology, 

some states and local governments restricted use of WBS, but many of these restrictions have 

since been lifted. As of December 2010, NACFE reports that there is virtual acceptance in North 

America with only a few provinces in Canada that disallow or require special permitting for the use 

of wide base tires.

46 A wide base single is a larger tire with a lower profile. The common wide 

base single sizes include 385/65R22.5, 425/65R22.5, 445/65R22.5, 435/50R22.5 and 

445/50R22.5. Generally, a wide base single tire has less sidewall flexing compared to a dual 

assembly and therefore less hysteresis occurs. Compared to a dual tire assembly, wide base 

singles also produce less aerodynamic resistance or drag. Wide base singles can contribute to 

improving a vehicle’s fuel efficiency through design as a low rolling resistance tire and/or 

through vehicle weight reduction. 

According to one study, the use of fuel efficient wide base singles can reduce rolling 

resistance by 3.7 to 4.9 percent compared to the most equivalent dual tire.47 An EPA study with 

a tractor-trailer demonstrated an improvement in fuel consumption of 6 percent at 55 mph on the 

highway, 13 percent at 65 mph on the highway and 10 percent on a suburban loop48 using wide 

base singles on the drive and trailer axles. EPA attributed the fuel consumption improvement to 

the reduction in rolling resistance and vehicle weight reduction from using wide base singles. In 

2008 the Department of Energy (DOE) compared the effect of different combinations of tires on 
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the fuel efficiency of Class 8 tractors. The data collected based on field testing indicates that 

tractors equipped with wide base singles on the drive axle experience better fuel efficiency than 

tractors equipped with dual tires, independent of the type of tire on the trailer.49 This study in 

particular indicated a 6.2 percent improvement in fuel efficiency from wide base singles. 

There is also a weight savings associated with wide base singles compared to dual tires. 

Wide base singles can reduce a tractor and trailer’s weight by as much as 1,000 lbs. when 

combined with aluminum wheels. Bulk haulers of gasoline and other liquids recognize the 

immediate advantage in carrying capacity provided by the reduction in the weight of tires and 

have led the transportation industry in retrofitting their tractors and trailers50. 

New generation wide base singles, which were first introduced in 2000, are designed to 

replace a set of dual tires on the drive and/or trailer positions. They are designed to be 

interchangeable with the dual tires without any change to the vehicle51. If the vehicle does not 

have hub-piloted wheels, there may be a need to retrofit axle components.50, 52 In addition to 

consideration of hub / bearing / axle, other axle-end components may be affected by use of wide 

base singles. To assure successful operation, suitable components should be fitted as 

recommended by the vehicle manufacturer.53

Current wide base singles are wider than earlier models and legal in all 50 states for a 5-

axle, 80,000 GVWR truck47. Wide base singles meet the “inch-width” requirements nationwide, 

but are restricted in certain states up to 17,500 lbs. on a single axle at 500 lbs/inch width limit, 

and are not allowed on single axle positions on certain double and triple combination vehicles51. 

An inch-width law regulates the maximum load that a tire can carry as a function of the tire 

width. Typically wide base singles are optimized for highway operation and not for city or 

on/off highway operation. However, newer wide base singles are being designed for better scrub 

resistance, which would allow an expansion of their use. The current market share of wide base 

singles in combination tractor applications is 5 percent and the potential market is all 

combination tractors.47 New generation wide base singles represent an estimated 0.5 percent of 

the 17.5 million tires sold each year in the U.S.51 

2.4.3.3 Tire Pressure Systems 

Proper tire inflation is critical to maintaining proper stress distribution in the tire, which 

reduces heat loss and rolling resistance. Tires with reduced inflation pressure exhibit more 

sidewall flexing and tread shearing, resulting in greater rolling resistance than a tire operating at 

its optimal inflation pressure. Bridgestone tested the effect of inflation pressure and found a 2 

percent variation in fuel consumption over a 40 psi range.42 Tractor-trailers operating with all 

tires under-inflated by 10 psi have been shown to increase fuel consumed by up to 1 percent.54 

Tires can gradually lose pressure from small punctures, leaky valves or simply diffusion through 

the tire casing. Changes in ambient temperature can also have an effect on tire pressure. Trailers 

that remain unused for long periods of time between hauls may experience any of these 

conditions. To achieve the intended fuel efficiency benefits of low rolling resistance tires, it is 

critical that tires are maintained at the proper inflation pressure. 

Although most truck fleets understand the importance of keeping tires properly inflated, 

it is likely that a substantial proportion of trucks on the road have one or more underinflated tires. 
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An industry survey conducted in 2002 at two truck stops found that fewer than half of the tires 

checked were within 5 pounds per square inch (psi) of their recommended inflation pressure. 

Twenty-two percent of the vehicles checked had at least one tire underinflated by at least 20 psi, 

and 4 percent of the vehicles were running with at least one flat tire, defined as a tire 

underinflated by 50 psi or more. The survey also found mismatches in tire pressure exceeding 5 

percent for dual tires on axle ends.55

A commercial vehicle tire condition study conducted by the Federal Motor Carrier Safety 

Administration (FMCSA) in 2003 found similar indicators of poor tire inflation pressure 

maintenance in commercial fleets. The FMCSA concluded that only 44 percent of all tires on 

commercial vehicles were inflated within 5 psi of the recommended pressure, while over 7 

percent of all tires in operation on commercial vehicles were underinflated by at least 20 psi. It 

was also determined that the rates of tires used in dual assemblies that differed in pressure by 

more than 5 psi was approximately 20 percent for tractor duals and 25 percent for trailer duals. 

Finally, the FMCSA concluded that there were significant differences in tire inflation 

maintenance practices between private and for-hire fleets, smaller and larger fleets, and local bus 

and motor coach fleets.56

If drivers or fleets are not diligent about checking and attending to under-inflated tires, 

the trailer may have much higher rolling resistance and much higher CO2 emissions and fuel 

consumption. Proper tire inflation pressure can be maintained with a rigorous tire inspection and 

maintenance program and EPA provides information on proper tire inflation pressure through its 

SmartWay program.57 Tire pressure monitoring (TPM) and automatic tire inflation (ATI) 

systems are designed to address under-inflated tires. Both systems alert drivers if a tire’s 

pressure drops below its set point. TPM systems monitor the tires and require user-interaction to 

reinflate to the appropriate pressure. Yet unless the vehicle experiences a catastrophic tire 

failure, simply alerting the driver that a tire’s pressure is low may not necessarily result in action 

to correct the problem. A driver may continue driving to their final destination before addressing 

the tires, resulting in many miles of driving with improperly inflated tires. Current ATI systems 

take advantage of trailers’ air brake systems to supply air back into the tires (continuously or on 

demand) until a selected pressure is achieved. In the event of a slow leak, ATI systems have the 

added benefit of maintaining enough pressure to allow the driver to get to a safe stopping area.58 

The agencies believe TPM systems cannot sufficiently guarantee the proper inflation of tires due 

to the inherent user-interaction required. Therefore, ATI systems are the only pressure systems 

the agencies are proposing to recognize in GEM. 

Estimates of the benefits of ATI systems vary depending on the base level of 

maintenance already performed by the driver or fleet, as well as the number of miles the trailer 

travels. Vehicles that are well maintained or that travel fewer miles would experience less 

benefits from ATI systems compared to vehicles that log many miles or have a history of driving 

with poorly inflated tires. The agencies believe ATI systems can provide a CO2 and fuel 

consumption benefit to most tractors and trailers. Drivers and fleets that diligently maintain their 

tires will spend less time and money to inspect each tire knowing that they are properly inflated. 

Vehicles that have lower annual VMT due to long periods between uses would be less 

susceptible to low tire pressures when they resume activity. Vehicles with high annual VMT 

would experience the fuel savings associated with consistent tire pressures. 
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2.4.3.4 Retreaded Tires 

The tread life of a tire is a measure of durability and some tires are designed specifically 

for greater durability. Commercial vehicle tires are designed to be retreaded, a process in which 

a new tread is bonded to the tire casing. The original tread of a tire will last anywhere from 

100,000 miles to over 300,000 miles, depending on vehicle operation, original tread depth, tire 

axle position, and proper tire maintenance. Retreading can extend the tire’s useful life by 

100,000 miles or more.59 In 2005, the Tire Industry Association estimated that approximately 

17.6 million retreaded truck tires were sold in North America60. 

All of the top commercial vehicle tire manufacturers are involved in tire retread 

manufacturing. Bridgestone Bandag Tire Solutions accounts for 42 percent of the domestic 

retreaded vehicle tire market with its Bandag retread products; Goodyear Tire and Rubber 

Company accounts for 28 percent, mostly through its Wingfoot Commercial Tire Systems; 

Michelin Retread Technologies Incorporated, with Megamile, Oliver, and Michelin retread 

products, accounts for 23 percent. Other tire companies like Continental and independent retread 

suppliers like Marangoni Tread North America (which also produces the Continental 

“ContiTread” retread product) make up the remaining 7 percent.61 The retreading industry itself 

consists of hundreds of retreaders who sell and service retreaded tires, often (but not always) 

using machinery and practices identified with one of the major retread producers. There are 

about 800 retread plants in North America.62 The top 100 retreaders in the U.S. retread 47,473 

truck tires per day. 

To maintain the quality of the casing and increase the likelihood of retreading, a tire 

should be retreaded before the tread depth is reduced to its legal limit. At any time, steer tires 

must have a tread depth of at least 4/32 of an inch and other tires, including drive tires and trailer 

tires, must have a tread depth of at least 2/32 of an inch (49 CFR § 393.75). Trucking fleets 

often retread tires before tire treads reach this minimum depth in order to preserve the integrity 

of the tire casing for retreading. If the casing remains in good condition, a truck tire can be 

safely retreaded multiple times. Heavy truck tires in line haul operation can be retread 2 to 3 

times and medium-duty truck tires in urban use can be retread 5 or more times.63 To 

accommodate this practice, many commercial vehicle tire manufacturers warranty their casings 

for up to five years, excluding damage from road hazards or improper maintenance. 

To protect the casing, a steer tire is generally retreaded once the tread is worn down to 

6/32 of an inch and a drive tire is retreaded once the tread is worn down to 8/32 of an inch.64 

Tires used on Class 8 vehicles are retreaded as many as three times. 

Both the casing and the tread contribute to a tire’s rolling resistance. It is estimated that 

35 to 50 percent of a tire’s rolling resistance is the result of the tread. Differences in drive tire 

rolling resistance of up to 50 percent for the same casing with various tread compounds have 

been demonstrated. For example, a fuel efficient tread (as defined by the manufacturer) was 

added to two different casings resulting in an average increase in rolling resistance of 48 percent. 

When a nonfuel efficient tread (also defined by the manufacturer) was added to the same casings, 

the rolling resistance increased by 125 percent on average. This characterizes the effect of the 

tread on the rolling resistance of a tire. 
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Because tires can be retreaded multiple times, changes in the casing due to wear, damage 

and material aging may impact rolling resistance to a greater degree than would occur in an 

original tire. Additionally, as evidenced above, if a tread compound different than the original 

tread is used, a retreaded tire can have higher or lower rolling resistance than the original tire. 

Since the agencies have no way of knowing whether the rolling resistance of retreaded tires will 

be higher or lower than the rolling resistance of the original tires, we similarly have no way of 

knowing whether low rolling resistance tire benefits will continue to accrue for a vehicle’s entire 

lifetime. 

2.4.4 Transmissions 

Transmissions are a significant vehicle component. They are part of the drive train, 

which also includes axles and tires. Ways to improve transmissions include shift strategy, gear 

efficiency, gear ratios, etc. The relative importance of having an efficient transmission increases 

when vehicles operate in conditions with a higher shift density. Each shift represents an 

opportunity to lose speed or power that would have to be regained after the shift is completed. 

Further, each shift engages gears that have their own inherent inefficiencies. 

Optimization of vehicle gearing to engine performance through selection of transmission 

gear ratios, final drive gear ratios and tire size can play a significant role in reducing fuel 

consumption and GHGs. Optimization of gear selection versus vehicle and engine speed 

accomplished through driver training or automated transmission gear selection can provide 

additional reductions. The 2010 NAS report found that the opportunities to reduce fuel 

consumption in heavy-duty vehicles due to transmission and driveline technologies in the 2015 

time frame ranged between 2 and 8 percent.65 

The design goal is for the transmission to deliver the needed power to the vehicle while 

maintaining engine operation within the engine’s “sweet spot” for most efficient operation. 

Truck and chassis manufacturers today offer a wide range of tire sizes, final gear ratios and 

transmission choices so that owners can work with application engineers to specify an optimal 

combination given the intended vehicle service class and other performance needs. 

2.4.4.1 Optimizing Number of Gears and Gear Ratios 

Manufacturers can choose to replace 6-speed transmissions with 8-speed or more 

automatic transmissions. Additional ratios allow for further optimization of engine operation 

over a wider range of conditions, but this is subject to diminishing returns as the number of 

speeds increases. As additional planetary gear sets are added (which may be necessary in some 

cases to achieve the higher number of ratios), additional weight and friction are introduced. 

Also, the additional shifting of such a transmission can be perceived as bothersome to some 

consumers, so manufacturers need to develop strategies for smooth shifts. 

The Phase 1 rulemaking projected that 8-speed transmissions could incrementally reduce 

fuel consumption by 1 to 3 percent from a baseline 6-speed automatic transmission over some 

test cycles. The SwRI report uses 2 to 3 percent fuel consumption reduction when replacing 6-

speed baseline automatic transmissions with improved 8-speed automatic transmissions. 
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2.4.4.2 Gear Efficiencies 

As described elsewhere for axles and engines, the efficiency of gears can be improved by 

reducing friction and minimizing mechanical losses. This can be done by reducing the friction 

between the two gears in contact. This friction is reduced mainly by improving the surface finish 

of the gears. The other way of doing is by reducing the amount of distance the gear faces are 

sliding against each other. 

2.4.4.3 Shift Strategies 

Calibrating the transmission shift schedule to upshift earlier and quicker, and to lock up 

or partially lock up the torque converter under a broader range of operating conditions can 

reduce fuel consumption and CO2 emissions. However, this operation can result in a perceptible 

degradation in noise, vibration, and harshness. The degree to which NVH can be degraded 

before it becomes noticeable to the driver is strongly influenced by characteristics of the vehicle, 

and although it is somewhat subjective, it always places a limit on how much fuel consumption 

can be improved by transmission control changes. 

During operation, an automatic transmission’s controller manages the operation of the 

transmission by scheduling the upshift or downshift, and locking or allowing the torque 

converter to slip based on a preprogrammed shift schedule. The shift schedule contains a 

number of lookup table functions, which define the shift points and torque converter lockup 

based on vehicle speed and throttle position, and other parameters such as temperature. 

Aggressive shift logic can be employed in such a way as to maximize fuel efficiency by 

modifying the shift schedule to upshift earlier and inhibit downshifts under some conditions, 

which reduces engine pumping losses and engine friction. The application of this technology 

does require a manufacturer to confirm that drivability, durability, and NVH are not significantly 

degraded. 

A torque converter is a fluid coupling located between the engine and transmission in 

vehicles with automatic transmissions and continuously-variable transmissions (CVT). This 

fluid coupling allows for slip so the engine can run while the vehicle is idling in gear (as at a stop 

light), provides for smoothness of the powertrain, and also provides for torque multiplication 

during acceleration, and especially launch. During light acceleration and cruising, the inherent 

slip in a torque converter causes increased fuel consumption, so modern automatic transmissions 

utilize a clutch in the torque converter to lock it and prevent this slippage. Fuel consumption can 

be further reduced by locking up the torque converter at lower vehicle speeds, provided there is 

sufficient power to propel the vehicle, and noise and vibration are not excessive. If the torque 

converter cannot be fully locked up for maximum efficiency, a partial lockup strategy can be 

employed to reduce slippage. Early torque converter lockup is applicable to all vehicle types 

with automatic transmissions. Some torque converters would require upgraded clutch materials 

to withstand additional loading and the slipping conditions during partial lock-up. As with 

aggressive shift logic, confirmation of acceptable drivability, performance, durability and NVH 

characteristics would be required to successfully implement this technology. 
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2.4.4.4 Architectures 

The manual transmission architecture has traditionally been considered the most efficient 

architecture since it did not experience the losses inherent in a torque converter required on a 

traditional automatic transmission (a traditional automatic transmission being a transmission with 

fully automated shifting and using a hydraulic lock-up torque converter for smooth vehicle 

launching from a stop). However, this traditional understanding has been called into question as 

advances in electronics and computer processing power allow for more efficiency from a manual 

transmission architecture with fully automated shifting. The two primary manual transmission 

architectures employing automated shifting are the automated manual transmission (AMT) and 

the dual-clutch transmission (DCT). When implemented well, these mechanically more efficient 

designs could inherently provide better fuel efficiency and lower greenhouse gas emissions than 

conventional torque converter automatic transmission designs and, potentially, even fully manual 

transmissions. These transmissions offer the inherently lower losses of a manual transmission 

with the efficiency and shift quality advantages of electronic controls. The lower losses stem 

from the elimination of the conventional lock-up torque converter, and a greatly reduced need for 

high pressure hydraulic circuits to hold clutches to maintain gear ratios (in automatic 

transmissions). 

2.4.4.4.1 AMT 

An AMT is mechanically similar to a conventional manual transmission, but shifting and 

launch functions are automatically controlled by electronics. The term AMT generally refers to 

a single clutch design (differentiating it from a dual-clutch transmission, or dual-clutch AMT, 

described below) which is essentially a manual transmission with automated clutch and shifting. 

Because of shift quality issues with single-clutch designs, dual-clutch designs are more common 

in light-duty applications where driver acceptance is of primary importance. In the HD sector, 

shift quality remains important but is less so when compared to light-duty. As a result, the 

single-clutch AMT architecture can be an attractive technology for HD vehicles. 

2.4.4.4.2 DCT 

A DCT uses separate clutches (and separate gear shafts) for the even-numbered and the 

odd-numbered gears. In this way, the next expected gear is pre-selected thereby allowing for 

faster and smoother shifting. For example, in a 6 speed DCT, if the vehicle is accelerating in 

third gear, the shaft with gears one, three and five has gear three engaged and is transmitting 

power to the wheels. The shaft with gears two, four, and six is idle but has gear four engaged. 

When a shift is required, the controller disengages the odd-gear clutch while simultaneously 

engaging the even-gear clutch, thus making a smooth shift. If, on the other hand, the driver 

slows the vehicle instead of continuing to accelerate, the transmission would have to change to 

second gear on the idling shaft to anticipate a downshift. This shift can be made quickly on the 

idling shaft since there is no torque being transferred on it. 

There are variations of the DCT design, with some having wet clutches and some dry 

clutches, and more recent versions that incorporate a torque converter similar to but smaller than 

the torque converter of a traditional automatic transmission. The wet clutch designs offer a 

higher torque capacity that comes from the use of a hydraulic system that cools the clutches. Wet 
2-34 

clutch systems are also less efficient than dry clutch systems due to the losses associated with the 

hydraulic pumping. They also are more costly due to the hydraulics. 

2.4.4.5 Hybrid Powertrain Systems 

The industry is currently developing many variations of hybrid powertrain systems. The 

hybrids developed to date have seen fuel consumption and CO2 emissions reductions between 20 

and 50 percent in the field. However, there are still some key issues that are restricting the 

penetration of hybrids, including overall system cost, battery technology, and lack of costeffective electrified accessories. 

A hybrid vehicle is a vehicle that combines two significant sources of propulsion energy, 

where one uses a consumable fuel (like diesel), and one is rechargeable (during operation, or by 

another energy source). Hybrid technology is well established in the U.S. light-duty market, 

some manufacturers have been producing heavy-duty hybrid models for many years, and others 

are looking to develop hybrid models in future years. 

Hybrids reduce fuel consumption through three major mechanisms: 

• The internal combustion engine can be optimized (through downsizing, modifying 

the operating cycle, or other control techniques) to operate at or near its most efficient point 

more of the time. Power loss from engine downsizing can be mitigated by employing power 

assist from the secondary power source. 

• Some of the energy normally lost as heat while braking can be captured and 

stored in the energy storage system for later use. 

• The engine is turned off when it is not needed, such as when the vehicle is 

coasting or when stopped. 

Hybrid vehicles utilize some combination of the three above mechanisms to reduce fuel 

consumption and CO2 emissions. The effectiveness of fuel consumption and CO2 reduction 

depends on the utilization of the above mechanisms and how aggressively they are pursued. One 

area where this variation is particularly prevalent is in the choice of engine size and its effect on 

balancing fuel economy and performance. Some manufacturers choose not to downsize the 

engine when applying hybrid technologies. In these cases, performance is vastly improved, 

while fuel efficiency improves significantly less than if the engine were downsized to maintain 

the same performance as the conventional version. The non-downsizing approach is used for 

vehicles where towing and/or hauling are an integral part of their performance requirements. In 

these cases, if the engine is downsized, the battery can be quickly drained during a long hill 

climb with a heavy load, leaving only a downsized engine to carry the entire load. Because 

towing capability is currently a heavily-marketed HD pickup truck attribute, manufacturers are 

hesitant to offer a truck with a downsized engine that can lead to a significantly diminished 

towing performance when the battery state of charge level is low, and therefore engines are 

traditionally not downsized for these vehicles. 
2-35 

Strong hybrid technology utilizes an axial electric motor connected to the transmission 

input shaft and connected to the engine crankshaft through a clutch. The axial motor is a 

motor/generator that can provide sufficient torque for launch assist, all electric operation, and the 

ability to recover significant levels of braking energy. 

A hybrid drive unit is complex and consists of discrete components such as the electric 

traction motor, transmission, generator, inverter, controller and cooling devices. Certain types of 

drive units may work better than others for specific vehicle applications or performance 

requirements. Several types of motors and generators have been developed for hybrid-electric 

drive systems, many of which merit further evaluation and development on specific applications. 

Series HEVs typically have larger motors with higher power ratings because the motor alone 

propels the vehicle, which may be applicable to Class 3-5 applications. In parallel hybrids, the 

power plant and the motor combine to propel the vehicle. Motor and engine torque are usually 

blended through couplings, planetary gear sets and clutch/brake units. The same mechanical 

components that make parallel heavy-duty hybrid drive units possible can be designed into series 

hybrid drive units to decrease the size of the electric motor(s) and power electronics. 

An electrical energy storage system is needed to capture energy from the generator, to 

store energy captured during vehicle braking events, and to return energy when the driver 

demands power. This technology has seen a tremendous amount of improvement over the last 

decade and recent years. Advanced battery technologies and other types of energy storage are 

emerging to give the vehicle its needed performance and efficiency gains while still providing a 

product with long life. The focus on the more promising energy storage technologies such as 

nickel metal-hydride (NiMH) and lithium technology batteries along with ultra-capacitors for the 

heavy-duty fleet should yield interesting results after further research and applications in the 

light-duty fleet. 

Heavy-duty hybrid vehicles also use regenerative braking for improved fuel economy, 

emissions, brake heat, and wear. A conventional heavy vehicle relies on friction brakes at the 

wheels, sometimes combined with an optional engine retarder or driveline retarder to reduce 

vehicle speed. During normal braking, the vehicle’s kinetic energy is wasted when it is 

converted to heat by the friction brakes. The conventional brake configuration has large 

components, heavy brake heat sinks, and high temperatures at the wheels during braking, audible 

brake squeal, and consumable components requiring maintenance and replacement. Hybrid 

electric systems recover some of the vehicle’s kinetic energy through regenerative braking, 

where kinetic energy is captured and directed to the energy storage system. The remaining 

kinetic energy is dissipated through conventional wheel brakes or in a driveline or transmission 

retarder. Regenerative braking in a hybrid electric vehicle can require integration with the 

vehicle’s foundation (friction) braking system to maximize performance and safety. 

Today’s systems function by simultaneously using the regenerative features and the 

friction braking system, allowing only some of the kinetic energy to be saved for later use. 

Optimizing the integration of the regenerative braking system with the foundation brakes would 

increase the benefits and is a focus for continued work. This type of hybrid regenerative braking 

system improves fuel economy, GHG emissions, brake heat, and wear. 
2-36 

In a hydraulic hybrid system, deceleration energy is taken from the drivetrain by an inline 

hydraulic pump/motor unit by pumping hydraulic fluid into high pressure cylinders. The fluid, 

while not compressible, pushes against a membrane in the cylinder that compresses an inert gas 

to 5,000 PSI or more when fully charged. Upon acceleration, the energy stored in the 

pressurized tank pushes hydraulic fluid back into the drivetrain pump/motor unit, allowing it to 

motor into the drivetrain and assist the vehicle’s engine with the acceleration event. This heavyduty vehicle hybrid approach has been demonstrated successfully, producing good results on a 

number of commercial and military trucks. 

Despite the significant future potential for hybrids discussed above, there are no simple 

solutions applicable for each heavy-duty hybrid application due to the large vocational vehicle 

fleet variation. A choice must be made relative to the requirements and priorities for the 

application. Challenges in motor subsystems such as gear reductions and cooling systems must 

be considered when comparing the specific power, power density, and cost of the motor 

assemblies. High speed motors can significantly reduce weight and size, but they require speed 

reduction gear sets that can offset some of the weight savings, reduce reliability and add cost and 

complexity. Air-cooled motors are simpler and generally less expensive than liquid cooled 

motors, but they are larger and heavier, and they require access to ambient air, which can carry 

dirt, water, and other contaminants. Liquid-cooled motors are generally smaller and lighter for a 

given power rating, but they may require more complex cooling systems that can be avoided 

with air-cooled versions. Various coolant options, including water, water-glycol, and oil, are 

available for liquid-cooled motors but must be further researched for long term durability. 

Electric motors, power electronics, electrical safety, regenerative braking, and power-plant 

control optimization have been identified as the most critical technologies requiring further 

research to enable the development of higher efficiency hybrid electric propulsion systems. 

2.4.5 Axles 

2.4.5.1 Axle Efficiency 

Axle efficiency is improved by reducing generally two categories of losses; mechanical 

losses and spin losses. 

Mechanical losses can be reduced by reducing the friction between the two gears in 

contact. This friction is reduced mainly by improving the surface finish of the gears. The other 

way of doing is by reducing the amount of distance the gear faces are sliding against each other. 

Generally speaking frictional losses are proportional to the torque on the axle not a function of 

rotational speed of the axle. 

Spin losses on the other hand are a function of speed and not torque. One of the main 

ways to reduce the spin losses of the axle is by using a lower viscosity lubricant. Some highperformance lower viscosity formulations have been designed to have superior performance at 

high operating temperatures, and may have extended change intervals. 

A study conducted by researchers at Shell Global Solutions on a Mercedes Benz OM 

460LA heavy-duty diesel engine run under the World Harmonized Transient Cycle (WHTC) and 

World Harmonized Stationary Cycle (WHSC), used a combination of a SAE 5W-30 engine oil, 
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SAE 75W-80 gearbox oil and SAE 75W-90 axle oil. The combination yielded average fuel 

economy improvements of 1.8 percent over the WHTC and 1.1 percent over the WHSC, relative 

to a SAE 15W-40 engine oil, SAE 80W gearbox and SAE 90 axle oil [VT-27]. The baseline 

lubricants represent current mainstream products, and the new lubricants were top-tier 

formulations focusing on modified viscometric effects. Using the WHSC cycle, significant 

variations in the individual lubricant contribution under different speed and load conditions 

within the cycle were identified. Additionally, an average fuel economy improvement of 1.8 

percent was observed using medium-duty trucks under a range of typical European driving 

conditions in a controlled field trial.66

Spin losses can also be reduced by lower the volume of lubricant in the sump. This 

reduces the surface area of the gears that is churning through the lubricant. One of the main 

challenges of doing this is making sure that there is still adequate coverage of lubricant on the 

gears and bearings as well as adequate circulation so that the lubricant temperature doesn’t rise 

too high and accelerate the aging of the lubricant. 

If a manufacturer wishes to demonstrate a benefit specific to any technology that 

improves axle efficiency, an axle efficiency test could be performed to support an off-cycle 

technology credit application. See draft RIA Chapter 3 for a description of the proposed test 

procedure for rear axle efficiency. 

2.4.5.2 Gear Ratio 

Combining with transmission ratio, selection of the axle ratio can play a significant role 

in vehicle performance. For an on-highway tractor, the axle ratio must be selected in such a way 

that the engine can constantly run inside the sweet spot, where the engine efficiency is optimal 

for a typical constant cruise speed like 65 mile per mile. Although many vehicles on the road 

already use the fast axle ratio as low as 2.64:1 with the direct drive of transmission, which moves 

the engine speed in the range of 1200 rpm or even lower, most vehicles still use higher or slower 

axle ratio, which puts the engine speed in the range of 1300-1400 rpm. In order to take 

advantage of optimal engine speed, which is typically in the range of 1100-1150 rpm for HHD 

diesel vehicles, it is expected that the faster axle ratio lower than 2.64:1 would be widely used in 

2018 and beyond for tractors. Furthermore, in order to enhance vehicle performance, many axle 

manufacturers are developing dual speed axles, allowing vehicles to switch to the higher axle 

ratio during transient driving conditions, such as city traffic. On the vocational side, the ability 

to start a heavy vehicle, climb hills, and operate smoothly at low speed is strongly influenced by 

axle ratio, and therefore, one can see a large of variation of axle ratios depending on the 

application. 

2.4.5.3 Tandem Drive Axle Improvements 

Manufacturers are developing technologies to enable heavy trucks with two rear drive 

axles to be driven solely by the lead rear axle either permanently or on a part time basis. 
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2.4.5.3.1 6 x 2 

Most tractors and heavy heavy-duty vocational vehicles today have three axles – a steer 

axle and two rear drive axles, which is commonly referred to as a 6x4 configuration. 

Manufacturers offer 6x2 tractors that include one rear drive axle and one rear non-driving axle. 

The 6x2 tractors offer three distinct benefits. First, the non-driving rear axle does not have 

internal friction and therefore reduces the overall parasitic losses in the drivetrain. In addition, 

the 6x2 configuration typically weighs approximately 300 to 400 pounds less than a 6x4 

configuration.67 Finally, the 6x2 typically costs less or is cost neutral when compared to a 6x4 

tractor. Sources cite the effectiveness of 6x2 axles at between 1 and 3 percent.68 Similarly, with 

the increased use of double and triple trailers, which reduce the weight on the tractor axles when 

compared to a single trailer, manufacturers offer 4x2 axle configurations. The 4x2 axle 

configuration would have as good as or better fuel efficiency performance than a 6x2. 

2.4.5.3.2 Enhanced 6x2 

One of the drawbacks of 6x2 axle is lack of traction, specifically during the winter 

condition and high grade road when the road is slippery. In order to overcome this deficiency, 

some axle manufacturers offer products that perform similar to the 6x4 configurations. 

SMARTandem offered by Meritor is just one of the examples.69 In this system, the axle runs 

6x2 for most time. Once the conditions that require more traction are experienced, the vehicle 

activates the system to add more loads into one the powered axle, thus instantly increasing 

traction. This system offers weight savings in the range of 300 to 400 lbs, as well as 2 percent 

fuel saving as compared with conventional 6x4 axle. 

2.4.5.3.3 2.4.4.3.3 Disconnect 6x4 Axle 

Based on confidential stakeholder discussions, the agencies anticipate that the axle 

market may offer, in the proposed time frame of Phase 2, a Class 8 version of the type of axle 

disconnect that today allows 4x4 operators of HD pickup trucks to automatically disconnect or 

reconnect the front axle depending on needs for traction in varying driving conditions. The Class 

8 version would likely function for the two tandem drive axles in a similar manner as the HD 

pickup trucks do for the front axle. The switching could be automated or user-commanded. In 

these cases, the axle actuator housing, sometimes called the axle disconnect housing, is part of 

the differential that houses the gears and shift fork required to lock two axles together. The axle 

actuator works together with the transfer case to send torque to all four wheel-ends. Recently, 

Dana Holding Corporation has developed an axle system that switches between the two modes 

based on driving conditions to maximize driveline efficiency.70 When high traction is required, 

the system operates in 6x4 mode. When 6x4 tractive effort is not required, the system operates 

in 6x2 mode. It is reported that this type of system can offer 2.5 percent benefits. 

In the 4x4 example, the transfer case connects the input from the transmission to the rear 

and front driveshafts. The axle actuator housing is found on the differential. In the 4x4 

example, a shift fork inside the axle actuator housing slides a locking collar over two gears 

locking both driver and passenger side axles together. In some 4x4 vehicles, those with 

automatic 4WD, this process occurs automatically. In others, with selective 4WD, the driver can 

choose to engage 4WD or RWD with a switch. These have slightly different axle actuator 
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housings and have actuator solenoids mounted to them.71 These systems would not provide the 

weight reduction benefit of the permanent 6x2 configuration, and may offer less fuel savings, 

especially with operator-switchable systems. 

2.4.6 Weight Reduction 

Mass reduction is a technology that can be used in a manufacturer’s strategy to meet the 

proposed Phase 2 standards. Vehicle mass reduction (also referred to as “down-weighting” or 

‘light-weighting”), decreases fuel consumption and GHG emissions by reducing the energy 

demand needed to overcome inertia forces, and rolling resistance. Reduced mass in heavy-duty 

vehicles can benefit fuel efficiency and CO2 emissions in two ways. If a truck is running at its 

gross vehicle weight limit with high density freight, more freight can be carried on each trip, 

increasing the truck’s ton-miles per gallon. If the vehicle is carrying lower density freight and is 

below the GVWR (or GCW) limit, the total vehicle mass is decreased, reducing rolling 

resistance and the power required to accelerate or climb grades. 

Many vehicle components are typically made of heavier material, such as steel. 

Manufacturers have worked with mass reduction technologies for many years and a lot of these 

technologies have been used in production vehicles. The weight savings achieved by adopting 

mass reduction technologies offset weight gains due to increased vehicle size, larger powertrains, 

and increased feature content (sound insulation, entertainment systems, improved climate 

control, etc.). Generally, an empty truck makes up about one-third of the total vehicle weight. 

Every 10 percent drop in vehicle weight reduces fuel use about 5 percent.72 

Although many gains have been made to reduce vehicle mass, many of the features being 

added to modern tractors to benefit fuel efficiency, such as additional aerodynamic features or 

idle reduction systems, have the effect of increasing vehicle weight, causing mass to stay 

relatively constant. Material and manufacturing technologies can also play a significant role in 

vehicle safety by reducing vehicle weight, and in the improved performance of vehicle passive 

and active safety systems. Hybrid powertrains, fuel cells and auxiliary power would not only 

present complex packaging and weight issues, they would further increase the need for 

reductions in the weight of the body, chassis, and powertrain components in order to maintain 

vehicle functionality. 

Manufacturers may employ a systematic approach to mass reduction, where the net mass 

reduction is the addition of a direct component or system mass reduction, also referred to as 

primary mass reduction, plus the additional mass reduction taken from indirect ancillary systems 

and components, also referred to as secondary mass reduction or mass compounding. 

Mass reduction can be achieved through a number of approaches, even while maintaining 

other vehicle functionalities. As summarized by NAS in its 2011 light duty vehicle report, there 

are two key strategies for primary mass reduction: 1) substituting lighter materials for heavier 

materials; and 2) changing the design to use less material.73 
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2.4.6.1 Material Substitution 

Substitution of a material used in an assembly or a component for one with lower density 

and/or higher strength includes replacing a common material such as mild steel with higherstrength and advanced steels, aluminum, magnesium, and composite materials. In practice, 

material substitution tends to be quite specific to the manufacturer and situation. Some materials 

work better than others for particular vehicle components, and unless strength is matched, some 

substituted components may need to be more numerous (i.e. two brackets instead of one). 

Further, one choices of material may lead a manufacturer to invest more heavily in adjusting its 

manufacturing process to its properties, thus possibly impeding its ability to consider other 

materials. The agencies recognize that like any type of mass reduction, material substitution has 

to be conducted not only with consideration to maintaining equivalent component strength, but 

also to maintaining all the other attributes of that component, system or vehicle, such as 

crashworthiness, durability, and noise, vibration and harshness (NVH). 

One example that combines material substitution with component-elimination is the use 

of wide-based single tires and aluminum rims to replace traditional dual tires and rims, 

eliminating eight steel rims and eight tires from a tractor. Using aluminum, metal alloys, metal 

matrix composites, and other lightweight components where appropriate can reduce empty 

vehicle weight (known as “tare weight”), improve fuel efficiency, and reduce greenhouse gas 

emissions. In addition, in weight-sensitive applications, lightweight components can allow more 

cargo and increased productivity. A report by the National Commission on Energy Policy 

estimates that a fuel economy gain of 5.0 percent on certain applications could be achieved by 

vehicle mass reduction further illustrating the fuel economy gains possible on heavy-duty 

applications.74 A report for the U.S. DOT estimated potential reductions in modal GHG 

emissions are 4.6 percent, though it also found that current light-weight materials are costly and 

are application- and vehicle-specific with need for further research and development for 

advanced materials.75

The principal barriers to overcome in reducing the weight of heavy vehicles are 

associated with the cost of lightweight materials, the difficulties in forming and manufacturing 

lightweight materials and structures, the cost of tooling for use in the manufacture of relatively 

low-volume vehicles (when compared to automotive production volumes), and ultimately, the 

extreme durability requirements of heavy vehicles. While light-duty vehicles may have a life 

span requirement of several hundred thousand miles, typical heavy-duty commercial vehicles 

must last over 1 million miles with minimum maintenance, and often are used in secondary 

applications for many more years. This requires high strength, lightweight materials that provide 

resistance to fatigue, corrosion, and can be economically repaired. Additionally, because of the 

limited production volumes and the high levels of customization in the heavy-duty market, 

tooling and manufacturing technologies that are used by the light-duty automotive industry are 

often uneconomical for heavy vehicle manufacturers. Lightweight materials such as aluminum, 

titanium and carbon fiber composites provide the opportunity for significant weight reductions, 

but their material cost and difficult forming and manufacturing requirements make it difficult for 

them to compete with low-cost steels. In addition, although mass reduction is currently 

occurring on both vocational vehicles and line haul tractors, the addition of other systems for fuel 

economy, performance or comfort increases the vehicle mass offsetting the mass reduction that 

has already occurred, thus is not captured in the overall vehicle mass measurement (e.g. 500 lbs 
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for WHR). Most vehicle manufacturers offer lightweight tractor models that are 1,000 or more 

pounds lighter than comparable models. Lighter-weight models combine different weight-saving 

options that may include:76

 Cast aluminum alloy wheels can save up to 40 pounds each for total savings of 400 

pounds 

 Aluminum axle hubs can save over 120 pounds compared to ductile iron or steel 

 Centrifuge brake drums can save nearly 100 pounds compared to standard brake drums 

 Aluminum clutch housing can save 50 pounds compared to iron clutch housing 

 Composite front axle leaf springs can save 70 pounds compared to steel springs 

 Aluminum cab frames can save hundreds of pounds compared to standard steel frames 

2.4.6.2 Synergistic Effects - Reduced Power Demand 

Manufacturers employ a systematic approach to mass reduction, where the net mass 

reduction is the addition of a direct component or system mass reduction plus the additional mass 

reduction that can be taken from indirect ancillary systems and components, as a result of full 

vehicle optimization, effectively compounding or obtaining a secondary mass reduction from a 

primary mass reduction. The strategy of using less material compared to the baseline component 

or system can be achieved by optimizing the design and structure of vehicle components, 

systems and vehicle structure. Vehicle manufacturers have long used these continuallyimproving CAE tools to optimize vehicle designs. For example, the Future Steel Vehicle (FSV) 

project sponsored by WorldAutoSteel used three levels of optimization: topology optimization, 

low fidelity 3G (Geometry Grade and Gauge) optimization, and subsystem optimization, to 

achieve 30 percent mass reduction in the body structure of a vehicle with a mild steel unibody 

structure.77 Using less material can also be achieved through improving the manufacturing 

process, such as by using improved joining technologies and parts consolidation. This method is 

often used in combination with applying new materials. 

If vehicle mass is reduced sufficiently through application of the two primary strategies 

of using less material and material substitution described above, secondary mass reduction 

options may become available. Secondary mass reduction is enabled when the load requirements 

of a component are reduced as a result of primary mass reduction. If the primary mass reduction 

reaches a sufficient level, a manufacturer may use a smaller, lighter, and potentially more 

efficient powertrain while maintaining vehicle performance. If a powertrain is downsized, a 

portion of the mass reduction may be attributed to the reduced torque requirement that results 

from the lower vehicle mass. The lower torque requirement enables a reduction in engine 

displacement, changes to transmission torque converter and gear ratios, and changes to final 

drive gear ratio. The reduced powertrain torque may enable the downsizing and/or mass 

reduction of powertrain components and accompanying reduced rotating mass (e.g., for 

transmission, driveshafts/halfshafts, wheels, and tires) without sacrificing powertrain durability. 

However, there may trade-offs, as it is possible that use of a downsized engine may require a 

transmission with more gears. The combined mass reductions of the engine, drivetrain, and body 

would reduce stresses on the suspension components, steering components, wheels, tires, and 

brakes, which can allow further reductions in the mass of these subsystems. Reducing the 
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unsprung masses such as the brakes, control arms, wheels, and tires further reduce stresses in the 

suspension mounting points, which would allow for further optimization and potential mass 

reduction. 

One example of a synergistic effect is rotational inertia. Reducing the weight of rotating 

components provides an enhanced fuel efficiency benefit over reducing the weight of static 

components. In theory, as components such as brake rotors, brake drums, wheels, tires, 

crankshafts, camshafts, and piston assemblies become lighter, the power consumption to rotate 

the masses would be directly proportional to the mass decrease. Using physical properties of a 

rotating component such as a wheel, it is relatively straightforward to calculate an equivalent 

mass. However, we do not have enough information to derive industry average values for 

equivalent mass, nor have we evaluated the best way for GEM to account for this. Using typical 

values for a heavy-duty steel wheel compared to a similar-sized aluminum wheel, the agencies 

estimate the equivalent mass ratio is in the range of 1.2 to 1.3. That means that by reducing the 

mass of a wheel by 20 pounds, the vehicle could theoretically perform as if 26 pounds had been 

reduced. 

Estimates of the synergistic effects of mass reduction and the compounding effect that 

occurs along with it can vary significantly from one report to another. For example, in 

discussing its estimate, an Auto-Steel Partnership report states that “These secondary mass 

changes can be considerable—estimated at an additional 0.7 to 1.8 times the initial mass 

change.”78 This means for each one pound reduction in a primary component, up to 1.8 pounds 

can be reduced from other structures in the vehicle (i.e., a 180 percent factor). The report also 

discusses that a primary variable in the realized secondary weight reduction is whether or not the 

powertrain components can be included in the mass reduction effort, with the lower end 

estimates being applicable when powertrain elements are unavailable for mass reduction. 

However, another report by the Aluminum Association, which primarily focuses on the use of 

aluminum as an alternative material for steel, estimated a factor of 64 percent for secondary mass 

reduction even though some powertrain elements were considered in the analysis.79 That report 

also notes that typical values for this factor vary from 50 to 100 percent. Although there is a 

wide variation in stated estimates, synergistic mass reductions do exist, and the effects result in 

tangible mass reductions. Mass reductions in a single vehicle component, for example a door 

side impact/intrusion system, may actually result in a significantly higher weight savings in the 

total vehicle, depending on how well the manufacturer integrates the modification into the 

overall vehicle design. Accordingly, care must be taken when reviewing reports on weight 

reduction methods and practices to ascertain if compounding effects have been considered or not. 

2.4.7 Vehicle Speed Limiter 

The power required to move a vehicle increases as the vehicle speed increases. 

Travelling at lower speeds provides additional efficiency to the vehicle performance. Most 

vehicles today have the ability to electronically control the maximum vehicle speed through the 

engine controller. This feature is used today by fleets and owners to provide increased safety 

and fuel economy. Currently, these features are designed to be able to be changed by the owner 

and/or dealer. 
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The impact of this feature is dependent on the difference between the governed speed and 

the speed that would have been travelled, which is dependent on road type, state speed limits, 

traffic congestion, and other factors. The agencies plan to assess the benefit of a vehicle speed 

limiter by reducing the maximum drive cycle speed on the 65 mph Cruise mode of the cycle. 

The maximum speed of the drive cycle is 65 mph, therefore any vehicle speed limit with a 

setting greater than this would show no benefit for purposes of these regulations, but may still 

show benefit in the real world in states where the interstate truck speed limit is greater than the 

national average of 65.5 mph. 

The benefits of this simple technology are widely recognized. The American Trucking 

Association (ATA) developed six recommendations to reduce carbon emissions from trucks in 

the United States. Their first recommendation is to enact a national truck speed limit of 65 mph 

and require that trucks manufactured after 1992 have speed governors set at not greater than 65 

mph.80 The SmartWay program includes speed management as one of their key Clean Freight 

Strategies and provides information to the public regarding the benefit of lower highway 

speeds.81

Some countries have enacted regulations to reduce truck speeds. For example, the United 

Kingdom introduced regulations in 2005 which require new trucks used for goods movement to 

have a vehicle speed limiter not to exceed 90 km/hr (56 mph).82 The Canadian Provinces of 

Ontario and Quebec developed regulations which took effect in January 2009 that requires onhighway commercial heavy-duty trucks to have speed limiters which limit the truck’s speed to 

105 km/hr (65 mph).83

Many truck fleets consider speed limiter application a good business practice in their 

operations. A Canadian assessment of heavy-duty truck speed limiters estimated that 60 percent 

of heavy truck fleets in North America use speed limiters.83 Con Way Freight, Con Way 

Truckload, and Wal-Mart currently govern the speeds of their fleets between 62 and 65 mph.84

A potential disbenefit of this technology is the additional time required for goods 

movement, or loss of productivity. The elasticity between speed reduction and productivity loss 

has not been well defined in industry. The Canadian assessment of speed limiters cited above 

found that the fuel savings due to the lower operating speeds outweigh any productivity losses. 

A general consensus among the OEMs is that a one percent decrease in speed might lower 

productivity by approximately 0.2 percent.84

2.4.8 Reduced Idling Time 

2.4.8.1 Engine Shutdown with Alternate Power Source during Hoteling 

Class 8 heavy-duty diesel truck extended engine idling expends significant amounts of 

fuel in the United States. Department of Transportation regulations require a certain amount of 

rest for a corresponding period of driving hours, as discussed in Chapter 1. Extended idle occurs 

when Class 8 long haul drivers rest in the sleeper/cab compartment during rest periods as drivers 

find it more convenient and economical to rest in the truck cab itself. In many cases it is the only 

option available. During this rest period a driver will generally idle the truck in order to provide 

heating or cooling or run on-board appliances. During rest periods the truck’s main propulsion 
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engine is running but not engaged in gear and it remains in a stationary position. In some cases 

the engine can idle in excess of 10 hours. During this period of time, fuel consumption will 

generally average 0.8 gallons per hour.113 Average overnight fuel usage would exceed 8 gallons 

in this example. When multiplied by the number of long haul trucks without idle control 

technology that operate on national highways on a daily basis, the number of gallons consumed 

by extended idling would exceed 3 million gallons per day. Fortunately, a number of 

alternatives (idling reduction technologies) are available to alleviate this situation. 

2.4.8.1.1 Idle Control Technologies 

Idle reduction technologies in general utilize an alternative energy source in place of 

operating the main engine. By using these devices the truck driver can obtain needed power for 

services and appliances without running the engine. A number of these devices attach to the 

truck providing heat, air conditioning, or electrical power for microwave ovens, televisions, etc. 

The idle control technologies (along with their typical hourly fuel rate) available today 

include the following:85

 Auxiliary Power Unit (APU) powers the truck’s heating, cooling, and electrical system. 

The fuel use of an APU is typically 0.2 gallons per hour 

 Fuel Operated Heater (FOH) provides heating services to the truck through small diesel 

fired heaters. The fuel use is typically 0.04 gallons per hour 

 Battery Air Conditioning Systems (BAC) provides cooling to the truck 

 Thermal Storage Systems provide cooling to trucks 

Another alternative involves electrified parking spaces, with or without modification to 

the truck. An electrified parking space system operates independently of the truck’s engine and 

allows the truck engine to be turned off while it supplies heating, cooling, and electrical power. 

These systems provide off-board electrical power to operate either: 

1. A single system electrification which requires no on-board equipment by providing an 

independent heating, cooling, and electrical power system, or 

2. A dual system which allows driver to plug in on-board equipment 

In the first case, power is provided to stationary equipment that is temporarily attached to 

the truck. In the second, the truck is modified to accept power from the electrical grid to operate 

on-board truck equipment. The retail price of idle reduction systems varies depending on the 

level of sophistication. For example, on-board technologies such as APUs can retail for over 

$7,000 while options such as electrified parking spaces require negligible up-front costs for 

equipment for the tractor itself, but will accrue fees with usage.86

CO2 emissions and fuel consumption during extended idling are significant contributors 

to emissions and fuel consumption from Class 8 sleeper cabs. The federal test procedure does 

evaluate idle emissions and fuel consumption as part of the drive cycle and related emissions 

measurement. However, long duration extended idle emissions and fuel consumption are not 

fully represented during the prescribed test cycle. To address the fact that real-world fuel and 
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emissions savings can occur with idle reduction technologies that cannot be reflected on the test 

cycle, the agencies adopted in Phase 1 a credit mechanism for manufacturers who provide for 

idle control using an automatic engine shutdown (AES) system on the tractor. This credit 

recognizes the CO2 reductions and fuel consumption savings attributed to idle control systems 

and allows vehicle manufacturers flexibility in product design and performance capabilities, 

compared to an alternative where the agencies would allow credits for specific idle control 

technologies. 

2.4.8.2 Stop Start 

For heavy-duty vehicles to apply engine stop-start technology without a reduction in 

vehicle function, some additional vehicle technologies are needed. To some extent this could be 

considered similar to a mild hybrid system, but it is not the same as the mild hybrid system 

described for HD pickups and vans described below in Chapter 2.5. The agencies are projecting 

the presence of a battery sufficient to offer electrified power steering, and some other electrified 

accessories. Some systems may replace the conventional alternator with a belt or crank driven 

starter/alternator and may add high voltage electrical accessories (which may include electric 

power steering and an auxiliary automatic transmission pump). The limited electrical 

requirements of these systems allow the use of lead-acid batteries or supercapacitors for energy 

storage, or the use of a small lithium-ion battery pack. 

The NACFE Idle Reduction Confidence report was written with long haul tractors in 

mind; however the section on vehicle electrification discusses inverters and on-vehicle solar 

energy capture, and offers some insights relevant to vocational vehicle electrification as it 

pertains to stop-start systems.87 Inverters and beltless alternators can use DC power stored in 

batteries to power on-board electrical devices and re-start engines. One example of a company 

that supplies battery-inverter idle reduction systems for vocational vehicles is Vanner.88 There 

are also systems available today that are designed to capture solar energy and store this energy 

for distribution to electrified accessories and engine re-starting. One example of a company that 

supplies on-vehicle solar energy capture for vocational vehicles is eNow.89

2.4.8.3 Neutral Idle 

Automatic transmissions historically apply torque to an engine when in gear at zero 

speed, such as when stopped at a traffic light. These transmissions can be programmed to place 

a smaller load on the engine, resulting in lower rpm and lower fuel consumption, essentially 

shifting the transmission to neutral at zero speed. 

2.4.9 Air Conditioning 

2.4.9.1 Refrigerant Leakage 

Hydrofluorocarbon (HFC) refrigerants, which are powerful GHG pollutants, can be 

emitted to the atmosphere through component and system leaks during operation, during 

maintenance and servicing, and with disposal at the end of the vehicle’s life. The current widelyused refrigerant – R134a, has a much higher global warming potential (GWP) than CO2, 
2-46 

therefore a small leakage of this refrigerant has a much greater global warming impact than a 

similar amount of emissions of CO2 or other mobile source GHGs. 

Direct emissions of HFC from air conditioning systems can be reduced by minimizing 

system leaks. Based on measurements from 300 European light-duty vehicles (collected in 2002 

and 2003), Schwarz and Harnisch estimate that the average HFC direct leakage rate from modern 

A/C systems was estimated to be 53 g/yr.90 This corresponds to a leakage rate of 6.9 percent per 

year. This was estimated by extracting the refrigerant from recruited vehicles and comparing the 

amount extracted to the amount originally filled (as per the vehicle specifications). The fleet and 

size of vehicles differs from Europe and the United States, therefore it is conceivable that 

vehicles in the United States could have a different leakage rate. The authors measured the 

average charge of refrigerant at initial fill to be about 747 grams (it is somewhat higher in the 

U.S. at 770g), and that the smaller cars (684 gram charge) emitted less than the higher charge 

vehicles (883 gram charge). Moreover, due to the climate differences, the A/C usage patterns 

also vary between the two continents, which may influence leakage rates. 

Vincent et al., from the California Air Resources Board estimated the in-use refrigerant 

leakage rate to be 80 g/yr.91 This is based on consumption of refrigerant in commercial fleets, 

surveys of vehicle owners and technicians. The study assumed an average A/C charge size of 

950 grams and a recharge rate of 1 in 16 years (lifetime). The recharges occurred when the 

system was 52 percent empty and the fraction recovered at end-of-life was 8.5 percent. 

Manufacturers today are complying with the HD Phase 1 program requirements to reduce 

A/C leakage emissions by utilizing high-quality, low-leakage air conditioning system 

components in the production of new tractors, and HD pickup trucks and vans. Some of the 

components available to manufacturers are low-permeation flexible hoses, multiple o-ring or seal 

washer connections, and multiple-lip compressor shaft seals. The availability of low leakage 

components in the market is being driven by the air conditioning credit program in the light-duty 

GHG rulemaking. The cooperative industry and government Improved Mobile Air Conditioning 

(IMAC) program has demonstrated that new-vehicle leakage emissions can be reduced by 50 

percent by reducing the number and improving the quality of the components, fittings, seals, and 

hoses of the A/C system.92

2.4.9.2 System Efficiency 

A program could be developed that includes efficiency improvements. CO2- equivalent 

emissions and fuel consumption are also associated with air conditioner efficiency, since air 

conditioners create load on the engine. See 74 FR at 49529. However, as in Phase 1, the 

agencies are not proposing air conditioning efficiency standards for heavy-duty vehicles, as the 

fuel consumption and CO2 emissions due to air conditioning systems in heavy-duty trucks are 

minimal (compared to their overall fuel consumption and emissions of CO2). For example, EPA 

conducted modeling of a Class 8 sleeper cab using GEM to evaluate the impact of air 

conditioning and found that it leads to approximately 1 gram of CO2/ton- mile. Therefore, a 

projected 24 percent improvement of the air conditioning system (the level projected in the lightduty GHG rulemaking), would only reduce CO2 emissions by less than 0.3 g CO2/ton-mile, or 

approximately 0.3 percent of the baseline Class 8 sleeper cab CO2 emissions. 
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2.4.9.3 Solar Control 

Solar reflective paint and solar control glazing can reduce the temperature inside a 

vehicle, and therefore reduce the air conditioning requirements. The reduction in air 

conditioning load can lead to reductions in fuel consumption and GHG emissions. CARB’s Low 

Emission Vehicle III Regulations (LEVIII) include a GHG credit for this technology.93 Solar 

reflective paints reflect approximately a half of the solar energy by reflecting the infrared portion 

of the solar spectrum. A study conducted by National Renewable Energy Laboratory found 

benefits to sleeper cab tractors using reflective paint.94 Solar control glazing reflects some of the 

solar energy from the glass. CARB found that most heavy-duty trucks today use solar absorbing 

glass. 

There are many factors that influence the level of emissions and fuel consumption 

reductions due to solar control glazing and solar reflective paint. The fraction of time spent 

idling during the daytime hours, the fraction of hours of the day that are sunny, the ambient 

temperatures, the wind conditions and/or vehicle speed, the fraction of the vehicles that are 

painted colors other than white, and other factors influence the potential impact of these 

technologies. Because of the difficulty in assessing the potential emission reductions from solar 

control paint and glazing, the agencies are not proposing this technology as part of HD Phase 2, 

but these types of technologies could be considered under the innovative technology program. 

2.4.10 Other Accessory Improvements 

Electric power steering (EPS) provides a potential reduction in CO2 emissions and fuel 

consumption over hydraulic power steering because of reduced overall accessory loads. This 

eliminates the parasitic losses associated with belt-driven power steering pumps which 

consistently draw load from the engine to pump hydraulic fluid through the steering actuation 

systems even when the wheels are not being turned. EPS is an enabler for all vehicle 

hybridization technologies since it provides power steering when the engine is off. EPS may be 

implemented on most vehicles with a standard 12V system. Some heavier vehicles such as Class 

2b and 3 may require a higher voltage system which may add cost and complexity. 

The 2017 light-duty final rule estimated a one to two percent effectiveness based on the 

2002 NAS report, a Sierra Research report, and confidential manufacturer data. The SwRI report 

estimated 0.8 percent to 1 percent effectiveness. The agencies reviewed these SwRI 

effectiveness estimates and found them to be accurate, thus they have been retained for this 

proposal. 

In addition to the purely hybrid technologies, which decreases the proportion of 

propulsion energy coming from the fuel by increasing the proportion of that energy coming from 

electricity, there are other steps that can be taken to improve the efficiency of auxiliary functions 

(e.g., power-assisted steering or air-conditioning) which also reduce CO2 emissions and fuel 

consumption. Optimization of the auxiliary functions is collectively referred to as vehicle or 

accessory load electrification because they generally use electricity instead of engine power. 

These improvements are considered enablers for hybrid systems. 
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2.4.11 Predictive Cruise Control 

Cruise control is commonly used in light-duty and heavy-duty applications to maintain a 

vehicle at a set speed. However, cruise control systems with additional intelligence and 

predictive control in are much more complex but offer opportunities to reduce fuel consumption 

and GHG emissions. Many of the heavy-duty manufacturers are developing intelligent cruise 

control systems and though they resemble each other in overall function, each manufacturer is 

doing it differently. 

As an example, an intelligent cruise control system partnered with a source of elevation 

information could detect when the vehicle is on a hill and know when it is close to cresting the 

hill. During this time, the vehicle may be allowed to temporarily travel at a lower speed to 

prevent the need for a transmission downshift, which consumes more fuel because it requires the 

engine to increase the rpm and run in a less efficient part of the fuel map. Similarly, predictive 

cruise control allows a vehicle to exceed the speed set point by a specified amount so that the 

vehicle will start the next hill at a higher speed and reduce the likelihood of needing to downshift 

on the next hill. 

The amount of reduction in fuel consumption and CO2 emissions depends significantly 

on the terrain. Sources estimate that the overall savings is approximately two percent.95

2.5 Technology Application and Estimated Costs – HD Pickups and Vans 

2.5.1 Gasoline Engines 

Spark ignited (gasoline) engines used in complete Class 2b and 3 pickups and vans 

include engines offered in a manufacturer’s light-duty truck counterparts, as well as engines 

specific to the Class 2b and 3 segment. Based on 2014 MY specifications, these engines 

typically range in displacement between 5 and 7 liters, though smaller and larger engines have 

also been used in this market. The majority of these engines are a V8 configuration, although the 

V10 configuration is also marketed. 

The engine technologies are based on the technologies described in the Light-Duty 

Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards 

Joint Technical Support Document and Sections 2.2 and 2.3 above.96 Some of the references 

come from the 2010 NAS Report, Technologies and Approaches to Reducing the Fuel 

Consumption of Medium and Heavy-Duty Vehicles. These technologies include engine friction 

reduction, cam phasing, cylinder deactivation and stoichiometric gas direct injection. Included 

with each technology description is an estimate of the improvement in fuel consumption and 

GHGs that is achievable through the use of the technology in heavy-duty pickup trucks and vans. 

2.5.1.1 Low Friction Lubricants 

One of the most basic methods of reducing fuel consumption in both gasoline and diesel 

engines is the use of lower viscosity engine lubricants. More advanced multi-viscosity engine 

oils are available today with improved performance in a wider temperature band and with better 

lubricating properties. This can be accomplished by changes to the oil base stock (e.g., 
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switching engine lubricants from a Group I base oils to lower-friction, lower viscosity Group III 

synthetic) and through changes to lubricant additive packages (e.g., friction modifiers and 

viscosity improvers). The use of 5W-30 motor oil is now widespread and auto manufacturers are 

introducing the use of even lower viscosity oils, such as 5W-20 and 0W-20, to improve coldflow properties and reduce cold start friction. However, in some cases, changes to the 

crankshaft, rod and main bearings and changes to the mechanical tolerances of engine 

components may be required. In all cases, durability testing would be required to ensure that 

durability is not compromised. The shift to lower viscosity and lower friction lubricants would 

also improve the effectiveness of valvetrain technologies such as cylinder deactivation, which 

rely on a minimum oil temperature (viscosity) for operation. 

Based on light-duty 2017-2025 MY vehicle rulemaking, and previously-received 

confidential manufacturer data, the agencies have estimated the effectiveness of low friction 

lubricants to be between 0 to 1 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.2 Engine Friction Reduction 

Manufacturers can reduce friction and improve fuel consumption by improving the 

design of engine components and subsystems. Approximately 10 percent of the energy 

consumed by a vehicle is lost to friction, and just over half is due to frictional losses within the 

engine. Examples include improvements in low-tension piston rings, piston skirt design, roller 

cam followers, improved crankshaft design and bearings, material coatings, material substitution, 

more optimal thermal management, and piston and cylinder surface treatments. Additionally, as 

computer-aided modeling software continues to improve, more opportunities for evolutionary 

friction reductions may become available. 

Estimations of fuel consumption improvements due to reduced engine friction from the 

2015 NHTSA Technology Study range from 1 percent to 2 percent. The agencies believe that 

this range is accurate. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.3 Engine Parasitic Demand Reduction 

Manufacturers can reduce mechanical engine loads and improve fuel consumption by 

implementing variable-displacement oil pumps, higher-efficiency direct injection fuel pumps, 

and variable speed/displacement coolant pumps. 

Estimations of fuel consumption improvements due to reduced engine parasitic demand 

from the 2015 NHTSA Technology Study range from 1 percent to 2 percent. The agencies 

believe that this range is accurate. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 
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2.5.1.4 Variable Valve Timing 

Variable valve timing (VVT) classifies a family of valve-train designs that alter the 

timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, increase 

specific power, and control the level of residual gases in the cylinder. VVT reduces pumping 

losses when the engine is lightly loaded by controlling valve timing closer to the optimum 

needed to sustain horsepower and torque. VVT can also improve volumetric efficiency at higher 

engine speeds and loads. Additionally, VVT can be used to alter (and optimize) the effective 

compression ratio where it is advantageous for certain engine operating modes (e.g., in the 

Atkinson Cycle). 

VVT has now become a widely adopted technology in the light duty fleet: in MY 2014, 

most of all new cars and light trucks had engines with some method of variable valve timing.97

Manufacturers are currently using many different types of variable valve timing, which have a 

variety of different names and methods. Therefore, the degree of further improvement across the 

fleet is limited by the level of valvetrain technology already implemented on the vehicles. The 

three major types of VVT are listed below. 

Each of the implementations of VVT uses a cam phaser to adjust the camshaft angular 

position relative to the crankshaft position, referred to as “camshaft phasing.” The phase 

adjustment results in changes to the pumping work required by the engine to accomplish the gas 

exchange process. The majority of current cam phaser applications use hydraulically-actuated 

units, powered by engine oil pressure and managed by a solenoid that controls the oil pressure 

supplied to the phaser. 

2.5.1.4.1 Coupled Cam Phasing for Overhead Valve (OHV) and Single 

Overhead Camshaft (SOHC) Engines 

Valvetrains with coupled (or coordinated) cam phasing (CCP) can modify the timing of 

both the inlet valves and the exhaust valves an equal amount by varying the phasing of the 

camshaft across an engine’s range of operating speeds; also known as VVT. For engines 

configured as an overhead valve (OHV) or as a single overhead camshaft (SOHC) only one cam 

phaser is required per camshaft to achieve CCP. 

Based on the heavy-duty 2014-2018 MY vehicle rulemaking, 2015 NHTSA Technology 

Study, and previously-received confidential manufacturer data, the agencies estimate the fuel 

consumption reduction effectiveness of this technology to be between 1 and 3 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.4.2 Intake Cam Phasing (ICP) for Dual Overhead Camshaft Engines 

(DOHC) 

Valvetrains with ICP, which is the simplest of the cam phasing technologies, can modify 

the timing of the inlet valves by phasing the intake camshaft while the exhaust valve timing 

remains fixed. This requires the addition of a cam phaser on each bank of intake valves on the 
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engine. An in-line 4-cylinder engine has one bank of intake valves, while V-configured engines 

have two banks of intake valves. 

Some newer Class 2b and 3 market entries are offering dual overhead camshaft (DOHC) 

engine designs where two camshafts are used to operate the intake and exhaust valves 

independently. Consistent with the heavy-duty 2014-2018 MY vehicle rulemaking and the SwRI 

report, the agencies agree with the effectiveness values of 1 to 2 percent reduction in fuel 

consumption for this technology. 

2.5.1.4.3 Dual Cam Phasing (DCP) for Dual Overhead Camshaft Engines 

(DOHC) 

The most flexible VVT design is dual (independent) cam phasing, where the intake and 

exhaust valve opening and closing events are controlled independently. This option allows the 

option of controlling valve overlap, which can be used as an internal EGR strategy. At low 

engine loads, DCP creates a reduction in pumping losses, resulting in improved fuel 

consumption. Increased internal EGR also results in lower engine-out NOX emissions. The 

amount by which fuel consumption is improved depends on the residual tolerance of the 

combustion system. Additional improvements are observed at idle, where low valve overlap 

could result in improved combustion stability, potentially reducing idle fuel consumption. DCP 

requires two cam phasers on each bank of the engine. 

Some newer Class 2b and 3 market entries are offering dual overhead camshaft (DOHC) 

engine designs where two camshafts are used to operate the intake and exhaust valves 

independently. Consistent with the light-duty 2012-2016 MY vehicle rulemaking and the SwRI 

report, the agencies agree with the effectiveness values of 1 to 3 percent reduction in fuel 

consumption for this technology. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.5 Variable Valve Lift (VVL) 

Controlling the lift of the valves provides a potential for further efficiency improvements. 

By optimizing the valve-lift profile for specific engine operating regions, the pumping losses can 

be reduced by reducing the amount of throttling required to produce the desired engine power 

output. By moving the throttling losses further downstream of the throttle valve, the heat 

transfer losses that occur from the throttling process are directed into the fresh charge-air mixture 

just prior to compression, delaying the onset of knock-limited combustion processes. Variable 

valve lift control can also be used to induce in-cylinder mixture motion, which improves fuel-air 

mixing and can result in improved thermodynamic efficiency. Variable valve lift control can 

also potentially reduce overall valvetrain friction. At the same time, such systems may also incur 

increased parasitic losses associated with their actuation mechanisms. A number of 

manufacturers have already implemented VVL into their fleets (Toyota, Honda, and BMW). 

There are two major classifications of variable valve lift, described below: 
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2.5.1.5.1 Discrete Variable Valve Lift (DVVL) 

Discrete variable valve lift (DVVL) systems allow the selection between two or three 

discrete cam profiles by means of a hydraulically-actuated mechanical system. By optimizing 

the cam profile for specific engine operating regions, the pumping losses can be reduced by 

reducing the amount of throttling required to produce the desired engine power output. This 

increases the efficiency of the engine. These cam profiles consist of a low and a high-lift lobe, 

and may include an inert or blank lobe to incorporate cylinder deactivation (in the case of a 3-

step DVVL system). DVVL is normally applied together with VVT control. DVVL is also 

known as Cam Profile Switching (CPS). DVVL is a mature technology with low technical risk. 

Based on the light-duty MY 2017-2025 final rule, previously-received confidential 

manufacturer data, 2015 NHTSA Technology Study, and report from the Northeast States Center 

for a Clean Air Future (NESCCAF), the agencies estimate the fuel consumption reduction 

effectiveness of this technology to be between 1 and 3 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.6 Cylinder Deactivation 

In conventional spark-ignited engines throttling the airflow controls engine torque output. 

At partial loads, efficiency can be improved by using cylinder deactivation instead of throttling. 

Cylinder deactivation can improve engine efficiency by disabling or deactivating (usually) half 

of the cylinders when the load is less than half of the engine’s total torque capability – the valves 

are kept closed, and no fuel is injected – as a result, the trapped air within the deactivated 

cylinders is simply compressed and expanded as an air spring, with reduced friction and heat 

losses. The active cylinders combust at almost double the load required if all of the cylinders 

were operating. Pumping losses are significantly reduced as long as the engine is operated in 

this “part-cylinder” mode. 

 Cylinder deactivation control strategy relies on setting maximum manifold absolute 

pressures or predicted torque within which it can deactivate the cylinders. Noise and vibration 

issues reduce the operating range to which cylinder deactivation is allowed, although 

manufacturers are exploring vehicle changes that enable increasing the amount of time that 

cylinder deactivation might be suitable. Some manufacturers may choose to adopt active engine 

mounts and/or active noise cancellations systems to address Noise Vibration and Harshness 

(NVH) concerns and to allow a greater operating range of activation. 

Effectiveness improvements scale roughly with engine displacement-to-vehicle weight 

ratio: the higher displacement-to-weight vehicles, operating at lower relative loads for normal 

driving, have the potential to operate in part-cylinder mode more frequently. 

Based on the 2015 NHTSA Technology Study and previously-received confidential 

manufacturer data, the agencies estimate the fuel consumption reduction effectiveness of this 

technology to be between 2.5 and 3.5 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 
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2.5.1.7 Stoichiometric Gasoline Direct Injection 

Stoichiometric gasoline direct injection (SGDI) engines inject fuel at high pressure 

directly into the combustion chamber (rather than the intake port in port fuel injection). SGDI 

requires changes to the injector design, an additional high pressure fuel pump, new fuel rails to 

handle the higher fuel pressures, and changes to the cylinder head and piston crown design. 

Direct injection of the fuel into the cylinder improves cooling of the air/fuel charge within the 

cylinder, which allows for higher compression ratios and increased thermodynamic efficiency 

without the onset of combustion knock. Recent injector design advances, improved electronic 

engine management systems and the introduction of multiple injection events per cylinder firing 

cycle promote better mixing of the air and fuel, enhance combustion rates, increase residual 

exhaust gas tolerance and improve cold start emissions. SGDI engines achieve higher power 

density and match well with other technologies, such as boosting and variable valvetrain designs. 

Several manufacturers have recently introduced vehicles with SGDI engines, including 

GM and Ford, who have announced their plans to increase dramatically the number of SGDI 

engines in their light-duty portfolios. 

Based on the heavy-duty 2014-2018 MY vehicle rulemaking, 2015 NHTSA Technology 

Study, and previously-received confidential manufacturer data, the agencies estimate the fuel 

consumption reduction effectiveness of SGDI to be between 1 and 2 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.1.8 Turbocharging and Downsizing (TRBDS) 

The specific power of a naturally aspirated engine is primarily limited by the rate at 

which the engine is able to draw air into the combustion chambers. Turbocharging and 

supercharging (grouped together here as boosting) are two methods to increase the intake 

manifold pressure and cylinder charge-air mass above naturally aspirated levels. Boosting 

increases the airflow into the engine, thus increasing the specific power level, and with it the 

ability to reduce engine displacement while maintaining performance. This effectively reduces 

the pumping losses at lighter loads in comparison to a larger, naturally aspirated engine. 

Almost every major manufacturer currently markets a vehicle with some form of 

boosting. While boosting has been a common practice for increasing performance for several 

decades, turbocharging has considerable potential to improve fuel economy and reduce CO2

emissions when the engine displacement is also reduced. Specific power levels for a boosted 

engine often exceed 100 hp/L, compared to average naturally aspirated engine power densities of 

roughly 70 hp/L. As a result, engines can be downsized roughly 30 percent or higher while 

maintaining similar peak output levels. In the last decade, improvements to turbocharger turbine 

and compressor design have improved their reliability and performance across the entire engine 

operating range. New variable geometry turbines and ball-bearing center cartridges allow faster 

turbocharger spool-up (virtually eliminating the once-common “turbo lag”) while maintaining 

high flow rates for increased boost at high engine speeds. Low speed torque output has been 

dramatically improved for modern turbocharged engines. However, even with turbocharger 

improvements, maximum engine torque at very low engine speed conditions, for example launch 
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from standstill, is increased less than at mid and high engine speed conditions. The potential to 

downsize engines may be less on vehicles with low displacement to vehicle mass ratios for 

example a very small displacement engine in a vehicle with significant curb weight, in order to 

provide adequate acceleration from standstill, particularly up grades or at high altitudes. 

Use of GDI systems with turbocharged engines and charge air cooling also reduces the 

fuel octane requirements for knock limited combustion and allows the use of higher compression 

ratios. Ford’s “EcoBoost” downsized, turbocharged GDI engines introduced on MY 2010 

vehicles allow the replacement of V8 engines with V6 engines with improved in 0-60 mph 

acceleration and with fuel economy improvements of up to 12 percent.98

Recently published data with advanced spray-guided injection systems and more 

aggressive engine downsizing targeted towards reduced fuel consumption and CO2 emissions 

reductions indicate that the potential for reducing CO2 emissions for turbocharged, downsized 

GDI engines may be as much as 15 to 30 percent relative to port-fuel-injected engines.14,15,16,17,18 

Confidential manufacturer data suggests an incremental range of fuel consumption and CO2

emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other publiclyavailable sources suggest a fuel consumption and CO2 emission reduction of 8 to 13 percent 

compared to current-production naturally-aspirated engines without friction reduction or other 

fuel economy technologies: a joint technical paper by Bosch and Ricardo suggesting fuel 

economy gain of 8 to 10 percent for downsizing from a 5.7 liter port injection V8 to a 3.6 liter 

V6 with direct injection using a wall-guided direct injection system;99 a Renault report 

suggesting a 11.9 percent NEDC fuel consumption gain for downsizing from a 1.4 liter port 

injection in-line 4-cylinder engine to a 1.0 liter in-line 4-cylinder engine, also with wall-guided 

direct injection;100 and a Robert Bosch paper suggesting a 13 percent NEDC gain for downsizing 

to a turbocharged DI engine, again with wall-guided injection.101 These reported fuel economy 

benefits show a wide range depending on the SGDI technology employed. 

The agencies reviewed estimates from the LD 2017-2025 final rule, the TSD, and 

existing public literature. The previous estimate from the MYs 2017-2025 suggested a 12 to 14 

percent effectiveness improvement, which included low friction lubricant (level one), engine 

friction reduction (level one), DCP, DVVL and SGDI, over baseline fixed-valve engines, similar 

to the estimate for Ford’s EcoBoost engine, which is already in production. Additionally, the 

agencies analyzed Ricardo vehicle simulation data and the 2015 NHTSA Technology Study for 

various turbocharged engine packages. Based on these data, and considering the widespread 

nature of the public estimates, the agencies assume that turbocharging and downsizing, would 

provide a 16.4 percent effectiveness improvement over naturally aspirated engines as applied to 

Class 2b and 3 vehicles. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

Note that for this analysis we determined that this technology path is only applicable to 

heavy duty applications that have operating conditions more closely associated with light duty 

vehicles. This includes vans designed mainly for cargo volume or modest payloads having 

similar GCWR to light duty applications. These vans cannot tow trailers heavier than similar 

light duty vehicles and are largely already sharing engines of significantly smaller displacement 

and cylinder count compared to heavy duty vehicles designed mainly for trailer towing. 
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2.5.1.9 Cooled Exhaust-Gas Recirculation 

Cooled exhaust gas recirculation or Boosted EGR is a combustion concept that involves 

utilizing EGR as a charge diluent for controlling combustion temperatures and cooling the EGR 

prior to its introduction to the combustion system. Higher exhaust gas residual levels at part load 

conditions reduce pumping losses for increased fuel economy. The additional charge dilution 

enabled by cooled EGR reduces the incidence of knocking combustion and obviates the need for 

fuel enrichment at high engine power. This allows for higher boost pressure and/or compression 

ratio and further reduction in engine displacement and both pumping and friction losses while 

maintaining performance. Engines of this type use GDI and both dual cam phasing and discrete 

variable valve lift. The EGR systems considered in this proposal would use a dual-loop system 

with both high and low pressure EGR loops and dual EGR coolers. The engines would also use 

single-stage, variable geometry turbocharging with higher intake boost pressure available across 

a broader range of engine operation than conventional turbocharged SI engines. Such a system is 

estimated to be capable of an additional 3 to 5 percent effectiveness relative to a turbocharged, 

downsized GDI engine without cooled-EGR. The agencies have also considered a more 

advanced version of such a cooled EGR system that employs very high combustion pressures by 

using dual stage turbocharging. 

2.5.2 Diesel Engines 

Diesel engines in this class of vehicle have emissions characteristics that present 

challenges to meeting federal NOX emissions standards. It is a significant systems-engineering 

challenge to maintain the fuel consumption advantage of the diesel engine while meeting U.S. 

emissions regulations. Fuel consumption can be negatively impacted by emissions reduction 

strategies depending on the combination of strategies employed. Emission compliance strategies 

for diesel vehicles sold in the U.S. are expected to include a combination of improvements of 

combustion, air handling system, aftertreatment, and advanced system control optimization. 

These emission control strategies are being introduced on Tier 2 light-duty diesel vehicles today. 

Some of the engine technologies are described in the Light-Duty Vehicle Greenhouse 

Gas Emission Standards and Corporate Average Fuel Economy Standards Joint Technical 

Support Document.102 Others are from the 2010 NAS Report, Technologies and Approaches to 

Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles, and the 2015 NHTSA 

Technology Study. Several key advances in diesel technology have made it possible to reduce 

emissions coming from the engine prior to aftertreatment. These technologies include engine 

friction and parasitic loss reduction, improved fuel systems (higher injection pressure and 

multiple-injection capability), advanced controls and sensors to optimize combustion and 

emissions performance, higher EGR levels and EGR cooling to reduce NOX, and advanced 

turbocharging systems. 

2.5.2.1 Low Friction Lubricants 

Consistent with the discussion above for gasoline engines (see Section 2.5.1.1), the 

agencies are expecting some engine changes to accommodate low friction lubricants. Based on 

the light-duty 2014-2018 MY HD vehicle rulemaking, and previously-received confidential 
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manufacturer data, the agencies estimated the effectiveness of low friction lubricants to be 

between 0 to 1 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

Based on a survey of the current powertrains being applied to the Class 2b and 3 segment 

and the level of powertrain sharing with the light duty vehicle market for these vehicles, the 

majority of light heavy duty gasoline engines in the 2014 Class 2b and 3 vehicle models are 

utilizing some form of low friction lubricants to achieve power and emission goals, and so this 

technology is considered to be in the baseline. 

2.5.2.2 Engine Friction Reduction 

Reduced friction in bearings, valve trains, and the piston-to-liner interface will improve 

efficiency. Friction reduction opportunities in the engine valve train and at its roller/tappet 

interfaces exist for several production engines. In virtually all production engines, the piston at 

its skirt/cylinder wall interface, wrist pin and oil ring/cylinder wall interface offer opportunities 

for friction reduction. Use of more advanced oil lubricant that could be available for production 

in the future may also eventually play a key role in reducing friction. Mechanical loads can also 

be reduced by converting the water, oil, and fuel pumps in the engine from fixed displacement to 

variable displacement. 

Estimations of fuel consumption improvements due to reduced engine friction from the 

2015 NHTSA Technology Study range from 1 percent to 2 percent. The agencies believe that 

this range is accurate. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.2.3 Turbocharger Technology 

Compact two stage turbochargers can increase the boost level with wider operation range, 

thus improving engine thermal efficiency. Ford’s new developed 6.7L Scorpion engine features 

a twin-compressor turbocharger103. Cummins has also developed its own two stage 

turbochargers.104 It is expected that this type of technology will continue to be improved by 

better system matching and development of higher compressor and turbine efficiency. 

Based on the 2015 NHTSA Technology Study and previously-received confidential 

manufacturer data, the agencies estimate the fuel consumption reduction effectiveness of this 

technology to be between 2 and 3 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.2.4 Reduction of Parasitic Loads 

Accessories that are traditionally gear- or belt-driven by a vehicle’s engine can be 

optimized and/or converted to electric power. Examples include the engine water pump, oil 

pump, fuel injection pump, air compressor, power-steering pump, cooling fans, and the vehicle’s 

air-conditioning system which can be converted to full electrically driven loads or an electro-
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mechanical arrangement that retains some mechanically connected aspects. Optimization and 

improved pressure regulation may significantly reduce the parasitic load of the water, air and 

fuel pumps. Electrification may result in a reduction in power demand, because electricallypowered accessories (such as the air compressor or power steering) operate only when needed if 

they are electrically powered, but they impose a parasitic demand all the time if they are enginedriven. In other cases, such as cooling fans or an engine’s water pump, electric power allows the 

accessory to run at speeds independent of engine speed, which can reduce power consumption. 

The 2015 NHTSA Technology Study used a 1 to 2 percent fuel consumption reduction for diesel 

engine parasitic improvements. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.2.5 Aftertreatment Improvements 

The HD diesel pickup and van segment has largely adopted the SCR type of 

aftertreatment system to comply with criteria pollutant emission standards. As the experience 

base for SCR expands over the next few years, many improvements in this aftertreatment system 

such as construction of the catalyst, thermal management, and reductant optimization may result 

in a reduction in the amount of fuel used in the process. However, due to uncertainties with 

these improvements regarding the extent of current optimization and future criteria emissions 

obligations, the agencies are not considering aftertreatment improvements as a fuel-saving 

technology in the rulemaking analysis for HD pickups and vans. 

2.5.3 Drivetrain 

The agencies have also reviewed the transmission technology estimates used in the lightduty 2012-2016 MY vehicle rulemaking. In doing so, the agencies have considered or 

reconsidered all available sources and updated the estimates as appropriate. The section below 

describes each of the transmission technologies considered for this rulemaking. 

2.5.3.1 Automatic 8-Speed Transmissions 

Manufacturers can also choose to replace 6-speed transmissions with transmissions 

capable of 8-speeds or more. Additional ratios allow for further optimization of engine operation 

over a wider range of conditions, but this is subject to diminishing returns as the number of 

speeds increases. As additional gear sets are added (which may be necessary in some cases to 

achieve the higher number of ratios), additional weight and friction are introduced. Also, the 

additional shifting of such a transmission can be perceived as bothersome to some consumers, so 

manufacturers continue to develop strategies for smooth operation. 

As discussed in the heavy-duty 2014-2018 MY vehicle rulemaking along with 

confidential manufacturer data projected that 8-speed transmissions could incrementally reduce 

fuel consumption by 1 to 3 percent from a baseline 6-speed automatic transmission. The SwRI 

report uses 2 to 3 percent fuel consumption reduction when replacing 6-speed baseline automatic 

transmissions with improved 8-speed automatic transmissions. 
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The agencies reviewed and revised these effectiveness estimates based on usage and 

testing methods for Class 2b and 3 vehicles. The agencies estimate the effectiveness for a 

conversion from a 6 to 8-speed transmission to be 2.7 percent. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.3.2 High Efficiency Transmission 

For this proposal, a high efficiency transmission refers to some or all of a suite of 

incremental transmission improvement technologies that should be available within the 2019 to 

2025 timeframe. The majority of these improvements address mechanical friction within the 

transmission. These improvements include but are not limited to: shifting clutch technology 

improvements, improved kinematic design, dry sump lubrication systems, more efficient seals, 

bearings and clutches (reducing drag), component superfinishing and improved transmission 

lubricants. 

2.5.3.3 Electric Power Steering (EPS) 

Electric power steering (EPS) provides a potential reduction in CO2 emissions and fuel 

consumption over hydraulic power steering because of reduced overall accessory loads. This 

eliminates the parasitic losses associated with belt-driven power steering pumps which 

consistently draw load from the engine to pump hydraulic fluid through the steering actuation 

systems even when the wheels are not being turned. EPS is an enabler for all vehicle 

hybridization technologies since it provides power steering when the engine is off. EPS may be 

implemented on most vehicles with a standard 12V system. Some heavier vehicles such as Class 

2b and 3 may require a higher voltage system which may add cost and complexity. 

The 2017 light-duty final rule estimated a one to two percent effectiveness based on the 

2002 NAS report, a Sierra Research report, and confidential manufacturer data. The SwRI report 

estimated 0.8 percent to 1 percent effectiveness. The agencies reviewed these SwRI 

effectiveness estimates and found them to be accurate, thus they have been retained for this 

proposal. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.3.4 Improved Accessories 

The accessories on an engine, including the alternator, coolant and oil pumps are 

traditionally mechanically-driven. A reduction in CO2 emissions and fuel consumption can be 

realized by driving them electrically, and only when needed (“on-demand”). 

Electric water pumps and electric fans can provide better control of engine cooling. For 

example, coolant flow from an electric water pump can be reduced and the radiator fan can be 

shut off during engine warm-up or cold ambient temperature conditions which would reduce 

warm-up time, reduce warm-up fuel enrichment, and reduce parasitic losses. 

Indirect benefit may be obtained by reducing the flow from the water pump electrically 

during the engine warm-up period, allowing the engine to heat more rapidly and thereby 
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reducing the fuel enrichment needed during cold operation and warm-up of the engine. Faster oil 

warm-up may also result from better management of the coolant warm-up period. Further benefit 

may be obtained when electrification is combined with an improved, higher efficiency engine 

alternator used to supply power to the electrified accessories. 

Intelligent cooling can more easily be applied to vehicles that do not typically carry 

heavy payloads, so larger vehicles with towing capacity present a challenge, as these vehicles 

have high cooling fan loads.A However, towing vehicles tend to have large cooling system 

capacity and flow scaled to required heat rejection levels when under full load situations such as 

towing at GCWR in extreme ambient conditions. During almost all other situations, this design 

characteristic may result in unnecessary energy usage for coolant pumping and heat rejection to 

the radiator. 

The agencies considered whether to include electric oil pump technology for the 

rulemaking. Because it is necessary to operate the oil pump any time the engine is running, 

electric oil pump technology has insignificant effect on efficiency. Therefore, the agencies 

decided to not include electric oil pump technology. 

2.5.3.5 Mild Hybrid (MHEV) 

Mild hybrid systems offer idle-stop functionality and a limited level of regenerative 

braking and power assist. These systems replace the conventional alternator with a belt or crank 

driven starter/alternator and may add high voltage electrical accessories (which may include 

electric power steering and an auxiliary automatic transmission pump). The limited electrical 

requirements of these systems allow the use of lead-acid batteries or supercapacitors for energy 

storage, or the use of a small lithium-ion battery pack. 

For the MHEV technology the agencies sized the system using a 7 kW starter/generator 

and 8 kWh Li-ion battery pack. The estimates were developed by Argonne National Laboratory 

as a supplement to the 2015 NHTSA Technology Study, resulting in an effectiveness range of 4 

to 5 percent depending on the vehicle’s engine. We present cost estimates for this technology in 

Chapter 2.12 of this draft RIA. 

2.5.3.6 Strong Hybrid (SHEV) 

A hybrid vehicle is a vehicle that combines two significant sources of propulsion energy, 

where one uses a consumable fuel (like gasoline), and one is rechargeable (during operation, or 

by another energy source). Hybrid technology is well established in the U.S. market and more 

manufacturers are adding hybrid models to their lineups. Hybrids reduce fuel consumption 

through three major mechanisms: 

 The internal combustion engine can be optimized (through downsizing, modifying the 

operating cycle, or other control techniques) to operate at or near its most efficient 

 A In the CAFE model, improved accessories refers solely to improved engine cooling. However, EPA has included 

a high efficiency alternator in this category, as well as improvements to the cooling system. 
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point more of the time. Power loss from engine downsizing can be mitigated by 

employing power assist from the secondary power source. 

 Some of the energy normally lost as heat while braking can be captured and stored in 

the energy storage system for later use. 

 The engine is turned off when it is not needed, such as when the vehicle is coasting or 

when stopped. 

Hybrid vehicles utilize some combination of the three above mechanisms to reduce fuel 

consumption and CO2 emissions. The effectiveness of fuel consumption and CO2 reduction 

depends on the utilization of the above mechanisms and how aggressively they are pursued. One 

area where this variation is particularly prevalent is in the choice of engine size and its effect on 

balancing fuel economy and performance. Some manufacturers choose not to downsize the 

engine when applying hybrid technologies. In these cases, performance is vastly improved, 

while fuel efficiency improves significantly less than if the engine was downsized to maintain 

the same performance as the conventional version. The non-downsizing approach is used for 

vehicles like trucks where towing and/or hauling are an integral part of their performance 

requirements. In these cases, if the engine is downsized, the battery can be quickly drained 

during a long hill climb with a heavy load, leaving only a downsized engine to carry the entire 

load. Because towing capability is currently a heavily-marketed truck attribute, manufacturers 

are hesitant to offer a truck with downsized engine which can lead to a significantly diminished 

towing performance when the battery state of charge level is low, and therefore engines are 

traditionally not downsized for these vehicles. 

Strong Hybrid technology utilizes an axial electric motor connected to the transmission 

input shaft and connected to the engine crankshaft through a clutch. The axial motor is a 

motor/generator that can provide sufficient torque for launch assist, all electric operation, and the 

ability to recover significant levels of braking energy. 

For SHEV, the agencies also relied on the study by Argonne National Laboratory to 

supplement the 2015 NHTSA Technology Study to determine that the effectiveness of these 

systems in terms of CO2 reduction. For the SHEV technology the agencies sized the system 

using a 50 kW starter/generator and a70 kWh Li-ion battery pack. The estimates resulted in an 

effectiveness range of 18 to 22 percent depending on the engine. The estimates assume no engine 

downsizing in order to maintain vehicle performance and/or maintain towing and hauling 

performance. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.4 Aerodynamics 

Aerodynamic drag is an important aspect of the power requirements for Class 2b and 3 

trucks. Because aerodynamic drag is a function of the cube of vehicle speed, small changes in 

the aerodynamics of a Class 2b and 3 can reduce drag, fuel consumption, and GHG emissions. 

Some of the opportunities to reduce aerodynamic drag in Class 2b and 3 vehicles are similar to 

those in Class 1 and 2 (i.e., light-duty) vehicles. In general, these transferable features make the 
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cab shape more aerodynamic by streamlining the airflow over the bumper, grill, windshield, 

sides, and roof. Class 2b and 3 vehicles may also borrow from light-duty vehicles certain drag 

reducing accessories (e.g., streamlined mirrors, operator steps, and sun visors). The great variety 

of applications for Class 2b and 3 trucks result in a wide range of operational speed profiles (i.e., 

in-use drive cycles) and functional requirements (e.g., shuttle buses that must be tall enough for 

standing passengers, trucks that must have racks for ladders). This variety makes it challenging 

to develop aerodynamic solutions that consider the entire vehicle. 

Many factors affect a vehicle’s aerodynamic drag and the resulting power required to 

move it through the air. While these factors change with air density and the square and cube of 

vehicle speed, respectively, the overall drag effect is determined by the product of its frontal area 

and drag coefficient. Reductions in these quantities can therefore reduce fuel consumption and 

CO2 emissions. Although frontal areas tend to be relatively similar within a vehicle class 

(mostly due to market-competitive size requirements), significant variations in drag coefficient 

can be observed. Significant changes to a vehicle’s aerodynamic performance may need to be 

implemented during a redesign (e.g., changes in vehicle shape). However, shorter-term 

aerodynamic reductions, with a somewhat lower effectiveness, may be achieved through the use 

of revised exterior components (typically at a model refresh in mid-cycle) and add-on devices 

that are currently being applied. The latter list would include revised front and rear fascias, 

modified front air dams and rear valances, addition of rear deck lips and underbody panels, and 

lower aerodynamic drag exterior mirrors. 

For this proposal, the agencies considered two levels of aero improvements. The first 

level includes such body features as air dams, tire spats, and perhaps one underbody panel 

resulting in a 5 percent aerodynamic drag reduction. The agencies estimated the CO2 and fuel 

consumption effectiveness of this first level of aerodynamic drag at 0.75 percent. 

The second level which includes the features of level 1 plus additional body features such 

as active grille shuttersB, rear visors, larger under body panels or low-profile roof racks resulting 

in a 10 percent aerodynamic drag reduction. The agencies estimated the CO2 and fuel 

consumption effectiveness of this second level of aerodynamic drag at 1.5 percent. We present 

cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.5 Tires 

Typically, tires used on Class 2b/3 vehicles are not designed specifically for the vehicle. 

These tires are designed for broader use and no single parameter is optimized. Similar to 

vocational vehicles, the market has not demanded tires with improved rolling resistance thus far; 

therefore, manufacturers have not traditionally designed tires with low rolling resistance for 

Class 2b/3 vehicles. The agencies believe that a regulatory program that incentivizes the 

optimization of tire rolling resistance, traction and durability can bring about GHG emission and 

fuel consumption reductions of 1.1 percent from this segment based on a 10 percent reduction in 

rolling resistance. 

 B For details on how active aerodynamics are considered for off-cycle credits, see TSD Chapter 5.2.2. 
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We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.5.6 Mass Reduction 

Mass reduction is a technology that can be used in a manufacturer’s strategy to meet the 

Heavy Duty Greenhouse Gas Phase 2 standards. Vehicle mass reduction (also referred to as 

“down-weighting” or ‘light-weighting”), decreases fuel consumption and GHG emissions by 

reducing the energy demand needed to overcome inertia forces, and rolling resistance. 

Automotive companies have worked with mass reduction technologies for many years and a lot 

of these technologies have been used in production vehicles. The weight savings achieved by 

adopting mass reduction technologies offset weight gains due to increased vehicle size, larger 

powertrains, and increased feature content (sound insulation, entertainment systems, improved 

climate control, panoramic roof, etc.). Sometimes mass reduction has been used to increase 

vehicle towing and payload capabilities. 

Manufacturers employ a systematic approach to mass reduction, where the net mass 

reduction is the addition of a direct component or system mass reduction, also referred to as 

primary mass reduction, plus the additional mass reduction taken from indirect ancillary systems 

and components, also referred to as secondary mass reduction or mass compounding. There are 

more secondary mass reductions achievable for light-duty vehicles compared to heavy-duty 

vehicles, which are limited due to the higher towing and payload requirements for these vehicles. 

Mass reduction can be achieved through a number of approaches, even while maintaining 

other vehicle functionalities. As summarized by NAS in its 2011 light duty vehicle report, there 

are two key strategies for primary mass reduction: 1) changing the design to use less material; 2) 

substituting lighter materials for heavier materials.105 

The first key strategy of using less material compared to the baseline component can be 

achieved by optimizing the design and structure of vehicle components, systems and vehicle 

structure. Vehicle manufacturers have long used these continually-improving CAE tools to 

optimize vehicle designs. For example, the Future Steel Vehicle (FSV) project sponsored by 

WorldAutoSteel used three levels of optimization: topology optimization, low fidelity 3G 

(Geometry Grade and Gauge) optimization, and subsystem optimization, to achieve 30 percent 

mass reduction in the body structure of a vehicle with a mild steel unibody structure.106 Using 

less material can also be achieved through improving the manufacturing process, such as by 

using improved joining technologies and parts consolidation. This method is often used in 

combination with applying new materials. 

The second key strategy to reduce mass of an assembly or component involves the 

substitution of lower density and/or higher strength materials. Material substitution includes 

replacing materials, such as mild steel, with higher-strength and advanced steels, aluminum, 

magnesium, and composite materials. In practice, material substitution tends to be quite specific 

to the manufacturer and situation. Some materials work better than others for particular vehicle 

components, and a manufacturer may invest more heavily in adjusting to a particular type of 

advanced material, thus complicating its ability to consider others. The agencies recognize that 

like any type of mass reduction, material substitution has to be conducted not only with 

consideration to maintaining equivalent component strength, but also to maintaining all the other 
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attributes of that component, system or vehicle, such as crashworthiness, durability, and noise, 

vibration and harshness (NVH). 

If vehicle mass is reduced sufficiently through application of the two primary strategies 

of using less material and material substitution described above, secondary mass reduction 

options may become available. Secondary mass reduction is enabled when the load requirements 

of a component are reduced as a result of primary mass reduction. If the primary mass reduction 

reaches a sufficient level, a manufacturer may use a smaller, lighter, and potentially more 

efficient powertrain while maintaining vehicle acceleration performance. If a powertrain is 

downsized, a portion of the mass reduction may be attributed to the reduced torque requirement 

which results from the lower vehicle mass. The lower torque requirement enables a reduction in 

engine displacement, changes to transmission torque converter and gear ratios, and changes to 

final drive gear ratio. The reduced powertrain torque enables the downsizing and/or mass 

reduction of powertrain components and accompanying reduced rotating mass (e.g., for 

transmission, driveshafts/halfshafts, wheels, and tires) without sacrificing powertrain durability. 

Likewise, the combined mass reductions of the engine, drivetrain, and body in turn reduce 

stresses on the suspension components, steering components, wheels, tires, and brakes, which 

can allow further reductions in the mass of these subsystems. Reducing the unsprung masses 

such as the brakes, control arms, wheels, and tires further reduce stresses in the suspension 

mounting points, which allows for further optimization and potential mass reduction. However, 

pickup trucks have towing and hauling requirements which must be taken into account when 

determining the amount of secondary mass reduction that is possible and so it is less than that of 

passenger cars. 

Ford’s MY 2015 F-150 is one example of a light duty manufacturer who has begun 

producing high volume vehicles with a significant amount of mass reduction identified, 

specifically 250 to 750 lb per vehicle.107 The vehicle is an aluminum intensive design and 

includes an aluminum cab structure, body panels, and suspension components, as well as a high 

strength steel frame and a smaller, lighter and more efficient engine. The Executive Summary 

to Ducker Worldwide’s 2014 report states that state that the MY 2015 F-150 contains 1080 

pounds of aluminum with at least half of this being aluminum sheet and extrusions for body and 

closures.108 Ford engine range for its light duty truck fleet includes a 2.7L EcoBoost V-6. It is 

possible that the strategy of aluminum body panels would be applied to the heavy duty F-250 and 

F-350 versions when they are redesigned.109 

The US EPA recently completed a multi-year study with FEV North America, Inc. on the 

lightweighting of a light-duty pickup truck, a 2011 GMC Silverado, titled “Mass Reduction and 

Cost Analysis –Light-Duty Pickup Trucks Model Years 2020-2025”.110 Results contain a cost 

curve for various mass reduction percentages with the main solution being evaluated for a 21.4 

percent (511 kg/1124 lb) mass reduction resulting in an increased direct incremental 

manufacturing cost of $2228. In addition, the report outlines the compounding effect that occurs 

in a vehicle with performance requirements including hauling and towing. Secondary mass 

evaluation was performed on a component level based on an overall 20 percent vehicle mass 

reduction. Results revealed 84 kg of the 511 kg, or 20 percent, were from secondary mass 

reduction. Information on this study is summarized in SAE paper 2015-01-0559. The US DOT 

has also sponsored an on-going pickup truck lightweighting project. This project uses a more 

recent baseline vehicle, a MY 2014 GMC Silverado, and the project will be finished by early 
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2016. Both projects will be utilized for the light-duty GHG Phase 2 Midterm Evaluation mass 

reduction baseline characterization and may be used to update assumptions of mass reduction for 

HD pickups and vans for the final Phase 2 rulemaking. 

In order to determine if technologies identified on light duty trucks are applicable to 

heavy-duty pickups, the U.S. EPA also contracted with FEV North America, Inc. to perform a 

scaling study in order to evaluate the technologies identified for the light-duty truck would be 

applicable for a heavy-duty pickup truck, in this study a Silverado 2500, a Mercedes Sprinter and 

a Renault Master. This report is currently being drafted and will be peer reviewed and finalized 

between the NPRM and FRM. In general, the heavy-duty pickup truck scaling study reveals 

results similar to the light-duty truck study; however, the mass reduction and cost for the Sprinter 

and Master were less in percent mass reduction and with much higher costs than the heavy-duty 

pickup truck. The specific results will be included in the final rulemaking. 

We present cost estimates for this technology in Chapter 2.12 of this draft RIA. 

2.6 Technology Application– SI Engines 

This section summarizes the technologies the agencies project as a feasible path to 

meeting the proposed engine standards for spark-ignition engines used in vocational vehicles – 

that is engines that are engine-certified and intended for vocational vehicles that will be GEMcertified. These standards apply with respect to emissions measured over the FTP test cycle. 

This cycle is described in Chapter 3.1. See Chapter 2.5 for spark-ignited engine technologies 

projected for the proposed Phase 2 HD pickup and van vehicle standards. 

Heavy-duty spark-ignited (SI) engines are used in almost 30 percent of vocational 

vehicles. Operators that choose gasoline engines do so for reasons similar to those for HD 

complete pickups and vans. Gasoline engines have the advantage of being less expensive and 

lower weight than diesels, but tend to also be less durable and have higher fuel consumption. 

Thus, gasoline engines are most likely to be purchased for applications with lower annual VMT, 

where fuel costs are less important than upfront costs. 

Today some SI-powered vocational vehicles are sold as incomplete vehicles by a 

vertically integrated chassis manufacturer, where the Phase 1 rules allow manufacturers to 

choose to certify incomplete vehicles with weight ratings between 8,501 and 14,000 lbs GVWR 

as vocational vehicles under the GEM certification procedures including separate engine GHG 

certification, if the engine is also engine-certified for criteria pollutants.C In this case, vertically 

integrated means both the engine and chassis are manufactured by the same entity. 

In Phase 1 we generally required that vehicles that are chassis-certified for criteria 

pollutants be chassis-certified for GHGs and fuel consumption, and likewise that vehicles with 

engines certified for criteria pollutants (which in this case would be engines installed in 

vocational vehicles exclusively) be certified to the vocational vehicle standards for GHGs and 

fuel consumption, with minor exceptions. We believe that this approach involving consistent 

 C See 76 FR 57106 (September 15, 2011) and 40 CFR 1037.104(f) 
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chassis- and engine-certification for criteria pollutants and GHG’s is the most sensible way to 

structure a program to minimize both the testing burden and the potential for gaming. 

There is a Phase 1 optional provision that allows manufacturers to certify Class 4 or 5 

(14,001 to 19,500 lb GVWR) complete or incomplete vehicles to be chassis certified and thereby 

included within the Class 2b/3 fleet average.D In Section XIV of the preamble to this 

rulemaking, EPA is requesting comment on some specific issues related to chassis certification 

of vehicles over 14,000 lbs GVWR for criteria pollutants. As adopted in Phase 1, the engines in 

these vehicles must be engine-certified for criteria pollutants, but the manufacturers may include 

the vehicles in their fleet average standard and annual compliance GHG calculations, using the 

same certification and compliance provisions as for the lighter vehicles. Such vehicles are not 

required to meet the vocational vehicle standards. Because sales volumes of Class 4 and 5 trucks 

are relatively small, and because we expect these Class 4 and 5 and Class 2b and 3 trucks to 

generally use the same technologies and face roughly the same technology challenge in meeting 

their standards targets, we do not believe that this provision dilutes the stringency of the fleet 

average standards. 

Another, less common way that SI-powered vocational vehicles are built is by a nonintegrated chassis manufacturer purchasing an engine from a company that also produces 

complete and/or incomplete HD pickup trucks and vans. The Phase 1 program allows SI engine 

manufacturers to sell these so-called “loose” SI engines to other chassis manufacturers for use in 

vocational vehicles. The primary certification path designed in the Phase 1 program in this 

scenario is for the “loose” engine to be engine certified and the vehicle to be GEM certified 

under the GHG rules. This is common practice for CI engines, and in Phase 2 the agencies 

propose to continue this as the primary certification path for SI engines intended for vocational 

vehicles. 

In Phase 1 we adopted a special provision aimed at simplifying compliance for 

manufacturers of complete HD pickups and vans that also sell a relatively small number of loose 

engines. This flexibility provision enables these manufacturers to avoid meeting the separate SI 

engine standard, instead averaging them into the applicable HD pickup and van fleet-wide 

average.E Loose engine sales account for the vast majority of CI-powered vocational vehicles, 

but represent a very small fraction of the SI-powered vocational vehicle market. 

The SI engines certified and sold as loose engines into the heavy-duty vocational vehicle 

market are typically large V8 and V10 engines produced by General Motors and Ford. The 

number of engine families certified in the past for this segment of vehicles is very limited and 

has ranged between three and five engine models.F

 Unlike the heavy-duty diesel engines typical 

of this segment that are built for vocational vehicles, these SI engines are primarily developed for 

chassis-certified heavy-duty pickup trucks and vans, but are also installed in incomplete 

vocational vehicles. 

 D See 76 FR 57259-57260, September 15, 2011 and 78 FR 36374, June 17, 2013 

E See 40 CFR 1037.150(m) and 49 CFR 535.5(a)(7). 

F See EPA’s heavy-duty engine certification database at http://www.epa.gov/otaq/certdata.htm#largeng. 
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Under this special Phase 1 provision, these loose engines need not be certified to enginebased GHG and fuel consumption standards, but instead may be treated under the regulations as 

though they are additional sales of the manufacturer’s complete pickup and van products, on a 

one-for-one basis. The pickup/van vehicle so chosen must be the vehicle with the highest 

emission test weight that uses the engine (as this vehicle is likely to have the highest GHG 

emissions and fuel consumption).G However, if this vehicle is a credit-generator under the HD 

pickup and van fleet averaging program, no credits would be generated by these engine-asvehicle contributors to the fleet average; they would be treated as just achieving the target 

standard. If, on the other hand, the vehicle is a credit-user, the appropriate number of additional 

credits would be needed to offset the engine-as-vehicle contributors. The purchaser of the 

engine would treat it as any other certified engine, and would still need to meet applicable 

vocational vehicle standards for the vehicles in which the engine is installed. 

2.6.1 Defining the Baseline Engines 

In deriving the stringency of the proposed Phase 2 SI engine standard, the agencies first 

reviewed the technology that was presumed in the MY 2010 Phase 1 baseline and the technology 

that was projected would be adopted to meet the MY 2016 SI engine standard, finalized as part 

of the Phase 1 program. Engines certified to this standard would represent a logical level at 

which to set a Phase 2 baseline performance level. 

The agencies finalized MY 2016 standards that require manufacturers to achieve a five 

percent reduction in CO2 compared to the Phase 1 MY 2010 baseline. That MY 2010 baseline 

engine was described in the Phase 1 preamble at Section III.B.2.a.iii, as a naturally aspirated, 

overhead valve V8 engine. H 

In deriving the stringency of the MY 2016 gasoline engine standards, the agencies 

projected 100 percent adoption of engine friction reduction, coupled cam phasing, and 

stoichiometric gasoline direct injection (SGDI) to produce an overall five percent reduction from 

the reference engine, over the engine FTP test cycle. Table 2-4 presents the technologies 

projected to be present on an engine following this technology path. 

Table 2-4 MY 2016 Technology Projection for SI Engines 

TECHNOLOGY ADOPTION 

RATE 

Coupled Cam Phasing 100% 

Engine friction reduction 100%

Gasoline direct injection 100%

In deciding whether to consider the above package as representing the Phase 2 baseline 

performance of SI engines, the agencies reviewed available certification information and 

 G Equivalent test weight is defined at 40 CFR 1037.104(d)(11) and is determined based on a vehicle’s adjusted 

loaded vehicle weight as specified in 40 CFR 86.129, except that for vehicles over 14,000 pounds, this may be 

rounded to the nearest 500 pound increment. 

H See 76 FR 57231 
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consulted with stakeholders to determine the degree to which these projections match with 

engines being produced today and engine product plans during the Phase 1 time frame. The 

agencies have learned that no SI engine manufacturer has applied SGDI to this type of engine to 

date, though cam phasing and engine friction reduction are widely being employed. 

Furthermore, no SI engine manufacturer has yet certified an engine to the future MY 2016 SI 

engine standard, and the agencies do not have specific information about what alternate 

technology paths the manufacturers may take. 

Another possible method to establish a Phase 2 SI engine baseline performance level 

would be to assess the engines that are currently being produced for complete HD SI pickup 

trucks. These vehicles are powered by engines that closely resemble engines intended for 

vocational vehicles. Further, cab-complete and box-delete vehicles sold into vocational 

applications are often derived from HD pickup truck chassis. The SI engine technologies 

assessed for the reference fleet for the HD pickup and van program are described in the preamble 

Section VI and in the draft RIA Chapter 10. As described in the draft RIA Chapter 10, vehicle 

manufacturers typically offer few models (i.e. only a pickup truck and/or a cargo van) and while 

there are a large number of variants of each model, the degree of component sharing across the 

variants can make diversified technology application either economically impractical or 

impossible. Similarly, these manufacturers produce a limited number of engines and tune them 

for slight variants in output for a variety of car and truck applications. Manufacturers limit 

complexity in their engine portfolio for much the same reason as they limit complexity in vehicle 

variants: they face engineering manpower limitations, and supplier, production and service costs 

that scale with the number of parts produced. 

The SI engine technologies that were considered in developing the proposed Phase 2 HD 

pickup truck standards and their projected adoption rates are shown in Table 2-5, as taken from 

Table VI-10 of the preamble. The vehicle-level technologies considered for the gasoline HD 

pickup truck standards and shown in Table VI-10 are not presented here. Considering the abovedescribed constraints on engine technology adoption, it’s not surprising the projections for 

technology packages for the Phase 2 HD pickup and van program include a limited set of SI 

engine technologies for HD pickup trucks. 

Table 2-5 CAFE Model Technology Adoption Rates for HD Gasoline Pickup Trucks 

TECHNOLOGY REFERENCE CASEa

 PROPOSAL (2.5% PER YEAR)b

2018 With strong 

hybrids 

Without strong 

hybrids 

Level 1 Low friction lubricants and Engine 

friction reduction 

100% 100% 100% 

Level 2 Low friction lubricants and Engine 

friction reductionc

35 to 40% 100% 100% 

Cylinder deactivation (overhead valve) 8 to 9% 56% 56%

Variable valve timing 0% 56% 56%

Gasoline direct injection 0% 0% 56%

Notes: 

a These values are taken from a spreadsheet file with CAFE model output, representing technology adoption rates 

projected in the no-action scenario. 

b These values are taken from Section VI.C.8, Table VI-10 of the preamble, and represent technology adoption rates 

projected in the flat baseline scenario. 
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c Level 2 friction reduction as shown here is incremental to Level 1 friction reduction. 

In comparing the technologies and projected adoption rates in Table 2-4 and Table 2-5, 

there are notable differences between what is projected for complete vehicles in MY 2018 and 

what was projected for engine-certified engines in MY 2016. The CAFE model used by the 

agencies treats different types of variable valve timing technologies as a group, so that coupled 

cam phasing would be included under the heading variable valve timing. The only type of 

variable valve timing that is feasible on an overhead valve engine is coupled cam phasing.I In 

light of the differences in projected adoption rates of SGDI and variable valve timing, there is 

uncertainty about the technology pathways that may be taken by SI engine manufacturers in the 

Phase 1 time frame. Thus, the agencies have concluded that it would be more appropriate to set 

the Phase 2 baseline performance level equal to the Phase 1 MY 2016 engine standard, rather 

than a performance level representing more or less technology than is represented by that 

standard. 

2.6.2 Phase 2 Technology Feasibility and Effectiveness 

A detailed description of many technologies potentially available to improve the fuel 

efficiency of SI engines can be found above in draft RIA Chapter 2.2. In deriving the stringency 

of the proposed Phase 2 SI engine standard, the agencies excluded the technologies already 

presumed in the baseline engine (see Table 2-4), and rejected technologies not considered as part 

of the proposed HD pickup truck standards (see Table 2-5). The agencies have not identified a 

single SI engine technology that we believe belongs on engine-certified vocational engines that 

we do not also project to be used on complete heavy-duty pickups and vans. 

It is also important to consider how these engines will be used. Engines in pickup trucks 

are likely to be driven very differently than engines in vocational vehicles. For example, a 

complete pickup truck may do an extensive amount of towing while vocational vehicles rarely 

tow trailers. Further, the most popular applications for SI engines in vocational vehicles are 

motor homes and school buses, which each have very different driving patterns, which also differ 

from those of pickup trucks. The agencies believe these differences in application and intended 

use may lead manufacturers to offer engines that may have small differences, and that such 

differences would be captured by the vehicle test procedures applicable to those applications. 

Specifically, complete HD pickups are certified using a chassis test procedure that is described in 

40 CFR part 86, while vocational vehicles are certified using the GEM vehicle simulation tool 

described in 40 CFR part 1037. 

In light of the market structure described above in Chapter 2.6, when the agencies 

considered the feasibility of more stringent Phase 2 standards for SI vocational engines, we 

identified the following key questions: 

1. Will there be technologies available that could reduce in-use emissions from 

vocational SI engines? 

 I

 See Preamble at Section VI.C.5(a)(iv) 
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2. Would these technologies be applied to complete vehicles and carried-over to 

engine certified engines without a new standard? 

3. Would these technologies be applied to meet the vocational vehicle standards? 

4. What are the drawbacks associated with setting a technology-forcing Phase 2 

standard for SI engines? 

With respect to the first question, the agencies have identified Level 2 lubricants, Level 2 

engine friction reduction, and cylinder deactivation as technologies available to be considered for 

a Phase 2 SI engine standard. With respect to the second question, based on Table 2-5, we 

project that these may be applied to complete vehicles. The agencies have further determined 

that to the extent these technologies would be viable for complete vehicles, they would also be 

applied to engine-certified engines, to the extent they would not detract from performance 

required by vocational vehicle owners. 

With respect to the third question, we believe that to the extent these engine technologies 

are viable and effective, they would be applied to meet the GEM-based standards for vocational 

vehicles. As described elsewhere in this proposal, the Phase 2 GEM would recognize engine 

technologies through interpolation of engine data generated by engine manufacturers and 

submitted to EPA and NHTSA for vehicle certification. Thus, it would be possible for cylinder 

deactivation to be recognized over the vocational vehicle GEM test cycles, if it were present on a 

vocational SI engine. 

Nevertheless, significant uncertainty remains about how much benefit would be provided 

by the identified Phase 2 candidate SI engine technologies. It is possible that the combined 

improvement of these technologies would be one percent or less. The degree of improvement for 

friction reduction is generally not cycle-dependent, but the effectiveness of cylinder deactivation 

is highly cycle-dependent. 

It appears the fourth question regarding drawbacks is the most important. The agencies 

could propose a technology forcing standard for engine-certified SI engines based on a 

projection of each of these identified candidate technologies being effective for all engines. 

However, the agencies see value in setting the standard at a level that would not require every 

projected technology to work as expected. Effectively requiring technologies to match our 

current projections would create the risk that the standard would not be feasible if even a single 

one of the technologies failed to match our projections. This risk is amplified for SI engines 

because of the very limited product offerings, which provide far fewer opportunities for 

averaging than exist for CI engines. 

Given the relatively small improvement projected, and the likelihood that most or all of 

this improvement would result anyway from the complete HD pickup and van standards and the 

vocational vehicle standards, we do not believe such risk is justified at the engine level. 

 Because one of the guiding principles of the Phase 2 program is maintaining customer 

choice, we have a strong interest in structuring a program that would enable SI engine 

manufacturers to continue supplying loose engines to the vocational vehicle market. For the 

reasons discussed above, rather than proposing a more stringent engine standard, the agencies are 

proposing to maintain the MY 2016 fuel consumption and CO2 emission standards for SI engines 
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for use in vocational vehicles: 7.06 gallon/100 bhp-hr and 627 g CO2/bhp-hr, as measured over 

the Heavy-duty FTP engine test cycle. 

In the preamble Section V and the draft RIA Chapter 2.9, the agencies describe the 

vocational vehicle standards, including details about ways we considered SI engine technologies 

such as advanced friction reduction over the GEM vehicle test cycles, as part of the proposed 

Phase 2 vocational vehicle standards. 

2.7 Technology Application and Estimated Costs – CI Engines 

2.7.1 Phase 1 Engines 

For analytical purposes, the agencies are projecting the technologies that may be used to 

meet the 2017 diesel engine standard. This technology package serves as a baseline for costs for 

this proposal. The agencies project that such engines will be equipped with an aftertreatment 

system which meets EPA’s 0.20 grams of NOX/bhp-hr standard with a selective catalytic 

reduction (SCR) system along with EGR and meets the PM emissions standard with a diesel 

particulate filter (DPF) with active regeneration. The following discussion of technologies 

describes improvements over the 2017 model year engine performance, unless otherwise noted. 

The CO2 performance over the FTP as well as SET for the baseline engines were 

developed through manufacturer reporting of CO2 in their non-GHG certification applications for 

2014 model year. This data was carefully considered to ensure that the baseline represented an 

engine meeting the 0.20 g/bhp-hr NOX standard. For those engines that were not at this NOX

level or higher, the agencies derived a CO2 correction factor to bring them to a 0.20 g/bhp-hr 

NOX emissions rate. The CO2 correction factor is derived based on available experimental data 

obtained from manufacturers and public literature. The agencies then sales-weighted the CO2

performance to derive a baseline CO2 performance for each engine subcategory. 

In order to establish baseline SET performance for the tractor engine and FTP 

performance for the vocational, several sources were considered. Some engine manufacturers 

provided the agencies with SET modal and FTP results or fuel consumption maps to represent 

their engine ranging from 2011 to 2013 model year engine fuel consumption performance. As a 

supplement to this, complete engine map CO2 data (including SET modes) acquired in EPA test 

cells as well as those obtained from Southwest Research Institute under the agency contract were 

also considered. Those maps are subsequently adjusted to represent 2021 and 2024 model year 

engine maps by using predefined technologies that are being used in current 2014 production. 

In summary, the baseline CO2 performance for each diesel engine category is included in 

Table 2-6. 

Table 2-6 Baseline CO2 Performance (g/bhp-hr) 

LHDD - FTP MHDD - FTP HHDD - FTP HHDD - SET HHDD - SET 

576 576 555 487 460 
2-71 

2.7.2 Individual Technology Feasibility and Cost 

The cost for combustion system optimization includes costs associated with several 

individual technologies, specifically, improved cylinder head, turbo efficiency improvements, 

EGR cooler improvements, higher pressure fuel rail, improved fuel injectors and improved 

pistons. The cost estimates for each of these technologies are presented in Section 2.12 of this 

draft RIA for heavy HD, medium HD and light HD engines, respectively. 

The agencies have included the costs of model-based control development in the research 

and development costs applied separately to each engine manufacturer. 

2.7.3 Test Cycle Weighting 

The current SET modes used for tractor engine certification in Phase 1 has relative large 

weighting in C speed as shown in the middle column of the following table: 

Table 2-7 SET Modes Weighting Factors 

SPEED/% LOAD WEIGHTING FACTOR IN 

PHASE 1 (%) 

PROPOSED WEIGHTING 

FACTOR IN PHASE 2 (%)

Idle 15 12

A, 100 8 9

B, 50 10 10

B, 75 10 10

A, 50 5 12

A, 75 5 12

A, 25 5 12

B, 100 9 9

B, 25 10 9

C, 100 8 2

C, 25 5 1

C, 75 5 1

C, 50 5 1

Total 100 100

A: 23 45

B: 39 38

C: 23 5

It can be seen from the above table that 23 percent weighting is in C speed, which is 

typically in the range of 1800 rpm for HHD engines. However, many of today’s HHD engines 

do not commonly operate in such a high speed in real world driving conditions, specifically 

during cruise vehicle speed between 55 and 65 mph. The agencies received confidential 

business information from a few vehicle manufacturers that support this observation. 

Furthermore, one of the key technology trends is to down speed, moving the predominant engine 

speed from the range of 1300-1400 rpm to the range of 1150-1200 rpm at vehicle speed of 

65mph. This trend would make the predominant engine speed even further away from C speed. 

Therefore, it can be argued that, if the current SET weighting factors were retained in Phase 2, 

the test would even more poorly reflect real-world driving operations. Further, some 

technologies developed the standard may not be as effective over real world driving conditions, 

while technologies that would be more likely to deliver real world reductions could be under-
2-72 

represented on the test. Accordingly, the agencies are proposing to adjust the weighting of the 

various modes in the SET cycle as presented in the third column of Table 2-7. 

As shown, the new proposed SET mode weighting basically would move most of the C 

weighting to A speed. It also would slightly reduce the weighting factor on the idle speed. 

These proposed values are based on the confidential business information obtained from vehicle 

manufacturers. 

2.7.4 Technology Packages 

The agencies assessed the impact of technologies over each of the SET modes to project 

an overall improvement for a tractor engine. The agencies considered improvements in parasitic 

and friction losses through piston designs to reduce friction, improved lubrication, and improved 

water pump and oil pump designs to reduce parasitic losses. The aftertreatment improvements 

are available through additional improvements to lower backpressure of the systems, further 

optimization of the engine-out NOX levels, and further reduction on ammonia slop out of SCR. 

Improvements to the EGR system and air flow through the intake and exhaust systems, along 

with turbochargers, can also produce engine efficiency improvements. Improvement of 

combustion and controls can reduce fuel consumption of the engine. Engine downsizing is part 

of consideration for improving efficiency, specifically when this technology is used together 

with down speeding. Although one of the most effective technologies to improve engine 

efficiency is the use of waste heat recovery (WHR) with Rankine cycle concept, the agencies do 

not project that this technology will have noticeable market penetration until MY 2024. The 

reason is that this type of WHR system involves many components that require extensive field 

testing to assure reliability. See Chapter 2.3.9 above. The high technology cost, longer pay back 

period (if the cost and benefit of using WHR is considered in isolation), concern about 

commercial acceptance (given the technology complexity, cost, concern about demurrage costs 

and warranty claims in early model years) again point to longer necessary lead time for 

introducing this technology. During the stringency development based on various technologies, 

the agencies received strong supports from various stakeholders, provided as many confidential 

business information (CBI). Table 2-8 lists those potential technologies together with the 

agencies’ estimated market penetration for tractor engine. However, as can be seen from this 

table, the agencies would not be able to release the more detailed numbers along each mode of 

13 SET modes to justify our stringency proposal due to nature of CBI. It should be pointed out 

that the stringency developed in Table 2-8 is based on the new proposed reweighting SET 

factors. 

With respect to market penetration, the agencies use the current market information and 

literature values to project what would be in the time frame beyond 2021. For example, only 

Daimler uses turbo-compound in their DD15 and DD16 engines currently. However, they are 

phasing out turbo-compound with the replacement of asymmetric turbo technology for most 

applications. In the meantime, Volvo just announced that they would put their new developed 

turbo-compound technology into the market. Combining both manufacturers’ market shares, the 

agencies estimate 5 percent market share in 2021. With the assumption that this technology 

could prove to be cost effective and be accepted by market well, more production from existing 

manufacturers or even some of other manufacturers could adopt this technology in some of their 

trucks, and therefore the market share could pick up 10 percent after 2024. The agencies assume 
2-73 

that the WHR with Rankine cycle will pick up momentum with more lead time because of the 

nature of high performance. However, as pointed out in Chapter 2.3.9, it would be hard to see 

massive production in the 2021 because of many potential issues. The agencies expect a small 

market penetration with one percent in 2021. Based on the industrial trend for typical 

complicated system like WHR, it would take time to have a sizeable market penetration, and 

therefore, it is estimated that 5 percent in 2024, and 15 percent in 2027. Except downsizing, all 

other technologies, such as parasitic/friction loss, aftertreatment, air breathing system, and 

combustion use the same assumption on the market penetration, such as 45 percent in 2021, 95 

percent in 2024, and 100 percent in 2027. With respect to engine downsizing, the agencies don’t 

expect high market penetration as others, because downsizing always has the trade-off with 

reliability and resale values. However, we do see the potential that this type of technology can 

be effective when combining with down speeding, specifically when power demand drops due to 

more efficient engine and vehicle. It is a matter of choices. We assume 10 percent, 20 percent, 

and 30 percent market penetration in 2021, 2024, and 2027 respectively. 

It should be pointed out that the technology road maps shown in Table 2-8 including both 

reduction and market penetration would be only one of many paths manufacturers might adopt in 

order to achieve 1.5 percent, 3.7, and 4.2 percent reduction goals in 2021, 2024 and 2027 

respectively. In addition, use of 1 percent, 5 percent, and 15 percent market penetration on WHR 

in 2021, 2024, and 2027 is one of many potential paths. Considering relatively small assumed 

market penetration, this only translates into very small percent improvements due to WHR. The 

manufacturers should be able to make up the difference or achieve the same reduction goals for 

2021, 2024 and 2027 by either increasing individual technology improvement factors or 

increasing market penetration or combination of both. 

Table 2-8 Projected Tractor Engine Technologies and Reduction, Percent Improvements Beyond Phase 1, 

2017 Engine as Baseline 

SET MODE SET 

WEIGHTED 

REDUCTION 

(%) 2020-2027 

MARKET 

PENETRATION 

(2021) 

MARKET 

PENETRATION 

(2024) 

MARKET 

PENETRATION 

(2027) 

Turbo compound with clutch 1.8% 5% 10% 10% 

WHR (Rankine cycle) 3.6% 1% 5% 15% 

Parasitic/Friction (Cyl Kits, 

pumps, FIE), lubrication 

1.4% 45% 95% 100% 

Aftertreatment (lower dP) 0.6% 45% 95% 100% 

EGR/Intake & exhaust 

manifolds/Turbo /VVT/Ports 

1.1% 45% 95% 100% 

Combustion/FI/Control 1.1% 45% 95% 100% 

Downsizing 0.3% 10% 20% 30% 

Weighted reduction (%) 1.5% 3.7% 4.2% 

For the vocational engines, the agencies considered the same technology package 

developed for the HHD diesel engines as for the LHD diesel and MHD diesel engines. Similar 

to tractor engines, the package includes parasitic and friction reduction, improved lubrication, 

aftertreatment improvements, EGR system and air flow improvements, and combustion 

improvement. However, WHR technology is not part of the package. The reason is that WHR is 
2-74 

not as efficient in transient mode, and since this is the principal operating mode for vocational 

vehicles, we project limited benefit for using WHR for vocational applications. Table 2-9 below 

lists those potential technologies together with the agencies’ estimated market penetration for 

vocational engines, which is developed by combining the various CBI data with the agencies’ 

engineering judgment. 

The market penetration estimate shown in this table uses the same principle as the one 

discussed in the tractor engine. In terms of effectiveness, the model based control would be one 

of the most effective technologies. However, it would take significant efforts to develop it and 

put into production, such as neural network approach developed by Daimler19,20, because one of 

the issues is that it is still not clear how this type of technology interact with on-board 

diagnostics (OBD). Therefore, we expect 25 percent market penetration in 2021, 30 percent in 

2024, and finally 40 percent in 2027. In contrast, all other technologies, such as 

parasitic/friction, air breathing system, aftertreatment, and combustion are relatively more 

mature than the model based control, and therefore, higher market penetration is assumed. 

Table 2-9 Projected Vocational Engine Technologies and Reduction, Percent Improvements Beyond Phase 1, 

2017 Engine as Baseline 

TECHNOLOGY GHG 

EMISSIONS 

REDUCTION 

2020-2027 

MARKET 

PENETRATION 

2021 

MARKET 

PENETRATION 

2024 

MARKET 

PENETRATION 

2027 

Model based control 2.0% 25% 30% 40% 

Parasitic /Friction 1.5% 60% 90% 100% 

EGR/Air/VVT /Turbo 1.0% 50% 90% 100% 

Improved AT 0.5% 50% 90% 100% 

Combustion Optimization 1.0% 50% 90% 100% 

Weighted reduction (%)-

L/M/HHD 

 2.0% 3.5% 4.0% 

2.7.5 2021 Model Year HHD Diesel Engine Package for Tractor 

As can be seen from Table 2-8 the weighted reduction for a MY2021 tractor engine is 1.5 

percent. With this reduction, the numerical stringency values for 2021 can be derived from the 

Phase 1 rules. These proposed standards are shown in Table 2-10. 

Table 2-10 2021 Model Year Proposed Standards – Tractors 

 MHDD- SET HHDD - 

SET 

CO2 Emissions (g CO2/bhp-hr) 479 453 

Fuel Consumption (gal/100 bhp-hr) 4.71 4.45 

The cost estimates for the complete HHD diesel engine packages can be developed 

accordingly as shown in Table 2-11. 
2-75 

Table 2-11 Technology Costs as Applied in Expected Packages for MY2021 Tractor Diesel Engines under the 

Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $7 $7

Valve Actuation $82 $82

Cylinder Head (flow optimized, increased firing pressure, improved thermal 

management) 

$3 $3

Turbocharger (improved efficiency) $9 $9

Turbo Compounding $50 $50

EGR Cooler (improved efficiency) $2 $2

Water Pump (optimized, variable vane, variable speed) $43 $43

Oil Pump (optimized) $2 $2

Fuel Pump (higher working pressure, increased efficiency, improved pressure 

regulation) 

$2 $2

Fuel Rail (higher working pressure) $5 $5

Fuel Injector (optimized, improved multiple event control, higher working pressure) $5 $5

Piston (reduced friction skirt, ring and pin) $1 $1

Valve Train (reduced friction, roller tappet) $39 $39

Waste Heat Recovery $105 $105

“Right sized” engine -$40 -$40

Total $314 $314

Note: 

a

 Costs presented here include application rates. 

2.7.6 2021 Model Year LHD/MHD/HHD Diesel Engine Package for 

Vocational Vehicles 

From Table 2-9, the proposed weighted reduction for 2021 model years of all 

LHD/MHD/HHD vocational diesel engines is 2.0 percent. Table 2-12 lists the numerical 

stringency values in 2021 model year. 

Table 2-12 2021 Model Year Proposed Standards -- Vocational 

 LHDD - 

FTP 

MHDD - 

FTP 

HHDD - 

FTP 

CO2 Emissions (g CO2/bhp-hr) 565 565 544 

Fuel Consumption (gal/100 bhp-hr) 5.55 5.55 5.34 

The cost estimates for the MY2021 vocational diesel engines are shown in Table 2-13. 

We present technology cost estimates along with adoption rates in Chapter 2.12 of this draft 

RIA. We present package cost estimates in greater detail in Chapter 2.13 of this draft RIA. 
2-76 

Table 2-13 Technology Costs as Applied in Expected Packages for MY2021 Vocational Diesel Engines under 

the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 LIGHT 

HD 

MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $8 $8 $8

Valve Actuation $91 $91 $91

Cylinder Head (flow optimized, increased firing pressure, improved 

thermal management) 

$6 $3 $3

Turbocharger (improved efficiency) $10 $10 $10

EGR Cooler (improved efficiency) $2 $2 $2

Water Pump (optimized, variable vane, variable speed) $57 $57 $57

Oil Pump (optimized) $3 $3 $3

Fuel Pump (higher working pressure, increased efficiency, improved 

pressure regulation)

$3 $3 $3

Fuel Rail (higher working pressure) $7 $6 $6

Fuel Injector (optimized, improved multiple event control, higher 

working pressure)

$8 $6 $6

Piston (reduced friction skirt, ring and pin) $1 $1 $1

Valve Train (reduced friction, roller tappet) $69 $52 $52

Model Based Controls $28 $28 $28

Total $293 $270 $270

Note: 

a

 Costs presented here include application rates. 

2.7.7 2024 Model Year HHDD Engine Package for Tractor 

The agencies assessed the impact of technologies over each of the SET modes to project 

an overall improvement in the 2024 model year. The agencies considered additional 

improvements in the technologies included in the 2021 model year package. Compared to 2021 

technology package, the technology package in 2024 considers higher market adoption as shown 

in Table 2-8, thus deriving the stringency at 3.7 percent. Table 2-14 below shows the proposed 

2024 model year tractor engine standards. 

Table 2-14 2024 Model Year Proposed Standards – Tractors 

MHDD- SET HHDD - SET 

CO2 Emissions (g CO2/bhp-hr) 469 443 

Fuel Consumption (gal/100 bhp-hr) 4.61 4.35 

The costs for the MY2024 tractor diesel engines are shown in Table 2-15. We present 

technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We 

present package cost estimates in greater detail in Chapter 2.13 of this draft RIA. 
2-77 

Table 2-15 Technology Costs as Applied in Expected Packages for MY2024 Tractor Diesel Engines under the 

Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $14 $14

Valve Actuation $166 $166

Cylinder Head (flow optimized, increased firing pressure, improved thermal 

management) 

$6 $6

Turbocharger (improved efficiency) $17 $17

Turbo Compounding $92 $92

EGR Cooler (improved efficiency) $3 $3

Water Pump (optimized, variable vane, variable speed) $84 $84

Oil Pump (optimized) $4 $4

Fuel Pump (higher working pressure, increased efficiency, improved pressure 

regulation) 

$4 $4

Fuel Rail (higher working pressure) $9 $9

Fuel Injector (optimized, improved multiple event control, higher working pressure) $10 $10

Piston (reduced friction skirt, ring and pin) $3 $3

Valve Train (reduced friction, roller tappet) $75 $75

Waste Heat Recovery $502 $502

“Right sized” engine -$85 -$85

Total $904 $904

Note: 

a

 Costs presented here include application rates. 

2.7.8 2024 Model Year LHD/MHD/HHD Diesel Engine Package for 

Vocational Vehicles 

The agencies developed the 2024 model year LHD/MHD/HHD diesel engine package 

based on additional improvements in the technologies included in the 2021 model year package 

as shown in Table 2-9. The projected impact of these technologies provides an overall reduction 

of 3.5 percent over the 2017 model year baseline. Table 2-16 below shows the proposed 2024 

model year standards in numerical values. 

Table 2-16 2024 Model Year Proposed Standards – Vocational 

 LHDD - 

FTP 

MHDD - 

FTP 

HHDD - 

FTP 

CO2 Emissions (g CO2/bhp-hr) 556 556 536 

Fuel Consumption (gal/100 bhp-hr) 5.46 5.46 5.26 

Costs for MY 2024 vocational diesel engines are shown in Table 2-17. We present 

technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We 

present package cost estimates in greater detail in Chapter 2.13 of this draft RIA. 
2-78 

Table 2-17 Technology Costs as Applied in Expected Packages for MY2024 Vocational Diesel Engines under 

the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 LIGHT 

HD 

MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $13 $13 $13

Valve Actuation $157 $157 $157

Cylinder Head (flow optimized, increased firing pressure, improved 

thermal management) 

$10 $6 $6

Turbocharger (improved efficiency) $16 $16 $16

EGR Cooler (improved efficiency) $3 $3 $3

Water Pump (optimized, variable vane, variable speed) $79 $79 $79

Oil Pump (optimized) $4 $4 $4

Fuel Pump (higher working pressure, increased efficiency, improved 

pressure regulation) 

$4 $4 $4

Fuel Rail (higher working pressure) $10 $9 $9

Fuel Injector (optimized, improved multiple event control, higher 

working pressure) 

$13 $10 $10

Piston (reduced friction skirt, ring and pin) $2 $2 $2

Valve Train (reduced friction, roller tappet) $95 $71 $71

Model Based Controls $31 $31 $31

Total $437 $405 $405

Note: 

a

 Costs presented here include application rates. 

2.7.9 2027 Model Year HHDD Engine Package for Tractor 

The agencies assessed the impact of technologies over each of the SET modes to project 

an overall improvement in the 2027 model year. The agencies considered additional 

improvements in the technologies included in the 2021 model year package. Compared to 2021 

technology package, the technology package in 2027 considers higher market adoption as shown 

in Table 2-8, thus deriving the stringency at 4.2 percent. Table 2-18 below shows the proposed 

2027 model year tractor engine standards. 

Table 2-18 2027 Model Year Proposed Standards – Tractors 

MHDD- SET HHDD - SET 

CO2 Emissions (g CO2/bhp-hr) 466 441 

Fuel Consumption (gal/100 bhp-hr) 4.58 4.33 

The costs for the MY2027 tractor diesel engines are shown in Table 2-19. We present 

technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We 

present package cost estimates in greater detail in Chapter 2.13 of this draft RIA. 
2-79 

Table 2-19 Technology Costs as Applied in Expected Packages for MY2027 Tractor Diesel Engines under the 

Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $14 $14

Valve Actuation $169 $169

Cylinder Head (flow optimized, increased firing pressure, improved thermal 

management) 

$6 $6

Turbocharger (improved efficiency) $17 $17

Turbo Compounding $87 $87

EGR Cooler (improved efficiency) $3 $3

Water Pump (optimized, variable vane, variable speed) $84 $84

Oil Pump (optimized) $4 $4

Fuel Pump (higher working pressure, increased efficiency, improved pressure 

regulation) 

$4 $4

Fuel Rail (higher working pressure) $9 $9

Fuel Injector (optimized, improved multiple event control, higher working pressure) $10 $10

Piston (reduced friction skirt, ring and pin) $3 $3

Valve Train (reduced friction, roller tappet) $75 $75

Waste Heat Recovery $1,340 $1,340

“Right sized” engine -$127 -$127

Total $1,698 $1,698

Note: 

a

 Costs presented here include application rates. 

2.7.10 2027 Model Year LHD/MHD/HHD Diesel Engine Package for 

Vocational Vehicles 

The agencies developed the 2027 model year LHD/MHD/HHD diesel engine package 

based on additional improvements in the technologies included in the 2021 model year package 

as shown in Table 2-9. The projected impact of these technologies provides an overall reduction 

of 4.0 percent over the 2017 model year baseline. Table 2-20 below shows the proposed 2027 

model year standards in numerical values. 

Table 2-20 2027 Model Year Proposed Standards – Vocational 

 LHDD - FTP MHDD- FTP HHDD - FTP 

CO2 Emissions (g CO2/bhp-hr) 553 553 533 

Fuel Consumption (gal/100 bhp-hr) 5.43 5.43 5.23 

Costs for MY 2027 vocational diesel engines are shown in Table 2-21. We present 

technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We 

present package cost estimates in greater detail in Chapter 2.13 of this draft RIA. 
2-80 

 Table 2-21 Technology Costs as Applied in Expected Packages for MY2027 Vocational Diesel Engines under 

the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a

 LIGHT 

HD 

MEDIUM 

HD 

HEAVY 

HD 

Aftertreatment system (improved effectiveness SCR, dosing, DPF) $14 $14 $14

Valve Actuation $169 $169 $169

Cylinder Head (flow optimized, increased firing pressure, improved 

thermal management) 

$10 $6 $6

Turbocharger (improved efficiency) $17 $17 $17

EGR Cooler (improved efficiency) $3 $3 $3

Water Pump (optimized, variable vane, variable speed) $84 $84 $84

Oil Pump (optimized) $4 $4 $4

Fuel Pump (higher working pressure, increased efficiency, improved 

pressure regulation) 

$4 $4 $4

Fuel Rail (higher working pressure) $11 $9 $9

Fuel Injector (optimized, improved multiple event control, higher 

working pressure) 

$13 $10 $10

Piston (reduced friction skirt, ring and pin) $3 $3 $3

Valve Train (reduced friction, roller tappet) $100 $75 $75

Model Based Controls $39 $39 $39

Total $471 $437 $437

Note: 

a

 Costs presented here include application rates. 

2.7.11 HD Diesel Engine Packages under the More Stringent Alternative 4 

The more stringent alternative 4 would impose new standards in MYs 2021 and 2024, 

with the MY2024 standards essentially equivalent to the MY2027 standards under the preferred 

alternative. The resultant HDD engine costs for both tractors and vocational engines in MYs 

2021 and 2024 are shown in Table 2-22. Note that, while the technology application rates in 

MY2024 under alternative 4 are essentially identical to those for MY2027 under alternative 3, 

the costs are higher under alternative 4 due to learning effects and markup changes that are 

estimated to have occurred by MY2027 under alternative 3. 

Table 2-22 Technology Costs as Applied in Expected Packages for HD Diesel Engines under the More 

Stringent Alternative 4 relative to the Less Dynamic Baseline (2012$)a

MODEL 

YEAR 

MHDD 

TRACTOR 

HHDD 

TRACTOR 

LHDD 

VOCATIONAL

MHDD 

VOCATIONAL 

HHDD 

VOCATIONAL

2021 $656 $656 $372 $345 $345

2024 $1,885 $1,885 $493 $457 $457

Note: 

a

 Costs presented here include application rates. 

2.8 Technology Application and Estimated Costs – Tractors 

2.8.1 Defining the Baseline Tractors 

The fuel efficiency and CO2 emissions of combination tractors vary depending on the 

configuration of the tractor. Many aspects of the tractor impact its performance, including the 
2-81 

engine, transmission, drive axle, aerodynamics, and rolling resistance. For each tractor 

subcategory, the agencies selected a theoretical tractor to represent the average 2017 model year 

tractor that meets the Phase 1 standards (see 76 FR 57212, September 15, 2011). These tractors 

are used as baselines from which to evaluate costs and effectiveness of additional technologies 

and standards. The specific attributes of each tractor subcategory are listed below in Table 2-23. 

Using these values, the agencies assessed the CO2 emissions and fuel consumption performance 

of the proposed baseline tractors using the proposed version of Phase 2 GEM. The results of 

these simulations are shown below in Table 2-24. 

The Phase 1 2017 model year tractor standards and the baseline 2017 model year tractor 

results are not directly comparable. The same set of aerodynamic and tire rolling resistance 

technologies were used in both setting the Phase 1 standards and determining the baseline of the 

Phase 2 tractors. However, there are several aspects that differ. First, a new version of GEM 

was developed and validated to provide additional capabilities, including more refined modeling 

of transmissions and engines. Second, the determination of the proposed HD Phase 2 CdA value 

takes into account a revised test procedure, a new standard reference trailer, and wind averaged 

drag. In addition, the proposed HD Phase 2 version of GEM includes road grade in the 55 mph 

and 65 mph highway cycles, as discussed in preamble Section III.E. Finally, the agencies 

assessed the current level of automatic engine shutdown and idle reduction technologies used by 

the tractor manufacturers to comply with the 2014 model year CO2 and fuel consumption 

standards. To date, the manufacturers are meeting the 2014 model year standards without the 

use of this technology. Therefore, the agencies are revising the baseline APU adoption rate back 

to 30 percent, the value used in the Phase 1 baseline. 

Table 2-23 GEM Inputs for the Baseline Class 7 and 8 Tractor 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

Low 

Roof 

Mid 

Roof 

High Roof Low Roof Mid 

Roof 

High Roof Low Roof Mid 

Roof 

High 

Roof 

Engine 

2017 MY 

11L 

Engine 

350 HP 

2017 MY 

11L 

Engine 

350 HP 

2017 MY 

11L 

Engine 

350 HP 

2017 MY 

15L 

Engine 

455 HP 

2017 MY 

15L 

Engine 

455 HP 

2017 MY 

15L 

Engine 

455 HP 

2017 MY 

15L 

Engine 

455 HP 

2017 MY 

15L 

Engine 

455 HP 

2017 MY 

15L 

Engine 

455 HP 

Aerodynamics (CdA in m2) 

5.00 6.40 6.42 5.00 6.40 6.42 4.95 6.35 6.22 

Steer Tires (CRR in kg/metric ton) 

6.99 6.99 6.87 6.99 6.99 6.87 6.87 6.87 6.54 

Drive Tires (CRR in kg/metric ton) 

7.38 7.38 7.26 7.38 7.38 7.26 7.26 7.26 6.92 

 Extended Idle Reduction Adoption Rate 

N/A N/A N/A N/A N/A N/A 30% 30% 30% 

Transmission = 10 Speed Manual Transmission 

Gear Ratios = 12.8, 9.25, 6.76, 4.90, 3.58, 2.61, 1.89, 1.38, 1.00, 0.73

Drive Axle Ratio = 3.70 
2-82 

Table 2-24 Class 7 and 8 Tractor Baseline CO2 Emissions and Fuel Consumption 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof

High 

Roof

CO2 (grams 

CO2/ton-mile) 

107 118 121 86 93 95 79 87 88 

Fuel 

Consumption 

(gal/1,000 tonmile) 

10.5 11.6 11.9 8.4 9.1 9.3 7.8 8.5 8.6 

The 2017 model year baseline fuel maps in the HD Phase 2 version of GEM are different 

than those used in 2017 year fuel maps in the HD Phase 1 version. The baseline map in the HD 

Phase 2 version takes two major factors into consideration. The first is the likelihood of engine 

down speeding beyond the 2020 model year and the second is to make the gradient of brake 

specific fuel consumption rate (BSFC) around the fuel consumption sweet spot less radical when 

compared to the HD Phase 1 version’s engine fuel map. 

Figure 2-11 gives an example of an engine fuel map for a 455hp rated engine. 

183

183

183

183

184

184

184

18

1

4

86

186

186

186

190

190

190

190

193

193

193

193

196

196

196

196

203

203

203

203

218

218

218

239 239

239

183

455 HP / 15 L : 2018 Baseline BSFC (g/kW-hr)

Engine Speed (RPM)

Torque (Nm)

600 800 1000 1200 1400 1600 1800 2000

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Figure 2-11 2018MY 15L Engine Fuel Map
2-83 

2.8.2 Defining the Proposed Tractor Technology Packages 

The agencies’ assessment of the proposed technology effectiveness was developed 

through the use of GEM in coordination with modeling conducted by Southwest Research 

Institute. The agencies developed the proposed standards through a three-step process, similar to 

the approach used in Phase 1. First, the agencies developed technology performance 

characteristics for each technology, as described below. Each technology is associated with an 

input parameter which in turn would be used as an input to the Phase 2 GEM simulation tool and 

its effectiveness thereby modeled. Second, the agencies combined the technology performance 

levels with a projected technology adoption rate to determine the GEM inputs used to set the 

stringency of the proposed standards. Third, the agencies input these parameters into Phase 2 

GEM and used the output to determine the proposed CO2 emissions and fuel consumption levels. 

All percentage improvements noted below are over the 2017 baseline tractor. 

2.8.2.1 Aerodynamics 

The aerodynamic packages are categorized as Bin I, Bin II, Bin III, Bin IV, Bin V, Bin 

VI, or Bin VII based on the wind averaged drag aerodynamic performance determined through 

testing conducted by the manufacturer. In general, the proposed CdA values for each package 

and tractor subcategory were developed through EPA’s coastdown testing of tractor-trailer 

combinations, the 2010 NAS report, and SAE papers. 

2.8.2.2 Tire Rolling Resistance 

The proposed rolling resistance coefficient target for Phase 2 was developed from 

SmartWay’s tire testing to develop the SmartWay verification, testing a selection of tractor tires 

as part of the Phase 1 and Phase 2 programs, and from 2014 MY certification data. Even though 

the coefficient of tire rolling resistance comes in a range of values, to analyze this range, the tire 

performance was evaluated at four levels determined by the agencies. The four levels are the 

baseline (average) from 2010, Level I and Level 2 from Phase 1, and Level 3 that achieves an 

additional 25 percent improvement over Level 2. The Level 1 rolling resistance performance 

represents the threshold used to develop SmartWay designated tires for long haul tractors. The 

Level 2 threshold represents an incremental step for improvements beyond today’s SmartWay 

level and represents the best in class rolling resistance of the tires we tested. The Level 3 values 

represent the long-term rolling resistance value that Michelin predicts could be achieved in the 

2025 timeframe. 111 The tire rolling resistance level assumed to meet the 2017 MY Phase 1 

standard high roof sleeper cab is considered to be a weighted average of 10 percent baseline 

rolling resistance, 70 percent Level 1, and 20 percent Level 2. The tire rolling resistance to meet 

the 2017MY Phase 1 standards for the high roof day cab, low roof sleeper cab, and mid roof 

sleeper cab includes 30 percent baseline, 60 percent Level 1 and 10 percent Level 2. Finally, the 

low roof day cab 2017MY standard can be met with a weighted average rolling resistance 

consisting of 40 percent baseline, 50 percent Level 1, and 10 percent Level 2. 
2-84 

2.8.2.3 Idle Reduction 

The benefits for the extended idle reductions were developed from literature, SmartWay 

work, and the 2010 NAS report. Additional details regarding the comments and calculations are 

included in RIA Section 2.4. 

2.8.2.4 Transmission 

The benefits for automated manual, automatic, and dual clutch transmissions were 

developed from literature and from simulation modeling conducted by Southwest Research 

Institute. The benefit of these transmissions is proposed to be set to a two percent improvement 

over a manual transmission due to the automation of the gear shifting. 

2.8.2.5 Drivetrain 

The reduction in friction due to low viscosity axle lubricants is set to 0.5 percent. 6x4 

and 4x2 axle configurations lead to a 2.5 percent improvement in vehicle efficiency. 

Downspeeding would be as demonstrated through the Phase 2 GEM inputs of transmission gear 

ratio, drive axle ratio, and tire diameter. Downspeeding is projected to improve the fuel 

consumption by 1.8 percent. 

2.8.2.6 Accessories and Other Technologies 

Compared to 2017MY air conditioners, air conditioners with improved efficiency 

compressors could reduce CO2 emissions by 0.5 percent. Improvements in accessories, such as 

power steering, can lead to an efficiency improvement of 1 percent over the 2017MY baseline. 

Based on literature information, intelligent controls such as predictive cruise control could 

reduce CO2 emissions by two percent while automatic tire inflation systems improve fuel 

consumption by one percent by keeping tire rolling resistance to its optimum based on inflation 

pressure. 

2.8.2.7 Weight Reduction 

The weight reductions were developed from tire manufacturer information, the 

Aluminum Association, the Department of Energy, SABIC and TIAX. 

2.8.2.8 Vehicle Speed Limiter 

The agencies did not include vehicle speed limiters in setting the Phase 1 stringency 

levels. The agencies are not including vehicle speed limiters in the technology package for 

setting the proposed standards for Class 7 and 8 tractors. 

2.8.2.9 Summary of Technology Performance 

Table 2-25 describes the performance levels for the range of Class 7 and 8 tractor vehicle 

technologies. 
2-85 

Table 2-25 Proposed Phase 2 Technology Inputs 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Engine 

 2021M

Y 11L 

Engine 

350 HP 

2021M

Y 11L 

Engine 

350 HP 

2021M

Y 11L 

Engine 

350 HP 

2021M

Y 15L 

Engine 

455 HP 

2021M

Y 15L 

Engine 

455 HP 

2021M

Y 15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

2021M

Y 15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

Aerodynamics (CdA in m2) 

Bin I 5.3 6.7 7.6 5.3 6.7 7.6 5.3 6.7 7.4 

Bin II 4.8 6.2 7.1 4.8 6.2 7.1 4.8 6.2 6.9 

Bin III 4.3 5.7 6.5 4.3 5.7 6.5 4.3 5.7 6.3 

Bin IV 4.0 5.4 5.8 4.0 5.4 5.8 4.0 5.4 5.6 

Bin V 5.3 5.3 5.1 

Bin VI 4.9 4.9 4.7 

Bin VII 4.5 4.5 4.3 

Steer Tires (CRR in kg/metric ton) 

Base 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 

Level 1 6.6 6.6 6.6 6.6 6.6 6.6 6.6 6.6 6.6 

Level 2 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 

Level 3 4.3 4.3 4.3 4.3 4.3 4.3 4.3 4.3 4.3 

Drive Tires (CRR in kg/metric ton) 

Base 8.2 8.2 8.2 8.2 8.2 8.2 8.2 8.2 8.2 

Level 1 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 

Level 2 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 

Level 3 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 

Idle Reduction (% reduction) 

APU N/A N/A N/A N/A N/A N/A 5% 5% 5% 

Other N/A N/A N/A N/A N/A N/A 7% 7% 7% 

Transmission Type (% reduction) 

Manual 0% 0% 0% 0% 0% 0% 0% 0% 0% 

AMT 2% 2% 2% 2% 2% 2% 2% 2% 2% 

Auto 2% 2% 2% 2% 2% 2% 2% 2% 2% 

Dual Clutch 2% 2% 2% 2% 2% 2% 2% 2% 2% 

Driveline (% reduction) 

Axle 

Lubricant 

0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 

6x2 or 4x2 

Axle 

2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 

Downspeed 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 

Accessory Improvements (% reduction) 

A/C 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 

Electric or 

Mech. 

Access. 

1% 1% 1% 1% 1% 1% 1% 1% 1% 
2-86 

Off-Cycle Technologies (% reduction) 

Predictive 

Cruise 

Control 

2% 2% 2% 2% 2% 2% 2% 2% 2% 

Automated 

Tire 

Inflation 

System 

1% 1% 1% 1% 1% 1% 1% 1% 1% 

2.8.3 Tractor Technology Adoption Rates 

As explained above, tractor manufacturers often introduce major product changes 

together, as a package. In this manner the manufacturers can optimize their available resources, 

including engineering, development, manufacturing and marketing activities to create a product 

with multiple new features. In addition, manufacturers recognize that a vehicle design will need 

to remain competitive over the intended life of the design and meet future regulatory 

requirements. In some limited cases, manufacturers may implement an individual technology 

outside of a vehicle’s redesign cycle. 

With respect to the levels of technology adoption used to develop the proposed HD Phase 

2 standards, NHTSA and EPA established technology adoption constraints. The first type of 

constraint was established based on the application of fuel consumption and CO2 emission 

reduction technologies into the different types of tractors. For example, extended idle reduction 

technologies are limited to Class 8 sleeper cabs using the assumption that day cabs are not used 

for overnight hoteling. A second type of constraint was applied to most other technologies and 

limited their adoption based on factors reflecting the real world operating conditions that some 

combination tractors encounter. This second type of constraint was applied to the aerodynamic, 

tire, powertrain, and vehicle speed limiter technologies. Table 2-26, Table 2-27 and Table 2-28 

specify the adoption rates that EPA and NHTSA used to develop the proposed standards. 

NHTSA and EPA believe that within each of these individual vehicle categories there are 

particular applications where the use of the identified technologies would be either ineffective or 

not technically feasible. The addition of ineffective technologies provides no environmental or 

fuel efficiency benefit, increases costs and is not a basis upon which to set a maximum feasible 

improvement under 49 USC Section 32902 (k), or appropriate under 42 U.S.C. Section 7521 

(a)(2). For example, the agencies are not predicating the proposed standards on the use of full 

aerodynamic vehicle treatments on 100 percent of tractors, because we know that in many 

applications (for example gravel truck engaged in local aggregate delivery) the added weight of 

the aerodynamic technologies would increase fuel consumption and hence CO2 emissions to a 

greater degree than the reduction that would be accomplished from the more aerodynamic nature 

of the tractor. 

2.8.3.1 Aerodynamics Adoption Rate 

The impact of aerodynamics on a tractor-trailer’s efficiency increases with vehicle speed. 

Therefore, the usage pattern of the vehicle will determine the benefit of various aerodynamic 

technologies. Sleeper cabs are often used in line haul applications and drive the majority of their 
2-87 

miles on the highway travelling at speeds greater than 55 mph. The industry has focused 

aerodynamic technology development, including SmartWay tractors, on these types of trucks. 

Therefore the agencies are proposing the most aggressive aerodynamic technology application to 

this regulatory subcategory. All of the major manufacturers today offer at least one SmartWay 

sleeper cab tractor model, which is represented as Bin III aerodynamic performance. The 

proposed aerodynamic adoption rate for Class 8 high roof sleeper cabs in 2024 (i.e., the degree 

of technology adoption on which the stringency of the proposed standard is premised) consists of 

30 percent of Bin IV, 25 percent Bin V, 13 percent Bin VI, and 2 percent Bin VII reflecting our 

assessment of the fraction of tractors in this segment that could successfully apply these 

aerodynamic packages. We believe that there is sufficient lead time to develop aerodynamic 

tractors that can move the entire high roof sleeper cab aerodynamic performance to be as good as 

or better than today’s SmartWay designated tractors. The changes required for Bin IV and better 

performance reflect the kinds of improvements projected in the Department of Energy’s 

SuperTruck program. That program assumes that such systems can be demonstrated on vehicles 

by 2017. In this case, the agencies are projecting that truck OEMs would be able to begin 

implementing these aerodynamic technologies as early as 2021 MY on a limited scale. 

Importantly, our averaging, banking and trading provisions provide manufacturers with the 

flexibility to implement these technologies over time even though the standard changes in a 

single step. 

The aerodynamic adoption rates used to develop the proposed standards for the other 

tractor regulatory categories are less aggressive than for the Class 8 sleeper cab high roof. 

Aerodynamic improvements through new tractor designs and the development of new 

aerodynamic components is an inherently slow and iterative process. The agencies recognize 

that there are tractor applications which require on/off-road capability and other truck functions 

which restrict the type of aerodynamic equipment applicable. We also recognize that these types 

of trucks spend less time at highway speeds where aerodynamic technologies have the greatest 

benefit. The 2002 VIUS data ranks trucks by major use.112 The heavy trucks usage indicates 

that up to 35 percent of the trucks may be used in on/off-road applications or heavier 

applications. The uses include construction (16 percent), agriculture (12 percent), waste 

management (5 percent), and mining (2 percent). Therefore, the agencies analyzed the 

technologies to evaluate the potential restrictions that would prevent 100 percent adoption of 

more advanced aerodynamic technologies for all of the tractor regulatory subcategories. 

2.8.3.2 Low Rolling Resistance Tire Adoption Rate 

For the tire manufacturers to further reduce tire rolling resistance, the manufacturers must 

consider several performance criteria that affect tire selection. The characteristics of a tire also 

influence durability, traction control, vehicle handling, comfort, and retreadability. A single 

performance parameter can easily be enhanced, but an optimal balance of all the criteria would 

require improvements in materials and tread design at a higher cost, as estimated by the agencies. 

Tire design requires balancing performance, since changes in design may change different 

performance characteristics in opposing directions. Similar to the discussion regarding lesser 

aerodynamic technology application in tractor segments other than sleeper cab high roof, the 

agencies believe that the proposed standards should not be premised on 100 percent application 

of Level IV tires in all tractor segments given the potential interference with vehicle utility that 

could result. 
2-88 

2.8.3.3 Weight Reduction Technology Adoption Rate 

The agencies propose setting the 2021 through 2027 model year tractor standards without 

using weight reduction as a technology to demonstrate the feasibility. The agencies view weight 

reduction as a technology with a high cost that offers a small benefit in the tractor sector. For 

example, our estimate of a 400 pound weight reduction would cost $2,050 (2012$) in MY2021, 

but offer a 0.3 percent reduction in fuel consumption and CO2 emissions. 

2.8.3.4 Idle Reduction Technology Adoption Rate 

Idle reduction technologies provide significant reductions in fuel consumption and CO2

emissions for Class 8 sleeper cabs and are available on the market today. There are several 

different technologies available to reduce idling. These include APUs, diesel fired heaters, and 

battery powered units. Our discussions with manufacturers indicate that idle technologies are 

sometimes installed in the factory, but it is also a common practice to have the units installed 

after the sale of the truck. We would like to continue to incentivize this practice and to do so in a 

manner that the emission reductions associated with idle reduction technology occur in use. 

Therefore, as adopted in Phase 1, we are allowing only idle emission reduction technologies 

which include an automatic engine shutdown (AES) with some override provisions. In the 

preamble in Section III, we request comment on other approaches that would appropriately 

quantify the reductions that would be experienced in the real world. 

We propose a 90 percent adoption rate for this technology for Class 8 sleeper cabs. The 

agencies are unaware of reasons why AES with extended idle reduction technologies could not 

be applied to this high fraction of tractors with a sleeper cab, except those deemed a vocational 

tractor, in the available lead time. 

The agencies are interested in extending the idle reduction benefits beyond Class 8 

sleepers, including day cabs. The agencies reviewed literature to quantify the amount of idling 

which is conducted outside of hoteling operations. One study, conducted by Argonne National 

Laboratory, identified several different types of trucks which might idle for extended amounts of 

time during the work day.113 Idling may occur during the delivery process, queuing at loading 

docks or border crossings, during power take off operations, or to provide comfort during the 

work day. However, the study provided only “rough estimates” of the idle time and energy use 

for these vehicles. The agencies are not able to appropriately develop a baseline of workday 

idling for day cabs and identify the percent of this idling which could be reduced through the use 

of AES. 

2.8.3.5 Vehicle Speed Limiter Adoption Rate 

As adopted in Phase 1, we propose to continue the approach where vehicle speed limiters 

may be used as a technology to meet the proposed standard. In setting the proposed standard, 

however, we assumed a zero percent adoption rate of vehicle speed limiters. Although we 

believe vehicle speed limiters are a simple, easy to implement, and inexpensive technology, we 

want to leave the use of vehicles speed limiters to the truck purchaser. Since truck fleets 

purchase tractors today with owner-set vehicle speed limiters, we considered not including VSLs 

in our compliance model. However, we have concluded that we should allow the use of VSLs 
2-89 

that cannot be overridden by the operator as a means of compliance for vehicle manufacturers 

that wish to offer it and truck purchasers that wish to purchase the technology. In doing so, we 

are providing another means of meeting that standard that can lower compliance cost and provide 

a more optimal vehicle solution for some truck fleets. For example, a local beverage distributor 

may operate trucks in a distribution network of primarily local roads. Under those conditions, 

aerodynamic fairings used to reduce aerodynamic drag provide little benefit due to the low 

vehicle speed while adding additional mass to the vehicle. A vehicle manufacturer could choose 

to install a VSL set at 55 mph for this customer. The resulting tractor would be optimized for its 

intended application and would be fully compliant with our program all at a lower cost to the 

ultimate tractor purchaser.J

 

As in Phase 1, we have chosen not to base the proposed standards on performance of 

VSLs because of concerns about how to set a realistic adoption rate that avoids unintended 

adverse impacts. Although we expect there will be some use of VSL, currently it is used when 

the fleet involved decides it is feasible and practicable and increases the overall efficiency of the 

freight system for that fleet operator. To date, the compliance data provided by manufacturers 

indicate that none of the tractor configurations include a tamper-proof VSL setting less than 65 

mph. At this point the agencies are not in a position to determine in how many additional 

situations use of a VSL would result in similar benefits to overall efficiency or how many 

customers would be willing to accept a tamper-proof VSL setting. We are not able at this time to 

quantify the potential loss in utility due to the use of VSLs. Absent this information, we cannot 

make a determination regarding the reasonableness of setting a standard based on a particular 

VSL level. Therefore, the agencies are not premising the proposed standards on use of VSL, and 

instead would continue to rely on the industry to select VSL when circumstances are appropriate 

for its use. The agencies have not included either the cost or benefit due to VSLs in analysis of 

the proposed program’s costs and benefits. 

2.8.3.6 Summary of the Adoption Rates used to Determine the Proposed Standards 

Table 2-26, Table 2-27, and Table 2-28 provide the adoption rates of each technology 

broken down by weight class, cab configuration, and roof height. 

Table 2-26 Technology Adoption Rates for Class 7 and 8 Tractors for Determining the Proposed 2021 MY 

Standards 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

2021 MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

 J

 The agencies note that because a VSL value can be input into GEM, its benefits can be directly assessed with the 

model and off cycle credit applications therefore are not necessary even though the proposed standard is not based 

on performance of VSLs (i.e. VSL is an on-cycle technology). 
2-90 

Bin II 75% 75% 0% 75% 75% 0% 75% 75% 0% 

Bin III 25% 25% 40% 25% 25% 40% 25% 25% 40% 

Bin IV 0% 0% 35% 0% 0% 35% 0% 0% 35% 

Bin V 20% 20% 20% 

Bin VI 5% 5% 5% 

Bin VII 0% 0% 0% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Level 2 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Level 3 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Level 2 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Level 3 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 80% 80% 80% 

Transmission Type 

Manual 45% 45% 45% 45% 45% 45% 45% 45% 45% 

AMT 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Auto 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Dual Clutch 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Driveline 

Axle 

Lubricant 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

6x2 or 4x2 

Axle 

 10% 10% 20% 10% 10% 20% 

Downspeed 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Accessory Improvements 

A/C 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Electric 

Access. 

10% 10% 10% 10% 10% 10% 10% 10% 10% 

Off-Cycle Technologies 

Predictive 

Cruise 

Control 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Automated 

Tire 

Inflation 

System 

20% 20% 20% 20% 20% 20% 20% 20% 20% 
2-91 

Table 2-27 Technology Adoption Rates for Class 7 and 8 Tractors for Determining the Proposed 2024 MY 

Standards 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

2024MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 60% 60% 0% 60% 60% 0% 60% 60% 0% 

Bin III 38% 38% 30% 38% 38% 30% 38% 38% 30% 

Bin IV 2% 2% 30% 2% 2% 30% 2% 2% 30% 

Bin V 25% 25% 25% 

Bin VI 13% 13% 13% 

Bin VII 2% 2% 2% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 2 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Level 3 15% 15% 15% 15% 15% 15% 15% 15% 15% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 2 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Level 3 15% 15% 15% 15% 15% 15% 15% 15% 15% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 90% 90% 90% 

Transmission Type 

Manual 20% 20% 20% 20% 20% 20% 20% 20% 20% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle 

Lubricant 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 or 4x2 

Axle 

 20% 20% 60% 20% 20% 60% 

Downspeed 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50%

Accessory Improvements 

A/C 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Electric 

Access. 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Other Technologies 

Predictive 

Cruise 

40% 40% 40% 40% 40% 40% 40% 40% 40% 
2-92 

Control 

Automated 

Tire 

Inflation 

System 

40% 40% 40% 40% 40% 40% 40% 40% 40% 
2-93 

Table 2-28 Technology Adoption Rates for Class 7 and 8 Tractors for Determining the Proposed 2027 MY 

Standards 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

2027 MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 50% 50% 0% 50% 50% 0% 50% 50% 0% 

Bin III 40% 40% 20% 40% 40% 20% 40% 40% 20% 

Bin IV 10% 10% 20% 10% 10% 20% 10% 10% 20% 

Bin V 35% 35% 35% 

Bin VI 20% 20% 20% 

Bin VII 5% 5% 5% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 90% 90% 90% 

Transmission Type 

Manual 10% 10% 10% 10% 10% 10% 10% 10% 10% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle 

Lubricant 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 Axle 20% 20% 60% 20% 20% 60% 

Downspeed 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Accessory Improvements 

A/C 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Electric 

Access. 

30% 30% 30% 30% 30% 30% 30% 30% 30% 

Other Technologies 

Predictive 

Cruise 

Control 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Automated 40% 40% 40% 40% 40% 40% 40% 40% 40% 
2-94 

Tire 

Inflation 

System 

2.8.4 Derivation of the Proposed Tractor Standards 

The agencies used the technology effectiveness inputs and technology adoption rates to 

develop GEM inputs to derive the proposed HD Phase 2 fuel consumption and CO2 emissions 

standards for each subcategory of Class 7 and 8 combination tractors. Note that we have 

analyzed one technology pathway for each proposed level of stringency as required by the Clean 

Air Act, but manufacturers would be free to use any combination of technology to meet the 

standards on average. As such, the agencies derived a scenario tractor for each subcategory by 

weighting the individual GEM input parameters included in Table 2-25 with the adoption rates in 

Table 2-26, Table 2-27, and Table 2-28. For example, the proposed CdA value for a 2021MY 

Class 8 Sleeper Cab High Roof scenario case was derived as 40 percent times 6.3 plus 35 percent 

times 5.6 plus 20 percent times 5.1 plus 5 percent times 4.7, which is equal to a CdA of 5.74 m2

. 

Similar calculations were made for tire rolling resistance, transmission types, idle reduction, and 

other technologies. To account for the proposed engine standards and engine technologies, the 

agencies assumed a compliant engine fuel map in GEM, as described in the section below.K The 

agencies then ran GEM with a single set of vehicle inputs, as shown in Table 2-30, to derive the 

proposed standards for each subcategory. 

2.8.4.1 2021 through 2027 MY Engine Fuel Maps 

One of the most significant changes in the HD Phase 2 version of GEM is the allowance 

for manufacturers to enter their own engine fuel maps by following the test procedure described 

in the Chapter 3 Test Procedure section of this draft RIA. The GEM engine fuel map input file 

consists of three types of information in csv format. The first set of information contains the 

engine fueling map and includes three columns: engine speed in rpm, engine torque in Nm, and 

engine fueling rate in g/s. In the second set of information contains the engine full torque or lug 

curve in two columns: engine speed in rpm and torque in NM. The third set of information 

contains the motoring torque and uses the same format and units as the full load torque curve. 

The agencies developed default engine fuel maps for all subcategories, utilizing the same 

format that the manufacturers would be required to provide. Fuel maps were developed for the 

2021, 2024, and 2027 model years by applying the technologies assumed in deriving the 

proposed engine standards to the 2018 baseline engine fuel maps. Those default maps are 

derived from multiple sources of confidential business information from different stakeholders 

together with engineering judgment. These maps cover a total of 18 vehicles subcategories 

including nine tractor subcategories. We would like to point out that some of the subcategories 

share the same engine fuel maps. A list of all of the engine fuel maps used in setting the 

 K See draft RIA Chapter 2.7 explaining the derivation of the proposed engine standards. 
2-95 

standards for each subcategory is given in Table 2-29. The model years covered by the maps are 

2021, 2024, and 2027 and are shown in Figure 2-12, Figure 2-13, and Figure 2-14. 

Table 2-29 GEM Default CI Engine Fuel Maps for Tractor Vehicles 

REGULATORY SUBCATEGORY ENGINE FUEL MAP 

Class 8 Combination Sleeper Cab - High Roof 15L - 455 HP 

Class 8 Combination Sleeper Cab - Mid Roof 15L - 455 HP 

Class 8 Combination Sleeper Cab - Low Roof 15L - 455 HP 

Class 8 Combination Day Cab - High Roof 15L - 455 HP 

Class 8 Combination Day Cab - Mid Roof 15L - 455 HP 

Class 8 Combination Day Cab - Low Roof 15L - 455 HP 

Class 7 Combination Day Cab - High Roof 11L - 350 HP 

Class 7 Combination Day Cab - Mid Roof 11L - 350 HP 

Class 7 Combination Day Cab - Low Roof 11L - 350 HP 

Figure 2-12 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2021MY Standards 
2-96 

Figure 2-13 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2024MY Standards 
2-97 

Figure 2-14 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2027MY Standard 
2-98 

Table 2-30 GEM Inputs for the Proposed 2021MY Class 7 and 8 Tractor Standard Setting 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Engine 

2021MY 

11L 

Engine 

350 HP 

2021MY 

11L 

Engine 

350 HP 

2021MY 

11L 

Engine 

350 HP 

2021MY 

15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

2021MY 

15L 

Engine 

455 HP 

Aerodynamics (CdA in m2) 

4.68 6.08 5.94 4.68 6.08 5.94 4.68 6.08 5.74 

Steer Tires (CRR in kg/metric ton) 

6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 

Drive Tires (CRR in kg/metric ton) 

6.6 6.6 6.6 6.6 6.6 6.6 6.6 6.6 6.6 

Extended Idle Reduction Weighted Effectiveness 

N/A N/A N/A N/A N/A N/A 2.5% 2.5% 2.5% 

Transmission = 10 speed Automated Manual Transmission 

Gear Ratios = 12.8, 9.25, 6.76, 4.90, 3.58, 2.61, 1.89, 1.38, 1.00, 0.73 

Drive axle Ratio = 3.55 

6x2 Axle Disconnect Weighted Effectiveness 

N/A N/A N/A 0.3% 0.3% 0.5% 0.3% 0.3% 0.5% 

Low Friction Axle Lubrication = 0.1% 

Transmission benefit = 1.1% 

Predictive Cruise Control =0.4% 

Accessory Improvements = 0.1% 

Air Conditioner Efficiency Improvements = 0.1% 

Automatic Tire Inflation Systems = 0.2% 

Weight Reduction = 0 pounds 
2-99 

Table 2-31 GEM Inputs for the Proposed 2024MY Class 7 and 8 Tractor Standard Setting 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Engine 

2024MY 

11L 

Engine 

350 HP 

2024MY 

11L 

Engine 

350 HP 

2024MY 

11L 

Engine 

350 HP 

2024MY 

15L 

Engine 

455 HP 

2024MY 

15L 

Engine 

455 HP 

2024MY 

15L 

Engine 

455 HP 

2024MY 

15L 

Engine 

455 HP 

2024MY 

15L 

Engine 

455 HP 

2024MY 

15L 

Engine 

455 HP 

Aerodynamics (CdA in m2) 

4.59 5.99 5.74 4.59 5.99 5.74 4.59 5.99 5.54 

Steer Tires (CRR in kg/metric ton) 

5.9 5.9 5.9 5.9 5.9 5.9 5.9 5.9 5.9 

Drive Tires (CRR in kg/metric ton) 

6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 

Extended Idle Reduction Adoption Rate 

N/A N/A N/A N/A N/A N/A 3% 3% 3% 

Transmission = 10 speed Automated Manual Transmission 

Gear Ratios = 12.8, 9.25, 6.76, 4.90, 3.58, 2.61, 1.89, 1.38, 1.00, 0.73 

Drive axle Ratio = 3.36 

6x2 Axle Disconnect Weighted Effectiveness

N/A N/A N/A 0.5% 0.5% 1.5% 0.5% 0.5% 1.5%

Low Friction Axle Lubrication = 0.2%

Transmission benefit = 1.8%

Predictive Cruise Control =0.8%

Accessory Improvements = 0.2%

Air Conditioner Efficiency Improvements = 0.1%

Automatic Tire Inflation Systems = 0.4%

Weight Reduction = 0 pounds

Direct Drive Weighted Efficiency = 1% for sleeper cabs; 0.8% for day cabs
2-100 

Table 2-32 GEM Inputs for the Proposed 2027MY Class 7 and 8 Tractor Standard Setting 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

Low 

Roof 

Mid 

Roof 

High Roof Low Roof Mid 

Roof 

High Roof Low Roof Mid 

Roof 

High 

Roof 

Engine 

2027MY 

11L 

Engine 

350 HP 

2027MY 

11L 

Engine 

350 HP 

2027MY 

11L 

Engine 

350 HP 

2027MY 

15L 

Engine 

455 HP 

2027MY 

15L 

Engine 

455 HP 

2027MY 

15L 

Engine 

455 HP 

2027MY 

15L 

Engine 

455 HP 

2027MY 

15L 

Engine 

455 HP 

2027MY 

15L 

Engine 

455 HP 

Aerodynamics (CdA in m2) 

4.52 5.92 5.52 4.52 5.92 5.52 4.52 5.92 5.32 

Steer Tires (CRR in kg/metric ton) 

5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 

Drive Tires (CRR in kg/metric ton) 

5.9 5.9 5.9 5.9 5.9 5.9 5.9 5.9 5.9 

Extended Idle Reduction Weighted Effectiveness 

N/A N/A N/A N/A N/A N/A 3% 3% 3% 

Transmission = 10 speed Automated Manual Transmission 

Gear Ratios = 12.8, 9.25, 6.76, 4.90, 3.58, 2.61, 1.89, 1.38, 1.00, 0.73

Drive axle Ratio = 3.2 

6x2 Axle Disconnect Weighted Effectiveness

N/A N/A N/A 0.5% 0.5% 1.5% 0.5% 0.5% 1.5% 

Low Friction Axle Lubrication = 0.2% 

Transmission benefit = 1.8% 

Predictive Cruise Control =0.8% 

Accessory Improvements = 0.3% 

Air Conditioner Efficiency Improvements = 0.2% 

Automatic Tire Inflation Systems = 0.4% 

Weight Reduction = 0 pounds 

Direct Drive Weighted Efficiency = 1% for sleeper cabs; 0.8% for day cabs

The level of the 2021, 2024, and 2027 model year proposed standards for each 

subcategory are included in Table 2-33. 
2-101 

Table 2-33 Proposed 2021, 2024, and 2027 Model Year Tractor Standards 

2021 MODEL YEAR CO2 GRAMS PER TON-MILE 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 97 78 70 

Mid Roof 107 84 78 

High Roof 109 86 77 

2021 Model Year Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 9.5285 7.5639 6.8762 

Mid Roof 10.5108 8.2515 7.6621 

High Roof 10.7073 8.4479 7.5639 

2024 Model Year CO2 Grams per Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 90 72 64 

Mid Roof 100 78 71 

High Roof 101 79 70 

2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 8.8409 7.0727 6.2868 

Mid Roof 9.8232 7.5639 6.9745 

High Roof 9.9214 7.7603 6.8762 

2027 Model Year CO2 Grams per Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 87 70 62 

Mid Roof 96 76 69 

High Roof 96 76 67 

2027 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 8.5462 6.8762 6.0904 

Mid Roof 9.4303 7.4656 6.7780 

High Roof 9.4303 7.4656 6.5815 

2.8.4.2 Heavy-Haul Tractor Standards 

For Phase 2, the agencies propose to add a tenth subcategory to the tractor category for 

heavy-haul tractors. The agencies recognize the need for manufacturers to build these types of 
2-102 

vehicles for specific applications and believe the appropriate way to prevent penalizing these 

vehicles is to set separate standards recognizing a heavy-haul vehicle’s unique needs, such as 

requiring a higher horsepower engine or different transmissions. The agencies are proposing this 

change in Phase 2 because unlike in Phase 1 the engine, transmission, and drivetrain 

technologies are included in the technology packages used to determine the stringency of the 

proposed tractor standards and are included as manufacturer inputs in GEM. 

The agencies recognize that certain technologies used to determine the stringency of the 

proposed Phase 2 tractor standards are less applicable to heavy-haul tractors. Heavy-haul 

tractors are not typically used in the same manner as long-haul tractors with extended highway 

driving, therefore would experience less benefit from aerodynamics. Aerodynamic technologies 

are very effective at reducing the fuel consumption and GHG emissions of tractors, but only 

when traveling at highway speeds. At lower speeds, the aerodynamic technologies may have a 

detrimental impact due to the potential of added weight. The agencies therefore are not 

considering the use of aerodynamic technologies in the development of the proposed Phase 2 

heavy-haul tractor standards. Moreover, because aerodynamics would not play a role in the 

heavy-haul standards, the agencies propose to combine all of the heavy-haul tractor cab 

configurations (day and sleeper) and roof heights (low, mid, and high) into a single heavy-haul 

tractor subcategory.L 

Certain powertrain and drivetrain components are also impacted during the design of a 

heavy-haul tractor, including the transmission, axles, and the engine. Heavy-haul tractors 

typically require transmissions with 13 or 18 speeds to provide the ratio spread to ensure that the 

tractor is able to start pulling the load from a stop. Downsped powertrains are typically not an 

option for heavy-haul operations because these vehicles require more torque to move the vehicle 

because of the heavier load. Finally, due to the loading requirements of the vehicle, it is not 

likely that a 6x2 axle configuration can be used in heavy-haul applications. 

The agencies used the following heavy-haul tractor inputs for developing the proposed 

2021, 2024, and 2027 MY standards, as shown in Table 2-34. 

 L Since aerodynamic improvements are not part of the technology package, the agencies likewise are not proposing 

any bin structure for the heavy-haul tractor subcategory. 
2-103 

Table 2-34 GEM Inputs for Proposed 2021, 2024 and 2027 MY Heavy-Haul Tractor Standards 

HEAVY-HAUL TRACTOR 

Baseline 2021MY 2024MY 2027MY 

Engine = 2017 MY 15L 

Engine with 600 HP 

Engine = 2021 MY 15L 

Engine with 600 HP 

Engine = 2024 MY 15L 

Engine with 600 HP 

Engine = 2027 MY 15L 

Engine with 600 HP 

Aerodynamics (CdA in m2) = 5.00 

Steer Tires (CRR in 

kg/metric ton) = 7.0 

Steer Tires (CRR in 

kg/metric ton) = 6.2 

Steer Tires (CRR in 

kg/metric ton) = 6.0 

Steer Tires (CRR in 

kg/metric ton) = 5.8 

Drive Tires (CRR in 

kg/metric ton) = 7.4 

Drive Tires (CRR in 

kg/metric ton) = 6.6 

Drive Tires (CRR in 

kg/metric ton) = 6.4 

Drive Tires (CRR in 

kg/metric ton) = 6.2 

Transmission = 13 speed 

Manual Transmission 

Gear Ratios = 12.29, 

8.51, 6.05, 4.38, 3.20, 

2.29, 1.95, 1.62, 1.38, 

1.17, 1.00, 0.86, 0.73 

Transmission = 13 speed 

Automated Manual 

Transmission 

Gear Ratios = 12.29, 8.51, 

6.05, 4.38, 3.20, 2.29, 1.95, 

1.62, 1.38, 1.17, 1.00, 0.86, 

0.73 

Transmission = 13 speed 

Automated Manual 

Transmission 

Gear Ratios = 12.29, 8.51, 

6.05, 4.38, 3.20, 2.29, 1.95, 

1.62, 1.38, 1.17, 1.00, 0.86, 

0.73 

Transmission = 13 speed 

Automated Manual 

Transmission 

Gear Ratios = 12.29, 8.51, 

6.05, 4.38, 3.20, 2.29, 1.95, 

1.62, 1.38, 1.17, 1.00, 0.86, 

0.73 

Drive axle Ratio = 3.55 Drive axle Ratio = 3.55 Drive axle Ratio = 3.55 Drive axle Ratio = 3.55 

 6x2 Axle Disconnect 

Weighted Effectiveness = 0% 

6x2 Axle Disconnect 

Weighted Effectiveness = 

0% 

6x2 Axle Disconnect 

Weighted Effectiveness = 0%

Low Friction Axle 

Lubrication = 0.1% 

Low Friction Axle 

Lubrication = 0.2% 

Low Friction Axle 

Lubrication = 0.2% 

AMT benefit = 1.1% AMT benefit = 1.8% AMT benefit = 1.8% 

Predictive Cruise Control 

=0.4% 

Predictive Cruise Control 

=0.8% 

Predictive Cruise Control 

=0.8% 

Accessory Improvements = 

0.1% 

Accessory Improvements = 

0.2% 

Accessory Improvements = 

0.3% 

Air Conditioner Efficiency 

Improvements = 0.1% 

Air Conditioner Efficiency 

Improvements = 0.1% 

Air Conditioner Efficiency 

Improvements = 0.2% 

Automatic Tire Inflation 

Systems = 0.2% 

Automatic Tire Inflation 

Systems = 0.4% 

Automatic Tire Inflation 

Systems = 0.4% 

Weight Reduction = 0 

pounds 

Weight Reduction = 0 

pounds 

Weight Reduction = 0 

pounds 

The baseline 2017 MY heavy-haul tractor would emit 57 grams of CO2 per ton-mile and 

consume 5.6 gallons of fuel per 1,000 ton-mile. The agencies propose the heavy-haul standards 

shown in Table 2-35. 

Table 2-35 Proposed Heavy-Haul Tractor Standards 

HEAVY-HAUL TRACTOR 

2021 MY 2024 MY 2027 MY 

Grams of CO2 per 

Ton-Mile Standard 

54 52 51 

Gallons of Fuel per 

1,000 Ton-Mile 

5.3045 5.1081 5.010 
2-104 

2.8.4.3 Tractor Package Costs under the Preferred and Alternative Standards 

A summary of the draft technology package costs under the preferred alternative and 

relative to the less dynamic baseline is included in Table 2-36 through Table 2-39 for MYs 2021, 

2024, and 2027, respectively. A summary of the draft technology package costs under 

alternative 4 and relative to the less dynamic baseline is included in Table 2-40 and Table 2-41 

for MYs 2021 and 2024, respectively. 

Table 2-36 Class 7 and 8 Tractor Technology Incremental Costs in the 2021 Model Yeara,b 

Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle) 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low/Mid 

Roof 

High 

Roof 

Low/ Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Enginec $314 $314 $314 $314 $314 $314 $314

Aerodynamics $687 $511 $687 $511 $656 $656 $535

Tires $49 $9 $81 $15 $59 $59 $15

Tire inflation 

system 

$180 $180 $180 $180 $180 $180 $180

Transmission $3,969 $3,969 $3,969 $3,969 $3,969 $3,969 $3,969

Axle & axle 

lubes 

$50 $50 $70 $90 $70 $70 $90

Idle reduction 

with APU 

$0 $0 $0 $0 $2,449 $2,449 $2,449

Air 

conditioning 

$45 $45 $45 $45 $45 $45 $45

Other vehicle 

technologies 

$174 $174 $174 $174 $174 $174 $174

Total $5,468 $5,252 $5,520 $5,298 $7,916 $7,916 $7,771

Notes: 

a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption 

rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular). c

 Engine costs are for a heavy HD diesel engine meant for a combination tractor. 
2-105 

Table 2-37 Class 7 and 8 Tractor Technology Incremental Costs in the 2024 Model Yeara,b 

Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle) 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low/Mid 

Roof 

High 

Roof 

Low/ Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Enginec $904 $904 $904 $904 $904 $904 $904

Aerodynamics $744 $684 $744 $684 $712 $712 $723

Tires $47 $11 $78 $18 $58 $58 $18

Tire inflation 

system 

$330 $330 $330 $330 $330 $330 $330

Transmission $5,883 $5,883 $5,883 $5,883 $5,883 $5,883 $5,883

Axle & axle 

lubes 

$92 $92 $128 $200 $128 $128 $200

Idle reduction 

with APU 

$0 $0 $0 $0 $2,687 $2,687 $2,687

Air 

conditioning 

$82 $82 $82 $82 $82 $82 $82

Other vehicle 

technologies 

$318 $318 $318 $318 $318 $318 $318

Total $8,400 $8,304 $8,467 $8,419 $11,102 $11,102 $11,145

Notes: 

a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption 

rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12). 

c

 Engine costs are for a heavy HD diesel engine meant for a combination tractor. 
2-106 

Table 2-38 Class 7 and 8 Tractor Technology Incremental Costs in the 2027 Model Yeara,b 

Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle) 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low/Mid 

Roof 

High 

Roof 

Low/ Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Enginec $1,698 $1,698 $1,698 $1,698 $1,698 $1,698 $1,698

Aerodynamics $771 $765 $771 $765 $733 $733 $802

Tires $45 $10 $75 $17 $56 $56 $17

Tire inflation 

system 

$314 $314 $314 $314 $314 $314 $314

Transmission $6,797 $6,797 $6,797 $6,797 $6,797 $6,797 $6,797

Axle & axle 

lubes 

$97 $97 $131 $200 $131 $131 $200

Idle reduction 

with APU 

$0 $0 $0 $0 $2,596 $2,596 $2,596

Air 

conditioning 

$117 $117 $117 $117 $117 $117 $117

Other vehicle 

technologies 

$302 $302 $302 $302 $302 $302 $302

Total $10,140 $10,099 $10,204 $10,209 $12,744 $12,744 $12,842

Notes: 

a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption 

rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular). c

 Engine costs are for a heavy HD diesel engine meant for a combination tractor. 
2-107 

Table 2-39 Heavy-Haul Tractor Technology Incremental Costs in the 2021, 2024, and 2027 Model Yeara,b 

Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle) 

2021 MY 2024 MY 2027 MY 

Enginec $314 $904 $1,698 

Tires $81 $78 $75 

Tire inflation system $180 $330 $314 

Transmission $3,969 $5,883 $6,797 

Axle & axle lubes $70 $128 $200 

Air conditioning $45 $82 $117 

Other vehicle technologies $174 $318 $302 

Total $4,833 $7,723 $9,503 

Notes: 

a Costs shown are for the specified model year and are incremental to the costs of a 

tractor meeting the phase 1 standards. These costs include indirect costs via 

markups along with learning impacts. For a description of the markups and 

learning impacts considered in this analysis and how it impacts technology costs 

for other years, refer to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology 

costs shown reflect the average cost expected for each of the indicated tractor 

classes. To see the actual estimated technology costs exclusive of adoption rates, 

refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular). c

 Engine costs are for a heavy HD diesel engine meant for a combination tractor. 
2-108 

Table 2-40 Class 7 and 8 Tractor Technology Incremental Costs in the 2021 Model Yeara,b 

Alternative 4 vs. the Less Dynamic Baseline (2012$ per vehicle) 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low/Mid 

Roof 

High 

Roof 

Low/ Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Enginec $656 $656 $656 $656 $656 $656 $656

Aerodynamics $769 $632 $769 $632 $740 $740 $665

Tires $50 $11 $83 $18 $61 $61 $18

Tire inflation 

system 

$271 $271 $271 $271 $271 $271 $271

Transmission $6,794 $6,794 $6,794 $6,794 $6,794 $6,794 $6,794

Axle & axle 

lubes 

$56 $56 $75 $95 $75 $75 $115

Idle reduction 

with APU 

$0 $0 $0 $0 $2,449 $2,449 $2,449

Air 

conditioning 

$90 $90 $90 $90 $90 $90 $90

Other vehicle 

technologies 

$261 $261 $261 $261 $261 $261 $261

Total $8,946 $8,769 $8,999 $8,816 $11,397 $11,397 $11,318

Notes: 

a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption 

rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular). c Engine costs are for a heavy HD diesel engine meant for a combination tractor. 
2-109 

Table 2-41 Class 7 and 8 Tractor Technology Incremental Costs in the 2024 Model Yeara,b 

Alternative 4 vs. the Less Dynamic Baseline (2012$ per vehicle) 

CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low/Mid 

Roof 

High 

Roof 

Low/ Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Enginec $1,885 $1,885 $1,885 $1,885 $1,885 $1,885 $1,885

Aerodynamics $805 $935 $805 $935 $773 $773 $997

Tires $50 $14 $83 $23 $63 $63 $23

Tire inflation 

system 

$330 $330 $330 $330 $330 $330 $330

Transmission $7,143 $7,143 $7,143 $7,143 $7,143 $7,143 $7,143

Axle & axle 

lubes 

$102 $102 $138 $210 $138 $138 $210

Idle reduction 

with APU 

$0 $0 $0 $0 $2,687 $2,687 $2,687

Air 

conditioning 

$123 $123 $123 $123 $123 $123 $123

Other vehicle 

technologies 

$318 $318 $318 $318 $318 $318 $318

Total $10,757 $10,851 $10,826 $10,968 $13,461 $13,461 $13,717

Notes: 

a Costs shown are for the 2024 model year and are incremental to the costs of a tractor meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to Chapter 2 of the draft RIA (see draft RIA 2.12). 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption 

rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular). c Engine costs are for a heavy HD diesel engine meant for a combination tractor. 

2.9 Technology Application and Estimated Costs – Vocational Vehicles 

The agencies are analyzing nine baseline vocational vehicle configurations: one for each 

of the nine proposed subcategories obtained with three weight class groups and the three 

proposed composite duty cycles. For each configuration, some of the attributes and parameters 

are proposed to be fixed by the agencies and would not be available as manufacturer inputs, 

while some are proposed to be available to manufacturers when identifying configurations to 

certify in the model years of the proposed HD Phase 2 program. 

2.9.1 Vocational Engines 

This section describes the engines the agencies selected to incorporate into the baseline 

vehicle configurations for the nine proposed subcategories of vocational vehicles, and how we 

used the GEM tool to establish performance levels of these baseline vehicles. The agencies have 
2-110 

developed models for engines that represent performance of the technologies we expect would 

be installed in vocational vehicles in the baseline year of 2017. A description of the technologies 

applied to our 2017 diesel engine models can be found above in Chapter 2.7 of this draft RIA. A 

description of the GEM engine simulation can be found in draft RIA Chapter 4. 

2.9.1.1 Baseline Vocational Engines 

One of the most significant changes in the HD Phase 2 version of GEM is the provision 

for manufacturers to enter their own engine fuel maps by following the test procedure described 

in the draft RIA Chapter 3. The GEM engine fuel map input file consists of three types of input. 

The first is the engine fueling map and includes: engine speed in rpm, engine torque in Nm, and 

engine fueling rate in g/s. Second is the engine full torque or lug curve by engine speed in rpm 

and torque in NM. The third is the motoring torque curve. 

The agencies have developed the proposed vehicle standards using engine fuel maps 

derived as discussed above in Chapter 2.7for all sub-categories, utilizing the same format that the 

OEMs would be required to provide. Four sets of diesel engine maps cover all nine vocational 

vehicle regulatory categories, as listed in Table 2-42. This means that some of the subcategories 

share the same engine fuel map. For example, all MHD vehicles use the same 7L engine with 

270 hp rating. 

The 15L engine was selected for the Regional HHD subcategory because these vocational 

vehicles often require a similar level of power as a day cab tractor. This is the same power and 

displacement of the engine simulated for HHD vocational vehicles in Phase 1, and is the engine 

powering the Kenworth T700 reference vehicle, as described in RIA Chapter 4 and summarized 

in Table 4-2. An 11L-345 hp engine was selected for the HHD Multi-purpose and Urban 

subcategories, and is the engine powering the New Flyer refuse truck as the applicable reference 

vehicle, as described in RIA Chapter 4. For these two subcategories, the agencies selected this 

engine because this is a more typical power rating for vehicles that are not long haul. For 

example, Volvo manufactures many vehicles that would likely be certified in these 

subcategories, and one product brochure describes an 11L engine as being fitted in all their 

TerraPro refuse trucks and other weight-sensitive applications.114 Although the displacement 

and horsepower rating of this engine are similar to those of the MHD engine described above in 

Chapter 2.8 for Class 7 tractors, the HHD vocational engine described here is very different from 

that MHD tractor engine, both in terms of technology and its engine certification cycle. The 

engine displacements and power ratings for the MHD and LHD vocational subcategories are the 

same as those simulated in GEM for Phase 1. The specifications for the Kenworth T270 truck 

and F650 tow truck that serve as reference vehicles for all MHD and LHD are shown in Table 4-

2 of draft RIA Chapter 4. 
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Table 2-42 GEM CI Engines for Vocational Vehicles 

REGULATORY SUBCATEGORY AND DUTY CYCLE ENGINE FUEL MAP 

Heavy Heavy-Duty (Class 8) Regional Duty Cycle 15L - 455 HP 

Heavy Heavy-Duty (Class 8) Multi-Purpose and Urban Duty Cycles 11L - 345 HP 

Medium Heavy-Duty (Class 6-7) Regional, Multi-Purpose, and Urban 

Duty Cycles 

7L - 270 HP 

Light Heavy-Duty (Class 2b-5) Regional, Multi-Purpose, and Urban 

Duty Cycles 

7L - 200 HP 

The 2017 baseline maps in the HD Phase 2 version of GEM are different than those used 

for simulating engines that would meet the MY2017 vehicle standards in the HD Phase 1 

rulemaking. The baseline map in the HD Phase 2 version takes two major factors into 

consideration. The first is the likelihood of engine down speeding beyond the 2020 model year 

and the second is to make the gradient of brake specific fuel consumption rate (BSFC) around 

the fuel consumption sweet spot less radical when compared to the HD Phase 1 version’s engine 

fuel map. Figure 2-15 gives an example of an engine fuel map for a 455 hp rated CI engine, for 

the baseline year. 

183

183

183

183

184

184

184

18

1

4

86

186

186

186

190

190

190

190

193

193

193

193

196

196

196

196

203

203

203

203

218

218

218

239 239

239

183

455 HP / 15 L : 2018 Baseline BSFC (g/kW-hr)

Engine Speed (RPM)

Torque (Nm)

600 800 1000 1200 1400 1600 1800 2000

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Figure 2-15 Engine fuel map for 455hp rated CI engine used in HHD Phase 2 Baseline 

The agencies do not have baseline fuel maps for SI engines intended for vocational 

vehicles. We have not obtained sufficient manufacturer data to construct a valid set of inputs 

that would reasonably represent a gasoline engine that will comply with the applicable MY 2016 

engine standard. In lieu of a SI engine map, we have approximated the performance of a 
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baseline gasoline engine over the vocational vehicle GEM duty cycles by applying a correction 

factor to the results of simulating identical vocational vehicles fitted with CI engines. This 

correction factor is derived using coefficients from the MY 2018 HD pickup and van Phase 1 

standards for HD gasoline pickup trucks. It is appropriate to do this, because the difference in 

performance of chassis-certified SI complete vehicles and similarly-sized CI complete vehicles 

would likely be proportional to the difference between SI-powered and CI-powered GEMcertified vocational vehicles. Using the HD pickup and van stringency curves and coefficients 

for the MY 2018 targets from Phase 1, the ratio of CO2 performance of gasoline powered 

vehicles to diesel powered vehicles calculates to 1.058. This was derived using the equations 

and coefficients found at 40 CFR 1037.104(a)(2), where a work factor of 9,000 was assumed, 

and the resulting calculated target value for SI vehicles was divided by the similarly calculated 

target value for CI vehicles. The correction factor approach is not the agencies’ preferred 

approach to establishing SI vocational vehicle baseline performance, as it has many drawbacks. 

One key drawback with this approach is that it does not account for the fact that SI engines 

operate very differently than CI engines at idle. Our current model includes information on CI 

engine idle performance, and assumes transmissions and torque converters appropriate for CI 

engines. We expect these driveline parameters would be very different for SI powered vehicles, 

which would affect performance over all the GEM duty cycles. 

2.9.1.2 Improved Vocational Engines for Phase 2 Standard-Setting 

Four model year versions of these engine maps have been developed for each of these 

four diesel engines: one set for MY 2017 as the baseline, a set of maps for MY2021, a set for 

MY2024, and a set for MY 2027, as improved over the 2017 baseline engine maps. 

Because the agencies are proposing to retain the Phase 1 SI separate engine standard for 

all implementation years of Phase 2, we developed the proposed Phase 2 standards for vocational 

vehicles powered by SI engines using the same analysis described above for development of the 

SI baseline engine, without further improvement. When developing improvement levels for the 

stringency of the MY 2027 proposed vehicle standards (and the MY 2024 Alternative 4 

standards), the agencies analyzed adoption rates, effectiveness, and cost of SI engine 

technologies that reduce friction. Consistent with our projection of adoption rates of advanced 

engine friction reduction on HD gasoline pickup trucks, the agencies projected that about 40 

percent of SI engines intended for vocational vehicles would already have technologies applied 

that achieve performance equivalent to Level 2 engine friction reduction, making the available 

population that could upgrade to Level 2 about 60 percent for MY 2027. In terms of 

effectiveness, the agencies relied on the data presented in the Joint Technical Support Document 

(TSD) published in support of the LD GHG final rulemaking.115 In Chapter 3 of that document, 

the agencies present effectiveness values for upgrading from baseline levels of engine friction 

reduction to Level 2 (EFR2) as ranging from 3.4 percent to 4.8 percent, for a range of LD vehicle 

types, and with large trucks falling in the middle of this range. The TSD describes example 

technologies as including low-tension piston rings, piston skirt design, roller cam followers, 

improved crankshaft design and bearings, material coatings, material substitution, more optimal 

thermal management, and piston and cylinder surface treatments. For this Phase 2 HD 

rulemaking, the agencies derived incremental EFR2 effectiveness values from the combined 

EFR1+EFR2 values that were relative to baseline-level friction reduction. We were able to do 

this because the TSD also presented incremental improvements for upgrading from EFR1 to 
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EFR2 as ranging from 0.83 to 1.37. The agencies then calculated a mid-range effectiveness 

value representing large trucks of about one percent. In terms of costs, the agencies have 

presented the costs of upgrading from EFR1 to EFR2, as shown in Chapter 2.12 below. 

Specifically, the tables in Chapter 2.12.2.17 present the incremental and total costs over the 

model years of the proposed Phase 2 standards for low friction lubricants and engine friction 

reduction. By applying our market adoption rate of approximately 60 percent to the incremental 

costs of upgrading to EFR2 from EFR1, we estimate a vocational vehicle package cost of 

approximately $70 for this technology. 

2.9.2 Defining the Baseline Vocational Vehicles 

Nine baseline vocational vehicle configurations have been developed. Vocational vehicle 

attributes that would be set by the agencies not only in the baseline but also in the executable 

version of the GEM include: transmission gear efficiencies, transmission inertia, engine inertia, 

axle efficiency, number of axles, axle inertia, axle efficiency, electrical and mechanical 

accessory power demand, vehicle mass and payload, and aerodynamic cross-section and drag 

coefficient. Other vehicle attributes that would be available as user inputs for compliance 

purposes and for which we have established baseline values include: engine power and 

displacement (and multi-point fuel map), axle ratio, transmission type and gear ratios, and tire 

loaded radius. 

In each of these proposed baseline configurations, the agencies have not applied any 

vehicle-level fuel saving or emission reduction technology beyond what is required to meet the 

Phase 1 standards. NHTSA and EPA reviewed available information regarding the likelihood 

that manufacturers of vocational vehicles would apply technology beyond what is required for 

Phase 1, and we concluded that the best approach was to analyze a reference case that maintains 

technology performance at the Phase 1 level. Thus, the nine GEM-simulated baseline vocational 

vehicle configurations as well as the programmatic vocational vehicle reference case analyzed in 

this proposal represent what is referred to as a nominally flat baseline. 

Tables 4-8, 4-9, and 4-10 in the draft RIA Chapter 4 present the non-user-adjustable 

modeling parameters for HHD, MHD and LHD vocational vehicles, respectively. In addition to 

those parameters, to completely define the proposed baseline vehicles, the agencies also selected 

parameters shown in Table 2-43, Table 2-44, and Table 2-45. 

 These attributes and parameters were selected to represent a range of performance across 

this diverse segment, and are intended to represent a reasonable range of vocational chassis 

configurations likely to be manufactured in the implementation years of the Phase 2 program. 

The tire radii and axle ratios were selected based on market research of publically available 

manufacturer product specifications, as well as some confidential manufacturer information 

about configurations sold in prior model years. The transmission gear ratios were selected based 

on the transmissions for which models have been validated in GEM. Using the reference 

vehicles noted above, the agencies were able to better determine an appropriate type of 

transmission and its gear ratios, for all vocational vehicles. Tire radii and axle ratios were 

selected using good engineering judgment and stakeholder input, to reflect reasonable final drive 

ratios to match with our modeled transmissions. In general, the trend is that vehicles with higher 
2-114 

final drive ratios have been selected for the subcategories with less weighting of the highway test 

cycles. 

The proposed Phase 2 weighting of steer tire CRR and drive tire CRR is different than in 

Phase 1. In Phase 1, the agencies weighted drive and steer tire CRR values as if 50 percent of 

the vehicle load was on the front axle and 50 percent on the rear axle(s). The agencies reviewed 

the Vehicle Valuation Services quick reference guide to obtain typical axle load ratings for a 

variety of vocational vehicle types.116 According to that guide, the examples of vocational 

vehicles with two axles had a rear axle designed to carry between 1.8 and three times the weight 

of the front axle. Examples of vehicles with three axles had combined weights of the rear axles 

designed to carry loads ranging from 2.4 to 3.7 times the weight rating of the front axle. Based 

on this, the agencies propose a Phase 2 weighting of 0.3 times the steer tire CRR and 0.7 times 

the drive tire CRR, representing a weight distribution of the rear axle(s) carrying 2.3 times the 

weight of the front axle. 

 The proposed allocation of 50 percent of reduced weight back to payload is described 

below in Section 2.9.3.5. 

Table 2-43 Heavy Heavy-Duty User-Enterable Modeling Parameters for Vocational Baseline 

GEM INPUT HHD (CLASS 8) HHD (CLASS 8) HHD (CLASS 8) 

Regional Duty 

Cycle 

Multi-Purpose 

Duty Cycle 

Urban Duty Cycle 

Transmission 

Number of Forward Gears 10 5 5 

Gear Ratio for Each Gear 12.8, 9.25, 6.76, 

4.9, 3.58, 2.61, 

1.89, 1.38, 1, 0.73 

4.6957, 2.213, 

1.5291, 1, 0.7643 

4.6957, 2.213, 

1.5291, 1, 0.7643 

Architecture Type Manual Automatic Automatic 

Axle 

Axle Ratio 3.76 4.33 5.29 

Advanced Axle Lubrication No No No 

6 x 2 Axle No No No 

Idle Reduction 

Neutral Idle No No No 

Stop-Start No No No 

Tires 

Steer Tire CRR 7.7 7.7 7.7 

Drive Tire CRR 7.7 7.7 7.7 

Tire Loaded Radius 0.483 0.483 0.483 

Weight Reduction (lbs) No No No 
2-115 

Table 2-44 Medium Heavy-Duty User-Enterable Modeling Parameters for Vocational Baseline 

GEM INPUT MHD (CLASS 6-

7) 

MHD (CLASS 6-

7) 

MHD (CLASS 6-7)

Regional Duty 

Cycle 

Multi-Purpose 

Duty Cycle 

Urban Duty Cycle 

Transmission 

Number of Forward Gears 5 5 5 

Gear Ratio for Each Gear 3.102, 1.8107, 

1.4063, 1, 0.7117 

3.102, 1.8107, 

1.4063, 1, 0.7117 

3.102, 1.8107, 

1.4063, 1, 0.7117 

Architecture Type Automatic Automatic Automatic 

Axle 

Axle Ratio 4.33 4.88 4.88

Advanced Axle Lubrication No No No

6 x 2 Axle No No No

Idle Reduction 

Neutral Idle No No No

Stop-Start No No No

Tires 

Steer Tire CRR 7.7 7.7 7.7

Drive Tire CRR 7.7 7.7 7.7

Tire Loaded Radius 0.462 0.462 0.426

Weight Reduction (lbs) No No No
2-116 

Table 2-45 Light Heavy-Duty User-Enterable Modeling Parameters for Vocational Baseline 

GEM INPUT LHD (CLASS 2B5) LHD (CLASS 2B5) LHD (CLASS 2B5) 

Regional Duty 

Cycle 

Multi-Purpose 

Duty Cycle 

Urban Duty Cycle 

Transmission 

Number of Forward Gears 5 5 5 

Gear Ratio for Each Gear 3.102, 1.8107, 

1.4063, 1, 0.7117 

3.102, 1.8107, 

1.4063, 1, 0.7117 

3.102, 1.8107, 

1.4063, 1, 0.7117 

Architecture Type Automatic Automatic Automatic 

Axle 

Axle Ratio 4.1 4.56 4.56 

Advanced Axle Lubrication No No No 

6 x 2 Axle No No No 

Idle Reduction 

Neutral Idle No No No 

Stop-Start No No No 

Tires 

Steer Tire CRR 7.7 7.7 7.7 

Drive Tire CRR 7.7 7.7 7.7 

Tire Loaded Radius 0.378 0.378 0.378 

Weight Reduction (lbs) No No No 

2.9.2.1 Setting Normalized Vocational Vehicle Baselines 

The agencies developed adjusted, normalized vocational vehicle GEM numerical 

baselines, from which the improvements due to the technology packages would be set. This 

process began with simulating the performance of each of the nine baseline vehicles defined 

above. In this simulation, the emissions for curb idle transmission torque were calculated for 

each vehicle over the idle cycle. 

Next, the best performing vehicle in each weight class was identified. For the HHD 

weight class, this was the Regional vehicle. For the MHD and LHD weight classes, these were 

the Urban vehicles. Next, we calculated a normalization factor for each of the nine subcategories 

by dividing each GEM result of the best vehicle by the fleet weighted average result per weight 

class of the best vehicle. For each weight class, we assumed that 25 percent of the vehicles 

would use the Regional cycle, 50 percent would use the Multipurpose cycle, and 25 percent 

would use the Urban cycle. Then, we calculated a population-weighted result of the actual 

baseline GEM results in each weight class group using a presumed population distribution (25-

50-25). Finally, we calculated the normalized baseline values for each of nine subcategories by 

multiplying the weighted baseline GEM result in each weight class by each respective 

normalization factor for that subcategory. This process is summarized in Table 2-46. 
2-117 

Table 2-46 Vocational Baseline Normalizing 

CLASS 2B-5 CLASS 6-7 CLASS 8 

Urban MultiPurpose Rural, 

Regional 

Urban MultiPurpose Rural, 

Regional 

Urban MultiPurpose Rural, 

Regional 

Straight GEM Baseline Performance 

318 328 331 200 204 199 223 216 187 

Normalization Factor 

0.97 1.00 1.04 1.00 1.01 0.99 1.01 1.02 0.96 

Population Weighted Best Vehicle Result

328.8 202.4 193.9 

Population Weighted Baseline Result 

326.3 201.8 210.5 

Normalized Baseline 

316 325 339 201 203 199 212 214 203 

2.9.2.2 Assigning Vocational Vehicles to Subcategories 

To determine appropriate engine speed cut-points for subcategory assignments, the 

agencies conducted GEM simulations for each of the nine defined baseline vocational vehicles 

using a sweep of axle ratios ranging from 2.47:1 to 12:1. We then compared the CO2 emission 

rates of the composite cycle to assess the sensitivity of the results to axle ratio, and identified the 

axle ratio for which the emission rate was lowest, and thus most optimized for that vehicle and 

duty cycle. Next the agencies compared the engine speeds attained during the 55 mph and 65 

mph cruise cycles for the optimized axle ratio simulation to the maximum engine test speed of 

the engine in each simulated vehicle. The diesel engines in each simulated vehicle are described 

above in Chapter 2.9.1. The agencies used two gasoline engine models for this analysis, which 

were not used for derivation of the proposed standards. 

We noted considerable variability in the ratio of attained engine speed at 55 mph vs. 

maximum test speed, but we reasoned that if an engine was rotating close to the engine’s rated 

speed (represented by a high percent of maximum test speed) while the vehicle is at 55 mph then 

it would logically be best certified using the Urban Duty Cycle. Based on our observations and 

good engineering judgment, we selected a cutpoint for the Urban Duty Cycle where a vehicle at 

55 mph would have an engine working above 90 percent of maximum engine test speed for 

vocational vehicles powered by diesel engines and above 50 percent for vocational vehicles 

powered by gasoline engines. We similarly noted considerable variability in the ratio of attained 

engine speed at 65 mph vs. maximum test speed, but we reasoned that if an engine was rotating 

slowly (represented by a low percent of maximum test speed) while the vehicle is at 65 mph then 

it would logically be best certified using the Regional Duty Cycle. Based on our observations 

and good engineering judgment, we selected a cutpoint for the Regional Duty Cycle where a 

vehicle at 65 mph would have an engine working below 75 percent of maximum engine test 

speed for vocational vehicles powered by diesel engines and below 45 percent for vocational 

vehicles powered by gasoline engines. The proposed regulations describe this subcategory 

assignment process at 40 CFR 1037.510. 
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2.9.3 Costs and Effectiveness of Vocational Vehicle Technologies 

The following paragraphs describe the vehicle-level technologies on which the proposed 

vocational vehicle standards are predicated, and their projected effectiveness over the proposed 

test cycles. The methodology for estimating costs, including indirect cost estimates and learning 

effects, is described in draft RIA Chapter 2.12.1. Certain elements of the cost estimating 

methodology are the same as for the Phase 1 program, but as described in that section, certain 

elements are different, including how the agencies apply the markups, how the markups change 

with time, and which cost elements are influenced by learning effects. As a result of different 

technology complexities, learning effects, and different short-term and long-term warranty and 

non-warranty-related indirect costs, some technology costs identified below may appear higher 

in MY 2021 than in MY 2027. These differences are not due to changes in adoption rates, since 

the costs in Chapter 2.12 and below in Chapter 2.9.3 to 2.9.4 are for applying a given technology 

to a single vehicle. Throughout this Chapter, where a dollar cost is given for a technology, note 

that these are adjusted to be valued as year 2012 dollars. Average costs for vocational vehicle 

technology packages, including adoption rates, are presented below in Chapter 2.9.5. Detailed 

descriptions of technology packages for SI engines can be found in the draft RIA Chapter 2.6. 

Detailed descriptions of technology packages and costs for CI engines can be found in the draft 

RIA Chapter 2.7. 

2.9.3.1 Transmissions 

Transmission improvements present a significant opportunity for reducing fuel 

consumption and CO2 emissions from vocational vehicles. Transmission efficiency is important 

for many vocational vehicles as their duty cycles involve high percentages of driving under 

transient operation. The three categories of transmission improvements the agencies considered 

for Phase 2 are driveline optimization, architectural improvements, and hybrid powertrain 

systems. 

Of the technologies described above in Chapter 2.4, the agencies are predicating the 

proposed vocational vehicle standards on performance improvements achieved by use of 

advanced transmissions as described in Table 2-47, below. The projected market adoption rates 

that inform the technology packages are described in Chapter 2.9.5. 
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Table 2-47 Projected Vocational Transmission Improvements over GEM Baseline 

TRANSMISSION 

TECHNOLOGY

PROJECTED 

IMPROVEMENT 

OVER TEST 

CYCLEA

REGIONAL 

COMPOSITE 

CYCLE

MULTI-PURPOSE 

COMPOSITE 

CYCLE

URBAN 

COMPOSITE 

CYCLE

Two More Gears 

(over 5-speed)

ARB Transient 2% 1.1 1.7 1.9

55 mph Cruise 0% 

65 mph Cruise 0% 

DCT or AMT 

(over automatic) 

ARB Transient 4% 2.1 3.4 3.8 

55 mph Cruise 0% 

65 mph Cruise 0% 

HHD DCT or 

AMT (over 

manual)b

ARB Transient 4% 2.3 (2.95) N/A N/A 

55 mph Cruise 1.5% 

65 mph Cruise 1.5% 

Strong Hybrid ARB Transient 27% 14.7 22.9 25.6 

55 mph Cruise 0% 

65 mph Cruise 0% 

Deep Driveline 

Integration 

ARB Transient 7% 4.7 6.2 6.7 

55 mph Cruise 2% 

65 mph Cruise 2% 

Notes: 

a

 Improvement is relative to a 5-speed automatic transmission in GEM, except where noted. Technology 

improvements would either be modeled in GEM or would be measured over the powertrain test, except as noted. b

 Fixed improvement for HHD AMT or DCT vs. manual transmission in GEM would be 2.3 percent as shown, in 

addition to the GEM-modeled improvement of one percent over the ARB Transient and zero over the cruise cycles. 

Combined these would be 2.95 percent. 

2.9.3.1.1 Deep Integration - Conventional 

The agencies believe an effective way to derive efficiency improvements from a 

transmission is by optimizing it with the engine and other driveline components to balance both 

performance needs and fuel savings. However, many vocational vehicles today are not operating 

with such optimized systems. Due to the fact that customers are able to specify their preferred 

components in a highly customized build process, many vocational vehicles are assembled with 

components that were designed more for compatibility than for optimization. To some extent, 

vertically integrated manufacturers are able to optimize their drivelines. However, this is not 

widespread in the vocational vehicle sector, resulting, primarily, from the multi-stage 

manufacture process. The agencies thus project that transmission and driveline optimization 

would yield a substantial proportion of vocational vehicle fuel efficiency improvements for 

Phase 2. On average, we anticipate that efficiency improvements of about five percent can be 

achieved from optimization, sometimes called deep integration, of drivelines. However, we are 

not assigning a fixed level of improvement; rather we have developed a test procedure, the 

powertrain test, for manufacturers to use to obtain improvement factors representative of their 

systems. See the draft RIA Chapter 3 for a discussion of this test procedure. Depending on the 

test cycle and level of integration, the agencies believe improvement factors greater than ten 

percent above the baseline vehicle performance could be achieved. To obtain such benefits 
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across more of the vocational vehicle fleet, the agencies believe there is opportunity for 

manufacturers to form strategic partnerships and explore commercial pathways to deploy deeper 

driveline integration. For example, one partnership of an engine manufacturer and a 

transmission manufacturer has led to development of driveline components that deliver improved 

fuel efficiency based on optimization that could not be realized without sharing of critical 

data.117 

The agencies project other related transmission technologies would be recognized over 

the powertrain test along with driveline optimization. These include improved mechanical gear 

efficiency, more sophisticated shift strategies, more aggressive torque converter lockups, 

transmission friction reduction, and reduced parasitic losses.118 Each of these attributes would be 

simulated in GEM using default values, unless the powertrain test were employed. The expected 

benefits of improved gear efficiency, shift logic, and torque converter lockup are included in the 

total projected effectiveness of optimized conventional transmissions using the powertrain test. 

For conventional powertrains, the agencies are projecting effectiveness of deep integration from 

3.5 to 4.8 percent, as shown in Table 2-47 above. 

The agencies estimate the total cost to apply a high efficiency gearbox, aggressive shift 

logic, and early torque converter lockup to a vocational vehicle at $372 in MY 2021 and $315 in 

MY 2027, as described in draft RIA 2.12.3.5. The agencies describe the capital and operational 

expenses of conducting powertrain testing in the draft RIA Chapter 7.1. 

2.9.3.1.2 Architectural Transmission Improvements 

One type of architectural improvement the agencies project can reasonably be developed 

by manufacturers of all transmission architectures is increased number of gears. The benefit of 

adding more gears varies depending on whether the gears are added in the range where most 

operation occurs. The TIAX 2009 report projected that 8-speed transmissions could 

incrementally reduce fuel consumption by 2 to 3 percent over a 6-speed automatic transmission, 

for Class 3-6 box and bucket trucks, refuse haulers, and transit buses.119 Although the agencies 

estimate the improvement could on average be about two percent for the adding of two gears in 

the range where significant vehicle operation occurs, we are not assigning a fixed improvement 

based solely on number of transmission gears. Manufacturers would enter the number of gears 

and gear ratios into GEM and the model would simulate the efficiency benefit over the 

applicable test cycle. The agencies estimate the total cost to add two gears to a vocational vehicle 

transmission at $495 in MY 2021 and $457 in MY 2027, as described in draft RIA 2.12.3.1. 

Transmission efficiency could also be improved in the time frame of the proposed rules 

by changes in the architecture of conventional transmissions. Most vocational vehicles currently 

use torque converter automatic transmissions (AT), especially in Classes 2b-6. According to the 

2009 TIAX report, approximately 70 percent of Class 3-6 box and bucket trucks use AT, and all 

refuse trucks, urban buses, and motor coaches use AT. 118 AT’s offer acceleration benefits over 

drive cycles with frequent stops, which can enhance productivity. However, with the diversity of 

vocational vehicles and drive cycles, other kinds of transmission architectures can meet customer 

needs, including automated manual transmissions (AMT) and even some manual transmissions 

(MT).120 
2-121 

Other architectural changes that the agencies project would offer efficiency 

improvements include improved automated manual transmissions (AMT) and introduction of 

dual clutch transmissions (DCT). Newer versions of AMT are showing significant improvements 

in reliability, such that the current generation of transmissions with this architecture is more 

likely to retain resale value and win customer acceptance than early models.121 The agencies 

believe AMT generally compare favorably to manual transmissions in fuel efficiency, and while 

the degree of improvement is highly driver-dependent, it can be two percent or greater, 

depending on the drive cycle. See Chapter 2.4.4 for additional discussion of AMT. The agencies 

are not assigning fixed average performance levels to compare an AMT with a traditional 

automatic transmission. Although the lack of a torque converter offers AMT an efficiency 

advantage in one respect, the lag in power during shifts is a disadvantage. For Phase 2, the 

agencies have developed validated models of both AMT and AT, as described in the draft RIA 

Chapter 4. Manufacturers installing AMT or AT would enter the relevant inputs to GEM and the 

simulation would calculate the performance. Dual clutch transmissions (DCT) are already in 

production for light-duty vehicles, and are expected to become available in the vocational vehicle 

market prior to the proposed beginning of Phase 2 in MY 2021.122 Based on supplier 

conversations, manufacturers intend to match varying DCT designs with the diverse needs of the 

heavy-duty market. The agencies do not yet have a validated DCT model in GEM, and we are 

not assigning a fixed performance level for DCT, though we expect the per-vehicle fuel 

efficiency improvement due to switching from automatic to DCT to be in the range of three 

percent over the GEM vocational vehicle test cycles. Selection of transmission architecture type 

(Manual, AMT, AT, DCT) would be made by manufacturers at the time of certification, and 

GEM would either use this input information to simulate that transmission using algorithms as 

described in the draft RIA Chapter 4, or fixed improvements may be assigned. The agencies are 

proposing to assign fixed levels of improvement that vary by test cycle in GEM for AMT when 

replacing a manual, which for vocational vehicles would be in the HHD Regional subcategory. 

If a manufacturer elected not to conduct powertrain testing to obtain specific improvements for 

use of a DCT, GEM would simulate a DCT as if it were an AMT, with no fixed assigned benefit. 

According to EPA’s light-duty teardown report, the direct incremental cost to build a sixspeed wet dual clutch transmission was determined to be roughly $100 less than the cost of a sixspeed automatic transmission.123 We estimate the components and engineering to design a 

heavy-duty torque converter automatic transmission are at least as costly and complex as those to 

design a dual clutch transmission. Therefore, the agencies estimate switching from AT to DCT 

would have zero incremental cost for vocational vehicles. 

The agencies have estimated the costs of upgrading from HHD manual transmissions to 

AT, AMT, and DCT, as summarized in Table 2-48, and described in detail below in 2.12.3. 

Table 2-48 Incremental Costs for HHD Transmissions Relative to Manual Transmissionsa

TECHNOLOGY 2021 COST 2027 COST

Manual to AMT $4,472 $3,795 

Manual to AT $3,764 $3,470 

Manual to DCT $4,472 $3,795 

Note: 

a

 Costs include markups (2012$) 
2-122 

2.9.3.1.3 Deep Integration - Hybrid 

The agencies are including hybrid powertrains as a technology on which the proposed 

vocational vehicle standards are predicated. We project a variety of mild and strong hybrid 

systems, with a wide range of effectiveness. Mild hybrid systems that offer an engine stop-start 

feature are discussed below under workday idle reduction. For hybrid powertrains, we are 

estimating a 22 to 25 percent fuel efficiency improvement over the powertrain test, depending on 

the duty cycle in GEM for the applicable subcategory. The agencies obtained these estimates by 

projecting a 27 percent effectiveness over the ARB Transient cycle, and zero percent over the 

highway cruise cycles. With the proposed cycle weightings, depending on the subcategory, this 

is projected to yield a 25 to 26 percent improvement over the Urban cycle, and 22 to 23 percent 

improvement over the Multi-Purpose cycle. According to the NREL Final Evaluation of UPS 

Diesel Hybrid-Electric Delivery Vans, the improvement of a hybrid over a conventional diesel in 

gallons per ton-mile on a chassis dynamometer over the NYC Composite test cycle was 28 

percent.124 NREL characterizes the NYC Composite cycle as more aggressive than most of the 

observed field data points from the study, and may represent an ideal hybrid cycle in terms of low 

average speed, high stops per mile, and high kinetic intensity (KI). NREL noted that most of the 

observed field data points were reasonably represented by the HTUF4 cycle, over which the chassis 

dynamometer results showed a 31 percent improvement in gallons per ton-mile. In units of grams 

CO2 per mile, NREL reported these test results as 22 percent improvement over the NYC Composite 

cycle and 26 percent improvement over the HTUF4 cycle. Based on these results, and the fact that 

any improvement from strong hybrids in Phase 2 would not be simulated in GEM, rather evaluated 

using the powertrain test, the agencies deemed it reasonable to estimate a conservative 27 percent 

effectiveness over the ARB Transient in setting the stringency of the standards. 

Hybrid powertrain systems are included under transmission technologies because, 

depending on the design and degree of hybridization, they may either replace a conventional 

transmission or be deeply integrated with a conventional transmission. Further, these systems are 

often manufactured by companies that also manufacture conventional transmissions. 

The Phase 1 standards were not predicated on any adoption of hybrid powertrains in the 

vocational vehicle sector. Because the first implementation year of Phase 1 came just three years 

after promulgation, it did not offer an opportunity to provide the lead time for development of 

technology. The agencies believe the Phase 2 rulemaking would offer sufficient lead time to 

develop, demonstrate, and conduct reliability testing for technologies that are still maturing. 

Several types of vocational vehicles are well suited for hybrid powertrains, and tend to be 

early adopters of this technology. Vehicles such as utility or bucket trucks, delivery vehicles, 

refuse haulers, and buses have operational usage patterns with either a significant amount of 

stop-and-go activity or spend a large portion of their operating hours idling the main engine to 

operate a PTO unit. 

The industry is currently developing many variations of hybrid powertrain systems. 

There are a few hybrid systems in the heavy-duty market today and several more under 

development. In addition, energy storage systems are getting better.125 Heavy-duty customers 

are getting used to these systems with the number of demonstration products on the road. Even 

so, manufacturers are uncertain how much investment to make in this technology without clear 
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signals from regulators. A list of hybrid manufacturers and their products intended for the 

vocational market is provided in Table 2-49. 

Table 2-49 Examples of Hybrid Manufacturers 

MANUFACTURER PRODUCT EXAMPLE APPLICATION 

Hino Class 5 cab-over-engine batteryelectric hybrid Delivery Trucks 

Allison HHD parallel hybrid Transit Bus 

BAE HHD series or parallel hybrid Transit Bus 

XL Class 3-4 mild electric hybrid Shuttle Bus 

Crosspoint Kinetics Class 3-7 mild electric hybrid Delivery trucks, shuttle buses 

Lightning Hybrids Class 2-5 hydraulic hybrid Delivery trucks 

Parker Hannifin MHD hydraulic hybrid Delivery trucks 

Freightliner Custom Chassis MHD hydraulic hybrid Delivery trucks 

Morgan Olson MHD hydraulic hybrid Delivery trucks 

Autocar-Parker Runwise hydraulic hybrid Refuse Trucks 

Eatona HHD parallel electric hybrid Trucks and Buses 

Odyne Plug-in electric hybrid, E-PTO Utility Trucks 

Note: 

a

 Currently selling in markets outside the U.S. 

Some low cost products on the simple end of the hybrid spectrum are available that 

minimize battery demand through the use of ultra-capacitors or only provide power assist at low 

speeds. Our regulations define a hybrid system as one that has the capacity for energy storage.M 

Unofficially, some systems are commonly known as mild hybrids, where some accessories are 

electrified, the engine is not downsized and there may or may not be capacity for regenerative 

braking. Strong hybrids are typically referred to as those that have larger energy storage capacity 

such that the engine may be downsized in some cases. Depending on the drive cycle and units of 

measurement, strong hybrids developed to date have seen fuel consumption and CO2 emissions 

reductions between 20 and 50 percent in the field.126 

The agencies estimate the total cost of a hybrid powertrain system for a LHD vocational 

vehicle at $15,207 in MY 2021 and $11,791 in MY 2027. For a MHD vocational vehicle, the 

total cost is estimated at $23,904 in MY 2021 and $18,534 in MY 2027. For a HHD vocational 

vehicle, the total cost is estimated at $39,919 in MY 2021 and $30,952 in MY 2027, as described 

in draft RIA 2.12.7. The estimated higher costs for heavier vehicles are related to higher power 

demands and greater energy storage needs. These estimates assume no engine downsizing in the 

design of hybrid packages. This is in part to be conservative in our cost estimates, and in part 

because in some applications a smaller engine may not be acceptable if it would risk that 

performance could be sacrificed during some portion of a work day. 

 M NHTSA’s and EPA’s regulations define a hybrid vehicle as one that “includes energy storage features ... in 

addition to an internal combustion engine or other engine using consumable chemical fuel....” 49 CFR 535.4 and 40 

CFR 1037.801. 
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2.9.3.2 Axles 

The agencies are considering two axle technologies for the vocational sector. The first is 

advanced low friction axle lubricants. SwRI tested improved driveline lubrication and found 

measurable improvements by switching from current mainstream products to top-tier 

formulations focusing on modified viscometric effects.127 The agencies believe that a 0.5 

percent improvement in vocational vehicle efficiency (as for tractors) is achievable through the 

application of low friction axle lubricants, and have included that value as a fixed technology 

improvement value in GEM. If a manufacturer wishes to demonstrate a greater benefit, an axle 

efficiency test could be performed to support an application for an innovative technology credit. 

See draft RIA Chapter 3 for a description of a test procedure for axle efficiency. We are 

estimating the axle lubricating costs for HHD to be the same as for tractors since those vehicles 

likewise typically have three axles. However, for LHD and MHD vocational vehicles, we scaled 

down the cost of this technology to reflect the presence of a single rear axle. The agencies 

estimate the total cost of low friction axle lubricants on a LHD or MHD vocational vehicle (with 

2 axles) at $132 in MY 2021 and $114 in MY 2027. For HHD vocational vehicles (with 3 

axles), the agencies estimate the cost at $197 in MY 2021 and $172 in MY 2027, as described in 

draft RIA 2.12.5.4. 

The second axle technology the agencies are considering is a design that enables one of 

the tandem axles to temporarily disconnect or permanently be a non-driven axle, on vehicles 

with two rear (drive) axles, commonly referred to as a 6x2 configuration. The agencies have 

considered two types of 6x2 configurations for vocational vehicles: those that are engaged full 

time on a vehicle, and those that may be engaged only during some types of vehicle operation, 

such as only when operating at highway cruise speeds. In prior years, manufacturers offered 

versions of this technology that were not accepted by vehicle owners. When the second drive 

axle is no longer powered, traction may be sacrificed in some cases. Vehicles with earlier 

versions of this technology have seen reduced residual values in the secondary market.128 Over 

the model years covered by the Phase 2 rules, the agencies expect the market to offer 

significantly improved versions of this technology, with traction control maintained at lower 

speeds and efficiency gains at highway cruise speeds.129 Mechanisms to automatically 

disconnect or reconnect drive axles would likely function in a similar manner as with two axle 

vehicles that can seamlessly switch from four-wheel drive to two-wheel drive and back. Further 

information about 6x2 axle technology is provided in the feasibility of the tractor standards, 

preamble Section III, as well as in draft RIA Chapter 2.4. 

The efficiency benefit of a 6x2 axle configuration is highly duty-cycle dependent. In 

many instances, vocational vehicles need to operate off-highway, such as at a construction site 

delivering materials or dumping at a refuse collection facility. Under these conditions, vehicles 

with two drive axles may need the full tractive benefit of both drive axles. The 6x2 axle 

disconnect technology is not expected to measurably improve a vehicle’s efficiency for vehicles 

whose normal duty cycle is performing off-highway work, but the agencies do expect this 

technology to be recognized on a cycle with a significant weighting of highway cruise. The 

agencies estimate the total cost of full time 6x2 at $197 in MY 2021 and $172 in MY 2027. The 

agencies estimate the total cost of part time 6x2 on a vocational vehicle at $120 in MY 2021 and 

$116 in MY 2027, as described in draft RIA 2.12.5.2. 
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Some vocational vehicles in the HHD Regional subcategory may see a 6x2 axle 

disconnect technology as a reasonable option for improving fuel efficiency. As in Phase 1, our 

vehicle simulation model assumes that only HHD vehicles have two rear axles, so only these 

could be recognized for adopting this technology. Further, the agencies don’t believe vehicles in 

the Multipurpose and Urban subcategories operate with a significant enough highway time to 

make this technology worthwhile. While the agencies project this can achieve over 2 percent 

benefit at highway cruise, we propose to assign a fixed 2.5 percent value in GEM for part time 

6x2 over the highway cruise cycles and zero over the ARB Transient cycle, where the specific 

benefit would be calculated according to the composite weighting of the applicable vocational 

vehicle test cycle.130

2.9.3.3 Lower Rolling Resistance Tires 

Tires are the second largest contributor to energy losses of vocational vehicles, as found 

in the energy audit conducted by Argonne National Lab.131 There is a wide range of rolling 

resistance of tires used on vocational vehicles today. This is in part due to the fact that the 

competitive pressure to improve rolling resistance of vocational vehicle tires has been less than 

that found in the line haul tire market. In addition, the drive cycles typical for these applications 

often lead vocational vehicle buyers to value tire traction and durability more heavily than rolling 

resistance. The agencies acknowledge there can be tradeoffs when designing a tire for reduced 

rolling resistance. These tradeoffs can include characteristics such as wear resistance, cost and 

scuff resistance. NHTSA, EPA, and ARB met with stakeholders from the tire industry 

(Bridgestone, Continental, Cooper, Goodyear, and Michelin) to discuss the next generation of 

lower rolling resistance (LRR) tires for the Phase 2 timeframe for all segments of Class 2b-8 

vehicles, including trailers. Manufacturers discussed forecasts for rolling resistance levels and 

production availability in the Phase 2 timeframe, as well as their plans for improving rolling 

resistance performance while maintaining other performance parameters such as traction, 

handling, wear, mass reduction, retreadability, and structural durability. 

The meetings included specific discussions of the impacts of the current generation of 

LRR tires on vehicle stopping distance and handling. Manufacturers indicated no known safety 

disbenefit in the current on-road fleet from use of LRR tires. While the next generation of tires 

may require some tradeoffs in wear performance and costs over the next 10 years to achieve 

better tire rolling resistance performance, manufacturers said they will not trade off safety for 

performance. They also emphasized that keeping tires inflated (through proper maintenance or 

automatic systems) was the best way to assure long term fuel efficiency and safety during 

vehicle operation. 

According to the 2015 NHTSA Technology Study, vocational vehicles are likely to see 

the most benefits from reduced tire rolling resistance when they are driving at 55 mph.132 This 

report also found an influence of vehicle weight on the benefits of LRR tires. The study found 

that both vocational vehicles tested had greater benefits of LRR tires at 100 percent payload than 

when empty. Also, the T270 delivery box truck that was 4,000 pounds heavier when fully 

loaded saw slightly greater efficiency gains from LRR tires than the F650 flatbed tow truck over 

the same cycles. At higher speeds, aerodynamic drag grows, which reduces the rolling resistance 

share of total vehicle power demand. In highly transient cycles, the power required to accelerate 

the vehicle inertia overshadows the rolling resistance power demand. In simulation, GEM 
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represents vocational vehicles with fixed vehicle weights, payloads and aerodynamic 

coefficients. Thus, the benefit of LRR tires would be reflected in GEM differently for vehicles 

of different weight classes. There will also be further differences arising from the different test 

cycles. Based on preliminary simulations, it appears the vehicles in GEM most likely to see the 

greatest fuel efficiency gains from use of LRR tires are those in the MHD weight classes tested 

over the Regional or Multipurpose duty cycles, where one percent efficiency improvement could 

be achieved by reducing CRR by four to five percent. Those seeing the least benefit from LRR 

tires would likely be Class 8 vocational vehicles tested over the Urban or Multipurpose cycles, 

where one percent efficiency improvement could be achieved by reducing CRR by seven to eight 

percent. 

As shown in draft RIA Chapter 2.12.8, the agencies estimate the total cost to apply LRR 

tires that have five percent lower CRR than baseline to be the same as the cost to apply baselinelevel LRR tires. The agencies estimate the cost to apply LRR tires that have 10 percent lower 

CRR than baseline to be about $4 more than the cost of baseline tires. The agencies estimate the 

cost to apply LRR tires that have 15 percent lower CRR than baseline to be about $6 more than 

the cost of baseline tires. Based on these costs, some illustrations of the costs associated with 

LRR tires are provided. To fit a LHD or MHD vocational vehicle with two steer tires improved 

by 10 percent and 4 drive tires improved by 5 percent would be roughly $9 to $10 in MY 2021 

as well as in MY 2024. Based on the estimated zero-cost to upgrade the drive tires by five 

percent, we estimate the cost to fit a HHD vocational vehicle (with 10 tires) with the same CRR 

upgrades would be roughly the same, $9 to $10. 

As another example, to fit a LHD or MHD vocational vehicle with two steer tires 

improved by 15 percent and 4 drive tires improved by 10 percent, it is estimated to cost $33 in 

MY 2024. For a HHD vehicle (with 8 drive tires) to make the same CRR upgrades, we estimate 

the cost to be $54 in MY 2024. Detailed tables of LRR tire costs in each year are provided in 

draft RIA Chapter 2.12.8. 

The agencies propose to continue the light truck (LT) tire CRR adjustment factor that was 

adopted in Phase 1. See generally 76 FR 57172-74. In Phase 1, the agencies developed this 

adjustment factor by dividing the overall vocational test average CRR of 7.7 by the LT 

vocational average CRR of 8.9. This yielded an adjustment factor of 0.87. After promulgation of 

the Phase 1 rules, the agencies conducted additional tire CRR testing on a variety of LT tires, 

most of which were designated as all-position tires. In addition, manufacturers have submitted to 

the agencies pre-certification data that include CRR values provided by tire suppliers. For the 

small subset of newer test tires that were designated as steer tires, the average CRR was 7.8 

kg/ton. For the subset of newer test tires that were designated as drive tires, the average CRR 

was 8.6 kg/ton. However all-position tires had an average CRR of 8.9 kg/ton.133 Therefore, for 

LT vocational vehicle tires, we propose to continue allowing the measured CRR values to be 

multiplied by the 0.87 adjustment factor before entering the values in the GEM for compliance, 

because this additional testing has not revealed compelling information that a change is needed. 

2.9.3.4 Workday Idle Reduction 

The Phase 2 idle reduction technologies considered for vocational vehicles are those that 

reduce workday idling, unlike the overnight idling of combination tractors. There are many 
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potential such technologies. The agencies in particular evaluated neutral idle and stop-start 

technologies, and the proposed standards are predicated on projected amounts of penetrations of 

these technologies. While neutral idle is necessarily a transmission technology, stop-start could 

range from an engine technology to one that would be installed by a secondary manufacturer 

under a delegated assembly agreement. 

The agencies are aware that for a vocational vehicle’s engine to turn off during workday 

driving conditions, there must be a reserve source of energy to maintain functions such as power 

steering, cabin heat, and transmission pressure, among others. Stop-start systems can be viewed 

as having a place on the low-cost end of the hybridization continuum. The agencies are 

including the cost of energy storage sufficient to maintain critical onboard systems and restart the 

engine as part of the cost of vocational vehicle stop-start packages. The technologies to capture 

this energy could include a system of photovoltaic cells on the roof of a box truck, or 

regenerative braking. The technologies to store the captured energy could include a battery or a 

hydraulic pressure bladder. According to CALSTART’s report to the NAFA 2014 Institute and 

Expo, examples of suppliers of on-vehicle energy storage systems that can enable idle reduction 

include Altec, Terex, and Time. More discussion of stop-start technologies is found in the draft 

RIA Chapter 2.4. 

The agencies are also proposing a certification test cycle, as described in draft RIA 

Chapter 3.4.2, which measures the amount of fuel saved and CO2 reduced by these two primary 

types of technologies: neutral idle and stop-start. Vocational vehicles frequently also idle while 

cargo is loaded or unloaded, and while operating a PTO such as compacting garbage or operating 

a bucket. In these rules, the agencies are proposing that the Regional duty cycle have ten percent 

idle, the Multi-purpose cycle have 15 percent idle, and the Urban cycle have 20 percent idle. 

These estimates are based on some publically available data published by NREL.134 Figure 2-16 

depicts a chart that illustrates the type of data on zero-speed operation data from delivery trucks 

available from NREL on its Fleet DNA web site. However, because engine parameters were not 

captured during the data-logging of this vehicle activity, these data cannot distinguish between 

zero speed conditions with the engine off and zero speed conditions with the engine idling. 
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Figure 2-16 Example Fleet DNA Vehicle Activity Data from NREL 

Combining the publically available zero-speed frequency charts from NREL on delivery 

trucks, service vans, delivery vans, and bucket trucks, the agencies observed that roughly half of 

the logged operating days had less than 10 percent time at zero speed, roughly 30 percent of the 

logged operating days had between 10 and 15 percent time at zero speed, roughly 20 percent of 

the logged operating days had between 15 and 20 percent time at zero speed, and roughly 6 

percent of the logged operating days had over 20 percent time at zero speed. School buses were 

excluded from this average, because the given distribution had two modes: over 40 percent of the 

school bus operating days logged had less than five percent time at zero speed, while nearly half 

of the logged operating days for those buses had roughly 40 percent time at zero speed. 

Without actual engine information, if we assume all the zero speed time is idling, then 

based on these rough estimates, it appears that 94 percent of these vehicles (excluding school 

buses) idle at frequencies of less than 20 percent on a daily basis. Thus, the agencies designed 

composite test cycles where the maximum weighting of idle was 20 percent. We assigned that 

value to the Urban cycle, where we expect a high incidence of traffic-related idling and city 

delivery routes involving frequent stops. The 15 percent and 10 percent idle weightings for the 

Multi-Purpose and Regional cycles, respectively, were selected as reasonably lesser values, 

given the distributions observed in the NREL charts. Table 2-50 presents a summary of this 

analysis. 
2-129 

Table 2-50 Daily Zero Speed Percentage Distribution by Vehicle Type 

LESS THAN 10 10 TO 14 15 TO 20 OVER 20 

Service Vans 59% 18% 23% 0% 

Delivery Trucks 76% 16% 9% 0% 

Delivery Vans 38% 35% 2% 25% 

Bucket Trucks 18% 45% 37% 0% 

School Buses 41% 3% 7% 48% 

Average 47% 23% 16% 15% 

Average 

Without Buses 

48% 28% 18% 6% 

Because these data are not representative of the national vocational vehicle fleet, EPA has 

entered into an interagency agreement with NREL to further characterize workday idle among 

vocational vehicles. One task of this agreement is to estimate the nationally representative 

fraction of idle operation for vocational vehicles for each proposed regulatory subcategory. The 

preliminary range of daily idle operation per vehicle indicated by this work is about 18 to 33 

percent when combining the data from all available vehicles. An analysis of possible 

vocational vehicle standards derived from alternate characterizations of idle operation has been 

prepared by the agencies, and is available for review in the public docket for this rulemaking.135 

Based on GEM simulations using the currently proposed vocational vehicle test cycles, 

the agencies estimate neutral idle for automatic transmissions to provide fuel efficiency 

improvements ranging from one percent to four percent, depending on the regulatory 

subcategory. The agencies estimate stop-start to provide fuel efficiency improvements ranging 

from 0.8 percent to seven percent, depending on the regulatory subcategory. Because of the 

higher idle weighting factor in the Urban test cycle, vehicles certified in these subcategories 

would derive the greatest benefit from applying idle reduction technologies. This presumes there 

is a correlation between amount of urban driving and amount of idle time. 

Although the primary program would not simulate vocational vehicles over a test cycle 

that includes PTO operation, the agencies are proposing to continue, with revisions, the hybridPTO test option that was in Phase 1. See 40 CFR 1037.525 and 76 FR 57247. Recall that we are 

proposing to regulate vocational vehicles at the incomplete stage when a chassis manufacturer 

may not know at the time of certification whether a PTO will be installed or how the vehicle will 

be used. Although chassis manufacturers will certainly know whether a vehicle’s transmission is 

PTO-enabled, that is very different from knowing whether a PTO will actually be installed and 

how it will be used. Chassis manufacturers may rarely know whether the PTO-enabled vehicle 

will use this capability to maneuver a lift gate on a delivery vehicle, to operate a utility boom, or 

merely as a reserve item to add value in the secondary market. In cases where a manufacturer 

can certify that a PTO with an idle-reduction technology will be installed either by the chassis 

manufacturer or by a second stage manufacturer, the hybrid-PTO test cycle may be utilized by 

the certifying manufacturer to measure an improvement factor over the GEM duty cycle that 

would otherwise apply to that vehicle. In addition, the delegated assembly provisions would 

apply. See preamble Section V.E for a description of the delegated assembly provisions. See 

draft RIA Chapter 3 for a discussion of the proposed revisions to the hybrid PTO test cycle. In 
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cases where a chassis manufacturer does not know whether a powertrain that is PTO-enabled 

will actually have a PTO-using tool installed, and whether there will be an energy storage system 

installed to save fuel during PTO operation, then the agencies do not see a way for the Phase 2 

program to recognize hybrid PTO technology. 

Our estimates are that applying neutral idle to a vocational vehicle with an automatic 

transmission would cost $9 in MY 2021, decreasing to $8 in MT 2027, as shown in draft RIA 

2.12.6. These costs are based on a small amount of engineering development and testing costs, 

with no hardware required. Our estimates are that the cost of applying stop-start to a vocational 

vehicle would vary by vehicle weight class. For LHD vocational vehicles, we estimate the total 

cost would range from $855 in MY 2021 to $709 in MY 2027. For MHD vocational vehicles, 

we estimate the total cost would range from $902 in MY 2021 to $748 in MY 2027. For HHD 

vocational vehicles, we estimate the total cost would range from $1,657 in MY 2021 to $1,374 in 

MY 2027. These costs, presented in draft RIA Chapter 2.12.6, are derived from costs reported 

by Tetra Tech for stop-start, along with costs for electrified accessories used in the light-duty 

GHG program, and scaled up for heavier vehicles. 

With either a stop-start engine feature or with a neutral idle transmission calibration, less 

fuel is burned at idle. Furthermore, it is expected that SCR catalyst function could be better 

managed when an engine shuts off than when it idles, because SCR systems are well insulated 

and can maintain temperature when an engine is shut off, however idling causes relatively cool 

air to flow through a catalyst. Therefore, the agencies have reason to believe there may be a NOX

co-benefit to stop-start idle reduction technologies, and possibly also to neutral idle. This would 

be true if the NOX reductions from reduced fuel consumption and retained aftertreatment 

temperature were greater than any excess NOX emissions due to engine re-starts. 

2.9.3.5 Weight Reduction 

The agencies believe there is opportunity for weight reduction in some vocational 

vehicles. The 2015 NHTSA Technology Study found that weight reduction provides a greater 

fuel efficiency benefit for vehicles driving under transient conditions than for those operating 

under constant speeds. In simulation, the study found that the two Class 6 trucks improved fuel 

efficiency by over two percent on the ARB transient cycle by removing 1,100 lbs. Further, 

SwRI observed that the improvements due to weight reduction behaved linearly.136 The 

proposed menu of components available for a vocational vehicle weight credit in GEM is 

presented in Table 2-52. It includes fewer options than for tractors, but the agencies believe 

there are a number of feasible material substitution choices at the chassis level, which could add 

up to weight savings on the order of a few hundred pounds. The agencies estimate the total cost 

to reduce the weight of a vocational vehicle by 200 pounds to be $683 in MY 2021 and $578 in 

MY 2027, as described in draft RIA 2.12.10.3. This is in the range of $3 to $4 per pound, as 

reported by TIAX 2009.137 

To assess the projected effectiveness of weight reduction of the proposed package of 200 

pounds, the agencies simulated a HHD, MHD and LHD vocational vehicle in GEM over each of 

the separate test cycles. Based on the results of this simulation, the agencies project a reduction 

of 200 pounds may yield a fuel efficiency improvement ranging from 0.8 percent to 2 percent 

over the ARB Transient cycle, depending on vehicle weight class. The results of this example 
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simulation are presented in Table 2-51. Consistent with the results of SwRI study mentioned 

above, these GEM results show a slightly greater benefit over transient operation than highway 

cruise. 

Table 2-51 Projected Effectiveness of Vocational Weight Reduction 

 HHD MHD LHD 

Weight Reduction 200 0 200 0 200 0 

Static Test Weight (kg) 

19,006 

 

19,051 

 

11,363 

 

11,408 

 

7,212 

 

7,257 

Dynamic Test Weight (kg) 

19,573 

 

19,618 

 

11,703 

 

11,748 

 

7,552 

 

7,597 

Payload (ton) 7.55 7.5 5.65 5.6 2.9 2.85

Transient CO2 Emission (g CO2 ptm) 240 242 224 227 351 358

55mph CO2 Emission (g CO2 ptm) 181 182 182 184 332 338

65mph CO2 Emission (g CO2 ptm) 216 217 230 232 394 401

Effectiveness over Transient -0.8% -1.1% -2.0% 

Effectiveness over 55 mph -0.7% -1.0% -1.8%

Effectiveness over 65 mph -0.7% -1.0% -1.8%

Urban Cycle Effectiveness (94% 

Transient, 6% 55 mph) 

-0.8% -1.1% -2.0%

Multi-Purpose Cycle Effectiveness 

(82% Transient, 15% 55 mph, 3% 65 

mph) 

-0.8% -1.1% -1.9%

Regional Cycle Effectiveness (50% 

Transient, 28% 55 mph, 22% 65 

mph) 

-0.8% -1.0% -1.9%

Without more specific data on which to base our assumptions, the agencies are proposing 

to allocate 50 percent of any mass reduction to increased payload, and 50 percent to reduce the 

chassis weight. We considered the data on which the tractor weight allocation (1/3:2/3) is based, 

but determined this would not be valid for vocational vehicles, as the underlying data pertained 

only to long haul tractor-trailers. The agencies propose that 50 percent of weight removed from 

vocational vehicle chassis would be added back as additional payload in GEM. This suggests an 

equal likelihood that a vehicle would be reducing weight for benefits of being lighter, or 

reducing weight to carry more payload. 

One reason why this effectiveness appears greater than would be expected based on the 

SwRI results is the change in payload. As shown in Table 2-51, this payload attribute has a 

stronger influence on the effectiveness than the duty cycle. For the LHD vehicles, reducing 200 

pounds would decrease the test weight by 0.6 percent and increase the payload by 1.8 percent. 

The agencies project the effectiveness of weight reduction for Phase 2 would be one percent or 

less for HHD and MHD vocational vehicles, and the effectiveness would be close to two percent 

for LHD vehicles over any of the vocational vehicle composite cycles. 
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Table 2-52 Proposed Vocational Weight Reduction Technologies 

COMPONENT MATERIAL 

VOCATIONAL VEHICLE CLASS 

Class 2b-5 Class 6-7 Class 8 

Axle Hubs - Non-Drive Aluminum 40 40 

Axle Hubs - Non-Drive High Strength Steel 5 5 

Axle - Non-Drive Aluminum 60 60 

Axle - Non-Drive High Strength Steel 15 15 

Brake Drums - Non-Drive Aluminum 60 60 

Brake Drums - Non-Drive High Strength Steel 8 8 

Axle Hubs - Drive Aluminum 40 80 

Axle Hubs - Drive High Strength Steel 10 20 

Brake Drums - Drive Aluminum 70 140 

Brake Drums - Drive High Strength Steel 5.5 11 

Clutch Housing Aluminum 34 40 

Clutch Housing High Strength Steel 9 10 

Suspension Brackets, 

Hangers 

Aluminum 67 100 

Suspension Brackets, 

Hangers 

High Strength Steel 20 30 

Transmission Case Aluminum 45 50 

Transmission Case High Strength Steel 11 12 

Crossmember – Cab Aluminum 10 14 15 

Crossmember – Cab High Strength Steel 2 4 5 

Crossmember - NonSuspension Aluminum 15 18 21 

Crossmember - NonSuspension High Strength Steel 5 6 7 

Crossmember -Suspension Aluminum 15 20 25 

Crossmember -Suspension High Strength Steel 4 5 6 

Driveshaft Aluminum 12 40 50 

Driveshaft High Strength Steel 5 10 12 

Frame Rails Aluminum 120 300 440 

Frame Rails High Strength Steel 24 40 87 

Wheels ‐ Dual Aluminum 126 126 210 

Wheels ‐ Dual High Strength Steel 48 48 80

Wheels ‐ Dual Lightweight

Aluminum

180 180 300

Wheels ‐ Wide Base Single Aluminum 278 278 556 
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COMPONENT MATERIAL 

VOCATIONAL VEHICLE CLASS 

Class 2b-5 Class 6-7 Class 8 

Wheels ‐ Wide Base Single High Strength Steel 168 168 336 

Wheels ‐ Wide Base Single Lightweight

Aluminum

294 294 588 

Permanent 6x2 Axle

Configuration

Multi N/A N/A 300 

2.9.3.6 HFC Leakage 

EPA believes the capacity of vocational vehicle air conditioning systems are sufficiently 

similar to those of other HD vehicles to apply a similar leakage standard as was applied in the 

HD Phase 1 program for tractors and HD pickup trucks and vans. Emissions due to direct 

refrigerant leakage are significant in all vehicle types. EPA is proposing a1.50 percent 

refrigerant leakage per year standard, to assure that high-quality, low-leakage components are 

used in the design of each air conditioning system with a refrigerant capacity greater than 733 

grams. Since refrigerant leakage past the compressor shaft seal is the dominant source of 

leakage in belt-driven air conditioning systems, the agency recognizes that this 1.50 percent 

leakage standard would not be feasible for systems with a refrigerant capacity of 733 grams or 

lower, as the minimum feasible leakage rate does not continue to drop as the capacity or size of 

the air conditioning system is reduced. The fixed leakage from the compressor seal and other 

system devices results in a minimum feasible yearly leakage rate, and further reductions in 

refrigerant capacity (the ‘denominator’ in the percent refrigerant leakage calculation) would 

result in a system which could not meet the 1.50 percent leakage per year standard. EPA does 

not believe that leakage reducing technologies will be available in MY 2021 to enable lower 

capacity systems to meet the percent per year standard, so we are proposing a maximum gram 

per year leakage standard of 11.0 grams per year for vocational vehicle air conditioning systems 

with a refrigerant capacity of 733 grams or lower, as was adopted in the HD Phase 1 program for 

tractors and HD pickup trucks and vans. 

The proposed standard is derived from the vehicles with the largest system refrigerant 

capacity based on the Minnesota GHG Reporting database.138 These are the same data on which 

the HD Phase 1 HFC leakage standard was based.139 

By requiring that all vocational vehicles achieve the leakage level of 1.50 percent per 

year, roughly half of the vehicles in the 2010 data sample would need to reduce their leakage 

rates, and an emissions reduction roughly comparable to that necessary to generate direct 

emission credits under the light-duty vehicle program would result. See 75 FR at 25426-247. 

However, no credits or trading flexibilities are proposed under this standard for heavy-duty 

vocational vehicles. We believe that a yearly system leakage approach would assure that highquality, low-leakage, components are used in each A/C system design, and we expect that 

manufacturers would reduce A/C leakage emissions by utilizing improved, leak-tight 

components. Some of the improved components available to manufacturers are low-permeation 

flexible hoses, multiple o-ring or seal washer connections, and multiple-lip compressor shaft 

seals. The availability of low leakage components in the market is being driven by the air 
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conditioning credit program in the light-duty GHG rulemaking (which applies to 2012 model 

year and later vehicles). EPA believes that reducing A/C system leakage is both highly costeffective and technologically feasible. The cooperative industry and government Improved 

Mobile Air Conditioning (IMAC) program has demonstrated that new-vehicle leakage emissions 

can be reduced by 50 percent by reducing the number and improving the quality of the 

components, fittings, seals, and hoses of the A/C system.140 All of these technologies are already 

in commercial use and exist on some of today’s A/C systems in other heavy-duty vehicles. 

EPA is proposing to adopt the same compliance method for control of leakage from A/C 

systems in vocational vehicles as was adopted for the HD Phase 1 HFC leakage standard. Under 

this approach, manufacturers would choose from a menu of A/C equipment and components 

used in their vehicles in order to establish leakage scores, which would characterize their A/C 

system leakage performance and calculate the percent leakage per year as this score divided by 

the system refrigerant capacity. The agencies estimate the total cost to apply low leakage A/C 

components to a vocational vehicle to be $22 in MY 2021 and $19 in MY 2027, as described in 

draft RIA 2.12.4.1. 

Consistent with the Light-Duty Vehicle Greenhouse Gas Emissions rulemaking, a 

manufacturer would compare the components of its A/C system with a set of leakage reduction 

technologies and actions that is based closely on that being developed through IMAC and the 

Society of Automotive Engineers (as SAE Surface Vehicle Standard J2727, August 2008 

version).141 See generally 75 FR at 25426. The SAE J2727 approach was developed from 

laboratory testing of a variety of A/C related components, and EPA believes that the J2727 

leakage scoring system generally represents a reasonable correlation with average real-world 

leakage in new vehicles. Like the IMAC approach, our proposed approach would associate each 

component with a specific leakage rate in grams per year identical to the values in J2727 and 

then sum together the component leakage values to develop the total A/C system leakage. As is 

currently done for other HD vehicles, for vocational vehicles, the total A/C leakage score would 

then be divided by the total refrigerant system capacity to develop a percent leakage per year 

value. 

2.9.4 Other Vocational Vehicle Technologies Considered 

2.9.4.1 Vocational Aerodynamics 

The agencies are not predicating the proposed standards on improved aerodynamics of 

vocational vehicles. However, the agencies are proposing to offer an option for manufacturers to 

receive recognition for a few specific aerodynamic technologies on vehicles where the criteria 

would be met to qualify for this credit, should a manufacturer decide to utilize the technology. 

We are partnering with CARB to incorporate into GEM some data from testing that is 

being conducted by CARB through NREL. A test plan is in place to assess the fuel efficiency 

benefit of three different devices to improve the aerodynamic performance of a Class 6 box 

truck, as well as two devices on a cutaway van. We propose that, if a manufacturer can certify 

that a final vehicle configuration will closely match one of the configurations on which testing 

was conducted, then an option may be selected to improve that vehicle’s GEM score based on 

installation of the applicable aerodynamic devices. The amount of improvement would be set by 
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EPA based on NREL’s test results. This credit provision would apply only to vocational 

vehicles certified over the Regional duty cycle. Manufacturers wishing to receive credit for 

other aerodynamic technologies or on other vehicle configurations would be able to apply for 

innovative credits using the established procedures. 

Table 2-53 shows the vocational aerodynamic technologies that are being tested, for 

which credit could be available through GEM. The agencies have not estimated manufacturing 

costs for these technologies on vocational vehicles. We project that a manufacturer would only 

apply these where it was found to be cost-effective for the specific application. For a description 

of the costs estimated for applying aerodynamic technologies to tractors, see the draft RIA at 

Chapter 2.12.9, where the estimated cost for a Bin2 package on a low roof day cab tractor is 

shown to be roughly $1,000. 

Table 2-53 Vocational Aerodynamic Technologies Being Assessed 

VEHICLE SKIRTS FRONT 

FAIRING 

(NOSE 

CONE) 

REAR 

FAIRING 

(TAIL)

WHEEL COVERS 

Class 6 Box 

Truck 

X X X 

Class 4 Box 

Truck 

 X 

The vehicles eligible for this GEM-based credit would be those for which the chassis 

manufacturer can certify that, through a delegated assembly agreement, the final built 

configuration would be reasonably similar to the dimensions of one of the test vehicles. A 

description of vehicles and aerodynamic technologies that could be eligible for this option, as 

well as a description of the testing conducted to obtain the assigned GEM improvements due to 

these technologies, are presented in a memorandum to the docket.142

2.9.4.2 Electric Vehicles 

Some heavy-duty vehicles can be powered exclusively by electric motors. Electric 

motors are efficient and able to produce high torque, giving e-trucks strong driving 

characteristics, particularly in stop-and-go or urban driving situations, and are well-suited for 

moving heavy loads. Electric motors also offer the ability to operate with very low noise, an 

advantage in certain applications. Currently, e-trucks have some disadvantages over 

conventional vehicles, primarily in cost, weight and range. Components are relatively expensive, 

and storing electricity using currently available technology is expensive, bulky, and heavy. 

The West Coast Collaborative, a public-private partnership, has estimated the incremental 

costs for electric Class 3-6 trucks in the Los Angeles, CA, area.143 Compared to a conventional 

diesel, the WCC estimates a battery-electric vehicle system would cost between $70,000 and 

$90,000 more than a conventional diesel system. The CalHEAT Technology Roadmap includes 

an estimate that the incremental cost for a fully-electric medium- or heavy- duty vehicle would 

be between $50,000 and $100,000. In draft RIA Chapter 2.12.7.6, the agencies estimate the cost 
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of a full electric LHD or MHD vocational vehicle at $55,216 in MY 2021 and $52,128 in MY 

2024. The CalHEAT roadmap report also presents several actions that must be taken by 

manufacturers and others, before heavy-duty e-trucks can reach what they call Stage 3 

Deployment.144 

Early adopters of electric drivetrain technology are medium-heavy-duty vocational 

vehicles that are not weight-limited and have drive cycles where they don’t need to go far from a 

central garage. According to CALSTART’s report to the NAFA 2014 Institute and Expo, there 

is an emerging market of MHD all-electric vocational vehicles, including models from Smith, 

EVI, Boulder, AMP, and others.126 CalHEAT has published results of a comprehensive 

performance evaluation of three battery electric truck models using information and data from 

in-use data collection, on road testing and chassis dynamometer testing.145 

Given the high costs and the developing nature of this technology, the agencies do not 

project fully electric vocational vehicles to be widely commercially available in the time frame 

of the proposed rules. For this reason, the agencies have not based the proposed Phase 2 

standards on adoption of full-electric vocational vehicles. However, in the more stringent 

alternatives discussed in detail in draft RIA Chapter 9, the agencies do project three percent 

adoption of full electric LHD and MHD vocational vehicles (only applicable for MY 2024 for 

Alternative 5). To the extent this technology is able to be brought to market in the time frame of 

the Phase 2 program, there is currently a certification path for these chassis from Phase 1, as 

described in the Preamble Section V and in the regulations at 40 CFR 1037.150 and 49 CFR 

535.8. 

2.9.5 Derivation of the Proposed Vocational Vehicle Technology Packages 

The agencies are proposing standards for vocational vehicles predicated on the same suite 

of technologies in both the 2021 and 2024 MY implementation years. The change in stringency 

between those years would be a result of different adoption rates of those technologies. Package 

costs for each model year are presented following each respective adoption rate discussion. 

2.9.5.1 Projected Technology Adoption Rates for Vocational Vehicles 

The agencies have estimated the extent to which technologies may be adopted by 

manufacturers to meet the proposed 2021 vocational vehicle standards. 

2.9.5.1.1 Transmissions 

The agencies project a compliance path whereby 30 percent of vocational vehicles would 

have one or more of the transmission technologies identified above in this chapter applied by 

MY 2021, increasing to nearly 60 percent by MY 2024 and over 80 percent by MY 2027. Most 

of this increase is due to a projected increase in adoption of technologies that represent deep 

driveline integration. The agencies project an adoption rate of 15 percent in MY 2021 and 30 

percent in MY 2024 for of various non-hardware technologies that enable driveline optimization, 

including gear efficiencies, shift strategies, and torque converter lockups. Manufacturers would 

use the powertrain test to certify these technology improvements. Due to the relatively high 

efficiency gains available from driveline optimization for relatively low costs, the agencies are 
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projecting a 70 percent application rate of driveline optimization (including the non-hardware 

enabling technologies) by MY 2027 across all subcategories. We do not have information about 

the extent to which integration may be deterred by barriers to information-sharing between 

component suppliers. Therefore we are projecting that major manufacturers would work to 

overcome these barriers, integrate and optimize their drivelines, and use the powertrain test on all 

eligible configurations, while smaller manufacturers may not adopt these technologies at all, or 

not to a degree that they would find value in this optional test procedure. 

For the technology of adding two gears, we are predicating the proposed MY 2021 

standard on a five percent adoption rate, except for zero in the HHD Regional subcategory, 

which is modeled with a 10-speed transmission. This adoption rate is projected to essentially 

remain at this level throughout the program, with an increase to ten percent only for two 

subcategories (Regional LHD and MHD) in MY 2027. This is because the manufacturers most 

likely to develop 8-speed transmissions are those that are also developing transmissions for HD 

pickups and vans, and the GEM-certified vocational market share among those manufacturers is 

relatively small. 

The HHD Regional subcategory is the only one where we assume a manual transmission 

in the baseline configuration. For these vehicles, the agencies project upgrades to electronic 

transmissions such as either AMT, DCT, or automatic, at collective adoption rates of 51 percent 

in MY 2021, 68 percent in MY 2024, and five percent in MY 2027. The decrease in MY 2027 

reflects a projection that a greater number of deeply integrated HHD powertrains would be used 

by MY 2027 (one consequence being that fewer HHD powertrains would be directly simulated 

in GEM in that year). The larger numbers in the phase-in years reflect powertrains that have 

been automated or electrified but not deeply integrated. The agencies have been careful to 

account for the cost of both electrifying and deeply integrating the MY 2027 powertrains. In 

draft RIA Chapter 11, the technology adoption rates for the HHD Regional subcategory 

presented in Table 11-42, Table 11-45, and Table 11-48 account for the assumption that a 

manual transmission cannot be deeply integrated, so there must also be an automation upgrade. 

These tables are inputs to the agencies’ cost analysis, thus the costs of both upgrading and 

integrating HHD powertrains are included. The adoption rates of the upgraded but not integrated 

transmission architectures represent a projection of three percent of all vocational vehicles in 

MY 2021 and four percent in MY 2024. This is based on an estimate that seven percent of the 

vocational vehicles would be in the HHD Regional subcategory. For more information about the 

assumptions that were made about the populations of vehicles in different subcategories, see the 

agencies’ inventory estimates in draft RIA Chapter 5. 

In the eight subcategories in which automatic transmissions are the base technology, the 

agencies project that five percent would upgrade to a dual clutch transmission in MY 2021. This 

projection increases to 15 percent in MY 2024 and decreases in MY 2027 to ten percent for two 

subcategories (Regional LHD and MHD) and five percent for the remaining 6 subcategories. 

The low projected adoption rates of DCT reflect the fact that this is a relatively new technology 

for the heavy-duty sector, and it is likely that broader market acceptance would be achieved once 

fleets have gained experience with the technology. Similar to the pattern described for the HHD 

Regional subcategory, the decrease in MY 2027 reflects a projection of greater use of deeply 

integrated powertrains. 
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In determining the proposed standard stringency, we have projected that hybrids on 

vehicles certified in the Multipurpose subcategories would achieve on average 22 percent 

improvement, and those in the Urban subcategories would see a 25 percent improvement. We 

have also projected zero hybrid adoption rate by vehicles in the Regional subcategories, 

expecting that the benefit of hybrids for those vehicles would be too low to merit use of that type 

of technology. However, there is no fixed hybrid value assigned in GEM and the actual 

improvement over the applicable test cycle would be determined by powertrain testing. By the 

full implementation year of MY 2027, the agencies are projecting an overall vocational vehicle 

adoption rate of ten percent hybrids, which we estimate would be 18 percent of vehicles certified 

in the Multi-Purpose and Urban subcategories. We are projecting a low adoption rate in the 

early years of the Phase 2 program, just four percent in these subcategories in MY 2021, and 

seven percent in MY 2024 for vehicles certified in the Multi-Purpose and Urban subcategories. 

Based on our assumptions about the populations of vehicles in different subcategories, these 

hybrid adoption rates are about two percent overall in MY 2021 and four percent overall in MY 

2024. 

Considering the combination of the above technologies and adoption rates, we project the 

CO2 and fuel efficiency improvements for all transmission upgrades to be approximately seven 

percent on a fleet basis by MY 2027. One subcategory in which we are projecting a very large 

advanced transmission adoption rate is the HHD Regional subcategory, in which we are 

projecting 75 percent of the transmissions would be either automated or automatic (upgraded 

from a manual) with 70 percent of those also being deeply integrated by MY 2027. By 

comparison, the agencies are projecting that HHD day cab tractors would have 90 percent 

adoption of automated or automatic transmissions by MY 2027. Although we are not prepared 

to predict what fraction of these would be upgraded in the absence of Phase 2, as noted above in 

Chapter 2.9.3, the agencies are confident that durable transmissions will be widely available in 

the Phase 2 time frame to support manufacture of HHD vocational vehicles. 

If the above technologies do not reach the expected level of market adoption, the 

vocational vehicle Phase 2 program has several other technology options that manufacturers 

could choose to meet the proposed standards. 

2.9.5.1.2 Axles 

The agencies project that 75 percent of vocational vehicles in all subcategories would 

adopt advanced axle lubricant formulations in all implementation years of the Phase 2 program. 

Fuel efficient lubricant formulations are widespread across the heavy-duty market, though 

advanced synthetic formulations are currently less popular.N Axle lubricants with improved 

viscosity and efficiency-enhancing performance are projected to be widely adopted by 

manufacturers in the time frame of Phase 2. Such formulations are commercially available and 

the agencies see no reason why they could not be feasible for most vehicles. Nonetheless, we 

have refrained from projecting full adoption of this technology. The agencies do not have 

specific information regarding reasons why axle manufacturers may specify a specific type of 

 N Based on conversations with axle suppliers. 
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lubricant over another, and whether advanced lubricant formulations may not be recommended 

in all cases. 

The agencies estimate that 45 percent of HHD Regional vocational vehicles would adopt 

either full time or part time 6x2 axle technology in MY 2021. This technology is most likely to 

be applied to Class 8 vocational vehicles (with 2 rear axles) that are designed for frequent 

highway trips. The agencies project a slightly higher adoption rate of 60 percent combined for 

both full and part time 6x2 axle technologies in MY 2024 and MY 2027. Based on our estimates 

of vehicle populations, this is about four percent of all vocational vehicles. 

2.9.5.1.3 Lower Rolling Resistance Tires 

The agencies estimate that the per-vehicle average level of rolling resistance from 

vocational vehicle tires could be reduced by 11 percent by full implementation of the Phase 2 

program in MY 2027, based on the tire development achievements expected over the next 

decade. This is estimated by weighting the projected improvements of steer tires and drive tires 

using an assumed axle load distribution of 30 percent on the steer tires and 70 percent on the 

drive tires, as explained in the draft RIA Chapter 2.9. By applying the assumed axle load 

distribution, the average vehicle CRR improvements projected for the proposed MY 2021 

standards would be four percent, which we project would achieve up to one percent reduction in 

fuel use and CO2 emissions, depending on the vehicle subcategory. Using that same method, the 

agencies estimate the average vehicle CRR in MY 2024 would be seven percent, yielding 

reductions in fuel use and CO2 emissions of between one and two percent, depending on the 

vehicle subcategory. 

The agencies understand that the vocational vehicle segment has access to a large variety 

of tires, including some that are designed for tractors, some that are designed for HD pickups and 

vans, and some with multiple use designations. In spite of the likely availability of LRR tires 

during the Phase 2 program, the projected adoption rates are intended to be conservative. The 

agencies believe that these tire packages recognize the variety of tire purposes and performance 

levels in the vocational vehicle market, and maintain choices for manufacturers to use the most 

efficient tires (i.e. those with least rolling resistance) only where it makes sense given these 

vehicles’ differing purposes and applications. The projected adoption rates and expected 

improvements in CRR are presented in Table 2-54. 

Table 2-54 Projected LRR Tire Adoption Rates 

TIRE 

POSITION 

LEVEL OF ROLLING 

RESISTANCE 

MY 2021 

ADOPTION RATE 

MY 2024 

ADOPTION RATE 

MY 2027 

ADOPTION 

RATE 

Drive Baseline CRR (7.7) 50 20 10 

Steer Baseline CRR (7.7) 20 10 0 

Drive 5% Lower CRR (7.3) 50 50 25 

Steer 10% Lower CRR (6.9) 80 30 20 

Drive 10% Lower CRR (6.9) 0 30 50 

Steer 15% Lower CRR (6.5) 0 60 30 

Drive 15% Lower CRR (6.5) 0 0 15 

Steer 20% Lower CRR (6.2) 0 0 50 
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Drive Average Improvement 

in CRR 

3% 6% 9% 

Steer Average Improvement 

in CRR 

8% 12% 17% 

For comparison purposes, the reader may note that these levels of tire CRR generally 

correspond with levels of tire CRR projected for tractors built for the Phase 1 standards. For 

example, the baseline level CRR for vocational tires is very similar to the baseline tractor steer 

tire CRR. Vocational vehicle tires with 10 percent better CRR have a similar CRR level as 

tractor tires of Drive Level 1. Vocational vehicle tires with 15 percent better CRR have a similar 

CRR level as tractor tires of Steer Level 1. Vocational vehicle tires with 20 percent better CRR 

have a similar CRR level as tractor tires of Drive Level 2, as described in preamble Section 

III.D.2. 

2.9.5.1.4 Workday Idle Reduction 

In this proposal, we are projecting a progression of idle reduction technology 

development that begins with 70 percent adoption rate of neutral idle for the MY 2021 standards, 

which by MY 2027 is replaced by a 70 percent adoption rate of stop-start idle reduction 

technology. Although it is possible that a vehicle could have both neutral idle and stop-start, we 

are only considering emissions reductions for vehicles with one or the other of these 

technologies. Also, as the program phases in, we do not see a reduction in the projected adoption 

rate of neutral idle to be a concern in terms of stranded investment, because it is a very low cost 

technology that could be an enabler for stop-start systems in some cases. 

We are not projecting any adoption of neutral idle for the HHD Regional subcategory, 

because any vehicle with a manual transmission must shift to neutral when stopped to avoid 

stalling the engine, vehicles in the HHD Regional subcategory would already essentially be 

idling in neutral, and no additional technology would be needed to achieve this. A similar case 

can be made for any vocational vehicle with an automated manual transmission, since these 

share inherently similar architectures with manual transmissions. The agencies are not 

projecting an adoption rate of 85 percent neutral idle until MY 2024, because it may take some 

additional development time to apply this technology to high-torque automatic transmissions 

designed for the largest vocational vehicles. Based on stakeholder input, the designs needed to 

avoid an uncomfortable re-engagement bump when returning to drive from neutral may require 

some engineering development time as well as some work to enable two-way communication 

between engines and transmissions. 

We are projecting a five percent adoption rate of stop-start in the six MHD and LHD 

subcategories for MY 2021 and zero for the HHD vehicles, because this technology is still 

developing for vocational vehicles and is most likely to be feasible in the early years of Phase 2 

for vehicles with lower power demands and lower engine inertia. Stopping a heavy-duty engine 

is not challenging. The real challenge is designing a robust system that can deliver multiple 

smooth restarts daily without loss of function while the engine is off. Many current light-duty 

products offer this feature, and some heavy-duty manufacturers are exploring this.146 The 

agencies are projecting an adoption rate of 15 percent stop-start across all subcategories in the 

intermediate year of MY 2024. The agencies are projecting this technology to have a relatively 
2-141 

high adoption rate (70 percent as stated above) by MY 2027 because we see it being technically 

feasible on the majority of vocational vehicles, and especially effective on those with the most 

time at idle in their workday operation. Although we are not prepared to predict what fraction of 

vehicles would adopt stop-start in the absence of Phase 2, above in draft RIA Chapter 2.9.3 the 

agencies explain why we are confident that this technology, which is on the entry-level side of 

the hybrid and electrification spectrum, will be widely available in the Phase 2 time frame. 

Based on these projected adoption rates and the effectiveness values described above in 

this section, we expect overall GHG and fuel consumption reductions from workday idle on 

vocational vehicles to be approximately three percent in MY 2027. 

2.9.5.1.5 Weight Reduction 

As described in the draft RIA Chapter 2.12, weight reduction is a relatively costly 

technology, at approximately $3 to $4 per pound for a 200-lb package. Even so, for vehicles in 

service classes where dense, heavy loads are frequently carried, weight reduction can translate 

directly to additional payload. The agencies project weight reduction would most likely be used 

for vocational vehicles in the refuse and construction service classes, as well as some regional 

delivery vehicles. The agencies are predicating the proposed standards on an adoption rate of 

five to eight percent, depending on the subcategory, in MY 2027, with slightly lower adoption 

rates in MY 2021 and MY 2024. 

For this technology package, NHTSA and EPA project manufacturers would use material 

substitution in the amount of 200 pounds. An example of how this weight could be reduced 

would be a complete set of aluminum wheels for a Class 8 vocational vehicle, or an aluminum 

transmission case plus high strength steel wheels, frame rails, and suspension brackets on a 

MHD or LHD vocational vehicle. The agencies have limited information about how popular the 

use of aluminum components is in the vocational vehicle sector. 

2.9.5.1.6 HFC Leakage 

We project 100 percent adoption rate in all implementation years of the Phase 2 program 

for use of low leakage air conditioning system components to reduce direct emissions of HFC 

compounds from vocational vehicles. 

2.9.5.2 Proposed Vocational Vehicle Standards 

The agencies applied the technology adoption rates shown in Table 2-55 through Table 

2-57 as GEM inputs, but have not directly transferred the GEM results from these inputs as the 

proposed standards. Rather, the proposed standards are the result of the normalizing process 

described in Chapter 2.9.2.1. The proposed standards are presented in Table 2-58 through Table 

2-63. 
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Table 2-55 GEM Inputs Used to Derive Proposed MY 2021 Vocational Vehicle Standards 

CLASS 2B-5 CLASS 6-7 CLASS 8 

Urban MultiPurpose Regional Urban MultiPurpose Regional Urban MultiPurpose Regional 

CI Enginea

2021 MY 7L, 200 hp Engine 2021 MY 7L, 270 hp Engine 2021 MY 11L, 345 

hp Engine

2021 MY 

15L 455hp 

Engine 

Transmission (improvement factor) 

0.023 0.021 0.008 0.023 0.021 0.009 0.023 0.022 0.022 

Axle (improvement factor)

0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.012 

Stop-Start (adoption rate) 

5% 5% 5% 5% 5% 5% 0% 0% 0% 

Neutral Idle (adoption rate) 

70% 70% 70% 70% 70% 70% 70% 70% 0% 

Steer Tires (CRR kg/metric ton) 

7.1 7.1 7.1 7.1 7.1 7.1 7.1 7.1 7.1 

Drive Tires (CRR kg/metric ton) 

7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 

Weight Reduction (lb) 

8 8 14 8 8 12 8 8 10 

Note: 

a

 SI engines were not simulated in GEM, rather a gas/diesel adjustment factor was applied to the results 

Table 2-56 GEM Inputs Used to Derive Proposed MY 2024 Vocational Vehicle Standards 

CLASS 2B-5 CLASS 6-7 CLASS 8 

Urban MultiPurpose Regional Urban MultiPurpose Regional Urban MultiPurpose Regional 

CI Enginea

2024 MY 7L, 200 hp Engine 2024 MY 7L, 270 hp Engine 2024 MY 11L, 345 

hp Engine

2024 MY 

15L 455hp 

Engine 

Transmission (improvement factor) 

0.045 0.04 0.017 0.045 0.041 0.018 0.045 0.042 0.035

Axle (improvement factor) 

0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.014

Stop-Start (adoption rate) 

15% 15% 15% 15% 15% 15% 15% 15% 15%

Neutral Idle (adoption rate)

85% 85% 85% 85% 85% 85% 85% 85% 0%

Steer Tires (CRR kg/metric ton) 

6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8

Drive Tires (CRR kg/metric ton) 

7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 

Weight Reduction (lb) 
2-143 

8 8 14 8 8 12 8 8 10 

Note: 

a

 SI engines were not simulated in GEM, rather a gas/diesel adjustment factor was applied to the results 

Table 2-57 GEM Inputs Used to Derive Proposed MY 2027 Vocational Vehicle Standards 

CLASS 2B-5 CLASS 6-7 CLASS 8 

Urban MultiPurpose Regional Urban MultiPurpose Regional Urban MultiPurpose Regional 

CI Enginea

2027 MY 7L, 200 hp Engine 2027 MY 7L, 270 hp Engine 2027 MY 11L, 345 

hp Engine

2027 MY 

15L 455hp 

Engine 

Transmission (improvement factor) 

0.096 0.085 0.034 0.096 0.088 0.037 0.097 0.089 0.036 

Axle (improvement factor)

0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.014 

Stop-Start (adoption rate) 

75% 70% 70% 75% 70% 70% 70% 70% 70% 

Neutral Idle (adoption rate)

25% 30% 30% 25% 30% 30% 30% 30% 0% 

Steer Tires (CRR kg/metric ton) 

6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 

Drive Tires (CRR kg/metric ton) 

7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 

Weight Reduction (lb) 

10 10 16 10 10 14 10 10 12 

Note: 

a

 SI engines were not simulated in GEM, rather a gas/diesel adjustment factor was applied to the results 

Table 2-58 and Table 2-59 present EPA’s proposed CO2 standards and NHTSA’s 

proposed fuel consumption standards, respectively, for chassis manufacturers of Class 2b 

through Class 8 vocational vehicles for the beginning model year of the program, MY 2021. As 

in Phase 1, the standards would be in the form of the mass of emissions, or gallons of fuel, 

associated with carrying a ton of cargo over a fixed distance. The EPA standards would be 

measured in units of grams CO2 per ton-mile and the NHTSA standards would be in gallons of 

fuel per 1,000 ton-miles. With the mass of freight in the denominator of this term, the program 

is designed to measure improved efficiency in terms of freight efficiency. As in Phase 1, the 

Phase 2 program would assign a fixed default payload in GEM for each vehicle weight class 

group (heavy heavy-duty, medium heavy-duty, and light heavy-duty). Even though this 

simplification does not allow individual vehicle freight efficiencies to be recognized, the general 

capacity for larger vehicles to carry more payload is represented in the numerical values of the 

proposed standards for each weight class group.
2-144 

Table 2-58 Proposed EPA CO2 Standards for MY2021 Class 2b-8 Vocational Vehicles 

EPA Standard for Vehicle with CI Engine Effective MY2021 (gram CO2/ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 296 188 198 

Multi-Purpose 305 190 200 

Regional 318 186 189 

EPA Standard for Vehicle with SI Engine Effective MY2021 (gram CO2/ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 320 203 214 

Multi-Purpose 329 205 216 

Regional 343 201 204 

Table 2-59 Proposed NHTSA Fuel Consumption Standards for MY2021 Class 2b-8 Vocational Vehicles 

NHTSA Standard for Vehicle with CI Engine Effective MY 2021 (Fuel Consumption 

gallon per 1,000 ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 29.0766 18.4676 19.4499 

Multi-Purpose 29.9607 18.6640 19.6464 

Regional 31.2377 18.2711 18.5658 

NHTSA Standard for Vehicle with SI Engine Effective MY 2021 (Fuel Consumption 

gallon per 1,000 ton-mile) 

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 36.0077 22.8424 24.0801 

Multi-Purpose 37.0204 23.0674 24.3052 

Regional 38.5957 22.6173 22.9549 

EPA’s proposed vocational vehicle CO2 standards and NHTSA’s proposed fuel 

consumption standards for the MY 2024 stage of the program are presented in Table 2-60 and 

Table 2-61, respectively. These reflect broader adoption rates of vehicle technologies already 

considered in the technology basis for the MY 2021 standards. The standards for vehicles 

powered by CI engines also reflect that in MY 2024, the separate engine standard would be more 

stringent, so the vehicle standard keeps pace with the engine standard. 
2-145 

Table 2-60 Proposed EPA CO2 Standards for MY2024 Class 2b-8 Vocational Vehicles 

EPA Standard for Vehicle with CI Engine Effective MY2024 (gram CO2/ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 284 179 190 

Multi-Purpose 292 181 192 

Regional 304 178 182 

EPA Standard for Vehicle with SI Engine Effective MY2024 (gram CO2/ton-mile) 

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 312 197 208 

Multi-Purpose 321 199 210 

Regional 334 196 199 

Table 2-61 Proposed NHTSA Fuel Consumption Standards for MY2024 Class 2b-8 Vocational Vehicles 

NHTSA Standard for Vehicle with CI Engine Effective MY 2024 (Fuel Consumption 

gallon per 1,000 ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 27.8978 17.5835 18.6640 

Multi-Purpose 28.6837 17.7800 18.8605 

Regional 29.8625 17.4853 17.8782 

NHTSA Standard for Vehicle with SI Engine Effective MY 2024 (Fuel Consumption 

gallon per 1,000 ton-mile) 

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 35.1075 22.1672 23.4050 

Multi-Purpose 36.1202 22.3923 23.6300 

Regional 37.5830 22.0547 22.3923 

EPA’s proposed vocational vehicle CO2 standards and NHTSA’s proposed fuel 

consumption standards for the full implementation year of MY 2027 are presented in Table 2-62 

and Table 2-63, respectively. These reflect even greater adoption rates of the same vehicle 

technologies considered in the basis for the previous stages of the Phase 2 standards. The 

proposed MY 2027 standards for vocational vehicles powered by CI engines reflect additional 

engine technologies consistent with those on which the separate proposed MY 2027 CI engine 

standard is based. The proposed MY 2027 standards for vocational vehicles powered by SI 

engines reflect improvements due to additional engine friction reduction technology, which is 

not among the technologies on which the separate SI engine standard is based. 
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Table 2-62 Proposed EPA CO2 Standards for MY2027 Class 2b-8 Vocational Vehicles 

EPA Standard for Vehicle With CI Engine Effective MY2027 (gram CO2/ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 272 172 182 

Multi-Purpose 280 174 183 

Regional 292 170 174 

EPA Standard for Vehicle with SI Engine Effective MY2027 (gram CO2/ton-mile) 

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 299 189 196 

Multi-Purpose 308 191 198 

Regional 321 187 188 

Table 2-63 Proposed NHTSA Fuel Consumption Standards for MY2027 Class 2b-8 Vocational Vehicles 

NHTSA Standard For Vehicle With CI Engine Effective MY 2027 (Fuel Consumption 

Gallon per 1,000 ton-mile)

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 26.7191 16.8959 17.8782 

Multi-Purpose 27.5049 17.0923 17.9764 

Regional 28.6837 16.6994 17.0923 

NHTSA Standard for Vehicle with SI Engine Effective MY 2027 (Fuel Consumption 

gallon per 1,000 ton-mile) 

Duty Cycle Light Heavy-Duty 

Class 2b-5 

Medium Heavy-Duty 

Class 6-7 

Heavy Heavy-Duty 

Class 8 

Urban 33.6446 21.2670 22.0547 

Multi-Purpose 34.6574 21.4921 22.2797 

Regional 36.1202 21.0420 21.1545 

2.9.5.3 Summary of Vocational Vehicle Package Costs 

The agencies have estimated the costs of the technologies expected to be used to comply 

with the proposed standards. Table 2-64 presents estimated incremental costs for MY2021 for 

light, medium and heavy HD vocational vehicles in each duty-cycle-based subcategory – Urban, 

Multi-Purpose, and Regional. As shown, in MY 2021 these range from approximately $600 for 

MHD and LHD Regional vehicles, up to $3,400 for HHD Regional vehicles. Those two lowercost packages reflect zero hybrids, and the higher-cost package reflects significant adoption of 

automated transmissions. In the draft RIA Chapter 2.13, the agencies present vocational vehicle 
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technology package costs differentiated by MOVES vehicle type. For example, in Table 2-231, 

intercity buses are estimated to have an average package cost of $2,900 and gasoline motor 

homes are estimated to have an average package cost of $450 in MY 2021. These costs do not 

indicate the per-vehicle cost that may be incurred for any individual technology. Chapter 2.12.7 

describes why a complex technology such as hybridization is estimated to range between 

$15,000 and $40,000 per vehicle for vocational vehicles in MY 2021. The engine costs listed 

represent the cost of an average package of diesel engine technologies. Individual technology 

adoption rates for engine packages are described above in Chapter 2.7. The details behind these 

costs are presented in draft RIA Chapter 2.12, including the markups and learning effects applied 

and how the costs shown here are weighted to generate an overall cost for the vocational sector. 

Table 2-64 Technology Package Incremental Costs for Vocational Vehicles for MY2021a,b (2012$) 

LIGHT HD MEDIUM HD HEAVY HD 

Urban Multipurpose Regional Urban Multipurpose Regional Urban Multipurpose Regional

Enginec $293 $293 $293 $270 $270 $270 $270 $270 $270

Tires $7 $7 $7 $7 $7 $7 $7 $7 $7

Transmission $81 $81 $81 $81 $81 $81 $81 $81 $2,852

Axle related $99 $99 $99 $99 $99 $99 $148 $148 $219

Weight 

Reduction 

$27 $27 $48 $27 $27 $41 $27 $27 $34

Idle reduction $49 $49 $49 $51 $51 $51 $6 $6 $0

Electrification 

& hybridization 

$547 $547 $0 $861 $861 $0 $1,437 $1,437 $0

Air 

Conditioning 

$22 $22 $22 $22 $22 $22 $22 $22 $22

Total $1,125 $1,125 $598 $1,418 $1,418 $571 $1,998 $1,998 $3,404

Notes: 

a

 Costs shown are for the 2021 model year and are incremental to the costs of a vehicle meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to draft RIA Chapter 2.12. 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated vehicle subcategories. c

 Engine costs shown are for a light HD, medium HD or heavy HD diesel engines. For gasoline-powered vocational 

vehicles we are projecting no additional engine-based costs beyond Phase 1.

Table 2-65 presents estimated incremental costs for MY2024 for light, medium and 

heavy HD vocational vehicles in each duty-cycle-based subcategory – Urban, Multi-Purpose, 

and Regional. As shown, these range from approximately $800 for MHD and LHD Regional 

vehicles, up to $4,800 for HHD Regional vehicles. The increased costs above the MY 2021 

values reflect increased adoption rates of individual technologies, while the individual 

technology costs are generally expected to remain the same or decrease, as explained in the draft 

RIA Chapter 2.12. For example, Chapter 2.12.7 presents MY 2024 hybridization costs that 

range from $13,000 to $33,000 per vehicle for vocational vehicles. 
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Table 2-65 Technology Package Incremental Costs for Vocational Vehicles for MY2024a,b (2012$) 

LIGHT HD MEDIUM HD HEAVY HD 

Urban Multipurpose Regional Urban MultipurposeRegional Urban Multipurpose Regional

Enginec $437 $437 $437 $405 $405 $405 $405 $405 $405

Tires $17 $17 $17 $17 $17 $17 $23 $23 $23

Transmission $123 $123 $123 $123 $123 $123 $123 $123 $3,915

Axle related $90 $90 $90 $90 $90 $90 $136 $136 $224

Weight Reduction $24 $24 $43 $24 $24 $37 $24 $24 $30

Idle reduction $119 $119 $119 $125 $125 $125 $224 $224 $217

Electrification & 

hybridization 

$906 $906 $0 $1,423 $1,423 $0 $2,377 $2,377 $0

Air Conditioning $20 $20 $20 $20 $20 $20 $20 $20 $20

Total $1,737 $1,737 $849 $2,228 $2,228 $817 $3,332 $3,332 $4,834

Notes: 

a

 Costs shown are for the 2024 model year and are incremental to the costs of a vehicle meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to draft RIA Chapter 2.12. 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated vehicle subcategories. c

 Engine costs shown are for a light HD, medium HD or heavy HD diesel engines. For gasoline-powered vocational 

vehicles we are projecting no additional engine-based costs beyond Phase 1. 

Table 2-66 presents estimated incremental costs for MY2027 for light, medium and 

heavy HD vocational vehicles in each duty-cycle-based subcategory – Urban, Multi-Purpose, 

and Regional. As shown, these range from approximately $1,400 for MHD and LHD Regional 

vehicles, up to $7,400 for HHD Urban and Multipurpose vehicles. These two subcategories are 

projected to have the higher-cost packages in MY 2027 due to an estimated 18 percent adoption 

of HHD hybrids, which are estimated to cost $31,000 per vehicle in MY 2027, as shown in 

Chapter 2.12.7 of the draft RIA. The engine costs shown represent the average costs associated 

with the proposed MY 2027 vocational diesel engine standard described in Section II.D. For 

gasoline vocational vehicles, the agencies are projecting adoption of Level 2 engine friction 

reduction with an estimated $68 added to the average SI vocational vehicle package cost in MY 

2027, which represents about 56 percent of those vehicles upgrading beyond Level 1 engine 

friction reduction. Further details on how these SI vocational vehicle costs were estimated are 

provided above in Chapter 2.9.1.
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Table 2-66 Technology Package Incremental Costs for Vocational Vehicles for MY2027a,b (2012$) 

LIGHT HD MEDIUM HD HEAVY HD 

Urban Multipurpose Regional Urban MultipurposeRegional Urban Multipurpose Regional

Enginec $471 $471 $471 $437 $437 $437 $437 $437 $437

Tires $20 $20 $20 $20 $20 $20 $29 $29 $29

Transmission $244 $244 $267 $244 $244 $267 $244 $244 $2,986

Axle related $86 $86 $86 $86 $86 $86 $129 $129 $215

Weight Reduction $29 $29 $46 $29 $29 $40 $29 $29 $35

Idle reduction $498 $499 $499 $526 $526 $526 $964 $964 $962

Electrification & 

hybridization 

$2,122 $2,122 $0 $3,336 $3,336 $0 $5,571 $5,571 $0

Air Conditioning $19 $19 $19 $19 $19 $19 $19 $19 $19

Total $3,489 $3,490 $1,407 $4,696 $4,696 $1,395 $7,422 $7,422 $4,682

Notes: 

a

 Costs shown are for the 2024 model year and are incremental to the costs of a vehicle meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to draft RIA Chapter 2.12. 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated vehicle subcategories. c

 Engine costs shown are for a light HD, medium HD or heavy HD diesel engines. For gasoline-powered vocational 

vehicles we are projecting $68 of additional engine-based costs beyond Phase 1.

2.9.6 Technologies and Costs of Alternative 4 

2.9.6.1 Derivation of Alternative 4 Standards 

2.9.6.1.1 Adoption Rates 

In developing the Alternative 4 standards, the agencies are projecting a set of technology 

packages in MY 2024 that is identical to those projected for the final phase-in year of the 

preferred alternative. In the package descriptions below, the agencies outline technologyspecific adoption rates in MY 2021 for Alternative 4 and offer insights on what market 

conditions could enable reaching adoption rates that would achieve the full implementation 

levels of stringency with less lead time. 

For transmissions including hybrids, the agencies project for Alternative 4 that 50 percent 

of vocational vehicles would have one or more of the transmission technologies identified above 

in this Section applied by MY 2021. This includes 25 percent deeply integrated conventional 

transmissions that would be recognized over the powertrain test, 10 percent DCT, 11 percent 

adding two gears (except zero for HHD Regional), and nine percent hybrids for vehicles certified 

in the Multi-Purpose and Urban subcategories, which we estimate would be five percent overall. 

In this alternative, the agencies project 21 percent of the vocational vehicles with manual 

transmissions in the HHD Regional subcategory would upgrade to either an AMT, DCT, or 

automatic transmission. The increased projection of driveline integration would mean that more 

manufacturers would need to overcome data-sharing barriers. In this alternative, we project that 

manufacturers would need to conduct additional research and development to achieve overall 

application of five percent hybrids. In the draft RIA Chapter 7.1, the agencies have estimated 
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costs for this additional accelerated research. In the preamble at Section V, the agencies request 

comment on the expected costs to accelerate hybrid development to meet the projected adoption 

rates of this alternative. 

For advanced axle lubricants, the agencies are projecting the same 75 percent adoption 

rate in MY 2021 as in the proposed program. For part time or full time 6x2 axles, the agencies 

project the HHD Regional vocational vehicles could apply this at the 60 percent adoption rate in 

MY 2021, where this level wouldn’t be reached until MY 2024 in the proposed program. One 

action that could enable this to be achieved is if information on the reliability of these systems 

were to be disseminated to more fleet owners by trustworthy sources. 

For lower rolling resistance tires in this alternative, the agencies project the same 

adoption rates of LRR tires as in the proposed program for MY 2021, because we don’t expect 

tire suppliers would be able to make greater improvements for the models that are fitted on 

vocational vehicles in that time frame. The tire research that is being conducted currently is 

focused on models for tractors and trailers, and we project further improved LRR tires would not 

be commercially available for vocational vehicles in the early implementation years of Phase 2. 

For the adoption rate of LRR tires in MY 2024 to reach the level projected for MY 2027 

in the proposed program, tire suppliers could promote their most efficient products to vocational 

vehicle manufacturers to achieve equivalent improvements with less lead time. Depending on 

how tire manufacturers focus their research and product development, it is possible that more of 

the LRR tire advancements being applied for tractors and trailers could be applied to vocational 

vehicles. To see the specific projected adoption rates of different levels of LRR tires for 

Alternative 4, see columns three and five of Table 2-54 above. 

For workday idle technologies, the agencies project an adoption rate of 12 percent stopstart in the six MHD and LHD subcategories for MY 2021 and zero for the HHD vehicles, on the 

expectation that manufacturers would have fewer challenges in the short term in bringing this 

technology to market for vehicles with lower power demands and lower engine inertia. In this 

alternative, the agencies project the overall workday idle adoption rate would approach 100 

percent, such that any vehicle without stop-start (except HHD Regional) would apply neutral idle 

in MY 2021. These adoption raters consider a more aggressive investment by manufacturers in 

developing these technologies. Estimates of research and development costs for this alternative 

are presented in the draft RIA Chapter 7.1. 

For weight reduction, in this alternative, the agencies project the same adoption rates of a 

200-lb lightweighting package as in the proposal for each subcategory in MY 2021, which is four 

to seven percent. 

2.9.6.1.2 Costs Associated with Alternative 4 Standards 

The agencies have estimated the costs of the technologies expected to be used to comply 

with the Alternative 4 standards, as shown in Table 2-67 for MY2021. Fleet average costs are 

shown for light, medium and heavy HD vocational vehicles in each duty-cycle-based 

subcategory – Urban, Multi-Purpose, and Regional. As shown, in MY 2021 these range from 

approximately $800 for MHD and LHD Regional vehicles, to $4,300 for HHD Urban and 
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Multipurpose vehicles. Those two subcategories are projected to have the higher-cost packages 

in MY 2021 due to an estimated 9 percent adoption of HHD hybrids, which are estimated to cost 

$40,000 per vehicle in MY 2021, as shown in Chapter 2.12.7 of the draft RIA. The engine costs 

listed represent the cost of an average package of diesel engine technologies with Alternative 4 

adoption rates described in the preamble at Section II.D.2(e). 

Table 2-67 Vocational Vehicle Technology Incremental Costs for Alternative 4Standards in the 2021 Model 

Yeara,b (2012$) 

LIGHT HD MEDIUM HD HEAVY HD 

Urban Multipurpose Regional Urban Multipurpose Regional Urban Multipurpose Regional

Enginec $372 $372 $372 $345 $345 $345 $345 $345 $345

Tires $7 $7 $7 $7 $7 $7 $7 $7 $7

Transmission $148 $148 $148 $148 $148 $148 $148 $148 $2,042

Axle related $99 $99 $99 $99 $99 $99 $148 $148 $243

Weight 

Reduction 

$27 $27 $48 $27 $27 $41 $27 $27 $34

Idle reduction $110 $110 $110 $116 $116 $116 $8 $8 $0

Electrification 

& 

hybridization 

$1,384 $1,384 $0 $2,175 $2,175 $0 $3,633 $3,633 $0

Air 

Conditioning 

$22 $22 $22 $22 $22 $22 $22 $22 $22

Total $2,169 $2,169 $805 $2,938 $2,938 $777 $4,337 $4,337 $2,693

Notes: 

a

 Costs shown are for the 2021 model year and are incremental to the costs of a vehicle meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to draft RIA Chapter 2.12. 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated vehicle classes. c

 Engine costs are for a light HD, medium HD or heavy HD diesel engine. We are projecting no additional costs 

beyond Phase 1 for gasoline vocational engines in MY2021 under this alternative. 

The estimated costs of the technologies expected to be used to comply with the 

Alternative 4 standards for MY2024 are shown in Table 2-68. Fleet average costs are shown for 

light, medium and heavy HD vocational vehicles in each duty-cycle-based subcategory – Urban, 

Multi-Purpose, and Regional. As shown, these range from approximately $1,500 for MHD and 

LHD Regional vehicles to $7,900 for HHD Urban and Multipurpose vehicles. These two 

subcategories are projected to have the higher-cost packages in MY 2024 due to an estimated 18 

percent adoption of HHD hybrids, which are estimated to cost $33,000 per vehicle in MY 2024, 

as shown in Chapter 2.12.7 of the draft RIA. The engine costs listed represent the cost of an 

average package of diesel engine technologies with Alternative 4 adoption rates described in the 

preamble at Section II.D.2(e). For gasoline vocational vehicles, the agencies are projecting 

adoption of Level 2 engine friction reduction with an estimated $74 added to the average SI 

vocational vehicle package cost in MY 2024, which represents about 56 percent of those vehicles 

upgrading beyond Level 1 engine friction reduction. Further details on how these SI vocational 

vehicle costs were estimated are provided above in Chapter 2.9.1. The details behind all these 

costs are presented in draft RIA Chapter 2.12, including the markups and learning effects applied 
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and how the costs shown here are weighted to generate an overall cost for the vocational vehicle 

segment. 

Table 2-68 Vocational Vehicle Technology Incremental Costs for Alternative 4 Standards in the 2024 Model 

Yeara,b (2012$) 

LIGHT HD MEDIUM HD HEAVY HD 

Urban Multipurpose Regional Urban Multipurpose Regional Urban Multipurpose Regional

Enginec $493 $493 $493 $457 $457 $457 $457 $457 $457

Tires $26 $26 $26 $26 $26 $26 $40 $40 $40

Transmission $256 $256 $280 $256 $256 $280 $256 $256 $3,123

Axle related $90 $90 $90 $90 $90 $90 $136 $136 $224

Weight Reduction $30 $30 $49 $30 $30 $43 $30 $30 $37

Idle reduction $561 $524 $524 $592 $553 $553 $1,014 $1,014 $1,011

Electrification & 

hybridization 

$2,264 $2,264 $0 $3,559 $3,559 $0 $5,943 $5,943 $0

Air Conditioning $20 $20 $20 $20 $20 $20 $20 $20 $20

Total $3,741 $3,704 $1,482 $5,030 $4,992 $1,469 $7,895 $7,895 $4,912

Notes: 

a

 Costs shown are for the 2024 model year and are incremental to the costs of a vehicle meeting the Phase 1 

standards. These costs include indirect costs via markups along with learning impacts. For a description of the 

markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer 

to draft RIA Chapter 2.12. 

b

 Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost 

expected for each of the indicated vehicle subcategories. Estimated technology costs exclusive of adoption rates are 

discussed in RIA 2.12. 

c

 Engine costs shown are for a light HD, medium HD or heavy HD diesel engines. For gasoline-powered vocational 

vehicles we are projecting $74 of additional engine-based costs beyond Phase 1 in MY2024.

2.10 Technology Application and Estimated Costs – Trailers 

The agencies are proposing standards for trailers specifically designed to be pulled by 

Class 7 and 8 tractors. These proposed standards are expressed as CO2 and fuel consumption 

standards, and would apply to each trailer with respect to the emissions and fuel consumption 

that would be expected for a specific standard type of tractor pulling such a trailer. EPA and 

NHTSA believe it is appropriate to establish standards for trailers separately from tractors 

because they are separately manufactured by distinct companies; the agencies are not aware of 

any manufacturers that currently assemble both the finished tractor and the trailer. This section 

of the draft RIA describes the analyses performed by the agencies as we developed the proposed 

trailer program. 

2.10.1 Trailer Subcategories Evaluated 

The agencies evaluated several trailer subcategories for this proposal. Though many of 

the same technologies are available for dry and refrigerated vans, the agencies evaluated these 

trailer types separately. The transport refrigeration unit (TRU) commonly located at the front of 

refrigerated trailers adds weight, has the potential to impact the aerodynamic characteristics of 

the trailer, and may limit the type of aerodynamic devices that can be applied. Additionally, 

“long box” trailers in lengths 50 feet or longer and “short box” trailers less than 50 feet in length 

were evaluated separately due to differences in both weight and use patterns. 
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The agencies identified a list of work-performing devices that are sometimes added to 

standard box trailers, which may inhibit the use of some aerodynamic devices. The agencies are 

proposing to recognize box trailers that are restricted from using aerodynamic devices in one 

location on the trailer as “partial-aero” box trailers. We believe these trailers have the ability to 

adopt some aerodynamic technologies, but do not expect them to be able to meet the same 

stringencies as the standard box vans. 

Additionally, we propose to consider box trailers that have work-performing devices in 

two locations such that they inhibit the use of all practical aerodynamic devices to be “non-aero” 

box trailers that would be not be expected to adopt aerodynamic technologies at any point in the 

program. The agencies are proposing that the non-aero box trailer subcategory include box 

trailers with more than three axles, since they are designed to be used in heavy-haul applications 

where aerodynamic devices are not generally practical. 

The agencies evaluated all non-box highway trailers (e.g., tankers, platforms, and car 

haulers) as a single representative trailer assuming a single stringency level for all trailers within 

the subcategory. These stringency levels did not include the use of aerodynamic technologies. 

In summary, the agencies are proposing ten trailer subcategories: 

– Long box (longer than 50 feet) dry vans 

– Long box (longer than 50 feet) refrigerated vans 

– Short box (50 feet and shorter) dry vans 

– Short box (50 feet and shorter) refrigerated vans 

– Partial-aero long box dry vans 

– Partial-aero long box refrigerated vans 

– Partial-aero short box dry vans 

– Partial-aero short box refrigerated vans 

– Non-aero box vans (all lengths of dry and refrigerated vans) 

– Non-box highway (tanker, platform, container chassis, and all other types of highway 

trailers that are not box trailers). 

The partial-aero box trailers would have similar stringencies as their corresponding full-aero 

trailers in the early phase-in years, but would have separate, reduced standards as the program 

becomes fully implemented. 

The analysis in the following sections describes our evaluation of two alternative 

stringencies with similar technologies. In the first analysis we projected adoption rates that were 

used to develop the proposed standards in MY 2027. The second analysis considers the same 

technologies with less lead time to achieve similar adoption rates. 

The agencies did consider an alternative that differentiated tanker trailers, platform 

trailers, and container chassis from the other non-box highway trailers to include aerodynamic 

technologies on a fraction of these trailer types. However, an evaluation of this alternative is not 

included here. As discussed in Section IV of the preamble for this proposal, a majority of the 
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non-box trailer manufacturers meet the definition of small business.O EPA convened and the 

agencies are proposing to follow the recommendations of the Small Business Advocacy Review 

panel required by the Small Business Regulatory Enforcement Fairness Act (SBREFA). This 

panel concluded that aerodynamic requirements for non-box trailer manufacturers would 

disproportionately burden small manufacturers and they recommended that no aerodynamic 

requirements be proposed. 

2.10.2 Defining the Proposed Trailer Technology Packages 

The impact of a trailer on the overall fuel efficiency and CO2 emissions of a tractor-trailer 

vehicle varies depending on three main characteristics of the trailer: aerodynamic drag, rolling 

resistance, and weight. In this section, we outline the technologies that the agencies evaluated 

for the proposed standards. 

2.10.2.1 Aerodynamic Drag Reduction 

The rigid, rectangular shape of box trailers creates significant aerodynamic drag and 

makes them ideal candidates for aerodynamic technologies that can reduce drag and improve 

fuel consumption and CO2 emissions. Current aerodynamic technologies for box trailers have 

shown significant drag reductions, as discussed below. These technologies are designed to 

create a smooth transition of airflow from the tractor, around the trailer, and beyond the trailer. 

Box trailers provide opportunities to address drag at the front, rear, and underside of the trailer, 

and the agencies considered several types of aerodynamic devices designed to address drag at all 

of these points. Table 2-69 lists general aerodynamic technologies that the EPA SmartWay 

program has evaluated for use on box trailers and a description of their intended impact. Several 

versions of each of these technologies are commercially available and have seen increased 

adoption over the past decade. Performance of these devices varies based on their design, their 

location and orientation on the trailer, and the vehicle speed. 

Table 2-69 Aerodynamic Technologies for Box Trailers 

LOCATION 

ON TRAILER

EXAMPLE TECHNOLOGIES INTENDED IMPACT ON AERODYNAMICS

Front Front fairings and gap-reducing 

fairings

Reduce cross-flow through gap and smoothly 

transition airflow from tractor to the trailer

Rear Rear fairings, boat tails and flow 

diffusers

Reduce pressure drag induced by the trailer wake

Underside Side fairings and skirts, and 

underbody devices

Manage flow of air underneath the trailer to reduce 

turbulence, eddies and wake

2.10.2.1.1 Performance of Aerodynamic Technologies 

SmartWay-verified technologies are evaluated on 53-foot dry vans. The verified 

technologies are grouped into bins that represent one percent, four percent, or five percent fuel 

 O The Regulatory Flexibility Act defines small entities as including “small businesses,” “small governments,” and 

“small organizations” (5 USC 601) and references the Small Business Act for the definition of “small businesses” 

using size standards based on the North American Industry Classification System (NAICS) (13 CFR 121.201). 
2-155 

savings relative to a typical long-haul tractor-trailer at 65-mph cruise conditions. Use of verified 

aerodynamic devices totaling at least five percent fuel savings, along with verified tires, qualifies 

a 53-foot dry van trailer for the “SmartWay Trailer” designation. In 2014, EPA expanded the 

program to include refrigerated vans and provided a “SmartWay Elite” designation if fleets adopt 

verified tires and aerodynamic equipment providing nine percent or greater fuel savings. Todate, nine aerodynamic technology packages from five manufacturers have received the 

SmartWay Elite designation. We may refer to SmartWay verification levels in this analysis, 

since the trailer industry is most familiar with these values as a measure of trailer performance. 

It is important to note that the cruise speed results presented in SmartWay do not 

necessarily represent performance that would be observed in real world operation. Additionally, 

EPA’s Greenhouse gas Emissions Model (GEM), which is the tool the agencies are proposing to 

use for trailer compliance, uses a weighted average of three drive cycles in its vehicle simulation. 

The CO2 and fuel consumption reductions calculated in GEM may differ from those measured in 

SmartWay’s performance tests. Figure 2-17 shows a comparison of the CO2 reductions observed 

for the three individual drive cycles simulated in GEM and the reductions using a combination of 

the three GEM cycles with the cycle weightings assigned to long-haul tractor-trailers in this 

proposed rulemaking (i.e., 86 percent 65-mph cruise, 9 percent 55-mph cruise, and 5 percent 

transient). These results could be used to estimate the difference in performance when 

comparing a constant, 65-mph cruise test similar to SmartWay’s performance tests or the results 

from GEM simulations used for compliance to other driving conditions. These results suggest 

that the SmartWay Elite target improvement of nine percent would be closer to eight percent 

using GEM’s long-haul simulation. It can also be seen that very little benefit is seen for tractortrailers driving under highly transient conditions. These results are for illustrative purposes only 

and do not provide an exact correlation between test results, real-world results, and results from 

GEM. 

0%

2%

4%

6%

8%

10%

12%

14%

16%

4.0 4.5 5.0 5.5 6.0 6.5

CO2 Reductions (%)

Aerodynamic Drag Area, CdA (m2)

CO2 Reductions for Simulated Long-Haul Dry Van 

(Compare GEM Cycles)

65-MPH Cruise Long-Haul Weighted 55-MPH Cruise Transient 

SmartWay

Verified

SmartWay

Elite

Figure 2-17 Comparison of Weighted and 65-mph Cruise Results using EPA's GEM for a Long Dry Van 
2-156 

In this analysis, the aerodynamic performance of a tractor-trailer vehicle is quantified by 

the aerodynamic drag area, CdA (coefficient of drag multiplied by frontal area), which is a 

function of both tractor and trailer aerodynamic characteristics. EPA’s aerodynamic testing of 

Class 8 high roof sleeper cab tractors pulling standard 53-foot dry vans with zero aerodynamic 

technologies produced an average CdA value of 6.2 m2 in coastdown testing and an average 

zero-yaw CdA value of 5.5 m2 in wind tunnel testing (see Chapter 3.2.4.1). EPA also performed 

wind tunnel tests on a Class 7 high roof day cab pulling several configurations of a solo 28-foot 

dry van trailer and two tandem 28-foot dry van trailers. The average zero-yaw CdA value for the 

solo 28-foot trailer configuration with zero aerodynamic technologies was 5.4 m2

 (a two percent 

difference compared the corresponding test of the 53-foot trailer). 

For this analysis, EPA grouped these common aerodynamic devices into packages of 

individual or combined technologies. Front fairings and gap reducers provide the smallest 

benefit of the aerodynamic technologies considered. Skirts and boat tails come in ranges of sizes 

and vary in effectiveness. For the purpose of this analysis, the agencies grouped these two 

technologies into “basic” and “advanced”. Basic boat tails and skirts achieve SmartWay’s 

verification threshold of four percent at cruise speeds. Advanced tails and skirts achieve 

SmartWay’s five percent verification. These technologies can be used individually, or in 

combination. The overall performance of a combination of devices could be nearly additive in 

terms of the effectiveness of its individual devices. Some devices may work synergistically to 

achieve greater reductions or counteract and provide less reduction. The trailer aerodynamics 

industry continues to evolve and the agencies anticipate further optimization of these devices in 

the future. In addition to these bolt-on technologies, some manufacturers are experimenting with 

physical changes to the trailer design such that the overall construction of the trailer is more 

aerodynamic. 

EPA collected aerodynamic test data for many of the technologies mentioned previously 

on several tractor-trailer configurations, including 53-foot dry vans and 28-foot dry van pup 

trailers. As described in Chapter 3, EPA’s aerodynamic testing included four tractor models, 

three trailer models, and several aerodynamic technologies. The wind tunnel results shown in 

Figure 2-18 indicate there is very little difference in performance between trailer manufacturers 

for their basic trailer models. 
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5.6

5.5

5.5

5.3

5.1

5.2

5.2

5.0

5.0

5.1

4.9

5.0

4.6

4.5

4.4

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Trailer 1 Trailer 2 Trailer 3

CdA (m2)

Wind Tunnel Tests: Average CdA

(Average of Four Tractors)

Baseline Tail Skirt 1 Skirt 2 Skirt 2 + Tail

Figure 2-18 Variation in Performance of Trailer Devices due to Trailer Manufacturer 

However, the results showed some variation in aerodynamic performance depending on 

the tractor type, device manufacturer, and test method. Figure 2-19 illustrates these variations 

for a single trailer. While there is some variability in the numerical CdA values of the baseline 

(zero technology) tractor-trailers tested, there is less variation in the effect of adding devices. 

For example, the wind tunnel CdA result for adding a skirt and tail to Tractor 1 (orange bar in the 

first column of Figure 2-19) is 4.5 m2 and the coastdown CdA result for adding the same skirt 

and tail to Tractor 3 (orange bar in the last column of Figure 2-19) is 5.8 m2. However, when we 

compare the effect of adding the devices by taking into account the change from the baseline 

result, we get a change in CdA (delta CdA) of 0.9 m2 for Tractor 1 and 0.8 m2 for Tractor 3. 

This reduced variation is one of the motivating factors in our decision to use a delta CdA 

approach for trailers in lieu of requiring an absolute CdA test.P

 

 P Additional considerations include the fact that an absolute test would require a specific standard tractor for testing 

to ensure an apples-to-apples comparison of all trailer test results and a delta CdA approach makes it possible to 

allow device manufacturers to perform tests on their devices and have them pre-approved for any trailer 

manufacturer to apply on their trailers. 
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5.4

5.6

5.6

5.6

6.1

5.9

6.6

5.5

5.7

5.6

5.1

5.2

5.4

5.3

5.0

5.2

5.1

4.9

5.0

5.1

5.0

5.6

5.5

4.5

4.6

4.6

4.5

5.0

5.8

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Wind Tunnel, 

Tractor 1

Wind Tunnel, 

Tractor 2

Wind Tunnel, 

Tractor 3

Wind Tunnel, 

Tractor 4

Coastdown, 

Tractor 1'

Coastdown, 

Tractor 2

Coastdown, 

Tractor 3'

CdA (m2)

Effect on Absolute CdA 

(Multiple Tractors, Multiple Test Methods, Single Trailer)

Baseline Gap Tail Skirt 1 Skirt 2 Skirt 2+Tail

Figure 2-19 Variation in Aerodynamic Performance of Trailer Devices due to Tractor Manufacturer and 

Test Method 

2.10.2.1.2 Performance Bins for Aerodynamic Technologies 

The agencies developed bins based on changes in CdA (or “delta CdA”) to encompass 

technologies that are expected to provide similar improvements in drag (e.g., most skirts would 

fall into the same bin) and cover the variability due to tractor model, test method, device 

manufacturer, and trailer manufacturer. Figure 2-20 summarizes the trailer aerodynamic test 

results that were used to establish the trailer certification bins. These results show the average 

delta CdA over four tractor types, and two test methods (i.e., wind tunnel and coastdown) using a 

single trailer. 
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Figure 2-20 Aerodynamic Trailer Testing Results used to Establish Bins for Trailer Certification 

As seen in Figure 2-21, results from EPA’s testing of solo 28-foot trailers show that a 

basic skirt would fall into Bin II and the addition of a gap reducer improves the aerodynamic 

performance to Bin III or Bin IV levels of drag reduction. It should be noted that while the 

agencies have chosen to test and regulate 28-foot box trailers individually, they are often pulled 

in a tandem configuration, which restricts the type of aerodynamic devices that can be applied on 

the rear of the trailers. We expect rear devices such as boat tails would not be practical for 28-

foot box trailers, since those devices are only deployable when the trailer is in the rear position. 

However, we recognize that other trailer lengths within the short box trailer subcategory (e.g., 

40-foot and 48-foot) would be able to use rear aerodynamic devices and achieve the 

improvements observed in the higher bins. 
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Figure 2-21 Aerodynamic Trailer Testing Results for a Solo 28-Foot Dry Van Relative to Proposed Bins 

Table 2-70 below illustrates the bin structure that the agencies are proposing as the basis 

for compliance. The table summarizes example technology packages that might be included in 

each bin for two example trailers. 

Table 2-70 Aerodynamic Technology Bins used to Evaluate Trailer Benefits and Costs 

BIN DELTA CDA AVERAGE 

DELTA CDA 

EXAMPLE TECHNOLOGIES 

53-FOOT DRY VAN 28-FOOT DRY VAN 

Bin I < 0.09 0.0 No Aero Devices No Aero Devices 

Bin II 0.10 - 0.19 0.1 Gap Reducer Skirt 

Bin 

III 

0.20 - 0.39 0.3 Basic Skirt or Basic Tail Skirt + Gap Reducer 

Bin 

IV 

0.40 - 0.59 0.5 Advanced Skirt or Tail Adv. Skirt + Gap Reducer 

Bin V 0.60 - 0.79 0.7 Basic Combinations 

Bin 

VI 

0.80 - 1.19 1.0 Advanced Combinations 

(including SmartWay Elite) 

Bin 

VII 

 1.20 - 1.59 1.4 Optimized Combinations 

Bin 

VIII 

> 1.60 1.8 Changes to Trailer Construction 

Within GEM, the aerodynamic performance of each trailer subcategory is evaluated by 

comparing the delta CdA from Table 2-70 to a CdA value representative of a tractor-trailer 

vehicle with zero aerodynamic trailer technologies (i.e., Bin I). The agencies chose to model the 

zero-technology long box dry van using a CdA value of 6.2 m2

 (the average CdA from EPA’s 

coastdown testing). For long box refrigerated vans, a two percent reduction in CdA was 

assumed to account for the aerodynamic benefit of the TRU at the front of those trailers. A short 

box dry van also received a two percent lower CdA value compared to its 53-foot counterpart, 
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consistent with the reduction observed in EPA’s wind tunnel testing. The CdA value assigned to 

a refrigerated short box van was an additional two percent lower than the short box dry van. 

Special purpose trailers of all lengths are modeled as a short dry van trailer in GEM and have the 

same Bin I CdA value of 6.1 m2

. Since there are no aerodynamic requirements for special 

purpose trailers, the rest of the bins are unnecessary. Non-box highway trailers, which are 

modeled as flatbed trailers, were assigned a drag area of 5.0 m2

, as was done in the Phase 1 

tractor program for low roof day cabs. Table 2-71 illustrates the absolute drag areas (CdA) 

associated with each aerodynamic bin for each trailer subcategory. 

Table 2-71 Baseline CdA Values Associated with Aerodynamic Bin I (Zero Trailer Technologies) within 

GEM 

TRAILER 

SUBCATEGORY 

DRY VAN 

Long Dry Van 6.2 

Short Dry Van 6.1 

Long Ref. Van 6.1 

Short Ref. Van 6.0 

Special Purpose Box 6.1 

Non-Box Highway 4.9 

2.10.2.1.3 Effect of Wind-Averaged Drag 

The agencies recognize that the benefits of aerodynamic devices for trailers can be better 

seen when measured considering multiple yaw angles. To evaluate the effect of wind, we 

compared the zero yaw and wind-averaged results from EPA’s wind tunnel tests. The windaverage results were calculated at 55 mph vehicle speeds, consistent with the procedures in 40 

CFR 1037.810. The results for three trailers, each an average of tests performed on four tractors, 

are shown in Figure 2-22. The wind-averaged analysis consistently results in a larger 

improvement (i.e., delta CdA). The gap reducer technology shows minimal benefit under a zero 

yaw analysis, but a measurable benefit when yaw angles are considered. 

The performance bins and the resulting proposed standards were developed using zero 

yaw drag results. The agencies are not proposing to accept wind averaged drag results, in order 

to maintain consistency between test methods, as was shown in Figure 2-19. The use of windaveraged drag data would result in larger benefits for trailers tested using a wind tunnel or CFD 

compared to same trailers tested using coastdown procedures. The tractor program, which is 

proposing to use wind-averaged drag results, has a reference test method and a correction factor 

to maintain consistency between methods. The trailer program is not proposing to require a 

reference test, in order to reduce the test burden for manufacturers and allow them to choose an 

appropriate test method for their needs and resources. 
2-162 

Figure 2-22 Comparison of Zero Yaw and Wind-Averaged Drag Results 

2.10.2.2 Tire Rolling Resistance 

2.10.2.2.1 Lower Rolling Resistance Tires 

On a typical Class 8 long-haul tractor-trailer, over 40 percent of the total energy loss from 

tires is attributed to rolling resistance from the trailer tires.147 Trailer tire rolling resistance 

values collected by the agencies for Phase 1 indicate that the average coefficient of rolling 

resistance (CRR) for new trailer tires was 6.0 kg/ton. This value was applied for the standard 

trailer used for tractor compliance in the Phase 1 tractor program. For Phase 2, the agencies 

consider all trailer tires with CRR values below 6.0 kg/ton to be “lower rolling resistance” (LRR) 

tires. For reference, a trailer tire that qualifies as a SmartWay-verified tire must meet a CRR 

value of 5.1 kg/ton, a 15 percent CRR reduction from the trailer tire identified in Phase 1. Our 

research of rolling resistance indicates an additional CRR reduction of 15 percent or more from 

the SmartWay verification threshold is possible with tires that are available in the commercial 

market today. 

For this proposal, the agencies are proposing to use the same rolling resistance baseline 

value of 6.0 kg/ton for all trailer subcategories. In the preamble at Section IV, the agencies 

request comments including information on current adoption rates of and CRR values for models 

of LRR tires in use by the various trailer types today. 

Similar to the case of tractor tires, LRR tires are available as either dual or as single widebased tires for trailers. Single wide-based tires achieve CRR values that are similar to their dual 
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counterparts, but have an added benefit of weight reduction, which can be an attractive option for 

trailers that frequently maximize cargo weight. 

2.10.2.2.2 Performance Levels for LRR Tires 

Similar to the proposed Phase 2 tractor and vocational vehicle programs, the agencies are 

proposing a tire program based on adoption of lower rolling resistance tires. Feedback from 

several box trailer manufacturers indicates that the standard tires offered on their new trailers are 

SmartWay-verified tires (i.e., CRR of 5.1 kg/ton or better). An informal survey of members 

from the Truck Trailer Manufacturers Association (TTMA) indicates about 35 percent of box 

trailers sold today have SmartWay tires.148 While some trailers continue to be sold with tires of 

higher rolling resistances, the agencies believe most box trailer tires currently achieve the Phase 

1 trailer tire CRR of 6.0 kg/ton or better. 

The agencies evaluated two levels of tire performance for this proposal beyond the 

baseline trailer tire with a CRR of 6.0 kg/ton. The first performance level was set at the criteria 

for SmartWay-verification for trailer tires, 5.1 kg/ton, which is a 15 percent reduction in CRR 

from the baseline. As mentioned previously, several tire models available today achieve rolling 

resistance values well below the present SmartWay threshold. Given the multiple year phase-in 

of the standards, the agencies expect that tire manufacturers will continue to respond to demand 

for more efficient tires and will offer increasing numbers of tire models with rolling resistance 

values significantly better than today’s typical LRR tires. In this context, we believe it is 

reasonable to expect a large fraction of the trailer industry could adopt tires with rolling 

resistances at a second performance level that would achieve an additional eight percent 

reduction in rolling resistance (a 22 percent reduction from the Level 1 tire), especially in the 

later stages of the program. The agencies project the CRR for this second level of performance 

to be a value of 4.7 kg/ton. The agencies evaluated these three tire rolling resistance levels, 

summarized in Table 2-72, in the feasibility analysis of the following sections. GEM simulations 

that apply Level 1 and 2 tires result in CO2 and fuel consumption reductions of two and three 

percent from the Level 1 tire, respectively. 

Table 2-72 Summary of Trailer Tire Rolling Resistance Levels Evaluated 

ROLLING 

RESISTANCE LEVEL 

CRR (KG/TON) 

Baseline 6.0 

Level 1 5.1 

Level 2 4.7 

2.10.2.3 Tire Pressure Systems 

The inflation pressure of tires also impacts the rolling resistance. Tractor-trailers 

operating with all tires under-inflated by 10 psi have been shown to increase fuel consumed by 

up to one percent.149 Tires can gradually lose pressure from small punctures, leaky valves or 

simply diffusion through the tire casing. Changes in ambient temperature can also affect tire 

pressure. Trailers that remain unused for long periods of time between hauls may experience any 

of these conditions. A 2003 FMCSA report found that nearly one in five trailers had at least one 

tire under-inflated by 20 psi or more. If drivers or fleets are not diligent about checking and 
2-164 

attending to under-inflated tires, the trailer may have much higher rolling resistance and much 

higher CO2 emissions and fuel consumption. 

2.10.2.3.1 Types of Tire Pressure Monitoring Systems 

Tire pressure monitoring (TPM) and automatic tire inflation (ATI) systems are designed 

to address under-inflated tires. Both systems alert drivers if a tire’s pressure drops below its set 

point. TPM systems simply monitor the tires and require user-interaction to reinflate to the 

appropriate pressure. Today’s ATI systems take advantage of trailers’ air brake systems to 

supply air back into the tires (continuously or on demand) until a selected pressure is achieved. 

In the event of a slow leak, ATI systems have the added benefit of maintaining enough pressure 

to allow the driver to get to a safe stopping area.150 The agencies believe TPM systems cannot 

sufficiently guarantee the proper inflation of tires due to the inherent user-interaction required. 

Therefore, ATI systems are the only pressure systems the agencies are proposing to recognize in 

Phase 2. 

2.10.2.3.2 Performance of ATI Systems 

Estimates of the benefits of ATI systems vary depending on the base level of 

maintenance already performed by the driver or fleet, as well as the number of miles the trailer 

travels. Trailers that are well maintained or that travel fewer miles would experience less 

benefits from ATI systems compared to trailers that often drive with poorly inflated tires or log 

many miles. The agencies believe ATI systems can provide a CO2 and fuel consumption benefit 

to most trailers. With ATI use, trailers that have lower annual vehicle miles traveled (VMT) due 

to long periods between uses would be less susceptible to low tire pressures when they resume 

activity. Trailers with high annual VMT or frequent changes in ambient conditions would 

experience the fuel savings associated with consistent tire pressures. Automatic tire inflation 

systems could provide a CO2 and fuel consumption savings of 0.5-2.0 percent, depending on the 

degree of under-inflation in the trailer system. 

Maintaining tire pressure is important to fuel consumption. Tire manufacturers estimate 

a tire pressure 10 psi below target results in a 0.9 percent increase in fuel consumption. Two 

studies have evaluated truck and trailer tire inflation including FMCSA (2003) and TMC 

(2002).151,152 In the 2003 FMCSA study, tire inflation (psi) was measured in 3,200 tractors and 

1,300 trailers. The TMC study measured tire inflation rates in two fleets and found that only 38 

percent of sampled trailer tires were within +/- 5 psi of target pressure as prescribed by tire 

manufacturers. The study also found that more than 20 percent of tires were 20 psi or more 

underinflated and four percent of tires were 50 psi or more underinflated compared to the target. 

The FMCSA study found similar results. These figures suggest under inflation of tractor and 

trailer tires in the U.S. fleet could result in an increase in fuel consumption of approximately one 

to two percent. Most recently, FMCSA (2014) evaluated trailer ATI systems in two test fleets.153 

The study found ATI systems improved fuel consumption 1.4 percent in test trucks as compared 

to control trucks in two fleets. 

NHTSA and EPA recognize the role of proper tire inflation in maintaining optimum tire 

rolling resistance during normal trailer operation. For this proposal, rather than require 

performance testing of ATI systems, the agencies are proposing to recognize the benefits of ATI 
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systems with a single default reduction for manufacturers that incorporate ATI systems into their 

trailer designs. Based on information available today, we believe that there is a narrow range of 

performance among technologies available and among systems in typical use. We propose to 

assign a 1.5 percent reduction in CO2 and fuel consumption for all trailers that implement ATI 

systems, based on information available today.154 We believe the use of these systems can 

consistently ensure that tire pressure and tire rolling resistance are maintained. We selected the 

levels of the proposed trailer standards with the expectation that a high rate of adoption of ATI 

systems would occur across all on-highway trailers and during all years of the phase-in of the 

program. For a target tire pressure of 100 psi, a 1.5 percent reduction could be achieved 

assuming 30 percent of trailer tires are at their target pressure, 30 percent are 10 psi below target, 

20 percent are 20 psi low, 10 percent are 30 psi low, 5 percent are 40 psi low and 5 percent are 

50 psi or more below target pressure. 

2.10.2.4 Weight Reduction 

Reduction in trailer tare (or empty) weight can lead to fuel consumption reductions in two 

ways. For applications where payload is not limited by weight restrictions, the overall weight of 

the tractor and trailer would be reduced and would lead to improved fuel efficiency. For 

applications where payload is limited by weight restrictions, the lower trailer weight would allow 

additional payload to be transported during the truck’s trip, so g/ton-mile emissions would 

decrease. Weight reduction opportunities in trailers exist in both the structural components and 

in the wheels and tires. Manufacturers commonly replace components such as roof posts, bows, 

side posts, cross members, floor joists, and floor sections with lighter weight options. 

Major lower-weight options are not offered consistently by all trailer manufacturers 

across the industry. For example, some manufacturers have already marketed lower-weight 

major components for many years, while others to date have not done so. There is no clear 

“baseline” for current trailer weight against which lower-weight designs could be compared for 

regulatory purposes. For this reason, the agencies do not believe it would be appropriate or fair 

across the industry to apply overall weight reductions toward compliance. However, the 

agencies do believe it would be appropriate to allow a manufacturer to account for weight 

reductions that involve substituting very specific, traditionally heavier components with lowerweight options that are not currently widely adopted in the industry. 

The agencies recognize that when weight reduction is applied to a trailer, some operators 

will replace that saved weight with additional payload. To account for this in EPA’s GEM 

vehicle simulation tool, it is assumed that one-third of the weight reduction is applied to the 

payload. For tractor-trailers simulated in GEM, it takes a weight reduction of nearly 1,000 

pounds before a one percent fuel savings is achieved and about a 2,500 pound reduction to reach 

three percent savings. The component substitutions identified by the agencies result in weight 

reductions of less than 500 pounds, yet can cost over $1,000. The agencies believe that few 

trailer manufacturers would apply weight reduction solely as a means of achieving reduced fuel 

consumption and CO2 emissions, and we are proposing standards that can be met without 

reducing weight. However, we are proposing to offer weight reduction as an option for box 

trailer manufacturers who wish to apply it to some of their trailers as part of their emissions 

averaging strategy. 
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2.10.2.4.1 Weight Reduction Options Recognized in this Proposal 

The agencies are proposing compliance provisions that would limit the weight-reduction 

options to the substitution of specified components that can be clearly isolated from the trailer as 

a whole. For this proposal, the agencies have identified several conventional components with 

available lighter-weight substitutes (e.g., substituting conventional dual tires with steel wheels 

with single wide-based tires and aluminum wheels). We are proposing values for the associated 

weight-related savings that would be applied with these substitutions for compliance. We 

believe that the initial cost of these component substitutions is currently substantial enough that 

only a relatively small segment of the industry has adopted these technologies today. 

In addition to weight reduction associated with replacing standard steel wheels with 

aluminum versions, and adopting single wide-based tires in place of dual tires, the agencies have 

identified 11 common trailer components that have lighter weight options available.155,156,157,158 

Some of the references include confidential data that outlined weight savings and costs 

associated with these material substitutions. Table 2-73 lists the components, and estimates of 

weight savings and costs obtained by the agencies. Manufacturers that adopt these technologies 

would sum the associated weight reductions and apply those values in GEM. Steel wheels can 

be replaced with aluminum wheels and two dual tires can be replaced with single wide-based 

tires on aluminum wheels. Relatively large weight savings are possible by replacing steel upper 

coupler assemblies or suspension sub-frames with aluminum versions, but these substitutions are 

more expensive and more labor-intensive to install. 

Table 2-73 Weight Reduction Options for Trailers 

COMPONENT MATERIAL 

SUBSTITUTION 

WEIGHT 

REDUCTION (LB) 

Hub and Drum (per axle) Cast Iron to Aluminum 80 

Floor Hardwood to Aluminum 375 

Floor Hardwood to Composite 245 

Floor Crossmembers Steel to Aluminum 203 

Landing Gear Steel to Aluminum 50 

Rear Door Steel to Aluminum 187 

Rear Door Surround Steel to Aluminum 150 

Roof Bows Steel to Aluminum 100 

Side Posts Steel to Aluminum 300 

Slider Box Steel to Aluminum 150 

Structure for Suspension Assembly Steel to Aluminum 280 

Upper Coupler Assembly Steel to Aluminum 430 

2.10.2.5 Effectiveness of Technologies 

The agencies are proposing to recognize trailer improvements via four performance 

parameters: aerodynamic drag reduction, tire rolling resistance reduction, and the adoption of 

ATI and weight reduction. Table 2-74 summarizes the performance levels for each of these 

parameters based on the technology characteristics outlined in Section 2.10.2. 
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Table 2-74 Performance Parameters for the Proposed Trailer Program 

AERODYNAMICS (DELTA CDA, M2

) 

Bin I 0.0 

Bin II 0.1 

Bin III 0.3 

Bin IV 0.5 

Bin V 0.7 

Bin VI 1.0 

Bin VII 1.4 

Bin VIII 1.8 

Tire Rolling Resistance (CRR, kg/ton) 

Tire Baseline 6.0 

Tire Level 1 5.1 

Tire Level 2 4.7 

Tire Inflation System (% reduction) 

ATI System 1.5 

Weight Reduction (pounds) 

Weight 1/3 added to payload, remaining 

reduces overall vehicle weight 

These performance parameters have different effects on each trailer subcategory due to 

differences in the simulated trailer characteristics. Table 2-75 shows the agencies’ estimates of 

the effectiveness of each parameter for four box trailer types. Each technology was evaluated in 

GEM using the baseline parameter values for the other technology categories. For example, each 

aerodynamic bin was evaluated using the Tire Level 1 (6.0 kg/ton) and the Base weight reduction 

option (zero pounds). The table shows that aerodynamic improvements offer the largest 

potential for CO2 emissions and fuel consumption reductions, making them relatively effective 

technologies. 
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Table 2-75 Effectiveness (Percent Change in CO2 Emissions and Fuel Consumption) of Technologies for the 

Proposed Trailer Program 

AERODYNAMICS DELTA CDA (M2

) DRY VAN REFRIGERATED VAN 

Long Short Long Short 

Bin I 0.0 0% 0% 0% 0% 

Bin II 0.1 -1% -1% -1% -1% 

Bin III 0.3 -2% -2% -2% -2% 

Bin IV 0.5 -3% -4% -3% -3% 

Bin V 0.7 -5% -5% -5% -5% 

Bin VI 1.0 -7% -7% -7% -7% 

Bin VII 1.4 -10% -10% -9% -10% 

Bin VIII 1.8 -13% -13% -12% -12% 

Tire Rolling Resistance CRR (kg/ton) Dry Van Refrigerated Van 

Long Short Long Short 

Baseline 6.0 0% 0% 0% 0% 

Level 1 5.1 -2% -1% -2% -1% 

Level 2 4.7 -3% -2% -3% -2% 

Weight Reduction Weight (lb) Dry Van Refrigerated Van 

Long Short Long Short 

Baseline 0.0 0.0% 0.0% 0.0% 0.0% 

Option 1 168 -0.2% -0.3% -0.2% -0.3% 

Option 2 280 -0.3% -1% -0.3% -1% 

Option 3 430 -0.5% -1% -0.5% -1% 

Option 4 556 -1% -1% -1% -1% 

2.10.3 Defining the Baseline Trailers 

2.10.3.1 Baseline Tractor-Trailer Vehicles within GEM 

The regulatory purpose of EPA’s heavy-duty vehicle compliance tool, GEM, is to 

combine the effects of trailer technologies through simulation so that they can be expressed as 

kg/ton-mile and gal/100 ton-mile and thus avoid the need for direct testing of each trailer model 

being certified. The proposed trailer program has separate standards for each trailer subcategory, 

and a unique tractor-trailer vehicle was chosen to represent each subcategory for compliance. In 

the Phase 2 update to GEM, each trailer subcategory is modeled as a particular trailer being 

pulled by a standard tractor depending on the physical characteristics and use pattern of the 

trailer. Table 2-76 highlights the relevant vehicle characteristics for the zero-technology baseline 

of each subcategory. Level 1 trailer tires are used, and the drag area, which is a function of the 

aerodynamic characteristics of both the tractor and trailer, is set to the Bin I values shown 

previously in Table 2-71. Weight reduction and ATI systems are not applied in these baselines. 

Chapter 2.10 of the draft RIA provides a detailed description of the development of these 

baseline tractor-trailers. 
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Table 2-76 Characteristics of the Zero-Technology Baseline Tractor-Trailer Vehicles 

 DRY VAN REFRIGERATED 

VAN 

AEROEXCLUDED 

BOX 

NON-BOX 

HIGHWAY 

Trailer Length Long Short Long Short All Lengths All Lengths 

Tractor Class Class 8 Class 8 Class 8 Class 8 Class 8 Class 8 

Tractor Cab Type Sleeper Day Sleeper Day Day Day 

Tractor Roof Height High High High High High Low 

Engine 2018 MY 

15L, 

455 HP 

2018 MY 

15L, 

455 HP 

2018 MY 

15L, 

455 HP 

2018 MY 

15L, 

455 HP 

2018 MY 

15L, 

455 HP 

2018 MY 

15L, 

455 HP 

Frontal Area (m2) 10.4 10.4 10.4 10.4 10.4 6.9 

Baseline Drag Area, CdA (m2) 6.2 6.1 6.1 6.0 6.1 4.9 

Steer Tire RR (kg/ton) 6.54 6.54 6.54 6.54 6.54 6.54 

Drive Tire RR (kg/ton) 6.92 6.92 6.92 6.92 6.92 6.92 

Trailer Tire RR (kg/ton) 6.00 6.00 6.00 6.00 6.00 6.00 

Total Weight (kg) 31978 21028 33778 22828 21028 29710 

Payload (tons) 19 10 19 10 10 19 

ATI System Use 0 0 0 0 0 0 

Weight Reduction (lb) 0 0 0 0 0 0 

Drive Cycle Weightings 

65-MPH Cruise 86% 64% 86% 64% 64% 64% 

55-MPH Cruise 9% 17% 9% 17% 17% 17% 

Transient Driving 5% 19% 5% 19% 19% 19% 

2.10.3.2 Reference Case Tractor-Trailer Vehicles to Evaluate Benefits and Costs 

In order to evaluate the benefits and costs of the proposed standards, it is necessary to 

establish a reference point for comparison. The technologies described in Section 2.10.2 exist in 

the market today, and their adoption is driven by available fuel savings as well as by the 

voluntary SmartWay Partnership and California’s Heavy Duty Greenhouse Gas Emission 

Reduction Measure tractor-trailer requirements. For this proposal, the agencies identified 

reference case tractor-trailers for each trailer subcategory based on the technology adoption rates 

we project would exist if this proposed trailer program was not implemented. 

The agencies believe research funded and conducted by the federal government, industry, 

academia and other organizations is likely to result in the adoption of some technologies beyond 

the levels required to comply with existing regulatory and voluntary programs. One example of 

such research is the Department of Energy Super Truck program159 which has a goal of 

demonstrating cost-effective measures to improve the efficiency of Class 8 long-haul freight 

trucks by 50 percent by 2015. For purposes of our reference case, we project that by 2018, 

absent further California regulation, EPA’s SmartWay program and these research programs will 

result in about 20 percent of 53-foot dry and refrigerated vans adopting basic SmartWay-level 

aerodynamic technologies (meeting SmartWay’s four percent verification level and Bin III from 

Table 2-74),160,161,162 30 percent adopting more advanced aerodynamic technologies at the five 

percent SmartWay-verification level (Bin IV), and five percent adding combinations of 

technologies (Bin V). In addition, we project half of these 53’ box trailers will be equipped with 

SmartWay-verified tires (i.e., 5.1 kg/ton) and ATI systems as well. The agencies project market 

forces will drive an additional one percent increase in adoption of the advanced SmartWay and 
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tire technologies each year through 2027. For analytical purposes, the agencies assumed 

manufacturers of the shorter box trailers and other trailer subcategories would not adopt these 

technologies in the timeframe considered and a zero-technology baseline is assumed. We are not 

assuming any weight reduction for any of the trailer subcategories in the reference cases. Table 

2-77 summarizes the reference case trailers for each trailer subcategory. 

Table 2-77 Adoption Rates and Average Performance Parameters for the Reference Case Trailers 

TECHNOLOGY LONG BOX 

DRY & REFRIGERATED 

VANS 

SHORT BOX, 

NON-AERO BOX, 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024 2027 2018 - 2027 

Aerodynamics 

Bin I 45% 41% 38% 35% 100% 

Bin II - - - - - 

Bin III 20% 20% 20% 20% - 

Bin IV 30% 34% 37% 40% - 

Bin V 5% 5% 5% 5% - 

Bin VI - - - - - 

Bin VII - - - - 

Bin VIII - - - - - 

Average Delta CdA (m2) a 0.2 0.3 0.3 0.3 0.0 

Tire Rolling Resistance 

Baseline tires 50% 47% 43% 40% 100% 

Level 1 tires 50% 53% 57% 60% - 

Level 2 tires - - - - - 

Average CRR (kg/ton) a 5.55 5.52 5.49 5.46 6.0 

Tire Inflation 

ATI 50% 53% 57% 60% 0% 

Average % Reduction a 0.8% 0.8% 0.9% 0.9% 0.0% 

Weight Reduction (pounds) 

Weight b 0 0 0 0 0 

Notes: 

a

 Combines adoption rates with performance levels shown in Table 2-74 b Weight reduction was not projected for the reference case trailers 

Also shown in Table 2-77 are average aerodynamic performance (delta CdA), average 

tire rolling resistance (CRR), and average reductions due to use of ATI and weight reduction for 

each stage of the proposed program. These values indicate the performance of theoretical 

average tractor-trailers that the agencies project would be in use if no federal regulations were in 

place for trailer CO2 and fuel consumption. The average tractor-trailer vehicles serve as 

reference cases for each trailer subcategory. 

In addition to the reference case described above, a second reference case was developed 

by the agencies. This alternative reflects the possibility that absent a Phase 2 regulation, there 

will be continuing adoption of technologies in the trailer market after 2027 that reduce fuel 

consumption and CO2 emissions. This alternative assumes that by 2040, 75 percent of new 

trailers will be equipped with SmartWay-verified aerodynamic devices and low rolling resistance 

tires, and ATI. Table 2-78 shows the adoption rates of technologies in the alternative reference 

case. 
2-171 

Table 2-78 Adoption Rates and Average Performance Parameters for the Alternative Reference Case 

TECHNOLOGY LONG BOX 

DRY & REFRIGERATED VANS 

SHORT BOX, 

NON-AERO BOX, 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024 2027 2040 2018 - 2027 

Aerodynamics 

Bin I 45% 41% 38% 35% 20% 100% 

Bin II - - - - - - 

Bin III 20% 20% 20% 20% 20% - 

Bin IV 30% 34% 37% 40% 55% - 

Bin V 5% 5% 5% 5% 5% - 

Bin VI - - - - - - 

Bin VII - - - - - 

Bin VIII - - - - - - 

Average Delta CdA (m2) a 0.2 0.3 0.3 0.3 0.4 0.0 

Tire Rolling Resistance 

Baseline tires 50% 47% 43% 40% 25% 100% 

Level 1 tires 50% 53% 57% 60% 75% - 

Level 2 tires - - - - - - 

Average CRR (kg/ton) a 5.6 5.5 5.5 5.5 5.3 6.0 

Tire Inflation 

ATI 50% 53% 57% 60% 75% 0% 

Average % Reduction a 0.8% 0.8% 0.9% 0.9% 1.1% 0.0% 

Weight Reduction (pounds) 

Weight b 0 0 0 0 0 0 

The agencies applied the vehicle attributes from Table 2-77 and the average performance 

values from Table 2-74 in the proposed Phase 2 GEM vehicle simulation to calculate the CO2 

emissions and fuel consumption performance of the reference tractor-trailers. The results of 

these simulations are shown in Table 2-79. We used these CO2 and fuel consumption values to 

calculate the relative benefits of the proposed standards. Note that the large difference between 

the per ton-mile values for long and short trailers is due primarily to the large difference in 

assumed payload (19 tons compared to 10 tons) as seen in and discussed further in the Chapter 

2.10.3. The alternative baseline in Table 2-78 impacts the long-term projections of benefits 

beyond 2027, which are analyzed in Chapters 5 through 7 of this draft RIA. The non-box trailers 

and non-aero box vans are not included in this reference case analysis, because we are proposing 

design standards for these trailers. As such, these trailers would not have standards to meet. 

Instead, they would have minimum tire requirements. 

Table 2-79 CO2 Emissions and Fuel Consumption Results for the Reference Case Tractor-Trailers 

DRY VAN REFRIGERATED 

VAN 

Length Long Short Long Short 

CO2 Emissions 

(kg/ton-mile) 85 147 87 151 

Fuel Consumption 

(gal/100 ton-miles) 8.3497 14.4401 8.5462 14.8330 
2-172 

2.10.4 Effectiveness and Costs of the Proposed Standards 

The agencies evaluated several alternatives for the proposed trailer program. The 

analysis below is for the alternative we believe reflects the agencies’ statutory authorities. This 

alternative is fully implemented in model year (MY) 2027. The agencies believe that a period of 

more than 10 years provides the industry sufficient lead time to meet the stringency requirements 

proposed. 

2.10.4.1 Projected Technology Adoption Rates for the Proposed Standards 

Table 2-80 and Table 2-81 provide a set of adoption rates that a manufacturer could apply 

to meet the standards of this proposed rulemaking. These adoption rates begin with 60 percent 

of long box trailers achieving current SmartWay level aerodynamics (Bin IV) and progress to 90 

percent achieving SmartWay Elite or better over the following nine years. Short box trailers 

adopt single aero devices in 2021 MY and combinations of devices by 2027 MY. Both long and 

short refrigerated vans have less stringent aerodynamic requirements in the later years to reflect 

the reduced number of aerodynamic options they have due to their TRUs. Similarly, we are 

proposing that partial-aero trailers of the various sizes would continue to be subject to the 

corresponding 2024 MY standards in 2027 and later model years to account for the workperforming devices that may inhibit the use of some technology combinations. The adoption 

rates for the long box trailers include some technologies that meet SmartWay Elite verification 

levels. The short box trailers would also include some combinations of devices. The agencies 

expect these adoption rates could be feasible in the next decade. The agencies believe trailer 

manufacturers and manufacturers of bolt-on aerodynamic devices will have incentive to design 

single-components or trailer features that accommodate work-performing devices and achieve 

these levels of performance by MY 2027. 

The agencies project that nearly all box trailers would adopt tire technologies to comply 

with the standards and the agencies projected consistent adoption rates across all lengths of dry 

and refrigerated vans. As mentioned previously, the agencies did not include weight reduction in 

their technology adoption projections, but manufacturers can use weight reduction as part of their 

compliance strategy. 

The adoption rates shown in these tables are one set of many possible combinations that 

box trailer manufacturers could apply to achieve the same average stringency. If a manufacturer 

chose these adoption rates, a variety of technology options exist within the aerodynamic bins, 

and several models of LRR tires exist for the levels shown. Alternatively, technologies from 

both higher and lower aero bins and tire levels could be used to comply. It should be noted that 

manufacturers are not limited to aerodynamic and tire technologies. Certain types of weight 

reduction, for example, may be used as a compliance pathway. Similar to the reference cases, 

the agencies derived a single set of performance parameters for each subcategory by weighting 

the performance levels included in Table 2-74 by the corresponding adoption rates. These 

performance parameters represent an average compliant vehicle for each trailer subcategory. 
2-173 

Table 2-80 Adoption Rates and Average Performance Parameters for the Long Box Trailers 

TECHNOLOGY LONG BOX 

DRY VANS 

LONG BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2027 2018 2021 2024 2027 

Aerodynamic Technologies 

Bin I 5% - - - 5% - - -

Bin II - - - - - - - -

Bin III 30% 5% - - 30% 5% - -

Bin IV 60% 55% 25% - 60% 55% 25% -

Bin V 5% 10% 10% 10% 5% 10% 10% 20%

Bin VI - 30% 65% 50% - 30% 65% 60%

Bin VII - - - 40% - - - 20%

Bin VIII - - - - - - - -

Average Delta CdA (m2) a 0.4 0.7 0.8 1.1 0.4 0.7 0.8 1.0

Trailer Tire Rolling Resistance 

Baseline tires 15% 5% 5% 5% 15% 5% 5% 5%

Level 1 tires 85% 95% - - 85% 95% - -

Level 2 tires - - 95% 95% - - 95% 95%

Average CRR (kg/ton) a 5.2 5.1 4.8 4.8 5.2 5.1 4.8 4.8

Tire Inflation System 

ATI 85% 95% 95% 95% 85% 95% 95% 95%

Average ATI Reduction (%) a 1.3% 1.4% 1.4% 1.4% 1.3% 1.4% 1.4% 1.4%

Weight Reduction (pounds) 

Weight b 0 0 0 0 0 0 0 0

Notes: 

a

 Combines adoption rates with performance levels shown in Table 2-74 b This set of adoption rates did not apply weight reduction to meet the proposed standards for these trailers 
2-174 

Table 2-81 Adoption Rates and Average Performance Parameters for the Short Box Trailers 

TECHNOLOGY SHORT BOX 

DRY VANS 

SHORT BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2027 2018 2021 2024 2027 

Aerodynamic Technologies a

Bin I 100% 5% - - 100% 5% - -

Bin II - 95% 70% 30% - 95% 70% 55%

Bin III - - 30% 60% - - 30% 40%

Bin IV - - - 10% - - - 5%

Bin V - - - - - - - -

Bin VI - - - - - - - -

Bin VII - - - - - - - -

Bin VIII - - - - - - - -

Average Delta CdA (m2) b 0.4 0.7 0.8 1.1 0.4 0.7 0.8 1.0

Trailer Tire Rolling Resistance 

Baseline tires 15% 5% 5% 5% 15% 5% 5% 5%

Level 1 tires 85% 95% - - 85% 95% - -

Level 2 tires - - 95% 95% - - 95% 95%

Average CRR (kg/ton) b 5.2 5.1 4.8 4.8 5.2 5.1 4.8 4.8

Tire Inflation System 

ATI 85% 95% 95% 95% 85% 95% 95% 95%

Average ATI Reduction (%) c 1.3% 1.4% 1.4% 1.4% 1.3% 1.4% 1.4% 1.4%

Weight Reduction (pounds) 

Weight b 0 0 0 0 0 0 0 0

Notes: 

a

 The majority of short box trailers are 28 feet in length. We recognize that they are often operated in tandem, which limits the 

technologies that can be applied (for example, boat tails). We established standards assuming these shorter trailers were run solo, 

but projected reduced aerodynamic improvements compared to the longer trailers with similar technologies to reflect their 

frequent tandem operation. 

b Combines adoption rates with performance levels shown in Table 2-74 c

 This set of adoption rates did not apply weight reduction to meet the proposed standards for these trailers

Non-box and non-aero box trailers, with two or more work-related special components, 

are not shown in the tables above. These trailers are projected to adopt tire technologies with 

zero adoption of aerodynamic technologies. As shown in Table 2-82, we are projecting 100 

percent adoption rates of these technologies at each stage of the program, which would 

significantly reduce the compliance burden for manufacturers by reducing the amount of tracking 

and eliminating the need to run GEM. The agencies are proposing these tire-only requirements 

in two stages. In 2018 MY, manufacturers would be required to use tires meeting a rolling 

resistance of Level 2 or better and apply ATI systems on all non-box and non-aero box trailers. 

In 2024 MY, ATI and LRR tires at a Level 3 or better would be required. The agencies are 

proposing ATI at all stages of the program. 
2-175 

Table 2-82 Adoption Rates and Average Performance Parameters for the Non-Aero Box and Non-Box 

Trailers 

TECHNOLOGY NON-AERO BOX 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024+ 

Aerodynamic Technologies 

Bin I 100% 100% 100% 

Bin II - - - 

Bin III - - - 

Bin IV - - - 

Bin V - - - 

Bin VI - - -

Bin VII - - - 

Bin VIII - - - 

Average Delta CdA (m2) a 0.0 0.0 0.0

Trailer Tire Rolling Resistance 

Baseline tires - - - 

Level 1 tires 100% 100% -

Level 2 tires - - 100%

Average CRR (kg/ton) a 5.1 5.1 4.7

Tire Inflation System 

ATI 100% 100% 100%

Average ATI Reduction (%) a 1.5% 1.5% 1.5% 

Weight Reduction (pounds) 

Weight b 0 0 0 

Notes: 

a

 Combines adoption rates with performance levels shown in Table 2-74 b This set of adoption rates did not apply weight reduction to meet the proposed standards 

for these trailers 

2.10.4.2 Derivation of the Proposed Standards 

The average performance parameters from Table 2-80 and Table 2-81 were applied as 

input values to the GEM vehicle simulation to derive the proposed HD Phase 2 fuel consumption 

and CO2 emissions standards for each subcategory of box trailers. 

The proposed standards are shown in Table 2-83. Over the four phases of the proposed 

rules, box trailers longer than 50 feet would, on average, reduce their CO2 emissions and fuel 

consumption by three percent, five percent and seven percent compared to their reference cases 

for each year in Table 2-79. Box trailers 50-foot and shorter would achieve reductions of two 

percent, four percent and five percent compared to their reference cases. The tire technologies 

used on non-box and special purpose box trailers would provide reductions of three percent in 

the first two stages and achieve four percent by 2027. 
2-176 

Table 2-83 CO2 Emissions and Fuel Consumption Based on Projected Technology Adoption Rates for 

Proposed Standards 

MODEL 

YEAR 

SUBCATEGORY DRY VAN REFRIGERATED 

VAN 

Length Long Short Long Short 

2018 - 2020 

EPA Standard 

(CO2 Grams per Ton-Mile) 83 144 84 147 

Voluntary NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.1532 14.1454 8.2515 14.4401 

2021 - 2023 

EPA Standard 

(CO2 Grams per Ton-Mile) 81 142 82 146 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.9568 13.9489 8.0550 14.3418 

2024 - 2026 

EPA Standard 

(CO2 Grams per Ton-Mile) 79 141 81 144 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.7603 13.8507 7.9568 14.1454 

2027 + 

EPA Standard 

(CO2 Grams per Ton-Mile) 77 140 80 144 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.5639 13.7525 7.8585 14.1454 

2.10.4.3 Projected Cost of Proposed Trailer Standards 

The agencies evaluated technology costs for 53-foot dry and refrigerated vans and 28-

foot dry vans, which we believe are representative of the majority of trailers in the long and short 

box trailer categories, respectively. Similar tire technology costs were assumed for the non-box 

trailer subcategory. We identified costs for each technology package evaluated and projected out 

the costs for each year of the program. A summary of the technology costs is included in Table 

2-84 through Table 2-86 for model years 2018, 2021 and 2024, respectively, with additional 

details available in Chapter 2.12. Costs shown in the following tables are for the specific model 

year indicated and are incremental to the average reference case costs, which includes some level 

of adoption of these technologies as shown in Table 2-77. Therefore, the technology costs in the 

following tables reflect the average cost expected for each of the indicated trailer subcategories. 

Note that these costs do not represent actual costs for the individual components, because some 

fraction of the component costs has been subtracted to reflect some use of these components in 

the reference case. These costs include indirect costs via markups along with learning impacts 

and also reflect estimated costs of the compliance process. For more on the estimated 

technology costs exclusive of adoption rates, refer to Chapter 2.12. 

Table 2-84 Trailer Technology Incremental Costs in the 2018 Model Year for the Proposed Alternative 

(2012 $) 

 53-FOOT 

DRY VAN 

53-FOOT 

REF. VAN 

28-FOOT 

DRY VAN 

NON-BOX 

HIGHWAY 

Aerodynamics $285 $285 $0 $0 

Tires $65 $65 $78 $185 

Tire inflation system $239 $239 $435 $683 

Total $588 $588 $514 $868 
2-177 

Table 2-85 Trailer Technology Incremental Costs in the 2021 Model Year for the Proposed Alternative 

(2012 $) 

 53-FOOT 

DRY VAN

53-FOOT 

REF. VAN

28-FOOT 

DRY VAN

NON-BOX 

HIGHWAY

Aerodynamics $602 $602 $468 $0 

Tires $65 $65 $79 $175 

Tire inflation system $234 $234 $426 $632 

Total $901 $901 $974 $807 

Table 2-86 Trailer Technology Incremental Costs in the 2024 Model Year for the Proposed Alternative 

(2012 $) 

 53-FOOT 

DRY VAN

53-FOOT 

REF. VAN

28-FOOT 

DRY VAN

NON-BOX 

HIGHWAY

Aerodynamics $836 $836 $608 $0 

Tires $61 $61 $76 $160 

Tire inflation system $220 $220 $412 $578 

Total $1,116 $1,116 $1,097 $739 

Table 2-87 Trailer Technology Incremental Costs in the 2024 Model Year for the Proposed Alternative 

(2012 $) 

 53-FOOT 

DRY VAN

53-FOOT 

REF. VAN

28-FOOT 

DRY VAN

NON-BOX 

HIGHWAY

Aerodynamics $1,163 $1,034 $788 $0 

Tires $54 $54 $74 $155 

Tire inflation system $192 $192 $391 $549 

Total $1,409 $1,280 $1,253 $704 

2.10.5 Effectiveness and Costs of a More Stringent Trailer Alternative 

The agencies also evaluated a more stringent alternative that considered the same 

technologies with less lead time. The reference cases from Section 2.10.3.2 apply for this 

alternative. Additionally, the projected adoption rates for the non-aero box and non-box trailer 

subcategories remain unchanged in this alternative, so their results are not repeated in this 

section. 

2.10.5.1 Projected Adoption Rates for More Stringent Alternative 

From Table 2-88 and Table 2-89, it can be seen that the 2018 MY aerodynamic 

technology adoption rates and the tire technology adoption rates for all model years are identical 

to those presented previously for the proposed standards. The aerodynamic projections for 2021 

MY and 2024 MY in this more stringent alternative are the same as those projected for 2024 MY 

and 2027 MY of the proposed standards, but are applied three years earlier. In this alternative, 

the 2021 MY adoption rates would continue to apply for the partial-aero box trailers in 2024 and 

later model years. 
2-178 

Table 2-88 Adoption Rates and Average Performance Parameters for the Long Box Trailers in the More 

Stringent Alternative 

TECHNOLOGY LONG BOX 

DRY VANS 

LONG BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies a

Bin I 5% - - 5% - -

Bin II - - - - - -

Bin III 30% - - 30% - -

Bin IV 60% 25% - 60% 25% -

Bin V 5% 10% 10% 5% 10% 20%

Bin VI - 65% 50% - 65% 60%

Bin VII - - 40% - - 20%

Bin VIII - - - - - -

Average Delta CdA (m2) a 0.4 0.8 1.1 0.4 0.8 1.0

Trailer Tire Rolling Resistance 

Baseline tires 15% 5% 5% 15% 5% 5%

Level 1 tires 85% 95% - 85% 95% -

Level 2 tires - - 95% - - 95%

Average CRR (kg/ton) a 5.2 5.1 4.8 5.2 5.1 4.8

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95%

Average ATI Reduction (%) a 1.3% 1.4% 1.4% 1.3% 1.4% 1.4%

Weight Reduction (pounds) 

Weight b 0 0 0 0 0 0

Notes: 

a

 Combines adoption rates with performance levels shown in Table 2-74 b This set of adoption rates did not apply weight reduction to meet the proposed standards for these trailers 
2-179 

Table 2-89 Adoption Rates and Average Performance Parameters for the Short Box Trailers in the More 

Stringent Alternative 

TECHNOLOGY SHORT BOX 

DRY VANS 

SHORT BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies a

Bin I 100% - - 100% - -

Bin II - 70% 30% - 70% 55%

Bin III - 30% 60% - 30% 40%

Bin IV - - 10% - - 5%

Bin V - - - - - -

Bin VI - - - - - -

Bin VII - - - - - -

Bin VIII - - - - - -

Average Delta CdA (m2) b 0.4 0.8 1.1 0.4 0.8 1.0

Trailer Tire Rolling Resistance 

Baseline tires 15% 5% 5% 15% 5% 5%

Level 1 tires 85% 95% - 85% 95% -

Level 2 tires - - 95% - - 95%

Average CRR (kg/ton) b 5.2 5.1 4.8 5.2 5.1 4.8

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95%

Average ATI Reduction (%) b 1.3% 1.4% 1.4% 1.3% 1.4% 1.4%

Weight Reduction (pounds) 

Weight c 0 0 0 0 0 0

Notes: 

a

 The majority of short box trailers are 28 feet in length. We recognize that they are often operated in tandem, which 

limits the technologies that can be applied (for example, boat tails). We established standards assuming these shorter 

trailers were run solo, but projected reduced aerodynamic improvements compared to the longer trailers with similar 

technologies to reflect their frequent tandem operation. 

b Combines adoption rates with performance levels shown in Table 2-74 

c

 This set of adoption rates did not apply weight reduction to meet the proposed standards for these trailers

2.10.5.2 Derivation of the More Stringent Alternative Standards 

Similar to the proposed standards of Section 2.10.4.2, the agencies applied the technology 

performance values from Table 2-88 and Table 2-89 as GEM inputs to derive the proposed 

standards for each subcategory. 

Table 2-90 shows the resulting standards for the more stringent alternative. Over the 

three phases of the alternative, box trailers longer than 50 feet would, on average, reduce their 

CO2 emissions and fuel consumption by four percent, six percent and eight percent. Box trailers 

50-foot and shorter would achieve reductions of two percent, four percent, and five percent 

compared to the reference case. Partial-aero box trailers would continue to be subject to the 

2021 MY standards for MY 2024 and later. The non-aero box and non-box trailers would meet 

the same standards and achieve the same three and four percent benefits as shown in the 

proposed alternative. 
2-180 

Table 2-90 Trailer CO2 and Fuel Consumption Standards for Box Trailers in the More Stringent Alternative 

MODEL 

YEAR 

SUBCATEGORY DRY VAN REFRIGERATED 

VAN 

Length Long Short Long Short 

2018 - 2020 

EPA Standard 

(CO2 Grams per Ton-Mile) 83 144 84 147 

Voluntary NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.1532 14.1454 8.2515 14.4401 

2021 - 2023 

EPA Standard 

(CO2 Grams per Ton-Mile) 80 142 81 145

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.8585 13.9489 7.9568 14.2436 

2024 + 

EPA Standard 

(CO2 Grams per Ton-Mile) 77 140 80 144

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.5639 13.7525 7.8585 14.1454 

2.10.5.3 Projected Cost of the More Stringent Trailer Alternative 

A summary of the technology costs is included in Table 2-91 to Table 2-93 for MYs 

2018, 2021 and 2024, with additional details available in Chapter 2.12. Costs shown in the 

following tables are for the specific model year indicated and are incremental to the average 

reference case costs, which includes some level of adoption of these technologies as shown in 

Table 2-77. Therefore, the technology costs in the following tables reflect the average cost 

expected for each of the indicated trailer classes. Note that these costs do not represent actual 

costs for the individual components because some fraction of the component costs has been 

subtracted to reflect some use of these components in the reference case. For more on the 

estimated technology costs exclusive of adoption rates, refer to Chapter 2.12 of this draft RIA. 

These costs include indirect costs via markups along with learning impacts. It can be seen that, 

despite the similar stringencies for MY 2024 of this more stringent alternative and MY 2027 of 

the proposed alternative, the costs shown below are slightly higher. The lower cost in the 

proposed MY 2027 can be partially attributed to the reduced costs due to three years of 

additional learning. For a description of the markups and learning impacts considered in this 

analysis and how it impacts technology costs for other years, refer to the draft RIA Chapter 2.12. 

Table 2-91 Trailer Technology Incremental Costs in the 2018 Model Year for the More Stringent Alternative 

(2012$) 

 >50-FOOT 

DRY VAN 

>50-FOOT 

REF. VAN 

<35-FOOT 

DRY VAN 

NON-BOX 

HIGHWAY 

Aerodynamics $285 $285 $0 $0 

Tires $65 $65 $78 $185 

Tire inflation system $239 $239 $435 $683 

Total $588 $588 $514 $868 
2-181 

Table 2-92 Trailer Technology Incremental Costs in the 2021 Model Year for the More Stringent Alternative 

(2012$) 

 >50-FOOT 

DRY VAN

>50-FOOT 

REF. VAN

<35-FOOT 

DRY VAN

NON-BOX 

HIGHWAY 

Aerodynamics $908 $908 $641 $0 

Tires $65 $65 $79 $175 

Tire inflation system $234 $234 $426 $632 

Total $1,207 $1,207 $1,146 $807 

Table 2-93 Trailer Technology Incremental Costs in the 2024 Model Year for the More Stringent Alternative 

(2012$) 

 >50-FOOT 

DRY VAN

>50-FOOT 

REF. VAN

<35-FOOT 

DRY VAN

NON-BOX 

HIGHWAY 

Aerodynamics $1,223 $1,090 $816 $0 

Tires $61 $61 $76 $160 

Tire inflation system $220 $220 $412 $578 

Total $1,504 $1,371 $1,304 $739 

2.10.6 Evaluation of Compliance Option using GEM-Based Equation 

EPA created the Greenhouse gas Emissions Model (GEM) as a compliance tool for 

heavy-duty vehicles. Users provide specific performance parameters to the model and GEM 

calculates CO2 emissions and fuel consumption results. As described previously, the proposed 

Phase 2 GEM is designed to accept four performance variables as trailer inputs: change in drag 

area (delta CDA), tire rolling resistance level (TRRL), automatic tire inflation (ATI) and weight 

reduction (WR). The reduction applied when using an automatic tire inflation system is 

accounted for after the vehicle simulation is complete. The other performance parameters 

directly impact the results of the vehicle simulation, by changing the drag, rolling resistance and 

weight of the simulated vehicle. 

We performed a sensitivity analysis for delta CDA, TRRL and WR to evaluate their effect 

on the model’s results. In the analysis to follow, all of the calculations are shown in terms of 

CO2 emissions; use a conversion of 10,180 grams CO2 per gallon of diesel fuel to calculate the 

corresponding fuel consumption values. Figure 2-23 through Figure 2-26 show GEM’s CO2

results for a parameter sweep of a simulated Class 8 tractor pulling each of the four box van 

trailers. It can be seen that each of the three parameters has a linear impact on CO2 emissions. A 

curve fit was applied to each data set and the equation is displayed on each plot. The intercept in 

each parameter sweep data set is the baseline CO2 result considering a zero-technology trailer, 

and this value is consistent for all parameters for a given trailer. The coefficients indicate the 

relationship between the assessed parameter and the model’s CO2 result. 
2-182 

(a) 

(b) 

(c) 

Figure 2-23 Impact of (a) Delta CDA, (b) Delta CRR, and (c) Weight Reduction on CO2 Results of a GEMSimulated Long Dry Van 
2-183 

(a) 

(b) 

(c) 

Figure 2-24 Impact of (a) Delta CDA, (b) Delta CRR, and (c) Weight Reduction on CO2 Results of a GEMSimulated Long Refrigerated Van 
2-184 

(a) 

(b) 

(c) 

Figure 2-25 Impact of (a) Delta CDA, (b) Delta CRR, and (c) Weight Reduction on CO2 Results of a GEMSimulated Short Dry Van 
2-185 

(a) 

(b) 

(c) 

Figure 2-26 Impact of (a) Delta CDA, (b) Delta CRR, and (c) Weight Reduction on CO2 Results of a GEMSimulated Short Refrigerated Van 
2-186 

Additional GEM simulations were performed for each of the four box trailer 

subcategories to assess the combined effect of these parameters. As seen in Figure 2-27 and 

Figure 2-28 for the long dry van simulation, the coefficients of the curve fit equations did not 

change, indicating that the combined impacts of these parameters on GEM’s CO2 results were 

additive. Similar trends were seen with the simulations for the other trailer subcategories, though 

the results are not shown here. 

Figure 2-27 Combined Impact of Drag Area and Tire Rolling Resistance Level on CO2 Results of a GEMSimulated Long Dry Van with No Weight Reduction 

Figure 2-28 Combined Impact of Drag Area and Weight Reduction on CO2 Results of a GEM-Simulated 

Long Dry Van at a Tire Rolling Resistance Level of 5.1 kg/ton 

The results presented Figure 2-27 and Figure 2-28 suggest that these parameters could be 

combined into a single equation to calculate CO2 emissions. Equation 1 is the result of 

combining the curve fit equations for long box dry vans. 
2-187 

ݕ ൌ 87.6 െ 6.1ሺ∆ܥ஽ܣሻ െ 1.7ሺ∆ܴܴܶܮሻ െ 0.001ሺܹܴሻ (1)

Our proposed regulations specify that TRRL be an absolute measure of a tire’s 

coefficient of rolling resistance (not a change in rolling resistance). As a result, Equation 1 was 

modified such that the variables of the equation matched the trailer inputs required by GEM. 

Equation 2 is the resulting equation. 

ݕ ൌ 77.4 െ 6.1ሺ∆ܥ஽ܣሻ ൅ 1.7ሺܴܴܶܮሻ െ 0.001ሺܹܴሻ (2)

Each of the trailer subcategories follows the same general format and a generic equation 

is shown in Equation 3. Table 2-94 summarizes the corresponding constants for each of the 

trailer subcategories. 

݁஼ைଶ ൌ ܥଵ ൅ ܥଶሺ∆ܥ஽ܣሻ ൅ ܥଷሺܴܴܶܮሻ ൅ ܥସሺܹܴሻ (3)

Table 2-94 Constants for GEM-Based CO2 Equation for Trailer Subcategories (See Equation 3) 

TRAILER SUBCATEGORY C1 C2 C3 C4

Long Dry Van 77.4 -6.1 1.7 -0.001 

Long Refrigerated Van 78.3 -6.0 1.8 -0.001 

Short Dry Van 134.0 -10.5 2.2 -0.003 

Short Refrigerated Van 136.3 -10.3 2.4 -0.003 

Over 100 GEM vehicle simulations were performed for a range of delta CDA, TRRL and 

weight reduction values. The results of these simulations were compared to CO2 results 

calculated using Equation 3 for each trailer subcategory. The following figures show the 

equation and GEM have nearly identical CO2 results. 
2-188 

Figure 2-29 Comparison of GEM and Calculated CO2 Results for a Long Dry Van 

Figure 2-30 Comparison of GEM and Calculated CO2 Results for a Long Refrigerated Van 
2-189 

Figure 2-31 Comparison of GEM and Calculated CO2 Results for a Short Dry Van 

Figure 2-32 Comparison of GEM and Calculated CO2 Results for a Short Refrigerated Van 
2-190 

The comparisons shown in Figure 2-29 through Figure 2-32 suggest that an equation may 

offer a simplified approach for trailer manufacturers to calculate CO2 without the use of GEM. 

Equation 4 below is a slight modification to Equation 3. The constants shown in Table 2-94 and 

used in Equation 3 were rearranged slightly to place the tire rolling resistance effect at the 

beginning of the equation, since we anticipate most trailers would adopt LRR tires. Equation 4 

and the corresponding Table 2-95 illustrate the rearrangement. As mentioned previously, the 

proposed trailer program is also offering the use of automatic tire inflation (ATI) systems as a 

means achieving the proposed standards. This parameter is not considered in Equation 3. 

Equation 4 includes a constant, C5, to address the use of ATI. Constant C5 is equal to unity for 

trailers that do not have ATI systems installed and equal to 0.985 (accounting for the 1.5% 

reduction assigned to ATI) for trailers that do include ATI systems. As mentioned previously, 

one can use a conversion factor of 10,180 grams CO2 per gallon of diesel fuel to calculate the 

corresponding fuel consumption values. 

݁஼ைଶ ൌ ሾܥଵ ൅ ܥଶ ∙ ሺܴܴܶܮሻ ൅ ܥଷ ∙ ሺ∆ܥ஽ܣሻ ൅ ܥସ ∙ ሺܹܴሻሿ ∙ ܥହ (4)

Table 2-95 Constants for GEM-Based CO2 Equation for Trailer Subcategories (See Equation 4) 

TRAILER SUBCATEGORY C1 C2 C3 C4

Long Dry Van 77.4 1.7 -6.1 -0.001 

Long Refrigerated Van 78.3 1.8 -6.0 -0.001 

Short Dry Van 134.0 2.2 -10.5 -0.003 

Short Refrigerated Van 136.3 2.4 -10.3 -0.003 

2.11 Natural Gas 

2.11.1 Sealed Crankcase 

EPA regulations allow venting to the atmosphere crankcase emissions from compressionignition engines, provided these vented crankcase emissions are measured and accounted for as 

part of an engine’s tailpipe emissions. This allowance has historically been in place to address 

the technical limitations related to recirculating diesel-fueled engines’ crankcase emissions, 

which have high PM emissions, back into the engine’s air intake. High PM emissions vented 

into the intake of an engine can foul turbocharger compressors and after-cooler heat exchangers. 

In contrast, historically EPA has mandated closed crankcase technology on all gasoline-fueled 

engines and all natural gas engines certified as spark-ignition. The inherently low PM emissions 

from these engines posed no technical barrier to a closed crankcase mandate. Because natural 

gas-fueled compression ignition engines also have inherently low PM emissions, there is no 

technological limitation that would prevent manufacturers from closing the crankcase and 

recirculating all crankcase gases into a natural gas-fueled compression ignition engine’s air 

intake. It is expected that costs for this requirement would be negligible. 
2-191 

2.11.2 Require 5 Day Hold Time 

Boil-off emissions from LNG vehicles were not addressed in the Phase 1 rulemaking. 

However, it is our understanding that the majority or all of the NG vehicles are already 

compliant with National Fire Protection Association standard NFPA 52 and the similar SAE 

standard SAE J2343, which are recommended practices for 3 and 5 day hold times, respectively. 

These are very similar to one another, but the SAE standards calls for more rigorous control. 

Although these standards were developed largely to address fire safety issues (boil-off emissions 

can lead to explosive mixtures in enclosed spaces), it is clear that following the industry 

recommended practice spelled out in SAE J2343 for five day hold time would substantially limit 

boil-off emissions from LNG vehicles. Therefore, EPA is proposing to require compliance with 

SAE J2343 as part of certification for LNG vehicles. Since the majority or all of the NG 

vehicles are already compliant with these requirements, there would be negligible costs 

associated with the requirement for 5 day hold time based on the SAE standard practice. 

2.12 Technology Costs 

2.12.1 Overview of Technology Cost Methodology Learning Effects on 

Technology Costs 

Section 2.12.1.2 presents the methods used to address indirect costs in this analysis. 

Section 2.12.1.3 presents the learning effects applied throughout this analysis. In Section 2.12.2 

through 2.12.10 we present individual technology costs including: the direct manufacturing costs 

(DMC), their indirect costs (IC) and their total costs (TC, TC=DMC+IC). Note that we also 

present technology adoption rates for most technologies and the resultant total cost as applied to 

a technology package (which we have denoted as TCp, where TCp=TC x Adoption Rate). The 

tables presented show the adoption rate for, generally, alternatives 1a and 3 where 1a represents 

the reference case (or the “no action” case) and 3 represents the preferred policy case. For TCp 

values under alternative 4, one would replace the alternative 3 adoption rates with the 

appropriate alternative 4 adoption rates to arrive at the TCp costs under alternative 4. Note also 

that some TCp values appear as negative values in some tables (notably the lower rolling 

resistance (LRR) tire tables). This is because certain LRR tires are expected in the reference 

case but are then expected to be removed in the policy case and replaced by more aggressive 

LRR tires. In such cases, the reference case tires show negative TCp costs since they are being 

removed and replaced. 

2.12.1.1 Direct Manufacturing Costs 

The direct manufacturing costs (DMCs) used throughout this analysis are derived from 

several sources. Many of the tractor, vocational and trailer DMCs can be sourced to the Phase 1 

rules which, in turn, were sourced largely from a contracted study by ICF International for 

EPA.163 There was no serious disagreement regarding these estimated costs in the public 

comments to the Phase 1 rules. We have updated those costs by converting them to 2012 dollars, 

as described in Section IX.B.1.e of the Preamble, and by continuing the learning effects 

described in the Phase 1 rules and in Section IX. B.1.c of the Preamble. The new tractor, 

vocational and trailer costs can be sourced to a more recent study conducted by Southwest 

Research Institute (SwRI) under contract to NHTSA.164 The cost methodology used by SwRI in 
2-192 

that study was to estimate retail costs then work backward from there to derive a DMC for each 

technology. The agencies did not agree with the approach used by Tetra Tech to move from 

retail cost to DMC. As such, the agencies have used an approach consistent with past 

GHG/CAFE/fuel consumption rules by dividing estimated retail prices by our estimated retail 

price equivalent markups to derive an appropriate DMC for each technology. We describe our 

RPEs in Section 2.12.1.2. 

For HD pickups and vans, we have relied primarily on the Phase 1 rules and the lightduty 2017-2025 model year rule since most technologies expected on these vehicles are, in 

effect, the same as those used on light-duty pickups. Many of those technology DMCs are based 

on cost teardown studies which the agencies consider to be the most robust method of cost 

estimation. However, many of the HD versions of those technologies would be expected to be 

more costly than their light-duty counterparts because of the heavier HD vehicles and/or the 

higher power and torque characteristics of their engines. Therefore, we have scaled upward 

where appropriate many of the light-duty DMCs for this analysis. We have also used some costs 

developed under contract to NHTSA by SwRI (the study mentioned above).165 

Importantly, in our methodology, all technologies are treated as being sourced from a 

supplier rather than being developed and produced in-house. As such, some portion of the total 

indirect costs of making a technology or system—those costs incurred by the supplier for 

research, development, transportation, marketing etc.—are contained in the sales price to the 

engine and/or vehicle/trailer manufacturer (i.e., the original equipment manufacturer (OEM)). 

That sale price paid by the OEM to the supplier is the DMC we estimate. 

2.12.1.2 Indirect Costs 

To produce a unit of output, engine and truck manufacturers incur direct and indirect 

costs. Direct costs include cost of materials and labor costs. Indirect costs are all the costs 

associated with producing the unit of output that are not direct costs – for example, they may be 

related to production (such as research and development [R&D]), corporate operations (such as 

salaries, pensions, and health care costs for corporate staff), or selling (such as transportation, 

dealer support, and marketing). Indirect costs are generally recovered by allocating a share of 

the costs to each unit of good sold. Although it is possible to account for direct costs allocated to 

each unit of good sold, it is more challenging to account for indirect costs allocated to a unit of 

goods sold. To make a cost analysis process more feasible, markup factors, which relate total 

indirect costs to total direct costs, have been developed. These factors are often referred to as 

retail price equivalent (RPE) multipliers. 

Cost analysts and regulatory agencies (including both EPA and NHTSA) have frequently 

used these multipliers to predict the resultant impact on costs associated with manufacturers’ 

responses to regulatory requirements. The best approach, if it were possible, to determining the 

impact of changes in direct manufacturing costs on a manufacturer’s indirect costs would be to 

actually estimate the cost impact on each indirect cost element. However, doing this within the 

constraints of an agency’s time or budget is not always feasible, or the technical, financial, and 

accounting information to carry out such an analysis may simply be unavailable. 
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RPE multipliers provide, at an aggregate level, the relative shares of revenues (Revenue = 

Direct Costs + Indirect Costs + Net Income) to direct manufacturing costs. Using RPE 

multipliers implicitly assumes that incremental changes in direct manufacturing costs produce 

common incremental changes in all indirect cost contributors as well as net income. However, a 

concern in using the RPE multiplier in cost analysis for new technologies added in response to 

regulatory requirements is that the indirect costs of vehicle modifications are not likely to be the 

same for different technologies. For example, less complex technologies could require fewer 

R&D efforts or less warranty coverage than more complex technologies. In addition, some 

simple technological adjustments may, for example, have no effect on the number of corporate 

personnel and the indirect costs attributable to those personnel. The use of RPEs, with their 

assumption that all technologies have the same proportion of indirect costs, is likely to 

overestimate the costs of less complex technologies and underestimate the costs of more 

complex technologies. Table 2-96 shows the RPE factors used in developing indirect costs in 

past, and this, agency analyses. 

Table 2-96 Industry Retail Price Equivalent (RPE) Factors 

INDUSTRY RPE

Heavy engine manufacturers 1.28 

Heavy truck manufacturers 1.36 

Light-duty vehicle manufacturers 1.50 

To address this concern, modified multipliers have been developed by EPA, working 

with a contractor, for use in rulemakings. These multipliers are referred to as indirect cost 

multipliers (or ICMs). In contrast to RPE multipliers, ICMs assign unique incremental changes 

to each indirect cost contributor as well as net income. 

 ICM = (direct cost + adjusted indirect cost)/(direct cost) 

Developing the ICMs from the RPE multipliers requires developing adjustment factors 

based on the complexity of the technology and the time frame under consideration: the less 

complex a technology, the lower its ICM, and the longer the time frame for applying the 

technology, the lower the ICM. This methodology was used in the cost estimation for the recent 

light-duty MYs 2012-2016 and MYs 2017-2025 rulemaking and for the heavy-duty MYs 2014-

2018 rulemaking. There was no serious disagreement with this approach in the public comments 

to any of these rulemakings. The ICMs for the light-duty context were developed in a peerreviewed report from RTI International and were subsequently discussed in a peer-reviewed 

journal article.166 Importantly, since publication of that peer-reviewed journal article, the 

agencies have revised the methodology to include a return on capital (i.e., profits) based on the 

assumption implicit in ICMs (and RPEs) that capital costs are proportional to direct costs, and 

businesses need to be able to earn returns on their investments. 

For the heavy-duty pickup truck and van cost projections in this proposal, the agencies 

have used ICM adjustment factors developed for light-duty vehicles, inclusive of a return on 

capital, primarily because the manufacturers involved in this segment of the heavy-duty market 

are the same manufacturers that build light-duty trucks. 
2-194 

For the combination tractors, vocational vehicles, and heavy-duty engine cost projections 

in this proposal, the agencies are again using the ICMs used in the HD Phase 1 rules. Those 

ICMs were developed by RTI International under EPA contract to update EPA’s methodology 

for accounting for indirect costs associated with changes in direct manufacturing costs for heavyduty engine and truck manufacturers.167 In addition to the indirect cost contributors varying by 

complexity and time frame, there is no reason to expect that the contributors would be the same 

for engine manufacturers as for truck manufacturers. The resulting report from RTI provides a 

description of the methodology, as well as calculations of the indirect cost multipliers that are 

being used as the basis for the markups used in this proposal. These indirect cost multipliers 

were used, along with calculations of direct manufacturing costs, to provide estimates of the full 

additional costs associated with new technologies. 

As explained in the Phase 1 final rules, and entirely consistent with the analysis 

supporting that program, the agencies have made some changes to both the ICMs factors and to 

the method of applying those factors relative to the factors developed by RTI and presented in 

their reports. The first of these changes was done in response to continued thinking among the 

agencies about how past ICMs have been developed and what are the most appropriate data 

sources to rely upon in determining the appropriate ICMs. The second change was done in 

response to both staff concerns and public feedback suggesting that the agencies were 

inappropriately applying learning effects to indirect costs via the multiplicative approach to 

applying the ICMs. 

Regarding the first change – to the ICM factors themselves – a little background must 

first be provided. In the original work done under contract to EPA by RTI International,168 EPA 

experts had undergone a consensus approach to determining the impact of specific technology 

changes on the indirect costs of a company. Subsequent to that effort, EPA experts underwent a 

blind survey to make this determination on a different set of technology changes. This 

subsequent effort, referred to by EPA as a modified-Delphi approach, resulted in different ICM 

determinations. This effort is detailed in a memorandum contained in the docket for this 

rulemaking.169 Upon completing this effort, EPA determined that the original RTI values should 

be averaged with the modified-Delphi values to arrive at the final ICMs for low and medium 

complexity technologies and that the original RTI values would be used for high complexity 

level 1 while the modified-Delphi values would be used for high complexity level 2. These final 

ICMs were used in the 2012-2016 light-duty GHG/CAFE rulemaking. Subsequent to that, EPA 

contracted RTI to update their light-duty report with an eye to the heavy-duty industry. In that 

effort, RTI determined the RPE of both the heavy-duty engine and heavy truck industries, then 

applied the light-duty indirect cost factors—those resulting from the averaging of the values 

from their original report with the modified-Delphi values—to the heavy-duty RPEs to arrive at 

heavy-duty specific ICMs. That effort is described in their final heavy-duty ICM report 

mentioned above.170

During development of the Phase 1 heavy-duty final rules, the agencies decided that the 

original light-duty RTI values, given the technologies considered for low and medium 

complexity, should no longer be used and that we should rely solely on the modified-Delphi 

values for these complexity levels. The original light-duty RTI study used low rolling resistance 

tires as a low complexity technology example and a dual clutch transmission as a medium 

complexity technology. Upon further thought, the technologies considered for the modified 
2-195 

Delphi values (passive aerodynamic improvements for low complexity and turbocharging with 

downsizing for medium complexity were considered to better represent the example 

technologies). As a result, the modified-Delphi values were to become the working ICMs for 

low and medium complexity rather than averaging those values with the original RTI report 

values. The agencies have also re-examined the technology complexity categories that were 

assigned to each light-duty technology and modified these assignments to better reflect the 

technologies that are now used as proxies for each category. This decision impacted the low and 

medium complexity heavy-duty ICMs too because the modified-Delphi values alone were to be 

applied to the heavy-duty RPEs to arrive at heavy-duty ICMs rather than using the averaged 

values developed for the light-duty 2012-2016 rulemaking. 

A secondary-level change was also made as part of this ICM recalculation to the lightduty ICMs and, therefore, to the ICMs used in the Phase 1 HD final rules and again in this 

proposed analysis for HD pickups and vans. That change was to revise upward the RPE level 

reported in the original RTI report from an original value of 1.46 to 1.5 to reflect the long term 

average RPE. The original RTI study was based on 2008 data. However, an analysis of 

historical RPE data indicates that, although there is year to year variation, the average RPE has 

remained roughly 1.5. ICMs are applied to future year’s data and therefore the agencies believed 

and continue to believe that it is most appropriate to base ICMs on the historical average rather 

than a single year’s result. Therefore, ICMs were adjusted to reflect this average level. As a 

result, the High 1 and High 2 ICMs used for HD pickups and vans were changed for the Phase 1 

final rules and we continue to use those changed values here. 

Table 2-97 shows the ICM values used in this proposal. Near term values are used in 

early years, depending on the technology, and account for differences in the levels of R&D, 

tooling, and other indirect costs that would be incurred. Once the program has been fully 

implemented, some of the indirect costs would no longer be attributable to the standards and, as 

such, a lower ICM factor is applied to direct costs in later years. 

Table 2-97 Indirect Cost Multipliers Used in this Analysisa

CLASS COMPLEXITY NEAR 

TERM 

LONG 

TERM 

HD Pickup Trucks and Vans Low 1.24 1.19 

Medium 1.39 1.29 

High1 1.56 1.35 

High2 1.77 1.50 

Loose diesel engines Low 1.15 1.13 

Medium 1.24 1.18 

High1 1.28 1.19 

High2 1.44 1.29 

Loose gasoline engines Low 1.24 1.19 

Medium 1.39 1.29 

High1 1.56 1.35 

High2 1.77 1.50 

Vocational Vehicles, 

Combination Tractors and 

Trailers 

Low 1.18 1.14 

Medium 1.30 1.23 

High1 1.43 1.27 
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High2 1.57 1.37 

Note: 

a

 Rogozhin, A., et. al., “Using indirect cost multipliers to estimate the total cost of adding 

new technology in the automobile industry,” International Journal of Production 

Economics (2009); “Documentation of the Development of Indirect Cost Multipliers for 

Three Automotive Technologies,” Helfand, G., and Sherwood, T., Memorandum dated 

August 2009; “Heavy Duty Truck Retail Price Equivalent and Indirect Cost Multipliers,” 

Draft Report prepared by RTI International and Transportation Research Institute, 

University of Michigan, July 2010. 

The second change made to the ICMs during development of the Phase 1 final rules had 

to do with the way in which the ICMs were applied. Until that time, we had applied the ICMs, 

as done in any analysis that relied on RPEs, as a pure multiplicative factor. This way, a direct 

manufacturing cost of, say, $100 would have been multiplied by an ICM of 1.24 to arrive at a 

marked up technology cost of $124. However, as learning effects (discussed below) are applied 

to the direct manufacturing cost, the indirect costs are also reduced accordingly. Therefore, in 

year 2 the $100 direct manufacturing cost might reduce to $97 and the marked up cost would 

become $120 ($97 x 1.24). As a result, indirect costs have been reduced from $24 to $23. Given 

that indirect costs cover many things such as facility-related costs, electricity, etc., it is perhaps 

not appropriate to apply the ICM to the learned direct costs, at least not for those indirect cost 

elements unlikely to change with learning. The agencies decided that it was more appropriate 

only to allow warranty costs to decrease with learning since warranty costs are tied to direct 

manufacturing costs (since warranty typically involves replacement of actual parts which should 

be less costly with learning).Q However, the remaining elements of the indirect costs should 

remain constant year-over-year, at least until some of those indirect costs are no longer 

attributable to the rulemaking effort that imposed them (such as R&D). 

As a result, the ICM calculation became more complex with the analysis supporting the 

Phase 1 final rules, and we continue to use that more complex calculation here. We first 

establish the year in which the direct manufacturing costs are considered “valid.” For example, a 

cost estimate might be considered valid today, or perhaps not until high volume production is 

reached in some future model year. That year is considered the base year for the estimated cost. 

That cost is the cost used to determine the “non-warranty” portion of the indirect costs. For 

example, the near term non-warranty portion of the loose diesel engine low complexity ICM is 

0.149 (the warranty versus non-warranty portions of the ICMs are shown in Table 2-98). For the 

improved water pump technology we have estimated a direct manufacturing cost of $82.66 

(2012$) in MY 2014. So the non-warranty portion of the indirect costs would be $12.32 ($82.66 

x 0.149). This value would be added to the learned direct manufacturing cost for each year 

through 2022 since the near term markup is considered appropriate for that technology through 

2022. Beginning in 2023, when long-term indirect costs begin, the additive factor would become 

$10.08 ($82.66 x 0.122). Additionally, the $82.66 cost in 2014 would become $80.18 in MY 

2015 due to learning ($82.66 x (1-3 percent)). So, while the warranty portion of the indirect 

costs would be $0.49 ($82.66 x 0.006) in 2014, they would decrease to $0.48 ($80.18 x 0.006) in 

2015 as warranty costs decrease with learning. The resultant indirect costs for the water pump 

 Q We note that the labor portion of warranty repairs does not decrease due to learning. However, we do not have 

data to separate this portion and so we apply learning to the entire warranty cost. Because warranty costs are a small 

portion of overall indirect costs, this has only a minor impact on the analysis. 
2-197 

would be $12.81 ($12.32+$0.49) in MY 2014 and $12.80 ($12.32+$0.48) in MY2015, and so on 

for subsequent years. 

Importantly, since the bulk of the indirect costs calculated using this methodology are the 

non-warranty costs, and since those costs do not change over with learning, one cannot look at 

the ICMs shown in Table 2-97 and assume that our HD pickup and van total costs are, in general, 

1.24 or 1.39 times the direct costs (since most technologies considered for application in HD 

pickups and vans are low and medium technologies). This can be illustrated by building on the 

example presented above for a water pump on a heavy diesel engine. We already calculated the 

MY 2014 total cost as $95.46 (2012$, $82.66+$12.32+$0.49). This is an effective markup of 

1.155 ($95.46/$82.66). This is expected since the cost is based in 2014 and the near term ICM is 

1.155. In MY2022, the final year of near term markups for this technology, the total cost would 

be $80.21 since the learned direct cost has reduced to $67.50, the non-warranty indirect costs 

(calculated above) remain $12.32, and the warranty indirect costs have become $0.39 

($67.50x0.006). So, in MY2022, we now have an effective markup of 1.19 ($80.21/$67.50).

Table 2-98 Warranty and Non-Warranty Portions of ICMs 

 SHORT-TERM LONG-TERM 

CLASS COMPLEXITY WARRANTY NONWARRANTY WARRANTY NONWARRANTY 

HD Pickup and 

Vans 

Low 0.012 0.230 0.005 0.187 

Medium 0.045 0.343 0.031 0.259 

High1 0.065 0.499 0.032 0.314 

High2 0.074 0.696 0.049 0.448 

Loose diesel 

engines 

Low 0.006 0.149 0.003 0.122 

Medium 0.022 0.213 0.016 0.165 

High1 0.032 0.249 0.016 0.176 

High2 0.037 0.398 0.025 0.265 

Loose gasoline 

engines 

Low 0.012 0.230 0.005 0.187 

Medium 0.045 0.343 0.031 0.259 

High1 0.065 0.499 0.032 0.314 

High2 0.074 0.696 0.049 0.448 

Vocational 

Vehicles, 

Combination 

Tractors and 

Trailers 

Low 0.013 0.165 0.006 0.134 

Medium 0.051 0.252 0.035 0.190 

High1 0.073 0.352 0.037 0.233 

High2 0.084 0.486 0.056 0.312 

The complexity levels and subsequent ICMs applied throughout this analysis for each 

technology are shown in Table 2-99. 

Table 2-99 Indirect Cost Markups and Near Term/Long Term Cutoffs Used in this Analysis 

TECHNOLOGY APPLIED TO ICM 

COMPLEXITY 

NEAR TERM 

THRU 

Cylinder head improvements 1 LH/MH/HH Engines Low 2022 

Cylinder head improvements 2 LH/MH/HH Engines Low 2027 

Turbo efficiency improvements 1 LH/MH/HH, HD Pickup & 

Van Engines 

Low 2022 

Turbo efficiency improvements 2 LH/MH/HH Engines Low 2027 

EGR cooler efficiency improvements 1 LH/MH/HH Engines Low 2022 
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EGR cooler efficiency improvements 2 LH/MH/HH Engines Low 2027 

Water pump improvements 1 LH/MH/HH Engines Low 2022 

Water pump improvements 2 LH/MH/HH Engines Low 2027 

Oil pump improvements 1 LH/MH/HH Engines Low 2022 

Oil pump improvements 2 LH/MH/HH Engines Low 2027 

Fuel pump improvements 1 LH/MH/HH Engines Low 2022 

Fuel pump improvements 2 LH/MH/HH Engines Low 2027 

Fuel rail improvements 1 LH/MH/HH Engines Low 2022 

Fuel rail improvements 2 LH/MH/HH Engines Low 2027 

Fuel injector improvements 1 LH/MH/HH Engines Low 2022 

Fuel injector improvements 2 LH/MH/HH Engines Low 2027 

Piston improvements 1 LH/MH/HH Engines Low 2022 

Piston improvements 2 LH/MH/HH Engines Low 2027 

Valve train friction reductions 1 LH/MH/HH Engines Low 2022 

Valve train friction reductions 2 LH/MH/HH Engines Low 2027 

Turbo compounding 1 LH/MH/HH Engines Low 2022 

Turbo compounding 2 LH/MH/HH Engines Low 2027 

Aftertreatment improvements 1 LH/MH/HH Engines Low 2022 

Aftertreatment improvements 2 LH/MH/HH Engines Low 2024 

Model based control LH/MH/HH Engines Low 2022 

Waste heat recovery HH Engines Medium 2025 

Engine friction reduction 1 HD Pickup & Van Engines Low 2018 

Engine friction reduction 2 HD Pickup & Van Engines Low 2024 

Engine changes to accommodate low friction 

lubes 

HD Pickup & Van Engines Low 2018 

Variable valve timing – coupled HD Pickup & Van Engines Low 2018 

Variable valve timing – dual HD Pickup & Van Engines Medium 2018 

Stoichiometric gasoline direct injection HD Pickup & Van Engines Medium 2018 

Cylinder deactivation HD Pickup & Van Engines Medium 2018 

Cooled EGR HD Pickup & Van Engines Medium 2024 

Turbocharging & downsizing HD Pickup & Van Engines Medium 2018 

“Right sized” diesel engine HD Pickup & Van vehicles, 

Tractors 

Low 2022 

6 speed transmission HD Pickup & Van vehicles Medium 2018 

8 speed transmission HD Pickup & Van vehicles, 

Vocational 

Medium 2018 

Automated manual transmission (AMT) Vocational, Tractors Medium 2022 

Auto transmission, power-shift Tractors Medium 2022 

Conversion from manual to auto trans Vocational Medium 2018 

Dual clutch transmission Vocational, Tractors Medium 2022 

Improved transmission Vocational Low 2022 

Lower RR tires 1 HD Pickup & Van vehicles Low 2018 

Lower RR tires 2 HD Pickup & Van vehicles Low 2024 

Low drag brakes HD Pickup & Van vehicles Low 2018 

Electric power steering HD Pickup & Van vehicles Low 2018 

High efficiency transmission HD Pickup & Van vehicles Low 2024 

Driveline friction reduction HD Pickup & Van vehicles Low 2022 

Improved accessories (electrification) HD Pickup & Van vehicles Low 2018 

Improved accessories (electrification) Tractors Low 2022 

Improved fan Tractors Low 2022 

Lower RR tires 1 Vocational , Tractors, 

Trailers 

Low 2022 

Lower RR tires 2 Vocational , Tractors, 

Trailers 

Low 2022 

Lower RR tires 3 Vocational , Tractors, 

Trailers s 

Medium 2025 

Lower RR tires 4 Vocational , Tractors, 

Trailers 

Medium 2028 
2-199 

Automated Tire Inflation System (ATIS) Tractors, Trailers Low 2022 

Aero 1 HD Pickup & Van vehicles Low 2018 

Aero 2 HD Pickup & Van vehicles Medium 2024 

Aero Bins 1 thru 4 Tractors Low 2022 

Aero Bin 5 thru 7 Tractors Medium 2025 

Aero Bins 1 thru 8 Trailers Low 2018 

Weight reduction (via single wide tires and/or 

aluminum wheels) 

Tractors Low 2022 

Weight reduction via material changes HD Pickup & Van vehicles Low 2018 

Weight reduction via material changes – 200 

lbs, 400 lbs 

Vocational Low 2022 

Weight reduction via material changes – 1000 

lbs 

Vocational Medium 2022 

Weight reduction via material changes Tractors Low 2022 

Auxiliary power unit Tractors Low 2022 

Air conditioning leakage Vocational, Tractors Low 2022 

Air conditioning efficiency Tractors Low 2022 

Neutral idle Vocational Low 2022 

Stop-start (no regeneration) HD Pickup & Van vehicles Medium 2018 

Stop-start (no regeneration) Vocational Medium 2022 

Mild hybrid HD Pickup & Van vehicles High1 2024 

Mild hybrid Tractors High1 2025 

Strong hybrid HD Pickup & Van vehicles High1 2024 

Strong hybrid Vocational High1 2022 

Full electric Vocational, Tractors High1 2028 

There is some level of uncertainty surrounding both the ICM and RPE markup factors. 

The ICM estimates used in this proposal group all technologies into three broad categories and 

treat them as if individual technologies within each of the three categories (low, medium, and 

high complexity) would have the same ratio of indirect costs to direct costs. This simplification 

means it is likely that the direct cost for some technologies within a category will be higher and 

some lower than the estimate for the category in general. More importantly, the ICM estimates 

have not been validated through a direct accounting of actual indirect costs for individual 

technologies. RPEs themselves are inherently difficult to estimate because the accounting 

statements of manufacturers do not neatly categorize all cost elements as either direct or indirect 

costs. Hence, each researcher developing an RPE estimate must apply a certain amount of 

judgment to the allocation of the costs. Moreover, RPEs for heavy- and medium-duty trucks and 

for engine manufacturers are not as well studied as they are for the light-duty automobile 

industry. Since empirical estimates of ICMs are ultimately derived from the same data used to 

measure RPEs, this affects both measures. However, the value of RPE has not been measured 

for specific technologies, or for groups of specific technologies. Thus, even if we assume that 

the examined technology accurately represents the average impact on all technologies in its 

representative category, applying a single average RPE to any given technology by definition 

overstates costs for very simple technologies, or understates them for more advanced 

technologies in that group. 

2.12.1.3 Learning Effects on Technology Costs 

For some of the technologies considered in this analysis, manufacturer learning effects 

would be expected to play a role in the actual end costs. The “learning curve” or “experience 

curve” describes the reduction in unit production costs as a function of accumulated production 
2-200 

volume. In theory, the cost behavior it describes applies to cumulative production volume 

measured at the level of an individual manufacturer, although it is often assumed—as both 

agencies have done in past regulatory analyses—to apply at the industry-wide level, particularly 

in industries that utilize many common technologies and component supply sources. Both 

agencies believe there are indeed many factors that cause costs to decrease over time. 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 these 

factors allow manufacturers to lower the per-unit cost of production (i.e., the manufacturing 

learning curve).171 

The agencies have a detailed description of the learning effect in the light-duty 2012-

2016 rulemaking. Most studies of the effect of experience or learning on production costs appear 

to assume that cost reductions begin only after some initial volume threshold has been reached, 

but not all of these studies specify this threshold volume. The rate at which costs decline beyond 

the initial threshold is usually expressed as the percent reduction in average unit cost that results 

from each successive doubling of cumulative production volume, sometimes referred to as the 

learning rate. Many estimates of experience curves do not specify a cumulative production 

volume beyond which cost reductions would no longer occur, instead depending on the 

asymptotic behavior of the effect for learning rates below 100 percent to establish a floor on 

costs. 

In past rulemaking analyses, as noted above, both agencies have used a learning curve 

algorithm that applied a learning factor of 20 percent for each doubling of production volume. 

NHTSA has used this approach in analyses supporting recent CAFE rules. In its analyses, EPA 

has simplified the approach by using an “every two years” based learning progression rather than 

a pure production volume progression (i.e., after two years of production it was assumed that 

production volumes would have doubled and, therefore, costs would be reduced by 20 percent). 

In the light-duty 2012-2016 rulemaking, the agencies employed an additional learning 

algorithm to reflect the volume-based learning cost reductions that occur further along on the 

learning curve. This additional learning algorithm was termed “time-based” learning simply as a 

means of distinguishing this algorithm from the volume-based algorithm mentioned above, 

although both of the algorithms reflect the volume-based learning curve supported in the 

literature.172 To avoid confusion, we now refer to this learning algorithm as the “flat-portion” of 

the learning curve. This way, we maintain the clarity that all learning is, in fact, volume-based 

learning, and the level of cost reductions depend only on where on the learning curve a 

technology’s learning progression is. We distinguish the flat-portion of the curve from the steepportion of the curve to indicate the level of learning taking place in the years following 

implementation of the technology. The agencies have applied the steep-portion learning 

algorithm for those technologies considered to be newer technologies likely to experience rapid 

cost reductions through manufacturer learning and the flat-portion learning algorithm for those 

technologies considered to be mature technologies likely to experience minor cost reductions 

through manufacturer learning. As noted above, the steep-portion learning algorithm results in 

20 percent lower costs after two full years of implementation (i.e., the 2016 MY costs are 20 

percent lower than the 2014 and 2015 model year costs). Once the steep-portion learning steps 

have occurred (for technologies having the steep-portion learning algorithm applied), flat-portion 
2-201 

learning at 3 percent per year becomes effective for 5 years. For technologies having the flatportion learning algorithm applied), flat-portion learning at 3 percent per year begins in year 2 

and remains effective for 5 years. Beyond 5 years of learning at 3 percent per year, 5 years of 

learning at 2 percent per year, then 5 at 1 percent per year become effective. There was no 

serious disagreement with this approach in the public comments to any of the GHG/fuel 

economy/consumption rulemakings. 

Learning effects are applied to most but not all technologies because some of the 

expected technologies are already used rather widely in the industry and, presumably, learning 

impacts have already occurred. The steep-portion learning algorithm was applied for only a 

handful of technologies that are considered to be new or emerging technologies. Most 

technologies have been considered to be more established given their current use in the fleet and, 

hence, the lower flat-portion learning algorithm has been applied. The learning algorithms 

applied to each technology are summarized in Table 2-100. 

Table 2-100 Learning Effect Algorithms Applied to Technologies Used in this Analysis 

TECHNOLOGY APPLIED TO LEARNING 

ALGORITHM 

LEARNING 

FACTOR 

“CURVE” A

Cylinder head improvements 1 LH/MH/HH Engines Flat 2 

Cylinder head improvements 2 LH/MH/HH Engines Flat 13 

Turbo efficiency improvements 1 LH/MH/HH, HD Pickup 

& Van Engines 

Flat 2 

Turbo efficiency improvements 2 LH/MH/HH Engines Flat 13 

EGR cooler efficiency improvements 1 LH/MH/HH Engines Flat 2 

EGR cooler efficiency improvements 2 LH/MH/HH Engines Flat 13 

Water pump improvements 1 LH/MH/HH Engines Flat 2 

Water pump improvements 2 LH/MH/HH Engines Flat 13 

Oil pump improvements 1 LH/MH/HH Engines Flat 2 

Oil pump improvements 2 LH/MH/HH Engines Flat 13 

Fuel pump improvements 1 LH/MH/HH Engines Flat 2 

Fuel pump improvements 2 LH/MH/HH Engines Flat 13 

Fuel rail improvements 1 LH/MH/HH Engines Flat 2 

Fuel rail improvements 2 LH/MH/HH Engines Flat 13 

Fuel injector improvements 1 LH/MH/HH Engines Flat 2 

Fuel injector improvements 2 LH/MH/HH Engines Flat 13 

Piston improvements 1 LH/MH/HH Engines Flat 2 

Piston improvements 2 LH/MH/HH Engines Flat 13 

Valve train friction reductions 1 LH/MH/HH Engines Flat 2 

Valve train friction reductions 2 LH/MH/HH Engines Flat 13 

Turbo compounding 1 LH/MH/HH Engines Flat 2 

Turbo compounding 2 LH/MH/HH Engines Flat 13 

Aftertreatment improvements 1 & 2 LH/MH/HH Engines Flat 2 

Model based control LH/MH/HH Engines Flat 13 

Waste heat recovery HH Engines Flat 12 

Engine friction reduction 1 & 2 HD Pickup & Van 

Engines 

None 1 

Engine changes to accommodate low 

friction lubes 

HD Pickup & Van 

Engines 

None 1 

Variable valve timing HD Pickup & Van Flat 8 
2-202 

Engines 

Stoichiometric gasoline direct injection HD Pickup & Van 

Engines 

Flat 7 

Cylinder deactivation HD Pickup & Van 

Engines 

Flat 8 

Cooled EGR HD Pickup & Van 

Engines 

Flat 7 

Turbocharging & downsizing HD Pickup & Van 

Engines 

Flat 7 

“Right sized” diesel engine HD Pickup & Van 

vehicles, Tractors 

None 1 

6 speed transmission HD Pickup & Van 

vehicles 

Flat 7 

8 speed transmission HD Pickup & Van 

vehicles, Vocational 

Flat 7 

Automated manual transmission (AMT) Vocational, Tractors Flat 12 

Auto transmission, power-shift Tractors Flat 12 

Conversion from manual to auto trans Vocational Flat 7 

Dual clutch transmission Vocational, Tractors Flat 12 

Improved transmission Vocational Flat 13 

Lower RR tires 1 HD Pickup & Van 

vehicles 

None 1 

Lower RR tires 2 HD Pickup & Van 

vehicles 

Steep 11 

Low drag brakes HD Pickup & Van 

vehicles 

None 1 

Electric power steering HD Pickup & Van 

vehicles 

Flat 8 

High efficiency transmission HD Pickup & Van 

vehicles 

Flat 6 

Driveline friction reduction HD Pickup & Van 

vehicles 

Flat 3 

Improved accessories (electrification) HD Pickup & Van 

vehicles 

Flat 8 

Improved accessories Tractors Flat 12 

Improved fan Tractors Flat 12 

Lower RR tires 1 Vocational , Tractors, 

Trailers 

Flat 2 

Lower RR tires 2 Vocational , Tractors, 

Trailers 

Flat 2 

Lower RR tires 3 Vocational , Tractors, 

Trailers 

Flat 12 

Lower RR tires 4 Vocational , Tractors, 

Trailers 

Flat 13 

Automated Tire Inflation System (ATIS) Tractors, Trailers Flat 12 

Aero 1 & 2 HD Pickup & Van 

vehicles 

Flat 8 

Aero Bins 1 & 2 Tractors None 1 

Aero Bin 3 Tractors Flat 2 

Aero Bins 4 thru 7 Tractors Steep 4 

Aero Bins 1 thru 8 Trailers Flat 2 

Weight reduction (via single wide tires 

and/or aluminum wheels) 

Tractors Flat 2 
2-203 

Weight reduction via material changes HD Pickup & Van 

vehicles 

Flat 6 

Weight reduction via material changes Vocational, Tractors Flat 13 

Auxiliary power unit Tractors Flat 2 

Air conditioning leakage Vocational, Tractors Flat 2 

Air conditioning efficiency Tractors Flat 12 

Neutral idle Vocational None 1 

Stop-start (no regeneration) HD Pickup & Van 

vehicles 

Steep 9 

Stop-start (no regeneration) Vocational Flat 13 

Mild hybrid HD Pickup & Van 

vehicles 

Flat 6 

Mild hybrid Tractors Flat 12 

Strong hybrid HD Pickup & Van 

vehicles, Vocational 

Steep 11 

Full electric Vocational, Tractors Steep 4 

Note: 

a

 See table and figure below. 

The actual year-by-year factors for the numbered curves shown in Table 2-100 are shown 

in Table 2-101 and are shown graphically in Figure 2-33. 

Table 2-101 Year-by-year Learning Curve Factors for the Learning Curves Used in this Analysis 

CURVEA 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2 1.000 0.970 0.941 0.913 0.885 0.868 0.850 0.833 0.817 0.800 0.784 0.769

3 1.031 1.000 0.970 0.941 0.913 0.894 0.877 0.859 0.842 0.825 0.808 0.792

4 1.000 1.000 0.800 0.800 0.640 0.621 0.602 0.584 0.567 0.550 0.533 0.517

6 1.096 1.063 1.031 1.000 0.970 0.941 0.913 0.885 0.859 0.842 0.825 0.808

7 0.941 0.913 0.885 0.868 0.850 0.833 0.817 0.800 0.784 0.769 0.753 0.738

8 1.031 1.000 0.970 0.951 0.932 0.913 0.895 0.877 0.859 0.842 0.825 0.809

9 1.250 1.000 1.000 0.970 0.941 0.913 0.885 0.859 0.833 0.808 0.784 0.760

11 1.563 1.563 1.563 1.563 1.563 1.250 1.250 1.000 0.970 0.941 0.913 0.885

12 1.130 1.096 1.063 1.031 1.000 0.970 0.941 0.913 0.894 0.877 0.859 0.842

13 1.238 1.201 1.165 1.130 1.096 1.063 1.031 1.000 0.970 0.941 0.913 0.894

Note: 

a

 Curves 5 and 10 were generated but subsequently not used so are not included in the table. 
2-204 

0.40

0.60

0.80

1.00

1.20

1.40

1.60

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

1

2

3

4

6

7

8

9

11

12

13

Figure 2-33 Year-by-year Learning Curve Factors for the Learning Curves used in this Analysis 

Importantly, where the factors shown in Table 2-101 and, therefore, the curves shown in 

Figure 2-33 equal “1.00” represents the year for which any particular technology’s cost is based. 

In other words, for example, the cost estimate that we have for cylinder head improvements 2 is 

“based” in 2021 (curve 13). Therefore, its learning factor equals 1.00 in 2021 and then decreases 

going forward to represent lower costs due to learning effects. Its learning factors are greater 

than 1.00 in years before 2021 to represent “reverse” learning, i.e., higher costs than our 2021 

estimate since production volumes have, presumably, not yet reached the point where our cost 

estimate can be considered valid. 

2.12.1.4 Technology Adoption Rates and Package Costs 

Determining the stringency of the proposed standards involves a balancing of relevant 

factors – chiefly technology feasibility and effectiveness, costs, and lead time. For vocational 

vehicles, tractors and trailers, the agencies have projected a technology path to achieve the 

proposed standards reflecting an application rate of those technologies the agencies consider to 

be available at reasonable cost in the lead times provided. The agencies do not expect each of 

the technologies for which costs have been developed to be employed by all engines and vehicles 

across the board. Further, many of today’s vehicles are already equipped with some of the 

technologies and/or are expected to adopt them by MY2018 to comply with the HD Phase 1 

standards. Estimated adoption rates in both the reference and control cases are necessary for 

each vehicle/trailer category. The adoption rates for many technologies are zero in the reference 
2-205 

case; however, for some technologies—notably aero and tire technologies—the adoption rate is 

not always zero in the reference case. These reference and control case adoption rates are then 

applied to the technology costs with the result being a package cost for each vehicle/trailer 

category. As such, package costs are rarely if ever a simple sum of all the technology costs since 

each technology would be expected to be adopted at different rates. 

For HD pickups and vans, the CAFE model determines the technology adoption rates that 

most cost effectively meet the standards being proposed. Similar to vocational vehicles, tractors 

and trailers, package costs are rarely if ever a simple sum of all the technology costs since each 

technology would be expected to be adopted at different rates. The methods for estimating 

technology adoption rates and resultant costs (and other impacts) for HD pickups and vans are 

discussed in Chapter 10 of this draft RIA. 

2.12.1.5 Conversion of Technology Costs to 2012 U.S. Dollars 

As noted above in Section IX.C.1, the agencies are using technology costs from many 

different sources. These sources, having been published in different years, present costs in 

different year dollars (i.e., 2009 dollars or 2010 dollars). For this analysis, the agencies sought to 

have all costs in terms of 2012 dollars to be consistent with the dollars used by AEO in its 2014 

Annual Energy Outlook.173 While the factors used to convert from 2009 dollars (or other) to 

2012 dollars are small, the agencies prefer to be overly diligent in this regard to ensure 

consistency across our benefit-cost analysis. The agencies have used the GDP Implicit Price 

Deflator for Gross Domestic Product as the converter, with the actual factors used as shown in 

Table 2-102.174

Table 2-102 Implicit Price Deflators and Conversion Factors for Conversion to 2012$ 

CALENDAR YEAR 2005 2006 2007 2008 2009 2010 2011 2012 2013 

Price index for GDP 91.991 94.818 97.335 99.236 100 101.211 103.199 105.002 106.588

Factor applied for 2012$ 1.141 1.107 1.079 1.058 1.050 1.037 1.017 1.000 0.985

The sections above describe the technologies expected to be used to enable compliance 

with the proposed standards and the adoption rates we estimate to be possible. Here we present 

the cost of each technology, the markups used for each, the learning effect applied, etc. The 

tables here present the direct manufacturing cost (DMC) we have estimated for each technology, 

the indirect costs (IC) associated with that technology, and the resultant total cost (TC) of each 

(where TC=DMC+IC). Each table also presents, where appropriate, the expected adoption rate 

of each technology in both the reference case (i.e., alternative 1a or the “no new controls” case) 

and the policy case (the proposed standards). For most technologies, the reference case adoption 

rate will be shown as 0 percent (or blanks in the tables) since the Phase 2 technologies are 

expected to be in limited or no use in the regulatory timeframe. However, for some 

technologies—notably tire and aero technologies—there is expected to considerably adoption of 

Phase 2 technologies in the reference case. The final row(s) of the tables shown here include the 

adoption rates applied to the technology costs to arrive at a total cost of each technology as it is 

applied to the ultimate package (noted as TCp). In Chapter 2.13 of this draft RIA, we sum these 

costs (the TCp costs) into total cost applied to the packages presented later in Chapter 7 of this 

draft RIA. We also describe how we moved from the total cost applied to the packages 

developed for the regulatory classes (i.e., Class 8 Sleeper cab, LH vocational medium-speed, 
2-206 

etc.) to the MOVES sourcetypes (i.e., transit bus, refuse truck, combination long haul, etc.) in 

order to develop program costs. This final step—moving from regulatory classes to MOVES 

sourcetypes, was necessary because MOVES populations, sales, inventory calculations, etc., are 

based on sourcetypes, not regulatory classes, and to allow for a more granular look at payback as 

presented in Chapter 7.4 of this draft RIA. 

Note that the text surrounding the tables presented here refer to low/medium/high 

complexity ICMs and to learning curves used. We discuss both the ICMs and the learning 

effects used in this analysis in Chapter 2.12.1.2 and 2.12.1.3 of this draft RIA, respectively. 

2.12.2 Costs of Engine Technologies 

2.12.2.1 Aftertreatment improvements 

We have estimated the cost of aftertreatment improvements based on the aftertreatment 

improvements technology discussed in the Phase 1 rules. That technology was estimated at $25 

(DMC, 2008$, in 2014) for each percentage improvement in fuel consumption, or $100 (DMC, 

2008$, in 2014) for the 4 percent improvement expected as a result of that program. In Phase 2, 

we are expecting only a 0.6 percent improvement in fuel consumption resulting from 

aftertreatment improvements. Therefore, the cost in Phase 2 including updates to 2012$ is $16 

(DMC, 2012$, in 2014). We consider this technology to be on the flat portion of the learning 

curve (curve 2) and have applied a low complexity ICM with short term markups through 2024. 

The resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-103 for vocational engines and in Table 2-104 for tractor engines. 

Table 2-103 Costs of Aftertreatment Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aftertreatment improvements 

– level 2 

DMC $14 $14 $13 $13 $13 $13 $12 $12 $12 $12

Aftertreatment improvements 

– level 2 

IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Aftertreatment improvements 

– level 2 

TC $17 $16 $16 $16 $15 $15 $15 $14 $14 $14

Aftertreatment improvements 

– level 2 

Alt 

1a 

 

Aftertreatment improvements 

– level 2 

Alt 3 50% 50% 50% 90% 90% 90% 100%

Aftertreatment improvements 

– level 2 

TCp $0 $0 $0 $8 $8 $8 $13 $13 $13 $14

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption
2-207 

Table 2-104 Costs of Aftertreatment Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aftertreatment improvements 

– level 2 

DMC $14 $14 $13 $13 $13 $13 $12 $12 $12 $12

Aftertreatment improvements 

– level 2 

IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Aftertreatment improvements 

– level 2 

TC $17 $16 $16 $16 $15 $15 $15 $14 $14 $14

Aftertreatment improvements 

– level 2 

Alt 

1a 

 

Aftertreatment improvements 

– level 2 

Alt 3 45% 45% 45% 95% 95% 95% 100%

Aftertreatment improvements 

– level 2 

TCp $0 $0 $0 $7 $7 $7 $14 $13 $13 $14

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.2 Cylinder head improvements 

We have estimated the cost of cylinder head improvements based on the cylinder head 

improvements technology discussed in the Phase 1 rules. That technology was estimated at $9 

(DMC, 2008$, in 2014) for light HDD engines and at $5 (DMC, 2008$, in 2014) for medium and 

heavy HDD engines. In Phase 2, we are estimating equivalent costs for an additional level of 

cylinder head improvements. With updates to 2012$, we estimate the costs at $10 (DMC, 

2012$, in 2021) for light HDD engines and at $6 (DMC, 2012$, in 2021) for medium and heavy 

HDD engines. We consider this technology to be on the flat portion of the learning curve (curve 

13) and have applied a low complexity ICM with short term markups through 2027. The 

resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-105 through Table 2-107. 

Table 2-105 Costs for Cylinder Head Improvements – Level 2 

Light HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Cylinder head improvements 

– level 2 

DMC $11 $11 $10 $10 $10 $9 $9 $9 $9 $9

Cylinder head improvements 

– level 2 

IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Cylinder head improvements 

– level 2 

TC $12 $12 $12 $11 $11 $11 $11 $10 $10 $10

Cylinder head improvements 

– level 2 

Alt 

1a 

 

Cylinder head improvements 

– level 2 

Alt 3 50% 50% 50% 90% 90% 90% 100%

Cylinder head improvements 

– level 2 

TCp $0 $0 $0 $6 $6 $5 $10 $9 $10 $10

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-208 

Table 2-106 Costs for Cylinder Head Improvements – Level 2 

Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Cylinder head improvements 

– level 2 

DMC $6 $6 $6 $6 $6 $5 $5 $5 $5 $5

Cylinder head improvements 

– level 2 

IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Cylinder head improvements 

– level 2 

TC $7 $7 $7 $7 $7 $6 $6 $6 $6 $6

Cylinder head improvements 

– level 2 

Alt 

1a 

 

Cylinder head improvements 

– level 2 

Alt 3 50% 50% 50% 90% 90% 90% 100%

Cylinder head improvements 

– level 2 

TCp $0 $0 $0 $3 $3 $3 $6 $5 $5 $6

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-107 Costs for Cylinder Head Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Cylinder head improvements 

– level 2 

DMC $6 $6 $6 $6 $6 $5 $5 $5 $5 $5

Cylinder head improvements 

– level 2 

IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Cylinder head improvements 

– level 2 

TC $7 $7 $7 $7 $7 $6 $6 $6 $6 $6

Cylinder head improvements 

– level 2 

Alt 

1a 

 

Cylinder head improvements 

– level 2 

Alt 3 45% 45% 45% 95% 95% 95% 100%

Cylinder head improvements 

– level 2 

TCp $0 $0 $0 $3 $3 $3 $6 $6 $6 $6

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.3 Turbocharger efficiency improvements 

We have estimated the cost of turbo efficiency improvements based on the turbo 

efficiency improvements technology discussed in the Phase 1 rules. That technology was 

estimated at $16 (DMC, 2008$, in 2014) for all HDD engines. In Phase 2, we are estimating 

equivalent costs for an additional level of turbo efficiency improvements. With updates to 

2012$, we estimate the costs at $17 (DMC, 2012$, in 2021) for all HDD engines. We consider 

this technology to be on the flat portion of the learning curve (curve 13) and have applied a low 

complexity ICM with short term markups through 2027. The resultant technology costs, 

adoption rates and total cost applied to the package are shown in Table 2-108 and Table 2-109 

for vocational and tractor engines. 
2-209 

Table 2-108 Costs for Turbocharger Efficiency Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Turbo efficiency 

improvements – level 2 

DMC $18 $18 $17 $17 $16 $16 $15 $15 $14 $14

Turbo efficiency 

improvements – level 2 

IC $3 $3 $3 $3 $3 $3 $3 $3 $3 $3

Turbo efficiency 

improvements – level 2 

TC $21 $20 $20 $19 $19 $18 $18 $17 $17 $17

Turbo efficiency 

improvements – level 2 

Alt 

1a 

 

Turbo efficiency 

improvements – level 2 

Alt 3 50% 50% 50% 90% 90% 90% 100%

Turbo efficiency 

improvements – level 2 

TCp $0 $0 $0 $10 $9 $9 $16 $16 $15 $17

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

Table 2-109 Costs for Turbocharger Efficiency Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Turbo efficiency 

improvements – level 2 

DMC $18 $18 $17 $17 $16 $16 $15 $15 $14 $14

Turbo efficiency 

improvements – level 2 

IC $3 $3 $3 $3 $3 $3 $3 $3 $3 $3

Turbo efficiency 

improvements – level 2 

TC $21 $20 $20 $19 $19 $18 $18 $17 $17 $17

Turbo efficiency 

improvements – level 2 

Alt 

1a 

 

Turbo efficiency 

improvements – level 2 

Alt 3 45% 45% 45% 95% 95% 95% 100%

Turbo efficiency 

improvements – level 2 

TCp $0 $0 $0 $9 $8 $8 $17 $16 $16 $17

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For HD diesel pickups and vans, we are estimating use of the Phase 1 level of turbo 

efficiency improvements, or $17 (DMC, 2012$, in 2014). We consider this technology to be on 

the flat portion of the learning curve (curve 2) and have applied a low complexity ICM with 

short term markups through 2022. The resultant technology costs are shown in Table 2-110. 

Table 2-110 Costs for Turbocharger Efficiency Improvements – Level 1 

HD Pickups & Vans (2012$) 

TECHNOLOGY 2021 2022 2023 2024 2025 2026 2027

Turbo efficiency improvements – level 1 DMC $14 $14 $13 $13 $13 $13 $13

Turbo efficiency improvements – level 1 IC $3 $3 $2 $2 $2 $2 $2

Turbo efficiency improvements – level 1 TC $16 $16 $15 $15 $15 $15 $15

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-210 

2.12.2.4 Turbo compounding 

We have estimated the cost of turbo compounding based on the turbo compounding 

technology discussed in the Phase 1 rules. That technology was estimated at $813 (DMC, 

2008$, in 2014) for all HDD tractor engines. In Phase 2, we are estimating equivalent costs for 

an additional level of turbo compounding improvements. With updates to 2012$, we estimate 

the costs at $860 (DMC, 2012$, in 2021) for all HDD tractor engines. We consider this 

technology to be on the flat portion of the learning curve (curve 13) and have applied a low 

complexity ICM with short term markups through 2027. The resultant technology costs, 

adoption rates and total cost applied to the package are shown in Table 2-111. 

Table 2-111 Costs for Turbocharger Compounding – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Turbo compounding – 

level 2 

DMC $942 $914 $886 $860 $834 $809 $785 $769 $754 $738

Turbo compounding – 

level 2 

IC $134 $133 $133 $133 $133 $133 $133 $133 $132 $132

Turbo compounding – 

level 2 

TC $1,076 $1,047 $1,020 $993 $967 $942 $917 $902 $886 $871

Turbo compounding – 

level 2 

Alt 

1a 

 

Turbo compounding – 

level 2 

Alt 3 5% 5% 5% 10% 10% 10% 10%

Turbo compounding – 

level 2 

TCp $0 $0 $0 $50 $48 $47 $92 $90 $89 $87

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.5 Valve actuation 

We have estimated the cost of valve actuation based on the dual cam phasing cost 

estimate used in the 2017-2025 light-duty rule. In that analysis, we estimated costs at $151 

(DMC, 2010$, in 2015) for a large V8 engine. In this HD Phase 2 program, we are estimating 

equivalent costs for this technology. With updates to 2012$, we estimate the costs at $157 

(DMC, 2012$, in 2015) for all HDD engines. We consider this technology to be on the flat 

portion of the learning curve (curve 8) and have applied a medium complexity ICM with short 

term markups through 2018. The resultant technology costs, adoption rates and total cost applied 

to the package are shown in Table 2-112 for vocational engines and in Table 2-113 for tractor 

engines. 
2-211 

Table 2-112 Costs for Valve Actuation 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Valve actuation DMC $146 $143 $141 $138 $135 $132 $130 $127 $126 $125

Valve actuation IC $60 $45 $45 $45 $45 $45 $45 $45 $45 $44

Valve actuation TC $207 $188 $186 $183 $180 $177 $174 $172 $170 $169

Valve actuation Alt 

1a 

 

Valve actuation Alt 3 50% 50% 50% 90% 90% 90% 100%

Valve actuation All $0 $0 $0 $91 $90 $89 $157 $154 $153 $169

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

Table 2-113 Costs for Valve Actuation 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Valve actuation DMC $146 $143 $141 $138 $135 $132 $130 $127 $126 $125

Valve actuation IC $60 $45 $45 $45 $45 $45 $45 $45 $45 $44

Valve actuation TC $207 $188 $186 $183 $180 $177 $174 $172 $170 $169

Valve actuation Alt 

1a 

 

Valve actuation Alt 3 45% 45% 45% 95% 95% 95% 100%

Valve actuation All $0 $0 $0 $82 $81 $80 $166 $163 $162 $169

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For HD pickups and vans, we have estimated the costs of dual cam phasing based on the 

DMC, IC and TC presented above in Table 2-112. 

For discrete variable valve lift (DVVL), we have again used the 2017-2025 light-duty 

FRM values updated to 2012$ to arrive at a cost of $259 (DMC, 2012$, in 2015). We consider 

this technology to be on the flat portion of the learning curve (curve 8) and have applied medium 

complexity markups with short term markups through 2024. The resultant costs are presented in 

Table 2-114. 

Table 2-114 Costs for Discrete Variable Valve Lift (DVVL) 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Discrete variable valve lift 

(DVVL) 

DMC $227 $223 $218 $214 $210 $207 $205

Discrete variable valve lift 

(DVVL) 

IC $74 $74 $74 $74 $74 $73 $73

Discrete variable valve lift 

(DVVL) 

TC $301 $297 $292 $288 $283 $281 $279

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.2.6 EGR 

We have estimated the cost of EGR cooler improvements based on the EGR cooler 

improvements technology discussed in the Phase 1 rules. That technology was estimated at $3 
2-212 

(DMC, 2008$, in 2014) for all HDD engines. In Phase 2, we are estimating equivalent costs for 

an additional level of EGR cooler improvements. With updates to 2012$, we estimate the costs 

at $3 (DMC, 2012$, in 2021) for all HDD engines. We consider this technology to be on the flat 

portion of the learning curve (curve 13) and have applied a low complexity ICM with short term 

markups through 2027. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-115 for vocational engines and in Table 2-116 for tractor 

engines. 

Table 2-115 Costs for EGR Cooler Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

EGR cooler – level 2 DMC $4 $4 $3 $3 $3 $3 $3 $3 $3 $3

EGR cooler – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

EGR cooler – level 2 TC $4 $4 $4 $4 $4 $4 $4 $3 $3 $3

EGR cooler – level 2 Alt 1a 

EGR cooler – level 2 Alt 3 50% 50% 50% 90% 90% 90% 100%

EGR cooler – level 2 TCp $0 $0 $0 $2 $2 $2 $3 $3 $3 $3

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

Table 2-116 Costs for EGR Cooler Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

EGR cooler – level 2 DMC $4 $4 $3 $3 $3 $3 $3 $3 $3 $3

EGR cooler – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

EGR cooler – level 2 TC $4 $4 $4 $4 $4 $4 $4 $3 $3 $3

EGR cooler – level 2 Alt 1a 

EGR cooler – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

EGR cooler – level 2 TCp $0 $0 $0 $2 $2 $2 $3 $3 $3 $3

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For HD pickups and vans, we have estimated the costs of adding cooled EGR to a 

gasoline engine based on the values used in the 2017-2025 light-duty FRM. We have scaled 

upward the light-duty value by 25 percent and converted to 2012$ to arrive at a cost of $317 

(DMC, 2012$, in 2012). We consider this technology to be on the flat portion of the learning 

curve (curve 7) and have applied medium complexity markups with near term markups through 

2024. The resultant costs are presented in Table 2-117. 

Table 2-117 Costs for Cooled EGR 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Cooled EGR DMC $253 $248 $243 $239 $234 $231 $229

Cooled EGR IC $120 $120 $119 $119 $89 $89 $89

Cooled EGR TC $373 $368 $363 $358 $323 $321 $318

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-213 

2.12.2.7 Water pump improvements 

We have estimated the cost of water pump improvements based on the water pump 

improvements technology discussed in the Phase 1 rules. That technology was estimated at $78 

(DMC, 2008$, in 2014) for all HDD engines. In Phase 2, we are estimating equivalent costs for 

an additional level of water pump improvements. With updates to 2012$, we estimate the costs 

at $83 (DMC, 2012$, in 2021) for all HDD engines. We consider this technology to be on the 

flat portion of the learning curve (curve 13) and have applied a low complexity ICM with short 

term markups through 2027. The resultant technology costs, adoption rates and total cost applied 

to the package are shown in Table 2-118 for vocational engines and in Table 2-119 for tractor 

engines. 

Table 2-118 Costs for Water Pump Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Water pump – level 2 DMC $91 $88 $85 $83 $80 $78 $75 $74 $72 $71

Water pump – level 2 IC $13 $13 $13 $13 $13 $13 $13 $13 $13 $13

Water pump – level 2 TC $103 $101 $98 $95 $93 $91 $88 $87 $85 $84

Water pump – level 2 Alt 1a 

Water pump – level 2 Alt 3 60% 60% 60% 90% 90% 90% 100%

Water pump – level 2 TCp $0 $0 $0 $57 $56 $54 $79 $78 $77 $84

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-119 Costs for Water Pump Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Water pump – level 2 DMC $91 $88 $85 $83 $80 $78 $75 $74 $72 $71

Water pump – level 2 IC $13 $13 $13 $13 $13 $13 $13 $13 $13 $13

Water pump – level 2 TC $103 $101 $98 $95 $93 $91 $88 $87 $85 $84

Water pump – level 2 Alt 1a 

Water pump – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

Water pump – level 2 TCp $0 $0 $0 $43 $42 $41 $84 $82 $81 $84

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.8 Oil pump improvements 

We have estimated the cost of oil pump improvements based on the oil pump 

improvements technology discussed in the Phase 1 rules. That technology was estimated at just 

under $4 (DMC, 2008$, in 2014) for all HDD engines. In Phase 2, we are estimating equivalent 

costs for an additional level of oil pump improvements. With updates to 2012$, we estimate the 

costs at just over $4 (DMC, 2012$, in 2021) for all HDD engines. We consider this technology 

to be on the flat portion of the learning curve (curve 13) and have applied a low complexity ICM 

with short term markups through 2027. The resultant technology costs, adoption rates and total 
2-214 

cost applied to the package are shown in Table 2-120 for vocational engines and in Table 2-121 

for tractor engines. 

Table 2-120 Costs for Oil Pump Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Oil pump – level 2 DMC $5 $4 $4 $4 $4 $4 $4 $4 $4 $4

Oil pump – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Oil pump – level 2 TC $5 $5 $5 $5 $5 $5 $4 $4 $4 $4

Oil pump – level 2 Alt 1a 

Oil pump – level 2 Alt 3 60% 60% 60% 90% 90% 90% 100%

Oil pump – level 2 TCp $0 $0 $0 $3 $3 $3 $4 $4 $4 $4

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-121 Costs for Oil Pump Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Oil pump – level 2 DMC $5 $4 $4 $4 $4 $4 $4 $4 $4 $4

Oil pump – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Oil pump – level 2 TC $5 $5 $5 $5 $5 $5 $4 $4 $4 $4

Oil pump – level 2 Alt 1a 

Oil pump – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

Oil pump – level 2 TCp $0 $0 $0 $2 $2 $2 $4 $4 $4 $4

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.9 Fuel pump improvements 

We have estimated the cost of fuel pump improvements based on the fuel pump 

improvements technology discussed in the Phase 1 rules. That technology was estimated at just 

under $4 (DMC, 2008$, in 2014) for all HDD engines. In Phase 2, we are estimating equivalent 

costs for an additional level of fuel pump improvements. With updates to 2012$, we estimate the 

costs at just over $4 (DMC, 2012$, in 2021) for all HDD engines. We consider this technology 

to be on the flat portion of the learning curve (curve 13) and have applied a low complexity ICM 

with short term markups through 2027. The resultant technology costs, adoption rates and total 

cost applied to the package are shown in Table 2-122 for vocational engines and in Table 2-123 

for tractor engines. 
2-215 

Table 2-122 Costs for Fuel Pump Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel pump – level 2 DMC $5 $4 $4 $4 $4 $4 $4 $4 $4 $4

Fuel pump – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Fuel pump – level 2 TC $5 $5 $5 $5 $5 $5 $4 $4 $4 $4

Fuel pump – level 2 Alt 1a 

Fuel pump – level 2 Alt 3 60% 60% 60% 90% 90% 90% 100%

Fuel pump – level 2 TCp $0 $0 $0 $3 $3 $3 $4 $4 $4 $4

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-123 Costs for Fuel Pump Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel pump – level 2 DMC $5 $4 $4 $4 $4 $4 $4 $4 $4 $4

Fuel pump – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Fuel pump – level 2 TC $5 $5 $5 $5 $5 $5 $4 $4 $4 $4

Fuel pump – level 2 Alt 1a 

Fuel pump – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

Fuel pump – level 2 TCp $0 $0 $0 $2 $2 $2 $4 $4 $4 $4

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.10 Fuel rail improvements 

We have estimated the cost of fuel rail improvements based on the fuel rail improvements 

technology discussed in the Phase 1 rules. That technology was estimated at $10 (DMC, 2008$, 

in 2014) for LHDD engines and just under $9 (DMC, 2008$, in 2014) for MHDD and HHDD 

engines. In Phase 2, we are estimating equivalent costs for an additional level of fuel rail 

improvements. With updates to 2012$, we estimate the costs at $11 (DMC, 2012$, in 2021) for 

LHDD and at just over $9 (DMC, 2012$, in 2021) for MHDD and HHDD engines. We consider 

this technology to be on the flat portion of the learning curve (curve 13) and have applied a low 

complexity ICM with short term markups through 2027. The resultant technology costs, 

adoption rates and total cost applied to the package are shown in Table 2-124 through Table 

2-126. 
2-216 

Table 2-124 Costs for Fuel Rail Improvements – Level 2 

Light HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel rail – level 2 DMC $12 $11 $11 $11 $10 $10 $10 $10 $9 $9

Fuel rail – level 2 IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Fuel rail – level 2 TC $13 $13 $13 $12 $12 $12 $11 $11 $11 $11

Fuel rail – level 2 Alt 1a 

Fuel rail – level 2 Alt 3 60% 60% 60% 90% 90% 90% 100%

Fuel rail – level 2 TCp $0 $0 $0 $7 $7 $7 $10 $10 $10 $11

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-125 Costs for Fuel Rail Improvements – Level 2 

Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel rail – level 2 DMC $10 $10 $9 $9 $9 $9 $8 $8 $8 $8

Fuel rail – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Fuel rail – level 2 TC $11 $11 $11 $11 $10 $10 $10 $10 $9 $9

Fuel rail – level 2 Alt 1a 

Fuel rail – level 2 Alt 3 60% 60% 60% 90% 90% 90% 100%

Fuel rail – level 2 TCp $0 $0 $0 $6 $6 $6 $9 $9 $8 $9

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-126 Costs for Fuel Rail Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel rail – level 2 DMC $10 $10 $9 $9 $9 $9 $8 $8 $8 $8

Fuel rail – level 2 IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Fuel rail – level 2 TC $11 $11 $11 $11 $10 $10 $10 $10 $9 $9

Fuel rail – level 2 Alt 1a 

Fuel rail – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

Fuel rail – level 2 TCp $0 $0 $0 $5 $5 $4 $9 $9 $9 $9

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.11 Fuel injector improvements 

We have estimated the cost of fuel injector improvements based on the fuel injector 

improvements technology discussed in the Phase 1 rules. That technology was estimated at $13 

(DMC, 2008$, in 2014) for LHDD engines and $9 (DMC, 2008$, in 2014) for MHDD and 

HHDD engines. In Phase 2, we are estimating equivalent costs for an additional level of fuel 

injector improvements. With updates to 2012$, we estimate the costs at $13 (DMC, 2012$, in 

2021) for LHDD and at $10 (DMC, 2012$, in 2021) for MHDD and HHDD engines. We 

consider this technology to be on the flat portion of the learning curve (curve 13) and have 

applied a low complexity ICM with short term markups through 2027. The resultant technology 
2-217 

costs, adoption rates and total cost applied to the package are shown in Table 2-127 through 

Table 2-129. 

Table 2-127 Costs for Fuel Injector Improvements – Level 2 

Light HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel injectors – level 2 DMC $14 $14 $14 $13 $13 $12 $12 $12 $12 $11

Fuel injectors – level 2 IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Fuel injectors – level 2 TC $17 $16 $16 $15 $15 $14 $14 $14 $14 $13

Fuel injectors – level 2 Alt 1a 

Fuel injectors – level 2 Alt 3 50% 50% 50% 90% 90% 90% 100%

Fuel injectors – level 2 TCp $0 $0 $0 $8 $7 $7 $13 $12 $12 $13

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-128 Costs for Fuel Injector Improvements – Level 2 

Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel injectors – level 2 DMC $11 $11 $10 $10 $10 $9 $9 $9 $9 $9

Fuel injectors – level 2 IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Fuel injectors – level 2 TC $12 $12 $12 $11 $11 $11 $11 $10 $10 $10

Fuel injectors – level 2 Alt 1a 

Fuel injectors – level 2 Alt 3 50% 50% 50% 90% 90% 90% 100%

Fuel injectors – level 2 TCp $0 $0 $0 $6 $6 $5 $10 $9 $9 $10

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-129 Costs for Fuel Injector Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Fuel injectors – level 2 DMC $11 $11 $10 $10 $10 $9 $9 $9 $9 $9

Fuel injectors – level 2 IC $2 $2 $2 $2 $2 $2 $2 $2 $2 $2

Fuel injectors – level 2 TC $12 $12 $12 $11 $11 $11 $11 $10 $10 $10

Fuel injectors – level 2 Alt 1a 

Fuel injectors – level 2 Alt 3 45% 45% 45% 95% 95% 95% 100%

Fuel injectors – level 2 TCp $0 $0 $0 $5 $5 $5 $10 $10 $10 $10

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.12 Piston improvements 

We have estimated the cost of piston improvements based on the piston improvements 

technology discussed in the Phase 1 rules. That technology was estimated at just over $2 (DMC, 

2008$, in 2014) for all HDD engines. In Phase 2, we are estimating equivalent costs for an 

additional level of fuel pump improvements. With updates to 2012$, we estimate the costs at 

over $2 (DMC, 2012$, in 2021) for all HDD engines. We consider this technology to be on the 

flat portion of the learning curve (curve 13) and have applied a low complexity ICM with short 
2-218 

term markups through 2027. The resultant technology costs, adoption rates and total cost applied 

to the package are shown in Table 2-130 for vocational engines and in Table 2-131 for tractor 

engines. 

Table 2-130 Costs for Fuel Pump Improvements – Level 2 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Piston improvements – level 

2 

DMC $3 $3 $3 $2 $2 $2 $2 $2 $2 $2

Piston improvements – level 

2 

IC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Piston improvements – level 

2 

TC $3 $3 $3 $3 $3 $3 $3 $3 $3 $3

Piston improvements – level 

2 

Alt 1a 

Piston improvements – level 

2 

Alt 3 50% 50% 50% 90% 90% 90% 100%

Piston improvements – level 

2 

TCp $0 $0 $0 $1 $1 $1 $2 $2 $2 $3

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-131 Costs for Fuel Pump Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Piston improvements – level 

2 

DMC $3 $3 $3 $2 $2 $2 $2 $2 $2 $2

Piston improvements – level 

2 

IC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Piston improvements – level 

2 

TC $3 $3 $3 $3 $3 $3 $3 $3 $3 $3

Piston improvements – level 

2 

Alt 1a 

Piston improvements – level 

2 

Alt 3 45% 45% 45% 95% 95% 95% 100%

Piston improvements – level 

2 

TCp $0 $0 $0 $1 $1 $1 $3 $2 $2 $3

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.13 Valvetrain friction reduction 

We have estimated the cost of valvetrain friction reduction based on the valvetrain 

friction reduction technology discussed in the Phase 1 rules. That technology was estimated at 

$94 (DMC, 2008$, in 2014) for LHDD engines and $70 (DMC, 2008$, in 2014) for MHDD and 

HHDD engines. In Phase 2, we are estimating equivalent costs for an additional level of fuel 

injector improvements. With updates to 2012$, we estimate the costs at $99 (DMC, 2012$, in 

2021) for LHDD and at $74 (DMC, 2012$, in 2021) for MHDD and HHDD engines. We 
2-219 

consider this technology to be on the flat portion of the learning curve (curve 13) and have 

applied a low complexity ICM with short term markups through 2027. The resultant technology 

costs, adoption rates and total cost applied to the package are shown in Table 2-132 and Table 

2-133 for vocational engines and in Table 2-134 for tractor engines. 

Table 2-132 Costs for Valvetrain Friction Improvements – Level 2 

Light HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Valvetrain friction reduction 

– level 2 

DMC $109 $105 $102 $99 $96 $93 $91 $89 $87 $85

Valvetrain friction reduction 

– level 2 

IC $15 $15 $15 $15 $15 $15 $15 $15 $15 $15

Valvetrain friction reduction 

– level 2 

TC $124 $121 $118 $115 $112 $109 $106 $104 $102 $100

Valvetrain friction reduction 

– level 2 

Alt 

1a 

 

Valvetrain friction reduction 

– level 2 

Alt 3 60% 60% 60% 90% 90% 90% 100%

Valvetrain friction reduction 

– level 2 

TCp $0 $0 $0 $69 $67 $65 $95 $94 $92 $100

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-133 Costs for Valvetrain Friction Improvements – Level 2 

Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Valvetrain friction reduction 

– level 2 

DMC $82 $79 $77 $74 $72 $70 $68 $67 $65 $64

Valvetrain friction reduction 

– level 2 

IC $12 $12 $12 $12 $12 $11 $11 $11 $11 $11

Valvetrain friction reduction 

– level 2 

TC $93 $91 $88 $86 $84 $81 $79 $78 $77 $75

Valvetrain friction reduction 

– level 2 

Alt 

1a 

 

Valvetrain friction reduction 

– level 2 

Alt 3 60% 60% 60% 90% 90% 90% 100%

Valvetrain friction reduction 

– level 2 

TCp $0 $0 $0 $52 $50 $49 $71 $70 $69 $75

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-220 

Table 2-134 Costs for Valvetrain Friction Improvements – Level 2 

HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Valvetrain friction reduction 

– level 2 

DMC $82 $79 $77 $74 $72 $70 $68 $67 $65 $64

Valvetrain friction reduction 

– level 2 

IC $12 $12 $12 $12 $12 $11 $11 $11 $11 $11

Valvetrain friction reduction 

– level 2 

TC $93 $91 $88 $86 $84 $81 $79 $78 $77 $75

Valvetrain friction reduction 

– level 2 

Alt 

1a 

 

Valvetrain friction reduction 

– level 2 

Alt 3 45% 45% 45% 95% 95% 95% 100%

Valvetrain friction reduction 

– level 2 

TCp $0 $0 $0 $39 $38 $37 $75 $74 $73 $75

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.14 “Right-sized” diesel engine 

We have estimated the cost of a slightly smaller diesel engine at a $500 savings (DMC, 

2013$, in any year) for all HDD tractor engines. We believe this represents an opportunity for 

lower costs because smaller diesel engines contain less materials and are, generally, less costly to 

produce than a larger diesel engine. In 2012$, we estimate the costs at $493 (DMC, 2012$, in 

any year) for all HDD tractor engines. As this cost is considered applicable in any year, we have 

applied not learning effects (curve 1). We have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-135 for tractor engines. For HD pickups and vans, we 

estimated the right-sized diesel engine cost as cost neutral to any reference case diesel engine 

and limited the technology to diesel vans. We have not included any costs associated with lost 

utility of the smaller diesel engine. We believe that the smaller engine would be attractive to 

some buyers, but not all, and that those buyers would not be concerned by any possible lost 

utility. For that reason, we have used a limited application rate for this technology since, as 

noted, not all buyers would be interested in this option due to the potential for lost utility. Note 

that, for HD pickups and vans, we have considered this technology to be cost neutral. 
2-221 

Table 2-135 Costs for “Right-sized” HDD Tractor Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Right-sized 

diesel engine 

DMC -$493 -$493 -$493 -$493 -$493 -$493 -$493 -$493 -$493 -$493

Right-sized 

diesel engine 

IC $88 $88 $88 $88 $88 $69 $69 $69 $69 $69

Right-sized 

diesel engine 

TC -$405 -$405 -$405 -$405 -$405 -$424 -$424 -$424 -$424 -$424

Right-sized 

diesel engine 

Alt 1a 

Right-sized 

diesel engine 

Alt 3 10% 10% 10% 20% 20% 20% 30%

Right-sized 

diesel engine 

TCp $0 $0 $0 -$40 -$40 -$42 -$85 -$85 -$85 -$127

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.15 Waste heat recovery 

We have estimated the cost of waste heat recovery based on the estimate from Tetra Tech 

showing it at $12,000 (retail, 2013$). Using that $12,000 estimate and dividing by a 1.36 RPE 

(see Chapter 2.12.1.2 of this draft RIA) and converting to 2012$, we arrive at our estimated 

DMC of $8,692 (DMC, 2012$, in 2018). We consider this technology to be on the flat portion of 

the learning curve (curve 12) because although waste heat recovery is a new technology and in 

the 2015 to 2017 timeframe remains, perhaps, on the steeper portion of the learning curve, 

applying such rapid learning effects to the cost estimate we have would result in costs too low in 

the MY2024 to 2027 timeframe. We have applied a medium complexity ICM with short term 

markups through 2025. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-136 for tractor engines. 
2-222 

Table 2-136 Costs for Waste Heat Recovery 

HDD Tractor Engines (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Waste 

heat 

recovery 

DMC $8,692 $8,431 $8,179 $7,933 $7,775 $7,619 $7,467 $7,317 $7,171 $7,028

Waste 

heat 

recovery 

IC $2,628 $2,615 $2,602 $2,589 $2,581 $2,574 $2,566 $2,558 $1,908 $1,903

Waste 

heat 

recovery 

TC $11,320 $11,046 $10,780 $10,523 $10,356 $10,193 $10,032 $9,876 $9,079 $8,931

Waste 

heat 

recovery 

Alt 

1a 

 

Waste 

heat 

recovery 

Alt 3 1% 1% 1% 5% 5% 5% 15%

Waste 

heat 

recovery 

TCp $0 $0 $0 $105 $104 $102 $502 $494 $454 $1,340

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.16 Model-based control 

We have estimated the cost of model-based controls at $100 (DMC, 2013$). Using that 

estimate and converting to 2012$, we arrive at our estimated DMC of $99 (DMC, 2012$, in 

2021). We consider this technology to be on the flat portion of the learning curve (curve 13) and 

have applied a low complexity ICM with short term markups through 2022. The resultant 

technology costs, adoption rates and total cost applied to the package are shown in Table 2-137 

for vocational engines. 

Table 2-137 Costs for Model Based Controls 

Light/Medium/Heavy HDD Vocational Engines (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Model-based control DMC $108 $105 $102 $99 $96 $93 $90 $88 $86 $85

Model-based control IC $15 $15 $15 $15 $15 $12 $12 $12 $12 $12

Model-based control TC $123 $120 $117 $114 $111 $105 $102 $100 $99 $97

Model-based control Alt 1a 

Model-based control Alt 3 25% 25% 25% 30% 30% 30% 40%

Model-based control TCp $0 $0 $0 $28 $28 $26 $31 $30 $30 $39

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.2.17 Engine friction reduction and accommodating low friction lubes 

We have based the costs for accommodating low friction lubes (LUB) on the costs used 

in the light-duty 2017-2025 FRM but have scaled upward that cost by 50 percent to account for 

the larger HD engines. Using that cost ($3 DMC, 2006$, in any year) and converting to 2012$ 
2-223 

results in a cost of $5 (DMC, 2012$, in any year). We consider this technology to be beyond 

learning (curve 1) and have applied low complexity markups with near term markups through 

2018. The resultant costs for HD pickups and vans are shown in are shown in Table 2-138. 

Table 2-138 Costs for Accommodating Low Friction Lubes 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Engine friction reduction - level 

1 

DMC $5 $5 $5 $5 $5 $5 $5

Engine friction reduction - level 

1 

IC $1 $1 $1 $1 $1 $1 $1

Engine friction reduction - level 

1 

TC $6 $6 $6 $6 $6 $6 $6

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

We have based the costs for engine friction reduction level 1 (EFR1) on the costs used in 

the light-duty 2017-2025 FRM. That cost is based on an original estimate of $11/cylinder 

(DMC, 2006$, in any year). Using that cost for an 8 cylinder engine and converting to 2012$ 

results in a cost of $97 (DMC, 2012$, in any year). We consider this technology to be beyond 

learning (curve 1) and have applied low complexity markups with near term markups through 

2018. The resultant costs for HD pickups and vans are shown in are shown in Table 2-139. 

Table 2-139 Costs for Engine Friction Reduction – Level 1 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Engine friction reduction - level 

1 

DMC $97 $97 $97 $97 $97 $97 $97

Engine friction reduction - level 

1 

IC $19 $19 $19 $19 $19 $19 $19

Engine friction reduction - level 

1 

TC $116 $116 $116 $116 $116 $116 $116

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

For engine friction reduction level 2 (EFR2, which includes costs for accommodating low 

friction lubes) we have used the same approach as used in the light-duty 2017-2025 rule in that 

we have doubled the DMC associated with LUB and EFR1. As with those technologies, we 

consider EFR2 to be beyond learning (curve 1) and have applied low complexity markups but 

have applied near term markups through 2024. The resultant costs for gasoline HD pickups and 

vans are shown in Table 2-140. 
2-224 

Table 2-140 Costs for Engine Friction Reduction – Level 2 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Engine friction reduction - level 

2 

DMC $205 $205 $205 $205 $205 $205 $205

Engine friction reduction - level 

2 

IC $50 $50 $50 $50 $39 $39 $39

Engine friction reduction - level 

2 

TC $254 $254 $254 $254 $244 $244 $244

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

For diesel HD pickups and vans, we have used the above costs for EFR level 2 and added 

to that costs associated with improvements to other parasitic loads on the engine. For that latter 

portion of the cost, we have used the light HDD engine DMCs for improved water pump level 1, 

improved oil pump level 1, improved fuel pump level 1, improved fuel injectors level 1 and 

valvetrain friction reduction level 1, which together result in a cost of $193 (DMC, 2012$, in and 

year). We consider this combined set of technologies to be beyond the effects of learning (curve 

1) and have applied low complexity markups with near term markups through 2022. The 

resultant costs for diesel HD pickups and vans are shown in Table 2-141. 

Table 2-141 Costs for Engine Friction Reduction & Improvements to Other Parasitics 

Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Engine friction reduction - 

diesel 

DMC $397 $397 $397 $397 $397 $397 $397

Engine friction reduction - 

diesel 

IC $96 $96 $87 $87 $77 $77 $77

Engine friction reduction - 

diesel 

TC $494 $494 $484 $484 $474 $474 $474

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.2.18 Cylinder deactivation 

For cylinder deactivation on HD pickups and vans, we have based the costs on values 

presented in the light-duty 2017-2025 FRM with updates to 2012$ to arrive at a cost of $169 

(DMC, 2012$, in 2015). We consider this technology to be on the flat portion of the learning 

curve (curve 8) and have applied medium complexity markups with near term markups through 

2018. The resultant costs are presented in Table 2-142. 

Table 2-142 Costs for Cylinder Deactivation 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Cylinder 

deactivation 

DMC $148 $145 $142 $139 $137 $135 $134

Cylinder 

deactivation 

IC $48 $48 $48 $48 $48 $48 $48

Cylinder 

deactivation 

TC $196 $193 $190 $187 $185 $183 $182

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-225 

2.12.2.19 Stoichiometric gasoline direct injection (SGDI) 

For gasoline direct injection on HD pickups and vans, we have based the costs on values 

presented in the light-duty 2017-2025 FRM with updates to 2012$ to arrive at a cost of $417 

(DMC, 2012$, in 2012). We consider this technology to be on the flat portion of the learning 

curve (curve 7) and have applied medium complexity markups with near term markups through 

2018. The resultant costs are presented in Table 2-143. 

Table 2-143 Costs for Direct Injection 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Gasoline direct 

injection 

DMC $333 $327 $320 $314 $307 $304 $301

Gasoline direct 

injection 

IC $118 $118 $118 $117 $117 $117 $117

Gasoline direct 

injection 

TC $451 $445 $438 $431 $425 $422 $418

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost.

2.12.2.20 Turbocharging & downsizing 

For turbocharging and downsizing (TDS) on HD pickups and vans, we have based the 

costs on values presented in the light-duty 2017-2025 FRM with updates to 2012$. For the twin 

turbo configuration expected on a V6 engine (downsized from a V8), we estimate the cost at 

$735 (DMC, 2012$, in 2012). We consider this technology to be on the flat portion of the 

learning curve (curve 7) and have applied medium complexity markups with near term markups 

through 2018. For downsizing from an overhead valve (OHV) V8 to an overhead cam (OHC) 

V6 valvetrain, we have estimated the cost at $340 (DMC, 2012$, in 2017). We consider this 

technology to be on the flat portion of the learning curve (curve 6) and have applied medium 

complexity markups with near term markups through 2018. For downsizing from an OHC V8 to 

an OHC V6, we have estimated the cost at -$295 (DMC, 2012$, in 2012). We consider this 

technology to be on the flat portion of the learning curve to arrive at a cost of $417 (DMC, 

2012$, in 2012). We consider this technology to be on the flat portion of the learning curve 

(curve 7) and have applied medium complexity markups with near term markups through 2024. 

The resultant costs for the turbocharging system are shown in Table 2-144, for downsizing from 

an OHV V8 to an OHC V6 in Table 2-145, and downsizing from an OHC V8 to an OHC V6 in 

Table 2-146. 

Table 2-144 Costs for Adding Twin Turbos 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Adding 

twin turbos 

DMC $588 $576 $565 $553 $542 $537 $531

Adding 

twin turbos 

IC $208 $208 $208 $207 $207 $207 $207

Adding 

twin turbos 

TC $796 $784 $772 $761 $749 $744 $738

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 
2-226 

Table 2-145 Costs for Downsizing from an OHV V8 to an OHC V6 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Downsizing from OHV 

V8 to OHC V6 

DMC $301 $292 $286 $280 $275 $269 $264

Downsizing from OHV 

V8 to OHC V6 

IC $97 $97 $97 $97 $96 $96 $96

Downsizing from OHV 

V8 to OHC V6 

TC $398 $389 $383 $377 $371 $365 $360

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 

Table 2-146 Costs for Downsizing from an OHC V8 to an OHC V6 

Gasoline HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Downsizing from OHC 

V8 to OHC V6 

DMC -$236 -$232 -$227 -$223 -$218 -$216 -$214

Downsizing from OHC 

V8 to OHC V6 

IC $112 $112 $111 $111 $83 $83 $83

Downsizing from OHC 

V8 to OHC V6 

TC -$125 -$120 -$116 -$111 -$135 -$133 -$131

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.3 Transmissions 

2.12.3.1 Adding additional gears (vocational) 

We have estimated the cost of adding 2 additional gears for vocational vehicles 

(light/medium HD, heavy HD urban/multipurpose) based on the light-duty cost for an 8 speed 

automatic transmission relative to a 6 speed automatic of $78 (DMC, 2010$, in 2012).R We have 

scaled that value by typical torque values of 2000 foot-pounds for vocational and 332 for a lightduty truck. With updates to 2012$, this DMC for vocational vehicles becomes $486 (DMC, 

2012$, in 2012). We consider this technology to be on the flat portion of the learning curve 

(curve 7) and have applied a medium complexity ICM with short term markups through 2018. 

The resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-147 and Table 2-148 for vocational vehicles. 

 R This cost was updated by FEV in early 2013. We are using the updated cost here, not the value used in the lightduty 2017-2025 final rule. 
2-227 

Table 2-147 Costs for Adding 2 Gears to an Automatic Transmission 

Vocational Light/Medium/Heavy HD Urban/Multipurpose Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Adding additional gears DMC $413 $405 $397 $389 $381 $374 $366 $359 $355 $352

Adding additional gears IC $143 $107 $107 $106 $106 $106 $105 $105 $105 $105

Adding additional gears TC $557 $512 $504 $495 $487 $479 $472 $464 $460 $457

Adding additional gears Alt 1a 

Adding additional gears Alt 3 5% 5% 5% 5% 5% 5% 5%

Adding additional gears TCp $0 $0 $0 $25 $24 $24 $24 $23 $23 $23

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-148 Costs for Adding 2 Gears to an Automatic Transmission 

Vocational Light/Medium HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Adding additional gears DMC $413 $405 $397 $389 $381 $374 $366 $359 $355 $352

Adding additional gears IC $143 $107 $107 $106 $106 $106 $105 $105 $105 $105

Adding additional gears TC $557 $512 $504 $495 $487 $479 $472 $464 $460 $457

Adding additional gears Alt 1a 

Adding additional gears Alt 3 5% 5% 5% 5% 5% 5% 10%

Adding additional gears TCp $0 $0 $0 $25 $24 $24 $24 $23 $23 $46

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.3.2 Automated manual transmissions (AMT) 

We have estimated the cost of an AMT transmission, relative to a manual transmission, 

based on an estimate by Tetra Tech of $5,100 (retail, 2013$). Using that estimate, we divided by 

an RPE of 1.36 and converted to 2012$ to arrive at an estimated cost of $3694 (DMC, 2012$, in 

2018). We consider this technology to be on the flat portion of the learning curve (curve 12) and 

have applied a medium complexity ICM with short term markups through 2022. The resultant 

technology costs, adoption rates and total cost applied to the package are shown in Table 2-149 

for vocational vehicles and in Table 2-150 for tractors. 
2-228 

Table 2-149 Costs for an AMT Transmission 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to AMT DMC $3,694 $3,583 $3,476 $3,372 $3,304 $3,238 $3,173 $3,110 $3,048 $2,987

Manual to AMT IC $1,117 $1,111 $1,106 $1,101 $1,097 $818 $815 $813 $811 $809

Manual to AMT TC $4,811 $4,695 $4,582 $4,472 $4,401 $4,056 $3,989 $3,923 $3,859 $3,795

Manual to AMT Alt 

1a 

 

Manual to AMT Alt 3 22% 22% 22% 33% 33% 33% 25%

Manual to AMT TCp $0 $0 $0 $984 $968 $892 $1,316 $1,295 $1,273 $949

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

Table 2-150 Costs for an AMT Transmission 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to AMT DMC $3,694 $3,583 $3,476 $3,372 $3,304 $3,238 $3,173 $3,110 $3,048 $2,987

Manual to AMT IC $1,117 $1,111 $1,106 $1,101 $1,097 $818 $815 $813 $811 $809

Manual to AMT TC $4,811 $4,695 $4,582 $4,472 $4,401 $4,056 $3,989 $3,923 $3,859 $3,795

Manual to AMT Alt 

1a 

 

Manual to AMT Alt 3 40% 40% 40% 50% 50% 50% 50%

Manual to AMT TCp $0 $0 $0 $1,789 $1,761 $1,622 $1,994 $1,961 $1,929 $1,898

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.3.3 Automatic Transmission Powershift 

We have estimated the cost of a powershift automatic transmission, relative to a manual 

transmission, based on an estimate by Tetra Tech of $15000 (retail, 2013$). Using that estimate, 

we divided by an RPE of 1.36 and converted to 2012$ to arrive at an estimated cost of $11670 

(DMC, 2012$, in 2018). We consider this technology to be on the flat portion of the learning 

curve (curve 12) and have applied a medium complexity ICM with short term markups through 

2022. The resultant technology costs, adoption rates and total cost applied to the package are 

shown in Table 2-151 for tractors. 
2-229 

Table 2-151 Costs for a Powershift Automatic Transmission 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to AT 

powershift 

DMC $11,670 $11,320 $10,981 $10,651 $10,438 $10,229 $10,025 $9,824 $9,628 $9,435

Manual to AT 

powershift 

IC $3,528 $3,511 $3,493 $3,477 $3,466 $2,583 $2,576 $2,569 $2,562 $2,555

Manual to AT 

powershift 

TC $15,199 $14,831 $14,474 $14,128 $13,904 $12,812 $12,601 $12,393 $12,190 $11,990

Manual to AT 

powershift 

Alt 

1a 

 

Manual to AT 

powershift 

Alt 3 10% 10% 10% 20% 20% 20% 30%

Manual to AT 

powershift 

TCp $0 $0 $0 $1,413 $1,390 $1,281 $2,520 $2,479 $2,438 $3,597

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.3.4 Dual-clutch transmissions (DCT) 

For vocational light/medium HD vehicles and for heavy HD urban and multipurpose 

vehicles, we estimate the cost of a move to a DCT from an automatic transmission to be cost 

neutral. For vocational heavy HD regional vehicles, we have estimated the cost of a DCT, 

relative to a manual transmission, as being equal to the move from a manual transmission to an 

AMT or $3694 (DMC, 2012$, in 2018, see 2.12.3.2 above). We consider this technology to be 

on the flat portion of the learning curve (curve 12) and have applied a medium complexity ICM 

with short term markups through 2022. The resultant technology costs, adoption rates and total 

cost applied to the package are shown in Table 2-154. 

Table 2-152 Costs for a Dual Clutch Transmission (DCT) 

Vocational Light/Medium/Heavy HD Urban/Multipurpose Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Auto trans to DCT DMC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT IC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT TC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT Alt 1a 

Auto trans to DCT Alt 3 5% 5% 5% 15% 15% 15% 5%

Auto trans to DCT TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-230 

Table 2-153 Costs for a Dual Clutch Transmission (DCT) 

Vocational Light/Medium HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Auto trans to DCT DMC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT IC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT TC $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Auto trans to DCT Alt 1a 

Auto trans to DCT Alt 3 5% 5% 5% 15% 15% 15% 10%

Auto trans to DCT TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-154 Costs for a Dual Clutch Transmission (DCT) 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to DCT DMC $3,694 $3,583 $3,476 $3,372 $3,304 $3,238 $3,173 $3,110 $3,048 $2,987

Manual to DCT IC $1,117 $1,111 $1,106 $1,101 $1,097 $818 $815 $813 $811 $809

Manual to DCT TC $4,811 $4,695 $4,582 $4,472 $4,401 $4,056 $3,989 $3,923 $3,859 $3,795

Manual to DCT Alt 

1a 

 

Manual to DCT Alt 3 22% 22% 22% 33% 33% 33% 25%

Manual to DCT TCp $0 $0 $0 $984 $968 $892 $1,316 $1,295 $1,273 $949

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For tractors, we have based our estimated cost of a DCT relative to a manual transmission 

on a Tetra Tech estimate of $17,500 (retail, 2013$). Using that estimate, we divided by an RPE 

of 1.36 and converted to 2012$ to arrive at an estimated cost of $12676 (DMC, 2012$, in 2018). 

We consider this technology to be on the flat portion of the learning curve (curve 12) and have 

applied a medium complexity ICM with short term markups through 2022. The resultant 

technology costs, adoption rates and total cost applied to the package are shown in Table 2-155 

for tractors. 

Table 2-155 Costs for a Dual Clutch Transmission (DCT) 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to DCT DMC $12,676 $12,296 $11,927 $11,569 $11,338 $11,111 $10,889 $10,671 $10,458 $10,248

Manual to DCT IC $3,832 $3,813 $3,794 $3,776 $3,765 $2,806 $2,798 $2,790 $2,783 $2,775

Manual to DCT TC $16,509 $16,109 $15,721 $15,346 $15,102 $13,917 $13,687 $13,461 $13,240 $13,024

Manual to DCT Alt 

1a 

 

Manual to DCT Alt 3 5% 5% 5% 10% 10% 10% 10%

Manual to DCT TCp $0 $0 $0 $767 $755 $696 $1,369 $1,346 $1,324 $1,302

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption
2-231 

2.12.3.5 Improved transmissions – vocational vehicles 

For this technology, we have relied on our light-duty technologies referred to as high 

efficiency gearbox (HEG), aggressive shift logic (ASL1) and early torque converter lockup 

(TORQ). Each of those technologies was estimated at $202, $27 and $25 (all are DMC, in 

2010$, in 2015 or 2017 (HEG)). For this analysis, we have used those estimates for ASL1 and 

TORQ, but have scaled upward the cost of HEG by 25 percent to account for differences 

between light-duty and HD. Converting to 2012$ results in costs for this technology of $316 

(DMC, 2012$, in 2021). We consider this technology to be on the flat portion of the learning 

curve (curve 13) and have applied a low complexity ICM with short term markups through 2022. 

The resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-156 for vocational vehicles. 

Table 2-156 Costs of Improved Transmissions 

All Vocational HD Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Improved trans DMC $346 $336 $326 $316 $307 $297 $288 $283 $277 $271

Improved trans IC $57 $57 $57 $56 $56 $44 $44 $44 $44 $44

Improved trans TC $403 $393 $382 $372 $363 $342 $333 $327 $321 $315

Improved trans Alt 1a 

Improved trans Alt 3 15% 15% 15% 30% 30% 30% 70%

Improved trans TCp $0 $0 $0 $56 $54 $51 $100 $98 $96 $221

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.3.6 Manual to automatic transmission, vocational heavy HD regional vehicles 

For this technology, we have estimated the cost as equal to the cost for moving from a 

manual transmission to an AMT, as presented above, but have considered that cost to be 

applicable to MY2012, or $3694 (DMC, 2012$, in 2012). We consider this technology to be on 

the flat portion of the learning curve (curve 7) and have applied a medium complexity ICM with 

short term markups through 2018. The resultant technology costs, adoption rates and total cost 

applied to the package are shown in Table 2-157. 
2-232 

Table 2-157 Cost for an Automatic Transmission 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Manual to auto 

trans 

DMC $3,141 $3,078 $3,017 $2,956 $2,897 $2,839 $2,782 $2,727 $2,699 $2,672

Manual to auto 

trans 

IC $1,089 $812 $810 $808 $806 $804 $802 $800 $799 $798

Manual to auto 

trans 

TC $4,230 $3,890 $3,826 $3,764 $3,703 $3,643 $3,584 $3,526 $3,498 $3,470

Manual to auto 

trans 

Alt 

1a 

 

Manual to auto 

trans 

Alt 3 22% 22% 22% 33% 33% 33% 25%

Manual to auto 

trans 

TCp $0 $0 $0 $828 $815 $801 $1,183 $1,164 $1,154 $868

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.3.7 8 speed transmission relative to a 6 speed, HD pickups & vans 

We have based the cost of this technology on several values used in the light-duty 2017-

2025 final rule. In that rule, we presented costs for 6 to 8 speed automatic transmission, high 

efficiency gearbox (HEG) and aggressive shift logic (ASL1) as separate technologies. Here we 

are treating these technologies as separate for costing (since some metrics differ for each) but 

considering them as being applied together as a complete group. As such, the cost for moving to 

an 8 speed transmission from the base 6 would always be the summation within any given year 

of the total costs shown in the tables that follow. For adding 2 gears, we have estimated the cost 

at $121 (DMC, 2012$, in 2012). We consider that technology to be on the flat portion of the 

learning curve (curve 7) and have applied medium complexity markups with near term markups 

through 2018. For HEG, we have estimated the cost at $263 (DMC, 2012$, in 2017). We 

consider this technology to be on the flat portion of the learning curve (curve 6) and have applied 

low complexity markups with near term markups through 2024. For shift logic, we have 

estimated the cost at $28 (DMC, 2012$, in 2015). We consider this technology to be on the flat 

portion of the learning curve (curve 8) and have applied low complexity markups with near term 

markups through 2018. The resultant costs for adding 2 gears are shown in Table 2-158, for 

HEG in Table 2-159 and for ASL1 in Table 2-160. 

Table 2-158 Costs to Add 2 Transmission Gears 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Move from 

6 to 8 gears 

DMC $97 $95 $93 $91 $89 $88 $88

Move from 

6 to 8 gears 

IC $34 $34 $34 $34 $34 $34 $34

Move from 

6 to 8 gears 

TC $131 $129 $127 $125 $123 $123 $122

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 
2-233 

Table 2-159 Costs for High Efficiency Gearbox (HEG) 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

High 

efficiency 

gearbox 

DMC $232 $225 $221 $217 $212 $208 $204

High 

efficiency 

gearbox 

IC $63 $63 $63 $63 $50 $50 $50

High 

efficiency 

gearbox 

TC $296 $288 $284 $279 $262 $258 $254

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 

Table 2-160 Costs for Aggressive Shift Logic Level 1 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Aggressive 

shift logic 1 

DMC $25 $24 $24 $23 $23 $22 $22

Aggressive 

shift logic 1 

IC $5 $5 $5 $5 $5 $5 $5

Aggressive 

shift logic 1 

TC $30 $30 $29 $29 $28 $28 $28

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 

Table 2-161 Complete Cost of Moving from the Base 6 Speed to 8 Speed Transmission 

2 Gears+HEG+ASL1 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Move from 6speed to 

8speed Transmission 

TC $457 $447 $440 $433 $414 $409 $403

Notes: TC=total cost. 

2.12.4 Air Conditioning 

2.12.4.1 Direct AC controls – vocational (all) 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$30 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $22 (DMC, 2012$, in 2014). We consider this technology to be on the flat 

portion of the learning curve (curve 2) and have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-162. 
2-234 

Table 2-162 Costs for Direct Air Conditioning Controls 

All Vocational HD Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

A/C direct DMC $19 $19 $18 $18 $18 $17 $17 $17 $17 $16

A/C direct IC $4 $4 $4 $4 $4 $3 $3 $3 $3 $3

A/C direct TC $23 $23 $22 $22 $22 $20 $20 $20 $20 $19

A/C direct Alt 1a 

A/C direct Alt 3 100% 100% 100% 100% 100% 100% 100%

A/C direct TCp $0 $0 $0 $22 $22 $20 $20 $20 $20 $19

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.4.2 Indirect AC controls – tractors (all) 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$218 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $158 (DMC, 2012$, in 2018). We consider this technology to be on the flat 

portion of the learning curve (curve 12) and have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-163. 

Table 2-163 Costs for Indirect AC Controls 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

A/C indirect DMC $158 $153 $148 $144 $141 $138 $135 $133 $130 $127

A/C indirect IC $28 $28 $28 $28 $28 $22 $22 $22 $22 $22

A/C indirect TC $186 $181 $176 $172 $169 $160 $157 $155 $152 $149

A/C indirect Alt 1a 

A/C indirect Alt 3 10% 10% 10% 20% 20% 20% 30%

A/C indirect TCp $0 $0 $0 $17 $17 $16 $31 $31 $30 $45

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.5 Axles 

2.12.5.1 6x2 Axle 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$250 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $181 (DMC, 2012$, in 2018). We consider this technology to be on the flat 

portion of the learning curve (curve 12) and have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-164 for vocational heavy HD regional vehicles, in Table 2-165 

for Class 8 tractors (day and sleeper cab) with low and mid roofs, and in Table 2-166 for Class 8 

tractors (day and sleeper cab) with high roofs. 
2-235 

Table 2-164 Costs for 6x2 Axles 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle 6x2 DMC $181 $176 $170 $165 $162 $159 $156 $152 $149 $146

Axle 6x2 IC $32 $32 $32 $32 $32 $25 $25 $25 $25 $25

Axle 6x2 TC $213 $208 $203 $197 $194 $184 $181 $178 $175 $172

Axle 6x2 Alt 1a 

Axle 6x2 Alt 3 23% 23% 23% 30% 30% 30% 30%

Axle 6x2 TCp $0 $0 $0 $44 $44 $41 $54 $53 $52 $51

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-165 Costs for 6x2 Axles 

Class 8 Day Cab Low and Sleeper Cab Low/Mid Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle 6x2 DMC $181 $176 $170 $165 $162 $159 $156 $152 $149 $146

Axle 6x2 IC $32 $32 $32 $32 $32 $25 $25 $25 $25 $25

Axle 6x2 TC $213 $208 $203 $197 $194 $184 $181 $178 $175 $172

Axle 6x2 Alt 1a 

Axle 6x2 Alt 3 10% 10% 10% 20% 20% 20% 20%

Axle 6x2 TCp $0 $0 $0 $20 $19 $18 $36 $36 $35 $34

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-166 Costs for 6x2 Axles 

Class 8 Day Cab and Sleeper Cab High Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle 6x2 DMC $181 $176 $170 $165 $162 $159 $156 $152 $149 $146

Axle 6x2 IC $32 $32 $32 $32 $32 $25 $25 $25 $25 $25

Axle 6x2 TC $213 $208 $203 $197 $194 $184 $181 $178 $175 $172

Axle 6x2 Alt 1a 

Axle 6x2 Alt 3 20% 20% 20% 60% 60% 60% 60%

Axle 6x2 TCp $0 $0 $0 $39 $39 $37 $108 $107 $105 $103

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.5.2 Axle disconnect 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$140 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $101 (DMC, 2012$, in all years). We consider this technology to be on the flat 

portion of the learning curve with no additional learning to occur (curve 1) and have applied a 

low complexity ICM with short term markups through 2022. The resultant technology costs, 

adoption rates and total cost applied to the package are shown in Table 2-167. 
2-236 

Table 2-167 Costs for Axle Disconnect 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle disconnect DMC $101 $101 $101 $101 $101 $101 $101 $101 $101 $101

Axle disconnect IC $18 $18 $18 $18 $18 $14 $14 $14 $14 $14

Axle disconnect TC $120 $120 $120 $120 $120 $116 $116 $116 $116 $116

Axle disconnect Alt 1a 

Axle disconnect Alt 3 46% 46% 46% 61% 30% 30% 30%

Axle disconnect TCp $0 $0 $0 $55 $55 $53 $71 $35 $35 $35

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.5.3 Axle downspeed 

We have estimated the cost of this technology based on engineering judgment at $50 

(DMC, 2013$, in 2018). This DMC is expected to cover development and some testing and 

integration work since there is no real hardware required for this technology. Converting this 

DMC to 2012$ results in a $49 cost (DMC, 2012$, in 2018). We consider this technology to be 

on the flat portion of the learning curve (curve 12) and have applied a low complexity ICM with 

short term markups through 2022. The resultant technology costs, adoption rates and total cost 

applied to the package are shown in Table 2-168. 

Table 2-168 Costs for Axle Downspeeding 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle downspeed DMC $49 $48 $46 $45 $44 $43 $42 $41 $41 $40

Axle downspeed IC $9 $9 $9 $9 $9 $7 $7 $7 $7 $7

Axle downspeed TC $58 $57 $55 $54 $53 $50 $49 $48 $47 $47

Axle downspeed Alt 1a 

Axle downspeed Alt 3 20% 20% 20% 40% 40% 40% 60%

Axle downspeed TCp $0 $0 $0 $11 $11 $10 $20 $19 $19 $28

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.5.4 Low friction axle lubes 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$250 (retail, 2013$), an estimate applicable to tractors having 3 axles. Using that estimate we 

divided by a 1.36 RPE and converted to 2012$ to arrive at a cost of $181 (DMC, 2012$, in 

2018). We consider this estimate to be applicable also to vocational HH vehicles since these 

generally have 3 axles. For vocational light/medium HD vehicles, which generally have 2 axles, 

we have estimated the DMC at 2/3 the vocational heavy HD/tractor cost, or $121 (DMC, 2012$, 

in 2018). We consider this technology to be on the flat portion of the learning curve (curve 12) 

and have applied a low complexity ICM with short term markups through 2022. The resultant 

technology costs, adoption rates and total cost applied to the package are shown in Table 2-169 

for vocational light and medium HD vehicles and in Table 2-170 for vocational heavy HD 

vehicles, and in Table 2-171 for tractors. 
2-237 

Table 2-169 Costs for Low Friction Axle Lubes 

Vocational Light/Medium HD Urban/Multipurpose/Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle low friction lubes DMC $121 $117 $114 $110 $108 $106 $104 $102 $100 $98

Axle low friction lubes IC $22 $22 $21 $21 $21 $17 $17 $17 $17 $17

Axle low friction lubes TC $142 $139 $135 $132 $129 $123 $121 $118 $116 $114

Axle low friction lubes Alt 1a 

Axle low friction lubes Alt 3 75% 75% 75% 75% 75% 75% 75%

Axle low friction lubes TCp $0 $0 $0 $99 $97 $92 $90 $89 $87 $86

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-170 Costs for Low Friction Axle Lubes 

Vocational Heavy HD Urban/Multipurpose/Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle low friction lubes DMC $181 $176 $170 $165 $162 $159 $156 $152 $149 $146

Axle low friction lubes IC $32 $32 $32 $32 $32 $25 $25 $25 $25 $25

Axle low friction lubes TC $213 $208 $203 $197 $194 $184 $181 $178 $175 $172

Axle low friction lubes Alt 1a 

Axle low friction lubes Alt 3 75% 75% 75% 75% 75% 75% 75%

Axle low friction lubes TCp $0 $0 $0 $148 $146 $138 $136 $133 $131 $129

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-171 Costs for Low Friction Axle Lubes 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Axle low friction lubes DMC $181 $176 $170 $165 $162 $159 $156 $152 $149 $146

Axle low friction lubes IC $32 $32 $32 $32 $32 $25 $25 $25 $25 $25

Axle low friction lubes TC $213 $208 $203 $197 $194 $184 $181 $178 $175 $172

Axle low friction lubes Alt 1a 

Axle low friction lubes Alt 3 20% 20% 20% 40% 40% 40% 40%

Axle low friction lubes TCp $0 $0 $0 $39 $39 $37 $72 $71 $70 $69

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.6 Idle Reduction 

2.12.6.1 Auxiliary power units (APU) 

We have estimated the cost of this technology based on the APU costs discussed in the 

Phase 1 rule. That technology was estimated at $4586 (DMC, 2008$, in 2014). With updates, 

that cost becomes $4853 (DMC, 2012$, in 2014) for Phase 2. We consider this technology to be 

on the flat portion of the learning curve (curve 2) and have applied a low complexity ICM with 

short term markups through 2022. The resultant technology costs, adoption rates and total cost 

applied to the package are shown in Table 2-172. 
2-238 

Table 2-172 Costs for Auxiliary Power Units (APU) 

Sleeper Cab Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

APU DMC $4,296 $4,210 $4,126 $4,043 $3,962 $3,883 $3,805 $3,729 $3,692 $3,655

APU IC $859 $857 $856 $855 $854 $674 $673 $673 $673 $672

APU TC $5,154 $5,067 $4,982 $4,899 $4,817 $4,557 $4,479 $4,402 $4,365 $4,327

APU Alt 

1a 

30% 30% 30% 30% 30% 30% 30% 30% 30% 30%

APU Alt 3 30% 30% 30% 70% 70% 70% 80% 90% 90% 90%

APU TCp $0 $0 $0 $1,959 $1,927 $1,823 $2,239 $2,641 $2,619 $2,596

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative

2.12.6.2 Neutral idle 

We have estimated the cost of this technology based on engineering judgment at $10 

(retail, 2013$). Using that estimate, we have divided by a 1.36 RPE and converted to 2012$ to 

arrive at a $7 cost (DMC, 2012$, in all years). This DMC is expected to cover development and 

some testing and integration work since there is no real hardware required for this technology. 

We consider this technology to be on the flat portion of the learning curve with no additional 

learning to occur (curve 1) and have applied a low complexity ICM with short term markups 

through 2022. The resultant technology costs, adoption rates and total cost applied to the package 

are shown in Table 2-173 and Table 2-174 for vocational vehicles. 

Table 2-173 Costs for Neutral Idle Technology 

Vocational Light/Medium HD Urban Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Neutral idle DMC $7 $7 $7 $7 $7 $7 $7 $7 $7 $7

Neutral idle IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Neutral idle TC $9 $9 $9 $9 $9 $8 $8 $8 $8 $8

Neutral idle Alt 1a 

Neutral idle Alt 3 70% 70% 70% 85% 85% 85% 25%

Neutral idle TCp $0 $0 $0 $6 $6 $6 $7 $7 $7 $2

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-174 Costs for Neutral Idle Technology 

Vocational Light/Medium/Heavy HD Multipurpose Vehicles and 

Vocational Light/Medium HD Regional Vehicles 

And Vocational Heavy HD Urban Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Neutral idle DMC $7 $7 $7 $7 $7 $7 $7 $7 $7 $7

Neutral idle IC $1 $1 $1 $1 $1 $1 $1 $1 $1 $1

Neutral idle TC $9 $9 $9 $9 $9 $8 $8 $8 $8 $8

Neutral idle Alt 1a 

Neutral idle Alt 3 70% 70% 70% 85% 85% 85% 30%

Neutral idle TCp $0 $0 $0 $6 $6 $6 $7 $7 $7 $2

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption
2-239 

2.12.6.3 Stop-start 

We have estimated the cost of this technology based on several cost estimates. First, an 

estimate from TetraTech of $700 (retail, 2013$) for gasoline HD pickups and vans and $1500 

(retail, 2013$) for diesel HD pickups and vans. Using these values, we divided by a 1.36 RPE 

and converted to 2012$ to arrive at $507 (DMC, 2012$, in 2021) and $1087 (DMC, 2012$, in 

2021) which were considered appropriate for vocational MH and HH vehicles, respectively. To 

these estimates, we have added the costs for improved accessories used for HD pickups and vans 

of $124 (DMC, 2012$, in 2015) which is based on values from the 2017-2025 light-duty FRM. 

However, to account for the heavier vocational vehicles relative to the HD pickup and vans, we 

have scaled upward the improved accessory value by 50 percent to arrive at a cost of $186 

(DMC, 2012$, in 2015). We have then added these values to arrive at costs of $693 (DMC, 

2012$, in 2021) and $1272 (DMC, 2012$, in 2021) and have applied the lower cost to vocational 

medium HD vehicles and the higher cost to vocational heavy HD vehicles. For vocational light 

HD, we have used the stop-start cost for the 2017-2025 rule for LD pickups ($377 DMC, 2012$, 

in 2015) but have scaled upward that value by 25 percent to account for the weight difference 

between the LD and vocational light HD vehicles. Doing this results in a cost of $471 (DMC, 

2012$, in 2021). Adding to that the $186 value for improved accessories mentioned earlier gives 

the resultant vocational light HD cost of $656 (DMC, 2012$, in 2021). We consider all of these 

technologies to be on the flat portion of the learning curve (curve 13) and have applied a medium 

complexity ICM with short term markups through 2022. The resultant technology costs, adoption 

rates and total cost applied to the package are shown in Table 2-175 for vocational light HD, 

Table 2-176 for vocational medium HD, and in Table 2-177 for vocational heavy HD vehicles. 

Table 2-175 Costs for Stop-start 

Vocational Light HD Urban/Multipurpose/Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Stop-start DMC $719 $698 $677 $656 $637 $618 $599 $587 $575 $564

Stop-start IC $202 $201 $200 $198 $197 $147 $146 $146 $145 $145

Stop-start TC $921 $898 $876 $855 $834 $764 $745 $733 $721 $709

Stop-start Alt 1a 

Stop-start Alt 3 5% 5% 5% 15% 15% 15% 70%

Stop-start TCp $0 $0 $0 $43 $42 $38 $112 $110 $108 $496

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-176 Costs for Stop-start 

Vocational Medium HD Urban/Multipurpose/Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Stop-start DMC $759 $736 $714 $693 $672 $652 $632 $620 $607 $595

Stop-start IC $213 $212 $211 $209 $208 $155 $154 $154 $153 $153

Stop-start TC $972 $948 $925 $902 $880 $807 $786 $773 $761 $748

Stop-start Alt 1a 

Stop-start Alt 3 5% 5% 5% 15% 15% 15% 70%

Stop-start TCp $0 $0 $0 $45 $44 $40 $118 $116 $114 $524

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 
2-240 

Table 2-177 Costs for Stop-start 

Vocational Heavy HD Urban/Multipurpose/Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Stop-start DMC $1,394 $1,352 $1,312 $1,272 $1,234 $1,197 $1,161 $1,138 $1,115 $1,093

Stop-start IC $391 $389 $387 $385 $383 $284 $283 $282 $282 $281

Stop-start TC $1,785 $1,741 $1,698 $1,657 $1,617 $1,482 $1,444 $1,420 $1,397 $1,374

Stop-start Alt 

1a 

 

Stop-start Alt 3 15% 15% 15% 70%

Stop-start TCp $0 $0 $0 $0 $0 $0 $217 $213 $210 $962

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For HD pickups and vans, we have based our costs for stop-start systems on the values 

used in the light-duty 2017-2025 final rule, but have scaled upward those costs by 25 percent to 

account for the larger and harder starting HD engines. Using this approach and converting to 

2012$ results in a cost of $471 (DMC, 2012$, in 2015). We consider this technology to be on the 

steep portion of the learning curve (curve 9, note the different year of cost-applicability relative 

to the vocational cost discussed above) and have applied medium complexity markups with near 

term markups through 2018. The resultant costs for HD pickups and vans are shown in Table 

2-178. 

Table 2-178 Costs of Stop-start 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Stop-start DMC $404 $392 $380 $369 $358 $351 $344

Stop-start IC $134 $134 $134 $133 $133 $133 $132

Stop-start TC $539 $526 $514 $502 $491 $483 $476

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.7 Electrification (strong/mild HEV, full EV) 

2.12.7.1 Strong hybrid electric vehicle (strong HEV) 

We have estimated the cost of this technology using the costs estimated in the 2017-2025 

light-duty rule for a light-duty pickup strong HEV. There we estimated the cost at $2729 (DMC, 

2010$, in 2021) for a LD truck with a 5200 pound curb weight. We have then scaled upward that 

value using the ratio of test weights for HD pickups in our MY2014 market file (8739 pounds) to 

the test weight of the 5200 pound LD truck (5500 pounds). The resultant strong hybrid costs 

become $4335 (DMC, 2012$, in 2021) for HD pickups and vans. We consider this technology to 

be on the steep portion of the learning curve today but on the flat portion by 2021 (curve 11) and 

have applied high complexity level 1 with short term markups through 2024. The resultant 

technology costs are shown in Table 2-179 for HD pickups and vans. 
2-241 

Table 2-179 Costs of Strong Hybrid 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Strong HEV DMC $4,335 $4,205 $4,079 $3,957 $3,838 $3,723 $3,648

Strong HEV IC $2,443 $2,435 $2,427 $2,419 $1,482 $1,478 $1,476

Strong HEV TC $6,779 $6,640 $6,506 $6,376 $5,320 $5,201 $5,124

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

For vocational vehicle strong hybrids, we have scaled upward from the HD pickup and 

van values using best estimates of curb weights. For vocational vehicles, we have used curb 

weights of 16,000 for light HD, 25,150 for medium HD and 42,000 for heavy HD relative to a 

6500 pound value for HD pickups. Scaling based on curb weight here should provide an 

acceptable scaling of costs with battery and motor sizes since those are generally directly 

correlated with the weight of the vehicle itself. Using these scaling factors results in costs for 

complete hybrid systems for light, medium and heavy HD, respectively, of $10,672, $16,774 and 

$28,013 (DMC, 2012$, in 2021). We consider this technology to be on the steep portion of the 

learning curve today but on the flat portion by 2021 (curve 11) and have applied high complexity 

level 1 with short term markups through 2022. The resultant technology costs, adoption rates and 

total cost applied to the package are shown are shown in Table 2-180 for light HD, in Table 

2-181 for medium HD and in Table 2-182 for heavy HD vocational vehicles. 

Table 2-180 Costs for Strong Hybrid 

Vocational Light HD Urban/Multipurpose Vehicles (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Strong 

HEV 

DMC $16,674 $13,340 $13,340 $10,672 $10,351 $10,041 $9,740 $9,448 $9,164 $8,981

Strong 

HEV 

IC $4,975 $4,731 $4,731 $4,536 $4,512 $2,849 $2,838 $2,827 $2,817 $2,810

Strong 

HEV 

TC $21,649 $18,070 $18,070 $15,207 $14,864 $12,890 $12,578 $12,275 $11,981 $11,791

Strong 

HEV 

Alt 

1a 

 

Strong 

HEV 

Alt 3 4% 4% 4% 7% 7% 7% 18%

Strong 

HEV 

TCp $0 $0 $0 $547 $535 $464 $906 $884 $863 $2,122

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-242 

Table 2-181 Costs for Strong Hybrid 

Vocational Medium HD Urban/Multipurpose Vehicles (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Strong 

HEV 

DMC $26,210 $20,968 $20,968 $16,774 $16,271 $15,783 $15,310 $14,850 $14,405 $14,117

Strong 

HEV 

IC $7,820 $7,436 $7,436 $7,129 $7,093 $4,478 $4,461 $4,444 $4,428 $4,418

Strong 

HEV 

TC $34,030 $28,404 $28,404 $23,904 $23,364 $20,262 $19,771 $19,295 $18,833 $18,534

Strong 

HEV 

Alt 

1a 

 

Strong 

HEV 

Alt 3 4% 4% 4% 7% 7% 7% 18%

Strong 

HEV 

TCp $0 $0 $0 $861 $841 $729 $1,424 $1,389 $1,356 $3,336

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-182 Costs for Strong Hybrid 

Vocational Heavy HD Urban/Multipurpose Vehicles (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Strong 

HEV 

DMC $43,770 $35,016 $35,016 $28,013 $27,173 $26,357 $25,567 $24,800 $24,056 $23,575

Strong 

HEV 

IC $13,059 $12,418 $12,418 $11,906 $11,844 $7,479 $7,450 $7,422 $7,395 $7,377

Strong 

HEV 

TC $56,829 $47,435 $47,435 $39,919 $39,017 $33,836 $33,017 $32,222 $31,451 $30,952

Strong 

HEV 

Alt 

1a 

 

Strong 

HEV 

Alt 3 4% 4% 4% 7% 7% 7% 18%

Strong 

HEV 

TCp $0 $0 $0 $1,437 $1,405 $1,218 $2,377 $2,320 $2,264 $5,571

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.7.2 Mild hybrid electric vehicle (mild HEV) 

We have estimated the cost of this technology using the costs estimated in the 2017-2025 

light-duty rule for a light-duty pickup mild HEV. There we estimated the cost at $983 (DMC, 

2010$, in 2021) for a LD truck with a 3500 pound curb weight. We have then scaled upward that 

value using the ratio of curb weights for HD pickups of 6500 pounds to the 3500 pound curb 

weight. The resultant mild hybrid costs become $1894 (DMC, 2012$, in 2017) for HD pickups 

and vans. We consider this technology to be on the flat portion of the learning curve (curve 6) 

and have applied high complexity level 1 with short term markups through 2024. The resultant 

technology costs are shown in Table 2-183 for HD pickups and vans. 
2-243 

Table 2-183 Costs of Mild Hybrid 

HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Mild HEV DMC $1,677 $1,626 $1,594 $1,562 $1,531 $1,500 $1,470

Mild HEV IC $1,053 $1,050 $1,048 $1,046 $643 $642 $641

Mild HEV TC $2,730 $2,677 $2,642 $2,608 $2,173 $2,142 $2,111

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

For tractors, we have estimated the cost of this technology based on an estimate from 

TetraTech of $20,000 (retail, 2013$). Using that value, we divided by a 1.36 RPE and converted 

to 2012$ to arrive at a cost of $14,487 (DMC, 2012$, in 2018). We consider this technology to 

be on the flat portion of the learning curve (curve 12) and have applied a high complexity level 1 

markup with short term markups through 2025. The resultant technology costs are shown in 

Table 2-184 (note that this technology is not expected to be used so the application rate is 0%). 

Table 2-184 Costs for Mild Hybrid 

Tractors (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Mild 

HEV 

DMC $14,487 $14,052 $13,631 $13,222 $12,958 $12,698 $12,444 $12,196 $11,952 $11,713

Mild 

HEV 

IC $6,157 $6,125 $6,094 $6,065 $6,045 $6,026 $6,008 $5,989 $3,806 $3,798

Mild 

HEV 

TC $20,644 $20,178 $19,725 $19,287 $19,003 $18,725 $18,452 $18,185 $15,758 $15,510

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative

2.12.7.3 Full electric vehicle (Full EV) 

For vocational vehicle full EVs, we have used an estimate of $77888 (retail, 2013$) 

based on an estimate from The West Coast Collaborative.175 Using that value, we divided by a 

1.36 RPE and converted to 2012$ to arrive at a cost of $56,418 (DMC, 2012$, in 2014). We 

consider this technology to be on the steep portion of the learning curve (curve 4) and have 

applied a high complexity level 1 markup with short term markups through 2028. The resultant 

technology costs are shown in Table 2-185. 

Table 2-185 Costs of Full Electric Vehicle 

Vocational Light/Medium HD (Urban/Multipurpose/Regional) Vehicles (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Full 

EV 

DMC $36,108 $35,025 $33,974 $32,955 $31,966 $31,007 $30,077 $29,174 $28,591 $28,019

Full 

EV 

IC $22,492 $22,413 $22,336 $22,262 $22,189 $22,119 $22,051 $21,985 $21,942 $21,900

Full 

EV 

TC $58,600 $57,438 $56,310 $55,216 $54,155 $53,126 $52,128 $51,159 $50,533 $49,920

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 

For day cab tractor full EVs, we have used an estimate of $203,000 (retail, 2012$) based 

on an estimate from the California Energy Commission. Using that value, we divided by a 1.36 
2-244 

RPE to arrive at a cost of $149,265 (DMC, 2012$, in 2014). We consider this technology to be 

on the steep portion of the learning curve (curve 4) and have applied a high complexity level 1 

markup with short term markups through 2028. The resultant technology costs are shown in 

Table 2-186. 

Table 2-186 Costs for Full Electric Vehicle 

Day Cab Tractors (2012$) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Full 

EV 

DMC $95,529 $92,664 $89,884 $87,187 $84,572 $82,034 $79,573 $77,186 $75,642 $74,130

Full 

EV 

IC $59,507 $59,297 $59,094 $58,897 $58,705 $58,520 $58,340 $58,165 $58,052 $57,941

Full 

EV 

TC $155,036 $151,961 $148,978 $146,084 $143,277 $140,554 $137,913 $135,351 $133,694 $132,071

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.8 Tires 

2.12.8.1 Lower rolling resistance tires ($/tire) 

We have estimated the cost of lower rolling resistance tires based on an estimate from 

TetraTech of $30 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 

2012$ to arrive at a cost of $22 (DMC, 2012$) but consider that cost valid in different years 

depending on the level of rolling resistance. For LRR tires level 1 and 2, we consider that $22 

value valid in 2014, level 3 in 2018, and level 4 in 2021. We consider this technology to be on 

the flat portion of the curve with LRR tires level 1 and 2 on curve 4, LRR tires level 3 on curve 

12 and LRR tires level 4 on curve 13. We have applied a low complexity markup to LRR tires 

levels 1 and 3 with short term markups through 2022. For LRR tires level 3, we have applied a 

medium complexity markup with short term markups through 2025 and, for LRR tires level 4, 

we have applied a medium complexity markup with short term markups through 2028. As a 

result, despite using the same DMC for each level of rolling resistance, our tire costs can vary 

considerably year-over-year for each of the 4 levels of rolling resistance considered. The 

resultant costs on a per-tire basis are shown in Table 2-187. Table 2-188 through Table 2-202 

show the costs per vehicle depending on the number of tires present on the vehicle. 
2-245 

Table 2-187 Costs for Lower Rolling Resistance Tires 

at each LRR level (2012$/tire) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

DMC $19 $19 $18 $18 $18 $17 $17 $17 $17 $16

LRR – 

level 2 

DMC $19 $19 $18 $18 $18 $17 $17 $17 $17 $16

LRR – 

level 3 

DMC $22 $21 $20 $20 $19 $19 $19 $18 $18 $18

LRR – 

level 4 

DMC $24 $23 $22 $22 $21 $20 $20 $19 $19 $19

LRR – 

level 1 

IC $4 $4 $4 $4 $4 $3 $3 $3 $3 $3

LRR – 

level 2 

IC $4 $4 $4 $4 $4 $3 $3 $3 $3 $3

LRR – 

level 3 

IC $7 $7 $7 $6 $6 $6 $6 $6 $5 $5

LRR – 

level 4 

IC $7 $7 $7 $7 $7 $7 $6 $6 $6 $6

LRR – 

level 1 

TC $23 $23 $22 $22 $22 $20 $20 $20 $20 $19

LRR – 

level 2 

TC $23 $23 $22 $22 $22 $20 $20 $20 $20 $19

LRR – 

level 3 

TC $28 $28 $27 $26 $26 $25 $25 $25 $23 $22

LRR – 

level 4 

TC $30 $30 $29 $28 $28 $27 $26 $26 $25 $25

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.8.2 Lower RR steer tires, All Vocational Vehicles 

Table 2-188 Costs for Lower Rolling Resistance Steer Tires 

All Vocational Vehicles 

(2012$/vehicle @ 2 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 2 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 3 

TC $57 $55 $54 $53 $52 $51 $50 $49 $45 $45

LRR – 

level 4 

TC $61 $59 $58 $57 $55 $54 $53 $52 $51 $50

LRR – 

level 1 

Alt 

1a 

100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

LRR – 

level 2 

Alt 

1a 

 

LRR – 

level 3 

Alt 

1a 

LRR – 

level 4 

Alt 

1a 

LRR – Alt 3 100% 100% 100% 20% 20% 20% 10% 10% 10% 0%
2-246 

level 1 

LRR – 

level 2 

Alt 3 

LRR – 

level 3 

Alt 3 80% 80% 80% 30% 30% 30% 20%

LRR – 

level 4 

Alt 3 60% 60% 60% 80%

LRR – 

level 1 

TCp $0 $0 $0 -$35 -$35 -$33 -$36 -$35 -$35 -$39

LRR – 

level 2 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

LRR – 

level 3 

TCp $0 $0 $0 $42 $41 $41 $15 $15 $14 $9

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $32 $31 $31 $40

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.3 Lower RR drive tires, All Vocational Light/Medium HD Vehicles 

Table 2-189 Costs for Lower Rolling Resistance Drive Tires 

Vocational Light/Medium HD Vehicles 

(2012$/vehicle @ 4 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 2 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 3 

TC $113 $110 $108 $105 $104 $102 $100 $99 $91 $89

LRR – 

level 4 

TC $122 $119 $116 $113 $110 $108 $105 $104 $102 $100

LRR – 

level 1 

Alt 

1a 

100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

LRR – 

level 2 

Alt 

1a 

 

LRR – 

level 3 

Alt 

1a 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 100% 100% 100% 50% 50% 50% 20% 20% 20% 10%

LRR – 

level 2 

Alt 3 50% 50% 50% 50% 50% 50% 25%

LRR – 

level 3 

Alt 3 30% 30% 30% 50%

LRR – 

level 4 

Alt 3 15%

LRR – 

level 1 

TCp $0 $0 $0 -$44 -$43 -$41 -$64 -$63 -$63 -$70

LRR – 

level 2 

TCp $0 $0 $0 $44 $43 $41 $40 $39 $39 $19

LRR – TCp $0 $0 $0 $0 $0 $0 $30 $30 $27 $45
2-247 

level 3 

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $15

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.4 Lower RR drive tires, Vocational Heavy HD Vehicles 

Table 2-190 Costs for Lower Rolling Resistance Drive Tires 

Vocational Heavy HD Vehicles 

(2012$/vehicle @ 8 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 2 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 3 

TC $226 $221 $216 $210 $207 $204 $201 $198 $182 $179

LRR – 

level 4 

TC $244 $238 $232 $226 $221 $216 $210 $207 $204 $201

LRR – 

level 1 

Alt 

1a 

100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

LRR – 

level 2 

Alt 

1a 

 

LRR – 

level 3 

Alt 

1a 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 100% 100% 100% 50% 50% 50% 20% 20% 20% 10%

LRR – 

level 2 

Alt 3 50% 50% 50% 50% 50% 50% 25%

LRR – 

level 3 

Alt 3 30% 30% 30% 50%

LRR – 

level 4 

Alt 3 15%

LRR – 

level 1 

TCp $0 $0 $0 -$88 -$86 -$82 -$128 -$126 -$125 -$140

LRR – 

level 2 

TCp $0 $0 $0 $88 $86 $82 $80 $79 $78 $39

LRR – 

level 3 

TCp $0 $0 $0 $0 $0 $0 $60 $59 $54 $89

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $30

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-248 

2.12.8.5 Lower RR steer tires, Day cab low roof tractors 

Table 2-191 Costs for Lower Rolling Resistance Steer Tires 

Day Cab Low Roof Tractors 

(2012$/vehicle @ 2 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 2 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 3 

TC $57 $55 $54 $53 $52 $51 $50 $49 $45 $45

LRR – 

level 4 

TC $61 $59 $58 $57 $55 $54 $53 $52 $51 $50

LRR – 

level 1 

Alt 

1a 

50% 50% 50% 50% 50% 50% 50% 50% 50% 50%

LRR – 

level 2 

Alt 

1a 

10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 50% 50% 50% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 10% 10% 10% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 $4 $4 $4 $0 $0 $0 -$12

LRR – 

level 2 

TCp $0 $0 $0 $7 $6 $6 $8 $8 $8 $16

LRR – 

level 3 

TCp $0 $0 $0 $5 $5 $5 $8 $7 $7 $11

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.6 Lower RR steer tires, Day cab high roof tractors 

Table 2-192 Costs for Lower Rolling Resistance Steer Tires 

Day Cab High Roof Tractors 

(2012$/vehicle @ 2 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 2 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – TC $57 $55 $54 $53 $52 $51 $50 $49 $45 $45
2-249 

level 3 

LRR – 

level 4 

TC $61 $59 $58 $57 $55 $54 $53 $52 $51 $50

LRR – 

level 1 

Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

LRR – 

level 2 

Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 70% 70% 70% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 20% 20% 20% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 -$4 -$4 -$4 -$8 -$8 -$8 -$19

LRR – 

level 2 

TCp $0 $0 $0 $2 $2 $2 $4 $4 $4 $12

LRR – 

level 3 

TCp $0 $0 $0 $5 $5 $5 $8 $7 $7 $11

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.7 Lower RR steer tires, Sleeper cab low/mid roof tractors 

Table 2-193 Costs for Lower Rolling Resistance Steer Tires 

Sleeper Cab Low/Mid Roof Tractors 

(2012$/vehicle @ 2 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 2 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 3 

TC $57 $55 $54 $53 $52 $51 $50 $49 $45 $45

LRR – 

level 4 

TC $61 $59 $58 $57 $55 $54 $53 $52 $51 $50

LRR – 

level 1 

Alt 

1a 

60% 60% 60% 60% 60% 60% 60% 60% 60% 60%

LRR – 

level 2 

Alt 

1a 

10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – Alt 3 60% 60% 60% 60% 60% 60% 50% 50% 50% 20%
2-250 

level 1 

LRR – 

level 2 

Alt 3 10% 10% 10% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 $0 $0 $0 -$4 -$4 -$4 -$16

LRR – 

level 2 

TCp $0 $0 $0 $7 $6 $6 $8 $8 $8 $16

LRR – 

level 3 

TCp $0 $0 $0 $5 $5 $5 $8 $7 $7 $11

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.8 Lower RR steer tires, Sleeper cab high roof tractors 

Table 2-194 Costs for Lower Rolling Resistance Steer Tires 

Sleeper Cab High Roof Tractors 

(2012$/vehicle @ 2 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 2 

TC $46 $45 $45 $44 $43 $41 $40 $39 $39 $39

LRR – 

level 3 

TC $57 $55 $54 $53 $52 $51 $50 $49 $45 $45

LRR – 

level 4 

TC $61 $59 $58 $57 $55 $54 $53 $52 $51 $50

LRR – 

level 1 

Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

LRR – 

level 2 

Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 70% 70% 70% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 20% 20% 20% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 -$4 -$4 -$4 -$8 -$8 -$8 -$19

LRR – 

level 2 

TCp $0 $0 $0 $2 $2 $2 $4 $4 $4 $12

LRR – TCp $0 $0 $0 $5 $5 $5 $8 $7 $7 $11
2-251 

level 3 

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.9 Lower RR drive tires, Class 7 Day cab low roof tractors 

Table 2-195 Costs for Lower Rolling Resistance Drive Tires 

Class 7 Day Cab Low Roof Tractors 

(2012$/vehicle @ 4 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 2 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 3 

TC $113 $110 $108 $105 $104 $102 $100 $99 $91 $89

LRR – 

level 4 

TC $122 $119 $116 $113 $110 $108 $105 $104 $102 $100

LRR – 

level 1 

Alt 

1a 

50% 50% 50% 50% 50% 50% 50% 50% 50% 50%

LRR – 

level 2 

Alt 

1a 

10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 50% 50% 50% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 10% 10% 10% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 $9 $9 $8 $0 $0 $0 -$23

LRR – 

level 2 

TCp $0 $0 $0 $13 $13 $12 $16 $16 $16 $31

LRR – 

level 3 

TCp $0 $0 $0 $11 $10 $10 $15 $15 $14 $22

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-252 

2.12.8.10 Lower RR drive tires, Class 8 Day cab low roof tractors 

Table 2-196 Costs for Lower Rolling Resistance Drive Tires 

Class 8 Day Cab Low Roof Tractors 

(2012$/vehicle @ 8 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 2 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 3 

TC $226 $221 $216 $210 $207 $204 $201 $198 $182 $179

LRR – 

level 4 

TC $244 $238 $232 $226 $221 $216 $210 $207 $204 $201

LRR – 

level 1 

Alt 

1a 

50% 50% 50% 50% 50% 50% 50% 50% 50% 50%

LRR – 

level 2 

Alt 

1a 

10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 50% 50% 50% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 10% 10% 10% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 $18 $17 $16 $0 $0 $0 -$47

LRR – 

level 2 

TCp $0 $0 $0 $26 $26 $24 $32 $32 $31 $62

LRR – 

level 3 

TCp $0 $0 $0 $21 $21 $20 $30 $30 $27 $45

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-253 

2.12.8.11 Lower RR drive tires, Class 7 Day cab high roof tractors 

Table 2-197 Costs for Lower Rolling Resistance Drive Tires 

Class 7 Day Cab High Roof Tractors 

(2012$/vehicle @ 4 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 2 

TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR – 

level 3 

TC $113 $110 $108 $105 $104 $102 $100 $99 $91 $89

LRR – 

level 4 

TC $122 $119 $116 $113 $110 $108 $105 $104 $102 $100

LRR – 

level 1 

Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

LRR – 

level 2 

Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 70% 70% 70% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 20% 20% 20% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 -$9 -$9 -$8 -$16 -$16 -$16 -$39

LRR – 

level 2 

TCp $0 $0 $0 $4 $4 $4 $8 $8 $8 $23

LRR – 

level 3 

TCp $0 $0 $0 $11 $10 $10 $15 $15 $14 $22

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-254 

2.12.8.12 Lower RR drive tires, Class 8 Day cab high roof tractors 

Table 2-198 Costs for Lower Rolling Resistance Drive Tires 

Class 8 Day Cab High Roof Tractors 

(2012$/vehicle @ 8 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 2 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 3 

TC $226 $221 $216 $210 $207 $204 $201 $198 $182 $179

LRR – 

level 4 

TC $244 $238 $232 $226 $221 $216 $210 $207 $204 $201

LRR – 

level 1 

Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

LRR – 

level 2 

Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 70% 70% 70% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 20% 20% 20% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 -$18 -$17 -$16 -$32 -$32 -$31 -$78

LRR – 

level 2 

TCp $0 $0 $0 $9 $9 $8 $16 $16 $16 $47

LRR – 

level 3 

TCp $0 $0 $0 $21 $21 $20 $30 $30 $27 $45

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-255 

2.12.8.13 Lower RR drive tires, Class 8 Sleeper cab low/mid roof tractors 

Table 2-199 Costs for Lower Rolling Resistance Drive Tires 

Class 8 Sleeper Cab Low/Mid Roof Tractors 

(2012$/vehicle @ 8 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 2 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 3 

TC $226 $221 $216 $210 $207 $204 $201 $198 $182 $179

LRR – 

level 4 

TC $244 $238 $232 $226 $221 $216 $210 $207 $204 $201

LRR – 

level 1 

Alt 

1a 

60% 60% 60% 60% 60% 60% 60% 60% 60% 60%

LRR – 

level 2 

Alt 

1a 

10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 60% 60% 60% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 10% 10% 10% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 $0 $0 $0 -$16 -$16 -$16 -$62

LRR – 

level 2 

TCp $0 $0 $0 $26 $26 $24 $32 $32 $31 $62

LRR – 

level 3 

TCp $0 $0 $0 $21 $21 $20 $30 $30 $27 $45

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-256 

2.12.8.14 Lower RR drive tires, Class 8 Sleeper cab high roof tractors 

Table 2-200 Costs for Lower Rolling Resistance Drive Tires 

Class 8 Sleeper Cab High Roof Tractors 

(2012$/vehicle @ 8 tires/vehicle) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR – 

level 1 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 2 

TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR – 

level 3 

TC $226 $221 $216 $210 $207 $204 $201 $198 $182 $179

LRR – 

level 4 

TC $244 $238 $232 $226 $221 $216 $210 $207 $204 $201

LRR – 

level 1 

Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

LRR – 

level 2 

Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

LRR – 

level 3 

Alt 

1a 

 

LRR – 

level 4 

Alt 

1a 

LRR – 

level 1 

Alt 3 70% 70% 70% 60% 60% 60% 50% 50% 50% 20%

LRR – 

level 2 

Alt 3 20% 20% 20% 25% 25% 25% 30% 30% 30% 50%

LRR – 

level 3 

Alt 3 10% 10% 10% 15% 15% 15% 25%

LRR – 

level 4 

Alt 3 

LRR – 

level 1 

TCp $0 $0 $0 -$18 -$17 -$16 -$32 -$32 -$31 -$78

LRR – 

level 2 

TCp $0 $0 $0 $9 $9 $8 $16 $16 $16 $47

LRR – 

level 3 

TCp $0 $0 $0 $21 $21 $20 $30 $30 $27 $45

LRR – 

level 4 

TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-257 

2.12.8.15 Lower RR tires, 53-foot dry van & reefer 

Table 2-201 Costs for Lower Rolling Resistance Tires 

53-foot Dry Van & Reefer Highway Trailers 

(2012$/trailer @ 8 tires/trailer) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR–level 1 TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR–level 2 TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR–level 1 Alt 1a 50% 51% 52% 53% 54% 56% 57% 58% 59% 60%

LRR–level 2 Alt 1a 

LRR–level 1 Alt 3 85% 85% 85% 90% 90% 90% 

LRR–level 2 Alt 3 95% 95% 95% 95%

LRR–level 1 TCp $65 $62 $59 $65 $62 $56 -$91 -$91 -$92 -$93

LRR–level 2 TCp $0 $0 $0 $0 $0 $0 $152 $150 $149 $147

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption

2.12.8.16 Lower RR tires, 28-foot dry van 

Table 2-202 Costs for Lower Rolling Resistance Tires 

28-foot Dry Van Trailers 

(2012$/trailer @ 4 tires/trailer) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR–level 1 TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR–level 2 TC $92 $91 $89 $88 $86 $82 $80 $79 $78 $78

LRR–level 1 Alt 1a 

LRR–level 2 Alt 1a 

LRR–level 1 Alt 3 85% 85% 85% 90% 90% 90% 

LRR–level 2 Alt 3 95% 95% 95% 95%

LRR–level 1 TCp $78 $77 $76 $79 $78 $73 $0 $0 $0 $0

LRR–level 2 TCp $0 $0 $0 $0 $0 $0 $76 $75 $74 $74

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption 

2.12.8.17 Lower RR tires, Non-box highway trailers 

Table 2-203 Costs for Lower Rolling Resistance Tires 

Other Non-Box Highway Trailers 

(2012$/trailer @ 4 tires/trailer) 

ITEM 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

LRR–level 1 TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR–level 2 TC $185 $182 $178 $175 $173 $163 $160 $158 $156 $155

LRR–level 1 Alt 1a 

LRR–level 2 Alt 1a 

LRR–level 1 Alt 3 100% 100% 100% 100% 100% 100% 

LRR–level 2 Alt 3 100% 100% 100% 100%

LRR–level 1 TCp $185 $182 $178 $175 $173 $163 $0 $0 $0 $0

LRR–level 2 TCp $0 $0 $0 $0 $0 $0 $160 $158 $156 $155

Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote 

0% adoption
2-258 

2.12.8.18 Lower RR tires, HD pickup & van ($/tire) 

We have estimated the costs of lower rolling resistance tires for HD pickups and vans 

using the costs used in the 2017-2025 light-duty FRM. In that rule, we estimated the costs of 

lower rolling resistance tires level 1 at $5/vehicle including a spare (DMC, 2010$, in all years) 

and level 2 at $40/vehicle assuming no spare (DMC, 2010$, in 2021). For HD pickups and vans, 

we have scaled upward both of those costs by 50 percent to account for the heavier and larger 

HD tires. We consider the level 1 tires to be learned out (curve 1) and the level 2 tires to be on 

the steep portion of the curve until 2021 after which it is on the flatter portion of the curve (curve 

11). We have applied a low complexity markup to both with short term markups through 2018 

for level 1 and through 2024 for level 2. With the exception of the 50 percent scaling factor, all 

LRR tire costs for HD pickups and vans are identical to the 2017-2025 light-duty FRM. The 

resultant costs are presented in Table 2-204. 

Table 2-204 Costs for Lower Rolling Resistance Tires 

HD Pickups & Vans 

(2012$ @ 4 tires/vehicle) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

LRR – level 1 DMC $8 $8 $8 $8 $8 $8 $8

LRR – level 2 DMC $63 $61 $59 $58 $56 $54 $53

LRR – level 1 IC $2 $2 $2 $2 $2 $2 $2

LRR – level 2 IC $15 $15 $15 $15 $12 $12 $12

LRR – level 1 TC $10 $10 $10 $10 $10 $10 $10

LRR – level 2 TC $78 $76 $74 $73 $68 $66 $65

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.8.19 Automatic Tire Inflation Systems (ATIS) 

For tractors, we have estimated the cost of this technology based on an estimate from 

TetraTech of $1143 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted 

to 2012$ to arrive at a cost of $828 (DMC, 2012$, in 2018). We consider this technology to be 

on the flat portion of the learning curve (curve 12) and have applied a low complexity ICM with 

short term markups through 2022. The resultant technology costs, adoption rates and total cost 

applied to the package are shown in Table 2-205 for tractors. 

Table 2-205 Costs for Automatic Tire Inflation Systems 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

ATIS DMC $828 $803 $779 $755 $740 $725 $711 $697 $683 $669

ATIS IC $148 $147 $147 $147 $147 $115 $115 $115 $115 $115

ATIS TC $975 $950 $926 $902 $887 $841 $826 $812 $798 $784

ATIS Alt 1a 

ATIS Alt 3 20% 20% 20% 40% 40% 40% 40%

ATIS TCp $0 $0 $0 $180 $177 $168 $330 $325 $319 $314

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption
2-259 

For trailers, we have estimated the cost of this technology based on an estimate from 

TetraTech of $800 (retail, 2013$). We consider this estimate to be valid for all trailers except 

tandems. For tandems, we have used an estimate of $600 (retail, 2013$) since they have just one 

axle. Using these estimates we divided by a 1.36 RPE and converted to 2012$ to arrive at a cost 

of $579 (DMC, 2012$, in 2018) for all but tandems and $435 (DMC, 2012$, in 2018) for 

tandems. We consider this technology to be on the flat portion of the learning curve (curve 12) 

and have applied a low complexity ICM with short term markups through 2022. The resultant 

technology costs, adoption rates and total cost applied to the package are shown in Table 2-206 

for 53-foot dry and reefer vans, in Table 2-207 for 28-foot dry vans and in Table 2-208 for nonbox highway trailers.

Table 2-206 Costs for Automatic Tire Inflation Systems 

53-foot Dry and Reefer Van Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

ATIS DMC $579 $562 $545 $529 $518 $508 $498 $488 $478 $469

ATIS IC $103 $103 $103 $103 $103 $81 $81 $81 $81 $80

ATIS TC $683 $665 $648 $632 $621 $589 $578 $568 $559 $549

ATIS Alt 1a 50% 51% 52% 53% 54% 56% 57% 58% 59% 60%

ATIS Alt 3 85% 85% 85% 90% 90% 90% 95% 95% 95% 95%

ATIS TCp $239 $226 $214 $234 $224 $200 $220 $210 $201 $192

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative 

Table 2-207 Costs for Automatic Tire Inflation Systems 

28-foot Dry Van Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

ATIS DMC $435 $422 $409 $397 $389 $381 $373 $366 $359 $351

ATIS IC $78 $77 $77 $77 $77 $61 $60 $60 $60 $60

ATIS TC $512 $499 $486 $474 $466 $441 $434 $426 $419 $412

ATIS Alt 1a 

ATIS Alt 3 85% 85% 85% 90% 90% 90% 95% 95% 95% 95%

ATIS TCp $435 $424 $413 $426 $419 $397 $412 $405 $398 $391

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-208 Costs for Automatic Tire Inflation Systems 

Non-box Highway Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

ATIS DMC $579 $562 $545 $529 $518 $508 $498 $488 $478 $469

ATIS IC $103 $103 $103 $103 $103 $81 $81 $81 $81 $80

ATIS TC $683 $665 $648 $632 $621 $589 $578 $568 $559 $549

ATIS Alt 1a 

ATIS Alt 3 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

ATIS TCp $683 $665 $648 $632 $621 $589 $578 $568 $559 $549

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the 

package; alt=alternative; empty cells for adoption rates denote 0% adoption
2-260 

2.12.9 Aerodynamic Improvements (aero) 

The agencies’ estimates for cost of tractor aero features are based the work done by ICF 

in support of the Phase 1 HD rules. For trailers, we have based our estimates on the work 

presented in the ICCT trailer technology report.176

2.12.9.1 Aero improvements, Day cab low roof tractors 

For low roof day cab tractors, Aero Bin 2 costs are estimated at $1001, Bin 3 at $2022 

and Bin 4 at $2578 (all are DMC, in 2012$, and applicable in 2014). We consider Bin 2 

technologies to be beyond the effects of learning (curve 1), Bin 3 technologies to be on the flat 

portion of the curve (curve 2) and Bin 4 technologies to be on the steep portion of the curve 

(curve 4). We have applied a low complexity ICMs to each with short term markups through 

2022. The resultant technology costs, adoption rates and total cost applied to the package are 

shown in Table 2-209. 

Table 2-209 Costs of Aero Technologies 

Day Cab Low Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin2 DMC $1,001 $1,001 $1,001 $1,001 $1,001 $1,001 $1,001 $1,001 $1,001 $1,001

Aero Bin3 DMC $1,790 $1,755 $1,719 $1,685 $1,651 $1,618 $1,586 $1,554 $1,539 $1,523

Aero Bin4 DMC $1,650 $1,601 $1,553 $1,506 $1,461 $1,417 $1,375 $1,333 $1,307 $1,281

Aero Bin2 IC $179 $179 $179 $179 $179 $140 $140 $140 $140 $140

Aero Bin3 IC $358 $357 $357 $356 $356 $281 $281 $280 $280 $280

Aero Bin4 IC $448 $447 $447 $446 $445 $354 $354 $353 $353 $353

Aero Bin2 TC $1,180 $1,180 $1,180 $1,180 $1,180 $1,141 $1,141 $1,141 $1,141 $1,141

Aero Bin3 TC $2,148 $2,112 $2,076 $2,041 $2,007 $1,899 $1,867 $1,835 $1,819 $1,803

Aero Bin4 TC $2,098 $2,048 $1,999 $1,952 $1,906 $1,771 $1,728 $1,687 $1,660 $1,634

Aero Bin2 Alt 

1a 

60% 60% 60% 60% 60% 60% 60% 60% 60% 60%

Aero Bin3 Alt 

1a 

 

Aero Bin4 Alt 

1a 

Aero Bin2 Alt 3 60% 60% 60% 75% 75% 75% 60% 60% 60% 50%

Aero Bin3 Alt 3 25% 25% 25% 38% 38% 38% 40%

Aero Bin4 Alt 3 2% 2% 2% 10%

Aero Bin2 TCp $0 $0 $0 $177 $177 $171 $0 $0 $0 -$114

Aero Bin3 TCp $0 $0 $0 $510 $502 $475 $709 $697 $691 $721

Aero Bin4 TCp $0 $0 $0 $0 $0 $0 $35 $34 $33 $163

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.9.2 Aero improvements, Day cab high roof tractors 

For high roof day cab tractors, Aero Bin 3 costs are estimated at $1028, Bin 4 at $2049, 

Bin 5 at $2612, Bin 6 at $3176 and Bin 7 at $3739 (all are DMC, in 2012$, and applicable in 

2014). We consider Bin 3 technologies to be on the flat portion of the curve (curve 2) and Bin 4 

through 7 technologies to be on the steep portion of the curve (curve 4). We have applied a low 
2-261 

complexity ICMs to Bins 3 and 4 with short term markups through 2022. We have applied 

medium complexity ICMs to Bins 5 through 7 with short term markups through 2025. The 

resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-210. 

Table 2-210 Costs of Aero Technologies 

Day Cab High Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin3 DMC $910 $892 $874 $856 $839 $822 $806 $790 $782 $774

Aero Bin4 DMC $1,311 $1,272 $1,234 $1,197 $1,161 $1,126 $1,092 $1,059 $1,038 $1,017

Aero Bin5 DMC $1,672 $1,622 $1,573 $1,526 $1,480 $1,436 $1,393 $1,351 $1,324 $1,297

Aero Bin6 DMC $2,032 $1,971 $1,912 $1,855 $1,799 $1,745 $1,693 $1,642 $1,609 $1,577

Aero Bin7 DMC $2,393 $2,321 $2,252 $2,184 $2,118 $2,055 $1,993 $1,933 $1,895 $1,857

Aero Bin3 IC $182 $182 $181 $181 $181 $143 $143 $142 $142 $142

Aero Bin4 IC $356 $355 $355 $354 $354 $281 $281 $281 $281 $281

Aero Bin5 IC $742 $740 $737 $735 $732 $730 $728 $726 $544 $543

Aero Bin6 IC $902 $899 $896 $893 $890 $888 $885 $882 $661 $660

Aero Bin7 IC $1,062 $1,059 $1,055 $1,052 $1,048 $1,045 $1,042 $1,039 $779 $777

Aero Bin3 TC $1,092 $1,073 $1,055 $1,037 $1,020 $965 $949 $932 $924 $916

Aero Bin4 TC $1,667 $1,627 $1,589 $1,551 $1,515 $1,407 $1,373 $1,340 $1,319 $1,298

Aero Bin5 TC $2,414 $2,361 $2,310 $2,260 $2,212 $2,166 $2,120 $2,077 $1,868 $1,840

Aero Bin6 TC $2,934 $2,870 $2,808 $2,748 $2,690 $2,633 $2,578 $2,524 $2,271 $2,237

Aero Bin7 TC $3,455 $3,380 $3,307 $3,236 $3,167 $3,100 $3,035 $2,972 $2,674 $2,634

Aero Bin3 Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

Aero Bin4 Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Aero Bin5 Alt 

1a 

 

Aero Bin6 Alt 

1a 

Aero Bin7 Alt 

1a 

Aero Bin3 Alt 3 70% 70% 70% 40% 40% 40% 30% 30% 30% 20%

Aero Bin4 Alt 3 20% 20% 20% 35% 35% 35% 30% 30% 30% 20%

Aero Bin5 Alt 3 20% 20% 20% 25% 25% 25% 35%

Aero Bin6 Alt 3 5% 5% 5% 13% 13% 13% 20%

Aero Bin7 Alt 3 2% 2% 2% 5%

Aero Bin3 TCp $0 $0 $0 -$311 -$306 -$290 -$379 -$373 -$370 -$458

Aero Bin4 TCp $0 $0 $0 $233 $227 $211 $137 $134 $132 $0

Aero Bin5 TCp $0 $0 $0 $452 $442 $433 $530 $519 $467 $644

Aero Bin6 TCp $0 $0 $0 $137 $134 $132 $335 $328 $295 $447

Aero Bin7 TCp $0 $0 $0 $0 $0 $0 $61 $59 $53 $132

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.9.3 Aero improvements, Sleeper cab low/mid roof tractors 

For low and mid roof sleeper cab tractors, Aero Bin 2 costs are estimated at $1222, Bin 3 

at $2313 and Bin 4 at $2949 (all are DMC, in 2012$, and applicable in 2014). We consider Bin 

2 technologies to be beyond the effects of learning (curve 1), Bin 3 technologies to be on the flat 
2-262 

portion of the curve (curve 2) and Bin 4 technologies to be on the steep portion of the curve 

(curve 4). We have applied a low complexity ICMs to each with short term markups through 

2022. The resultant technology costs, adoption rates and total cost applied to the package are 

shown in Table 2-211. 

Table 2-211 Costs of Aero Technologies 

Sleeper Cab Low/Mid Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin2 DMC $1,222 $1,222 $1,222 $1,222 $1,222 $1,222 $1,222 $1,222 $1,222 $1,222

Aero Bin3 DMC $2,048 $2,007 $1,967 $1,928 $1,889 $1,851 $1,814 $1,778 $1,760 $1,742

Aero Bin4 DMC $1,888 $1,831 $1,776 $1,723 $1,671 $1,621 $1,572 $1,525 $1,495 $1,465

Aero Bin2 IC $218 $218 $218 $218 $218 $171 $171 $171 $171 $171

Aero Bin3 IC $409 $409 $408 $408 $407 $321 $321 $321 $321 $320

Aero Bin4 IC $512 $512 $511 $510 $509 $405 $405 $404 $404 $404

Aero Bin2 TC $1,440 $1,440 $1,440 $1,440 $1,440 $1,393 $1,393 $1,393 $1,393 $1,393

Aero Bin3 TC $2,457 $2,416 $2,375 $2,335 $2,296 $2,172 $2,135 $2,099 $2,081 $2,063

Aero Bin4 TC $2,400 $2,343 $2,287 $2,233 $2,181 $2,026 $1,977 $1,930 $1,899 $1,869

Aero Bin2 Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

Aero Bin3 Alt 

1a 

 

Aero Bin4 Alt 

1a 

Aero Bin2 Alt 3 70% 70% 70% 75% 75% 75% 60% 60% 60% 50%

Aero Bin3 Alt 3 25% 25% 25% 38% 38% 38% 40%

Aero Bin4 Alt 3 2% 2% 2% 10%

Aero Bin2 TCp $0 $0 $0 $72 $72 $70 -$139 -$139 -$139 -$279

Aero Bin3 TCp $0 $0 $0 $584 $574 $543 $811 $797 $791 $825

Aero Bin4 TCp $0 $0 $0 $0 $0 $0 $40 $39 $38 $187

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.9.4 Aero improvements, Sleeper cab high roof tractors 

For high roof sleeper cab tractors, Aero Bin 3 costs are estimated at $1387, Bin 4 at 

$2379, Bin 5 at $3034, Bin 6 at $3688 and Bin 7 at $4342 (all are DMC, in 2012$, and 

applicable in 2014). We consider Bin 3 technologies to be on the flat portion of the curve (curve 

2) and Bin 4 through 7 technologies to be on the steep portion of the curve (curve 4). We have 

applied a low complexity ICMs to Bins 3 and 4 with short term markups through 2022. We have 

applied medium complexity ICMs to Bins 5 through 7 with short term markups through 2025. 

The resultant technology costs, adoption rates and total cost applied to the package are shown in 

Table 2-212. 
2-263 

Table 2-212 Costs of Aero Technologies 

Sleeper Cab High Roof Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin3 DMC $1,228 $1,204 $1,180 $1,156 $1,133 $1,110 $1,088 $1,066 $1,056 $1,045

Aero Bin4 DMC $1,523 $1,477 $1,433 $1,390 $1,348 $1,308 $1,268 $1,230 $1,206 $1,182

Aero Bin5 DMC $1,942 $1,883 $1,827 $1,772 $1,719 $1,667 $1,617 $1,569 $1,537 $1,507

Aero Bin6 DMC $2,360 $2,290 $2,221 $2,154 $2,090 $2,027 $1,966 $1,907 $1,869 $1,832

Aero Bin7 DMC $2,779 $2,696 $2,615 $2,536 $2,460 $2,387 $2,315 $2,246 $2,201 $2,157

Aero Bin3 IC $245 $245 $245 $245 $244 $193 $192 $192 $192 $192

Aero Bin4 IC $413 $413 $412 $412 $411 $327 $326 $326 $326 $326

Aero Bin5 IC $862 $859 $856 $853 $851 $848 $845 $843 $632 $631

Aero Bin6 IC $1,048 $1,044 $1,041 $1,037 $1,034 $1,031 $1,028 $1,025 $768 $767

Aero Bin7 IC $1,234 $1,229 $1,225 $1,221 $1,217 $1,214 $1,210 $1,207 $904 $903

Aero Bin3 TC $1,474 $1,449 $1,425 $1,401 $1,377 $1,303 $1,281 $1,259 $1,248 $1,237

Aero Bin4 TC $1,936 $1,890 $1,845 $1,801 $1,759 $1,634 $1,595 $1,557 $1,532 $1,508

Aero Bin5 TC $2,803 $2,742 $2,683 $2,625 $2,569 $2,515 $2,463 $2,412 $2,169 $2,137

Aero Bin6 TC $3,408 $3,334 $3,262 $3,192 $3,124 $3,058 $2,994 $2,932 $2,637 $2,598

Aero Bin7 TC $4,013 $3,925 $3,840 $3,758 $3,678 $3,600 $3,525 $3,452 $3,105 $3,060

Aero Bin3 Alt 

1a 

70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

Aero Bin4 Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Aero Bin5 Alt 

1a 

 

Aero Bin6 Alt 

1a 

Aero Bin7 Alt 

1a 

Aero Bin3 Alt 3 70% 70% 70% 40% 40% 40% 30% 30% 30% 20%

Aero Bin4 Alt 3 20% 20% 20% 35% 35% 35% 30% 30% 30% 20%

Aero Bin5 Alt 3 20% 20% 20% 25% 25% 25% 35%

Aero Bin6 Alt 3 5% 5% 5% 13% 13% 13% 20%

Aero Bin7 Alt 3 2% 2% 2% 5%

Aero Bin3 TCp $0 $0 $0 -$420 -$413 -$391 -$512 -$503 -$499 -$619

Aero Bin4 TCp $0 $0 $0 $270 $264 $245 $159 $156 $153 $0

Aero Bin5 TCp $0 $0 $0 $525 $514 $503 $616 $603 $542 $748

Aero Bin6 TCp $0 $0 $0 $160 $156 $153 $389 $381 $343 $520

Aero Bin7 TCp $0 $0 $0 $0 $0 $0 $71 $69 $62 $153

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.9.5 Aero improvements, trailers 

For dry and reefer van trailers, Aero Bin 3 costs are based on and ICCT estimate of $700 

(retail, 2013$), Bin 4 costs are based on an ICCT estimate of $1000 (retail, 2013$), Bin 5 costs 

are based on an ICCT estimate of $1600 (retail, 2013$), Bin 6 costs are based on an ICCT 

estimate of $1900 (retail, 2013$), and Bin 7 costs are based on an ICCT estimate of $2200 

(retail, 2013$). We have used these costs and divided by a 1.36 RPE and converted to 2012$ to 

arrive at direct manufacturing costs of $507, $724, $1159, $1376 and $1594 for Bins 3 through 

7, respectively (all are DMC, in 2012$, applicable in 2014). We consider each of these 

technologies to be on the flat portion of the learning curve (curve 2) and have applied low 
2-264 

complexity ICMs with short term markups through 2018. The resultant technology costs, 

adoption rates and total cost applied to the package are shown in Table 2-213 and Table 2-214 

for 53-foot dry and reefer van trailers, respectively, and in Table 2-215 for 28-foot dry van 

trailers. 

Table 2-213 Costs of Aero Technologies 

53-foot Dry Van Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin3 DMC $449 $440 $431 $422 $414 $406 $398 $390 $386 $382

Aero Bin4 DMC $641 $628 $616 $604 $591 $580 $568 $557 $551 $546

Aero Bin5 DMC $1,026 $1,006 $985 $966 $946 $927 $909 $891 $882 $873

Aero Bin6 DMC $1,218 $1,194 $1,170 $1,147 $1,124 $1,101 $1,079 $1,058 $1,047 $1,037

Aero Bin7 DMC $1,411 $1,383 $1,355 $1,328 $1,301 $1,275 $1,250 $1,225 $1,212 $1,200

Aero Bin8 DMC $1,860 $1,822 $1,786 $1,750 $1,715 $1,681 $1,647 $1,614 $1,598 $1,582

Aero Bin3 IC $90 $71 $71 $71 $70 $70 $70 $70 $70 $70

Aero Bin4 IC $128 $101 $101 $101 $101 $101 $100 $100 $100 $100

Aero Bin5 IC $205 $161 $161 $161 $161 $161 $161 $161 $161 $161

Aero Bin6 IC $244 $192 $191 $191 $191 $191 $191 $191 $191 $191

Aero Bin7 IC $282 $222 $222 $222 $221 $221 $221 $221 $221 $221

Aero Bin8 IC $372 $293 $292 $292 $292 $292 $291 $291 $291 $291

Aero Bin3 TC $539 $511 $502 $493 $484 $476 $468 $460 $456 $452

Aero Bin4 TC $769 $729 $717 $704 $692 $680 $669 $657 $652 $646

Aero Bin5 TC $1,231 $1,167 $1,147 $1,127 $1,107 $1,088 $1,070 $1,051 $1,042 $1,034

Aero Bin6 TC $1,462 $1,386 $1,362 $1,338 $1,315 $1,292 $1,270 $1,249 $1,238 $1,227

Aero Bin7 TC $1,693 $1,604 $1,577 $1,549 $1,523 $1,496 $1,471 $1,446 $1,433 $1,421

Aero Bin8 TC $2,231 $2,115 $2,078 $2,042 $2,007 $1,973 $1,939 $1,906 $1,889 $1,873

Aero Bin3 Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Aero Bin4 Alt 

1a 

30% 31% 32% 33% 34% 36% 37% 38% 39% 40%

Aero Bin5 Alt 

1a 

5% 5% 5% 5% 5% 5% 5% 5% 5% 5%

Aero Bin6 Alt 

1a 

 

Aero Bin7 Alt 

1a 

Aero Bin8 Alt 

1a 

Aero Bin3 Alt 3 30% 30% 30% 5% 5% 5% 

Aero Bin4 Alt 3 60% 60% 60% 55% 55% 55% 25% 25% 25% 

Aero Bin5 Alt 3 5% 5% 5% 10% 10% 10% 10% 10% 10% 10%

Aero Bin6 Alt 3 

Aero Bin7 Alt 3 30% 30% 30% 65% 65% 65% 50%

Aero Bin8 Alt 3 40%

Aero Bin3 TCp $54 $51 $50 -$74 -$73 -$71 -$94 -$92 -$91 -$90

Aero Bin4 TCp $231 $212 $201 $155 $145 $129 -$80 -$85 -$91 -$258

Aero Bin5 TCp $0 $0 $0 $56 $55 $54 $53 $53 $52 $52

Aero Bin6 TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Aero Bin7 TCp $0 $0 $0 $465 $457 $449 $956 $940 $932 $711

Aero Bin8 TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $749

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-265 

Table 2-214 Costs of Aero Technologies 

Reefer Van Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin3 DMC $449 $440 $431 $422 $414 $406 $398 $390 $386 $382

Aero Bin4 DMC $641 $628 $616 $604 $591 $580 $568 $557 $551 $546

Aero Bin5 DMC $1,026 $1,006 $985 $966 $946 $927 $909 $891 $882 $873

Aero Bin6 DMC $1,218 $1,194 $1,170 $1,147 $1,124 $1,101 $1,079 $1,058 $1,047 $1,037

Aero Bin7 DMC $1,411 $1,383 $1,355 $1,328 $1,301 $1,275 $1,250 $1,225 $1,212 $1,200

Aero Bin8 DMC $1,860 $1,822 $1,786 $1,750 $1,715 $1,681 $1,647 $1,614 $1,598 $1,582

Aero Bin3 IC $90 $71 $71 $71 $70 $70 $70 $70 $70 $70

Aero Bin4 IC $128 $101 $101 $101 $101 $101 $100 $100 $100 $100

Aero Bin5 IC $205 $161 $161 $161 $161 $161 $161 $161 $161 $161

Aero Bin6 IC $244 $192 $191 $191 $191 $191 $191 $191 $191 $191

Aero Bin7 IC $282 $222 $222 $222 $221 $221 $221 $221 $221 $221

Aero Bin8 IC $372 $293 $292 $292 $292 $292 $291 $291 $291 $291

Aero Bin3 TC $539 $511 $502 $493 $484 $476 $468 $460 $456 $452

Aero Bin4 TC $769 $729 $717 $704 $692 $680 $669 $657 $652 $646

Aero Bin5 TC $1,231 $1,167 $1,147 $1,127 $1,107 $1,088 $1,070 $1,051 $1,042 $1,034

Aero Bin6 TC $1,462 $1,386 $1,362 $1,338 $1,315 $1,292 $1,270 $1,249 $1,238 $1,227

Aero Bin7 TC $1,693 $1,604 $1,577 $1,549 $1,523 $1,496 $1,471 $1,446 $1,433 $1,421

Aero Bin8 TC $2,231 $2,115 $2,078 $2,042 $2,007 $1,973 $1,939 $1,906 $1,889 $1,873

Aero Bin3 Alt 

1a 

20% 20% 20% 20% 20% 20% 20% 20% 20% 20%

Aero Bin4 Alt 

1a 

30% 31% 32% 33% 34% 36% 37% 38% 39% 40%

Aero Bin5 Alt 

1a 

5% 5% 5% 5% 5% 5% 5% 5% 5% 5%

Aero Bin6 Alt 

1a 

 

Aero Bin7 Alt 

1a 

Aero Bin8 Alt 

1a 

Aero Bin3 Alt 3 30% 30% 30% 5% 5% 5% 

Aero Bin4 Alt 3 60% 60% 60% 55% 55% 55% 25% 25% 25% 

Aero Bin5 Alt 3 5% 5% 5% 10% 10% 10% 10% 10% 10% 20%

Aero Bin6 Alt 3 

Aero Bin7 Alt 3 30% 30% 30% 65% 65% 65% 60%

Aero Bin8 Alt 3 20%

Aero Bin3 TCp $54 $51 $50 -$74 -$73 -$71 -$94 -$92 -$91 -$90

Aero Bin4 TCp $231 $212 $201 $155 $145 $129 -$80 -$85 -$91 -$258

Aero Bin5 TCp $0 $0 $0 $56 $55 $54 $53 $53 $52 $155

Aero Bin6 TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Aero Bin7 TCp $0 $0 $0 $465 $457 $449 $956 $940 $932 $853

Aero Bin8 TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $375

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-266 

Table 2-215 Costs of Aero Technologies 

28-foot Dry Van Trailers (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Aero Bin3 DMC $449 $440 $431 $422 $414 $406 $398 $390 $386 $382

Aero Bin4 DMC $641 $628 $616 $604 $591 $580 $568 $557 $551 $546

Aero Bin5 DMC $1,090 $1,068 $1,047 $1,026 $1,006 $985 $966 $946 $937 $928

Aero Bin3 IC $90 $71 $71 $71 $70 $70 $70 $70 $70 $70

Aero Bin4 IC $128 $101 $101 $101 $101 $101 $100 $100 $100 $100

Aero Bin5 IC $218 $171 $171 $171 $171 $171 $171 $171 $171 $171

Aero Bin3 TC $539 $511 $502 $493 $484 $476 $468 $460 $456 $452

Aero Bin4 TC $769 $729 $717 $704 $692 $680 $669 $657 $652 $646

Aero Bin5 TC $1,308 $1,240 $1,218 $1,197 $1,177 $1,156 $1,137 $1,117 $1,108 $1,098

Aero Bin3 Alt 

1a 

 

Aero Bin4 Alt 

1a 

Aero Bin5 Alt 

1a 

Aero Bin3 Alt 3 95% 95% 95% 70% 70% 70% 30%

Aero Bin4 Alt 3 30% 30% 30% 60%

Aero Bin5 Alt 3 10%

Aero Bin3 TCp $0 $0 $0 $468 $460 $452 $328 $322 $319 $136

Aero Bin4 TCp $0 $0 $0 $0 $0 $0 $281 $276 $274 $543

Aero Bin5 TCp $0 $0 $0 $0 $0 $0 $0 $0 $0 $110

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.9.6 Aero improvements, HD pickups and vans 

For HD pickups and vans, we have based our aero improvement costs on values used in 

our light-duty 2017-2025 final rule. Using those values updated to 2012$ results in costs for 

aero 1 (passive aero treatments) and active aero treatments of $47 and $142 (both are DMC, in 

2012$, in 2015). Note that the aero 2 costs are the passive aero 1 plus the active aero costs. We 

consider both of these technologies to be on the flat portion of the learning curve (curve 8) and, 

to aero 1, have applied low complexity markups with near term markups through 2018 and, to 

active aero, have applied medium complexity markups with near term markups through 2024. 

The resultant costs for HD pickups and vans are shown in Table 2-216 (aero 1) and in Table 

2-217 (active aero) and in Table 2-218 (aero 2, passive+active aero). 

Table 2-216 Costs for Passive Aero Treatments – Aero 1 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Aero 1 – passive aero DMC $42 $41 $40 $39 $38 $38 $38 

Aero 1 – passive aero IC $9 $9 $9 $9 $9 $9 $9 

Aero 1 – passive aero TC $51 $50 $49 $48 $47 $47 $47 

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 
2-267 

Table 2-217 Costs for Active Aero Treatments 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Aero 2 – active aero DMC $125 $122 $120 $118 $115 $114 $113 

Aero 2 – active aero IC $54 $54 $54 $54 $40 $40 $40 

Aero 2 – active aero TC $179 $177 $174 $172 $156 $154 $153 

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost 

Table 2-218 Costs for Aero 2 (passive plus active aero) 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Aero 2 – active aero DMC $166 $163 $160 $157 $154 $152 $151 

Aero 2 – active aero IC $63 $63 $63 $63 $50 $49 $49 

Aero 2 – active aero TC $230 $227 $223 $220 $203 $201 $200 

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.10 Other Technologies 

2.12.10.1 Advanced cruise controls, tractors 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$1100 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $797 (DMC, 2012$, in 2018). We consider this technology to be on the flat 

portion of the learning curve (curve 12) and have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-219 for tractors. 

Table 2-219 Costs for Advanced Cruise Controls 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Advanced cruise control DMC $797 $773 $750 $727 $713 $698 $684 $671 $657 $644

Advanced cruise control IC $142 $142 $142 $141 $141 $111 $111 $111 $111 $111

Advanced cruise control TC $939 $915 $891 $868 $854 $809 $795 $782 $768 $755

Advanced cruise control Alt 1a 

Advanced cruise control Alt 3 20% 20% 20% 40% 40% 40% 40%

Advanced cruise control TCp $0 $0 $0 $174 $171 $162 $318 $313 $307 $302

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

2.12.10.2 Improved accessories 

We have estimated the cost of this technology based on an estimate from TetraTech of 

$350 (retail, 2013$). Using that estimate we divided by a 1.36 RPE and converted to 2012$ to 

arrive at a cost of $254 (DMC, 2012$, in 2018). We consider this technology to be on the flat 

portion of the learning curve (curve 12) and have applied a low complexity ICM with short term 

markups through 2022. The resultant technology costs, adoption rates and total cost applied to 

the package are shown in Table 2-220 for tractors. 
2-268 

Table 2-220 Costs for Improved Accessories 

Tractors (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Improved accessories DMC $254 $246 $239 $231 $227 $222 $218 $213 $209 $205

Improved accessories IC $45 $45 $45 $45 $45 $35 $35 $35 $35 $35

Improved accessories TC $299 $291 $284 $276 $272 $258 $253 $249 $244 $240

Improved accessories Alt 1a 

Improved accessories Alt 3 10% 10% 10% 20% 20% 20% 30%

Improved accessories TCp $0 $0 $0 $28 $27 $26 $51 $50 $49 $72

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption

For HD pickups and vans, we have estimated the costs for two levels of improved 

accessories based on estimates presented in the light-duty 2017-2025 final rule. In that rule, we 

estimated the costs of IACC1 and IACC2 at $73 and $118, respectively (both are DMC, 2009$, 

in 2015). With updates to 2012$, these costs become $77 and $124, respectively (both are DMC, 

2012$, in 2015). Note that IACC2 includes IACC1. We consider these technologies to be on the 

flat portion of the learning curve (curve 8) and have applied low complexity markups with near 

term markups through 2018. The resultant cost for both are shown in Table 2-221. 

Table 2-221 Costs for Improved Accessories 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Improved accessories 1 (IACC1) DMC $67 $66 $64 $63 $62 $61 $61

Improved accessories 1 (IACC2) DMC $109 $106 $104 $102 $100 $99 $98

Improved accessories 1 (IACC1) IC $15 $15 $15 $15 $15 $15 $15

Improved accessories 1 (IACC2) IC $24 $24 $24 $24 $24 $24 $24

Improved accessories 1 (IACC1) TC $82 $80 $79 $78 $77 $76 $75

Improved accessories 1 (IACC2) TC $132 $130 $128 $126 $124 $123 $122

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.10.3 Weight reduction, vocational vehicles 

We have estimated the cost of a 200, 400 and 1000 pound weight reduction on vocational 

vehicles at $4/pound, $6/pound and $8/pound, respectively (all are retail, 2013$). Using those 

costs we have divided by a 1.36 RPE and converted to 2012$ to arrive at costs of $579, $1738 

and $5795 for 200, 400 and 1000 pound reductions, respectively (all are DMC, in 2012$, 

applicable in 2021). We consider each of these weight reduction levels to be on the flat portion 

of the learning curve (curve 13) and have applied low complexity ICMs with short term markups 

through 2022 for 200 and 400 pound reductions, and medium complexity ICMs with short term 

markups through 2022 for a 1000 pound reduction. The resultant technology costs, adoption 

rates and total cost applied to the package are shown in Table 2-222 though Table 2-227. 
2-269 

Table 2-222 Costs for a 200 Pound Weight Reduction 

Vocational Light/Medium/Heavy HD Urban/Multipurpose Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Weight reduction, 200 lbs DMC $635 $616 $597 $579 $562 $545 $529 $518 $508 $498

Weight reduction, 200 lbs IC $104 $104 $104 $103 $103 $81 $81 $81 $81 $81

Weight reduction, 200 lbs TC $739 $720 $701 $683 $665 $626 $610 $599 $589 $578

Weight reduction, 200 lbs Alt 1a 

Weight reduction, 200 lbs Alt 3 4% 4% 4% 4% 4% 4% 5%

Weight reduction, 200 lbs TCp $0 $0 $0 $27 $27 $25 $24 $24 $24 $29

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-223 Costs for a 200 Pound Weight Reduction 

Vocational Light HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Weight reduction, 200 lbs DMC $635 $616 $597 $579 $562 $545 $529 $518 $508 $498

Weight reduction, 200 lbs IC $104 $104 $104 $103 $103 $81 $81 $81 $81 $81

Weight reduction, 200 lbs TC $739 $720 $701 $683 $665 $626 $610 $599 $589 $578

Weight reduction, 200 lbs Alt 1a 

Weight reduction, 200 lbs Alt 3 7% 7% 7% 7% 7% 7% 8%

Weight reduction, 200 lbs TCp $0 $0 $0 $48 $47 $44 $43 $42 $41 $46

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-224 Costs for a 200 Pound Weight Reduction 

Vocational Medium HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Weight reduction, 200 lbs DMC $635 $616 $597 $579 $562 $545 $529 $518 $508 $498

Weight reduction, 200 lbs IC $104 $104 $104 $103 $103 $81 $81 $81 $81 $81

Weight reduction, 200 lbs TC $739 $720 $701 $683 $665 $626 $610 $599 $589 $578

Weight reduction, 200 lbs Alt 1a 

Weight reduction, 200 lbs Alt 3 6% 6% 6% 6% 6% 6% 7%

Weight reduction, 200 lbs TCp $0 $0 $0 $41 $40 $38 $37 $36 $35 $40

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 

Table 2-225 Costs for a 200 Pound Weight Reduction 

Vocational Heavy HD Regional Vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Weight reduction, 200 lbs DMC $635 $616 $597 $579 $562 $545 $529 $518 $508 $498

Weight reduction, 200 lbs IC $104 $104 $104 $103 $103 $81 $81 $81 $81 $81

Weight reduction, 200 lbs TC $739 $720 $701 $683 $665 $626 $610 $599 $589 $578

Weight reduction, 200 lbs Alt 1a 

Weight reduction, 200 lbs Alt 3 5% 5% 5% 5% 5% 5% 6%

Weight reduction, 200 lbs TCp $0 $0 $0 $34 $33 $31 $30 $30 $29 $35

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative; empty cells for adoption rates denote 0% adoption 
2-270 

Table 2-226 Costs for a 400 Pound Weight Reduction 

Vocational vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Weight 

reduction, 400 

lbs 

DMC $1,905 $1,848 $1,792 $1,738 $1,686 $1,636 $1,587 $1,555 $1,524 $1,493

Weight 

reduction, 400 

lbs 

IC $312 $312 $311 $310 $310 $243 $243 $242 $242 $242

Weight 

reduction, 400 

lbs 

TC $2,217 $2,159 $2,103 $2,049 $1,996 $1,879 $1,829 $1,797 $1,766 $1,735

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package; 

alt=alternative 

Table 2-227 Costs for a 1000 Pound Weight Reduction 

Vocational vehicles (2012$) 

TECHNOLOGY 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Weight 

reduction, 1000 

lbs 

DMC $6,349 $6,159 $5,974 $5,795 $5,621 $5,452 $5,289 $5,183 $5,079 $4,978

Weight 

reduction, 1000 

lbs 

IC $1,780 $1,770 $1,761 $1,752 $1,743 $1,296 $1,290 $1,286 $1,283 $1,279

Weight 

reduction, 1000 

lbs 

TC $8,129 $7,929 $7,735 $7,547 $7,364 $6,748 $6,579 $6,469 $6,362 $6,257

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.10.4 Weight reduction in HD pickups and vans 

 For this proposal, we are estimating weight reduction costs for HD pickups and vans 

using the same cost curve used in support of the 2017-2025 light-duty GHG/CAFE FRM. That 

curve can be expressed as: 

Mass Reduction Direct Manufacturing Cost (DMC) ($/lb) = 4.55 x Percentage of Mass 

Reduction (2012$) 

For example, this results in an estimated $80 (2012$) DMC increase for a 5 percent mass 

reduction of a 7,000 pound vehicle and $318 (2012$) DMC increase for a 10 percent mass 

reduction of a 7,000 pound vehicle, or $0.227 $/lb and $0.455/lb, respectively (both in 2012$). 

Consistent with the 2017-2025 light-duty FRM, the agencies consider this DMC to be 

applicable to MY2017 and consider mass reduction technology to be on the flat portion of the 

learning curve in the 2017-2025MY timeframe. To estimate indirect costs for applied mass 

reduction of up to 10 percent, the agencies have applied a low complexity ICM with near term 

markups through 2018. 
2-271 

2.12.10.5 Electric power steering, HD pickups and vans 

We have based the costs for electric power steering on the costs used in the light-duty 

2017-2025 FRM but have scaled upward that cost by 50 percent to account for the larger HD 

vehicles. Using that cost and converting to 2012$ results in a cost of $141 (DMC, 2012$, in 

2015). We consider this technology to be on the flat portion of the learning curve (curve 8) and 

have applied low complexity markups with near term markups through 2018. The resultant costs 

for HD pickups and vans are shown in are shown in Table 2-228. 

Table 2-228 Costs for Electric Power Steering 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Electric power steering (EPS) DMC $124 $121 $119 $117 $114 $113 $112

Electric power steering (EPS) IC $27 $27 $27 $27 $27 $27 $27

Electric power steering (EPS) TC $151 $148 $146 $144 $141 $140 $139

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.10.6 Low drag brakes, HD pickups and vans 

We have based the costs for low drag brakes on the costs used in the light-duty 2017-

2025 FRM but have scaled upward that cost by 50 percent to account for the larger HD vehicles. 

Using that cost and converting to 2012$ results in a cost of $91 (DMC, 2012$, in any year). We 

consider this technology to be beyond the learning curve (curve 1) and have applied low 

complexity markups with near term markups through 2018. The resultant costs for HD pickups 

and vans are shown in are shown in Table 2-229. 

Table 2-229 Costs for Low Drag Brakes 

Gasoline & Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Low drag brakes DMC $91 $91 $91 $91 $91 $91 $91

Low drag brakes IC $18 $18 $18 $18 $18 $18 $18

Low drag brakes TC $109 $109 $109 $109 $109 $109 $109

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.12.10.7 Driveline friction reduction, diesel HD pickups & vans 

We have estimated the cost of driveline friction reduction based on the cost of secondary 

axle disconnect in the light-duty 2017-2025 final rule. Using that cost of $80 (DMC, 2009$, in 

2015), we have scaled upward by 50 percent to account for the larger HD componentry to arrive 

at a cost of $126 (DMC, 2012$, in 2015). We consider this technology to be on the flat portion 

of the learning curve (curve 3) and have applied low complexity markups with near term 

markups through 2022. The resultant costs for driveline friction reduction (applied only to diesel 

HD pickups & vans) are shown in Table 2-230. 
2-272 

Table 2-230 Costs for Driveline Friction Reduction 

Diesel HD Pickups and Vans (2012$) 

ITEM 2021 2022 2023 2024 2025 2026 2027 

Driveline friction reduction DMC $108 $106 $104 $102 $100 $99 $98

Driveline friction reduction IC $30 $30 $24 $24 $24 $24 $24

Driveline friction reduction TC $139 $136 $128 $126 $124 $123 $122

Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost

2.13Package Costs 

Section 0presents detailed technology costs along with adoption rates to illustrate how 

each technology is accounted for in the package costs. Here we present package costs by 

regulated sector (i.e., vocational heavy HD, urban vehicles) and package costs by MOVES 

sourcetype (i.e., diesel refuse trucks). We determine package costs by MOVES sourcetype so 

that we can calculate total program costs (i.e., package costs multiplied by vehicle sales) since 

sourcetypes are the sales figures that we can glean from MOVES. As a result, the sourcetype 

package costs presented here are the costs used in our program cost estimations. 

2.13.1 Package Costs by Regulated Sector 

2.13.1.1 Vocational vehicles 

We have estimated costs for 9 vocational segments and 2 fuels. We present package 

costs in Table 2-231 through Table 2-238 for these for alternatives 3 and 4, both relative to 

alternatives 1a and 1b and separately for diesel and gasoline vehicles. 

Table 2-231 Package Costs for Regulated Vocational Segment 

Alternative 3 Incremental to Alternative 1a 

Diesel (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $1,125 $1,100 $1,008 $1,737 $1,701 $1,664 $3,489 $3,427

Light HD Multipurpose $1,125 $1,100 $1,008 $1,737 $1,701 $1,664 $3,490 $3,427

Light HD Regional $598 $585 $563 $849 $835 $819 $1,407 $1,378

Medium HD Urban $1,418 $1,386 $1,254 $2,228 $2,180 $2,132 $4,696 $4,616

Medium HD Multipurpose $1,418 $1,386 $1,254 $2,228 $2,180 $2,132 $4,696 $4,616

Medium HD Regional $571 $559 $537 $817 $803 $788 $1,395 $1,367

Heavy HD Urban $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

Heavy HD Multipurpose $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

Heavy HD Regional $3,404 $3,348 $3,160 $4,834 $4,755 $4,683 $4,682 $4,607
2-273 

Table 2-232 Package Costs for Regulated Vocational Segment 

Alternative 3 Incremental to Alternative 1a 

Gasoline (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $832 $814 $729 $1,299 $1,271 $1,240 $3,086 $3,037

Light HD Multipurpose $832 $814 $729 $1,299 $1,271 $1,240 $3,087 $3,038

Light HD Regional $305 $299 $284 $412 $405 $395 $1,004 $989

Medium HD Urban $1,147 $1,122 $996 $1,823 $1,782 $1,740 $4,327 $4,259

Medium HD Multipurpose $1,147 $1,122 $996 $1,823 $1,782 $1,740 $4,328 $4,259

Medium HD Regional $300 $294 $279 $412 $405 $395 $1,026 $1,010

Heavy HD Urban $1,728 $1,690 $1,491 $2,927 $2,860 $2,791 $7,053 $6,941

Heavy HD Multipurpose $1,728 $1,690 $1,491 $2,927 $2,860 $2,791 $7,053 $6,941

Heavy HD Regional $3,134 $3,083 $2,902 $4,429 $4,357 $4,290 $4,314 $4,251

Table 2-233 Package Costs for Regulated Vocational Segment 

Alternative 4 Incremental to Alternative 1a 

Diesel (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $2,169 $2,120 $1,911 $3,741 $3,661 $3,582 $3,525 $3,462

Light HD Multipurpose $2,169 $2,120 $1,911 $3,704 $3,625 $3,546 $3,490 $3,427

Light HD Regional $805 $788 $756 $1,482 $1,457 $1,430 $1,407 $1,378

Medium HD Urban $2,938 $2,873 $2,560 $5,030 $4,920 $4,810 $4,733 $4,652

Medium HD Multipurpose $2,938 $2,873 $2,560 $4,992 $4,882 $4,772 $4,696 $4,616

Medium HD Regional $777 $760 $729 $1,469 $1,444 $1,417 $1,395 $1,367

Heavy HD Urban $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

Heavy HD Multipurpose $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

Heavy HD Regional $2,693 $2,647 $2,504 $4,912 $4,831 $4,752 $4,682 $4,607

Table 2-234 Package Costs for Regulated Vocational Segment 

Alternative 4 Incremental to Alternative 1a 

Gasoline (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $1,796 $1,757 $1,556 $3,322 $3,245 $3,172 $3,122 $3,072

Light HD Multipurpose $1,796 $1,757 $1,556 $3,285 $3,209 $3,136 $3,087 $3,038

Light HD Regional $433 $424 $401 $1,063 $1,041 $1,020 $1,004 $989

Medium HD Urban $2,594 $2,536 $2,232 $4,648 $4,539 $4,435 $4,365 $4,296

Medium HD Multipurpose $2,594 $2,536 $2,232 $4,609 $4,501 $4,398 $4,328 $4,259

Medium HD Regional $432 $423 $400 $1,086 $1,063 $1,042 $1,026 $1,010

Heavy HD Urban $3,992 $3,903 $3,416 $7,512 $7,339 $7,168 $7,053 $6,941

Heavy HD Multipurpose $3,992 $3,903 $3,416 $7,512 $7,339 $7,168 $7,053 $6,941

Heavy HD Regional $2,348 $2,310 $2,175 $4,529 $4,450 $4,378 $4,314 $4,251
2-274 

Table 2-235 Package Costs for Regulated Vocational Segment 

Alternative 3 Incremental to Alternative 1b 

Diesel (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $1,125 $1,100 $1,008 $1,737 $1,701 $1,664 $3,489 $3,427

Light HD Multipurpose $1,125 $1,100 $1,008 $1,737 $1,701 $1,664 $3,490 $3,427

Light HD Regional $598 $585 $563 $849 $835 $819 $1,407 $1,378

Medium HD Urban $1,418 $1,386 $1,254 $2,228 $2,180 $2,132 $4,696 $4,616

Medium HD Multipurpose $1,418 $1,386 $1,254 $2,228 $2,180 $2,132 $4,696 $4,616

Medium HD Regional $571 $559 $537 $817 $803 $788 $1,395 $1,367

Heavy HD Urban $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

Heavy HD Multipurpose $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

Heavy HD Regional $3,404 $3,348 $3,160 $4,834 $4,755 $4,683 $4,682 $4,607

Table 2-236 Package Costs for Regulated Vocational Segment 

Alternative 3 Incremental to Alternative 1b 

Gasoline (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $843 $825 $740 $1,311 $1,282 $1,251 $3,097 $3,048

Light HD Multipurpose $843 $825 $740 $1,311 $1,282 $1,251 $3,097 $3,049

Light HD Regional $316 $310 $295 $423 $416 $406 $1,015 $999

Medium HD Urban $1,159 $1,134 $1,008 $1,835 $1,793 $1,750 $4,338 $4,270

Medium HD Multipurpose $1,159 $1,134 $1,008 $1,835 $1,793 $1,750 $4,339 $4,270

Medium HD Regional $312 $306 $291 $424 $416 $406 $1,037 $1,021

Heavy HD Urban $1,740 $1,702 $1,502 $2,938 $2,871 $2,802 $7,064 $6,952

Heavy HD Multipurpose $1,740 $1,702 $1,502 $2,938 $2,871 $2,802 $7,064 $6,952

Heavy HD Regional $3,146 $3,095 $2,914 $4,441 $4,368 $4,301 $4,324 $4,262

Table 2-237 Package Costs for Regulated Vocational Segment 

Alternative 4 Incremental to Alternative 1b 

Diesel (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $2,169 $2,120 $1,911 $3,741 $3,661 $3,582 $3,525 $3,462

Light HD Multipurpose $2,169 $2,120 $1,911 $3,704 $3,625 $3,546 $3,490 $3,427

Light HD Regional $805 $788 $756 $1,482 $1,457 $1,430 $1,407 $1,378

Medium HD Urban $2,938 $2,873 $2,560 $5,030 $4,920 $4,810 $4,733 $4,652

Medium HD Multipurpose $2,938 $2,873 $2,560 $4,992 $4,882 $4,772 $4,696 $4,616

Medium HD Regional $777 $760 $729 $1,469 $1,444 $1,417 $1,395 $1,367

Heavy HD Urban $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

Heavy HD Multipurpose $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

Heavy HD Regional $2,693 $2,647 $2,504 $4,912 $4,831 $4,752 $4,682 $4,607
2-275 

Table 2-238 Package Costs for Regulated Vocational Segment 

Alternative 4 Incremental to Alternative 1b 

Gasoline (2012$) 

WEIGHT CLASS SPEED 2021 2022 2023 2024 2025 2026 2027 2028 

Light HD Urban $1,808 $1,768 $1,567 $3,334 $3,256 $3,183 $3,132 $3,083

Light HD Multipurpose $1,808 $1,768 $1,567 $3,297 $3,220 $3,147 $3,097 $3,049

Light HD Regional $445 $436 $413 $1,075 $1,052 $1,031 $1,015 $999

Medium HD Urban $2,605 $2,547 $2,243 $4,659 $4,550 $4,446 $4,375 $4,306

Medium HD Multipurpose $2,605 $2,547 $2,243 $4,620 $4,512 $4,408 $4,339 $4,270

Medium HD Regional $444 $435 $412 $1,097 $1,074 $1,053 $1,037 $1,021

Heavy HD Urban $4,004 $3,915 $3,428 $7,524 $7,350 $7,178 $7,064 $6,952

Heavy HD Multipurpose $4,004 $3,915 $3,428 $7,524 $7,350 $7,178 $7,064 $6,952

Heavy HD Regional $2,360 $2,321 $2,187 $4,540 $4,461 $4,389 $4,324 $4,262

2.13.1.2 Tractors 

We have estimated costs for 7 tractor segments and 1 fuel. We present package costs in 

Table 2-239 through Table 2-242 for these for alternatives 3 and 4, both relative to alternatives 

1a and 1b. 

Table 2-239 Package Costs for Regulated Tractor Segment 

Alternative 3 Incremental to Alternative 1a 

Diesel (2012$) 

CLASS TYPE 2021 2022 2023 2024 2025 2026 2027 2028 

7 Day cab, low roof $5,468 $5,381 $5,008 $8,400 $8,259 $8,095 $10,140 $9,968

7 Day cab, high roof $5,252 $5,161 $4,811 $8,304 $8,160 $7,913 $10,099 $9,923

8 Day cab, low roof $5,520 $5,432 $5,057 $8,467 $8,325 $8,159 $10,204 $10,031

8 Day cab, high roof $5,298 $5,206 $4,854 $8,419 $8,274 $8,024 $10,209 $10,030

8 Sleeper cab, 

low roof 

$7,916 $7,786 $7,281 $11,102 $10,912 $10,723 $12,744 $12,548

8 Sleeper cab, 

mid roof 

$7,916 $7,786 $7,281 $11,102 $10,912 $10,723 $12,744 $12,548

8 Sleeper cab, 

high roof 

$7,771 $7,637 $7,156 $11,145 $10,952 $10,666 $12,842 $12,640
2-276 

Table 2-240 Package Costs for Regulated Tractor Segment 

Alternative 4 Incremental to Alternative 1a 

Diesel (2012$) 

CLASS TYPE 2021 2022 2023 2024 2025 2026 2027 2028 

7 Day cab, low roof $8,946 $8,799 $8,180 $10,757 $10,574 $10,306 $10,140 $9,968

7 Day cab, high roof $8,769 $8,621 $8,035 $10,851 $10,664 $10,270 $10,099 $9,923

8 Day cab, low roof $8,999 $8,852 $8,230 $10,826 $10,642 $10,371 $10,204 $10,031

8 Day cab, high roof $8,816 $8,666 $8,079 $10,968 $10,780 $10,382 $10,209 $10,030

8 Sleeper cab, 

low roof 

$11,397 $11,208 $10,456 $13,461 $13,229 $12,934 $12,744 $12,548

8 Sleeper cab, 

mid roof 

$11,397 $11,208 $10,456 $13,461 $13,229 $12,934 $12,744 $12,548

8 Sleeper cab, 

high roof 

$11,318 $11,126 $10,411 $13,717 $13,481 $13,039 $12,842 $12,640

Table 2-241 Package Costs for Regulated Tractor Segment 

Alternative 3 Incremental to Alternative 1b 

Diesel (2012$) 

CLASS TYPE 2021 2022 2023 2024 2025 2026 2027 2028 

7 Day cab, low roof $5,244 $5,086 $4,642 $7,966 $7,765 $7,564 $9,504 $9,337

7 Day cab, high roof $5,159 $5,040 $4,681 $8,156 $7,995 $7,726 $9,885 $9,710

8 Day cab, low roof $5,296 $5,137 $4,691 $8,033 $7,831 $7,628 $9,569 $9,400

8 Day cab, high roof $5,205 $5,085 $4,724 $8,271 $8,108 $7,836 $9,995 $9,818

8 Sleeper cab, 

low roof 

$7,642 $7,426 $6,863 $10,610 $10,381 $10,140 $12,078 $11,882

8 Sleeper cab, 

mid roof 

$7,556 $7,332 $6,770 $10,528 $10,283 $10,044 $11,944 $11,758

8 Sleeper cab, 

high roof 

$7,653 $7,497 $7,019 $10,996 $10,788 $10,497 $12,640 $12,443

Table 2-242 Package Costs for Regulated Tractor Segment 

Alternative 4 Incremental to Alternative 1b 

Diesel (2012$) 

CLASS TYPE 2021 2022 2023 2024 2025 2026 2027 2028 

7 Day cab, low roof $8,721 $8,505 $7,815 $10,323 $10,080 $9,774 $9,504 $9,337

7 Day cab, high roof $8,676 $8,499 $7,905 $10,703 $10,499 $10,083 $9,885 $9,710

8 Day cab, low roof $8,775 $8,557 $7,865 $10,392 $10,148 $9,840 $9,569 $9,400

8 Day cab, high roof $8,722 $8,545 $7,949 $10,820 $10,614 $10,195 $9,995 $9,818

8 Sleeper cab, 

low roof 

$11,124 $10,848 $10,038 $12,969 $12,698 $12,351 $12,078 $11,882

8 Sleeper cab, 

mid roof 

$11,037 $10,753 $9,944 $12,887 $12,599 $12,255 $11,944 $11,758

8 Sleeper cab, 

high roof 

$11,200 $10,985 $10,273 $13,567 $13,317 $12,870 $12,640 $12,443
2-277 

2.13.1.3 Trailers 

We have estimated costs for 7 trailer types. We present package costs in Table 2-243 and 

Table 2-244 for these for alternatives 3 and 4 relative to alternative 1a. We present package 

costs in Table 2-245 and Table 2-246 for alternative 3 and 4 relative to alternative 1b. 

Table 2-243 Costs for Trailers 

Alternative 3 Incremental to Alternative 1a (2012$) 

TYPE 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

53-foot dry van $588 $550 $524 $901 $870 $817 $1,116 $1,083 $1,059 $1,409 $1,396

53-foot reefer van $588 $550 $524 $901 $870 $817 $1,116 $1,083 $1,059 $1,280 $1,267

Container chassis $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28-foot dry van $514 $501 $489 $974 $957 $923 $1,097 $1,078 $1,065 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other highway $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Table 2-244 Costs for Trailers 

Alternative 4 Incremental to Alternative 1a (2012$) 

TYPE 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

53-foot dry van $588 $550 $524 $1,207 $1,172 $1,113 $1,504 $1,465 $1,437 $1,409 $1,396

53-foot reefer van $588 $550 $524 $1,207 $1,172 $1,113 $1,371 $1,333 $1,306 $1,280 $1,267

Container chassis $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28-foot dry van $514 $501 $489 $1,146 $1,127 $1,090 $1,304 $1,282 $1,267 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other highway $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Table 2-245 Costs for Trailers 

Alternative 3 Incremental to Alternative 1b (2012$) 

TYPE 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

53-foot dry van $588 $550 $524 $901 $856 $817 $1,116 $1,083 $1,059 $1,409 $1,382

53-foot reefer van $588 $550 $524 $901 $856 $817 $1,116 $1,083 $1,059 $1,280 $1,254

Container chassis $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28-foot dry van $514 $501 $489 $974 $957 $923 $1,097 $1,078 $1,065 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other highway $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693
2-278 

Table 2-246 Costs for Trailers 

Alternative 4 Incremental to Alternative 1b (2012$) 

TYPE 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

53-foot dry van $588 $550 $524 $1,207 $1,157 $1,113 $1,504 $1,465 $1,437 $1,409 $1,382

53-foot reefer van $588 $550 $524 $1,207 $1,157 $1,113 $1,371 $1,333 $1,306 $1,280 $1,254

Container chassis $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28-foot dry van $514 $501 $489 $1,146 $1,127 $1,090 $1,304 $1,282 $1,267 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other highway $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

2.13.1.4 HD Pickups and Vans 

The costs presented in Table 2-247 are CAFE model outputs used in this analysis. We 

describe the CAFE model and how these costs were generated in Chapter 11 of this draft RIA. 

The costs presented here spread evenly over MYs 2021 through 2026 the costs estimated by the 

CAFE model for MYs 2017-2020. 

Table 2-247 Package Costs for HD Pickups and Vans (2012$) 

ALTERNATIVE BASELINE CASE 2021 2022 2023 2024 2025 2026 2027 

3 1a $516 $508 $791 $948 $1,161 $1,224 $1,342

4 1a $1,050 $1,033 $1,621 $1,734 $1,825 $1,808 $1,841

3 1b $493 $485 $766 $896 $1,149 $1,248 $1,366

4 1b $909 $894 $1,415 $1,532 $1,627 $1,649 $1,684

2.13.2 Package Costs by MOVES Sourcetype 

The package costs by segment can then be used to calculate package costs by MOVES 

sourcetype. To do this, we need the percentage of the MOVES sourcetype fleet comprised of 

each regulated sector. Table 2-248 shows this breakout for the vocational sector and Table 2-249 

shows it for tractors. 
2-279 

Table 2-248 Fleet Mix by MOVES Sourcetype and Regulated Sector -- Vocationala 

ENGINE FUEL SPEED INTERCITY 

BUS 

TRANSIT 

BUS 

SCHOOL 

BUS 

REFUSE 

TRUCKS 

SINGLE 

UNIT 

SHORT 

HAUL 

SINGLE 

UNIT 

LONG 

HAUL 

MOTOR 

HOMES

Light 

HD 

Gasoline Urban 0% 7% 0% 0% 4% 0% 0%

Light 

HD 

Gasoline Multipurpose 0% 20% 1% 0% 49% 0% 0%

Light 

HD 

Gasoline Regional 0% 0% 0% 0% 28% 0% 54%

Medium 

HD 

Gasoline Urban 0% 2% 0% 0% 1% 0% 0%

Medium 

HD 

Gasoline Multipurpose 0% 7% 95% 0% 11% 0% 0%

Medium 

HD 

Gasoline Regional 0% 0% 0% 0% 7% 0% 41%

Heavy 

HD 

Gasoline Urban 0% 16% 0% 0% 0% 0% 0%

Heavy 

HD 

Gasoline Multipurpose 0% 48% 4% 0% 0% 0% 0%

Heavy 

HD 

Gasoline Regional 0% 0% 0% 0% 0% 0% 5%

Light 

HD 

Diesel Urban 0% 0% 0% 0% 2% 0% 0%

Light 

HD 

Diesel Multipurpose 0% 0% 1% 0% 25% 0% 0%

Light 

HD 

Diesel Regional 2% 0% 0% 0% 14% 25% 54%

Medium 

HD 

Diesel Urban 0% 0% 0% 3% 2% 0% 0%

Medium 

HD 

Diesel Multipurpose 0% 0% 95% 0% 20% 0% 0%

Medium 

HD 

Diesel Regional 15% 0% 0% 0% 12% 37% 41%

Heavy 

HD 

Diesel Urban 0% 25% 0% 97% 1% 0% 0%

Heavy 

HD 

Diesel Multipurpose 0% 75% 5% 0% 15% 0% 0%

Heavy 

HD 

Diesel Regional 83% 0% 0% 0% 9% 37% 5%

Note: 

a

 Columns add to 100% or 0% for gasoline rows and 100% for diesel rows. 

 
2-280 

Table 2-249 Fleet Mix by MOVES Sourcetype and Regulated Sector – Tractorsa 

ENGINE MOVES 

SOURCETYPE 

CLASS 

7 

DAY 

CAB 

LOW 

ROOF 

CLASS 

7 

DAY 

CAB 

HIGH 

ROOF 

CLASS 

8 

DAY 

CAB 

LOW 

ROOF 

CLASS 

8 

DAY 

CAB 

HIGH 

ROOF 

CLASS 8 

SLEEPER 

CAB 

LOW 

ROOF 

CLASS 8 

SLEEPER 

CAB 

MID 

ROOF 

CLASS 8 

SLEEPER 

CAB 

HIGH 

ROOF 

Medium 

HD 

Combination 

Short haul 

11% 11% 

Heavy 

HD 

Combination 

Short haul 

 39% 39% 

Heavy 

HD 

Combination 

Long haul 

 5% 15% 80%

Note: 

a

 Combination short haul adds to 100% and long haul to 100%; empty cells denote 0%. 

Using the fleet mix information shown in Table 2-248 and Table 2-249, along with the 

package costs shown in Table 2-231 through Table 2-246, we can generate the package costs by 

MOVES sourcetype (note that package costs by MOVES sourcetype differ from package costs 

by regulated sector only for vocational vehicles and tractors; trailer and HD pickup and van costs 

do not change). These costs are shown in Table 2-250 through Table 2-253. 
2-281 

Table 2-250 Package Costs by MOVES Sourcetype 

Alternative 3 Incremental to Alternative 1a (2012$) 

SOURCETYPE FUEL 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

Intercity Bus Diesel $0 $0 $0 $2,913 $2,864 $2,705 $4,138 $4,070 $4,008 $4,112 $4,045

Transit Bus Diesel $0 $0 $0 $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

School Bus Diesel $0 $0 $0 $1,442 $1,410 $1,275 $2,275 $2,226 $2,177 $4,813 $4,730

Refuse Truck Diesel $0 $0 $0 $1,983 $1,939 $1,735 $3,302 $3,230 $3,156 $7,350 $7,226

SingleUnit 

ShortHaul 

Diesel $0 $0 $0 $1,400 $1,371 $1,261 $2,155 $2,112 $2,069 $3,975 $3,905

SingleUnit 

LongHaul 

Diesel $0 $0 $0 $1,637 $1,608 $1,525 $2,327 $2,289 $2,252 $2,627 $2,581

MotorHome Diesel $0 $0 $0 $736 $721 $690 $1,047 $1,030 $1,011 $1,576 $1,545

Intercity Bus Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Transit Bus Gasoline $0 $0 $0 $1,434 $1,402 $1,240 $2,388 $2,334 $2,277 $5,736 $5,645

School Bus Gasoline $0 $0 $0 $1,170 $1,145 $1,016 $1,868 $1,826 $1,782 $4,439 $4,368

Refuse Truck Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

SingleUnit 

ShortHaul 

Gasoline $0 $0 $0 $686 $671 $606 $1,053 $1,031 $1,006 $2,512 $2,472

SingleUnit 

LongHaul 

Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

MotorHome Gasoline $0 $0 $0 $453 $444 $421 $625 $615 $602 $1,189 $1,170

Comb 

ShortHaul 

Diesel $0 $0 $0 $5,398 $5,309 $4,945 $8,423 $8,279 $8,072 $10,187 $10,012

Comb 

LongHaul 

Diesel $0 $0 $0 $7,800 $7,667 $7,181 $11,137 $10,944 $10,677 $12,823 $12,622

53’ dry van $588 $550 $524 $901 $870 $817 $1,116 $1,083 $1,059 $1,409 $1,396

53’ rfr van $588 $550 $524 $901 $870 $817 $1,116 $1,083 $1,059 $1,280 $1,267

Container ch $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28’ dry van $514 $501 $489 $974 $957 $923 $1,097 $1,078 $1,065 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other trailer $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Vocational Diesel $0 $0 $0 $1,366 $1,337 $1,230 $2,102 $2,060 $2,018 $3,892 $3,824

Vocational Gasoline $0 $0 $0 $612 $599 $547 $916 $898 $877 $2,086 $2,053

Vocational Weighted 

Avg 

$0 $0 $0 $1,152 $1,128 $1,037 $1,766 $1,731 $1,695 $3,381 $3,323

Tractor Weighted 

Avg 

$0 $0 $0 $6,708 $6,605 $6,184 $9,935 $9,774 $9,542 $11,684 $11,503

Trailer Weighted 

Avg 

$639 $613 $592 $898 $877 $832 $1,012 $989 $971 $1,165 $1,152

Tractor/Trailer Weighted 

Avg 

$639 $613 $592 $7,606 $7,482 $7,016 $10,947 $10,763 $10,513 $12,849 $12,655
2-282 

Table 2-251 Package Costs by MOVES Sourcetype 

Alternative 4 Incremental to Alternative 1a (2012$) 

SOURCETYPE FUEL 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

Intercity Bus Diesel $0 $0 $0 $2,361 $2,320 $2,197 $4,314 $4,243 $4,174 $4,112 $4,045

Transit Bus Diesel $0 $0 $0 $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

School Bus Diesel $0 $0 $0 $2,997 $2,930 $2,610 $5,115 $5,002 $4,890 $4,813 $4,730

Refuse Truck Diesel $0 $0 $0 $4,300 $4,204 $3,714 $7,819 $7,645 $7,470 $7,350 $7,227

SingleUnit 

ShortHaul 

Diesel $0 $0 $0 $2,388 $2,336 $2,109 $4,216 $4,128 $4,040 $3,976 $3,907

SingleUnit 

LongHaul 

Diesel $0 $0 $0 $1,500 $1,473 $1,400 $2,760 $2,714 $2,668 $2,627 $2,581

MotorHome Diesel $0 $0 $0 $894 $875 $838 $1,658 $1,630 $1,601 $1,576 $1,545

Intercity Bus Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Transit Bus Gasoline $0 $0 $0 $3,273 $3,201 $2,807 $6,112 $5,971 $5,832 $5,739 $5,648

School Bus Gasoline $0 $0 $0 $2,649 $2,590 $2,279 $4,727 $4,616 $4,510 $4,439 $4,368

Refuse Truck Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

SingleUnit 

ShortHaul 

Gasoline $0 $0 $0 $1,417 $1,386 $1,235 $2,673 $2,612 $2,554 $2,513 $2,474

SingleUnit 

LongHaul 

Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

MotorHome Gasoline $0 $0 $0 $534 $524 $495 $1,256 $1,231 $1,207 $1,189 $1,170

Comb 

ShortHaul 

Diesel $0 $0 $0 $8,896 $8,748 $8,144 $10,877 $10,691 $10,357 $10,187 $10,012

Comb 

LongHaul 

Diesel $0 $0 $0 $11,334 $11,142 $10,420 $13,666 $13,430 $13,018 $12,823 $12,622

53’ dry van $588 $550 $524 $1,207 $1,172 $1,113 $1,504 $1,465 $1,437 $1,409 $1,396

53’ rfr van $588 $550 $524 $1,207 $1,172 $1,113 $1,371 $1,333 $1,306 $1,280 $1,267

Container ch $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28’ dry van $514 $501 $489 $1,146 $1,127 $1,090 $1,304 $1,282 $1,267 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other trailer $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Vocational Diesel $0 $0 $0 $2,333 $2,282 $2,061 $4,126 $4,041 $3,955 $3,892 $3,825

Vocational Gasoline $0 $0 $0 $1,134 $1,109 $997 $2,218 $2,168 $2,121 $2,087 $2,054

Vocational Weighted 

Avg 

$0 $0 $0 $1,994 $1,950 $1,760 $3,586 $3,511 $3,436 $3,382 $3,324

Tractor Weighted 

Avg 

$0 $0 $0 $10,225 $10,065 $9,404 $12,431 $12,227 $11,859 $11,684 $11,503

Trailer Weighted 

Avg 

$639 $613 $592 $1,084 $1,059 $1,012 $1,231 $1,204 $1,184 $1,165 $1,152

Tractor/Trailer Weighted 

Avg 

$639 $613 $592 $11,310 $11,124 $10,416 $13,662 $13,431 $13,043 $12,849 $12,655
2-283 

Table 2-252 Package Costs by MOVES Sourcetype 

Alternative 3 Incremental to Alternative 1b (2012$) 

SOURCETYPE FUEL 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

Intercity Bus Diesel $0 $0 $0 $2,913 $2,864 $2,705 $4,138 $4,070 $4,008 $4,112 $4,045

Transit Bus Diesel $0 $0 $0 $1,998 $1,954 $1,748 $3,332 $3,258 $3,183 $7,422 $7,298

School Bus Diesel $0 $0 $0 $1,442 $1,410 $1,275 $2,275 $2,226 $2,177 $4,813 $4,730

Refuse Truck Diesel $0 $0 $0 $1,983 $1,939 $1,735 $3,302 $3,230 $3,156 $7,350 $7,226

SingleUnit 

ShortHaul 

Diesel $0 $0 $0 $1,400 $1,371 $1,261 $2,155 $2,112 $2,069 $3,975 $3,905

SingleUnit 

LongHaul 

Diesel $0 $0 $0 $1,637 $1,608 $1,525 $2,327 $2,289 $2,252 $2,627 $2,581

MotorHome Diesel $0 $0 $0 $736 $721 $690 $1,047 $1,030 $1,011 $1,576 $1,545

Intercity Bus Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Transit Bus Gasoline $11 $12 $12 $1,445 $1,414 $1,252 $2,399 $2,345 $2,288 $5,747 $5,655

School Bus Gasoline $11 $12 $12 $1,182 $1,156 $1,028 $1,880 $1,837 $1,793 $4,449 $4,379

Refuse Truck Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

SingleUnit 

ShortHaul 

Gasoline $11 $12 $12 $697 $683 $617 $1,065 $1,041 $1,017 $2,522 $2,483

SingleUnit 

LongHaul 

Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

MotorHome Gasoline $11 $12 $12 $465 $456 $432 $636 $625 $613 $1,199 $1,181

Comb 

ShortHaul 

Diesel $0 -$59 -$114 $5,240 $5,101 $4,698 $8,132 $7,950 $7,713 $9,763 $9,590

Comb 

LongHaul 

Diesel $0 -$70 -$115 $7,638 $7,468 $6,974 $10,906 $10,692 $10,411 $12,508 $12,312

53’ dry van $588 $550 $524 $901 $856 $817 $1,116 $1,083 $1,059 $1,409 $1,382

53’ rfr van $588 $550 $524 $901 $856 $817 $1,116 $1,083 $1,059 $1,280 $1,254

Container ch $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28’ dry van $514 $501 $489 $974 $957 $923 $1,097 $1,078 $1,065 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other trailer $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Vocational Diesel $0 $0 $0 $1,366 $1,337 $1,230 $2,102 $2,060 $2,018 $3,892 $3,824

Vocational Gasoline $11 $12 $12 $624 $611 $558 $928 $909 $888 $2,096 $2,063

Vocational Weighted 

Avg 

$3 $3 $3 $1,156 $1,132 $1,040 $1,770 $1,734 $1,698 $3,384 $3,326

Tractor Weighted 

Avg 

$0 -$65 -$114 $6,547 $6,402 $5,958 $9,678 $9,488 $9,235 $11,321 $11,145

Trailer Weighted 

Avg 

$639 $613 $592 $898 $870 $832 $1,012 $989 $971 $1,165 $1,146

Tractor/Trailer Weighted 

Avg 

$639 $548 $478 $7,445 $7,273 $6,790 $10,690 $10,476 $10,206 $12,487 $12,292
2-284 

Table 2-253 Package Costs by MOVES Sourcetype 

Alternative 4 Incremental to Alternative 1b (2012$) 

SOURCETYPE FUEL 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 

Intercity Bus Diesel $0 $0 $0 $2,361 $2,320 $2,197 $4,314 $4,243 $4,174 $4,112 $4,045

Transit Bus Diesel $0 $0 $0 $4,337 $4,240 $3,745 $7,895 $7,719 $7,542 $7,422 $7,298

School Bus Diesel $0 $0 $0 $2,997 $2,930 $2,610 $5,115 $5,002 $4,890 $4,813 $4,730

Refuse Truck Diesel $0 $0 $0 $4,300 $4,204 $3,714 $7,819 $7,645 $7,470 $7,350 $7,227

SingleUnit 

ShortHaul 

Diesel $0 $0 $0 $2,388 $2,336 $2,109 $4,216 $4,128 $4,040 $3,976 $3,907

SingleUnit 

LongHaul 

Diesel $0 $0 $0 $1,500 $1,473 $1,400 $2,760 $2,714 $2,668 $2,627 $2,581

MotorHome Diesel $0 $0 $0 $894 $875 $838 $1,658 $1,630 $1,601 $1,576 $1,545

Intercity Bus Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Transit Bus Gasoline $11 $12 $12 $3,285 $3,212 $2,819 $6,124 $5,981 $5,843 $5,750 $5,659

School Bus Gasoline $11 $12 $12 $2,661 $2,602 $2,290 $4,738 $4,627 $4,521 $4,449 $4,379

Refuse Truck Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

SingleUnit 

ShortHaul 

Gasoline $11 $12 $12 $1,429 $1,397 $1,246 $2,685 $2,623 $2,565 $2,524 $2,484

SingleUnit 

LongHaul 

Gasoline $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

MotorHome Gasoline $11 $12 $12 $546 $535 $507 $1,268 $1,241 $1,218 $1,199 $1,181

Comb ShortHaul Diesel $0 -$59 -

$114 

$8,737 $8,540 $7,897 $10,586 $10,361 $9,998 $9,763 $9,590

Comb LongHaul Diesel $0 -$70 -

$115 

$11,172 $10,944 $10,212 $13,435 $13,178 $12,752 $12,508 $12,312

53’ dry van $588 $550 $524 $1,207 $1,157 $1,113 $1,504 $1,465 $1,437 $1,409 $1,382

53’ rfr van $588 $550 $524 $1,207 $1,157 $1,113 $1,371 $1,333 $1,306 $1,280 $1,254

Container ch $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

28’ dry van $514 $501 $489 $1,146 $1,127 $1,090 $1,304 $1,282 $1,267 $1,253 $1,239

Platform $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Tanker $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Other trailer $868 $847 $827 $807 $793 $752 $739 $726 $715 $704 $693

Vocational Diesel $0 $0 $0 $2,333 $2,282 $2,061 $4,126 $4,041 $3,955 $3,892 $3,825

Vocational Gasoline $11 $12 $12 $1,146 $1,121 $1,009 $2,230 $2,179 $2,132 $2,098 $2,065

Vocational Weighted 

Avg 

$3 $3 $3 $1,997 $1,954 $1,763 $3,589 $3,514 $3,439 $3,385 $3,327

Tractor Weighted 

Avg 

$0 -$65 -

$114 

$10,065 $9,862 $9,179 $12,174 $11,941 $11,552 $11,321 $11,145

Trailer Weighted 

Avg 

$639 $613 $592 $1,084 $1,053 $1,012 $1,231 $1,204 $1,184 $1,165 $1,146

Tractor/Trailer Weighted 

Avg 

$639 $548 $478 $11,149 $10,915 $10,191 $13,404 $13,144 $12,736 $12,487 $12,292
2-285 

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73 Committee on the Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy; National 

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80 American Trucking Association. Last viewed on January 29, 2010 at 

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http://www.epa.gov/smartway/transport/documents/tech/reducedspeed.pdf 82 Department for Transport, Vehicle and Operator Services Agency. Last viewed on January 6, 2010 at 

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85 Gaines, L. and D. Santini. Argonne National Laboratory, Economic Analysis of Commercial Idling Reduction 

Technologies 

86 ICF International. Investigation of Costs for Strategies to Reduce Greenhouse Gas Emissions for Heavy-Duty OnRoad Vehicles. July 2010. Docket Identification Number EPA-HQ-OAR-2010-0162-0044 87 NACFE, June 2014. Confidence Report: Idle-Reduction Solutions. Available at 

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November 2014) 

88 See Vanner battery-inverter Systems at http://www.vanner.com/ 

89 See eNow solar systems. http://www.enowenergy.com/. 

90 Schwarz, W., Harnisch, J. 2003. “Establishing Leakage Rates of Mobile Air Conditioners.” Prepared for the 

European Commission (DG Environment), Doc B4-3040/2002/337136/MAR/C1 
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 91 Vincent, R., Cleary, K., Ayala, A., Corey, R. 2004. “Emissions of HFC-134a from Light-Duty Vehicles in 

California.” SAE 2004-01-2256 

92 Society of Automotive Engineers, “IMAC Team 1 – Refrigerant Leakage Reduction, Final Report to Sponsors,” 

2006 

93 California Air Resources Board. Letter from Michael Carter to Matthew Spears dated December 3, 2014. Solar 

Control: Heavy-Duty Vehicles White Paper. Docket EPA-HA-OAR-2014-0827. 

94 National Renewal Energy Laboratory. Reducing Long-Haul Truck Idle Loads and Resulting Fuel Use. Presented 

by Jason A. Lustbader to EPA on July 29, 2014. See Docket EPA-HA-OAR-2014-0827. 

95 See NHTSA Technology Report #1 (2015), Note 6. 

96 U.S. EPA and NHTSA, “Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards 

and Corporate Average Fuel Economy Standards – Joint Technical Support Document,” 2010. Last viewed on June 

3, 2010 at http://www.epa.gov/otaq/climate/regulations/420r10901.pdf 

97 “Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 - 2014”, EPA420-R-14-023, October 2014. Available at http://www.epa.gov/otaq/fetrends.htm (last accessed October 31, 2014). 

98 “Development and Optimization of the Ford 3.5L V6 EcoBoost Combustion System,” Yi,J., Wooldridge, S., 

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99 David Woldring and Tilo Landenfeld of Bosch, and Mark J. Christie of Ricardo, “DI Boost: Application of a 

High Performance Gasoline Direct Injection Concept,” SAE 2007-01-1410. Available at 

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“The New Renault TCE 1.2L Turbocharged Gasoline Engine,” 28th Vienna Motor Symposium, April 2007. 

101 Tobias Heiter, Matthias Philipp, Robert Bosch, “Gasoline Direct Injection: Is There a Simplified, Cost-Optimal 

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September 2005. 102 U.S. EPA and NHTSA, “Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards 

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3, 2010 at http://www.epa.gov/otaq/climate/regulations/420r10901.pdf 

103 “All-new-Ford-engineered, Ford-tested, Ford-built diesel maximizes 2011 Super Duty productivity,” Ford press 

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Department of Energy. May 21, 2009. Last accessed on August 25, 2010 at 

http://www1.eere.energy.gov/vehiclesandfuels/pdfs/merit_review_2009/advanced_combustion/ace_40_stanton.pdf. 105 Committee on the Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy; National 

Research Council, “Assessment of Fuel Economy Technologies for Light-Duty Vehicles”, 2011. Available at 

http://www.nap.edu/catalog.php?record_id=12924 (last accessed Jun 27, 2012) 

106 SAE World Congress, “Focus B-pillar ‘tailor rolled’ to 8 different thicknesses,” Feb. 24, 2010. Available at 

http://www.sae.org/mags/AEI/7695 (last accessed Jun. 10, 2012) 

107 “2008/9 Blueprint for Sustainability,” Ford Motor Company. Available at: http:// 

www.ford.com/go/sustainability (last accessed February 8, 2010). 108 “2015 North American Light Vehicle Aluminum Content Study – Executive Summary”, June 2014, 

http://www.drivealuminum.org/research-resources/PDF/Research/2014/2014-ducker-report (last accessed February 

26, 2015) 

109 Fox News September 2014, “Ford confirms increased aluminum use on next-gen Super Duty pickups,” 

http://www.foxnews.com/leisure/2014/09/30/ford-confirms-increased-aluminum-use-on-next-gen-super-dutypickups/ (last accessed February 2015) 110 “Mass Reduction and Cost Analysis – Light-Duty Pickup Trucks Model Years 2020-2025”, FEV, North 

America, Inc., April 2015, Document no. EPA-420-R-15-006 

111 Michelin. SAE Presentation. 112 U.S. Department of Energy. Transportation Energy Data Book, Edition 28-2009. Table 5.7. 113 Gaines, L., A. Vyas, J. Anderson. Estimation of Fuel Use by Idling Commercial Trucks. January 2006. 114 Mack 2010 Powertrain Brochure 
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 115 Joint Technical Support Document: Final Rulemaking for 2017-2025 Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards, August 2012, available at 

http://www.epa.gov/otaq/climate/documents/420r12901.pdf 

116 Vehicle Valuation Services, Inc, 2000, available at http://www.vvsi.com/training/TrainingGuide.pdf (accessed 

June 2014) 

117 See Cummins-Eaton partnership at http://smartadvantagepowertrain.com/ 118 See TIAX 2009, Note 2 at 4.5.2 

119 See TIAX 2009, Note 2, Table 4-48 120 Heavy Duty Trucking October 2014, “2015 Medium-Duty Truck Trends,” 

http://www.truckinginfo.com/channel/equipment/article/story/2014/10/2015-medium-duty-trucks-the-vehicles-andtrends-to-look-for/page/3.aspx (downloaded November 2014) 121 See NACFE Confidence Report: Electronically Controlled Transmissions, at 

http://www.truckingefficiency.org/powertrain/automated-manual-transmissions (January 2015). See also 

http://www.overdriveonline.com/auto-vs-manual-transmission-autos-finding-solid-ground-by-sharing-data-withengines/ (accessed November 2014) 122 See Eaton Announcement September 2014, available at 

http://www.ttnews.com/articles/lmtbase.aspx?storyid=2969&t=Eaton-Unveils-Medium-Duty-ProcisionTransmission

123 Light-Duty Technology Cost Analysis, Report on Additional Case Studies, Revised final Report, EPA-420-R-13-

008, page 2-16, April 2013, available at http://www.epa.gov/otaq/climate/documents/420r13008.pdf 124 Lammert, M., Walkowivz, K., NREL, Eighteen-Month Final Evaluation of UPS Second Generation Diesel 

Hybrid-Electric Delivery Vans, September 2012, NREL/TP-5400-55658 

125 Green Fleet Magazine, The Latest Developments in EV Battery Technology, November 2013, available at 

http://www.greenfleetmagazine.com/article/story/2013/12/the-latest-developments-in-ev-battery-technologygrn/page/1.aspx 

126 Van Amburg, Bill, CALSTART, Status Report: Alternative Fuels and High-Efficiency Vehicles, Presentation to 

National Association of Fleet Administrators (NAFA) 2014 Institute and Expo, April 8, 2014 127 See NHTSA Technology Report #1 (2015), Note 6. See T-270 Delivery Truck Vehicle Technology Results 128 Heavy Duty Trucking, “Rise of the 6x2”, May 2013, accessed from 

http://www.truckinginfo.com/article/story/2013/05/rise-of-the-6x2.aspx 129 NACFE, Confidence Findings on the Potential of 6x2 Axles, available at http://nacfe.org/wpcontent/uploads/2014/01/Trucking-Efficiency-6x2-Confidence-Report-FINAL-011314.pdf, January 2014 

(downloaded November 2014). 

130 See NHTSA Technology Report #1 (2015), Note 6. 131 Argonne National Laboratory. “Evaluation of Fuel Consumption Potential of Medium and Heavy Duty Vehicles 

through Modeling and Simulation.” October 2009. Page 91. 

132 See SwRI Technology Report 1 (2014), Note 6. See Section 3.3.4.3 T270 Delivery Truck Vehicle Technology 

Results. 

133 See memorandum dated May 2015 on Vocational Vehicle Tire Rolling Resistance Test Data Evaluation. 134 See NREL data at http://www.nrel.gov/vehiclesandfuels/fleettest/research_fleet_dna.html 135 See memorandum dated May 2015 on Analysis of Possible Vocational Vehicle Standards Based on Alternative 

Idle Cycle Weightings 

136 See NHTSA Technology Report #1 (2015) Note 6, T-270 and F-650 weight reduction results 137 See TIAX 2009, Note 2. 138 The Minnesota refrigerant leakage data can be found at 

http://www.pca.state.mn.us/climatechange/mobileair.html#leakdata 

139 See Phase 1 RIA, Chapter 2.7 140 Society of Automotive Engineers, “IMAC Team 1 – Refrigerant Leakage Reduction, Final Report 

to Sponsors,” 2006 

141 Society of Automotive Engineers Surface Vehicle Standard J2727, issued August 2008, 

http://www.sae.org 

142 EPA Docket memo on Vocational Aerodynamics, May 2015 143 See http://westcoastcollaborative.org/files/sector-fleets/WCC-LA-BEVBusinessCase2011-08-15.pdf
2-291 

 144 Silver, Fred, and Brotherton, Tom. (CalHEAT) Research and Market Transformation Roadmap to 2020 for 

Medium- and Heavy-Duty Trucks. California Energy Commission, June 2013. 

145 Gallo, Jean-Baptiste, and Jasna Tomic (CalHEAT). 2013. Battery Electric Parcel Delivery Truck Testing and 

Demonstration. California Energy Commission. 

146 See Ford announcement December 2013, 

https://media.ford.com/content/fordmedia/fna/us/en/news/2013/12/12/70-percent-of-ford-lineup-to-have-auto-startstop-by-2017--fuel-.html. See also Allison-Cummins announcement July 2014, 

http://www.oemoffhighway.com/press_release/12000208/allison-stopstart?utm_source=OOH+Industry+News+eNL&utm_medium=email&utm_campaign=RCL140723006

147 “Tires & Truck Fuel Economy: A New Perspective”, The Tire Topic Magazine, Special Edition Four, 2008, 

Bridgestone Firestone, North American Tire, LLC. Available online: 

http://www.trucktires.com/bridgestone/us_eng/brochures/pdf/08-Tires_and_Truck_Fuel_Economy.pdf 148 Truck Trailer Manufacturers Association letter to EPA. October 16, 2014. Docket EPA-HQ-OAR-2014-0827 

149 “Tire Pressure Systems - Confidence Report”. North American Council for Freight Efficiency. 2013. Available 

online: http://nacfe.org/wp-content/uploads/2014/01/TPS-Detailed-Confidence-Report1.pdf 150 ”A Day in the Life of a Tire”, Pressure Systems International, Presented to EPA on August 20, 2014. 

151 Federal Motor Carriers Safety Administration, “Commercial Vehicle Tire Condition Sensors,” conducted by 

Booz-Allen-Hamilton, Inc. November, 2003. 

152 TMC Technology & Maintenance Council, “TMC Tire Air Pressure Study,” May 2002 153 FMCSA “Advanced Sensors and Applications: Commercial Motor Vehicle Tire Pressure Monitoring and 

Maintenance,” February 2014 

154 “Underinflated Commercial Low Rolling Resistance Truck Tires & It’s Impact on Fuel Economy”, October 

2010. Pressure Systems International, Presentation Material shared with EPA on May 21, 2014. 155 Scarcelli, Jamie. "Fuel Efficiency for Trailers" Presented at ACEEE/ICCT Workshop: Emerging Technologies 

for Heavy-Duty Vehicle Fuel Efficiency, Wabash National Corporation. July 22, 2014 156 "Weight Reduction: A Glance at Clean Freight Strategies", EPA SmartWay. EPA420F09-043. Available at: 

http://permanent.access.gpo.gov/gpo38937/EPA420F09-043.pdf 157 Memo to docket regarding confidential weight reduction information obtained during SBREFA Panel, June 4, 

2015

158 Randall Scheps, Aluminum Association, “The Aluminum Advantage: Exploring Commercial Vehicles 

Applications,” presented in Ann Arbor, Michigan, June 18, 2009 

159 http://energy.gov/eere/articles/supertruck-making-leaps-fuel-efficiency 160 Truck Trailer Manufacturers Association letter to EPA. October 16, 2014. Docket EPA-HQ-OAR-2014-0827 161 Ben Sharpe (ICCT) and Mike Roeth (North American Council for Freight Efficiency), “Costs and Adoption 

Rates of Fuel-Saving Technologies for Trailer in the North American On-Road Freight Sector”, Feb 2014 

162 Frost & Sullivan, “Strategic Analysis of North American Semi-trailer Advanced Technology Market”, Feb 2013 163 ICF International. Investigation of Costs for Strategies to Reduce Greenhouse Gas Emissions for Heavy-Duty 

On-Road Vehicles. July 2010. See also, TIAX, LLC. “Assessment of Fuel Economy Technologies for Mediumand Heavy-Duty Vehicles,” Final Report to the National Academy of Sciences, November 19, 2009. 164 Schubert, R., Chan, M., Law, K. (2015). Commercial Medium- and Heavy-Duty (MD/HD) Truck Fuel Efficiency 

Cost Study. Washington, DC: National Highway Traffic Safety Administration, Tetra Tech Technology Cost Report 

(2015), Note 8. 

165 Schubert, R., Chan, M., Law, K. (2015). Commercial Medium- and Heavy-Duty Truck Fuel Efficiency 

Technology Cost Study. Washington, DC: National Highway Traffic Safety Administration. 

166 A. Rogozhin et al., Int. J. Production Economics 124 (2010) 360–368. 167 RTI International. Heavy Duty Truck Retail Price Equivalent and Indirect Cost Multipliers. July 2010. 168 Rogozhin, A., et al., “Using indirect cost multipliers to estimate the total cost of adding new technology in the 

automobile industry,” International Journal of Production Economics (2009), doi:10.1016/j.ijpe.2009.11.031. 

169 Helfand, Gloria, and Todd Sherwood, “Documentation of the Development of Indirect Cost Multipliers for Three 

Automotive Technologies,” August 2009. 

170 RTI International. Heavy Duty Truck Retail Price Equivalent and Indirect Cost Multipliers. July 2010. 
2-292 

 171 See “Learning Curves in Manufacturing”, L. Argote and D. Epple, Science, Volume 247; “Toward Cost Buy 

down Via Learning-by-Doing for Environmental Energy Technologies, R. Williams, Princeton University, 

Workshop on Learning-by-Doing in Energy Technologies, June 2003; “Industry Learning Environmental and the 

Heterogeneity of Firm Performance, N. Balasubramanian and M. Lieberman, UCLA Anderson School of 

Management, December 2006, Discussion Papers, Center for Economic Studies, Washington DC. 172 See “Learning Curves in Manufacturing”, L. Argote and D. Epple, Science, Volume 247; “Toward Cost Buy 

down Via Learning-by-Doing for Environmental Energy Technologies, R. Williams, Princeton University, 

Workshop on Learning-by-Doing in Energy Technologies, June 2003; “Industry Learning Environments and the 

Heterogeneity of Firm Performance, N. Balasubramanian and M. Lieberman, UCLA Anderson School of 

Management, December 2006, Discussion Papers, Center for Economic Studies, Washington DC. 173 U.S. Energy Information Administration, Annual Energy Outlook 2014, Early Release; Report Number 

DOE/EIA-0383ER (2014), December 16, 2013. 

174 Bureau of Economic Analysis, Table 1.1.9 Implicit Price Deflators for Gross Domestic Product; as revised on 

March 27, 2014. 

175 Business Case for Battery-Electric Trucks in Los Angeles, California, at 

http://westcoastcollaborative.org/files/sector-fleets/WCC-LA-BEVBusinessCase2011-08-15.pdf, last accessed on 

12/8/2014. 

176 Ben Sharpe (ICCT) and Mike Roeth (North American Council for Freight Efficiency), “Costs and Adoption 

Rates of Fuel-Saving Technologies for Trailer in the North American On-Road Freight Sector”, Feb 2014. 
3-1 

Chapter 3: Test Procedures 

Test procedures are a crucial aspect of the heavy-duty vehicle GHG and fuel 

consumption program. This rulemaking is proposing to establish several new test procedures for 

both engine and vehicle compliance. This chapter will describe the development process for the 

test procedures being proposed, including the assessment of engines, aerodynamics, rolling 

resistance, chassis dynamometer testing, powertrain testing, and duty cycles. 

3.1 Heavy-Duty Engine Test Procedure 

The agencies are controlling heavy-duty engine fuel consumption and greenhouse gas 

emissions through the use of engine certification. The program will mirror existing engine 

regulations for the control of both GHG and non-GHG pollutants in many aspects. The 

following sections provide an overview of the test procedures. 

3.1.1 Existing Regulation Reference 

Heavy-duty engines currently are certified for GHG and non-GHG pollutants using test 

procedures developed by EPA. The Heavy-Duty Federal Test Procedure (FTP) is a transient test 

consisting of second-by-second sequences of engine speed and torque pairs with values given in 

normalized percent of maximum form. The cycle was computer generated from a dataset of 88 

heavy-duty trucks in urban operation in New York and Los Angeles. These procedures are welldefined, mirror in-use operating parameters, and thus we believe appropriate also for the 

assessment of GHG emissions from heavy duty engines. Further, EPA is concerned that we 

maintain a regulatory relationship between the non-GHG emissions and GHG emissions, 

especially for control of CO2 and NOX. Therefore, the agencies are proposing to continue using 

the same criteria pollutant test procedures for both the CO2 and fuel consumption standards. 

For 2007 and later Heavy-Duty engines, 40 CFR Parts 86 – “Control of Emissions from 

New and In-Use Highway Vehicles and Engines” and 1065 – “Engine Testing Procedures” detail 

the certification process. 40 CFR 86.007-11 defines the standard settings of Oxides of Nitrogen, 

Non-Methane Hydrocarbons, Carbon Monoxide, and Particulate Matter. The duty cycles are 

defined in Part 86. The Federal Test Procedure engine test cycle is defined in 40 CFR part 86 

Appendix I. The Supplemental Emissions Test engine cycle is defined in 40 CFR 86.1360(b). 

All emission measurements and calculations are defined in Part 1065, with exceptions as noted 

in 40 CFR 86.007-11. The data requirements are defined in 40 CFR 86.001-23 and 40 CFR 

1065.695. 

The measurement method for CO2 is described in 40 CFR 1065.250. For measurement of 

CH4 refer to 40 CFR 1065.260. For measurement of N2O refer to 40 CFR 1065.275. We 

recommend that you use an analyzer that meets performance specifications shown in Table 1 of 

40 CFR 1065.205. Note that your system must meet the linearity verification of 40 CFR 

1065.307. To calculate the brake specific mass emissions for CO2, CH4 and N2O refer to 40 

CFR 1065.650. 
3-2 

3.1.2 Engine Dynamometer Test Procedure Proposed Modifications 

3.1.2.1 Fuel Consumption Calculation 

EPA and NHTSA propose to calculate fuel consumption, as defined as gallons per brake 

horsepower-hour, from the CO2 measurement, just as in the HD Phase 1 rule. The agencies are 

proposing that manufacturers use 8,887 grams of CO2 per gallon of gasoline and 10,180 g CO2

per gallon of diesel fuel. 

3.1.2.2 Regeneration Impact on Fuel Consumption and CO2 Emissions 

The current engine test procedures also require the development of regeneration emission 

rate and frequency factors to account for the emission changes during a regeneration event.1

 In 

Phase 1, the agencies adopted provisions to exclude CO2 emissions and fuel consumption due to 

regeneration. However, for Phase 2, we propose to include CO2 emissions and fuel consumption 

due to regeneration over the FTP and RMC cycles as determined using the infrequently 

regenerating aftertreatment devices (IRAF) provisions in 40 CFR 1065.680. However, we are 

not proposing to include fuel consumption due to regeneration in the creation of the fuel map 

used in GEM for vehicle compliance Our assessment of the current non-GHG regulatory 

program indicates that engine manufacturers have significantly reduced the frequency of 

regeneration events. In addition, market forces already exist which create incentives to reduce 

fuel consumption during regeneration. 

3.1.2.3 Fuel Heating Value Correction 

In the HD Phase 1 rule, the agencies collected baseline CO2 performance of diesel 

engines from testing which used fuels with similar properties. The agencies are proposing to 

continue using a fuel-specific correction factor for the fuel’s energy content in case this changes 

in the future. The agencies found the average energy content of the diesel fuel used at EPA’s 

National Vehicle Fuel and Emissions Laboratory was 21,200 BTU per pound of carbon. This 

value is determined by dividing the Net Heating Value (BTU per pound) by the carbon weight 

fraction of the fuel used in testing. We are also proposing to continue using the Phase 1 

corrections for gasoline, natural gas, and liquid petroleum gas in 40 CFR 1036.530. We are also 

proposing to expand the table by adding dimethyl ether. 

In addition to the fuel heating value correction, we are proposing the addition of 

reference carbon mass fraction values for these fuels to the Table in 40 CFR 1036.530. These 

reference values are used in the powertrain calculations 40 CFR 1037.550 to account for the 

difference in carbon mass fraction between the test fuel and the reference fuel prior to correcting 

for the test fuel’s mass-specific net energy content. 

The agencies are not proposing fuel corrections for alcohols because the fuel chemistry is 

homogeneous. 
3-3 

3.1.2.4 Urea Derived CO2 Correction 

The agencies are proposing to allow manufacturers to correct compression ignition 

engine and powertrain CO2 emission results (for engines utilizing urea SCR for NOX control) to 

account for the contribution of urea derived CO2 emissions to the total engine CO2 emissions. 

Urea derived CO2 can account for up to 1 percent of the total CO2 emissions. Urea is 

produced from gaseous NH3 and gaseous CO2 that is captured from the atmosphere, thus CO2

derived from urea decomposition in diesel SCR emission control systems results in a net 

emission of zero CO2 to the environment. In our proposed test procedures for Phase 2, we allow 

manufacturers to determine CO2 emissions either by measuring the CO2 emitted from the engine 

or to determine it by measuring fuel flow rate during the test. If we do not allow for correction 

of the urea derived CO2 emissions, this will result in a positive CO2 bias for CO2 emissions 

determined by measuring the CO2 emitted from the engine. To perform this correction, we are 

proposing that you determine the mass rate of urea injected over the duty cycle from the engine’s 

J1939 CAN signal. This value is used as an input to an equation that allows you to determine the 

mass rate of CO2 from urea during the duty cycle. This resulting CO2 mass emission rate value 

is then used as an input to the steady-state fuel map fuel mass flow rate calculation in 40 CFR 

1036.535 and the total mass of CO2 emissions over the duty cycle calculations in 40 CFR 

1037.550. Note that this correction is only allowed for CO2 measured from the engine and not 

CO2 derived from fuel flow measurement. 

The calculation for determination of the mass rate of CO2 from urea requires the user to 

input the urea solution urea percent by mass. This calculation uses prescribed molecular weights 

for CO2 and urea as given in 40 CFR 1065.1005 of 44.0095 and 60.05526 respectively. A 1:1 

molar ratio of urea reactant to CO2 product is assumed. 

To facilitate the ability of the agencies to make this correction, we are proposing that the 

urea mass flow rate be broadcasted on the non-proprietary J1939 PGN (Parameter Group 

Number) 61475 (and 61478 if applicable). 

3.1.2.5 Multiple Fuel Maps 

Modern heavy-duty engines may have multiple fuel maps, commonly meant to improve 

performance or fuel efficiency under certain operating conditions. CO2 emissions can also be 

different depending on which map is tested, so it is important to specify a procedure to properly 

deal with engines with multiple fuel maps. Consistent with criteria-pollutant emissions 

certification, engine manufacturers should submit CO2 data from all fuel maps on a given test 

engine. This includes fuel map information as well as the conditions under which a given fuel 

map is used (i.e. transmission gear, vehicle speed, etc). 

3.1.2.6 Measuring GEM Engine Inputs 

To recognize the contribution of the engine in GEM the engine fuel map, full load torque 

curve and motoring torque curve have to be input into GEM. To insure the robustness of each of 

those inputs, a standard procedure has to be followed. Both the full load and motoring torque 

curve procedures are already defined in 40CFR part 1065 for engine testing. However, the fuel 
3-4 

mapping procedure being proposed would be new. The agencies have compared the proposed 

procedure against other accepted engine mapping procedures with a number of engines at 

various labs including EPA’s NVFEL, Southwest Research Institute sponsored by the agencies, 

and Environment Canada’s laboratory.1

 The proposed procedure was selected because it proved 

to be accurate and repeatable, while limiting the test burden to create the fuel map. This proposed 

provision is consistent with NAS’s recommendation (3.8). 

The agencies are proposing that engine manufacturers must certify fuel maps as part of 

their certification to the engine standards, and that they be required to provide those maps to 

vehicle manufacturers. The one exception to this requirement would be for cases in which the 

engine manufacturer certifies based on powertrain testing, as described in Section 3.6. In such 

cases, engine manufacturers would not be required to also certify the otherwise applicable fuel 

maps. We are not proposing that vehicle manufacturers will be allowed to develop their own 

fuel maps for engines they do not manufacture. 

3.1.3 Engine Family Definition and Test Engine Selection 

3.1.3.1 Criteria for Engine Families 

The current regulations outline the criteria for grouping engine models into engine 

families sharing similar emission characteristics. A few of these defining criteria include borecenter dimensions, cylinder block configuration, valve configuration, and combustion cycle; a 

comprehensive list can be found in 40 CFR 86.096-24(a)(2). While this set of criteria was 

developed with criteria pollutant emissions in mind, similar effects on CO2 emissions can be 

expected. For this reason, this methodology should continue to be followed when considering 

CO2 emissions, just as it was in the HD Phase 1 rule. 

3.1.3.2 Emissions Test Engine 

We are proposing that manufacturers select at least one engine per engine family for 

emission testing. The methodology for selecting the test engine(s) should be consistent with 40 

CFR 86.096-24(b)(2) (for heavy-duty Otto cycle engines) and 40 CFR 86.096-24(b)(3) (for 

heavy-duty diesel engines). An inherent characteristic of these methodologies is selecting the 

engine with the highest fuel feed per stroke (primarily at the speed of maximum rated torque and 

secondarily at rated speed) as the test engine, as this is expected to produce the worst-case 

criteria pollutant emissions. To be consistent, however, it is recommended that the same 

methodology continue to be used for selecting test engines. 

3.2 Aerodynamic Assessment 

3.2.1 Aerodynamics for Tractors 

For the Phase 1 rule, the agencies promulgated requirements whereby the coefficient of 

drag assessment was a product of test data and modeling using good engineering judgment. A 

 1

 U.S. Environmental Protection Agency. Memo to Docket EPA-HQ-OAR-2014-0827. 
3-5 

group of aerodynamic bins for tractors corresponding to certain known aerodynamic design 

features (e.g., Classic, Conventional, SmartWay, etc.) were established based on the results of an 

agency sponsored aerodynamic assessment test program. The rules require tractor manufacturers 

to take the aerodynamic test result from a tractor and determine the tractor’s appropriate bin. To 

ensure the consistency of the drag assessment results, certain aspects of the truck were defined, 

including the trailer, location of payload, and tractor-trailer gap. In addition, the agencies 

specified test procedures for aerodynamic assessment: coastdown testing (also used as the 

reference method), wind tunnel testing (reduced and full scale), and computational fluid 

dynamics (CFD). Constant speed testing was also permitted as an alternative test procedure, but 

the agencies did not develop a specific procedure. 

For the HD Phase 2 proposal, we are retaining all the current HD Phase 1 aspects of the 

aerodynamic assessment protocols with the following revisions and additions: enhancement of 

the analysis methodology for the coastdown test procedure, which we are proposing to keep as 

the reference method for the proposed tractor program; specifications for the constant speed test 

procedure; inclusion of trailers in the aerodynamic assessment test protocols; modifications to 

the reference trailer used for tractor aerodynamic assessment and establishing a reference tractor 

for trailer aerodynamic assessment; proposal of wind-average coefficient of drag (CDwa) as the 

required aerodynamic Greenhouse Gas Emissions Model (GEM) input for tractors; and 

considering potential aerodynamic performance for advanced aerodynamic performance of 

tractor-trailer combinations in the proposed timeframe for this rulemaking. Another proposed 

modification to the aerodynamic assessment for HD Phase 2 is the use of drag area (coefficient 

of drag multiplied by the frontal area, or CdA), rather than the coefficient of drag (Cd), for tractor 

aerodynamic bin standards. Although this modification would not alter the aerodynamic 

assessment protocols, it is important to note this since all HD Phase 2 aerodynamic assessment 

results will be presented in this format, rather than the Cd format used for HD Phase 1. 

3.2.2 Modifications to Aerodynamic Assessment Methods for HD Phase 2 

Currently, tractor manufacturers are successfully using the established aerodynamic 

assessment methods established under HD Phase 1 for implementation and compliance with HD 

Phase 1 emissions standards. Accordingly for HD Phase 2, we are proposing to continue to use 

the existing aerodynamic assessment methods for generating aerodynamic inputs to GEM with 

the coastdown test procedure as the reference method, and the constant speed test procedure, 

wind tunnels, and CFD as the allowed alternative methods (or any other EPA pre-approved test 

methods). As a result, for HD Phase 2, we are only considering modifications to further 

enhance, improve or specify the existing aerodynamic assessment methods. 

During development and since the beginning of HD Phase 1 implementation, we have 

received suggestions for improving the coastdown test procedure analysis methodology to reduce 

data post processing and improve data resolution. Also, as mentioned above, although constant 

speed testing is allowed as an alternative method, we did not define the specifications and 

protocols for conducting the testing as was done for the coastdown test procedure reference 

method and other alternative methods. Finally, by virtue of CFD being software-based, it may 

be possible to improve the conditions specified for performing CFD analysis to provide a more 

realistic result. Accordingly, for HD Phase 2 aerodynamic assessment methods, we are 

proposing to modify the coastdown test procedure analysis methodology, define the 
3-6 

specifications and protocols for conducting and analyzing the results of the constant speed test 

procedure, and update the conditions for performing CFD analysis. 

For HD Phase 2, we are not proposing any changes to the existing wind tunnel 

specifications and protocols other than revising the measured yaw angles to incorporate the wind 

average coefficient of drag. Under HD Phase 1, we only required manufacturers to conduct wind 

tunnel testing at a zero degree yaw angle. In contrast, for HD Phase 2, we are proposing to 

incorporate the wind average coefficient of drag and, consequently, to require measurement at 

additional yaw angles for generating the yaw sweep curve and calculating the wind average 

coefficient of drag. 

Wind tunnels are pivotal in redefining a standard trailer for tractors, assessing different 

trailer types to support the proposed trailer standards, and assessing wind averaged drag. 

Therefore, the test results using the existing wind tunnel specifications and protocols, and the 

revisions to incorporate the wind average coefficient of drag will be discussed in the context of 

these areas later in this section. 

3.2.2.1 Modification of Coastdown Testing Data Analysis Procedures for HD 

Phase 2 

Based on feedback from the heavy-duty vehicle manufacturing industry and other 

entities, the agencies finalized a Modified SAE J1263 coastdown procedure in the HD Phase 1 

rulemaking. During and since the finalization of HD Phase 1 regulations, stakeholders have 

suggested analyzing portions of the data rather than the full data set generated during coastdown 

testing to increase measurement accuracy and/or precision. One OEM suggested the use of the 

high speed portion of the coastdown test procedure speed range to solely or predominantly 

isolate the aerodynamic forces. Another OEM suggested using the high speed and low speed 

portions of the coastdown test procedure speed range in an iterative fashion to isolate the 

mechanical/frictional losses and rolling resistance predominantly present at lower speeds and 

removing these forces from the higher speed forces to capture predominantly aerodynamic 

forces. 

As a result of these suggestions, the agencies (via contractors ICF Corporation and 

Southwest Research Institute (SwRI)) coasted down combination tractors on Farm-to-Market 

Highway 70, a rural highway, between Bishop, Texas and Chapman Ranch, Texas. A grade 

survey was performed by SwRI. Filtered USGS elevation data were also obtained for the same 

stretch of roadway.2

 The grade information was incorporated into our analysis. The testing was 

conducted overnight, usually between 12 am and 4 am, to minimize traffic and wind. To get a 

comprehensive data set to conduct various analysis techniques, the vehicles were coasted down 

from 70 mph to 0 mph. Approximately 20 runs (10 in each direction) were planned for each test, 

but the number was reduced to 14 runs (7 in each direction), due to the increase in test time 

associated with coasting all the way to 0 mph. An ultrasonic anemometer was mounted 0.85 m 

above the leading edge of the trailer at the midpoint of the trailer width. This anemometer 

recorded air speed and direction onboard the vehicle at 10 Hz. A weather station, which 

measured wind speed, wind direction, temperature, and air pressure at 1 Hz, was placed 

alongside the road at the approximate midpoint of the stretch of road being used for the tests. 

Details of the test setup and vehicle information can be found in the on-road testing summary 
3-7 

report from SwRI.3

 The average and maximum wind speeds were calculated for each run to 

determine validity of the run with respect the wind restrictions. Table 3-1 below shows the 

ambient conditions desired during coastdown testing, which resembles the SAE J1263 

recommended practice. 

Table 3-1 Coastdown Ambient Conditions 

PARAMETER RANGE 

Average wind speed at the test site 

(for each run in each direction) 

< 10 mph 

Maximum wind speed (for each run in each 

direction) 

<12.3 mph 

Average cross wind speed 

(for each run in each direction at the site) 

< 5 mph 

The position of the onboard anemometer is such that the air speed readings need to be 

corrected. Located above the trailer, the anemometer’s air velocity readings will typically be 

greater than the free stream air speed. The roadside weather station was used to correct the 

onboard air speed measurements, using the trigonometric calculations below. For this 

correction, each coastdown run was split into 5-mph segments, over which the vehicle speed v, 

measured air speed vr,meas, wind speed w, and wind direction θw were averaged. 

 
3-8 

Figure 3-1 Diagram of vehicle speed and air speed vectors during coastdowns in opposite directions 

 

v

r, th

w

θ

r, th

v

r, th

w

r, th

Direction 1 

Direction 2 
3-9 

The law of cosines was used to calculate the theoretical air speed vr,th from the vehicle 

speed and weather station measurements, as described in the equation below. The bars over the 

variables indicate averages over the 5-mph segments. 

ݒ

ഥr,th ൌ

ە

۔

1 ൌ ݊݋݅ݐ݁ܿݎ݀݅ , ഥwߠ cos ഥݓഥݒ2 െ 2ഥݒ ൅ 2ഥݓටۓ

2 ൌ ݊݋݅ݐ݁ܿݎ݀݅ , ഥwߠ cos ഥݓഥݒ2 ൅ 2ഥݒ ൅ 2ഥݓට

The resulting theoretical air speed values were regressed against the measured air speed 

values for every test, and this linear relationship was used to correct the air speed measurements 

in the real-time data. This relationship is shown by the equations below and from the test results 

given in Figure 3-2. 

Regression equation for air speed correction: ݒ

ݒ1ߙ ൅ 0ߙ ൌ th,ഥr

ഥr,meas

Applied to air speed measurements: ݒ୰ ൌ ߙ ൅ ଴ߙଵݒ୰,୫ୣୟୱ

Figure 3-2 Example of theoretical air speed vs. measured air speed shows a consistent relationship that can 

be used to correct the onboard air speed measurements. 

 

vr,th = -0.872 + 0.937vr,meas [mph] 
3-10 

The 10-Hz data were filtered using a one-second weighted centered moving average prior 

to further analysis. 

3.2.2.1.1 High-low Iteration Analysis 

This analysis involves analyzing the coastdown over two separate speed ranges. A lowspeed range is used to estimate mechanical losses and subtract them out of a high-speed range to 

estimate aerodynamic drag. This process is iterated until mechanical and aerodynamic drag 

forces converge. The force is not calculated at each measurement, but instead the net force over 

each speed range is calculated by measuring the time taken to decelerate through each speed 

range. We assumed a linear decrease in speed (i.e. constant deceleration), because the speed 

ranges are small. We are also incorporating a simple speed-dependent rear axle loss adjustment 

to subtract out what we have learned to be a small but speed-dependent non-aerodynamic drag. 

We are also in the process of collecting data on the speed effect of tire rolling resistance and are 

considering including a simple adjustment for this in the final rule. 

While this analysis can be done for any pair of speed ranges, we are focusing this 

discussion on a low speed range of 25 to 15 mph and a high speed range of 70 to 60 mph. Table 

3-2 below describes the analysis methodology step by step. 
3-11 

Table 3-2 Drag Area Calculation Steps for High-Low Iteration Analysis 

STEP 0: FIND THE TIMES 

BRACKETING THE LOWSPEED AND HIGH-SPEED 

RANGES 

VLO1<VI<VLO2 (LOW SPEED)

VHI1<VI<VHI2 (HIGH SPEED) 

ݐ୓୐ଵ, ݐ୓୐ଶ, ݐୌ୍ଵ, ݐୌ୍ଶ V = VEHICLE SPEED 

LO1=15MPH, 

LO2=25MPH 

HI1=60MPH, 

HI2=70MPH 

Step 1: Calculate acceleration 

for each speed range. ܽ୪୭ ൌ ݒ୪୭ଶ െ ݒ୪୭ଵ

ݐ୪୭ଶ െ ݐ୪୭ଵ

ൌ ∆ݒ୪୭

୪୭ݐ∆

ܽ୦୧ ൌ ݒ୦୧ଶ െ ݒ୦୧ଵ

ݐ୦୧ଶ െ ݐ୦୧ଵ

ൌ ∆ݒ୪୭

୪୭ݐ∆

a = vehicle acceleration 

t = time 

Step 2: Calculate average road 

grade force over each speed 

range. 

ܨ୥୰ୟୢୣ,୪୭ ൌ ܯ݃ ൬

∆݄

൰ݏ∆

୪୭

݃ܯ ൌ

݄୪୭ଶ െ ݄୪୭ଵ

ݏ୪୭ଶ െ ݏ୪୭ଵ

ܨ୥୰ୟୢୣ,୦୧ ൌ ܯ݃ ൬

∆݄

൰ݏ∆

୦୧

݃ܯ ൌ

݄୦୧ଶ െ ݄୦୧ଵ

ݏ୦୧ଶ െ ݏ୦୧ଵ

M = vehicle mass 

h = elevation (relative) 

s = travel distance 

g = gravitational 

acceleration = 9.81 m/s2

Step 3: Inertial and Effective 

Mass 

(Add 125 lbm per tire to 

account for rotational inertia). 

ܯ୧୬ୣ୰୲୧ୟ୪ ൌ 125

݈ܾ݉

ୱୣ୧୰୲݊ ∙ ݁ݎ݅ݐ

ൌ 56.7 ݇݃

ୱୣ୧୰୲݊ ∙ ݁ݎ݅ݐ

ܯୣ ൌܯ൅ܯ୧୬ୣ୰୲୧ୟ୪

Minertial = additional inertia 

from rotating components 

Me = effective mass 

ntires = total number of tires 

in test configuration

Step 4: Road load force for 

each speed range, also 

accounting for rear axle loss 

estimate (Faxle). 

ܨ୪୭ ൌ െܯୣܽ୪୭ ൅ ܨ୥୰ୟୢୣ,୪୭ െ ܨୟ୶୪ୣ,୪୭

ܨ୦୧ ൌ െܯୣܽ୦୧ ൅ ܨ୥୰ୟୢୣ,୦୧ െ ܨୟ୶୪ୣ,୦୧

F = force 

Faxle,lo = 100 N 

Faxle,hi = 200 N

Step 5: Air density during 

each high speed section. ߩ ൌ

1000 ൈ P

RሺT ൅ 273.15ሻ

ρ = density of air

P= average ambient 

pressure during high speed 

run in kPa 

T = average ambient 

temperature during high 

speed run in °C 

R = gas constant for air 

=287.058 J/(kg-K) 

Step 6: Average relative air 

speed over each speed range. 

ݒ୰,୪୭,ୟ୴୥ ൌ ∑ ௩౨

௡ౢ౥

௩ౢ౥మ

௩ౢ౥భ ݒ୰,୦୧,ୟ୴୥ ൌ ∑ ௩౨

௡౞౟

௩౞౟మ

௩౞౟భ

vr = relative air speed 

Step 7: Initial conditions (i=0). 

Start with no aerodynamic 

forces in the low speed range. 

ܨୟୣ୰୭,୪୭,଴ ൌ 0
3-12 

Step 8: Subtract low-speed 

aerodynamic forces from low 

speed forces to estimate 

mechanical forces. 

ܨ୫ୣୡ୦,୧ ൌ ܨ୪୭ െ ܨୟୣ୰୭,୪୭,୧

Step 9: Subtract mechanical 

forces from high speed forces 

to estimate aerodynamic 

forces. 

ܨୟୣ୰୭,୦୧,୧ ൌ ܨ୦୧ െ ܨ୫ୣୡ୦,୧

Step 10: Adjust aerodynamic 

forces by speed to estimate 

low-speed aerodynamic 

forces. 

ܨୟୣ୰୭,୪୭,୧ାଵ ൌ ܨୟୣ୰୭,୦୧,୧ାଵ ቆ

ݒ୰,୪୭,ୟ୴୥

ଶ

ݒ୰,୦୧,ୟ୴୥

ଶ ቇ

Step 11: Repeat steps 8-10 

until both high-speed 

aerodynamic and low-speed 

mechanical forces both 

converge less than 1%. 

Repeat steps 8-10 until: 

ቤ1 െ ܨୟୣ୰୭,୦୧,୧ାଵ

ܨୟୣ୰୭,୦୧,୧ ቤ ൏ 0.01	

and 

ቤ1 െ ܨ୫ୣୡ୦,୪୭,୧ାଵ

ܨ୫ୣୡ୦,୪୭,୧ ቤ ൏ 0.01

Step 12: Calculate drag area. ܥୢܣ ൌ 2ܨୟୣ୰୭,୦୧,୧ାଵ

ݒߩ୰,୦୧,ୟ୴୥

ଶ

There are some advantages to using this method over the Phase 1 method. Focusing on 

segmented speed ranges may open up more test locations, as less road or track space would be 

required to collect a full data set. The middle range of speeds that would be eliminated contains 

a higher proportion of rolling resistance forces and also sweeps through greater yaw angles, even 

at modest crosswind conditions, which can increase the aerodynamic drag of certain runs and 

subsequently increase the variability of a test. This method does not account for yaw angle, but 

with the proper wind constraints and the use of a high-speed range to estimate aerodynamic drag, 

the yaw angle effect should be small or statistically indiscernible. The result of this analysis 

method is considered to be a zero-yaw CdA. 

3.2.2.1.2 High-low Intercept Analysis 

This method is similar to the previous method, with a few important differences. Instead 

of calculating force over speed intervals, like in the iteration method, the force is calculated at 

every speed measurement, similar to the HD Phase 1. The mechanical forces are determined 

through the low speed range that goes all the way to 0 mph though a force versus vehicle speed 

regression. The force intercept is then adjusted by a generic rolling resistance speed adjustment 

to estimate rolling resistance forces throughout the coastdown. These forces are then subtracted 
3-13 

from the total road load forces to estimate aerodynamic drag forces. The aerodynamic drag 

forces are then adjusted for yaw angle through an adjustment previous determined by a CFD run. 

The agencies did not evaluate this method in detail for the proposal due to lack of CFD or 

other yaw sweep data for the specific tractors that were tested. The agencies also did not have 

data to support a specific rolling resistance speed correction. The agencies are in the process of 

collecting some of this data and may evaluate this method for the final rule. 

3.2.2.1.3 High-speed Yaw Angle Analysis 

Another suggested analysis method was to use only the high-speed range from each 

coastdown (70-50 mph) and use the onboard anemometry to develop a wind-average CdA, as 

opposed to a zero-yaw CdA. One main advantage to this method is that it would allow for less 

road or track space to be used because only a high speed range would be needed. This allows for 

minimal acceleration and deceleration, which consume significant amounts of space and time, 

and subsequently allows for more runs to be conducted. 

A second-order regression with no linear term is used to separate the mechanical forces 

from the aerodynamic forces; the regression is done similar to Phase 1, but using the smaller 

speed range and air speed instead of vehicle speed. 

ୣܯൌܨ

ݒ∆

୰ݒܦ ൅ ܣ ൌ ݐ∆

ଶ

The D coefficient is used to estimate drag area CdA, using the temperature and pressure 

during the speed range to calculate air density, just as in the analysis methods discussed earlier in 

this section. The yaw angle θr is averaged over the speed range, and the absolute value is used. 

The CdA is fit to a second order regression with the absolute value of the yaw angle. 

ൌ ܣୢܥ

ܦ2

ߩ

തതതܥ

d

തത

ܣത ൌ ܽ0 ൅ ܽ1หߠr തതതห ൅ ܽ2หߠr തതതห

2

This CdA curve would then be used to conduct a yaw sweep to develop a wind-average 

CdA value for certification, rather than a zero-yaw CdA. While air direction was measured 

onboard, the accuracy of the anemometer is only ±2°, according the product specifications.4

 

Over the speed range recommend for analysis, yaw angles would only occur between 0° (for a 

direct headwind/tailwind) and 6° (for a direct crosswind). The accuracy of the instrument is not 

sufficient to measure average yaw angle. Instead, the roadside weather station was used to more 

accurately determine the average yaw angle. Trigonometric equations using the weather station 

measurements were used to calculate the average yaw angle for each coastdown run; the 

anemometer air direction readings were not used. The yaw angle was calculated using the law of 

sines. See Figure 3-1 earlier for variable references. 
3-14 

ߠr തതത ൌ ߠr,th തതതതതത ൌ

ە

۔

ۓ sinെ1 ൬

ഥݓ

ݒ

ݎഥ

1 ൌ ݊݋݅ݐ݁ܿݎ݀݅		 , ഥw൰ߠ sin

െsinെ1 ൬

ഥݓ

ݒ

ഥr

2 ൌ ݊݋݅ݐ݁ܿݎ݀݅		 , ഥw൰ߠ sin

Using the recommended speed range (70-50 mph) and regression fit equation, we found 

that the standard errors for the D values for individual runs ranged from 16 to 76 percent, with a 

median value of 31 percent. The standard error for the A value was even larger, ranging from 25 

to 186 percent. These values are extremely high, compared to applying this method through the 

full speed range (70-0 mph), where the median of the standard errors for the D values was 

approximately 3 to 4 percent. 

In a typical unloaded coastdown from 70 to 50 mph, the average vehicle speed is 

approximately 59 mph. The SAE J1263 and HD Phase 1 limit for cross wind is 5 mph. Under 

this limiting scenario, the average yaw angle experienced during this speed range would be 4.8° 

[tan-1(5/59)]. This does not provide a large enough spread in yaw angle to develop a yaw sweep 

to produce a statistically meaningful wind averaged drag area. Furthermore, if winds are 

relatively constant throughout a test, the constant cross wind would provide yaw angles around 

5° and -5°, without much in between. If winds are not exactly perpendicular, then there would 

be more of a distribution closer to 0°, but with less data at wider angles. 

The data collected here show that the bulk of the yaw angles from the data collected 

within Phase 1 wind requirements falls between -4° and 4°. Further analysis shows that 

statistically significant curves of CdA versus yaw angle could not be formed due to the variability 

of the data. Since the absolute value of the yaw angle is used in the analysis, this method also 

assumes that the aerodynamic characteristics are symmetrical, which is a not a safe assumption. 

Even using the absolute values, the CdA versus yaw angle curve is not statistically meaningful, as 

shown in Figure 3-3. 

 
3-15 

Figure 3-3 Example data from the high-speed yaw angle analysis shows high uncertainty of the statistical fit. 

The solid line is the yaw curve with the dashed lines representing the upper and lower 95% confidence limits 

of the mean. 

 
3-16 

After evaluating the methods described above, the agencies are proposing that the highlow iteration analysis method be used to determine the drag area GEM input. In general, while 

coastdown tests are used to determine the aerodynamic drag, the procedure itself is a road load 

procedure, not an aerodynamic one. That is, the measured or calculated forces represent the total 

road load from which the aerodynamic forces must be calculated or inferred; they are not 

measured directly. As a result, all analysis methods that are used in conjunction with 

coastdowns must calculate or infer aerodynamic forces. 

3.2.2.2 Specifications and Protocols for Conducting Constant Speed Testing 

Similar to the Coastdown Test Procedure, the Constant Speed Test Procedure is 

conducted on road and used to measure the forces acting on the tractor. In contrast to the 

Coastdown Test where the vehicle is accelerated to a set speed and then allowed to coast to a 

lower speed in neutral, the Constant Speed Test is conducted by measuring torque along the 

driveline at various constant speeds. This helps to reduce measurement uncertainty due to 

potential driveline vibration experienced during coastdown and better isolate the force 

contributions between speed transitions over the speed range (e.g., aerodynamic drag dominance 

at high speed; a mix of aero drag and mechanical/frictional forces in middle speeds; and 

mechanical/frictional force dominance at low speeds). In addition, whereas the total force, and 

consequently the total drag force, is derived based on the speed and time for the coastdown test, 

the constant speed test measures the total force at the wheels using wheel hub torque meters 

and/or a driveshaft torque meter. It can also incorporate, where needed and available, engine 

speed and torque, transmission gear ratio, rear axle losses, or other relevant data. The constant 

speed test has the potential to reduce uncertainty compared to a coastdown because it can collect 

data at a single speed for a sustained amount of time. For HD Phase 1, we allowed the use of 

Constant Speed testing as an alternate aerodynamic method but did not promulgate specific test 

procedure requirements. In lieu of this, a manufacturer would have been required to develop its 

own test procedure for constant speed testing and submit it to the agencies for approval. 

For HD Phase 2, we are proposing specific requirements for conducting the constant 

speed test, to be used by manufacturers choosing this testing method. Accordingly, we evaluated 

the constant speed testing using the same vehicles receiving the coastdown test procedure. For 

our evaluations, we used several speeds to determine the optimal speeds for constant speed 

testing. In addition, we performed the testing with both wheel hub torque meters and a 

driveshaft torque meter to quantify the benefits and detriments of both methods. More details on 

our test set up for constant speed testing and our results are discussed below. 

In addition to evaluating the constant speed test procedure, we are also seeking comment 

regarding making the constant speed test procedure the reference aerodynamic method. We 

received suggestions to this effect from industry during the HD Phase 1 rulemaking process, 

since some of the OEMs have European controlling interest. Thus, use of the constant speed test 

would allow them to harmonize test procedures with their European counterparts, who are 

required to use constant speed testing. We did not have data to support use of the constant speed 

test as the preferred method for Phase 1. Since then we have evaluated and are proposing 

specific constant speed testing requirements for HD Phase 2. Thus, we are taking this 

opportunity to explore this approach again. 
3-17 

3.2.2.2.1 Constant Speed Test Procedure Specifications 

For our evaluation of the constant speed test, we used the following procedure and 

specifications: 

 Used four High Resolution Truck Torque Wheel Transducers with approximate 5 lb-ft 

resolution during testing of each of the tractors. Mechanical protection against high 

torque application (both acceleration and braking) telemetry, and associated wheel 

adapters, encoder, amplifier, and power supply was employed. 

 Used an in-line strain-gauged torque flange, which was used to measure driveshaft torque 

during the testing each of the tractors (i.e., driveshaft torque sensor). The torque flange 

was an ANSI C12.20 0.5 class meter with a range of 0 to 5,000 Newton- meters (N-m). 

This torque meter utilizes special adapters and cannot be connected directly to the drive 

shaft. A modified drive shaft shall was acquired for each tractor to accommodate the 

torque meter. These drive shafts were dynamically balanced. 

 Used a driveshaft torque sensor and wheel hub meter simultaneously to collect data at the 

drive shaft and the wheels for comparison. The driveshaft torque sensor was calibrated 

according to 40 CFR 1065.310. 

 During the test, the following parameters were monitored (data collected), similar to the 

coastdown tests: 

o Air speed data using an anemometer mounted on the trailer approximately 0.85 m 

above the trailer roof, at the midpoint of the trailer width, at the leading edge of 

the trailer. 

o Vehicle speed using an optical fifth wheel. 

o Engine speed using the electronic control unit (ECU). 

o Grade data along the location of track or road where testing was performed. 

 The vehicle was warmed-up by being driven for 30 minutes prior to the test. The same 

road was used for the constant speed testing coastdown to ensure grade/location 

consistency. 

 Testing was performed at the following speeds and durations while recording torque and 

engine data. Cruise control was used to maintain speeds, except for the lower one or two 

speeds for certain tests. 

o 10 mph – 7.5 minutes in each direction 

o 20 mph – 7.5 minutes in each direction 

o 30 mph – 7.5 minutes in each direction 

o 50 mph – 8-10 minutes in each direction 

o 70 mph – Approximately 11.25 miles or 9.6 minutes in each direction. 

 If necessary, multiple passes were conducted. 

The agencies conducted constant speed testing through Southwest Research Institute 

along the same stretch of roadway as the coastdown testing. 

3.2.2.2.2 Constant Speed Test Procedure Analysis Methodology 

For analysis of the constant speed test procedure data, the 10-Hz data were filtered using 

a two-second centered moving average and then split into 10-second segments over which the 
3-18 

forces, air speed, and air direction were averaged. For tractors equipped with the driveshaft 

torque meter, the road load force was calculated as follows: 

ܨୖ୐,ୱ୦ୟ୤୲ ൌ ߬ୱ୦ୟ୤୲߱ୣ୬୥

ୢୣ୰ୟ୥ܨ ൅ ݒ ∙ ܴܩ

For tractors also equipped with the wheel torque meters, the road load force was 

calculated as follows: 

ܨୖ୐,୵୦ୣୣ୪ ൌ ߬୵୦ୣୣ୪߱୵୦ୣୣ୪

ݒ

ୢୣ୰ୟ୥ܨ ൅

Where: 

τshaft = driveshaft torque 

ωeng = engine speed 

GR = transmission gear ratio 

FRL,shaft = road load force calculated from the driveshaft torque 

τwheel = wheel torque, sum of all four wheel torque measurements 

ωwheel = wheel speed, average of all four wheel speed measurements 

FRL,wheel = road load force calculated from the wheel torque 

The analysis method involved a force subtraction method where the average road load 

force at 10 mph was assumed to be made up of just rolling resistance and mechanical forces. 

This value was subtracted from each 50-mph and 70-mph road load force measurement to 

estimate aerodynamic forces, and CdA was calculated similar to coastdowns. 

ܨ୫ୣୡ୦ ൌ ܨത

ୖ୐,ଵ଴୫୮୦

ܨୟୣ୰୭ ൌ ܨୖ ୐െ ܨ୫ୣୡ୦ (50 & 70 mph runs only) 

ൌ ܣୢܥ

2ܨୟୣ୰୭

୰ݒߩ

ଶ

Since wind conditions would vary from one test day to another, the yaw angle would also 

change, creating larger yaw angles and different yaw angle spreads for some tests. In order to 

compare the constant speed results to one another, the CdA values were regressed against a 

fourth-order polynomial of yaw angle to determine the zero-yaw CdA. Any tests that were 

conducted outside of the wind speed constraints were included along with tests within the wind 

constraints because the yaw angle distribution helped developed the polynomial fit. 

ܥୢܣሺߠ௥ሻൌܥୢܣሺ0ሻ ൅ ܽଵߠ୰ ൅ ܽଶߠ୰

ଶ ൅ ܽଷߠ୰ଷ ൅ ܽସߠ୰ସ

 If over 75 percent of the points occurred at yaw angles between -2 and +2 degrees, then 

the zero-yaw CdA was determined by using the average of the CdA values between those yaw 

angles. This would occur on test days where the prevailing winds were more parallel to the 

direction of travel, resulting in low yaw angles. A regression was not used in this case because 

the F test showed a low level significance of the polynomial fit given above. These regressed or 
3-19 

averaged zero-yaw CdA values are the constant speed test results that are reported in this 

document. 

Since the wheel torque measurements are downstream of the rear axle losses, they are a 

more accurate measurement of actual road load forces than the driveshaft torque measurements. 

The first several tests were conducted without wheel torque meters, so those tests’ results, which 

were determined only through the driveshaft torque measurements, were reduced by 6 percent, 

based on the ratio of CdA determined from one of the vehicles with a similar axle that was 

equipped with both driveshaft and wheel torque meters. 

3.2.2.3 Results for Coastdown and Constant Speed Testing Using 

Modified/Specified Test Procedures 

Using the coastdown test procedure with modified analysis methodology and the 

specifications and protocols for the constant speed test procedure, we evaluated four Class 8, 

high roof sleeper cabs, one from each of the heavy-duty tractor OEMs, and a Class 8, high roof, 

tandem axle sleeper cab with a 53’ dry box van trailer. The results using these procedures on 

tractor-trailer combinations with the trailer in the standard configuration (e.g., a 53’ dry box van 

with no trailer devices installed) are shown below in Table 3-3 and Table 3-4 for the coastdown 

and constant speed test procedure, respectively. 

Table 3-3 Summary of Results from Coastdown Testing 

CAB 

TYPE 

ROOF 

HEIGHT 

TRAILER 

CONFIGURATION 

CdA

[m2]

STD. 

ERROR 

# OF VALID 

RUNS 

Sleeper 1 High Standard 5.9 0.5 % 14 

Sleeper 2 High Standard 6.2 2.1 % 14 

Sleeper 3 High Standard 6.1 2.3 % 14 

Based on this test procedure, the results from our constant speed testing are shown in 

Table 3-4 below. 

Table 3-4 Summary of Results from Constant Speed Testing 

CAB 

TYPE 

ROOF 

HEIGHT 

TRAILER 

CONFIGURATION 

CdA

[m2

] 

STD. 

ERROR 

Sleeper 1 High Standard 6.1 0.7% 

Sleeper 2 High Standard 5.9 1.8% 

Sleeper 3 High Standard 5.9 0.3% 

The coastdown and constant speed results show a similar range of CdA values. Using 

different speed ranges in the coastdown method or analysis techniques in either method may 

produce slightly different CdA ranges, depending on certain factors such as the temperature and 

speed dependence of tire rolling resistance. 

The uncertainties, however, are unlikely to change significantly, since they are based on 

run-to-run variability for a given analysis technique. To ensure the required test repeatability 
3-20 

and method acceptance needed for future certification and compliance purposes, we looked at the 

standard error of the test and the alignment between the test procedures for each tractor. The 

standard error of the constant speed tests was determined through the statistical fit of the tensecond segments through the yaw polynomial where the polynomial fit was used. In this case, 

the standard error represents that of the zero-yaw CdA from that fit. Otherwise, the standard error 

is of the mean of the CdA values between -2 and 2 degrees. This may slightly underestimate the 

uncertainty due to the precision error that may be introduced in the low-speed segments. Other 

trailer configurations tested with coastdowns show lower uncertainties than the standard trailer. 

Consequently, either on-road test should be acceptable for aerodynamic assessment. The 

agencies are still collecting data using both test procedures and will continue to evaluate the 

accuracy and repeatability of both. 

Accordingly, we are proposing to require the use of the coastdown test procedure as the 

reference method with the high-low iteration analysis method discussed above. We are also 

specifying the constant speed aerodynamic assessment test procedure based on our above 

analysis. For constant speed testing, manufacturers may use a driveshaft torque meter or wheel 

hub torque meters for constant speed testing provided they meet or exceed the specifications 

given above for each type of device. 

We are completing testing on an additional sleeper cab tractor from another OEM and on 

a day cab tractor. Although all of the on-road testing for tractors is not complete, we believe the 

results of future on-road testing will be consistent with the trends emerging from the existing 

data. Once all on-road testing is complete, a full report will be included in the docket and further 

considered in the context of the tractor aerodynamic standards. 

3.2.2.4 Modifications to Computational Fluid Dynamics for HD Phase 2 

Computational Fluid Dynamics, or CFD, capitalizes on today’s computing power by 

modeling a full size vehicle and simulating the flows around this model to examine the fluid 

dynamic properties, in a virtual environment. CFD tools are used to solve either the NavierStokes equations that relate the physical law of conservation of momentum to the flow 

relationship around a body in motion or a static body with fluid in motion around it, or the 

Boltzmann equation that examines fluid mechanics and determines the characteristics of discreet, 

individual particles within a fluid and relates this behavior to the overall dynamics and behavior 

of the fluid. CFD analysis involves several steps: defining the model structure or geometry 

based on provided specifications to define the basic model shape; applying a closed surface 

around the structure to define the external model shape (wrapping or surface meshing); dividing 

the control volume, including the model and the surrounding environment, up into smaller, 

discreet shapes (gridding); defining the flow conditions in and out of the control volume and the 

flow relationships within the grid (including eddies and turbulence); and solving the flow 

equations based on the prescribed flow conditions and relationships. 

This approach can be beneficial to manufacturers since they can rapidly prototype (e.g., 

design, research, and model) an entire vehicle without investing in material costs; they can 

modify and investigate changes easily; and the data files can be re-used and shared within the 

company or with corporate partners. 
3-21 

For HD Phase 1, we established some CFD procedures based on our results and industry 

collaboration since there were no standardized practices at the time. In addition, to ensure data 

consistency, a minimum set of characteristics and criteria was included for CFD analysis to 

ensure that the boundary and surface conditions are not too coarse and, thus, not representative 

of the real tractor and environmental conditions. 

For HD Phase 2, we are proposing to either use the existing criteria from HD Phase 1 or 

require adherence to a newly established, Society of Automotive Engineering (SAE) standard for 

CFD, SAE J2966.5 Accordingly, we are comparing the criteria we set forth in HD Phase 1 with 

SAE J2966 to assess the efficacy of adopting this new standard. 

In addition, we are considering enhancements to the specified conditions for performing 

CFD analysis. Specifically, we specified the use of a simulated vehicle speed of 55 miles per 

hour and “ambient conditions consistent with the coastdown test procedures.” These conditions 

are ambiguous and may require greater specificity to ensure that the CFD analysis is more 

closely simulating the real world conditions. As a result, we are seeking comment on the need to 

improve the simulated ambient environmental conditions for conducting CFD analysis. 

Also, similar to wind tunnels, we are proposing to modify the measured yaw angles used 

in the CFD analysis to incorporate the concept of the wind average coefficient of drag. Under 

HD Phase 1, we only required manufacturers to perform the CFD analysis at a zero degree yaw 

angle. For HD Phase 2, we are requiring the CFD analysis to be performed at additional yaw 

angles for generating the yaw sweep curve and calculating the wind average coefficient of drag. 

This is discussed further in the following section. 

Finally, our CFD specifications do not include or require the use of turbulence intensity 

in the CFD analysis, and the use of a turbulence model is only required “if applicable.” As a 

result, there is less ability to capture transient flow phenomena in the CFD analysis. Based on 

developments since HD Phase 1, the ability to include turbulence in the CFD analysis without 

severely impacting the analysis run time has improved. Therefore, we are seeking comment on 

the inclusion of turbulence in CFD analysis. 

As we consider these proposed modifications and a revised standard reference trailer for 

tractor aerodynamic assessment, it is important to evaluate the ability of CFD to characterize the 

aerodynamics when aerodynamic trailer devices are employed. Therefore, we will be evaluating 

trailer aerodynamic devices for CFD method validation to support HD Phase 2. 

3.2.3 Aerodynamic Assessment and Use of Wind Averaged Drag Area 

Finally, we received comments in HD Phase 1 regarding the use of the wind averaged 

coefficient of drag (WACd) since WACd accounts for aerodynamic performance across a broader 

spectrum of wind conditions rather than a pure headwind or tailwind (e.g., zero degree yaw). 

Consequently, the use of WACd for aerodynamic assessment may better reflect real-world 

aerodynamic performance and fuel consumption. Therefore, we assessed the use of WACd for 

HD Phase 2 and the results are discussed below in this section. 
3-22 

EPA and NHTSA recognize that wind conditions have a greater impact on real world 

CO2 emissions and fuel consumption of heavy-duty tractors than light-duty vehicles. As stated 

in the NAS report6, the wind averaged drag coefficient is about 15 percent higher than the zero 

degree coefficient of drag (Cd). The large ratio of the side area of a combination tractor and 

trailer to the frontal area illustrates that winds will have a significant impact on drag. One 

disadvantage of the agencies’ approach to aerodynamic assessment in Phase 1 is that the test 

methods have varying but limited degrees of ability to assess wind conditions. Wind tunnels are 

currently the only demonstrated tool to measure the influence of wind speed and direction on a 

vehicle’s aerodynamic performance. The coastdown test and computational fluid dynamics 

modeling both have limited ability to assess yaw conditions. The constant speed test has the 

potential for yaw angle measurement capability but it is not certain how its ability to measure the 

influence of wind speed and direction on a vehicle’s aerodynamic performance compares to the 

wind tunnel. 

To address this issue in HD Phase 1, the agencies finalized the use of coefficient of drag 

values that represented zero yaw (i.e., representing wind from directly in front of the vehicle, not 

from the side). The agencies recognized that the results of using the zero-yaw approach will 

produce fuel consumption results in the regulatory program which are slightly lower (i.e. predict 

better fuel consumption results) than in-use, but we believed this approach was appropriate since 

not all manufacturers were using wind tunnels for the aerodynamic assessment to the extent 

needed for wind averaged Cd quantification purposes. 

During HD Phase 1, we examined full yaw sweep data from the reduced-scale wind tunnel 

test for three manufacturer 1/8th scale models. Below in Figure 3-4 are the yaw sweep graphs for 

three manufacturers’ vehicles in the reduced-scale wind tunnel with the WACd shown for 

comparison. This graph indicates that, although the zero-yaw Cd results for two tractors can be 

nearly identical at zero yaw, their aerodynamic performance may diverge as the yaw angle is 

increased. As a result, although the two tractors exhibit similar zero-yaw aerodynamic 

performance, their aerodynamic performance in non-zero yaw conditions may be drastically 

different and, consequently, so would their real-world fuel efficiency. 

 
3-23 

Figure 3-4 Full yaw sweeps and wind-average coefficients of drag (WACds) for three manufacturer, 1/8th 

scale, tractor models in the reduced scale wind tunnel. 

 

0.000

0.050

0.100

0.150

0.200

0.250

0.300

‐10.000 ‐8.000 ‐6.000 ‐4.000 ‐2.000 0.000 2.000 4.000 6.000 8.000 10.000

Truck A Yaw Sweep

Truck A WACd

Truck B Yaw Sweep

Truck B WACd

Truck C Yaw Sweep

Truck C WACd
3-24 

Table 3-5 below shows the results of an analysis on the impact of improving the non-zero 

yaw performance on the coefficient of drag for Tractor A versus Tractor B and Tractor C, 

respectively. 

Table 3-5 Absolute Deltas and Percent Difference for Individual Yaw Points for Tractor A versus Tractor B 

and Tractor C. 

+/- YAW 

ANGLE 

AVERAGES 

TRACTOR A VERSUS 

TRACTOR B 

TRACTOR A VERSUS 

TRACTOR C 

Delta Cd Delta Cd % 

Difference 

Delta Cd Delta Cd % 

Difference 

Zero Degree 0.001 0.18% -0.031 -5.84% 

One Degree -0.003 -0.53% -0.035 -6.65% 

Three Degrees -0.018 -3.33% -0.052 -9.58% 

Six Degrees -0.035 -5.85% -0.078 -13.05% 

Nine Degrees -0.042 -6.24% -0.098 -14.52% 

Based on the results, although Tractor A and Tractor B have similar zero degree 

aerodynamic performance, their aerodynamic performance at higher yaw angles results in a 0.53 

to 6.24 percent loss in aerodynamic efficiency. This difference is exacerbated for the case of 

Tractor A versus Tractor C where the zero degree performance of Tractor C is worse than 

Tractor A. 

Due to this decreased aerodynamic performance at each individual yaw angle, the WACd

values are impacted as follows when again comparing Tractor A versus Tractor B and Tractor C, 

respectively, as shown in Table 3-6. 

Table 3-6 Absolute Deltas and Percent Difference for Wind Averaged Coefficient of Drag (WACd) for 

Tractor A versus Tractor B and Tractor C. 

COMPARISON ABSOLUTE 

DELTA 

% 

DIFFERENCE 

Tractor A vs. B -0.021 -3.5% 

Tractor A vs. C -0.069 -11.7% 

Consequently, by focusing solely on zero degree yaw angle drag, there is an additional 

3.5 to 11.7 percent of benefit lost due to higher aerodynamic drag at greater yaw angles. 

As a result of this data and comments we received during and since HD Phase 1, we are 

proposing the use of additional yaw data to develop a wind-average drag area to be used for 

input into the GEM model and assigning a GHG emissions score. Further, the agencies are 

proposing to require manufacturers to use the yaw sweep calculation in SAE J1252 to determine 

WACd and, coupled with the frontal area, the wind averaged drag area (WACdA). 

Specifically, we are proposing to require the CdA data for zero degrees, positive/negative 

one degree, positive/negative three degrees, positive/negative six degrees and positive/negative 

nine degrees to calculate WACdA. This is in accordance with SAE J1252 which requires a 

minimum of six points for the calculating WACd rather than just using zero degrees as is 
3-25 

currently required for the HD Phase 1 version of GEM input and positive/negative six degrees 

currently used for the generation of the HD Phase 1 WACd as a yaw sweep correction factor. 

This proposed methodology would apply for any aerodynamic method that is used to generate 

the WACd using an approved series of yaw angles. Alternatively, a manufacturer may use 

request to use a series of different angles (e.g., 0, ±2, ±4, ±6, ±8 , ±10 degrees) or fewer angles 

(e.g., ±3, ±6, ±9) with advanced approval by and demonstration to the agencies that the series of 

different/reduced yaw angles provides equivalent results to the required yaw angles. 

We will also need to generate a yaw sweep curve and WACdA based on the coastdown 

reference method results to determine the appropriate GEM model inputs. At present, the 

coastdown procedure does not account for the varying wind direction well enough to reliably 

generate a yaw sweep curve. Therefore, one approach we are proposing to require is for a 

manufacturer to generate the yaw sweep curve using an alternate method, in particular, the wind 

tunnel or CFD, by taking the differential of the coastdown result and zero-yaw result from the 

alternate method and additively applying the coastdown-alternate method zero yaw to each of the 

points on the alternate method yaw sweep curve. Both of these techniques are demonstrated 

below in Figure 3-5. 

 
3-26 

Figure 3-5 Proposed development of a coastdown yaw sweep curve based on additive offset from alternate 

method yaw sweep curve. 

 

0.531

0.724

0.801

0.607

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

‐10.000 ‐8.000 ‐6.000 ‐4.000 ‐2.000 0.000 2.000 4.000 6.000 8.000 10.000

Yaw Sweep Curve Shift Concept

Multiplicative Approach

Additive Approach
3-27 

Using the results in Figure 3-5 as it applies to the entire yaw sweep curve; a 

multiplicative approach changes the shape of the yaw sweep curve whereas the additive 

approach maintains the yaw sweep curve shape. Therefore, the additive approach is a more 

appropriate to use than the multiplicative approach when using the full yaw sweep curve to 

generate the coastdown yaw sweep curve. This, however, requires the addition of the offset to 

each point on the yaw sweep curve, to shift and maintain the shape of the yaw sweep curve, and 

recalculating the wind average drag area based on the shifted curve. 

Alternatively, a simpler approach is to use the offsets between the zero degree yaw and 

the coastdown values to calculate Falt-aero; the zero degree yaw and WACdA values to calculate a 

wind average drag offset from the alternate method; and apply them to the coastdown results 

which reduces the complexity of calculating a virtual coastdown wind average drag area. This 

approach is explored in further detail in Section 3.2.9. 

Accordingly, we are proposing to require WACdA as the aerodynamic GEM input for HD 

Phase 2 and invite comment on the methodology used to generate a coastdown equivalent 

WACdA. Although the data above is shown in Cd format, we will use WACdA format for alternate 

method results for HD Phase 2. The WACdA values used to support HD Phase 2 are discussed 

below in Section 3.2.9. 

3.2.4 HD Phase 1 Yaw Sweep Correction 

In this proposal, the agencies propose to correct the Phase 1 yaw sweep correction factor 

equation to create equivalency between the two methods currently allowed by the regulations to 

determine the yaw sweep coefficient of drag area. The HD Phase 1 aerodynamic bins are based 

on zero degrees yaw. However, the current regulations allow manufacturers the option of 

determining the aerodynamic bin levels based on their wind average drag performance relative to 

the nominal wind averaged drag performance of the baseline fleet evaluated in Phase 1. The yaw 

sweep correction factor, as defined currently in 40 CFR 1037.521, allows the determination of 

the CDA based on (1) averaging the measurements at positive six degrees and negative six 

degrees (± 6 degrees) of yaw and (2) a full yaw sweep as defined by SAE J1252. However, 

these two methods do not produce the same wind averaged drag CDA value, as shown below in 

Table 3-7. The CDA based full yaw sweeps produce lower values, on the average of 3.3 percent. 

Table 3-7: CDA Values 

CDA (ZERO YAW) CDA (AVG ± 6 DEGREES) CDA (FULL YAW 

SWEEP) 

Sleeper Cab 1 5.425 6.239 6.043 

Sleeper Cab 2 5.553 6.619 6.405 

Sleeper Cab 3 5.622 6.442 6.285 

Sleeper Cab 4 5.563 6.663 6.373 

The value used to represent the nominal wind average drag performance of the fleet 

developed for Phase 1 was based on ± 6 degree measurement values. In order to remove the 

discrepancy in measurement methods, the agencies developed a new factor for determining the 

yaw sweep correction factor for data based on full yaw sweeps. We propose that manufacturers 

use the following equation when using wind averaged drag values based on the full yaw sweep. 
3-28 

CFys = [CDA from full yaw sweep * 0.8330] / [CDA at zero yaw] 

3.2.5 Aerodynamics for Trailers 

For HD Phase 2, the agencies are proposing GHG standards reflecting GHG and fuel 

consumption reductions from trailers. Aerodynamic improvements are among the technologies 

on which those proposed standards are predicated. New aerodynamic technologies have been 

implemented on box trailers to improve their aerodynamic efficiency and lower overall tractortrailer fuel consumption. In addition, the agencies have assessed the extent that some of these 

technologies may migrate to the trailer sector without regulation, and the extent these 

improvements should be reflected in the reference trailer used in tractor certification testing. 

Consistent with the proposed trailer regulations, our aerodynamic assessment of different 

trailer configurations (applicable to coastdown, constant speed and reduced-scale wind tunnel 

testing) and trailer types (applicable to reduced scale wind tunnel testing only) was limited to 

box van type trailers including dry box, reefer, and pup. Specifics on the applicable trailer types 

and certification protocols are discussed further in Section IV. D(2), of the preamble. 

3.2.5.1 On-Road Aerodynamic Assessment of Different Trailer Configurations 

We are also proposing to use the on-road test procedures as one method to determine 

aerodynamic performance for the proposed trailer program. Specifically, the on-road testing 

would estimate the delta drag area between a tractor-trailer combination for a trailer equipped 

without and with aerodynamic trailer devices (i.e., A to B testing; A constitutes a test without the 

technology; B constitutes a test with the technology). In addition, we used these test procedures 

to evaluate the ability of on-road testing to capture the aerodynamics of trailers equipped with 

and without aerodynamic devices for the standard trailer used in the tractor program. 

Consequently, we assessed different trailer configurations using the proposed coastdown 

and constant speed test procedures with and without trailer aerodynamic devices installed on the 

trailer. Specifically, we performed coordinated, HD Phase 2 and SmartWay testing on-road for 

53’ dry box van trailers; which represent the bulk of the trailer market. The results of testing 53’ 

dry box van trailers in different configurations are shown below in Table 3-8 and Table 3-9. 

Table 3-8 Summary of Results from Coastdown Testing with Different Trailer Configurations 

CAB 

TYPE 

ROOF 

HEIGHT 

CONFIGURATION CdA

[m2] 

STANDARD 

ERROR 

DELTA CdA

(VS. STANDARD) 

% DELTA CdA

(VS. STANDARD) 

Sleeper 1 High Standard 5.9 0.5% -- -- 

With Trailer Skirt 5.4 1.0% 0.5 8.5% 

With Trailer Skirt and Boat Tail 5.0 1.0% 0.9 15% 

Sleeper 2 High Standard 6.2 2.1% -- -- 

With Trailer Skirt 5.6 1.3% 0.6 10% 

With Trailer Skirt and Boat Tail 5.1 1.1% 1.1 18% 

Sleeper 3 High Standard 6.1 2.3% -- -- 

With Trailer Skirt 5.6 1.2% 0.5 8.2% 

With Trailer Skirt and Boat Tail 5.4 1.1% 0.7 11% 
3-29 

Table 3-9 Summary of Results from Constant Speed Testing 

CAB 

TYPE 

ROOF 

HEIGHT 

CONFIGURATION CDA

[m2] 

STANDARD 

ERROR 

DELTA CdA

(VS. STANDARD) 

% DELTA CdA

(VS. STANDARD) 

Sleeper 1 High Standard 6.1 0.7% -- -- 

With Trailer Skirt 5.2 2.2% 0.7 12% 

With Trailer Skirt and Boat Tail 4.9 0.7% 1.2 20% 

Sleeper 2 High Standard 5.9 1.8% -- -- 

With Trailer Skirt 5.5 0.7% 0.4 6.7% 

With Trailer Skirt and Boat Tail 5.1 1.0% 0.8 14% 

Sleeper 3 High Standard 5.9 0.3% -- -- 

With Trailer Skirt 5.7 0.4% 0.2 3.4% 

With Trailer Skirt and Boat Tail 5.2 0.6% 0.7 12% 

In addition to our on-road testing of 53’ dry box vans, we are coordinating with the 

California Air Resources Board (ARB) and U.S. Department of Energy’s (U.S. DOE) National 

Renewable Energy Laboratory (NREL) to evaluate twin, 28-foot (also known as “pup”) trailers 

using the coastdown and the SAE J1526 Type III7 test procedure. Also, we are planning 

additional on-road testing of reefer box van trailers in the same location and identical tractors 

used for our evaluations of 53’ dry box vans.8

The agencies used the results from the on-road testing of 53’dry box vans to assist in 

developing aerodynamic standards for the trailer types covered under this proposed rulemaking. 

Specifically, the delta CdA for trailers configured with various devices will be used to determine 

categories of values for GEM input; as discussed further under Section 3.2.7. 

Although all of the on-road testing for different trailer types is not complete at the time of 

proposal, we do have data on other trailer types using alternate aerodynamic methods (e.g., 

reduced-scale wind tunnel) and discussed further below. Therefore, the combination of existing, 

on-road and alternate method data on other trailer types is a good foundation to develop 

aerodynamic standards for other trailer types. Further, we believe the results of future on-road 

testing will be consistent with the trends emerging from the existing data. Once all testing is 

complete, a full report will be included in the docket and further considered in the context of the 

trailer aerodynamic standards. 

3.2.5.2 Reduced-Scale Wind Tunnel Testing of Different Trailer OEMs, 

Configurations, and Trailer Types 

In addition to the on-road coastdown and constant speed procedures presented previously, 

we are proposing to allow additional aerodynamic assessment methods (e.g., wind tunnel, CFD) 

to generate delta CdA values for the HD Phase 2 trailer program. In contrast to the on-road test 

procedures, the wind tunnel provides a stable, controllable environment yielding a more 

repeatable test and allows for more accurate measurement of the aerodynamic impact of tractortrailer modifications. In addition, wind tunnels provide testers with the ability to yaw the vehicle 

in a controlled, specific manner at positive and negative angles relative to the original centerline 

of the vehicle to accurately capture the influence of non-uniform wind direction on the Cd (e.g., 

wind averaged Cd). Most trailers in the U.S. are 28’ or longer and the agencies are not aware of 
3-30 

any viable, available wind tunnels that can accommodate full scale tractor-trailer combination 

vehicles. As a result, we exclusively used reduced-scale wind tunnel (RSWT) testing to support 

the HD Phase 2 aerodynamic assessment rather than a mix of full scale wind tunnel (FSWT) and 

RSWT testing as performed in HD Phase 1. 

For HD Phase 2, we are proposing to carry over the use of SAE J1252 with the 

exceptions and modifications noted above. In addition, since the finalization of HD Phase 1, 

SAE J1252 has been updated and we are incorporating by reference the most recent version for 

HD Phase 2. For HD Phase 2, we are performing wind tunnel testing to inform revisions to the 

standard trailer test article for the tractor program and to develop separate trailer standards. 

The RSWT testing was performed at the Automotive Research Center (ARC) in 

Indianapolis Indiana. The ARC wind tunnel is a closed single return tunnel with 3/4 open-jet 

working section and moving ground plane (2.3 m wide x 2.1 m high x 5.5 m long (7.5 ft x 6.8 ft 

x 18 ft)). It is powered by an air-cooled 373kW (274 hp) variable speed DC motor that drives a 

9-bladed fan with carbon fiber blades. Its speed may be varied and set at any value from 0 to 

610 rpm. The maximum wind speed is about 50 m/s (164 ft/s). The wind tunnel can 

accommodate a model up to 50 percent scale (1/2 scale) for race car applications down to 12.5 

percent scale (1/8th scale) for Class 8 tractor and trailer combinations. The wind tunnel is 

equipped with a moving ground plane (i.e., rolling road), four-stage boundary layer suction 

system, and a top-mounting “Sting” system that allows for yawing of the model. For model 

development, ARC has in-house model developers and can create highly detailed scale models 

using original computer aided design and engineering (CAD/CAE) drawings or using in-house 

scanning equipment to perform scanning and digitizing to create CAD/CAE drawings (see 

Figure 3-6 below). 
3-31 

Figure 3-6 1/8th scale tractor-trailer model in ARC reduced scale wind tunnel. 

 
3-32 

The RSWT testing was conducted simulating an actual vehicle speed of 65 miles per hour 

(mph) using a tunnel speed of 111.8 mph and Reynolds number (Re) of greater than one million 

(1x106

) on one eighth scale models of heavy-duty, Class 8 sleeper and day cab tractors equipped 

with the full aerodynamics package components sold on the full size version of the tractor; 

consistent with our HD regulatory requirements. For our test program, we assumed a base 

tractor-trailer gap of 45 inches and a bogey position of 40 feet (California position) from the 

leading edge of the trailer. 

To support HD Phase 2, we tested the latest model year tractor available from each of the 

four tractor OEMs in sleeper cab form and one tractor OEM in day cab form. The tractor models 

used in the RSWT matched the tractor models used for the on-road testing to the extent feasible. 

For one manufacturer, we were not able to match the exact tractor model but did test models 

from that OEM in the RSWT and on-road. While the results across the tractor models are not 

directly comparable, the data provides some representativeness of the expected performance 

from that OEM’s tractors. 

In addition, we also tested three 53 foot dry box van trailers from three different trailer 

OEMs: Wabash, Great Dane, and Hyundai Translead; to evaluate aerodynamic trailer devices. 

For aerodynamic trailer devices, we focused on technologies that may improve areas on the 

tractor-trailer where large amounts of aerodynamic drag can occur and tested: one OEM trailer 

front treatment (e.g., front end trailer gap reduction device), two OEM side treatments (e.g., 

trailer side skirts) and one OEM aft treatment (e.g., trailer boat tail). In the case of the trailer 

side skirts and boat tail, a portion of the trailer devices used in the RSWT matched those used for 

the on-road testing. Thus, we ensured some overlap between the RSWT and on-road testing, 

although we were able to test a broader range of trailer devices. 

Below in Table 3-10 are the RSWT results for the various configurations of four OEM 

sleeper cabs, one OEM day cab, three OEM 53-foot dry box van trailers, and four aerodynamic 

trailer devices (two trailer skirts, one rear boat tail, one front trailer gap reducer); averaged across 

trailer OEMs. The variation across the three OEM 53-foot dry box van trailers was small and 

should be considered negligible at 1.0 percent on average. The variation ranged from 0.5 to 0.9 

percent for three of the four sleeper cab tractors and the day cab and a high of 2.3 percent for one 

of the four sleeper cab tractors. Where there were multiple runs on the same tractor, trailer, and 

device, the results were averaged across those runs and then included in the overall average. 
3-33 

 Table 3-10 Results of Reduced-Scale Wind Tunnel Testing to Assess Tractor Trailer Performance with 

Various Aerodynamic Trailer Devices. 

CAB TYPE ROOF 

HEIGHT

CONFIGURATION CdA 

[m2]

DELTA CdA

(VS. STANDARD)

% DELTA CdA

(VS. STANDARD)

Sleeper 1 High Standard 5.425 -- -- 

With Trailer Skirt #1 5.002 0.423 7.8% 

With Trailer Skirt #2 4.914 0.511 9.4% 

With Boat Tail 5.085 0.340 6.3% 

With Trailer Gap Reducer 5.450 -0.024 -0.5% 

With Trailer Skirt #2 and 

Boat Tail 

4.453 0.972 17.9% 

With Trailer Skirt #2, 

Boat Tail, and Trailer Gap 

Reducer 

4.460 0.965 17.8% 

Sleeper 2 High Standard 5.556 -- -- 

With Trailer Skirt #1 5.054 0.586 10.5% 

With Trailer Skirt #2 4.962 0.594 10.7% 

With Boat Tail 5.191 0.365 6.6% 

With Trailer Gap Reducer 5.565 -0.020 -0.4% 

With Trailer Skirt #2 and 

Boat Tail 

4.464 1.092 19.6% 

With Trailer Skirt #2, 

Boat Tail, and Trailer Gap 

Reducer 

4.449 1.106 19.9% 

Sleeper 3 High Standard 5.622 -- -- 

With Trailer Skirt #1 5.145 0.477 8.5% 

With Trailer Skirt #2 5.056 0.566 10.1% 

With Boat Tail 5.305 0.318 5.6% 

With Trailer Gap Reducer 5.632 -0.009 -0.2% 

With Trailer Skirt #2 and 

Boat Tail 

4.566 1.056 18.8% 

With Trailer Skirt #2, 

Boat Tail, and Trailer Gap 

Reducer 

4.560 1.062 18.9% 

Sleeper 4 High Standard 5.563 -- -- 

With Trailer Skirt #1 5.079 0.481 8.7% 

With Trailer Skirt #2 5.003 0.557 10.1% 

With Boat Tail 5.229 0.332 6.0% 

With Trailer Gap Reducer 5.547 0.015 -0.3% 

With Trailer Skirt #2 and 

Boat Tail 

4.436 1.121 20.2% 

With Trailer Skirt #2, 

Boat Tail, and Trailer Gap 

Reducer 

4.445 1.112 20.1% 

Day High Standard 5.609 -- -- 

With Trailer Skirt #1 5.353 0.256 4.6% 

With Trailer Skirt #2 5.298 0.311 5.6% 

With Boat Tail 5.240 0.370 6.6% 

With Trailer Gap Reducer 5.596 0.014 0.2% 
3-34 

With Trailer Skirt #2 and 

Boat Tail 

4.910 0.699 12.5% 

With Trailer Skirt #2, 

Boat Tail, and Trailer Gap 

Reducer 

4.902 0.707 12.6% 

We believe that most trailers in the U.S. fleet will adopt some aerodynamic technologies 

by 2021. We are proposing to revise the standard trailer definition for 53-foot air ride dry box 

van trailers to include aerodynamic trailer devices. Specifically, we are proposing trailer skirts 

as part of the standard configuration for tractor aerodynamic evaluation. We are using the results 

in Table 3-10, to inform the proposed tractor aerodynamic bin categories for use as GEM inputs. 

In addition to evaluating the aerodynamics of 53 foot dry box vans in the RSWT, we also 

evaluated the aerodynamics of shorter, 28 foot dry box van trailers, singular or in 

tandem/dual/twin configuration, known as “pup” trailers. This testing was performed using a 

single-axle, day cab tractor using similar trailer devices (e.g., trailer skirts, rear boat tail, and 

front trailer gap reducer) with a tractor-to-first trailer gap of 44 inches and a first pup-to-second 

pup trailer gap of 48 inches. Table 3-11 shows the results of this testing for the configurations 

tested: 

Table 3-11 Results of Reduced-Scale Wind Tunnel Testing to Assess Tractor Trailer Performance with 

Various Aerodynamic Trailer Devices. 

DUAL PUP TRAILER RESULTS 

Configuration CdA 

[m2

] 

Delta CdA

(vs. Standard) 

% Delta CdA

(vs. Standard) 

Standard 6.022 -- -- 

Skirt on First Trailer 

Only 

5.902 0.120 1.99% 

Skirt on Second 

Trailer Only 

5.735 0.286 4.76% 

Skirt on Both 

Trailers 

5.586 0.436 7.24% 

Skirt and Gap 

Reducers on Both 

Trailers 

5.289 0.733 12.2% 

Skirt and Gap 

Reducers on Both 

Trailers w/ tail on 

second trailer 

5.208 0.814 13.5% 

SINGLE PUP TRAILER RESULTS 

Standard 5.384 -- -- 

Skirt on Trailer 5.226 0.159 2.95% 

Skirt and Gap 

Reducer on Trailer 

4.971 0.414 7.68% 

Based on the results in Table 3-11 above, the potential to significantly improve the 

aerodynamic performance of pup trailers, singular or in tandem/dual/twin, exists using various 
3-35 

trailer devices. The maximum package of trailer side skirts on both trailers, gap reducers on both 

trailers, and a boat tail on the second trailer produced a benefit of 13.5 percent over the standard 

dual pup trailer configuration. 

However, since pup trailers may be frequently interchanged (e.g., drop off the second pup 

trailer or both pup trailers at one destination; proceed to the next destination to pick up a new 

first pup trailer, second pup trailer or both pup trailers), the most practical application may be 

dual pup trailers with just trailer side skirts and gap reducers on both trailers. The presence of 

boat tail/aft treatment restricts that trailer to a second/rear position, depending on the length of 

the aft trailer device, and omitting this device allows for pup trailer symmetry. Even with the 

omission of an aft trailer device, the trailer side skirts and gap reducers on both trailers 

configuration still achieves a benefit of 12.2 percent. Notwithstanding, the added use of aft 

trailer devices; with proper consideration for the length of the device to accommodate 

operational constraints versus the potential tradeoff in aerodynamic performance with reduced, 

aft trailer device length; should be investigated further to identify additional dual pup trailer 

optimization. 

Installing skirts/gap reducers on both trailers for dual pup trailer configurations also 

produces benefits from the use of a single pup trailer with trailers side skirts and gap reducer; 

achieving a benefit of 7.68 percent versus the standard, single pup trailer without any devices. 

There may be occasions where a dual pup tractor-trailer must travel between destinations in 

single pup trailer configuration. Thus, the presence of trailer side skirts and gap reducers on both 

pup trailers also aids the aerodynamic performance for the occasions where it is operated in a 

single pup trailer configuration. 

Finally, we also evaluated the impact of aerodynamic devices on reefer trailer 

performance. Reefer trailers are similar in basic shape to 53-foot dry box van trailers, but are 

equipped with a thermal refrigeration unit (TRU) on the front of the trailer. Thus, the TRU fills 

the gap space between the tractor-trailer where gap reduction technology would normally be 

fitted. In addition, based on conversations with reefer trailer manufacturers, although reefer 

trailers are less prevalent in the field, they tend to see much more operation than the typical 53-

foot dry box van trailer due to their smaller numbers (1.5-2.2 to 1 for tractor to reefer trailer ratio 

versus 3 to 1 for conventional 53-foot dry box van trailers). As such, there is more opportunity 

to realize the benefits from optimizing reefer trailers. 
3-36 

Table 3-12 Results of Reduced-Scale Wind Tunnel Testing of Reefer Trailer Using a 2012 Model Year 

Tractor 

CONFIGURATION CDA 

(ZERO 

YAW) [m2

] 

DELTA CdA

FROM 

STANDARD 

REEFER (% 

DELTA) 

DELTA CdA VS. 

STANDARD DRY 

BOX VAN TRAILER 

FROM THE SAME 

TRAILER OEM 

% DELTA (VS. 

STANDARD 

TRAILER) 

Standard Reefer 5.840 -- -0.264 -4.7% 

Skirt Only 5.141 0.699 (12.0%) -0.196 -4.0% 

Boat tail only 5.607 0.232 (4.0%) -0.448 -8.7% 

Skirt +Boat Tail 4.596 1.244 (21.3%) -0.114 -2.5% 

Vs. OEM standard 

trailer w/ Skirt + 

Boat Tail + Gap 

-- -- -0.146 -3.3% 

The results in Table 3-10, Table 3-11, and Table 3-12 will also be used to support the 

newly proposed, trailer regulations. Specifically, the delta CdA for trailers configured with 

various devices will be used to determine categories of values for GEM input; as discussed 

further in Section 3.2.7. 

3.2.6 Standardized Trailer Definitions for Heavy-Duty Tractor Testing 

In HD Phase 1, we finalized the use of a model input (i.e., an input to GEM at 

certification) reflecting a standardized trailer for each subcategory of the Class 7/8 tractors based 

on tractor roof height. The height of the roof fairing is designed to minimize the height 

differential between the tractor and typical trailer to reduce the air flow disruption. Low roof 

tractors are designed to carry flatbed or low-boy trailers. Mid roof tractors are designed to carry 

tanker and bulk carrier trailers. High roof tractors are designed to optimally pull box trailers. 

However, we recognize that during actual operation tractors sometimes pull trailers that do not 

provide the optimal roof height that matches the tractor. In order to assess how often tractor and 

trailer mismatches are found in operation, EPA conducted a study based on observations of 

traffic across the U.S.9

 Data was gathered on over 4,000 tractor-trailer combinations using 33 

live traffic cameras in 22 states across the United States. Approximately 95 percent of tractors 

were “matched” – i.e. optimized – per our definition (e.g. box trailers were pulled by high roof 

tractors and flatbed trailers were pulled with low roof tractors). The amount of mismatch varied 

depending on the type of location. Over 99 percent of the tractors were observed to be in 

matched configuration in Indiana at the I-80/I-94/I-65 interchange, which is representative of 

long-haul operation. On the other hand, only about 90 percent of the tractors were matched with 

the appropriate trailer in metro New York City, where all mismatches consisted of a day cab and 

a tall container trailer. The study also found that approximately 3 percent of the tractors were 

traveling without a trailer or with an empty flatbed. The agencies therefore concluded in Phase 1 

that given this very limited degree of mismatch, it is reasonable to use a standardized definition 

which optimizes tractor-trailer matching. For purposes of compliance testing, the agencies also 

finalized bob-tail testing for low roof and mid roof tractors to facilitate repeatability and 

reproducibility of test data in response to concerns raised by tractor manufacturers. 
3-37 

Recently, trailer OEMs and aftermarket manufacturers have begun implementing new 

aerodynamic technologies on box trailers, to improve their aerodynamic efficiency and to lower 

overall tractor-trailer fuel consumption. There are a range of technologies to affix to the front 

(e.g., nose cones, gap reducers, front lip devices); sides and underbody (e.g., skirts, wedge 

shaped devices aft of trailer landing gear and fore of trailer rear axles/bogeys); top (e.g., vortex 

generating devices to promote turbulent flow and delay boundary layer separation along the 

length of the trailer) and rear (e.g., tails, rear lip spoilers and diffusers) of the trailer. However, 

the most widely implemented devices in the market today are trailer side skirts that extend in the 

gap between the fifth wheel and the trailer bogey to restrict underbody air flow and subsequent 

drag and rear/aft trailer treatments extending from the rear of the trailer (e.g., boat tails, that 

reduce the low pressure region, and associated, subsequent drag, on the rear of the tractortrailer). As discussed in Section III.E(2)(a)(iii) and Section IV.D(2) of the preamble, we estimate 

that approximately 50 percent of the new trailers sold in 2018 will have trailer side skirts.10,11 As 

the agencies are proposing tractor standards for model year 2021 and beyond, we believe that it 

is appropriate to update the standardized box trailer definition to reflect the technologies we 

project will be used on the majority of the trailers in the fleet during that timeframe. Therefore, 

we are proposing for Phase 2 that the standardized box trailer used for tractor certification be 

updated to include a trailer skirt starting in model year 2021. Although we are proposing GHG 

standards for trailers that capture the drag area improvement for various trailer devices, including 

trailer skirts, beginning in model year 2018, this proposed update to include a test article to the 

standardized trailer is strictly for the purpose of certifying tractors beginning in model year 2021, 

and is not related to the proposed trailer GHG standards themselves. 

Based on the test results shown above in Table 3-10, Table 3-11, and Table 3-12 and 

comments from stakeholders, the agencies have chosen to update the test article specifications 

for 53’ dry box van trailers below in Table 3-13. Specifically, the standardized, 53’ air ride dry 

van test article we are proposing for Phase 2 will include trailer side skirts on the standardized 

trailer for tractor aerodynamic assessment. We propose that the skirt meet the dimensions shown 

in Table 3-13 based on the devices used for the agencies’ aerodynamic assessment, accounting 

for potential design, differences, variations and tolerances for other trailer attributes (e.g., 

landing gear overlap, rear bogey clearance, ground clearance). Trailer side skirts that do not 

meet the size specifications (e.g., length and width) below can be used, with prior approval from 

the agencies , on the standardized trailer used for the aerodynamic assessment of tractors, 

provided that they do not exceed the dimensions in Table 3-13 but are no shorter than 270 inches 

± 4 inches in length when measured along the top edge of the trailer side skirt; and meet all other 

specifications for positioning on the trailer; except for the distance from the front and rear trailer 

edge which may vary based on a shorter skirt length. Finally, the tractor must be a stock OEM 

tractor without additional devices installed, unless devices are included as part of a stock OEM 

aero package (e.g., drive axle wheel covers, fairings or fenders). 
3-38 

Table 3-13 53’ Dry Box Van Trailer Test Article 

53’ AIR RIDE DRY BOX VAN TRAILER 

Length: 53 feet (636 inches; 1615.44 cm) ± 1 inch

Width: 102 inches ± 0.5 inches

Height: 102 inches (162 inches or 13 feet, 6 inches (+0.0 inch/-1 inch) from the 

ground)

Capacity: 3,800 cubic feet

Assumed trailer load/capacity: 45,000 lbs

Suspension: Any (see “trailer ride height” below) 

Corners: Rounded with a radius of 5.5 inches ± 0.5 inches 

Bogie/Rear Axle Position: Tandem axle (std), 146 inches ± 4.0 inches from rear axle centerline to 

rear of trailer. Set to California position.

Skin: Generally smooth with flush rivets

Scuff band: Generally smooth, flush with sides (protruding ≤ 1/8 inch)

Wheels: 22.5 inches. Duals. Std mud flaps.

Doors: Swing doors.

Undercarriage/Landing Gear: Std landing gear, no storage boxes, no tire storage, 105 inches ± 4.0 

inches from front of trailer to centerline of landing gear

Underride Guard Equipped in accordance with per 49 CFR 393.86

Aerodynamic Trailer Device 

Type: 

Trailer Side Skirts 

Aerodynamic Trailer Device 

Specifications Applicable to 

Trailer Side Skirts: 

Skirts must be installed on both sides of the trailer 

Skirts must be designed to fit between the landing gear and the rear 

bogey, in the most forward position (California), of the trailer and must 

be: 

118 inches ± 4 inches measured from the front of the trailer to the 

leading, forward-most point of the upper/top edge of the skirt 

Skirts must be straight and flush with the trailer sides 

Skirts must be: 

341 inches, ± 4 inches in total length, measured along the top/upper edge 

The same total length on the bottom/lower edge as the top/upper edge 

(e.g., rectangular in shape) but shall be no less than 268 inches ± 4 inches 

in length along the bottom edge (e.g., angled front/rear edge or front 

edge only) 

36 inches ± 2 inches in total width, measured between the top/upper and 

bottom/lower edge, at the midpoint of the skirt 

Skirts may minimally (e.g., 10% or less) overlap the trailer landing gear 

at the forward-most point along the upper/top edge but should not cover 

the trailer landing gear with any portion of the skirt leading/front edge 

(e.g., skirt may not extend forward beyond the landing gear position 

Tires for the Standard Trailer and the Tractor:

a. Size: 295/75R22.5 or 275/80R22.5

b. Crr<5.1 kg/metric ton

c. Broken in per Section 8.1 of SAE J1263

d. Pressure per Section 8.5 of SAE J1263

e. No uneven wear

f. No re-treads

g. If these tires or appropriate Smart Way tires are unavailable, the Administrator testing may include tires 
3-39 

used by the manufacturer for certification.

Test Conditions: 

Tractor-trailer gap: 45 inches ± 2.0 inches. 

King pin setting: 36 inches ± 0.5 inches from front of trailer to king pin center line. 

Trailer ride height: 115 inches ±1.0 inches from top of trailer to fifth wheel plate, measured at the front of 

the trailer, and set within trailer height boundary from ground as described above.

Mud flaps: Positioned immediately following wheels of last axle.

For special cases in which a trailer would be used for coastdown testing for mid-roof and low-roof tractors, 

the specifications for the tanker and flatbed trailers are being carried over from HD Phase 1, and are shown, 

unchanged, in Table 3-14 and 

Table 3-15 below, respectively. 

Table 3-14 Tanker Trailer Specifications for Special Testing 

TANKER TRAILER 

Length: 42 feet ± 1 foot, overall 

40 feet ± 1 foot, tank 

Width: 96 inches ± 2 

Height: 140 inches 

(overall, from ground)

Capacity: 7,000 gallons

Suspension: Any (see “trailer ride height” below)

Tank: Generally cylindrical with rounded ends.

Bogie: Tandem axle (std). Set to furthest rear position.

Skin: Generally smooth

Structures: (1) Centered, manhole (20 inch opening), (1) ladder generally centered 

on side, (1) walkway (extends lengthwise)

Wheel fairings:

Wheels: 24.5 inches. Double wide. 

Tanker Operation Empty

Table 3-15 Flatbed Trailer Specifications for Special Testing 

FLATBED TRAILER 

Length 53 feet

Width 102 inches

Flatbed Deck Heights: Front: 60 inches ± 0.5 inches 

Rear: 55 inches ± 0.5 inches

Wheels / Tires 22.5 inch diameter tire with steel or aluminum wheels

Bogie Tandem axles, may be in “spread” configuration up to 10 feet ± 2 

inches. 

Air suspension 

Load Profile: 25 inches from the centerline to either side of the load; 

Mounted 4.5 inches above the deck. 

Load height 31.5 inches above the load support. 

Trailer should be empty.
3-40 

The regulations in 40 CFR 1037.501 prescribe the standardized trailer for each tractor 

subcategory (low, mid, and high roof) including trailer dimensions based on the tables above. 

3.2.7 Standardized Tractor Definition for Heavy-Duty Trailer Testing 

Similar to the standardized trailer definition for tractor aerodynamic assessment, the 

agencies are proposing to define a standardized tractor definition for trailer aerodynamic 

assessment. The proposed standardized tractor definition is based on tractor attributes such as a 

Class 8, high roof, tandem axle tractor equipped with, at a minimum, a roof fairing, cab side 

extenders and fuel tank/chassis skirts. This type of tractor typically meets a Bin III or better 

tractor aerodynamic level under HD GHG Phase 1 and is expected to meet the proposed Bin III 

level for HD GHG Phase 2. We believe the majority of tractors in the U.S. trucking fleet will be 

Bin III or better in the timeframe of this rulemaking and trailer manufacturers have the option to 

choose higher-performing tractors in later years as tractor technology improves. As with the 

standardized trailer’s test article specification, the aerodynamic specification for the standardized 

tractor here is strictly for the purpose of certifying trailers beginning in model year 2018 model 

year. Therefore, although the aerodynamic level of the standardized tractor for trailer 

certification potentially overlaps with tractors designed to meet the 2021 and beyond tractor 

GHG standards, it is only intended to serve as a specification for trailer certification to the 2018 

and beyond trailer GHG standards. 

Accordingly, we are proposing that trailer manufacturers would use this standardized 

tractor definition with their trailers to conduct A to B testing to capture the delta CdA for their 

trailers that are either: equipped with aerodynamic devices to meet the proposed trailer standards 

or are designed to be more aerodynamic than current, standard trailers. Specifically, the trailer 

manufacturers would use the standardized tractor to generate A to B test values where the “A” 

represents a standard test and “B” represents the modified/advanced trailer; both tests performed 

using the same standardized tractor. 

Subsequently, the tractor manufacturer would input their trailer OEM-specific delta CdA

value in the GEM model and the model would determine the appropriate, default trailer delta 

CdA GEM value; where the default delta CdA GEM value is based on the test results in Sections 

3.2.5.1 and 3.2.5.2 above. This value is applied to a default tractor/trailer CdA value based on a 

Bin III or greater Class 8 sleeper cab tractors with a standard trailer. Finally, the default 

tractor/trailer CdA value with the default trailer CdA value applied would be used to determine the 

greenhouse gas emissions for this configuration by the GEM model. 

For trailer OEMs that are certifying a trailer where devices are added to an existing OEM 

trailer design, the trailer used for both “A” and “B” test uses a trailer meeting our standardized 

trailer definition for 53’ air ride dry box vans shown above in Section 3.2.6, Table 3-13, without 

any trailer devices installed; with the same standard reference tractor used for both tests. 

In contrast, for trailer OEMs that certify a completely new trailer design, the “A” test 

uses a trailer meeting our standardized trailer definition for 53’ air ride dry box vans shown 

above in Section 3.2.6, Table 3-13, without any trailer devices installed and the “B” test would 

be the new, OEM trailer design; with a standard reference tractor used for both tests. In 

summary, the standard reference tractor would be used for all trailer OEM “component” level 
3-41 

testing; where the “component” in the B test can range from an add-on trailer device up to a 

completely different trailer design. 

To assist in defining the standardized tractor for different trailer types, below are the 

proposed trailer sub-categories used for GEM from Chapter IV of the preamble. 

Table 3-16 Description of Baseline Tractor-Trailers Used In GEM from Section IV. D(2)(b)(ii), of the 

Preamble 

TRAILER SUBCATEGORY FEATURES 

Dry van 50 feet and shorter Class 8 high roof day cab, pulling solo 28’ dry van 

CdA = 6.1, Crr = 6.0 kg/ton 

Dry van longer than 50 feet Class 8 high roof sleeper cab pulling a solo 53’ dry van 

CdA = 6.2, Crr = 6.0 kg/ton 

Refrigerated van 50 feet and shorter Class 8 high roof day cab pulling a solo 28’ ref van 

CdA = 6.0, Crr = 6.0 kg/ton 

Refrigerated van longer than 50 feet Class 8 high roof sleeper cab pulling a solo 53’ ref van 

CdA = 6.1, Crr = 6.0 kg/ton 

Based on this table, we are proposing standardized tractor definitions based on tractor 

type and attributes that reflect the types of tractors used for trailers in each of these 

subcategories. 

Specifically, we are proposing that all tractors for all trailers greater than 50 feet shall use 

a standardized tractor meeting the following criteria for A to B testing: a Class 8, high-roof 

sleeper cab, tandem axle tractor that meets HD Phase 2 Bin III or better Class 8 high roof sleeper 

cab tractor aerodynamic standards. For all trailers less than 50 feet, a standardized tractor 

meeting the following criteria shall be used for A-B testing: Class 8, high-roof day cab, dual axle 

tractor that meets HD Phase 2 Bin III or better Class 8 high roof day cab tractor aerodynamic 

standards. 

Table 3-17 Characteristics of Standard Tractor for Aerodynamic Assessment of Trailers 

BOX TRAILERS 50 FEET AND 

LONGER 

CLASS 8 HIGH ROOF SLEEPER 

CAB 

DUAL-AXLE 

BIN III OR BETTER TRACTOR, 

CDA < 6.5 

CAB SIDE EXTENDERS 

FUEL TANK COVER/CHASSIS 

SKIRTS 

ROOF FAIRING 

Box trailers shorter than 50 feet Class 8 high roof day cab 

Dual-axle 

Bin III or better tractor, CdA <6.7 

Cab Side extenders 

Fuel tank covers/Chassis Skirts 

Roof Fairing 
3-42 

3.2.8 Continued Use of the Aerodynamic Alternate Method Factor (Falt-aero) for HD 

Phase 2 Tractors 

 As the agencies showed in Phase 1, aerodynamic test methods differed in their 

predictions of drag coefficient.12 On-road methods, such as coastdown and constant speed tests, 

are performed in uncontrolled real-world environments, whereas wind tunnel testing is 

performed in constrained, controlled conditions, and CFD is a simulation that attempts to 

replicate complex aerodynamic events. Different test methods have differences with regards to 

environmental conditions, assumptions for non-aerodynamic drag forces, tunnel geometry, 

boundary conditions, and simulation characteristics. These differences can lead to different 

results, even though they are used to measure or calculate the same parameter. The agencies 

acknowledged that there will never be perfect alignment between the predicted drag area values 

from the aerodynamic methods even with full, appropriate correction for every factor, but 

wanted to allow the use of these methods, which are currently being used by the manufacturers, 

to limit test burden for certification 

As a result, for HD Phase 1, we employed the use of an aerodynamic method adjustment 

factor (or Falt-aero) factor, to relate the results from the reference method, a coastdown test, to the 

results from the alternative method as a ratio of the coastdown result to the alternate method 

result for selected Class 8 high roof sleeper cab. The Falt-aero is then multiplied to the results 

generated using the alternative method for all other OEM configurations This allowed 

manufacturers the convenience and lower test burden of using existing aerodynamic protocols 

rather than pursuing extensive data correction to produce equivalent results across the 

aerodynamic methods. 

For HD Phase 2, we are proposing to continue to allow the use of data from alternate 

aerodynamic test methods, and subsequently the aerodynamic method adjustment factor. Thus, 

for HD GHG Phase 2, we explored the level of agreement between the aerodynamic methods 

similar to HD GHG Phase 1. The method comparison was used in HD GHG Phase 1 to 

demonstrate that for a given tractor and trailer model with specified conditions for each of the 

methods, testing meeting these specifications performed with the required level of precision 

(e.g., repeated with a low level of error) could yield a reasonable level of agreement across the 

aerodynamic method. Consequently, there is no guarantee that the same level of agreement 

would be produced using different facilities, conditions, or test articles. However, although this 

level of agreement is only applicable for that tractor and trailer model using the specified 

conditions for those tools in a specific facility, this data was sufficient to demonstrate the level of 

agreement possible and support the use of specified conditions in HD GHG Phase 1 (as shown in 

Figure 3-7 a and b). 
3-43 

These trends were similarly observed in the HD GHG Phase 2 data. Accordingly, below in 

Figure 3-7a is the aerodynamic method comparison using HD Phase 2 test results. The 

results are shown are for three of the four Class 8, high roof, sleeper cab tractors from 

coastdown, constant speed and zero degree CdA RSWT testing; normalized to the coastdown 

results. The data in Figure 3-7a is presented using the zero yaw Cd values for the reduced scale 

wind tunnel (RSWT) and the constant speed test results normalized to the coastdown test results 

(e.g., RSWT or constant speed results divided by the coastdown result) similar to the comparison 

used for HD GHG Phase 1 for a standard trailer. The HD GHG Phase 2 data shows a similar 

level of method agreement as HD GHG Phase 1 for the coastdown test and RSWT (constant 

speed testing was not performed for HD GHG Phase 1). 

1.00 1.00 1.00

1.03

0.95

0.97

0.92

0.90 0.93

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT

(Cd*A @ zero yaw)
3-44 

Figure 3-7a Method Comparison for Coastdown, Constant Speed and RSWT (Zero-Yaw Cd) Testing 

Normalized to the Coastdown Results for Class 8 High Roof Sleeper Cab Tractors and Standard Trailers 

following a HD GHG Phase 1 approach. (Note: The results of Sleeper 1 and Sleeper 3 are not directly 

comparable since the test articles do not match but are shown to represent the overall industry trend given a 

manufacturer’s fleet variance.) 

 

1.00 1.00 1.00

1.03

0.95

0.97

0.92

0.90 0.93

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT

(Cd*A @ zero yaw)
3-45 

It is interesting to note that if the WACd concept had been adopted in HD GHG Phase 1, 

the level of method agreement, and consequently the Falt-aero factor used to characterize and 

account for method inequality, may have been closer to parity (e.g., Falt-aero closer to 1) based 

on the HD GHG Phase 2 WACd data for a standard trailer, as shown in Figure 3-8b below. 

 
3-46 

Figure 3-8b Method Comparison for Coastdown, Constant Speed and RSWT (WACdA @ 55 mph w/ 7 mph 

wind) Testing Normalized to the Coastdown Results for Class 8 High Roof Sleeper Cab Tractors and 

Standard Trailers. This approach was recommended by commenters for HD GHG Phase 1. (Note: The 

results of Sleeper 1 and Sleeper 3 are not directly comparable since the test articles do not match but are 

shown to represent the overall industry trend given a manufacturer’s fleet variance.) 

 

1.00 1.00 1.00

1.03

0.95

0.97

1.025 1.033 1.035

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT

(Cd*A @ zero yaw)
3-47 

For HD GHG Phase 2, we are proposing to include the wind average drag and will 

discuss this in Section 3.2.9. Further, we will revisit the method comparison with the inclusion 

of wind average drag for a trailer equipped with side skirts; consistent with the proposed HD 

GHG Phase 2 approach. 

3.2.9 Wind-Averaged Drag Area and General Aerodynamic Assessment Results 

Based on Proposed HD Phase 2 Tractor Modifications 

For HD Phase 2, we are proposing to use the WACdA as the main GEM input (Section 

3.2.3), revise the standard reference trailer for tractors (Chapter 3.2.6), and continue to use a Faltaero when alternative aerodynamic methods are used for certification and compliance 

demonstration (Chapter 3.2.8). Based on the proposed modifications, this section uses the HD 

Phase 2 WACdA results from RSWT testing for a tractor-trailer combination with trailer side 

skirts installed to demonstrate the coastdown yaw sweep shift concept, calculation of the 

alternate method factor (Falt-aero), and provide a cross-method comparison for illustrative 

purposes. 

To begin, below in Table 3-18 are the values for the zero yaw drag areas (CdA (zero)) and 

WACdA at 55 miles per hour (mph) for each sleeper cab tractor and 53’ trailer, averaged across 

three OEM trailers with trailer side skirt #2 installed on the trailer. 

Table 3-18 Zero Yaw and Wind Average Drag Area RSWT Results for Tractor-Trailer Combinations with a 

Trailer Side Skirt (Results for Trailer Side Skirt #2 Shown). 

TRACTOR CdA

(ZERO) 

CdA DELTA 

VS. 

STANDARD 

TRAILER 

CdA % 

DELTA VS. 

STANDARD 

TRAILER 

WACdA

(@ 55 

MPH) 

WACdA

DELTA VS. 

STANDARD 

TRAILER 

WACdA % 

DELTA VS. 

STANDARD 

TRAILER 

WACDA –

CdA

DELTA 

(WACdA

OFFSET) 

WACDA

– CdA % 

DELTA 

(VS.

CdA) 

Sleeper 1 4.914 0.511 9.42% 5.419 0.624 10.33% 0.505 10.27% 

Sleeper 2 4.962 0.594 10.68% 5.705 0.700 10.94% 0.742 14.96% 

Sleeper 3 5.056 0.566 10.07% 5.598 0.687 10.93% 0.542 10.73% 

Sleeper 4 5.003 0.560 10.07% 5.627 0.714 11.26% 0.625 12.49% 

Day 5.298 0.311 5.55% 5.755 0.612 9.61% 0.457 8.63% 

Sleeper 

Average (2 

out of 4 

tractors w/ 

matching 

coastdown 

data) 

4.938 0.552 10.1% 5.562 0.662 10.6% 0.624 12.6% 

Based on the results in Table 3-18, there is good alignment in the data (CdA standard 

deviation of 0.06, 1.21 percent relative standard error; WACdA standard deviation of 0.12, 2.16 

percent relative standard deviation) across sleeper cab tractors and trailers equipped with trailer 

side skirts (e.g., a similar trend was demonstrated in the data for trailer side skirt #1 as well). 
3-48 

Similar to the method comparison in 

Figure 3-7 for zero degree drag area and a standard trailer, Figure 3-9 below shows the 

WACdA method comparison for three of the four Class 8, high roof, sleeper cab tractors with a 

trailer equipped with trailer side skirts consistent with the proposed HD GHG Phase 2 reference 

trailer modifications. The results are shown for constant speed testing and WACdA RSWT 

testing at 55mph assuming a 7mph wind speed, normalized to the coastdown test results. As 

discussed above, these results are for a specific set of tractor-trailer models, specifications and 

facilities for those tools and, thus, a similar or equivalent level of agreement is not guaranteed for 

using different criterion. However, as shown for HD GHG Phase 1, there is a certain level of 

agreement achievable using set conditions for a given tractor-trailer model as proposed under 

HD GHG Phase 2. 

This is prior to any adjustment of the coastdown result to incorporate wind averaging 

which would theoretically increase the coastdown values upward since the wind average drag 

area is higher than zero degree yaw drag area. However, this is a useful comparison to 

demonstrate how the use of wind average drag in HD GHG Phase 1 may have influenced the 

level of agreement between the methods, and the resulting OEM Falt-aero values (e.g., Falt-aero 

values closer to 1), versus the use of zero yaw drag adopted in HD GHG Phase 1. We will 

explore the method comparison further with the inclusion of wind average drag later in this 

section. 

1.00 1.00 1.00

1.03

0.95

0.97

0.92

0.90 0.93

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT

(Cd*A @ zero yaw)
3-49 

Figure 3-9 Unadjusted Method Comparison for Coastdown, Constant Speed and RSWT (WACdA @ 55 mph 

w/ 7 mph wind) Testing Normalized to the Coastdown Results, for Class 8 High Roof Sleeper Cab Tractors 

and Trailers Equipped with Trailer Side Skirts. Note: The results of Sleeper 1 and Sleeper 3 are not directly 

comparable since the test articles do not match but are shown to represent overall industry trend given a 

manufacturer’s unique fleet. 

 

1.00 1.00 1.00

0.96

0.98 1.00 1.02

1.02

1.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT (WACD*A @ 7/55)
3-50 

Next, the results from the RSWT were used to develop a virtual WACdA value for 

coastdown testing. Specifically, the coastdown results and the WACdA to CdA delta will be used 

to correct the coastdown results. As previously mentioned, we could not match the exact tractor 

model for RSWT and on-road testing for one of the tractors. In addition, we have not completed 

on-road testing for another tractor model that has been tested in the RSWT. Therefore, for this 

exercise, we will use: the average of the RSWT results above for two out of the four sleeper cab 

tractors with trailer skirts shown in the last row of Table 3-18; and the average of coastdown 

results for two of the three tested sleeper cab tractors with trailer skirts (average: 5.549 [n=2]); to 

demonstrate the coastdown yaw sweep correction. 

To begin the conversion from alternate method to coastdown, we first calculate certain 

parameters using the results from above: the wind average drag area – zero yaw drag area offset; 

and the Falt-aero using the coastdown and the zero yaw drag area results; shown in Table 3-19 

below. 

Table 3-19 Converting Wind Average Drag Area from the Alternate Method to Virtual Wind Average Drag 

Area for the Coastdown Reference Method: Sample Calculations for Certified Configuration Calculations. 

CERTIFIED CONFIGURATION USING COASTDOWN REFERENCE METHOD AND ALTERNATE 

METHOD 

(SAMPLE DATA: RSWT RESULTS FOR DAY CAB IN TABLE 3-18)

Test Method/Source Variable Value 

Coastdown (reference 

method) 

(CdA)zero,coastdown 5.5 

Drag area from 

RSWT (or other alt. 

method) 

(CdA)zero,wind 

tunnel

4.9 

Wind average drag 

area RSWT (or other 

alt. method) 

(CdA)wad wind 

tunnel

5.6 

Alternate Method 

Factor 

Falt-aero Falt-aero = (CdA)zero, coastdown / (CdA)zero,wind tunnel = 5.5/4.9 = 1.12 

Wind Average Offset WACd-CdA0 

Offset 

(CdA)wad,wind tunnel – (CdA)zero,wind tunnel = 5.6 – 4.9 = 0.7 

CdAwad Calculations 

(for bin determination 

and GEM input): 

Wind Average 

Equivalent 

Coastdown 

(CdA)wad

(CdA)wad = (CdA)zero, coastdown + ((CdA)wad,wind tunnel – (CdA)zero,wind tunnel)*Faltaero 

= 5.5+(5.6-4.9)*1.12 

= 5.5 + 0.7*1.12 

(CdA)wad = 6.3

Using the Wind Average Equivalent Coastdown value of 6.2, a manufacturer would 

identify the appropriate bin for that value and use the associated aerodynamic GEM input for 

determining CO2 emissions and fuel consumption; which is discussed below. For now, the 

alternate method factor, Falt-aero, is not used, however, it will be important later for certifying 

additional configurations. 

Once the certified configuration values have been derived, the alternate aerodynamic 

method can be used to certify additional configurations using the Falt-aero from the certified 

configuration. For this example, we will use the same data from Table 3-18 and from Table 3-19 

above for the Day Cab to represent a completely different configuration since the data from the 

sleeper cab tractors is all very similar (Note: In reality, a manufacturer would not be allowed to 
3-51 

use the day cab for sleeper certification, and vice versa). Thus, this approach is limited to the 

example calculations for illustrative purposes). Table 3-20 shows the calculations using the data 

from Table 3-18 and Table 3-19 for the day cab. 

Specifically, Table 3-20 shows two cases: a case where the manufacturer has generated 

the wind average drag area from the alternate method and another case where the manufacturer 

does not have the wind average drag area but, instead, has the zero yaw drag area from the 

alternate method. In the latter case where the manufacturer does not have the wind average drag 

area, we are proposing that the manufacturer would use a default wind average drag area-zero 

yaw drag area of 0.80. 

Table 3-20 Converting Wind Average Drag Area from the Alternate Method to Virtual Wind Average Drag 

Area for the Coastdown Reference Method: Sample Calculations for Additional Configurations Using RSWT 

Day Cab Data 

ADDITIONAL CERTIFIED CONFIGURATIONS USING ALTERNATE METHOD 

(SAMPLE DATA: RSWT RESULTS FOR DAY CAB IN TABLE 3-18) 

Test Method/Source Variable Example: 

Previous Certified Configuration 

Data 

Additional Configuration using 

Alternate Method 

(Example: Day Cab Data) 

Coastdown 

(reference method) 

(CdA)zero,coastdown 5.5 Not Available; 

alternate method used 

Alt. method Drag 

area 

(CdA)zero,alt method 4.9 5.3 

Alt. method wind 

average drag area 

(CdA)wad,alt method 5.6 5.8 

Calculated Using 

Certified 

Configuration in 

Table 3-19 

Alternate Method 

Factor (Falt-aero) 

Falt-aero = (CdA)zero,coastdown / (CdA)zero,wind tunnel = 5.5/4.9 = 1.12 

(from Table 3-19 for certified case)

Actual Wind 

Average Offset 

WACd-CdA0 

Offset 

0.7 0.5 

Default Wind 

Average Offset 

Default Offset 0.8 0.8 

CdAwad Calculations 

(for bin 

determination and 

GEM input): 

With (CdA)wad, alt 

method

(CdA)wad = (CdA)wad,alt method * Faltaero 

= 5.6 * 1.12 

= 6.3

(CdA)wad = (CdA)wad,alt method * 

Falt-aero 

= 5.8 * 1.12 

= 6.5

Without (CdA)wad, 

alt method; Instead, 

use (CdA)zero, 

coastdown (if 

applicable) or 

(CdA)zero, alt method

with a default 

offset value of 

0.80 

(CdA)wad = (CdA)zero,coastdown + 

default offset 

= 5.5 + 0.80 

= 6.3

Not Applicable; 

Only alternate method is used 

(CdA)wad = (CdA)zero,alt method * Faltaero + default offset 

= 4.9 * 1.12 + 0.80 

= 6.3

(CdA)wad = (CdA)zero, alt method * 

Falt-aero + default offset 

= 5.3 * 1.12 + 0.80 

= 6.7

In contrast to Falt-aero that is calculated once for the certified configuration and may be 

applied to additional configurations, the wind average drag area – drag area (WACdA – CdA) 

offset is configuration dependent, and must be calculated for and used solely for that 

configuration. It may be possible to use a single wind average – drag area offset for grouping 
3-52 

similar configurations but this would need to be supported by data showing the level of variation 

between the configurations to justify such a grouping, and approved by the agencies prior to use. 

Now that we have applied the adjustment for wind average drag area to the coastdown, 

we will revisit the method comparison using this approach. Below in Figure 3-10 is the data 

from Figure 3-9 with the values for coastdown adjusted to account for wind average drag. In 

addition, the constant speed test results have been adjusted by the same factor to adjust for wind 

average and maintain its relative agreement with the coastdown test results. As shown in Figure 

3-10, the results for the RSWT are lower than the coastdown test results, as expected. Further, of 

note is the fact that if we analyze the method agreement for the zero yaw RSWT test results for a 

trailer with a skirt, prior to wind average adjustment and normalized to the coastdown test 

results, you would get results of 0.91 for Sleeper 1, 0.89 for Sleeper 2, and 0.90 for Sleeper 3. 

These are the results shown in Figure 3-10, demonstrating that the equations above are 

adequately accounting for wind average drag area in the coastdown results (e.g., the relative 

difference between the adjusted coastdown and RSWT wind average drag area is the same as the 

relative difference between the original coastdown result and the zero yaw drag area). 

 
3-53 

Figure 3-9 Adjusted Method Comparison for Coastdown, Constant Speed and RSWT (WACdA @ 55 mph 

w/ 7 mph wind) Testing Normalized to the Coastdown Results with wind average adjustment applied, for 

Class 8 High Roof Sleeper Cab Tractors and Trailers Equipped with Trailer Side Skirts. Note: The results of 

Sleeper 1 and Sleeper 3 are not directly comparable since the test articles do not match but are shown to 

represent overall industry trend given a manufacturer’s unique fleet. 

 

1.00 1.00 1.00

0.96

0.98 1.02

0.91

0.89 0.90

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Sleeper 1 Sleeper 2 Sleeper 3

Coastdown

Constant Speed

RSWT

(Cd*A @ zero yaw)
3-54 

Finally, below in Table 3-21 are the wind average equivalent coastdown values to 

illustrate how today’s tractors fit into the proposed HD Phase 2 aerodynamic standards. These 

values were developed using the coastdown results for each tractor and trailer equipped with a 

trailer side skirt, and applying the principles above with the corresponding zero yaw drag area 

and wind-average drag area from the RSWT. The corresponding aerodynamic bins and GEM 

input are provided as well based on the proposed bin values in Section III.E.(2)(a)(iv) of the 

preamble. A manufacturer would use the resulting aerodynamic GEM input to generate the 

configuration-specific CO2 emission and fuel consumption value for certification. As shown 

below, most tractors today would qualify for Bin III with varying degrees of opportunity to move 

into improved bins. 

Table 3-21 Wind Average Equivalent Coastdown Values Used to Develop the Proposed HD Phase 2 

Aerodynamic Standards and Corresponding Aero Bin and Aero GEM Input. 

TRACTOR CONFIG CSTDWN CdA0 WACd Falt-aero WACd

OFFSE

T 

WACd X 

Falt-aero

CSTDWN

+ WACd

OFFSET 

HD PHASE 

2 

PROPOSED 

BIN # 

BIN III 

AERO TEST 

RESULT 

BIN III 

AERO GEM 

INPUT 

Sleeper 1 skirt 5.4 4.91 5.42 1.11 0.505 6.0 6.0 Bin III 6.0-6.5 6.3 

Sleeper 2 skirt 5.6 4.96 5.70 1.15 0.742 6.5 6.4 Bin III 6.0-6.5 6.3 

Sleeper 3 skirt 5.6 5.06 5.60 1.11 0.542 6.2 6.2 Bin III 6.0-6.5 6.3 

This table is only intended to illustrate how the tractors would fit into the proposed bin 

structure but may not be completely appropriate for bin setting. Specifically, in two cases, there 

were tractors where either the model designation or model year did not match between the onroad and RSWT testing. As a result, it is better to look at the data in aggregate rather than on an 

individual vehicle basis. 

Using the average of the coastdown drag areas (avg = 5.5), the average of the wind 

average offsets (avg = 0.6), and the alternate method factor (Falt-aero) of 1.15 from the tractor 

(Sleeper 2) with matching vehicles between on-road and wind tunnel testing; in the equation in 

Table 3-18 for a certified configuration (e.g., CdAzero, coastdown + (CdAwad, wind tunnel – CdAzero, wind tunnel) * Faltaero) you get a CdAwad value of 6.23. This value is almost exactly in the middle of the proposed 

Bin III standards and, therefore, we think that the proposed standards are appropriate. Further, 

the data in the Table 3-21 above provides some insight into the variability and range of high roof 

sleeper cab tractors from across the manufacturers that might be expected to be certified for HD 

Phase 2. Thus, although we do not have a complete set of matching datasets in all cases, we 

believe the values and bandwidth are appropriate for the HD Phase 2 proposed bin standards 

based on the data and calculations above. 

3.2.10 Delta Drag Area Results to Support Proposed HD Phase 2 Trailer 

Regulations 

Similar to the tractor aerodynamic assessment in Section 3.2.9 above, we can use the data 

generated during our aerodynamic assessment test programs to support development of trailer 

aerodynamic standards. As previously discussed in Section 3.2.5, the proposed trailer 

regulations would use the delta CdA from any of the accepted aerodynamic methods. Table 3-22 

below shows the delta CdAs across tractors, trailers, trailer configurations and aerodynamic test 
3-55 

methods using data from Table 3-8, Table 3-9 and Table 3-10. The corresponding trailer 

certification bin and trailer aerodynamic GEM inputs are provided as well based on the proposed 

values in Section IV of the preamble, IV.F.(3)(b)(iv), Table IV-31. 

Table 3-22 Delta CDAs from Various Tractors, Trailers, Trailer Configurations and Aerodynamic Test 

Methods with Corresponding Trailer Aerodynamic Bins 

CAB TYPE CONFIGURATION DELTA CdA

(VS. STANDARD) 

TRAILER CERT. BIN TRAILER AERO 

GEM INPUT 

Coastdown Testing 

Sleeper 1 With Trailer Skirt #2 0.5 III 0.5 

With Trailer Skirt and Boat Tail 0.9 IV 0.9 

Sleeper 2 With Trailer Skirt #2 0.6 III 0.5 

With Trailer Skirt and Boat Tail 1.1 IV 0.9 

Sleeper 3 With Trailer Skirt #2 0.5 III 0.5 

With Trailer Skirt and Boat Tail 0.7 IV 0.9 

Constant Speed Testing 

Sleeper 1 With Trailer Skirt #2 0.7 IV 0.9 

With Trailer Skirt and Boat Tail 1.1 IV 0.9 

Sleeper 2 With Trailer Skirt #2 0.5 III 0.5 

Reduced Scale Wind Tunnel Testing (RSWT) 

Sleeper 1 With Trailer Skirt #1 0.423 III 0.5 

With Trailer Skirt #2 0.511 III 0.5 

 With Boat Tail 0.340 III 0.5 

With Trailer Skirt #2 and Boat Tail 0.972 IV 0.9 

Sleeper 2 With Trailer Skirt #1 0.586 III 0.5 

With Trailer Skirt #2 0.594 III 0.5 

 With Boat Tail 0.365 III 0.5 

With Trailer Skirt #2 and Boat Tail 1.092 IV 0.9 

Sleeper 3 With Trailer Skirt #1 0.477 III 0.5 

With Trailer Skirt #2 0.566 III 0.5 

 With Boat Tail 0.318 III 0.5 

With Trailer Skirt #2 and Boat Tail 1.056 IV 0.9 

Sleeper 4 With Trailer Skirt #1 0.481 III 0.5 

With Trailer Skirt #2 0.557 III 0.5 

 With Boat Tail 0.332 III 0.5 

With Trailer Skirt #2 and Boat Tail 1.121 IV 0.9 

Day With Trailer Skirt #1 0.256 II 0.2 

With Trailer Skirt #2 0.311 III 0.5 

 With Boat Tail 0.370 III 0.5 

With Trailer Skirt #2 and Boat Tail 0.699 III 0.5 

In general, Table 3-22 shows that individual components (e.g., trailer side skirts, boat 

tails) added to a trailer might qualify for Bin III while combinations of devices (e.g., trailer side 

skirt and boat tail) added to a trailer might qualify for Bin IV. For the day cab, a basic skirt such 
3-56 

as Skirt #1 would qualify for Bin II as opposed to an advance skirt such as Skirt #2 which would 

qualify for Bin III. 

Trailer manufacturers would follow a similar process of using the delta CdA values for 

their trailer devices or improved trailer designs to identify the appropriate trailer certification bin 

and the trailer aero GEM input for that bin. Accordingly, these values were used to develop the 

trailer certification bins and aero GEM inputs to support the trailer regulations in HD Phase 2. 

3.3 Tire Rolling Resistance 

The agencies are proposing that the ISO 28580 test method be used to determine rolling 

resistance and the coefficient of rolling resistance. A copy of the test method can be obtained 

through the American National Standards Institute.13

3.3.1 Reason for Using ISO 28580 

EPA’s SmartWay Partnership Program started to identify equipment and feature 

requirements for SmartWay-designated Class 8 over-the-road tractors and trailers in 2006. In 

order to develop a tire rolling resistance specification for SmartWay-designated commercial 

trucks, EPA researched different test methods used to evaluate tire rolling resistance, reviewing 

data and information from tire manufacturers, testing laboratories, the State of California, the 

Department of Transportation, tractor manufacturers, and various technical organizations. After 

assessing this information, EPA determined that its SmartWay program would use the SAE 

J126914 tire rolling resistance method until the ISO 2858015 method (at that time under 

development) was finalized, at which time the Agency would consider moving to this method for 

its SmartWay program. 

During this same time period, the National Highway Traffic Safety Administration 

(NHTSA) conducted an evaluation of passenger vehicle tire rolling resistance test methods and 

their variability.16 Five different laboratory test methods at two separate labs were evaluated. 

The NHTSA study focused on passenger tires; however, three of the four test methods evaluated 

can be used for medium-duty and heavy-duty tractor tires. The methods evaluated were SAE 

J1269, SAE J245217 (not applicable for medium-duty or heavy-duty tractor tires), ISO 1816418

and ISO 28580. The NHTSA study showed significant lab to lab variability between the labs 

used. The variability was not consistent between tests or types of tire within the same test. The 

study concluded that a method to account for this variability is necessary if the rolling resistance 

value of tires is to be compared (NHTSA, 2009). Because of laboratory variability, NHTSA 

recommended that the use of ISO 28580 is preferred over the other test methods referenced. 

ISO 28580 is preferred because the test method involves laboratory alignment between a 

“reference laboratory” and “candidate laboratory.” The ISO technical committee involved in 

developing this test method also has the responsibility for determining the laboratory that will 

serve as the reference laboratory. The reference laboratory would make available an alignment 

tire that can be purchased by candidate laboratories. The candidate laboratory would identify its 

reference machine. However, at this time, the reference laboratory and alignment tires have not 

been identified. 
3-57 

3.3.2 Measurement Method and Results 

The ISO 28580 test method includes a specific methodology for “light truck, commercial 

truck and bus” tires, and it has 4 measurement methods, force, torque, deceleration, and power, 

all of which appear to be suitable for use. 

The results of the ISO 28580 test are intended for use in vehicle simulation modeling, 

such as the model used to assess the effects of various technology options for national 

greenhouse gas and fuel economy requirements for commercial trucks (see Chapter 4). The 

results are usually expressed as a rolling resistance coefficient and measured as kilogram per 

metric ton (kg/metric ton) or as dimensionless units (1 kg/metric ton is the same as the 

dimensionless unit 0.001). The results are corrected for ambient temperature drum surface and 

drum diameter as specified in the proposed test method. 

3.3.3 Sample Size 

The rolling resistance of tires within the same model and construction are expected to be 

relatively uniform. In the study conducted by NHTSA, only one individual tire had a rolling 

resistance value that was significantly different from the other tires of the same model. The 

effect of production variability can be further reduced by conducting three replicate tests and 

using the average as the value for the rolling resistance coefficient. Tire models available in 

multiple diameters may have different values of rolling resistance for each diameter because 

larger diameter tires can produce lower rolling resistance than smaller diameters under the same 

load and inflation conditions. If the size range within a tire model becomes large enough that a 

given tire size is no longer “substantially similar” in rolling resistance performance to all other 

tire sizes of that model, then good engineering judgment should be exercised as to whether the 

differently-sized tire shall be treated, for testing and vehicle simulation purposes, as a distinct tire 

model. For Class 8 tractors that typically use tires that fit on 22.5” or 24.5” wheels, this situation 

might occur with 17.5” tires, more commonly used on moving vans and other applications that 

require a low floor. 

3.3.4 Tire Size 

In Phase 2, the agencies propose to require manufacturers to enter the tire loaded radius 

as a GEM input. While this rulemaking does not include tire size among the technologies 

applied to improve fuel efficiency, this measurement is among the driveline parameters 

necessary for GEM to calculate a vehicle speed for a given engine speed. Because there is a 

wide range of possible measurements for loaded radius, the agencies are specifying a proposed 

measurement procedure. Tire sizes can be measured using an overall diameter or a static loaded 

radius. Deflection is typically between 24 and 33 percent depending on the tire design. In the 

first 100-200 miles of a tire’s useful life, there will be a break-in process during which a 

commercial tire can “grow” one to two percent, up to 18 mm. Because this growth affects the air 

pressure in the tire, it’s important to specify the air pressure under which the loaded radius 

measurement is performed. The Society of Automotive Engineers (SAE) has published 

recommended practice J1025 for determining the revolutions per mile of new truck tires.19 

Consistent with that recommended practice, the agencies propose that manufacturers would 

quantify the loaded tire radius of the drive tire, NIST traceable within ±0.5 percent uncertainty, 
3-58 

by measuring the perpendicular distance from the axis of rotation of the loaded tire to the surface 

on which it is rolling. Load the tire to 85 percent of the maximum load capacity specified by the 

manufacturer, at the corresponding air inflation level. See 40 CFR 1037.520(j). 

3.4 Duty Cycle 

Certification duty cycles have a significant impact on the GHG emissions from a truck 

and how technologies are assessed. Every truck has a different duty cycle in-use. Therefore, it is 

very challenging to develop a uniform duty cycle which accurately assesses GHG improvements 

and fuel efficiency from technologies relative to their performance in the real world. 

The duty cycle attributes that impact a vehicle’s performance include average speed, 

maximum speed, acceleration rates, deceleration rates, number of stops, road grade, power takeoff operation, and idling time. Average and maximum speeds are the attributes which have the 

greatest impact on aerodynamic technologies. Vehicle speed also impacts the effect of low 

rolling resistance tires. The effectiveness of extended idle reduction measures is determined by 

the amount of time spent idling. Lastly, hybrid technologies demonstrate the greatest 

improvement on cycles which include a significant amount of stop-and-go driving due to the 

opportunities to recover braking energy. In addition, the amount of power take-off operation will 

impact the effectiveness of some vocational hybrid applications. 

The ideal duty cycle for a line-haul truck would account for a significant amount of time 

spent cruising at high speeds. A pickup and delivery truck duty cycle would contain a 

combination of urban driving, some number of stops, and limited highway driving. If the 

agencies propose an ill-suited duty cycle for a regulatory subcategory, it may drive technologies 

where they may not see the in-use benefits. For example, requiring all trucks to use a constant 

speed highway duty cycle would drive significant aerodynamic improvements. However, in the 

real world a pickup and delivery truck may spend too little time on the highway to realize the 

benefits of aerodynamic enhancements. In addition, the extra weight of the aerodynamic fairings 

would actually penalize the GHG performance of that truck in urban driving and may reduce its 

freight carrying capability. 

3.4.1 Duty Cycles Considered 

In HD Phase 1, the agencies selected three duty cycles for certification testing: the 

Transient portion of the California Air Resource Board (CARB) Heavy Heavy-Duty Truck 5 

Mode Cycle, 55 mph cruise (without grade), and 65 mph cruise (without grade). 

For HD Phase 2, the agencies carefully considered which duty cycles are appropriate for 

the different regulatory subcategories. We considered several duty cycles in the development of 

the rulemaking including EPA’s MOVES model; the Light-Duty FTP75 and HFET; Heavy-Duty 

UDDS; World Wide Transient Vehicle Cycle (WTVC); Highway Line Haul; Hybrid Truck User 

Forum (HTUF) cycles; and California CARB’s Heavy-Heavy-Duty Truck 5 Mode Cycle. 

MOVES Medium-Duty and Heavy-Duty schedules were developed based on three 

studies. Eastern Research Group (ERG) instrumented 150 medium and heavy-duty vehicles, 

Battelle instrumented 120 vehicles instrumented with GPS, and Faucett instrumented 30 trucks 
3-59 

to characterize their in-use operation.20 ERG then segregated the driving into freeway and nonfreeway driving for medium and heavy-duty vehicles, and then further stratified vehicle trips 

according the predefined ranges of average speed covering the range of vehicle operation. 

Driving schedules were then developed for each speed bin by creating combinations of idle-toidle “microtrips” until the representative target metrics were achieved. The schedules developed 

by ERG are not contiguous schedules which would be run on a chassis dynamometer, but are 

made up of non-contiguous “snippets” of driving meant to represent target distributions. This 

gives MOVES the versatility to handle smaller scale inventories, such as intersections or sections 

of interstate highway, independently. 

The FTP75 and HFET duty cycles are used extensively for Light-Duty emissions and 

CAFE programs. Our assessment is that these cycles are not appropriate for HD trucks for two 

primary reasons. First, the FTP has 24 accelerations during the cycle which are too steep for a 

Class 8 combination tractor to follow. Second, the maximum speed is 60 mph during the 

HWFEC, while the national average truck highway speed is 65 mph. 

The Heavy-Duty Urban Dynamometer Driving Cycle was developed to determine the 

Heavy-Duty Engine FTP cycle. The cycle was developed from CAPE-21 survey data which 

included information from 44 trucks and 3 buses in Los Angeles and 44 trucks and 4 buses in 

New York in 1977. The cycle was computer generated and weighted to represent New York 

non-freeway (254 sec), Los Angeles non-freeway (285 sec), Los Angeles freeway (267 sec), 

New York non-freeway (254 sec) to produce a nearly 50/50 weighting of highway cruise and 

urban transient. We believe this cycle is not appropriate for our program for several reasons. 

The maximum speed on the UDDS is 58 mph which is low relative to the truck speed limits in 

effect today. The 50/50 weighting of cruise to transient is too low for combination tractors and 

too high for vocational vehicles and the single cycle does not provide flexibility to change the 

weightings. Lastly, the acceleration rates are low for today’s higher power trucks. 

The World Harmonized WTVC was developed by the UN ECE GRPE group. It 

represents urban, rural, and motorway operation. The cycle was developed based on data from 

20 straight trucks, 18 combination tractors, and 11 buses total from Australia, Europe, Japan, and 

the US. EPA has a desire to harmonize internationally, however, we believe that this single 

cycle does not optimally cover the different types of truck operation in the United States and 

does not provide the flexibility to vary the weightings of a single cycle. 

The Highway Line Haul schedule was created by Southwest Research Institute, using 

input from a group of stakeholders, including EPA, Northeastern States for Coordinated Air Use 

Management (NESCAUM), several truck and engine manufacturers, state organizations, and 

others, for a NESCAUM heavy truck fuel efficiency modeling and simulation project. The cycle 

is 103 miles long and incorporates grade and altitude. This cycle is a good representation of line 

haul operation. However, the altitude changes cannot be incorporated into a chassis 

dynamometer or track test and the cycle is also too long for a typical chassis dynamometer test. 

The Calstart-Weststart Hybrid Truck Users Forum is developing cycles to match the 

characteristics of truck applications which are expected to be first to market for hybrids. The 

cycles include the Manhattan Bus Cycle, Orange County Bus Cycle, Class 4 Parcel Delivery, 

Class 6 Parcel Delivery, Combined International Local and Commuter Cycle (CILCC), 
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Neighborhood Refuse, Utility Service, and Intermodal Drayage cycles. The cycles are very 

application-specific and appropriately evaluate each vocation. However, the use of these types 

of application specific cycles in a regulatory scheme would lead to a proliferation of cycles for 

every application, an outcome that is not desirable. 

The CARB 5 Mode cycle was developed by California CARB from heavy-duty truck 

data gathered from 1997 through 2000.21 Data was collected from real world driving from 

randomly selected vehicles. The data was gathered from 140 heavy-duty trucks by Battelle and 

from 31 heavy-duty trucks in a study conducted by Jack Faucett and Associates. The final data 

set included 84 of these heavy duty trucks covering over 60,000 miles and 1,600 hours of 

activity. The cycles were developed to reflect typical in-use behavior as demonstrated from the 

data collected. The four modes (idle, creep, transient, and cruise) were determined as distinct 

operating patterns, which then led to the four drive schedules. The cycle is well accepted in the 

heavy-duty industry. It was used in the CRC E55/59 Study which is the largest HD chassis 

dynamometer study to date and used in MOVES and EMFAC to determine emission rate inputs; 

EPA’s biodiesel study which used engine dynamometer schedules created from CARB cruise 

cycle; the HEI ACES Study: WVU developed engine cycles from CARB 4-mode chassis cycles; 

CE/CERT test; and by WVU to predict fuel efficiency performance on any duty cycle from 

CARB 5 mode results. The modal approach to the cycles provides flexibility in cycle weightings 

to accommodate a variety of truck applications. A downside of the cycle is that it was developed 

from truck activity in California only. 

3.4.2 Proposed Duty Cycles 

3.4.2.1 Highway Cruise Cycles 

The agencies analyzed the average truck speed limit on interstates and other freeways to 

identify the appropriate speed of the highway cruise cycles. State speed limits for trucks vary 

between 55 and 75 mph, depending on the state.22 The median urban and rural interstate speed 

limit of all states is 65 mph. The agencies also analyzed the speed limits in terms of VMTweighting. The agencies used the Federal Highway Administration data on Annual Vehicle 

Miles for 2008 published in November 2009 to establish the vehicle miles travelled on rural and 

urban interstates broken down by state. The VMT-weighted national average speed limit is 63 

mph based on the information provided in Table 3-23. The results of this analysis led to the 

adoption of the High Speed (65 mph) and Low Speed (55 mph) Cruise duty cycles in Phase 1. 
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Table 3-23 VMT-Weighted National Truck Speed Limit 

STATE RURAL 

INTERSTATE 

SPEED LIMIT 

URBAN 

INTERSTATE 

SPEED LIMIT 

RURAL 

INTERSTATE 

MILES 

URBAN 

INTERSTATE 

AND OTHER 

FREEWAYS 

MILES 

U.S. 

WEIGHTED 

VMT 

FRACTION 

RURAL 

U.S. 

WEIGHTED 

VMT 

FRACTION 

URBAN 

VMT 

WEIGHTED 

SPEED 

LIMIT 

AL 70 65 5,643 7,950 0.6% 0.8% 0.968 

AK 55 55 803 662 0.1% 0.1% 0.086 

AZ 75 65 6,966 13,324 0.7% 1.4% 1.474 

AR 65 55 4,510 4,794 0.5% 0.5% 0.591 

CA 55 55 17,681 123,482 1.9% 13.1% 8.242 

CO 75 65 4,409 11,745 0.5% 1.2% 1.161 

CN 65 55 715 13,485 0.1% 1.4% 0.837 

DE 55 55 - 1,694 0.0% 0.2% 0.099 

DC 55 55 - 813 0.0% 0.1% 0.047 

FL 70 65 9,591 37,185 1.0% 3.9% 3.279 

GA 70 55 9,433 21,522 1.0% 2.3% 1.958 

HA 60 60 110 2,403 0.0% 0.3% 0.160 

ID 65 65 2,101 1,250 0.2% 0.1% 0.231 

IL 65 55 8,972 23,584 1.0% 2.5% 1.996 

IN 65 55 7,140 10,850 0.8% 1.2% 1.126 

IA 70 55 4,628 2,538 0.5% 0.3% 0.492 

KA 75 75 3,242 5,480 0.3% 0.6% 0.694 

KE 65 65 6,566 6,834 0.7% 0.7% 0.925 

LA 70 70 5,489 7,708 0.6% 0.8% 0.981 

ME 65 65 2,207 958 0.2% 0.1% 0.218 

MA 65 65 3,484 18,792 0.4% 2.0% 1.537 

MS 70 70 1,257 20,579 0.1% 2.2% 1.623 

MI 60 60 5,245 20,931 0.6% 2.2% 1.667 

MN 70 60 4,150 12,071 0.4% 1.3% 1.077 

MS 70 70 4,103 4,004 0.4% 0.4% 0.602 

MO 70 60 5,972 16,957 0.6% 1.8% 1.524 

MT 65 65 2,350 343 0.2% 0.0% 0.186 

NE 75 65 2,590 1,653 0.3% 0.2% 0.320 

NV 75 65 1,826 5,286 0.2% 0.6% 0.510 

NH 65 65 1,235 2,574 0.1% 0.3% 0.263 

NJ 65 55 1,609 25,330 0.2% 2.7% 1.590 

NM 75 65 4,530 2,667 0.5% 0.3% 0.545 

NY 65 55 6,176 37,306 0.7% 4.0% 2.604 

NC 70 70 5,957 19,216 0.6% 2.0% 1.871 

ND 75 75 1,394 374 0.1% 0.0% 0.141 
3-62 

OH 65 65 9,039 27,830 1.0% 3.0% 2.544 

OK 75 70 5,029 7,223 0.5% 0.8% 0.937 

OR 55 55 4,109 5,734 0.4% 0.6% 0.575 

PA 65 55 10,864 21,756 1.2% 2.3% 2.020 

RI 65 55 404 2,948 0.0% 0.3% 0.200 

SC 70 70 7,355 6,879 0.8% 0.7% 1.058 

SD 75 75 1,960 648 0.2% 0.1% 0.208 

TN 70 70 8,686 13,414 0.9% 1.4% 1.642 

TX 70 70 15,397 71,820 1.6% 7.6% 6.481 

UT 75 65 3,117 6,165 0.3% 0.7% 0.674 

VT 65 55 1,216 443 0.1% 0.0% 0.110 

VA 70 70 8,764 18,907 0.9% 2.0% 2.056 

WA 60 60 4,392 15,816 0.5% 1.7% 1.287 

WV 70 65 3,195 3,175 0.3% 0.3% 0.456 

WI 65 65 5,197 9,139 0.6% 1.0% 0.989 

WY 75 75 2,482 474 0.3% 0.1% 0.235 

In establishing the highway cruise cycles in Phase 1, we realized that we did not address 

the effect of road grade on emissions. Therefore, for Phase 2, we are proposing to alter the High 

Speed Cruise and Low Speed Cruise modes to reflect road grade for the constant speed cycles at 

65 mph and 55 mph respectively. Based on input from trucking fleets and truck manufacturers, 

we believe this is representative of in-use operation, wherein truck drivers use cruise control 

whenever possible during periods of sustained higher speed driving and road grade varies. 

To this end, the U.S. Department of Energy and EPA have partnered to support a project 

aimed at evaluating, refining and/or developing the appropriate road grade profiles for the duty 

cycles that would be used in the certification of heavy-duty vehicles to the GHG emission and 

fuel efficiency Phase 2 standards. The National Renewable Energy Laboratory (NREL) is 

leading a project which will refine the existing highway cruise duty cycles. In the course of this 

work, NREL has developed several activity-weighted road grade profiles which are 

representative of U.S. limited-access highways using high-accuracy road grade and countyspecific data for vehicle miles traveled. Either a single road grade profile representative of the 

nation’s limited-access highways will be chosen for use in the highway cruise cycles or two 

activity-weighted grade profiles will be selected if analysis demonstrates that they should be 

different for speed limits of 55 and 65 mph. The profiles are distance-based and cover a 

maximum distance of 15 miles. In addition to NREL work, the agencies have independently 

developed another candidate road grade profile for use in the 55 mph and 65 mph highway cruise 

cycles. While based on the same road grade database generated by NREL for U.S. restrictedaccess highways, its design is predicated on a different approach. This analysis of road grade 

profile options was not completed in time for use in developing the primary proposal. Therefore, 

for the proposal, the agencies selected an interim road grade profile for development of the 

proposed standards, which is described in Section III.E of the preamble to these rules. Based on 

preliminary results, it appears the interim road grade profile closely matches a national road 

grade profile on an absolute basis, before VMT weighting. The report documenting the NREL’s 
3-63 

and the agencies’ road grade work is available to the public in the docket, as is the agencies’ 

analysis of possible alternative vehicle standards developed using alternative road grade profiles. 

3.4.2.2 Transient Cycle 

The Phase 1 rule requires use of the Transient portion of the CARB’s Heavy Heavy-Duty 

Truck 5 Mode Cycle. The agencies have found that this cycle reasonably represents transient 

operation of many heavy-duty vehicles, though it is a very short test cycle - less than 3 miles – 

and can be driven in roughly 11 minutes. We are not proposing any changes to that cycle, and 

would continue to use it when certifying vehicles to the HD Phase 2 standards. 

The agencies would like to note that we have also launched a project at the National 

Renewable Energy Laboratory (NREL) to determine the extent to which the Transient mode of 

the CARB Heavy-Duty Truck 5 Mode Cycle is representative of transient operation of Class 2b8 vocational vehicles. This analysis is being performed using NREL’s extensive vehicle activity 

database and a variety of metrics such as average driving speed, kinetic intensity, idle time, 

maximum driving speed and standard deviation of speed. Should NREL recommend, and the 

agencies agree, that any subcategory of vocational vehicles is poorly represented by the 

Transient mode of the CARB cycle, a more representative transient test cycle will be adopted, 

possibly selected from test cycles already in use. This analysis was not completed in time for use 

in developing the primary proposal. The report documenting this work is available to the public 

in the docket, as is the agencies’ analysis of possible alternative vocational vehicle standards 

developed using an alternative transient duty cycle. 

3.4.2.3 Idle Cycle 

We are also proposing the addition of an idle-only cycle to determine both fuel 

consumption and CO2 emissions when a vehicle is idling, and recognize technologies that either 

reduce the fuel consumption rate or shut the engine off (and restart) during short-term idle events 

during the workday. The agencies are not expecting that this cycle would recognize technologies 

that allow the main engine to remain off during stationary vehicle operation with a PTO engaged 

and performing work. Those technologies would be recognized over the Hybrid-PTO test 

procedure defined in 40 CFR 1037.525. In this proposed idle-only cycle, based on user inputs, 

GEM would calculate CO2 emissions and fuel consumption at both zero torque (neutral idle) and 

with torque set to Curb-Idle Transmission Torque (as defined in 40 CFR 1065.510(f)(4) for 

variable speed engines) for use in the CO2 emission calculation in 40 CFR 1037.510(b). We are 

also proposing that GEM would calculate reduced CO2 and fueling for stop-start systems, based 

on an assumption that the effectiveness would represent a 90 percent reduction of the emissions 

that would occur if the vehicle had operated at Curb-Idle Transmission Torque over this cycle. 

This cycle is proposed to be applicable only for vocational vehicles using either the Regional, 

Multi-Purpose, or Urban composite duty cycles. 

3.4.3 Weightings of Each Cycle per Regulatory Subcategory 

Table 3-24 presents the Phase 1 final GEM duty cycle composite weightings for 

vocational vehicles and tractors. 
3-64 

Table 3-24 Phase 1 Vehicle Duty Cycle Composite Weightings 

VEHICLE 

CATEGORY 

PHASE 1 COMPOSITE WEIGHTINGS OF DUTY 

CYCLE MODE 

Transient 55 mph Cruise 65 mph Cruise 

Vocational 42% 21% 37% 

Vocational Hybrid 

Vehicles 

75% 9% 16% 

Day Cabs 19% 17% 64% 

Sleeper Cabs 5% 9% 86% 

3.4.3.1 Vocational Vehicles 

In order to properly weight the “idle” time of each vehicle class and category 

independently of the idle time in the duty cycles, EPA is proposing that idle emissions are 

weighted with the driving cycles. In the HD Phase 1 rule the duty cycles were weighted by 

distance to properly reflect the vehicle miles traveled by each category. To incorporate “idle” 

emissions, the equation had to be modified to allow for the “idle” emissions to be time weighted 

with the driving cycles. The result of this is that the weighting factors for the driving cycles will 

still add up to 100 percent while the idle weighting factor will be less than 100 percent, reflecting 

the actual idle time of the vehicles by category. The agencies are proposing to modify the 

equation in 40 CFR 1037.510(b) to accommodate both the distance (non-idle) and time based 

(idle) weighting factors. 

The proposed duty cycle weightings for each vocational vehicle test cycle are included in 

Table 3-25. 

Table 3-25 Proposed Phase 2 Duty Cycle Mode Composite Weightings 

VEHICLE 

CATEGORY 

DUTY CYCLE MODE 

Transient 55 mph Cruise 65 mph Cruise Idle 

Vocational 

Regional 

50% 28% 22% 10% 

Vocational MultiPurpose 82% 15% 3% 15% 

Vocational Urban 94% 6% 0% 20% 

3.5 Tare Weights and Payload 

We propose to continue defining the total weight of a truck as the combination of the 

truck’s tare weight, a trailer’s tare weight (if applicable), and the payload; as it was defined in the 

HD Phase 1 rule. The total weight of a truck is important because it in part determines the 

impact of technologies, such as rolling resistance, on GHG emissions and fuel consumption. As 
3-65 

the HD program is designed, it is important that the agencies define weights which are 

representative of the fleet while recognizing that the final weights are not representative of a 

specific vehicle. The sections below describe the agencies’ approach to defining each of these 

weights. 

3.5.1 Truck Tare Weights 

The tare weight of a truck will vary depending on many factors, including the choices 

made by the manufacturer in designing the truck (such as the use of lightweight materials, the 

cab configuration (such as day or sleeper cab), whether it has aerodynamic fairing (such as a roof 

fairing), and the specific options on the truck. 

The Class 8 combination tractor tare weights were developed based on the weights of 

actual tractors tested in EPA’s coastdown program. The empty weight of the Class 8 sleeper 

cabs with a high roof tested ranged between 19,000 and 20,260 pounds. The empty weight of the 

Class 8 day cab with a high roof tested was 17,840 pounds. The agencies derived the tare weight 

of the Class 7 day cabs based on the guidance of truck manufacturers. The agencies then 

assumed that a roof fairing weighs approximately 500 pounds. Based on this, the agencies are 

proposing the tractor tare weights as shown in Table 3-26. 

Table 3-26 Tractor Tare Weights 

MODEL 

TYPE 

CLASS 8 CLASS 8 CLASS 8 CLASS 8 CLASS 8 CLASS 7 CLASS 7

Regulatory 

Subcategory 

Sleeper 

Cab High 

Roof 

Sleeper 

Cab Mid 

Roof 

Sleeper 

Cab Low 

Roof 

Day Cab 

High 

Roof 

Day Cab 

Low 

Roof 

Day Cab 

High 

Roof 

Day Cab 

Low 

Roof 

Tractor Tare 

Weight (lbs) 

19,000 18,750 18,500 17,500 17,000 11,500 11,000 

The agencies developed the empty tare weights of the vocational vehicles based on the 

EDF report23 on GHG management for Medium-Duty Fleets. The EDF report found that the 

average tare weight of a Class 4 truck is 10,343 pounds, of a Class 6 truck is 13,942 pounds, and 

a Class 8 truck is 23,525 pounds. The agencies are proposing to continue to use the following 

tare weights: 

 Light Heavy (Class 2b-5) = 10,300 pounds 

 Medium Heavy (Class 6-7) = 13,950 pounds 

 Heavy Heavy (Class 8) = 23,500 pounds 

3.5.2 Trailer Tare Weights 

We propose to continue to define the trailer tare weights used in the tractor program 

based on measurements conducted during EPA’s coastdown testing and information gathered by 

ICF in the cost report to EPA, as adopted in the HD Phase 1 rule.24 
3-66 

A typical 53 foot box (or van) trailer has an empty weight ranging between 13,500 and 

14,000 pounds per ICF’s findings. The box trailer tested by EPA in the coastdown testing 

weighed 13,660 pounds. Therefore, the agencies are defining the empty box trailer weight as 

13,500 pounds. 

A typical flatbed trailer weighs between 9,760 and 10,760 per the survey conducted by 

ICF. EPA’s coastdown work utilized a flatbed trailer which weighed 10,480 pounds. Based on 

this, the agencies are defining a flatbed trailer weight of 10,500 pounds. 

Lastly, a tanker trailer weight typically ranges between 9,010 and 10,500 pounds based 

on ICF findings. The tanker trailer used in the coastdown testing weighed 9,840 pounds. The 

agencies are defining the empty tanker trailer weight of 10,000 pounds. 

3.5.3 Payload 

The amount of payload by weight that a tractor can carry depends on the class (or 

GVWR) of the vehicle. For example, a typical Class 7 tractor can carry fewer tons of payload 

than a Class 8 tractor. Payload impacts both the overall test weight of the truck and is used to 

assess the “per ton-mile” fuel consumption and GHG emissions. The “tons” represent the 

payload measured in tons. 

M.J. Bradley analyzed the Truck Inventory and Use Survey and found that approximately 

9 percent of combination tractor miles travelled empty, 61 percent are “cubed-out” (the trailer is 

full before the weight limit is reached), and 30 percent are “weighed out” (operating weight 

equal 80,000 pounds which is the gross vehicle weight limit on the Federal Interstate Highway 

System or greater than 80,000 pounds for vehicles traveling on roads outside of the interstate 

system).25 The Federal Highway Administration developed Truck Payload Equivalent Factors to 

inform the development of highway system strategies using Vehicle Inventory and Use Survey 

(VIUS) and Vehicle Travel Information System (VTRIS) data. Their results, as shown in Table 

3-27, found that the average payload of a Class 8 truck ranged from 29,628 to 40,243 pounds, 

depending on the average distance travelled per day.26 The same results found that Class 7 

trucks carried between 18,674 and 34,210 pounds of payload also depending on average distance 

travelled per day. 

Table 3-27 National Average Payload (lbs.) per Distance Travelled and Gross Vehicle Weight Group 

(VIUS)27

 CLASS 3 CLASS 4 CLASS 5 CLASS 6 CLASS 7 CLASS 8 

< 50 miles 3,706 4,550 8,023 10,310 18,674 29,628 

51 to100 miles 3,585 4,913 6,436 10,628 23,270 36,247 

101 to 200 miles 4,189 6,628 8,491 12,747 30,180 39,743 

201 to 500 miles 4,273 7,029 6,360 10,301 25,379 40,243 

> 500 mile 3,216 8,052 6,545 12,031 34,210 40,089 

Average 3,794 6,234 7,171 11,203 26,343 37,190 

The agencies are prescribing a fixed payload of 25,000 pounds for Class 7 tractors and 

38,000 pounds for Class 8 tractors for their respective test procedures. These payload values 
3-67 

represent a heavily loaded trailer, but not maximum GVWR, since as described above the 

majority of tractors "cube-out" rather than "weigh-out.” 

NHTSA and EPA are also proposing to continue using the payload requirements for each 

regulatory subcategory in the vocational vehicle category that were finalized in the HD Phase 1 

rule. The payloads were developed from Federal Highway statistics based on the averaging the 

payloads for the weight classes of represented within each vehicle category.28 The payload 

requirement is 5,700 pounds for the Light Heavy trucks based on the average payload of Class 3, 

4, and 5 trucks from Table 3-27. The payload for Medium Heavy trucks is 11,200 pounds per 

the average payload of Class 6 trucks as shown in Table 3-27. Lastly the agencies are defining 

38,000 pounds payload for the Heavy Heavy trucks based on the average Class 8 payload in 

Table 3-27. 

3.5.4 Total Weight 

In summary, the total weights of the combination tractors are shown in Table 3-28. 

Table 3-28 Combination Tractor Total Weight 

MODEL 

TYPE 

CLASS 8 CLASS 8 CLASS 8 CLASS 8 CLASS 8 CLASS 8 CLASS 7 CLASS 7 CLASS 7 

Regulatory 

Subcategory 

Sleeper 

Cab High 

Roof 

Sleeper 

Cab Mid 

Roof 

Sleeper 

Cab Low 

Roof 

Day Cab 

High 

Roof 

Day Cab 

Mid Roof 

Day Cab 

Low Roof 

Day Cab 

High 

Roof 

Day Cab 

Mid Roof 

Day Cab 

Low 

Roof 

Tractor Tare 

Weight (lbs) 

19,000 18,750 18,500 17,500 17,100 17,000 11,500 11,100 11,000 

Trailer 

Weight (lbs) 

13,500 10,000 10,500 13,500 10,000 10,500 13,500 10,000 10,500 

Payload 

(lbs) 

38,000 38,000 38,000 38,000 38,000 38,000 25,000 25,000 25,000 

Total 

Weight (lbs) 

70,500 66,750 67,000 69,000 65,100 65,500 50,000 46,100 46,500 

The total weights of the vocational vehicles are as shown in Table 3-29. 

Table 3-29 Vocational Vehicle Total Weights 

REGULATORY 

SUBCATEGORY 

LIGHT 

HEAVY 

MEDIUM 

HEAVY 

HEAVY 

HEAVY 

Truck Tare 

Weight (lbs) 

10,300 13,950 27,000 

Payload (lbs) 5,700 11,200 15,000 

Total Weight (lbs) 16,000 25,150 42,000 

3.6 Powertrain Test Procedures 

In the HD Phase 1 rule the agencies introduced a powertrain test procedure to allow 

manufacturers to generate credits for selling advanced powertrains that reduced CO2 emissions 

and fuel consumption. In Phase 2 we are proposing to bring the powertrain test procedure into 

the main program and project that 15 to 30 percent of the vocational vehicles (including both 

hybrid and non-hybrid applications) would certify using this method. To accommodate this 
3-68 

change we are proposing a number of improvements to the test procedure in 40 CFR 1037.550 

and reducing the test burden by only requiring testing of the powertrain that is to be certified. 

The agencies are also proposing modifications to 40 CFR 1037.550 to separate out the hybrid 

specific testing protocols. 

3.6.1 Reason Behind Use Of Powertrain Test Method for Conventional and Hybrid 

Powertrain Certification 

The agencies are proposing a powertrain test option to afford a robust mechanism to 

quantify the benefits of CO2 reducing technologies that are a part of the powertrain (conventional 

or hybrid), that are not captured in the GEM simulation. Among these technologies are transient 

fuel control, engine and transmission control integration, and hybrid systems. The largest 

proposed change from the Phase 1 powertrain procedure is that only the advanced powertrain 

will need to be tested as opposed to the requirement in Phase 1 where the result was an 

improvement factor calculated from the powertrain results of both the advanced powertrain and 

the conventional powertrain. This proposed change is possible because the proposed GEM 

simulation tool uses the engine fuel map and torque curve from the actual engine in the vehicle 

that is to be certified for all vehicles that do not use the powertrain method to certify the vehicle. 

3.6.2 Use of Generic Vehicles to Apply Measurements Broadly Across All Vehicles 

That the Powertrain Will Be Installed In 

To limit the amount of testing, under the proposal, powertrains will be divided into 

families and will be tested in a limited number of simulated vehicles that will cover the range of 

vehicles in which the powertrain will be used. 

A matrix of 8 to 9 tests would be needed per vehicle cycle, to enable the use of the 

powertrain results broadly across all the vehicles in which the powertrain will be installed. The 

individual tests differ by the vehicle that is being simulated during the test. Table 3-30 and Table 

3-31 define the unique vehicles being proposed that would cover the range of coefficient of drag, 

coefficient of rolling resistance, vehicle mass and axle ratio of the vehicles that the powertrain 

will be installed in. 

To allow for a generic tire size definition that will cover the tires and axles installed on 

the certified vehicles, the agencies are proposing that each tire radius will be set so that when the 

vehicle is cruising at 65 mph the engine speed will equal the corresponding minimum NTE 

exclusion speed as defined in 40 CFR part 86.1370(b)(1), intermediate test speed (A, B, or C), or 

maximum test speed defined in 40 CFR part 1065. To calculate the tire radius, use the equation 

in 40 CFR 1037.550. 
3-69 

Table 3-30 Proposed Generic Vehicle Definitions for Class 2b-7 Vehicles 

 TEST 1 TEST 2 TEST 3 TEST 4 TEST 5 TEST 6 TEST 7 TEST 8 

Mass (kg) 7,257 11,408 7,257 11,408 7,257 11,408 7,257 11,408 

CdA 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 

Tire Crr

(kg/ton) 

6.7 6.9 6.7 6.9 6.7 6.9 6.7 6.9 

Tire Radius 

(m) 

0.426 0.426 0.426 0.426 0.426 0.426 0.426 0.426 

Rotating 

Inertia (kg)

454 454 454 454 454 454 454 454 

Axle Gear 

Efficiency (%)

95.5 95.5 95.5 95.5 95.5 95.5 95.5 95.5 

Accessory 

Power (W) 

1300 1300 1300 1300 1300 1300 1300 1300 

Axle ratio at 

engine speed

A A B B C C Maximum 

engine 

speed 

Maximum 

engine 

speed 

Table 3-31 Proposed Generic Vehicle Definitions for Tractors and Class 8 Vocational Vehicles—General 

Purpose 

 TEST 1 TEST 2 TEST 3 TEST 4 TEST 5 TEST 6 TEST 7 TEST 8 TEST 9 

Mass (kg) 31,978 22,679 19,051 31,978 22,679 19,051 31,978 22,679 19,051 

CdA 5.4 4.7 4.0 5.4 4.7 4.0 5.4 4.7 4.0 

Tire Crr

(kg/ton)

6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 

Tire 

Radius 

(m)

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

Rotating 

Inertia 

(kg) 

1,134 907 680 1,134 907 680 1,134 907 680 

Axle Gear 

Efficiency 

(%)

95.5 95.5 95.5 95.5 95.5 95.5 95.5 95.5 95.5 

Accessory 

Power 

(W) 

1300 1300 1300 1300 1300 1300 1300 1300 1300 

Axle ratio 

at engine 

speed 

Minimum 

NTE 

exclusion 

speed 

Minimum 

NTE 

exclusion 

speed 

Minimum 

NTE 

exclusion 

speed 

B B B Maximum 

engine 

speed 

Maximum 

engine 

speed 

Maximum 

engine 

speed 
3-70 

Table 3-32 Proposed Generic Vehicle Definitions for Class 8 Combination— Heavy-Haul Vehicle 

 TEST 1 TEST 2 TEST 3 TEST 4 TEST 5 TEST 6 TEST 7 TEST 8 TEST 9 

Mass (kg) 40,895 31,978 22,679 40,895 31,978 22,679 40,895 31,978 22,679 

CdA 6.1 5.4 4.7 6.1 5.4 4.7 6.1 5.4 4.7 

Tire Crr

(kg/ton)

6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 

Tire 

Radius 

(m)

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

Rotating 

Inertia 

(kg) 

1,134 907 680 1,134 907 680 1,134 907 680 

Axle Gear 

Efficiency 

(%)

95.5 95.5 95.5 95.5 95.5 95.5 95.5 95.5 95.5 

Accessory 

Power 

(W) 

1300 1300 1300 1300 1300 1300 1300 1300 1300 

Axle ratio 

at engine 

speed 

Minimum 

NTE 

exclusion 

speed 

Minimum 

NTE 

exclusion 

speed 

Minimum 

NTE 

exclusion 

speed 

B B B Maximum 

engine 

speed 

Maximum 

engine 

speed 

Maximum 

engine 

speed 

The main outputs of this matrix of tests is grams of CO2, the average transmission output 

shaft speed divided by the average vehicle speed and positive work measured at the output shaft 

of the powertrain. This matrix of test results will then be used to calculate the vehicle’s CO2

emissions in GEM taking the work per ton-mile from the GEM simulation and multiplying it by 

the interpolated work specific CO2 mass emissions from the powertrain test. 

3.6.3 Measurement Method and Results 

The agencies are proposing to expand upon the test procedures defined 40 CFR 1037.550 

for HD Phase 1. The Phase 2 proposed expansion will migrate the current Phase 1 test procedure 

to a new 40 CFR 1037.555 and will modify the current test procedure in 40 CFR 1037.550, 

allowing its use for Phase 2 only. The Phase 2 modifications to 40 CFR 1037.550 include the 

addition of the rotating inertia of the driveline and tires, the axle efficiency and the vehicle’s 

accessory loads. This revised procedure also requires that each of the powertrain components be 

cooled so that the temperature of each of the components is kept in the normal operation range. 

In addition to changing the vehicle model, we are proposing changes to the drive model 

so that it can compensate when the powertrain gets ahead or falls behind in the duty cycle. Use 

of this compensation algorithm will ensure that every powertrain drives the complete distance of 

the cycle, regardless of whether or not it can maintain the target speed of the cycle at a given 

moment in time. 

Although detailed equations for the vehicle and driver models can be found in the 

proposed 40 CFR 1037.550, the agencies are recommending that manufactures use the 

MATLAB and Simulink models provided by the agencies. These models can be found at 

http://www.epa.gov/otaq/climate/gem.htm. 
3-71 

Conventional Powertrain Test Results

The agencies have performed internal test programs, contracted with outside labs, as well 

as collaborated with manufactures to test out the improvements to the powertrain test procedure. 

The data presented in Figure 3-10 is from a conventional powertrain that consisted of a Cummins 

ISX engine and Eaton 10 speed automated manual transmission that was tested in one of these 

test programs. This data summarizes the results from three different types of tests. The first set 

of data, labeled “Engine Only”, was collected from engine tests where the speed and torque 

setpoints were determined by GEM. The simulations were done with 9 different vehicle 

configurations over the three duty cycles that are being proposed as certification duty cycles (55 

mph with grade, 65 mph with grade and ARB transient cycle). The “GEM Model” data contains 

the CO2 emissions as determined by GEM using the engine’s fuel map and the transmission’s 

gear ratios using with the default shift strategy. The x-axis defines the Powertrain test results. 

The data shows that across all three test cycles the powertrain test procedure produces 2.5 

percent less CO2 emission than the GEM simulation predicted. One must, however, take into 

account the fact that the GEM simulation was done using the engine steady-state fuel map; thus, 

the GEM results don’t fully take into account the effect of transient fueling on CO2 emissions. 

This is evident when looking at the data collected when operating over the transient test cycles 

(highest CO2 g/ton-mile results). Here you see that the engine consumed greater than 3 percent 

more fuel than GEM predicted. When taking the transient test results into account, the 

powertrain performed 5 to 8 percent better than GEM predicted. 

 
3-72 

Figure 3-10 Engine only and GEM CO2 results vs. powertrain. 
3-73 

3.6.4 Powertrain Family Definition 

To complement the agencies powertrain procedures we are proposing criteria for defining 

a powertrain family. The specifics of these criteria can be found in 40 CFR 1037.231 but 

nominally a powertrain family is made up of one engine family and one transmission family. 

3.6.4.1 Criteria for Powertrain Families 

The proposed regulations in 40 CFR 1037.231 outline the criteria for grouping 

transmission models into powertrain families sharing similar emission characteristics. A few of 

these defining criteria include the transmission’s architecture (manual, automatic, automated 

manual, dual-clutch and hybrid), number of gears in the front box, number of meshes in the back 

box and dry sump versus regular sump. In addition to the criteria for the transmission, all the 

engines in the powertrain family have to be from the same engine family. 

3.6.4.2 Emissions Test Powertrain 

We are proposing that manufacturers select at least one powertrain per powertrain family 

for emission testing. The methodology for selecting the test powertrain(s) should be consistent 

with 40 CFR 1037.231. The test powertrain(s) should consist of the engine and transmission 

combination that results in the highest CO2 emissions. 

3.6.5 Vehicle Certification with Powertrain Results in GEM 

For manufactures that choose to use the powertrain method when certifying a vehicle, the 

powertrain results from the test will be input into GEM instead of the engine’s fuel map, torque 

curve, motoring curve and the transmissions gear ratios. GEM will use the default powertrain 

inputs, as described in Table 3-33, and the inputs of the to-be certified vehicle to calculate the 

cycle work (W) of the powertrain and the ratio of rotational speed over the vehicle speed (N/V) as 

defined by the tire radius and rear-axle ratio. 

Table 3-33 GEM Default Parameters for Vehicle Certification Using Powertrain Testing. 

REGULATORY CLASS ENGINE TRANSMISSION GEAR RATIOS 

Class 8 

Combination 

Heavy-Haul 2017 MY 15L 

Engine with 600 

HP 

13 speed 

Automated Manual 

Transmission 

12.29, 8.51, 

6.05, 4.38, 3.20, 

2.29, 1.95, 1.62, 

1.38, 1.17, 1.00, 

0.86, 0.73 

Sleeper Cab - High Roof 2017 MY 15L 

Engine with 455 

HP 

10 speed 

Automated Manual 

Transmission 

12.8, 9.25, 6.76, 

4.9, 3.58, 2.61, 

1.89, 1.38, 1, 

0.73 

Sleeper Cab - Mid Roof 

Sleeper Cab - Low Roof 

Day Cab - High Roof 

Day Cab - Mid Roof 

Day Cab - Low Roof 

Class 7 Day Cab - High Roof 2017 MY 13L 
3-74 

Combination Day Cab - Mid Roof Engine with 350 

HP 

Day Cab - Low Roof 

HHD 

Vocational 

Regional Duty Cycle 2017 MY 15L 

Engine with 455 

HP 

Multi-Purpose Duty Cycle 2017 MY 11L 

Engine with 345 

HP 

5 speed HHD 

Automatic 

Transmission 

4.6957, 2.213, 

1.5291, 1, 

0.7643 Urban Duty Cycle 

MHD 

Vocational 

Regional Duty Cycle 2017 MY 7L 

Engine with 270 

HP 

5 speed MLHD 

Automatic 

Transmission 

3.102, 1.8107, 

1.4063, 1, Multi-Purpose Duty Cycle 0.7117 

Urban Duty Cycle 

LHD 

Vocational 

Regional Duty Cycle 2017 MY 7L 

Engine with 200 Multi-Purpose Duty Cycle HP 

Urban Duty Cycle 

In GEM the cycle work from the powertrain testing will be corrected for the electrical 

and mechanical accessory power according to the following equation. The accessory power is 

defined for each vehicle category in Chapter 4 of this RIA. 

ܹ୮୭୵ୣ୰୲୰ୟ୧୬	ୡ୭୰୰ୣୡ୲ୣୢ ൌ ܹ୲ୣୱ୲ െ ܲୟୡୡ ∙ ݐ୲ୣୱ୲ ∙

ܹ୲୰ୟ୬ୱ.୭୳୲	୭୰	୵୦ୣୣ୪	୦୳ୠሺାሻ

ܹୣ୬୥୧୬ୣሺାሻ

GEM will use the calculated cycle work and N/V of the powertrain for the to-be certified 

vehicle to interpolate the powertrain input table. For vehicle configurations that have cycle work 

or N/V outside of the powertrain input table, we are proposing that the closest end points of the 

table be used instead of extrapolating. GEM will then use the following equation to calculate the 

CO2 g/ton-mile result per cycle before any technology inputs are applied. Finally the technology 

inputs are applied, all the cycles are weighted and the gallons of fuel are then calculated from the 

mass of CO2. 

݁஼ைమ ൌ݁ቂ݃୤୳ୣ୪

ܹ݄݇ቃ

୧୬୲ୣ୰୮୭୪ୟ୲ୣୢ

∙ ܹୋ୉୑ ∙ 1∙ ܽ݀݋݈ݕܽ݌ ∙ ୑୉ୋݏ݈݉݅݁

݉େ୓మ

݉୤୳ୣ୪

3.7 Hybrid Powertrain Test Procedures 

As discussed in Section V of the preamble, the agencies see an opportunity to help drive 

the technology’s advancement by predicating the vocational vehicle standards on a small 

adoption rate of hybrid powertrains in this rulemaking. However, since the projected 

effectiveness of this technology over the proposed Urban vocational duty cycle is 25 percent, the 

agencies believe it is no longer appropriate to provide a 1.5 multiplier for credits generated by 

vehicles applying this technology. EPA and NHTSA are proposing two methods to demonstrate 

benefits of a hybrid powertrain – chassis and engine testing. 
3-75 

3.7.1 Measurement Method and Results 

The agencies are proposing that hybrid powertrains be tested just like conventional 

powertrains, with the dynamometer connected at either the input shaft of the rear axle or the 

input shaft to the wheels, using the powertrain method described in Section 3.6 with some 

additional requirements for the rechargeable energy storage systems (RESS) net energy change 

(NEC) over the test. 

As in the Phase 1 rule, the agencies are proposing that hybrids will be tested under charge 

sustain operation so that all the energy to drive the cycle comes from the on board hybrid 

powertrain. The NEC of the RESS must meet the requirements of SAE J2711 for each test. 

3.7.2 Engine Hybrid Method 

To address hybrid powertrain system performance for hybrids that recover energy 

between the engine and transmission, the agencies are proposing to retain the engine hybrid 

procedures defined in 40 CFR 1036.525. The control volume for these hybrids is drawn so as to 

include the battery, battery support and control systems, power electronics, the engine, motor 

generator and hybrid control module. The performance of this system is an engine based 

evaluation in which emission rates are determined on a brake-specific work basis. As such, the 

duty cycles being proposed to assess this system performance are engine speed and torque 

command cycles that are similar but not identical to the cycles used for criteria pollutant 

standards. In addition to the cycles being slightly different between the test for GHG emissions 

and the test for criteria emissions, the system boundary of the engine for the criteria emission test 

will remain unchanged and will not include the hybrid components. It is expected that, parallel 

engine hybrids would be the most likely choice for engine-based hybrid certification. Details 

related to engine hybrid test procedures may be found in 40 CFR 1036.525. 

 

Figure 3-11 Engine Hybrid Test Configuration 
3-76 

3.7.3 Removal of the Chassis Test Option for Hybrids 

In the Phase 1 rule the agencies finalized a powertrain and chassis test option for hybrid 

testing. The agencies are proposing to remove the chassis test option for the Phase 2 program 

because it appears to be incompatible with the proposed changes regarding use of results from 

the hybrid test procedure. In the proposed procedure, the output of the hybrid test is brake 

specific CO2 emission where the positive work is measured at the output shaft of the hybrid 

powertrain. Since work cannot be measured at this location on a chassis dynamometer without 

modifying the vehicle, the agencies are proposing the removal of the chassis testing option. 

Another reason for the removal of the chassis test option is that there are a number of additional 

sources of variability when testing a vehicle on a chassis dynamometer. These include electrical 

and mechanical accessory load, tire temperature and driver variability to name a few. 

3.7.4 Electrified PTO Test Method 

A power take off (PTO) is a system on a vehicle that allows energy to be drawn from the 

vehicle’s drive system and used to power an attachment or a separate machine. Typically in a 

heavy-duty truck, a shaft runs from the transmission of the truck and operates a hydraulic pump. 

The operator of the truck can select to engage the PTO shaft in order for it to do work, or 

disengage the PTO shaft when the PTO is not required to do work. The pressure and flow from 

this hydraulic fluid can be used to do work in implements attached to the truck. Common 

examples of this are utility trucks that have a lift boom on them, refuse trucks that pick up and 

compact trash, and cement trucks that have a rotating barrel. In each case the auxiliary 

implement is typically powered by a PTO that uses energy from the truck’s primary drive engine. 

 In most PTO equipped trucks, it is necessary to run the primary drive engine at all times 

when the PTO might be needed. This is an unoptimized configuration. Typical PTO systems 

require no more than 19 kW at any time, which is far below the optimal operation range of the 

primary drive engine of most trucks. Furthermore, in intermittent operations, the primary drive 

engine is kept running at all times in order to ensure that the PTO can operate instantaneously. 

This results in excess GHG emissions and fuel consumption due to idle time. Additionally, 

idling a truck engine for prolonged periods of time while operating auxiliary equipment like a 

PTO could cause the engine to cycle into a higher idle speed, wasting even more fuel. 

Hybridization and changing the operation of a conventional PTO equipped truck are two 

viable means to lower the GHG emissions and fuel consumption in the real world. The proposed 

test procedures will allow for manufactures to quantify the reduction of CO2 emissions and fuel 

consumption from electrified PTO systems. 

In Phase 1, hybrid PTO testing was performed either via chassis or powertrain testing of 

both the conventional and hybrid systems over the PTO duty cycles described in Appendix II of 

40 CFR 1037, in addition to the vehicle duty cycles. An improvement factor was then generated 

as described in 40 CFR 1037.615 and applied to the g/ton-mile CO2 emission rate resulting from 

the GEM output for the advanced vehicle as described in 40 CFR 1037.520. 

EPA and NHTSA are proposing to continue the Phase 1 testing methodology outlined in 

40 CFR 1037.525 where A to B testing is used to generate an improvement factor either via 
3-77 

powertrain or chassis testing. The one change that the agencies are proposing for Phase 2 is how 

the results are used to calculate the vehicle’s emission result. For Phase 2, the agencies are 

proposing that the reduction in emissions from the electrified PTO system versus the 

conventional PTO system be subtracted from the composite driving emissions result. Specifics 

on the applicability of electrified PTOs is discussed further in Chapter V. C of the preamble. 

3.8 Rear Axle Efficiency Test 

In Phase 2, the agencies developed test procedures to measure axle efficiency. See 40 

CFR 1037.515. This procedure ultimately provides for the determination of torque loss versus 

input speed and input torque for use in the GEM simulation tool. The procedure provides 

limitations on axle break in procedures and prescribes dynamometer set ups for axles with and 

without lockable differentials as well as drive-through axles. This procedure puts limitations on 

the test cell ambient temperature, sump oil temperature, and requires the use of representative 

commercially available axle lubricating oil. The mapping process requires that you map the axle 

by testing with an input torque in the range of 0 to 4000 Nm in 1000 Nm steps at wheel speeds 

that range from 50 rpm to the maximum wheel speed in 50 rpm steps. The procedure sweeps 

though the torque points at a given wheel speed from the minimum to the maximum torque point 

and back, with the process repeated twice for a given wheel speed. The four values generated at 

each speed and torque point are then averaged resulting in one map point per wheel and output 

torque value. 

3.9 HD Pickup Truck and Van Chassis Test Procedure 

The agencies are proposing that HD pickup trucks and vans continue to demonstrate 

compliance using the 40 CFR part 1066 chassis test procedures. For each test vehicle from a 

family required to comply with the GHG and fuel consumption requirements, the manufacturer 

would supply representative road load forces for the vehicle at speeds between 15 km/hr (9.3 

mph) and 115 km/hr (71.5 mph). The road load force would represent vehicle operation on a 

smooth level road, during calm winds, with no precipitation, at an ambient temperature of 20 °C 

(68 °F), and atmospheric pressure of 98.21 kPa. Road load force for speeds below 9.3 mph may 

be extrapolated. 

The dynamometer's power absorption would be set for each vehicle's emission test 

sequence such that the force imposed during dynamometer operation matches actual road load 

force at all speeds. Required test dynamometer inertia weight class selections are determined by 

the test vehicle test weight basis using adjusted loaded vehicle weight from which the 

corresponding equivalent test weight is determined. 

3.9.1 LHD FTP and HWFE Testing 

The FTP dynamometer schedule consists of two tests, a “cold” start UDDS test after a 

minimum 12-hour and a maximum 36-hour soak according to the provisions of 40 CFR 

1066.801, 1066.815, and 1066.816, and a “hot” start test following the “cold” start after a 10 

minute soak. Engine startup (with all accessories turned off), operation over the UDDS, and 

engine shutdown constitutes a complete cold start test. Engine startup and operation over the 

first 505 seconds of the driving schedule complete the hot start test. The driving schedule for 
3-78 

EPA’s Urban Dynamometer Driving Schedule is contained in Appendix I of 40 CFR part 86. 

The driving schedule is defined by a smooth trace drawn through the specified speed versus time 

relationship. The schedule consists of a distinct non-repetitive series of idle, acceleration, cruise, 

and deceleration modes of various time sequences and rates. 

The Highway Fuel Economy Dynamometer Procedure (HFET) consists of 

preconditioning highway driving sequence and a measured highway driving sequence. The 

HFET is designated to simulate non-metropolitan driving with an average speed of 48.6 mph and 

a maximum speed of 60 mph. The cycle is 10.2 miles long with 0.2 stops per mile and consists 

of warmed-up vehicle operation on a chassis dynamometer through a specified driving cycle. 

The Highway Fuel Economy Driving Schedule is set forth in Appendix I of 40 CFR Part 600, 

while the test is carried out according to 40 CFR 1066.840. The driving schedule is defined by a 

smooth trace drawn through the specified speed versus time relationships. 

Practice runs over the prescribed driving schedules may be performed, provided an 

emission sample is not taken, for the purpose of finding the appropriate throttle action to 

maintain the proper speed-time relationship, or to permit sampling system adjustment. 

Both smoothing of speed variations and excessive accelerator pedal perturbations are to be 

avoided. The driver should attempt to follow the target schedule as closely as possible. The 

speed tolerance at any given time on the dynamometer driving schedules specified in Appendix I 

of parts 86 and 600 is defined by upper and lower limits in 40 CFR 1066.425. The upper limit is 

2 mph higher than the highest point on trace within 1 second of the given time. The lower limit 

is 2 mph lower than the lowest point on the trace within 1 second of the given time. Speed 

variations greater than the tolerances (such as may occur during gear changes) are acceptable 

provided they occur for less than 2 seconds on any occasion. Speeds lower than those prescribed 

are acceptable provided the vehicle is operated at maximum available power during such 

occurrences. 

3.9.2 LHD FTP and HWFE Hybrid Testing 

Since LHD chassis certified vehicles share test schedules and test equipment with much 

of Light-Duty Vehicle testing, EPA believes it is appropriate to continue to use the HD Phase 1 

test procedure which references SAE J1711 “Recommended Practice for Measuring the Exhaust 

Emissions and Fuel Economy of Hybrid-Electric Vehicles, Including Plug-in Hybrid Vehicles” 

instead of SAE J2711 “Recommended Practice for Measuring Fuel Economy and Emissions of 

Hybrid-Electric and Conventional Heavy-Duty Vehicles”. 

3.9.2.1 Charge Depleting Operation – FTP or “City” Test and HFET or 

“Highway” Test 

EPA would like comment on incorporating by reference SAE J1711 Chapters 3 and 4, as 

published June 2010, testing procedures for Light-Heavy-Duty chassis certified vehicles with the 

following exceptions and clarifications: 

Test cycles will continue, until the end of the phase of the test cycle, in which charge 

sustain operation is confirmed. Charge sustain operation is confirmed when one or more phases 
3-79 

or cycles satisfy the Net Energy Change requirements below. Optionally, a manufacturer may 

terminate charge deplete testing before charge sustain operation is confirmed provided that the 

Rechargeable Energy Storage System (RESS) has a higher State of Charge (SOC) at charge 

deplete testing termination than in charge sustain operation. In the case of Plug-In Hybrid 

Electric Vehicles (PHEV) with an all-electric range, engine start time will be recorded but the 

test does not necessarily terminate with engine start. PHEVs with all electric operation follow 

the same test termination criteria as blended mode PHEVs. Testing can only be terminated at the 

end of a test cycle. The regulation allows EPA to approve alternate end of test criteria as 

described in 40 CFR 1066.501. 

For the purposes of charge depleting CO2 and fuel efficiency testing, manufacturers may 

elect to report one measurement per phase (one bag per UDDS). Exhaust emissions need not be 

reported or measured in the phases of the test the engine does not operate. 

End of test recharging procedure is intended to return the RESS to a full charge 

equivalent to pretest conditions. The recharge AC watt hours must be recorded throughout the 

charge time and soak time. Vehicle soak conditions must not be violated. The AC watt hours 

must include the charger efficiency. The measured AC watt hours are intended to reflect all 

applicable electricity consumption including charger losses, battery and vehicle conditioning 

during the recharge and soak, and the electricity consumption during the duty cycles. 

Net Energy Change Tolerance (NEC), is to be applied to the RESS to confirm charge 

sustaining operation. The agencies are proposing to continue to use the 1 percent of fuel energy 

NEC state of charge criteria as expressed in SAE J1711 and described in 40 CFR 1066.501. The 

Administrator may approve alternate NEC tolerances and state of charge correction factors. 

3.9.2.2 Hybrid Charge Sustaining Operation – FTP or “City” Test and HFET 

or “Highway” Test 

The agencies are proposing to continue incorporating by reference SAE J1711 Chapters 3 

and 4 for definitions and test procedures, respectively, where appropriate, with the following 

exceptions and clarifications. 

The agencies are adopting the 1 percent of fuel energy NEC state of charge criteria as 

expressed in SAE J1711 and described in 40 CFR 1066.501. The Administrator may approve 

alternate NEC tolerances and state of charge correction factors. 

Preconditioning special procedures are optional for traditional “warm” test cycles that are 

now required to test starting at full RESS charge due to charge depleting range testing. If the 

vehicle is equipped with a charge sustain switch, the preconditioning cycle may be conducted per 

40 CFR 600.111 provided that the RESS is not charged. Exhaust emissions are not taken in 

preconditioning drives. Alternate vehicle warm up strategies may be approved by the 

Administrator. 

State of Charge tolerance correction factors may be approved by the Administrator as 

described in 40 CFR 1066.501. RESS state of charge tolerances beyond the 1 percent of fuel 

energy may be approved by the Administrator. 
3-80 

The agencies are seeking comment on modifying the minimum and maximum allowable 

test vehicle accumulated mileage for both EVs and PHEVs. Due to the nature of PHEV and EV 

operation, testing may require many more vehicle miles than conventional vehicles. 

Furthermore, EVs and PHEVs either do not have engines or may use the engine for only a 

fraction of the miles driven. 

Electric Vehicles and PHEVs are to be recharged using the supplied manufacturer 

method provided that the methods are available to consumers. This method could include the 

electricity service requirements such as service amperage, voltage, and phase. Manufacturers 

may employ the use of voltage regulators in order to reduce test to test variability with prior 

Administrator approval. 

3.10 Alternative Certification Approach 

3.10.1 Purpose and Scope 

Under the Phase 1 rule, vocational vehicles and tractors are certified by using GEM with 

the default engine fuel map pre-defined by the agency, while the engine is certified by either 

using the SET cycle for tractor engines or the FTP cycle for vocational engines, which are totally 

different from vehicle drive cycles. 

 In this section, a new concept as an alternative to the engine fuel mapping test, proposed 

in Phase 2 is explored. This approach would allow use of the same drive cycles for both the 

vehicle and engine compliance process, without the need for engine manufacturer providing the 

steady state engine fuel map for vehicle certification. Therefore, this approach has the potential 

to totally integrate vehicle and engine certification, while more accurately quantifying the 

transient engine operation than is possible using a traditional steady state engine fuel map. 

The potential approach discussed here would be an alternative to certifying vocational 

vehicles, tractors, and their engines using a steady-state fuel map. The agencies solicit comment 

on this alternative, and commenters should include their thoughts on whether this concept can be 

adequately fleshed out in the time remaining in this rulemaking. 

3.10.1.1 Phase 1 Certification Approach 

In order to help to understand the vocational vehicle and tractor vehicle certification 

process under the Phase 1 rule, Figure 3-12 summarizes the GEM-based certification process 

flow. 
3-81 

Figure 3-12 Phase 1 Rule for Certification Using GEM 

 
3-82 

In this approach, vehicle manufacturers can only use up to five input parameters – aero 

dynamic coefficient, rolling resistance, weight reduction, speed limiter, and idle reduction, to 

conduct vehicle certifications (although improvements not recognized by GEM can be certified 

as off-cycle credits under the Phase 1 rules). All other vehicle and engine parameters are the 

default parameters specified by the agencies. 

Figure 3-12 shows that use of the agencies’ default engine fuel maps under the Phase 1 

rules. This default engine fuel map approach would not be able to recognize the benefits of 

advanced engine technologies packages. The three drive cycles shown in Figure 3-12used in 

Phase 1 are 55 mph and 65 mph cruise speed cycles, and ARB transient cycles. In the two cruise 

speed cycles, there is no road grade, meaning that the vehicle operates with one single operating 

point inside engine fuel maps, which does not represent the real-life driving condition on the 

road. On the other hand, engine certifications use completely different cycles. For example, 

tractor engines use the SET cycle, while vocational engines use the FTP cycle. Those engine 

cycles have very little connection with how the engine operates in a vehicle over Phase 1 vehicle 

drive cycles. This means whatever the optimized engine calibration developed for the FTP and 

SET cycles cell may not be able to be realized in the real-life driving condition on the road. 

Furthermore, there is no direct linkage between GHG emissions and criteria emissions, since 

vehicle and engine certification cycles are different. 

3.10.1.2 Primary Certification Approach in Phase 2 

In order to overcome the deficiencies mentioned in the above section, the agencies are 

proposing significant improvements and enhancements as part of the Phase 2 proposal. Many of 

the Phase 1 predefined parameters used in GEM now become the vehicle-specific user-entered 

inputs in Phase 2. Most significantly, vehicle manufacturers can use engine fuel maps 

representing the actual engine in the vehicle for certification, which means that it can recognize 

the benefits due to advanced technologies developed for the engines. Chapter 4 of this draft RIA 

details the enhancements of GEM and the extensive validations of these proposed changes. 

Another significant proposed enhancement is the addition of the road grade into 55 mph and 65 

mph cruise speed cycles, which represents more realistic driving conditions. In addition, the 

agencies propose reweighting on the SET cycles with more emphasis on A and B speed modes. 

The more detailed description on addition of the SET and road grade weighting can be seen in 

Section II.D(1) and Section III.C of the preamble, respectively. 

Even with these improvements, however, certain issues would not be directly addressed. 

First of all, there would still be no direct linkage between GHG emissions and criteria pollutant 

emissions, since vehicle and engine certification cycles would still be different. Second, the 

engine fuel maps used in GEM from individual vehicle manufacturers are still obtained under 

steady state conditions. The transient behaviors due to smoke control and thermal management 

control, for example, would not be able to be modeled using steady state engine fuel maps. 

Third, the agencies’ primary certification approach introduces a new concern from independent 

engine manufacturers, namely concern regarding proprietary technology information that can be 

found from engine fuel maps. 
3-83 

3.10.2 Description of Alternative Certification Concept 

In view of these concerns mentioned above two sections for both Phase 1 and Phase 2, 

the agencies would like to specifically ask for comments on this alternative approach to Phase 2 

certification to address these concerns. This section will introduce the concept and principal of 

this alternative approach. 
3-84 

3.10.2.1 Vehicle Certification

Figure 3-13 Alternative Phase 2 Certification Option II 

Figure 3-14 Phase 2 Certification Process with Option II 
3-85 

Displayed in Figure 3-13 and Figure 3-14 is the potential alternative vehicle certification 

approach. The entire process can be further simplified and described as follows. 

1. Define nine vehicles that will cover the range of vehicles that the engine will be 

certified in. In one dimension the vehicles will cover the range of engine cycle 

work (W) by varying the vehicles mass, CdA and Crr. The other dimension would 

cover the range of average engine speed over average vehicle speed (N/V), by 

varying the vehicles tire size and rear axle ratio or transmission (see the left part 

of Figure 3-13). 

2. Run GEM with the nine vehicle configurations and the three certification cycles 

to generate 27 engine cycles (three certification cycles multiplied by nine 

vehicles). Each cycle will define engine speed and torque as a function of time. 

These cycles will be generated at 10hz to capture engine torque during shifting 

(see the right part of Figure 3-13). 

3. Test the parent engine of the engine family using the 27 engines cycles to create a 

matrix of brake specific CO2 consumption in g/hp-hr as a function of Work in hphr and N/V in rpm/(mile/hr). 

4. For certification, run GEM for each vehicle that will be certified with an engine 

from the engine family. For each simulation the actual vehicle parameters shall 

be used. The output of the simulation will be work in hp-hr and N/V in 

rpm/(mile/hr) for each drive cycle (See the left part of Figure 3-14 ). 

5. Use work and N/V obtained from Step 4 to interpolate CO2 in g/hp-hr for the 

engine in the specific vehicle being certified (see the middle part of Figure 3-14. 

6. Multiply the interpolated CO2 in g/hp-hr by work in (hp-hr) from the simulation 

of the certified vehicle (Step 4) and divide by the ton-miles of each vehicle 

category and drive cycle (see the right part of Figure 3-14). 

7. Supply this final CO2 in g/ton-mile for certification. 

3.10.2.2 Engine Certification 

One of the key features for the alternative vehicle certification approach is that 

supplementation of the engine tests requires running a number of tests at a certified engine dyno. 

These tests will result in a fuel consumption or CO2 map in g/kw-hr as function of cycle work 

and N/V over three certification cycles. Therefore, the engine certification point could be 

selected from one of the testing points through this fuel consumption or CO2 map, and therefore 

there is no need to run engine certification test alone, thus reducing engine test burden from 

manufacturers as far as GHG emission certification is concerned. How this single point is to be 

selected is something we need to work out in the near future.

3.10.3 Discussion of Alternative Certification Approach 

The previous section only describes the principles of this alternative approach. This 

section will provide more detailed supporting information, addressing the following questions: 
3-86 

 Would the fuel consumption or CO2 maps be very well behaved, so that the 

interpolation or curve fitting can be carried out without losing accuracy? 

 Would one map that combines all three certification cycles be adequate to represent 

the engine or would an individual map for each cycle be needed? 

 What would be the most suitable independent axis of the fuel consumption maps to be 

used to minimize the impact of type of transmissions and their shifting strategies? 

 What is the minimum number of engine tests and GEM simulations required to cover 

the range of vehicles certified for a given engine family? 

In order to answer the above questions, a test matrix based on a Class 8 Kenworth T700 

tractor with a Cummins ISX engine was carefully designed as follows: 

• 32 variations of the Kenworth T700 

– Axel ratio: 2.64, 3.36,3.9 and 4.56 

– Cd: 0.35, 0.45, 0.55, 0.65, and 0.75 

– Crr: 0.005, 0.006, 0.007 and 0.008 

• Cycles – with distance correction 

– 55 w/SwRI grade profile 

– 65mph w/SwRI grade profile 

– ARB Transient 

• Transmissions – Eaton AMT 

– 10 speed: F016E310C-LAS 

– 13 speed: FO_16E313A_MHP 

– 18 speed: FO-16E318B-MXP 

• Transmission shift strategy 

– Eaton’s table shift 

– EPA’s shift optimizer 

In addition, a child rating engine is selected to show its impacts on the accuracy of this 

approach. This results in a total of 1,152 simulation runs. 

3.10.4 GEM Simulations 

 Phase 2 GEM (as proposed) was used by EPA to perform a large scale of simulations 

based on the matrix proposed above. The results and plots are arranged as follows. Figure 3-15 

to Figure 3-17 display the BSFC (g/hp-hr) surface plots over 55mph, 65mph, and ARB cycles 

with all points simulated. The next section discusses the evaluation of the results with the engine 

child ratings. 
3-87 

Figure 3-15 Contour plot of CO2 as function of cycle work and N/V for 55 mph cycle 

Figure 3-16 Contour plot of CO2 as function of cycle work and N/V for 65 mph cycle 

 

20

25

30

20 25 30 35 40 45

140

150

160

170

180

190

200

Work (hp-hr)

55mph

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

450hp rating

15

20

25

30

25 30 35 40 45 50

140

150

160

170

180

190

200

Work (hp-hr)

65mph

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

450hp rating
3-88 

Figure 3-17 Contour plot of CO2 as function of cycle work and N/V for ABB cycle 

As can be seen from these three figures (Figure 3-15 to Figure 3-17), a surface is used to 

curve fit all points. Also shown in these figures is how well the actual points are fitted with the 

surface. It is seen from Figure 3-15 and Figure 3-17 that all test cycles are collapsed into one 

surface with different transmissions, different shifting strategies, and axles. The behavior that all 

simulation points are very well fitted into one surface plane suggests that impact of transmissions 

including shifting strategies and numbers of gears, axle ratio be minimal if the plots are designed 

in such a way that average engine speed (N) over average vehicle speed (V) defined as N/V is 

selected. 

Figure 3-18 shows the three-dimensional surface plot of BSFC as function of cycle work 

and N/V with all cycles combined into one plot. It can be seen that all points are not very well 

fitted into one surface plot, suggesting that certification be done in an individual cycle manner. 

 

60

70

80

90

11.5 12 12.5 13 13.5 14 14.5

170

175

180

185

190

195

200

205

210

Work (hp-hr)

ARB

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

450hp rating
3-89 

 

Figure 3-18 Surface plot of CO2 as function of cycle work and N/V for all three cycles 

The simulations are then carried out in order to address child rating impact on this surface 

fit with each cycle. Figure 3-19 to Figure 3-21 show the behavior of all three individual cycles. 

It can be seen that all parent and child rating points are still collapsed into one surface plot, 

which is very similar to the results where only parent ratings are shown in Figure 3-16 to Figure 

3-18, suggesting that the same interpolation schemes or the same surface fitting could be applied 

to both parent and child ratings for those points that are located between the testing points. 

 

20 40

60 80 10

20

30

40

50 140

160

180

200

220

240

260

280

Work (hp-hr)

All Cycles

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

450hp rating
3-90 

Figure 3-19 Surface plot of CO2 with child rating engine for 55mph cycle 

Figure 3-20 Surface plot of CO2 with child rating engine N/V for 65 mph cycle 

 

20

25

30

25 20 35 30 45 40

145

150

155

160

165

170

175

180

185

190

195

Work (hp-hr)

55mph

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

400hp rating

450hp rating

15

20

25

30

25 30 35 40 45 50

140

150

160

170

180

190

200

Work (hp-hr)

65mph

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

400hp rating

450hp rating
3-91 

Figure 3-21 Surface plot of CO2 with child rating engine for ABB cycle 

Figure 3-19 to Figure 3-21 demonstrate the well behaving nature of CO2 surface as a 

function of work and N/V as long as the surface fitting is conducted in an individual cycle, 

showing the potential to use surface fitting or interpolation scheme to determine the point 

resulted from the actual vehicle certification. However, please note that the entire simulations 

consist of over one thousand points, and it would be impossible to run all engine tests in order to 

generate a CO2 map for use in certification. Efforts must be made to greatly simplify the process 

by reducing the points to a minimum level, which can still very well represent a to-be-certified 

vehicle. After numerous trials, it is found that a minimum 9 points per cycle is needed, which 

covers three final drive ratios and three vehicle loads or work. Displayed in Figure 3-22 to 

Figure 3-24 are the same plots as Figure 3-19 to Figure 3-21, but the numbers of points are 

reduced to 9 points per cycle. 

 

60

70

80

90

11.5 12 12.5 13 13.5 14 14.5

170

175

180

185

190

195

200

205

210

Work (hp-hr)

ARB

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

400hp rating

450hp rating
3-92 

Figure 3-22 Contour plot of CO2 with only 9 points for 55mph cycle 

Figure 3-23 Contour plot of CO2 with only 9 points N/V for 65 mph cycle 

20

25

30

20 25 30 35 40 45

140

150

160

170

180

190

200

Work (hp-hr)

55mph

N/V (rpm/(km/hr)) bsfc (g/hp-hr)

bsfc

All Points

9 Points

15

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30

25 30

35 40

45 50

140

150

160

170

180

190

200

Work (hp-hr)

65mph

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

All Points

9 Points
3-93 

Figure 3-24 Contour plot of CO2 with only 9 points for ABB cycle 

Comparing Figure 3-19 to Figure 3-21, very similar behaviors for all three cycles are 

observed, and therefore, it can be said that 9 points would be acceptable at least for the engines 

and vehicles that are under consideration. 

3.10.5 Generic Vehicle Definition 

Chapter 3.10.4 shows that nine vehicle configurations for each certification cycle could 

be used to cover the range of the vehicles to be certified. However, it is not clear how those 

vehicle configurations can be defined in a generic way. This section attempts to achieve this 

objective. 

 To cover the range of vehicle configuration that the engine could be sold in the agencies 

are considering the vehicle configuration defined in Table 3-30 to Table 3-32. To cover the 

range of axles, the axle ratios would be calculated from the regulatory defined engines speeds 

and the maximum duty cycle speed of 65 mph. With the engine speed, tire radius, top gear ratio 

and vehicle speed, the axle ratio can be calculated. To cover the range of engine work the 

agencies are proposing a range of drag area (CdA), coefficients of rolling resistance (Crr) and 

vehicle masses. For Class 8 vehicles the highest mass and highest CdA be on the same vehicle 

and that both CdA and vehicle mass drop together for the lower average power vehicles. The 

reason for this is that, the cruise cycle’s average power is most affected by the vehicle’s CdA, 

where mass has the largest effect on average power on the transient cycle. For Class 2b-7 

60

70

80

90

11.5 12 12.5 13 13.5 14 14.5

170

175

180

185

190

195

200

205

210

Work (hp-hr)

ARB

N/V (rpm/(km/hr))

bsfc (g/hp-hr)

bsfc

All Points

9 Points
3-94 

vehicles mass and Crr are varied to change the average power over the cycle. For these vehicles 

Crr is varied instead of CdA because CdA is not an input into GEM for vocational vehicles. 

In addition to defining the vehicle parameters the agencies are proposing that the 

transmissions be defined for each vehicle configuration. Table 3-34 defines the transmission 

type and gear ratios for each of these vehicles. 

Table 3-34 Default Transmissions 

GEAR 

NUMBER 

TRANSMISSION TYPE AND GEAR RATIOS 

10 speed AMT 6 speed HH AT 6 speed MH AT 

1 12.8 4.6957 3.102 

2 9.25 2.213 1.8107 

3 6.76 1.5291 1.4063 

4 4.9 1 1 

5 3.58 0.7643 0.7117 

6 2.61 0.6716 0.61 

7 1.89 N/A 

8 1.38 

9 1 

10 0.73 

 

3.10.6 Certification Point Determination from Alternative 

Table 3-30 to Table 3-32 define the numbers of vehicle configurations so that a welldefined map can be generated for a certain vehicle family to be certified. Section 3.10.4 shows 

that a surface fitting could approximate the surface for the entire vehicle applications. It 

indicates that the fitted surface could be well behaved only if an individual cycle is plotted. 

However, it can be imagined that the surface could be distorted if those mapping points shown in 

Table 3-30 to Table 3-32 may not be general enough to define the range of applications. As a 

result, an alternative to surface fitting should be considered. This section discusses a numerical 

scheme that is used to determine to-be-certified CO2 of the vehicle family from the CO2 map 

generated from the engine tests as well as GEM simulation. It should be pointed out that the 

numerical scheme discussed in this section is only the first attempt to derive a numerical scheme 

that can interpolate or extrapolate certification point, and many other alternatives and 

optimization schemes can be used as well. 

In the approach, it is assumed that one map per certification cycle is considered, each 

consisting of 8-9 data points, but the map data does not need to be in an exact rectangular matrix 

or in any particular order. In order to demonstrate the concept, the generic vehicle defined in 

Table 3-31 is used, where there are a total of nine configurations as shown in Figure 3-25 to 

Figure 3-27 for three certification cycles. Shown in these figures are also a number of vehicle 
3-95 

configurations that could be certified under this map, covering a wide range of applications 

including both tractor and vocational vehicles and different transmission packages. 

In these figures, X axis is SN/SV defined as average engine speed over average vehicle 

speed in rpm/km-ph. Y axis is the cycle work in kw-hr over the individual driving cycle. The 

legend of “Map Points” stands for the generic class 8 nine vehicle configurations using the same 

engine defined in Table 3-31. In these transmission packages, the same engine as the generic 

vehicle is used, and vehicle variables are varied, such as tire, axle, and aerodynamic packages. 

Veh 1 and Veh 2 stand for class 8 vehicles for typical vocational applications using the same 

engine as the generic vehicle. They all have similar vehicle weight, but with different tires, axle 

ratios, and aerodynamic packages. The purpose of these practices is to demonstrate whether the 

generic vehicle points (Map Points) can be used to interpolate or extrapolate all other points 

under different vehicle and transmission configurations with a high confidence level. 

From these figures, it can be seen that quite a few vehicle configurations are outside 

generic vehicle points, which are shown in “Map Points”. This means that extrapolations must 

be used. It is hoped that whatever numerical scheme is used can offer reasonably good accuracy 

in terms of interpolation as well as extrapolation. 

Figure 3-25 Alternative certification map for 55 mph certification cycle

 

16.0

17.0

18.0

19.0

20.0

21.0

22.0

23.0

24.0

25.0

26.0

27.0

28.0

29.0

30.0

10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0

kW‐hr

SN/SV

gCO2/kW‐hr

Map

Points

Tran 1

Veh 1

Veh 2

Tran 2

Tran 3
3-96 

Figure 3-26 Alternative certification map for 65 mph certification cycle 

Figure 3-27 Alternative certification map for ARB certification cycle 

 

19.0

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10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0

kW‐hr

SN/SV

gCO2/kW‐hr

Map

Points

Tran 1

Veh 1

Veh 2

Tran 2

Tran 3

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37.5 38.5 39.5 40.5 41.5 42.5

kW‐hr

SN/SV

gCO2/kW‐hr

Map

Points

Tran 1

Veh 1

Veh 2

Tran 2

Tran 3
3-97 

In order to prove the concept and numerical accuracy in determining appropriate CO2 for 

certification, those nine points defined by Table 3-31 as shown in “Map Points” of these three 

figures which are used to interpolate or extrapolate CO2 for any other vehicle configurations 

points and then are compared with the actual simulation points. In the numerical scheme used in 

this study, within, above, and to the right of a mapped region defined by nine Map Points, 

interpolation and extrapolation occur in a plane defined by the interpolated point’s 3-nearest 

mapped neighbors. Below and to the left of the mapped region extrapolation occur between the 

2 nearest neighbors along the non-extrapolated axis and there is no change in the extrapolated 

value along any extrapolated axis. The interpolation scheme is to pick the 3 closest neighbors 

that have a maximum included angle less than 120 degrees, which eliminates picking the narrow 

triangular planes formed just outside of the mapped region. Table 3-35, Table 3-36, and Table 

3-37 show the comparisons for those points. 

In these tables, there are 76 vehicle configurations. Among them, nine points with green 

color filled are the generic vehicle defined in Table 3-31. All other points represent different 

vehicle configurations under the same engine family. The column with error (%) is the 

comparison of CO2 g/hp-hr between the test points and interpolated or extrapolated values. For 

the sake of simplicity, both values of CO2 g/hp-hr for both test and interpolated/extrapolate 

points are not shown in these tables. As can be seen from Table 3-35 and Table 3-36, only one 

point shows over 4 percent numerical accuracy, and most points are under 2.5 percent difference 

for cruise speed 55 mph and 65 mph cycles, even for those points located outside mapped region. 

On the other hand, ARB cycle shows quite a few points in the range of 4.0-4.5 percent 

difference, which is a concern as far as the certification is concerned. These results suggest that 

more work and numerical scheme development will be required moving forward. 
3-98 

Table 3-35 Proof of Concept and Numerical Scheme Accuracy for 55MPH Cycle 

INTERP/EXTRAP POINTS    INTERP/EXTRAP POINTS

i N/Vi kW‐hri Error

(%)

i N/Vi kW‐hri Error

(%)

1 12.61 27.37 0.5%    39 12.71 24.11 1.0%

2 12.31 27.73  ‐0.4%    40 14.18 24.17 0.4%

3 13.30 27.78  ‐0.2%    41 18.06 24.36 2.3%

4 12.47 23.81 1.6%    42 13.31 24.13 0.8%

5 12.31 24.09 1.0%    43 12.71 20.74 1.2%

6 13.30 24.13 0.8%    44 14.18 20.80 0.9%

7 12.42 20.46 1.8%    45 18.06 20.98 3.0%

8 12.31 20.72 1.7%    46 13.31 20.76 1.2%

9 13.30 20.76 1.2%    47 11.0727 20.474 0.5%

10 11.85 25.84 0.2%    48 11.3574 20.298 1.5%

11 12.80 25.89 0.3%    49 11.9048 20.342 1.9%

12 11.85 22.36 1.3%    50 12.8518 20.428 1.7%

13 12.80 22.40 1.6%    51 11.1814 22.769 0.3%

14 12.14 20.97 0.2%    52 11.3602 22.55 1.0%

15 12.04 21.19  ‐0.3%    53 11.9048 22.609 1.5%

16 13.01 21.24  ‐0.6%    54 12.8518 22.7 1.6%

17 14.19 21.30  ‐2.1%    55 12.882 26.145 0.5%

18 19.26 21.61  ‐2.0%    56 15.1703 21.851 ‐0.7%

19 11.99 20.14 0.3%    57 17.4585 22.132 ‐0.8%

20 12.04 20.33  ‐0.2%    58 19.7468 22.449 ‐1.8%

21 13.01 20.38  ‐0.9%    59 22.0351 22.745 ‐3.3%

22 14.19 20.44  ‐1.6%    60 23.1592 22.9  ‐4.0%

23 19.26 20.73  ‐1.4%    61 18.3492 22.253 ‐1.1%

24 11.98 19.36 0.4%    62 20.7542 22.579 ‐2.8%

25 12.04 19.54 0.0%    63 15.9442 21.943 ‐0.2%

26 13.01 19.59  ‐0.3%    64 13.5392 26.215 0.4%

27 14.19 19.65  ‐1.2%    65 10.8136 25.072 0.0%

28 19.26 19.93  ‐0.9%    66 10.4952 19.698 0.0%

29 14.44 21.32  ‐2.3%    67 10.4952 16.748 0.0%

30 14.90 21.34  ‐2.9%    68 14.4455 26.005 0.0%

31 12.97 21.24  ‐0.6%    69 14.4455 19.938 0.0%

32 17.08 21.47  ‐1.4%    70 14.4455 16.927 0.0%

33 13.82 21.28  ‐1.8%    71 19.5963 26.267 0.0%

34 18.55 21.56  ‐1.7%    72 19.5963 20.22 0.0%

35 12.71 27.75  ‐0.2%    73 19.5963 17.21 0.0%

36 14.18 27.82  ‐0.5%    74 17.0015 26.129 1.5%

37 18.06 28.03  ‐1.3%    75 17.0015 20.072 1.8%
3-99 

38 13.31 27.78  ‐0.2%    76 17.0015 17.061 2.0%

Table 3-36 Proof of Concept and Numerical Scheme Accuracy for 65MPH Cycle 

   INTERP/EXTRAP POINTS    INTERP/EXTRAP POINTS

i N/Vi kW‐

hri

Error

(%)

i N/Vi kW‐hri Error

(%)

1 11.40 32.68 0.6%    39 12.71 27.96 1.8%

2 12.31 32.85  ‐0.9%    40 14.18 28.04 0.8%

3 13.30 32.94  ‐1.2%    41 18.06 28.26 2.1%

4 11.40 27.86 0.9%    42 13.31 27.99 1.5%

5 12.31 27.93 1.8%    43 12.71 23.39 2.2%

6 13.30 27.99 1.5%    44 14.18 23.47 1.9%

7 11.40 23.33 1.5%    45 18.06 23.67 4.2%

8 12.31 23.38 2.1%    46 13.31 23.42 2.1%

9 13.30 23.42 2.1%    47 10.287 23.201 0.1%

10 11.85 30.33 1.1%    48 11.1382 23.307 1.3%

11 12.80 30.41 0.6%    49 11.9048 23.383 2.0%

12 11.85 25.57 1.9%    50 12.8518 23.471 2.3%

13 12.80 25.61 2.4%    51 10.4864 26.581 0.1%

14 11.15 27.02 1.1%    52 11.1382 26.689 0.9%

15 12.04 27.08 1.2%    53 11.9048 26.797 1.2%

16 13.01 27.13 1.7%    54 12.8518 26.895 2.3%

17 14.19 27.21 0.4%    55 12.882 31.126 0.1%

18 19.26 27.57 1.2%    56 15.1703 27.695 0.5%

19 11.15 25.75 1.2%    57 17.4585 28.007 1.6%

20 12.04 25.80 2.0%    58 19.7468 28.315 0.1%

21 13.01 25.86 0.7%    59 22.0351 25.395 2.3%

22 14.19 25.94 0.2%    60 23.1592 23.919 3.4%

23 19.26 26.30 1.0%    61 18.3492 28.124 2.0%

24 11.15 24.56 0.3%    62 20.7542 27.401 ‐0.3%

25 12.04 24.61 1.5%    63 15.9442 27.802 1.0%

26 13.01 24.67 0.4%    64 13.5392 31.201 ‐0.3%

27 14.19 24.75  ‐0.3%    65 10.4952 29.49 0.0%

28 19.26 25.11 0.4%    66 10.4953 23.234 0.0%

29 14.44 27.23 0.0%    67 10.4952 19.715 0.0%

30 14.90 27.26  ‐0.7%    68 14.4455 29.917 0.0%

31 12.97 27.13 1.8%    69 14.4456 23.441 0.0%

32 17.08 27.41 1.5%    70 14.4455 19.921 0.0%

33 13.82 27.18 0.8%    71 19.5963 30.142 0.0%

34 18.55 27.52 2.0%    72 19.5964 23.766 0.0%

35 12.71 32.89  ‐1.0%    73 19.5963 20.247 0.0%
3-100 

36 14.18 33.00  ‐1.8%    74 17.0015 30.063 1.0%

37 18.06 33.20  ‐1.6%    75 17.0016 23.594 0.6%

38 13.31 32.94  ‐1.2%    76 17.0015 20.075 0.3%

Table 3-37 Proof Of Concept and Numerical Scheme Accuracy for ARB Cycle 

   INTERP/EXTRAP POINTS    INTERP/EXTRAP POINTS

i N/Vi kW‐

hri

Error

(%)

i N/Vi kW‐hri Error

(%)

1 41.00 9.66  ‐1.2%    39 39.26 9.29 ‐2.7%

2 40.16 9.65  ‐0.9%    40 40.34 9.30 ‐1.3%

3 39.48 9.64  ‐1.0%    41 40.56 9.33 ‐1.9%

4 40.98 9.30  ‐1.0%    42 39.49 9.28 ‐2.5%

5 40.13 9.30  ‐2.9%    43 39.23 8.92 ‐2.3%

6 39.43 9.28  ‐2.3%    44 40.31 8.93 ‐2.6%

7 40.95 8.93  ‐2.1%    45 40.53 8.97 ‐2.8%

8 40.03 8.93  ‐2.0%    46 39.00 8.91 ‐2.0%

9 39.39 8.91  ‐1.9%    47 39.1403 9.2227 1.0%

10 41.40 9.48  ‐0.7%    48 40.0123 9.2143 0.4%

11 39.47 9.45  ‐1.3%    49 39.2724 9.1879 0.7%

12 41.13 9.10  ‐1.1%    50 38.9924 9.1411 1.0%

13 39.43 9.09  ‐2.3%    51 39.2179 9.3688 0.4%

14 40.17 6.25  ‐4.1%    52 40.0236 9.3563 0.2%

15 39.40 6.25  ‐4.2%    53 39.3053 9.3359 0.4%

16 38.82 6.26  ‐4.3%    54 38.9921 9.2828 0.9%

17 38.89 6.26  ‐4.4%    55 39.1243 10.353 1.2%

18 40.73 6.32  ‐3.9%    56 38.1574 7.0408 0.2%

19 40.03 6.17  ‐4.5%    57 40.5059 7.0397 0.7%

20 39.40 6.17  ‐4.5%    58 42.0372 7.087 1.2%

21 38.75 6.19  ‐4.6%    59 43.4797 7.1226 1.6%

22 38.88 6.18  ‐4.6%    60 43.1689 7.1632 0.9%

23 40.73 6.25  ‐4.1%    61 44.334 7.1471 0.8%

24 40.02 6.11  ‐4.8%    62 44.0081 7.1626 0.0%

25 39.31 6.11  ‐4.7%    63 41.7231 7.0508 ‐2.9%

26 38.75 6.12  ‐4.8%    64 41.8206 10.136 1.5%

27 38.88 6.12  ‐4.8%    65 39.4411 11.832 0.0%

28 40.73 6.18  ‐4.4%    66 38.2432 8.6276 0.0%

29 39.85 6.27  ‐4.2%    67 37.8895 7.3752 0.0%

30 40.32 6.29  ‐3.9%    68 41.2466 11.14 0.0%

31 38.40 6.23  ‐4.5%    69 40.3708 8.1927 0.0%

32 40.88 6.73  ‐3.3%    70 40.0346 7.1138 0.0%

33 38.21 6.34  ‐4.5%    71 42.1071 10.8 0.0%
3-101 

34 41.58 6.69  ‐2.9%    72 41.6921 8.0995 0.0%

35 39.31 9.65  ‐1.3%    73 41.4929 7.0552 0.0%

36 40.36 9.66  ‐1.3%    74 42.1548 10.83 0.2%

37 40.69 9.69  ‐1.8%    75 41.6017 8.0873 0.4%

38 39.57 9.64  ‐1.2%    76 41.4366 7.0472 0.4%

3.10.7 Preliminary Comparisons of Primary and Alternative Approaches 

The main purpose of introduction of this alternative approach for vehicle certification is 

to integrate vehicle certification with engine certification, being able to address both GHG 

emissions and criteria emission in one set of tests. Chapter 3.10.4 shows the principle of this 

approach. However, one of the key questions remains – how this alternative certification 

approach is credible in terms of accuracy as opposed to the primary vehicle certification 

approach? In addition, there are also many other questions that still need answers, such as parent 

and child rating impacts, the impact of vocational sector with a large variation on the ratio of 

N/V (average engine speed over average vehicle speed), surface fit or interpolation scheme, and 

engine certification. 

Shown in Figure 3-28 is the comparison between alternative approaches discussed in this 

Section and GEM with steady state map based approach discussed in Chapter 4 of this draft RIA. 

In both engine and GEM simulations, a test matrix consisting of twenty seven points is tested (3 

axle ratios x 3 Cd values x 3 cycles) as shown below. 

• Nine variations of the Kenworth T700 to cover the range of vehicle loads 

– Axel ratio: 2.64, 2.85, and 3.08 

– Cd: 0.41, 0.55, and 0.7 

• Three certification cycles – with distance correction 

– 55 w/SwRI grade profile 

– 65mph w/SwRI grade profile 

– ARB Transient 

• Eaton AMT model: 10 speed: F016E310C-LAS 
3-102 

Figure 3-28 Comparisons between Alternative and GEM Based Approaches

 

120

130

140

150

160

170

180

190

130 140 150 160 170 180 190

Engine BSFC (g/hp‐hr) ‐ GEM

Engine BSFC (g/hp‐hr) ‐ measured from Engine tests

1:1

GEM sim ‐ SS map based

Alternative

1:1 +5%

1:1 ‐5%
3-103 

As shown in Figure 3-28, both alternative approach and GEM based approach seem to 

produce similar results in 55 mph and 65 mph cruise cycles with road grade. However, it is 

clearly shown that the alternative approach seems to be better in the transient cycle than GEM 

based approach with the steady state map. 

3.10.8 Remaining Questions and Future Plans 

 The comparison shown in Figure 3-28 only partially answers this alternative approach 

fidelity with one specific engine and transmission platform and because of this there are still 

many unanswered questions. The following questions are only a subset of the questions that 

need to be answered: 

 How does this alternative approach address parent and child rating? Would nine 

points be adequate to cover the practical range of the vehicle operation on the road? 

 How can this approach address those points that may be out of map ranges with much 

higher or lower N/V? 

 What kinds of numerical schemes, interpolation or surface fitting, shall be used to 

interpolate those points that are located between testing points? 

 What the numerical scheme shall be used to extrapolate those points outside the 

maps? 

 How robust are these numerical schemes? 

 Can what we have learned so far be applied to other engines? 

 How the single engine certification point shall be selected among the number of 

engine tests? 

 Are there potential unintended consequences? 

The agencies welcome comments on these questions and on the alternative vehicle certification 

approach in general. 

 
3-104 

References

1

 For more information, see CFR Title 40, Part 86.004-28 

2

 National Renewable Energy Laboratory, 2014. Technical Report: Appending High-Resolution Elevation Data to 

GPS Speed Traces for Vehicle Energy Modeling and Simulation. http://www.nrel.gov/docs/fy14osti/61109.pdf 3

 Southwest Research Institute coastdown and constant speed testing summary report for EPA. 4

 R.M. Young. Product specifications for Ultrasonic Anemometer Model 81000. 

http://www.youngusa.com/products/6/3.html 

5

 SAE International, 2013, Guidelines for Aerodynamic Assessment of Medium and Heavy Commercial Ground 

Vehicles Using Computational Fluid Dynamics, SAE J2966, 2013-09 

6

 2010 NAS Report. Finding 2-4 on page 39. 

7

 SAE International, 1987, Fuel Consumption In-Service Test Procedure Type III, SAE J1526, 1987-06 

8

 CARB-NREL Aero testing Report (2014) 

9

 U.S. EPA. Truck and Trailer Roof Height Match Analysis Memorandum from Amy Kopin to the Docket, August 

9, 2010. Docket Identification Number EPA-HQ-OAR-2010-0162-0045. 

10 Ben Sharpe (ICCT) and Mike Roeth (North American Council for Freight Efficiency), “Costs and Adoption Rates 

of Fuel-Saving Technologies for Trailer in the North American On-Road Freight Sector”, Feb 2014. 11 Frost & Sullivan, “Strategic Analysis of North American Semi-trailer Advanced Technology Market”, Feb 2013. 12 EPA, 2011, HD Phase 1 Regulatory Impact Analysis, Figure 3-16. EPA-420-R-11-901. 

13 (http://webstore.ansi.org/RecordDetail.aspx?sku=ISO+28580%3a2009) 

14 SAE International, 2006, Rolling Resistance measurement Procedure for Passenger Car, Light Truck, and 

Highway Truck and Bus Tires, SAE J1269, 2006-09 

15 ISO, 2009, Passenger Car, Truck, and Bus Tyres – Methods of Measuring Rolling Resistance – Single Point Test 

and Correlation of Measurement Results: ISO 28580:2009(E), First Edition, 2009-07-01 16 NHTSA, 2009. “NHTSA Tire Fuel Efficiency Consumer Information Program Development: Phase 1 – 

Evaluation of Laboratory Test Protocols.” DOT HS 811 119. June. (www.regulations.gov, Docket ID: NHTSA2008-0121-0019). 

17 SAE International, 1999, Stepwise Coastdown Methodology for Measuring Tire Rolling Resistance, SAE J2452, 

1999-06 

18 ISO, 2005, Passenger Car, Truck, Bus, and Motorcycle Tyres – Methods of Measuring Rolling Resistance, ISO 

18164:2005(E) 

19 SAE International, 2012, Test Procedures for Measuring Truck Tire Revolutions Per Kilometer/Mile, SAE J1025, 

2012-08 

20 Based on MOVES analysis. 

21 Gautam, M., N. Clark, W. Riddle, R. Nine, S. Wayne, H. Maldonado, A. Agrawal, M. Carlock. “Development 

and Initial Use of a Heavy-Duty Diesel Truck Test Schedule for Emissions Characterization.” SAE Paper 2002-01-

1753. 2002. 

22 Governors Highway Safety Association. Speed Limit Laws May 2011. Last viewed on May 9, 2011 at 

http://www.ghsa.org/html/stateinfo/laws/speedlimit_laws.html 23 Environmental Defense Fund. “Greenhouse Gas Management for Medium-Duty Truck Fleets.” Viewed at 

http://edf.org/documents/10860_fleets-med-ghg-management.pdf. Page 6. 

24 ICF International. Investigation of Costs for Strategies to Reduce Greenhouse Gas Emissions for Heavy-Duty 

On-Road Vehicles. July 2010. Pages 4-16. Docket Identification Number EPA-HQ-OAR-2010-0162-0044. 

25 M.J. Bradley & Associates. Setting the Stage for Regulation of Heavy-Duty Vehicle Fuel Economy and GHG 

Emissions: Issues and Opportunities. February 2009. Page 35. Analysis based on 1992 Truck Inventory and Use 

Survey data, where the survey data allowed developing the distribution of loads instead of merely the average loads. 26 The U.S. Federal Highway Administration. Development of Truck Payload Equivalent Factor. Table 11. Last 

viewed on March 9, 2010 at 

http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_11_12_tables.htm 
3-105 

27 Excerpted from The U.S. Federal Highway Administration. Development of Truck Payload Equivalent Factor. 

Table 11. Last viewed on March 9, 2010 at 

http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_11_12_tables.htm 28 The U.S. Federal Highway Administration. Development of Truck Payload Equivalent Factor. Table 11. Last 

viewed on March 9, 2010 at 

http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_11_12_tables.htm 
4-1 

Chapter 4: Vehicle Simulation Model 

4.1 Purpose and Scope 

In designing a regulatory GHG emission control and fuel consumption program, it is 

necessary to estimate the performance of technologies, verify compliance with the regulatory 

standards, and estimate overall benefits of the program. The agencies developed the Greenhouse 

gas Emission Model (GEM) to serve these purposes for Phase 1, which was consistent with 

recommendations by the National Academies of Sciences (NAS) to use vehicle simulation to 

demonstrate compliance.A GEM is currently being used to certify the fuel consumption and CO2

benefits of the Phase 1 rulemaking for all heavy duty vehicles except for HD pickups and vans, 

which require a chassis dynamometer test for certification. While the version of GEM used in 

Phase 1 contained most of the technical and mathematical features needed to run a vehicle 

simulation, the model was limited. For example: 

 Only manual transmissions were used in the model for all tractor and vocational vehicle 

simulations, which is not always the case for real world applications, especially for 

vocational vehicle applications

 The model did not include engine torque interruption during gear shifting

 Engine control were simplified, with no fueling cut-off features

 Only the agencies’ pre-specified engine fuel maps were used 

The Phase 1 certification process only required up to five user inputs, and all other 

vehicle parameters and their inputs were pre-specified by the agencies.1 Phase 1 GEM only 

recognized the benefits of aerodynamics improvement, tire rolling resistance, vehicle speed 

limiter, weight reduction, and idle reduction (only for high roof sleeper tractors). 

Because the proposed Phase 2 standards are predicated on the performance of a broader 

range of technological improvements than Phase 1, including changes to transmissions and better 

integration of engines and transmissions, a more comprehensive vehicle simulation model is 

required. This chapter describes a new version of this vehicle simulation model, referred to as 

Phase 2 GEM. It should be noted that all changes to GEM described in this chapter remain 

potential, since the agency is proposing these changes, and will make a final determination as to 

what changes are appropriate only after considering the entire record after the close of the public 

comment period. 

 

A National Academies of Science. “Technologies and Approaches to Reducing the Fuel Consumption of Mediumand Heavy-Duty Vehicles.” 2010. Recommendation 8-4. 
4-2 

4.2 Model Code Description 

4.2.1 Engineering Foundations of the Model 

EPA developed GEM to be a forward-looking Matlab /Simulink-based model for heavyduty (Class 2b-8) vehicle compliance in 2011.1

 A more detailed description of this model and its 

engineering foundation can be found in Reference 1. The underlying GEM code was originally 

developed to simulate a broad range of vehicle speeds over essentially any in-use duty cycle. 

However, the official version that is used for determining compliance with the Phase 1 standards 

incorporates the regulatory duty cycles into the code. In other words, manufacturers cannot run 

other duty cycles with the official version of GEM. We propose to continue this approach for 

Phase 2. 

In order to meet proposed Phase 2 rulemaking requirements in recognizing most of the 

technologies that are measured in both engine and chassis dynamometers, GEM has been 

considerably enhanced. Specifically, the agencies are proposing to implement the following key 

technical features into Phase 2 GEM: 

• An upgraded engine controller, which includes engine fuel cut-off during braking and 

deceleration 

• An upgraded transmission model, which includes an upgraded manual transmission, 

along with newly developed automatic and automated manual transmissions 

• An upgraded driver model with a distance-compensated driver that will drive the 

certification drive trace over a prescribed distance regardless of increased drive time due 

to vehicle under-performance, for example. 

4.2.2 Model Components 

The GEM architecture is comprised of four systems: Ambient, Driver, Powertrain, and 

Vehicle as seen in Figure 4-1. With the exception of Ambient and Driver, each system consists 

of one or more subcomponents. The function of each system and its respective component 

models, wherever applicable, is discussed in this chapter. Many changes and modifications 

described in this chapter have resulted from numerous constructive comments from both public 

comments and GEM peer reviews.2

 The model has been upgraded to improve the fidelity of the 

model and better match the function of the simulated vehicles, which also meets our primary 

goal to accurately reflect changes in technology for compliance purposes. As part of this effort, 

substantial effort has been put forth to accurately track and audit power flows through the model 

to ensure conservation of energy. 
4-3 

Figure 4-1 GEM model structure 

4.2.2.1 Ambient Subsystem 

This system defines ambient conditions such as pressure, temperature, and road gradient, 

where vehicle operations are simulated. Just like in Phase 1 GEM, the conditions have been 

maintained in accordance with standard SAE practices. The road gradient has been modified to 

accept a road grade that varies as a function of distance traveled. 

4.2.2.2 Driver Subsystem 

The driver model in Phase 2 GEM has been substantially reorganized to simplify 

operation and to add support for distance compensated drive cycles. The result is a purely 

proportional-integral control driver that features a small lookahead to anticipate the drive cycle, 

especially useful at launch where the vehicle response may be delayed due to the large effective 

inertia in low gears. The proposed target drive cycle consists of a road grade versus distance and 

a vehicle speed target as a function of the time required to achieve those speeds as a function of 

distance (i.e. desired cycle time). The drive cycle speed can be converted to a target speed 
4-4 

versus distance travelled, however such a conversion involves the complication of tracking 

vehicle stop times separately since they necessarily occur over zero distance. 

Because the simulation itself is time-based, we consider the driver to be distance 

compensated rather than distance-based. The driver always operates in the time domain. To 

implement the distance compensated driver, the cycle time is tracked separately from simulation 

time, based on the ability of the target vehicle to meet the target speed trace. If the vehicle meets 

the target speed trace then cycle time is equivalent to simulation time as there is no difference in 

the distance travelled. If the vehicle under-performs the drive cycle then cycle time proceeds 

more slowly than simulation time, forcing the vehicle to drive for a longer amount of time in 

order to cover an equivalent distance. 

In terms of implementation, to apply distance compensation at each time step, the current 

model vehicle speed is divided by the target speed from the drive cycle. This value is integrated 

to produce the current cycle time and an updated speed target. The result is that if a simulated 

vehicle is traveling at half the drive cycle speed the simulation will progress through the drive 

cycle at half the rate. This behavior is disabled at speeds below 1 meter per second to provide 

reasonable launch behavior (which necessarily occurs over short distances), division by zero and 

to maintain vehicle stop times independent of small discrepancies in total distance travelled. 

The addition of distance compensation allows all simulated vehicles to complete an 

equivalent trip such as traveling from point A to point B. Without distance compensation, underpowered vehicles might complete the drive cycle by time but not distance and would have done 

less work than higher powered vehicles as measured in ton-miles. Distance compensation also 

allows for the variation in road grade to be kept in synchronization with the drive cycle speed 

trace. 

4.2.2.3 Powertrain Subsystem 

The engine, transmission, electric accessories, and portions of the vehicle models from 

Phase 1 GEM have been upgraded and merged into a conventional vehicle powertrain system as 

shown in Figure 4-2. The conventional powertrain system contains sub-models representing the 

engine, transmission, electric accessories, and driveline. Only conventional powertrains are 

modeled in Phase 2 GEM, and no hybrid power systems are modeled and certified with GEM. 

Rather, hybrid powertrains would be certified through powertrain dynamometer tests as 

described in Chapter 3 of the draft RIA. 
4-5 

Figure 4-2 GEM Powertrain Model 

4.2.2.3.1 Engine Subsystem 

The engine model is based on a steady-state fuel map covering all engine speed and 

torque conditions with torque curves for wide open throttle (full load) and closed throttle (no 

load). The engine fuel map in Phase 2 is the input provided by users. The engine fuel map 

features three sets of data: engine speed, torque, and fueling rate at pre-specified engine speed 

and torque intervals. In-cylinder combustion processes are not modelled. The engine speed at a 

given point in the drive cycle is calculated from the physics of the downstream speeds. The 

quantity of torque required is calculated from the driver model accelerator demand, an idle speed 

governor, and requests from the transmission during shifts. The torque request is then limited by 

the maximum torque curve. The engine torque and speed are used to interpolate a fuel rate from 

the fuel map. The engine model also includes a constant power loss to simulate mechanical 

accessories. Most vehicles run a number of accessories that are driven via mechanical power 

from the engine. Some of these accessories are necessary for the vehicle to run, like the engine 

coolant pump or power steering, while others are only used occasionally and at the operator’s 

discretion, such as the air conditioning compressor. Some heavy-duty vehicles also use Power 

Take Off (PTO) to operate auxiliary equipment, such as refuse compactors or lift forks. These 

would also be modeled as a mechanical accessory. The mechanical accessory load is proposed 

to be fixed for all vehicles based on regulatory subcategory, as shown below in Table 4-6, Table 

4-7, and Table 4-8. The actual power consumed for this loss would differ for actual vehicle 

configurations, but the agencies do not propose to allow users to change this value in GEM. If a 

manufacturer uses a hybrid powertrain for power take-off devices, it may make use of the 

hybrid-PTO test procedure. See, 40 CFR 1037.525. 
4-6 

4.2.2.3.2 Electric Subsystem 

The electric subsystem is modeled as a constant power loss. The power consumed for 

this loss is based on the vehicle subcategory. It represents the power loss associated with the 

starter, electric energy system, alternator and the electrically driven accessories. The 

simplification has a negligible impact on the fuel consumption and CO2 emissions results. The 

power losses for different vehicles are shown in tables from Table 4-5 to Table 4-10. 

4.2.2.3.3 Transmission Subsystem 

The transmission subsystem features three different variants representing the three major 

types of transmissions that are currently in use in the heavy-duty sector, which are the 

transmission types on whose performance the various proposed standards are predicated. The 

variants are manual transmission (MT), automated manual transmission (AMT), and automatic 

transmission (AT) (planetary gear set with torque converter). The different transmission models 

are built from similar components, but each features a unique control algorithm matching 

behaviors observed during vehicle testing3. 

4.2.2.3.3.1 Transmission Gear Selection 

All of the transmission models use a dynamic shift algorithm to determine the operating 

gear over the cycle3. This employs a rule based approach utilizing the engine torque curve and 

fuel map to select gears that optimize efficient engine operation and provide a torque reserve as a 

traditional transmission calibration would. 

4.2.2.3.3.2 Clutch 

The clutch model in Phase 2 GEM replaces the simplified model found in Phase 1 GEM. 

The original clutch model had no transition between the fully engaged and fully disengaged 

states and provided no commensurate torque impulse to the driveline. In the new clutch model, 

engagement and disengagement occur over time, torque is conserved across the clutch and the 

inertial effects of accelerating and decelerating the upstream inertias are captured. 

4.2.2.3.3.3 Gearbox 

The gearbox model has also been substantially revised in Phase 2 GEM to provide more 

realistic operation. The gearbox contains gear ratios and efficiencies for each gear. Each gear 

also has spin (churning) loss torques that can vary by current gear number and input speed. 

GEM assumes higher efficiency for direct drive than any other gear for manual and automated 

manual transmissions. Shifting behavior is more realistic than in Phase 1 GEM with appropriate 

delays provided by a synchronizer clutch model. This layout is most similar to a manual 

transmission, but the application for a planetary gearbox is a reasonable approximation as this 

type of gearbox can utilize a variety of topologies. A detailed description on the shifting strategy 

can be seen in reference3

. The gearbox rotational inertias are split between a common input 

inertia, common output inertia and a gear specific inertia. The common inertias represent 

rotational inertia always coupled to the input or output shafts. The gear specific inertias are 

added or removed as gears are engaged or disengaged and incur additional losses. 
4-7 

4.2.2.3.3.4 Hydrodynamic Torque Converter 

The torque converter model in Phase 2 GEM simulates a lockup-type torque converter. 

The torque multiplication and resulting engine load are calculated via torque ratio and K-factor 

curves that vary as a function of speed ratio. A base torque ratio curve is used for all simulations 

and the K-factor curve is scaled based on the engine torque curve to provide a good match 

between the torque converter stall speed and the engine’s speed at maximum torque. This 

approximation could result in some simulation differences for highly specialized vehicles 

equipped with torque converters matched to their specialized duty cycle, but for the vast majority 

of vehicles, the effect of this approximation on simulated CO2 emissions is negligible. The 

lockup behavior of the torque converter is accomplished by integrating a clutch model similar to 

the one discussed in Chapter 4.2.2.3.3.2. The torque converter model also contains a pump loss 

torque that varies with input speed to simulate the power required to operate the pump on an 

automatic transmission. 

4.2.2.3.3.5 Manual Transmission & Control 

The manual transmission (MT) is composed of the clutch and gearbox systems discussed 

above. The gearbox spin losses for a particular simulation are scaled with the vehicle class. The 

manual transmission features minimal gear specific inertia. Control of the MT is accomplished 

via a low speed clutch engagement model that gets the vehicle moving by feathering the clutch 

during launch. Shifts are accomplished by reducing the requested engine load, disengaging the 

clutch, shifting the gearbox to the new gear and reengaging the clutch. In heavier vehicles 

shifting is accomplished by double-clutching to match transmission input speeds rather than 

relying purely on the gearbox synchronizers. 

4.2.2.3.3.6 Automatic Transmission & Control 

The automatic transmission (AT) is composed of the torque converter and gearbox 

systems discussed above. The gearbox gear specific inertias and spin loss torques are higher as 

would be expected from a conventional planetary automatic transmission gearbox. The AT is 

allowed to shift under load. During upshifts and torque converter lockup the engine output 

torque is slightly reduced to minimize the resultant torque pulse encountered by decelerating the 

engine inertia. 

Torque converter lockup will be controlled by a predetermined lookup table or lockup 

strategy algorithm and at this time is not expected to be among the available user inputs with the 

possible exception of indicating the lowest gear in which lockup may occur. 

4.2.2.3.3.7 Automated Manual Transmission & Control 

The automated manual transmission (AMT) features the same clutch and gearbox models 

as the manual transmission with the addition of an inertia brake to slow the gearbox input inertia 

during upshifts. Control of the AMT during launch features a clutch feathering routine similar to 

the MT. Upshifts are handled by limiting the engine load, disengaging the clutch and shifting the 

gearbox to neutral. The inertia brake is then applied to slow the transmission input inertia before 

the gearbox engages the new gear. With the new gear engaged the clutch is reengaged and the 

engine is again allowed to operate at full load. Downshifts are handled by shifting the gearbox to 
4-8 

neutral and accelerating the gearbox input up to a speed matching the desired gear using the 

engine. 

4.2.2.3.4 Driveline 

The driveline system contains all of the components that convert the torque at the 

transmission output to force at the wheels. This includes drive shafts as well as driven axles, 

consisting of a differential, brakes and tires. Except as specified below, we are not proposing to 

change the Phase 1 GEM approach to driveline modeling. For example, both Phase 1 and Phase 

2 GEM can model all axles individually, or a single composite axle can be substituted to reduce 

simulation time. 

4.2.2.3.4.1 Driveshaft 

The driveshaft is a simple component for transferring torque while adding additional 

rotational inertia. 

4.2.2.3.4.2 Final Drive 

The final drive is modeled as a gear ratio change and an associated fixed efficiency. 

Various combinations of spin loss and efficiency, including a look-up table as a function of 

wheel speed and axle output torque, were considered. Table 4-1 below shows the comparisons 

between single value and look-up table efficiency approaches for a Class 6 box truck simulation. 

Table 4-1 Axle Efficiency Modeling and Comparisons 

 Axle Study Results (Box Truck) 

 Fixed Efficiency v. Look-up Table 

Fuel 

Economy (MPG)

Drive Cycle

Look-up Table 

MPG

Fixed 95.5% 

Efficiency MPG Difference

55 mph 11.19 11.15 0.36%

65 mph 9.13 9.06 0.77%

CARB HHDDT 8.43 8.29 1.67%

In this table, 95.5 percent is a fixed efficiency, which is intended to be used as the GEM 

predefined value. As can be seen, the difference between these two approaches is fairly small, 

and the fuel economy with the fixed efficiency is more conservative (lower) than the look-up 

table approach as far as certifications are concerned. We are open to the approach of using a 

look-up table and request comment on its use. At this time, however, a single efficiency value 
4-9 

was selected to simplify simulation and model inputs. The final drive model also adds some 

rotational inertia to the system. 

4.2.2.3.4.3 Brakes 

The brake system on each axle applies a torque to the axle proportional to the brake pedal 

position from the driver model. The scaling factor to determine this force is based on engine 

maximum torque, transmission multiplication and final drive ratio. 

4.2.2.3.4.4 Tires 

The tire component model transfers the torques and rotational inertias from upstream 

components to a force and equivalent mass that is passed to the vehicle model. This conversion 

uses the loaded tire radius and adds the tire’s rotational inertia. The force associated with the tire 

rolling resistance is also applied when the vehicle is moving. The magnitude of this force is 

determined by the coefficient of rolling resistance, vehicle static mass and current grade. 

The proposed new version of GEM would make tire size a manufacturer-specified input 

rather than use a predefined value as was done for Phase 1. Manufacturers would specify tire 

size in terms of loaded radius or perhaps tire revolutions per mile. Other than this, tires are being 

modeled the same as in Phase 1. 

4.2.2.4 Vehicle 

The vehicle system consists of the chassis, its mass and forces associated with 

aerodynamic drag, rolling resistance, and changes in road grade. The aerodynamic force is 

calculated from the air density, vehicle speed, frontal area and drag coefficient. The vehicle 

system also contains the vehicle speed integrator that computes acceleration from the input force 

and equivalent mass which is integrated to generate vehicle speed and distance traveled. 

4.2.3 Capability, Features, and Computer Resources 

GEM is a flexible simulation platform that can model a wide variety of vehicles with 

conventional powertrains from Class 2b to Class 8. The key to this flexibility is the component 

description files that can be modified or adjusted to accommodate vehicle-specific information. 

Parameters such as vehicle weight, engine fuel map, transmission gear ratios, tire radius, or axle 

ratio can all be changed as inputs by the user in this fashion. The proposed Phase 2 GEM 

predefines all drive cycles (the Transient mode, as defined by the California Air Resources 

Board (CARB) in their Highway Heavy-Duty Diesel Transient (HHDDT) cycle, and EPA GEM 

constant speed cycles at 65 mph and 55 mph, each with varying road grade). The agencies also 

pre-defined many key parameters, since those parameters are either hard to quantify due to lack 

of certified testing procedures or difficult to obtain due to proprietary barriers. Examples of 

these parameters include transmission shifting strategies, transmission gear mechanical 

efficiency, and transmission spin and pumping loss. The values selected for these parameters are 

a result of substantial testing found in the Southwest Research Institute Report4

, as well as 

confidential discussions with engine, chassis and component manufacturers. 
4-10 

During simulation the GEM tracks the status of many components and the status of all of 

the modeled losses. This information provides an energy audit to ensure the model conserves 

energy. The fuel consumed and vehicle speed traces are immediately available in the generated 

report, while the larger data set is available in a Matlab .mat file or a comma-separated values 

(CSV) file. 

4.2.3.1 GEM Executable 

The agencies propose to require that vehicle manufacturers use the Phase 2 GEM 

executable version, which does not require the use of Matlab or Simulink software, for 

demonstrating compliance with the proposed CO2 and fuel consumption standards. In this form, 

a precompiled executable format is used for certification. Its computational requirements are 

minimal. When using the minimum recommended 2 GHz processor and 4 GB of RAM, a single 

simulation should complete in 10 seconds and generate 100 MB output files. Inputs from the 

manufacturers are provided in a text file, and the results are available in a generated report. 

4.2.3.2 GEM Matlab /Simulink Model 

The Matlab/Simulink version of the GEM source code will be released for users that 

desire a more detailed look at the inner workings of the model. The system requirements for the 

Matlab /Simulink version of GEM include Matlab, Simulink and StateFlow software from 

Mathworks (version 2014a or later) and a compatible compiler.5 The recommended hardware 

for the Matlab release of GEM is 2+ GHz processor and 4 GB of RAM. The output data from a 

GEM simulation into Matlab is approximately 500 MB, depending on the simulation 

configuration and outputs selected. Simulations inside Matlab /Simulink using the source code 

take approximately 2 to 3 minutes. Although the source code is available to users, all of the 

component initialization files, control strategies and the underlying Matlab /Simulink/Stateflowbased models may not be used for determining compliance. Only the executable version can be 

used when producing official truck certification results. Also, it should be pointed out that EPA 

will not provide any technical support for the use of the GEM source code because it is beyond 

the scope of the agency’s responsibilities and resources. 

4.2.4 Peer Review of Phase 2 GEM 

The agencies conducted a peer review of Phase 2 GEM which has been submitted for 

public review in this NPRM. The peer review was conducted by an independent contractor and 

includes four reviewers. Additional details regarding the peer review and EPA’s responses to the 

peer review comments can be found in the docket2. 

The agencies also met with and received comments from the Engine Manufacturers 

Association, along with other industry stakeholders, during the development of Phase 2 GEM, 

which identified some areas of concern with GEM. In response, the agencies made changes as 

necessary. 
4-11 

4.3 Validation of Phase 2 GEM Simulations 

This chapter presents the results of an engineering evaluation of the ability of the 

computer model in GEM to accurately simulate actual engine and vehicle performance. Note 

that this version differs from the compliance version in that it was possible to use actual values 

for vehicle parameters that are locked in the compliance version of GEM. For example, 

validations used actual vehicle curb weights. They also incorporated actual shift strategies where 

available. This is appropriate because the purpose of the validations was to evaluate the 

engineering basis of the model, rather than to evaluate whether the policy of locking certain 

parameters is appropriate. 

4.3.1 Experimental Tests for GEM Validation 

Working with Southwest Research Institute (SwRI), EPA has invested significantly in 

various truck tests in order to collect data to validate Phase 2 GEM. The technical research 

workshop held at SwRI, San Antonio, TX, December 10-11 details all of these tests6

. The 

following truck tests were carried out by SwRI for the purpose of model validation: 

• Class 6 Kenworth T270 vocational box truck with AT 

• Class 6 Ford F-650 vocational tow truck with AT 

• Class 8 Kenworth T700 line haul truck with AMT 

• Class 8 New Flyer refuse truck with AT 

The key specifications for those trucks are listed in Table 4-2. 

Table 4-2 Vehicle Specifications of Heavy-Duty Trucks Tested at Southwest Research Institute 

Truck 2013 Kenworth 

T700 

2012 Kenworth 

T270 

2011 Ford 

F-650 Tow truck 

2012 New Flyer 

Refuse 

Engine /Rated 

Power (hp) 

Cummins ISX 

455 

Cummins ISB 

240 

Cummins 

ISB 270 

Cummins 

ISL 345 

Transmission Eaton 

F016E310C-LAS 

Allison 

2100 

Allison 

2200 RDS 

Eaton 

FO16E310C-LA 

In order to fully validate the model, each truck was tested over six different driving 

cycles including regulatory cycles and non-regulatory cycles. They are the EPA GEM 55mph 

(with and without grade), EPA GEM 65mph (with and without grade), the transient portion of 

the CARB Heavy-Duty Diesel Truck (HDDT) cycle, the World Harmonized Vehicle Cycle 

(WHVC), the High-efficiency Truck Users Forum (HTUF) Class 6 Parcel Delivery Cycle, and 

the National Renewable Energy Laboratory (NREL) Combined International Local and 

Commuter Cycle (CILCC) cycle (which is a utility vehicle cycle). The inclusion of driving 

cycles in addition to those used for Phase 1 certification was done to expand the range of 

operation. Some of the cycles are very aggressive (especially for Class 8 trucks), such as the 

CILCC and Parcel Delivery cycles, with many stops and rapid accelerations. EPA evaluated the 
4-12 

results from these additional cycles to improve the modeling capability and its response to highly 

transient conditions, thus providing additional confidence in model fidelity. All trucks were 

tested on a chassis dynamometer. In addition, the engine and transmission from the F-650 tow 

truck were tested in a powertrain dynamometer cell. More information on the vehicle chassis 

and powertrain dynamometer setups and tests can found in the Southwest Research Institute 

Report7

. 

Considering that procurement of trucks for model validations would be time consuming 

and expensive, EPA developed a comprehensive approach to quantify variants of vehicles in 

order to maximize testing efficiency. This was done by varying aerodynamic drag and tire 

rolling resistance, as well as using weights to simulate different trucks, affording coverage of a 

wide range of vehicles. For tractors, varying these parameters also reflects the effects of pulling 

different types of trailers, which would impact the combined drag, rolling resistance, and weight 

of the vehicle. In this sense, this simultaneously provides validation data for both tractors and 

trailers. 

Three vehicles were selected for this portion of the test program and they are: the 

Kenworth T270 box truck, the Kenworth T700 truck with a 53 foot box trailer, and the F-650 

tow truck. The first two trucks were tested on a chassis dynamometer, while the third one was 

tested on a powertrain system dynamometer. A total of six drive cycles were tested: EPA GEM 

55 mph, EPA GEM 65 mph, CARB HHDDT, WHVC, NREL CILCC, and HTUF Parcel 

Delivery cycle. An additional set of six tests were run for each driving cycle listed above to 

evaluate the impact of various vehicle characteristics on CO2 emissions and fuel efficiency. The 

characteristics of the six test modifications are listed below: 

1. Adding 800 to 1,000 pounds to the vehicle’s tare weight depending on the vehicle class 

2. Adding 15 percent to the vehicle-specific constant value representing the vehicle’s 

frictional load to simulate higher rolling resistance tires 

3. Reducing the vehicle-specific constant value representing the vehicle’s frictional load by 

15 percent to simulate lower rolling resistance tires 

4. Increasing the vehicle-specific coefficient representing aerodynamic effects by 15 percent 

to simulate a higher aerodynamic drag vehicle 

5. Decreasing the vehicle-specific coefficient representing aerodynamic effects by 15 

percent to simulate a lower aerodynamic drag vehicle 

6. Running a new set of road load coefficients, to represent a vehicle configuration 

optimized for fuel efficiency for each vehicle that was tested, which consists of the 

lowest rolling resistance as well as the lowest aerodynamic drag coefficient 

Three valid replicate tests were conducted for each vehicle and characteristic over each 

driving cycle. A valid replicate was defined as a successful test run in which all data was 

collected without regeneration of the diesel particulate filter. The following parameters were 

measured or recorded during all tests: 
4-13 

 Vehicle speed as a function of time 

 Engine fuel rate as a function of time 

 Engine speed as a function of time 

 Gear number as a function of time 

 Engine load (Nm) as a function of time 

 Emissions (NOX, HC, CO, CO2, N2O, CH4) as a function of time in g/s 

 Measured cycle fuel economy (MPG) and emissions (NOX, HC, CO, CO2, PM, N2O, 

CH4) 

 Grade as function of time for the cycle with road grade if tested 

4.3.2 Results of the GEM Validations 

Taking into account all of the vehicles and test configurations mentioned above, more than 

130 vehicle variants were tested, allowing GEM to be comprehensively validated against a very 

well-defined and robust set of test data. 

The results displayed in through Figure 4-3 through Figure 4-6 show the of comparison 

between the GEM simulations and testing data of the Class 8 Kenworth T700 truck, Class 6 Ford 

F-650 tow truck, Class 6 Kenworth T270 box truck, and New Flyer refuse truck respectively. In 

all figures shown here for 55 and 65mph cycles, road grade is not included. 
4-14 

Figure 4-3 GEM Validation against Class 8 Kenworth T700 Truck chassis tests 

Figure 4-4 GEM validation against Class 6 Ford F-650 tow truck powertrain tests 

2

3

4

5

6

7

8

9

10

11

2 4 6 8 10

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1

1:1‐5%

1:1+5%

6

7

8

9

10

11

12

13

14

15

16

6 8 10 12 14 16

GEM [MPG]

Powertrain Test [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1‐5%

1:1+5%

1:1
4-15 

Figure 4-5 GEM validation against Class 6 Kenworth T270 box truck chassis tests 

Figure 4-6 GEM validation against New Flyer refuse truck chassis tests 

5

6

7

8

9

10

11

12

13

14

5 7 9 11 13

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1

1:1‐5%

1:1+5%

2

3

4

5

6

7

234567

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Utility

WHVC

1:1

1:1‐5%

1:1+5%
4-16 

A review of the data indicates that there is good agreement between the GEM simulations 

and testing data obtained over the wide range of vehicles and conditions. In general, the 

accuracy of the model simulations against the testing data is very well controlled with an error of 

less than ±5 percent, although there are a few outliers of the transient simulation cases for Class 

8 trucks due to the nature of the high variability of chassis dynamometer tests. The range of 

vehicles tested and simulated included vehicles that varied in terms of all of the proposed 

regulatory inputs. Thus, the agencies believe that the accuracy of GEM is sufficient to simulate 

the benefits of the range technologies that form the basis of the proposed standards. 

Figure 4-7 shows the overall comparison between the simulation and test results when 

combining all of the testing and simulation into one figure. Overall, the simulation and test result 

correlate well. 

Figure 4-7 Comparison of model simulations and chassis test results for the 130 vehicle test configurations 

While it is encouraging that GEM accurately simulates overall vehicle performance in an 

absolute sense, it is actually more important that GEM is accurate in relative comparisons. This 

is because the agencies used the same version of GEM to calculate the stringency of the 

proposed standards as was used to evaluate baseline performance for this rulemaking. The 

ultimate purpose of this new version of GEM will be to evaluate changes or additions in 

technology, and compliance is demonstrated on a relative basis to the numerical standards that 

were also derived from GEM. The importance of relative comparisons can be further explained 

with the following simplified example. 

2

4

6

8

10

12

14

16

2 4 6 8 10 12 14 16

GEM (MPG)

Experimental Tests  (MPG)

1:1

1:1+5%

1:1‐5%

Results
4-17 

Assume you have two simulation models: one that says a baseline vehicle with Bin 3 

aerodynamics and a conventional automatic transmission would be 90 g/ton-mile, and another 

that said the same vehicle would be 95 g/ton-mile. Assume also that there was a similar vehicle 

that was the basis of the new standards that had Bin 4 aerodynamics and a dual-clutch 

transmission. If both models simulated the second vehicle as being 10 g/ton-mile better than the 

baseline vehicle, then the models would work equally well for compliance as long as they were 

also used to set the standards. With the first model, we would set the standard at 80 g/tonmile. And with the second, we would set the standard at 85 g/ton-mile. In both cases, 

manufacturers adding Bin 4 aerodynamics and dual-clutch transmissions would meet the 

standard. In other words, the two models would be equivalent in terms of measuring the effect 

of the change in technology on emissions, even though the absolute values were significantly 

different. 

As is shown below, GEM indeed performs better in this relative sense. The results from 

the T700 and T270 trucks, and powertrain tests for the F-650 tow truck shown in Figure 4-3 

through Figure 4-5, can also be presented in a format to evaluate GEM’s ability to measure the 

relative impact of a technology. Table 4-3 shows an example of relative comparisons to 

illustrate how the relative comparison is done with the T700 truck. For simplicity, only the 

results from the Class 8 T700 tractor on the 65mph cycle are shown in this table. The column 

labeled as Chassis Test Fuel Economy Result (MPG) shows the testing results, while the column 

with GEM Fuel Economy Result (MPG) shows the GEM simulation results. Each row 

represents a single change to the vehicle configuration, relative to the baseline case. The “Delta” 

in the last column is the difference between the impact of the vehicle configuration change as 

measured on the chassis dynamometer and simulated in GEM (which sometimes differs from the 

apparent delta due to rounding). For example, the row with the “+15 percent Crr” variable 

compares GEM results to chassis test results for a vehicle that is the same as the baseline vehicle 

except that it has tires with a coefficient of rolling resistance 15 percent higher than the baseline 

vehicle. For this example, chassis testing indicates the change in rolling resistance increases fuel 

consumption for this cycle by 3.9 percent, while GEM predicts it would increase by 4.9 percent, 

but the delta difference is only 1.0 percent as shown in the last column. 

Table 4-3 Sample of Relative Comparisons for T700 Truck 

Drive

Cycle

Vehicle

Attribute

Variables

Chassis

Test Fuel

Economy

Result

(MPG)

GEM Fuel

Economy

Result

(MPG)

Impact of

Variable

on Chassis

Test Result

Impact of

Variable on

GEM

Simulation

Result Delta

65 mph Baseline 6.84 6.61 0.0% 0.0% 0.0%

65 mph +907 kg 6.86 6.55  ‐0.3% 0.9%  ‐1.2%

65 mph +15% Crr 6.57 6.28 3.9% 4.9%  ‐1.0%

65 mph  ‐15% Crr 7.27 6.96  ‐6.3%  ‐5.3%  ‐1.0%

65 mph +15% Cd 6.31 6.05 7.7% 8.4%  ‐0.7%

65 mph  ‐15% Cd 7.63 7.25  ‐11.5%  ‐9.8%  ‐1.8%

65 mph

Optimized

Package 8.08 7.65  ‐18.1%  ‐15.8%  ‐2.3%
4-18 

The same methodology was applied to all other cases including three different trucks, and 

six driving cycles and six vehicle variables. The differences between the chassis test and GEM 

results from all of these comparisons are plotted in Figure 4-8. 

Figure 4-8 Relative comparisons between tests and GEM results 

In Figure 4-8, the horizontal axis represents the test number of each truck. It can be seen 

that the majority of cases have less than ±2-3 percent difference. Excellent correlation was 

obtained between the F-650 tow truck powertrain test data and GEM results, where all of the 

comparisons had an error less than ±2 percent. However, a few outliers with an error greater 

than 3 percent can be found in the Class 8 T700 and Class 6 T270 data. This is not unexpected 

since all the tests for both T700 and T270 trucks were conducted on the chassis dynamometer, 

while the tests for F-650 tow truck were conducted in the powertrain dynamometer cell8

. The 

recent findings from the SwRI program sponsored by EPA show that chassis dynamometer tests 

have higher variability than powertrain tests, as discussed below9

. 

The driver behavior in the chassis dynamometer is one of the biggest contributors to the 

variability. This becomes even more an issue when a driver drives a very heavy vehicle like a 

Class 8 truck to follow a targeted vehicle speed trace in the chassis dynamometer cell, 

specifically for those highly transient cycles, such as CARB HHDDT, NREL CILCC, and HTUF 

Class 6 Parcel Delivery cycles. In contrast, a robot driver is used in the powertrain test for F-650 

truck tests, thus removing this major source of variability. In addition, many other testing 

conditions, such as air temperature and coolant temperature, can be more stably controlled 

during powertrain than in chassis dynamometer tests. The findings also include many other 

sources of variability in the chassis dynamometer tests, such as tire temperature, thermal 

management during idle, and transmission oil temperature.9,10 Because of the many 

‐5%

‐4%

‐3%

‐2%

‐1%

0%

1%

2%

3%

4%

5%

0 10 20 30 40 50

Relative Error between GEM and Test

T700

T270

F650
4-19 

uncertainties due to the variability of chassis dynamometer testing, it has been very challenging 

to match GEM results with chassis dynamometer test results in the same range of accuracy as the 

comparisons against the powertrain tests, specifically for those highly transient cycles. In some 

cases, it is hard to quantify which method, vehicle simulation or chassis dynamometer test, is 

more accurate. Therefore, considering the favorable comparison between the powertrain tests 

and GEM simulation results, it is fair to say that the overall accuracy of the GEM to represent the 

relative changes in fuel economy of a real world vehicle should be in the range of ±2-3 percent. 

Figure 4-7 and Figure 4-8, respectively, show the GEM accuracy against over 130 

vehicle variants on an absolute and a relative basis. All are done in a total vehicle configuration, 

which includes all vehicle components, such as engine, transmission, and driveline. Since 

certification would be done in a total vehicle form for CO2 emissions and fuel efficiency, these 

types of comparisons are the most important because they demonstrate that GEM is capable of 

capturing the impact on the total vehicle CO2 emissions and fuel consumption due to technology 

improvement of individual components. In order to show the fidelity of GEM in modeling 

individual components in a more detailed level, the comparisons for the key components must be 

demonstrated as well. Displayed in Figure 4-9 through Figure 4-11 are the comparisons of 

engine speed, fuel rate, and transmission gear numbers as function of time over the CARB 

HHDDT cycle for Class 8 T700 truck. 

Figure 4-9 Engine speed comparisons over the WHVC for a Class 8 T700 truck 

0 100 200 300 400 500 600 700

400

600

800

1000

1200

1400

1600

1800

2000

2200

Time (s)

Engine Speed (RPM)

GEM

Test Data
4-20 

Figure 4-10 Engine fuel rate comparisons over the WHVC for a Class 8 T700 truck 

Figure 4-11 Transmission gear comparisons over the WHVC for a Class 8 T700 truck 

0 100 200 300 400 500 600 700

0

5

10

15

20

25

Time (s)

Fuel Rate (g/s)

GEM

Test Data

0 100 200 300 400 500 600 700

0

1

2

3

4

5

6

7

8

9

10

Time (s)

Transmission Gear

GEM

Test Data
4-21 

As shown in Figure 4-9 through Figure 4-11, reasonably good comparisons between 

GEM simulations and tests are obtained. GEM basically can capture detailed behaviors of the 

engine and transmission. To further provide more complete picture on the GEM validations, 

Figure 4-12 and Figure 4-13 show another set of examples for an F-650 tow truck. Shown in 

these two figures are the comparisons of engine speed and transmission output shaft torque over 

the World Harmonized Vehicle Cycle (WHVC) between powertrain dynamometer tests and 

GEM results. As can be seen from these two figures, reasonable comparisons are again obtained 

between GEM and actual test results. 

Figure 4-12 Engine speed comparisons over the WHVC for an F-650 tow truck 

0

500

1000

1500

2000

2500

0 500 1000 1500 2000

Engine Speed (RPM)

Time (s)

Test

GEM
4-22 

Figure 4-13 Transmission output torque comparisons over the WHVC for an F-650 tow truck 

4.4 EPA and NHTSA HD Vehicle Compliance Model 

As described earlier, GEM is a computer model that simulates vehicle operation to 

predict CO2 emissions and fuel consumption for a wide variety of heavy-duty vehicles. This 

section describes how that computer model is used as a compliance tool to evaluate vehicle 

performance relative to the applicable standards. The engineering evaluation of GEM discussed 

in Chapter 4.3 was not limited by computing time and presumes all inputs to be accurate. 

However, using GEM as a compliance tool requires some simplification of the model. It also 

requires the elimination of user inputs that cannot be verified by the agencies. As discussed 

below, such simplifications are being proposed for Phase 2, but to a lesser degree than was done 

for Phase 1. 

The Phase 2 GEM of EPA and NHTSA’s vehicle compliance simulation model is similar 

to Phase 1 GEM in many respects. However, it differs from the Phase 1 version in two major 

aspects. The first involves the significant improvements described in Chapter of 4.2.1. Second, 

Phase 2 GEM provides users the opportunity to enter additional vehicle and engine parameters 

for the actual vehicle being simulated. As noted above, Phase 1 GEM only allows a maximum 

of five user defined inputs for tractors. These are: the aerodynamic drag coefficient, tire rolling 

resistance, vehicle speed limiter, weight reduction, and idle reduction. For vocational vehicles 

there is only one user defined input: tire rolling resistance. In contrast, the proposed Phase 2 

GEM allows the user to input many more engine and vehicle parameters, including most of those 

that have the biggest impact on emissions. In particular, it allows vehicle manufacturers to input 

their own engine fuel maps. Key driveline parameters, such as transmission gear number versus 

gear ratio, axle ratio, and tire rolling radius, are also part of the manufacturer inputs. 

‐1000

‐500

0

500

1000

1500

2000

0 500 1000 1500 2000

Transmission Ouput Torque (NM)

Time (s)

Test

GEM
4-23 

There are still some GEM input parameters that are proposed to be pre-defined by the 

agencies. For some, such as shifting strategy, this is due to the fact that the parameters are hard 

to measure and quantify due to the lack of well-defined test procedures. For others, the 

manufacturers consider the parameter values to be proprietary and are reluctant to share the 

information with other parties. Examples of those items include the transmission gear shifting 

strategy table and gear mechanical efficiency. The modeling parameters associated with torque 

converters for automatic transmission would be also pre-defined by the agencies. The inertias of 

all rotational parts, vehicle weights and accessory power losses are also default parameters 

defined by the agencies. Finally, in order to have a consistent basis for the standards, the vehicle 

weights and payloads are predefined by vehicle class and duty cycle. 

Table 4-6 and Table 4-7 list all of the proposed GEM input parameters for tractors and 

Table 4-8 through Table 4-10 list the predefined parameters for vocational vehicles. These 

tables also include weighting factors for each driving cycle for the determination of composite 

CO2 in g/ton-mile. 

It is important to note that, for many of these parameters, publicly available information 

on the values for current and future vehicles is limited. Manufacturers have provided values to 

the agencies, but have generally identified them as confidential business information. 

Nevertheless, we have used this information to inform our estimation of appropriate default 

values. 

4.4.1 Predefined GEM Values 

4.4.1.1 Transmissions 

One of the major changes in Phase 2 GEM is to allow manufacturers to enter their 

transmission gear ratio versus gear number. When entering this information, manufacturers also 

have an option to select the type of transmission, which is either manual, automated manual or 

automatic with a torque converter. Mechanical efficiency for each gear is pre-defined by the 

agencies as shown in Table 4-5 through Table 4-10. Pre-specification was required due to the 

lack of a reliable, repeatable, and cost-effective test procedure. 

One of the areas that required significant development work was the transmission shift 

strategy for use in the compliance tool. This was required because transmission suppliers have 

been reluctant to provide their shifting strategies to vehicle manufacturers for vehicle 

certification due to their concern over protecting intellectual property. The shifting strategy in 

the proposed Phase 2 GEM includes the agencies’ internally developed automatic shift 

algorithm3. The impact of the use of the agencies’ default transmission shifting as opposed to 

using manufacturers’ shifting strategies has been evaluated and the results are presented in Table 

4-4. 
4-24 

Table 4-4 Impact of the Agencies’ Default Shifting Strategy Compared to Transmission Manufacturers’ 

Strategies 

Truck Cycle 

Manufacturer 

Shift Strategy 

Fuel Economy 

(MPG) 

EPA Defined 

Shift Strategy 

Fuel Economy 

(MPG) 

Difference (%) 

T270 Box Truck GEM 55 mph 11.36 11.36 0 

T270 Box Truck GEM 65 mph 9.24 9.24 0 

T270 Box Truck CARB HHDDT 8.39 8.44 0.60 

F-650 Tow Truck GEM 55 mph 14.49 14.57 0.55 

F-650 Tow Truck GEM 65 mph 12.03 12.14 0.91 

F-650 Tow Truck CARB HHDDT 10.32 10.72 3.88 

T700 Class 8 Truck GEM 55 mph 7.96 7.96 0 

T700 Class 8 Truck GEM 65 mph 6.59 6.59 0 

T700 Class 8 Truck CARB HHDDT 3.9 3.94 1.02 

The Manufacturer column in Table 4-4 represents the simulation results using the shifting 

tables and strategies provided by the transmission manufacturer, while the EPA column 

represents the results using EPA’s default shift algorithm. The transmission manufacturer and 

EPA fuel economy results are essentially the same despite the different shifting strategies. There 

is a noticeable difference in fuel economy results for the CARB HHDDT cycle, but it is still 

relatively small. It should be pointed out that in the case of 55 and 65 mph cruise speed cycles, 

there are few, if any, shifts. 

Phase 2 GEM includes three types of transmissions as discussed in Chapter 4.2.2.3.3. 

They are manual transmissions (MT), automated manual transmissions (AMT) and automatic 

transmissions (AT). Due to lack of test data for other types of transmissions, GEM was not able 

to be validated in time against these three cases: 

1. Dual clutch transmission (DCT) 

2. Dual clutch transmission with a torque converter 

3. Allison TC-10 automatic transmission 

The agencies are proposing use of AMT to model case 1; use of AT to model case 2, and 

use of AT to model case 3. The manufacturers would still have the option to use powertrain 

dynamometer tests to quantify the benefits of these or any other special transmissions, rather 

than use the pre-defined values. The detailed test procedure of the powertrain dynamometer tests 

are described in Chapter 3 of the draft RIA 

4.4.1.2 Axles 

Axle ratios for all model sub-categories would be user defined. Default axle mechanical 

efficiency is pre-defined by the agencies. Based on comments that the agencies receive related 

to this NPRM, we may adopt provisions in the final rule that would allow manufacturers to 

override the default mechanical efficiency and input their own values; however, the inputs would 

be determined by using the prescribed test procedure described by Chapter 3.8 of the draft RIA. 
4-25 

Typical base Class 8 tractors have one steer and two drive axles, while typical base Class 

7 tractors have one steer and one drive axle. The trailer used for both Class 7 and Class 8 

tractors in simulation modeling has two axles. All HHD vocational vehicle categories have 3 

axles, while all others only have two. 

4.4.1.3 Weights 

It is assumed that the vehicle unloaded weight will vary by vehicle subcategory. Taking 

tractors as an example, the total weight ranges from 65,500 to 70,500 lbs, while for Class 7 

tractors weight ranges from 46,500 to 50,000 lbs. The payload capacity varies as shown in Table 

4-5 through Table 4-10. The development of these weights is discussed in Chapter 3 of the draft 

RIA. 

4.4.1.4 Inertia 

All of the inertias for rotational parts, including engine, transmission and axle, are predefined based on a combination of the agencies’ engineering judgment and confidential business 

information from OEMs. The default inertia values were used during GEM validation against 

respective trucks and they will be used as the default values for all of the vehicles certified using 

GEM. Thus, the vehicle OEM will not have flexibility to enter their own inertias. 

4.4.1.5 Accessory Load 

The agencies are assuming that all trucks, including tractors and vocational vehicles, 

carry a constant electrical load as well as mechanical load when operated over the agencies’ 

certification drive cycles. Those agency derived values were used when GEM validations were 

carried out against experimentally derived data from SwRI. All of the default, pre-specified 

values are shown in Table 4-5 through Table 4-10, and would be used as the default values for 

all vehicle certification. 

4.4.1.6 Tires 

 Tire radius is a user defined input; however, the agencies do provide default values for 

all vehicle sub-categories. Static loaded tire radius is used in GEM for all simulations for every 

combination tractor and the default value can be overridden by the vehicle OEM. 

The trailer tire coefficient of rolling resistance (Crr, trailer tires) assumes a constant value 

for all trailer tires. This value was developed through tire testing performed by the SmartWay 

Transport Partnership.11 

4.4.1.7 Idle Cycle and Its Modeling 

As described in Chapter 3.4.2 of this draft RIA, we are proposing the addition of an idleonly cycle to determine both fuel consumption and CO2 emissions when a vocational vehicle is 

idling, and to recognize technologies that either reduce the fuel consumption rate or shut the 

engine off (and restart) during short-term idle events during the workday. Based on user inputs, 

GEM would calculate CO2 emissions and fuel consumption at both zero torque (neutral idle) and 
4-26 

with torque set to Curb-Idle Transmission Torque (as defined in 40 CFR 1065.510(f)(4) for 

variable speed engines) for use in the CO2 emission calculation in 40 CFR 1037.510(b). We are 

also proposing that GEM would calculate reduced CO2 and fueling for stop-start systems, based 

on an assumption that the effectiveness would represent a 90 percent reduction of the emissions 

that would occur if the vehicle had operated at Curb-Idle Transmission Torque over this cycle. 

This cycle is proposed to be applicable for all subcategories of vocational vehicles (HHD, MHD 

and LHD) using any of the proposed composite duty cycles (Regional, Multi-Purpose, or Urban 

composite duty cycles). More can be seen in Chapter 3.4.2.3 about the idle cycle. Chapter 4.5 

discusses how these idle technologies are modeled as part of technology improvements that are 

recognized in GEM. 

4.4.1.8 Transient Adjustment Factor 

As described in Chapter 2, fuel consumption during transient engine operation typically 

is higher than during steady-state operation. The difference can vary significantly, but the trend 

is generally consistent. Because the GEM simulation relies on steady-state fuel maps to predict 

emissions for all the cycles, including the transient cycle, the agencies are proposing to apply a 

transient adjustment to GEM results for the transient cycle. 

4.4.1.8.1 Transient Engine Testing 

To evaluate the need for a transient adjustment factor, we compared the results from 28 

individual engine dynamometer tests. Three different engines were used to generate this data, 

and these engines were produced by two different engine manufacturers. One engine was tested 

at three different power ratings (13 liters at 410, 450 & 475 hp) and the other engines ranged 

from medium heavy-duty (6.7 liters, 300 hp) to heavy heavy-duty (15 liters, 455 hp) service 

classes. For each engine and rating our proposed steady-state engine dynamometer test 

procedure was conducted to generate the data table to represent that particular engine in GEM. 

Next, GEM simulated various vehicles in which the engine could be installed. For each of the 

GEM duty cycles we are proposing, namely the urban local (CARB HHDDT), urban highway 

with road grade (GEM 55 mph), and rural highway with road grade (GEM 65 mph) duty cycles, 

we determined the GEM result for each vehicle configuration, and we saved the engine output 

shaft speed and torque information that GEM utilized to interpolate the steady-state engine data 

table for each vehicle configuration We then had this same engine output shaft speed and torque 

information programmed into the engine dynamometer controller, and we had each engine 

perform the same duty cycles that GEM demanded of the simulated version of the engine. We 

then compared the GEM interpolated results to the measured engine dynamometer results. We 

concluded that for the 55 mph and 65 mph duty cycles, GEM’s interpolation of the steady-state 

data tables was sufficiently accurate versus the measured results. This is reasonable because 

even with changes in road grade, the 55 mph and 65 mph duty cycles do not demand rapid 

changes in engine speed or load. They are nearly steady-state, just like the data tables 

themselves. However, for the CARB HHDDT cycle, we observed a consistent bias, where GEM 

consistently under-predicted fuel consumption and CO2 emissions. This low bias over the 28 

engine tests ranged from 4.2 percent low to 7.8 percent low. When we aggregated these results 

by engine, the results were between 5 and 6 percent. We understand that use of an engine 

dynamometer test with GEM inputs of torque and speed to quantify the impact of using a steady 

state engine fuel map would not be perfect, since this approach would not consider the 
4-27 

interaction of the driver model of GEM in response to individual vehicle component dynamics. 

Also, the input torque and speed values calculated in GEM and input into the engine 

dynamometer are only targeted values and they are not the real measured engine torque and 

speed. Since the engine may not be able to follow the targeted speed trace, there may be some 

discrepancy compared to the vehicle performance obtained from GEM. Accessory loads 

between the engine test and GEM simulation are also different. In spite of these differences, it is 

a common fact that steady state operation is different from transient operation, specifically in a 

diesel engine. 

The most significant difference between steady state and transient behavior is the smoke 

control during acceleration. Diesel engines must limit the fueling in order to prevent smoke 

during rapid acceleration. In contrast, there is no such issue during steady state mapping. 

Furthermore, all modern diesel engines use a fueling cut-off technique to shut off the fueling 

during deceleration based on a manufacturer-defined set of conditions. In addition, thermal 

management is another major factor to that may create a difference between steady state and 

transient fueling. The aftertreatment system is very sensitive to exhaust temperature in order to 

maintain optimal performance of selective catalytic reduction devices. Post fueling must be 

injected into the exhaust stream once the exhaust temperature is below certain criteria, typically 

in the range of 200 degrees Celsius. In steady state fueling mapping, the engine always follows 

the defined testing procedure; thus, the engine runs hotter even at light loads than in a transient 

condition at the same speed and torque, thus thermal management may not even kick in. 

Because of these differences, engine manufacturers typically include at least two distinguished 

engine calibrations into the engine control unit – one for typically on-highway operation, and the 

other for transient operation, such as urban and mountainous areas. 

We are confident that this low bias in GEM results using a steady state fuel map would 

continue to exist well into the future if we were to test additional engines. However, with the 

range of the results that we have generated so far we are somewhat less confident in proposing a 

single numerical value to correct for this bias over the CARB HHDDT drive cycle. The 

procedure used to derive this proposed correction adjustment factor, although reasonable, may 

still need more refinement. 

4.4.1.8.2 Proposed Value for the Transient Adjustment Factor 

Based on the limited testing that was performed for this analysis, the agencies are 

proposing a transient adjustment factor of 1.05. This means that the simulated transient cycle 

GEM results (that are generated based on steady-state fuel maps) would be multiplied by 1.05 

before being output from the GEM compliance tool. The higher output value would be the 

official GEM result. 

This 1.05 factor reflects the engine at the lower end of the range for the three engines, 

with the other two engines indicating values of 1.06 or 1.07 might be appropriate. The agencies 

are proposing the lower value because we do not want to allow manufacturers to gain an 

advantage from powertrain testing without making some improvement to the powertrain. The 

test results indicate that an adjustment of 1.07 would allow the ISX engine (which showed a 

lower transient difference) to show a 2 percent improvement without making any improvements. 

The agencies recognize that there is significant uncertainty in the proposed value and will 
4-28 

continue to evaluate the adjustment. The agencies would likely consider values in the range of 

1.00 to 1.07. 

4.4.1.9 Tractor Tables 

Table 4-5 through Table 4-7 display the predefined GEM parameters proposed for the 

Phase 2 tractor compliance model. The predefined parameters were developed using the same 

methodology used in Phase 1. Many of the parameters are based on the vehicles EPA selected to 

test at SwRI and are considered to reasonably represent the fleet in their respective categories. 

For example, the transmission gear ratio, axle ratio, tire diameters, and all accessory losses used 

for all tractors shown in these three tables are from the Kenworth T700 truck tested at SwRI. All 

of the other predefined parameters, such as the engine power rating, vehicle weight, payload, 

follow the Phase 1 structure. The gear mechanical efficiency as well as axle mechanical 

efficiency was developed based on several verbal communications from stakeholders. For 

further detail regarding how these parameters are chosen and used in GEM see Chapters 4.4.3 to 

4.4.9. 

Table 4-5 Class 8 Combination Tractor Sleeper Cab Predefined Modeling Parameters 

Regulatory Class 

Class 8 Combination Class 8 Combination Class 8 Combination 

Sleeper Cab - High 

Roof 

Sleeper Cab - Mid 

Roof Sleeper Cab - Low Roof 

Gearbox Efficiency 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other 

gears 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears 

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 31978 30277 30390 

Number of Axles 5 5 5 

Default Axle Configuration 6x4 6x4 6x4 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental Air Temperature (°C) 25 25 25 

Payload (tons) 19 19 19 

Weight Reduction (lbs) 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Tire Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

Drive Cycles & Weightings: 

CARB HHDDT 0.05 0.05 0.05 

GEM 55 mph 0.09 0.09 0.09 

GEM 65 mph 0.86 0.86 0.86 
4-29 

Table 4-6 Class 8 Combination Tractor Day Cab Predefined Modeling Parameters 

Regulatory Subcategory 

Class 8 Combination Class 8 Combination Class 8 Combination 

Day Cab - High Roof Day Cab - Mid Roof Day Cab - Low Roof 

Gearbox Efficiency 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears 

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 31297 29529 29710 

Number of Axles 5 5 5 

Default Axle Configuration 6x4 6x4 6x4 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental air temperature (°C) 25 25 25 

Payload (tons) 19 19 19 

Weight Reduction (lbs) 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Tire Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

Drive Cycles & Weightings: 

CARB HHDDT 0.19 0.19 0.19 

GEM 55 mph 0.17 0.17 0.17 

GEM 65 mph 0.64 0.64 0.64 
4-30 

Table 4-7 Class 7 Combination Tractor Predefined Modeling Parameters 

Regulatory Subcategory 

Class 7 Combination Class 7 Combination Class 7 Combination 

Day Cab - High Roof Day Cab - Mid Roof Day Cab - Low Roof 

Gearbox Efficiency 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears 

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 22679 20910 21091 

Axle Base 4 4 4 

Default Axle Configuration 4x2 4x2 4x2 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental air temperature (°C) 25 25 25 

Payload (tons) 12.5 12.5 12.5 

Weight Reduction (lbs) 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Add 1/3*weight 

reduction to Payload 

tons 

Tire Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr 

Drive Cycles & Weightings: 

CARB HHDDT 0.19 0.19 0.19 

GEM 55 mph 0.17 0.17 0.17 

GEM 65 mph 0.64 0.64 0.64 

4.4.1.10 Vocational Tables 

Table 4-8 through Table 4-10 display the predefined GEM parameters proposed for use 

for the vocational vehicle compliance model. Many of the parameters are based on the vehicles 

EPA selected to test at SwRI and are considered to reasonably represent the fleet in their 

respective categories. For example, the Kenworth T270 truck and Ford F-650 tow truck are used 

as vehicles to represent the MHD and LHD vocational vehicle fleet, while the Kenworth T700 

and New Flyer refuse trucks are used to represent the fleet of HHD vocational vehicles. With 

those vehicles as reference, it helps to determine the type of transmission and its gear ratio, tire 

diameters, and all accessory losses used for all vocational vehicles shown in these three tables. 

Tire radius and axle ratios were selected, using good engineering judgment and stakeholder 

input, to reflect reasonable final drive ratios to match with our modeled transmissions. With the 

exception of the Multi-purpose and Urban HHD vehicles, the engine power rating is the same as 

in Phase 1. For these two subcategories, the agencies selected 11L-345 hp engines because this 

is a more typical power rating for vehicles that are not long haul. Other parameters, such as the 

engine power rating, vehicle weight, payload, weight reduction, tire rolling resistance, frontal 

area, and axle base, etc. follow the Phase 1 structure. The gear mechanical efficiency as well as 

axle mechanical efficiency is selected based on the inputs from stakeholders. The weighting of 

steer tire Crr and drive tire Crr is different than in Phase 1 to better reflect the weight distribution 

over the steer and drive axles. The assignment of 50 percent of reduced weight back to payload 
4-31 

is different than in Phase 1 and not the same as for tractors. See the draft RIA Chapter 2 for 

details. Chapter 4.4.3 to 4.4.9 explains how these parameters are used in GEM. 

The agencies propose to expand the number of vocational subcategories from three (in 

Phase 1) to nine (in Phase 2). It can be seen from Table 4-8 through Table 4-10, the agencies are 

also proposing to add an idle cycle for vocational vehicles to the duty cycles used in Phase 1 

certification. 

Table 4-8 Vocational HHD Vehicle Predefined Modeling Parameters 

Regulatory Subcategory 

HHD HHD HHD 

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency 

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other 

gears 

98% for all gears 98% for all gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 19051 19051 19051 

Number of Axles 3 3 3 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental Air Temperature (°C) 25 25 25 

CdA (m2) 6.86 6.86 6.86 

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr 

0.7*Drive Crr + 

0.3*Steer Crr 

=0.7*Drive Crr + 

0.3*Steer Crr 

Payload (tons) 7.50 7.50 7.50 

Weight Reduction (lbs) 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Drive Cycles & Weightings: 

CARB HHDDT 0.50 0.82 0.94 

GEM 55 mph 0.28 0.15 0.06 

GEM 65 mph 0.22 0.03 0.00 

Idle cycle 0.10 0.15 0.20 
4-32 

Table 4-9 Vocational MHD Vehicle Predefined Modeling Parameters 

Regulatory Subcategory 

MHD MHD MHD 

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency 98% for all gears 98% for all gears 98% for all gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 11408 11408 11408 

Number of Axles 2 2 2 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental Air Temperature (°C) 25 25 25 

CdA (m2) 5.40 5.40 5.40 

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr 

0.7*Drive Crr + 

0.3*Steer Crr 

=0.7*Drive Crr + 

0.3*Steer Crr 

Payload (tons) 5.60 5.60 5.60 

Weight Reduction (lbs) 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Drive Cycles & Weightings: 

CARB HHDDT 0.50 0.82 0.94 

GEM 55 mph 0.28 0.15 0.06 

GEM 65 mph 0.22 0.03 0.00 

Idle cycle 0.10 0.15 0.20 
4-33 

Table 4-10 Vocational LHD Vehicle Predefined Modeling Parameters 

Regulatory Subcategory 

LHD LHD LHD 

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency 98% for all gears 98% for all gears 98% for all gears 

Axle Mechanical Efficiency 95.5% 95.5% 95.5% 

Total weight (kg) 7257 7257 7257 

Number of Axles 2 2 2 

Electrical Accessory Power (W) 300 300 300 

Mechanical Accessory Power (W) 1000 1000 1000 

Environmental Air Temperature (°C) 25 25 25 

CdA (m2) 5.40 5.40 5.40 

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr 

0.7*Drive Crr + 

0.3*Steer Crr 

=0.7*Drive Crr + 

0.3*Steer Crr 

Payload (tons) 2.85 2.85 2.85 

Weight Reduction (lbs) 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Add 0.5*weight 

reduction to Payload 

tons 

Drive Cycles & Weightings: 

CARB HHDDT 0.50 0.82 0.94 

GEM 55 mph 0.28 0.15 0.06 

GEM 65 mph 0.22 0.03 0.00 

Idle cycle 0.10 0.15 0.20 

4.4.1.11 Trailer Tables 

Trailers are simulated using the same GEM models as the tractor program. There are 

only minor differences between the trailer and tractor modeling parameters and inputs. Table 

4-11 lists all of the predefined vehicle parameters of trailer baseline models. The predefined 

modeling parameters for the long box dry van trailer subcategory are identical to the Class 8 

high-roof sleeper cab tractor subcategory. The other trailer subcategories differ in tractor cab 

type, total weight, aerodynamic characteristics, number of axles, payload, and drive cycle. For 

example, the refrigerated trailers include a refrigeration unit which adds weight and slightly 

improves aerodynamic performance (reduces CdA). Short box vans are half the length, have a 

single axle, and are pulled by a day cab tractor which reduces total weight and the total payload 

carrying capacity. The drive cycle weightings are consistent with the tractor program. Long box 

trailers are simulated as being pulled by sleeper cabs, and therefore have the long-haul drive 

cycle weightings. The short box trailers are pulled by day cabs and have the short-haul 

weightings. 

Similar to the tractor program, trailer manufacturers can provide aerodynamic drag, tire 

rolling resistance and weight reduction inputs to the model. The key differences between the 

trailer and tractor options are that aerodynamic drag is submitted as a change in drag (delta CdA) 

for trailers, which is compared to the baseline CdA values shown in Table 4-11 within GEM, and 
4-34 

only adjustments to trailer tire rolling resistance are allowed. A list of weight reduction options 

is available in 40 CFR 1037.520 and manufacturers have the option to indicate that their trailers 

use Automatic Tire Inflation Systems (ATIS) for a predefined additional performance 

improvement. Additional information about each trailer subcategory is found in Chapter 2.10 of 

this draft RIA. 

Table 4-11 Predefined Modeling Parameters for Box Trailers 

Regulatory Subcategory Long Box 

Dry Van 

Long Box 

Refrigerated Van 

Short Box 

Dry Van 

Short Box 

Refrigerated Van 

Tractor Type C8 Sleeper Cab - High Roof C8 Day Cab - High Roof 

Engine Fuel Map 15L - 455 HP 

Gear ratio 12.8, 9.25, 6.76, 4.9, 3.58, 2.61, 1.89, 1.38, 1, 0.73 

Gearbox Efficiency 98% for 1:1 gear ratio, and 96 for all other gears 

Axle Mechanical Efficiency 95.5% 

Total weight (kg) 31978 33778 15191 16991 

Baseline CdA Values (m2) 6.2 6.1 6.1 6.0 

Number of Axles 5 4 

Payload (tons) 19 10 

Default Axle Configuration 6x4 

Electrical Accessory Power (W) 300 

Mechanical Accessory Power (W) 1000 

Steer Tire RR 6.54 

Drive Tire RR 6.92 

Tire Radius (m) 0.5 

Axle Drive Ratio 3.7 

Tire Crr =0.425*Trailer Crr+0.425*Drive Crr+0.15*Steer Crr 

Weight Reduction (lbs) Add 1/3*weight reduction to Payload tons 

Drive Cycles & Weightings: 

CARB HHDDT 0.05 0.19 

GEM 55 mph 0.09 0.17 

GEM 65 mph 0.86 0.64 

Payload (tons) 19 7 

4.5 Technology Improvements that Are Recognized in GEM without 

Simulation 

The development of GEM as a compliance tool has required the agencies to balance the 

need for simplicity against the rigor of the model. As part of that process, the agencies have 

identified several technologies and technological improvements that would be difficult to 

accurately simulate, but that should be recognized during certification. These would be 

recognized in the proposed Phase 2 GEM through post-simulation adjustments to the results. 

This is similar to what was done in Phase 1, where the GEM interface included pull-down menus 

for manufacturers to select these adjustments. For this reason, these adjustments have come to 

be known as pull-down technologies. 
4-35 

Phase 2 GEM would continue to recognize those technologies that would be difficult to 

model accurately. In addition to those recognized in Phase 1, the technology list is expanded to a 

much wider range as discussed in the next few paragraphs of this Chapter. In contrast to the 

Phase 1 approach, Phase 2 GEM uses a different approach in recognizing these technologies. 

First of all, all technology improvements are built into GEM. Default improvement values for 

each of these technologies, developed by the agencies after consulting various stakeholders and 

searching for literature values, are implemented into GEM. The user can only select either “Y” 

or “N” in the GEM input file, where Y means that the technology is included, while N means the 

vehicle does not have this technology. This means that users have no flexibility to enter their 

own values. 

For some of these technologies, such as predictive cruise control and Automatic Tire 

Inflation Systems (ATIS), the actual benefit is dependent on how operators behave in the real 

world. For example, ATIS would be of very little benefit where a driver made sure on a daily 

basis that the tires were properly inflated, but would have large benefits where a driver never 

checked the tires. For other technologies, the benefits of the technology are small relative to the 

difficulty of rigorously simulating it. The agencies believe the technology improvement 

approach is an appropriate compromise that will achieve the regulatory goal of incentivizing the 

use of the technology. 

In this proposed approach, the GEM software would adjust the simulation results to 

decrease the g/ton-mile results that are output by the model. For example, with a technology that 

is assigned a 1 percent benefit, the official result for a vehicle that was simulated as having 500 

g/ton-mile CO2 emissions would be reported as having an emission rate of 495 g/ton-mile. 

The technology improvement values used for tractors are shown in Table 4-12. These 

values represent the agencies’ best judgment about the appropriate value for each of these 

technologies. We are generally assigning minimum values to be conservative and not 

overestimate the actual in-use benefits. These values were developed based on all available 

information, including information from stakeholders. 
4-36 

Table 4-12 Tractor Technology Improvement Values 

Technology Improvement Class 8 Sleeper 

Cabs 

Class 8 Day 

Cabs 

Class 7 Day 

Cabs 

Class 8 Heavy Haul 

Tractors 

Single Drive Axle (6x2 or 4x2) 

Configuration 

2.5% 2.5% N/A 2.5% 

Part-time Single Drive Axle 6x2 

Configurationa

2.5% 2.5% N/A 2.5% 

Low Friction Axle Lubricant 0.5% 0.5% 0.5% 0.5% 

Automated Manual, Automatic, 

and Dual Clutch Transmissions 

2% 2% 2% 2% 

Predictive Cruise control 2% 2% 2% 2% 

High Efficiency Air 

Conditioning Compressor 

0.5% 0.5% 0.5% 0.5% 

Electric Accessories 1% 1% 1% 1% 

Extended Idle Reduction 5% N/A N/A N/A 

Automatic Tire Inflation System 

(ATIS) 

1% 1% 1% 1% 

Note: 

a

 A 2.5% reduction over the 55 mph and 65 mph cycles and no reduction over the CARB HHDDT cycle 

For vocational vehicles, the technologies in Table 4-13 would be considered. 

Table 4-13 Vocational Vehicle Technology Improvement Values 

Technology Improvement Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

HHD Single Drive Axle (6x2 or 

4x2) Configuration 

2.5% 2.5% 2.5% 

HHD Part-time Single Drive Axle 

6x2 Configurationa

1.0% 0.3% 0.1% 

Low Friction Axle Lubricant 0.5% 0.5% 0.5% 

HHD Automated Manual or Dual 

Clutch Transmissions 

2.3% N/A N/A 

PTO Delta Fuel (g/ton-mile) 0 to 30 0 to 30 0 to 30 

Neutral Idle Emissions during idle cycle calculated using torque and speed values 

from idle fuel map with the transmission in drive and neutral, 10% and 

90% of the cycle time, respectivelyb

Stop-Start Idle Reduction 90 percent reduction of idle-cycle emissions calculated using torque and 

speed values from idle fuel map with the transmission in driveb

Notes: 

a

 Based on 2.5% reduction over the 55 mph and 65 mph cycles and no reduction over the CARB HHDDT cycle 

b

 See idle fuel consumption test procedure at 40 CFR 1036.535(d). 

For trailers, the following technologies in Table 4-14 would be considered. 

Table 4-14 Trailer Technology Improvement Values 

Technology Improvement Effectiveness 

Automatic Tire Inflation Systems 1.5% 
4-37 

If a manufacturer believes that the CO2 reduction benefits assigned by the agencies are an 

underestimate, they have the option to perform powertrain testing or request (and demonstrate) 

credit in the off-cycle technology process. 

 
4-38 

References

1

 http://www.epa.gov/otaq/climate/gem.htm 2

 “Peer Review of the Greenhouse gas Emissions Model (GEM) and EPA's Response to Comments,” found in the 

docket of this rulemaking, EPA–420-R-15-009 

3

 K. Newman, J. Kargul, and D. Barba, “Development and Testing of an Automatic Transmission Shift Schedule 

Algorithm for Vehicle Simulation, “SAE Int. J. Engines 8(3):2015, doi:10.4271/2015-01-1142 4 Reinhart, T.E. (2015, June). Commercial Medium- and Heavy-Duty Truck Fuel Efficiency Technology Study – 

Report #1. (Report No. DOT HS 812 146). Washington, DC: National Highway Traffic Safety Administration. 

5

 MatLab information (© 1994-2010 The MathWorks, Inc.) can be found at 

http://www.mathworks.com/products/matlab. Simulink information (© 1994-2010 The MathWorks, Inc.) can be 

found at http://www.mathworks.com/products/simulink. StateFlow information (© 1994-2010 The MathWorks, 

Inc.) can be found at http://www.mathworks.com/products/stateflow 6 US EPA, “Technical Research Workshop supporting EPA and NHTSA Phase 2 Standards for MD/HD Greenhouse 

Gas and Fuel Efficiency — December 10 and 11, 2014,” http://www.epa.gov/otaq/climate/regs-heavy-duty.htm 7 J. W. Anthony, J.V. Sarlashkar, A. Phan, C. E. Roberts, Jr., H. Zhang, J. Sanchez, M. Spears, A. Cullen, 

Powertrain test cell and its test procedure development for greenhouse gas emission measurements, ASME Int. J. , 

[in press] 

8 SwRI, “GEM Validation”, Technical Research Workshop supporting EPA and NHTSA Phase 2 Standards for 

MD/HD Greenhouse Gas and Fuel Efficiency — December 10 and 11, 2014, http://www.epa.gov/otaq/climate/regsheavy-duty.htm

9

 J. Anthony, J. Sarlashkar, H. Zhang, J. Sanchez, M. Spears, A. Cullen, Chassis testing versus powertrain testing 

for heavy-duty vehicle greenhouse gas emission measurements, SAE 2014 Commercial Vehicle Engineering 

Congress, Oct. 7-9, 2014 

10 H. Zhang, M. Spears, and A. Cullen, Technology recognition for the next phase heavy-duty greenhouse gas 

emission and fuel efficiency standards, SAE 2014 Commercial Vehicle Engineering Congress, Oct. 7-9, 2014. 

11 United States Environmental Protection Agency. SmartWay Transport Partnership July 2010 e-update accessed 

July 16, 2010, from http://www.epa.gov/smartwaylogistics/newsroom/documents/e-update-july-10.pdf
5-1 

Chapter 5: Impacts on Emissions and Fuel Consumption 

5.1 Executive Summary 

Climate change is widely viewed as the most significant long-term threat to the global 

environment. According to the IPCC, it is extremely likely (>95 percent probability) that human 

influence was the dominant cause of the observed warming since the mid-20th century. The 

primary GHGs of concern are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), 

hydrofluorocarbons (HFCs), perfluorocarbons, and sulfur hexafluoride.1 Mobile sources emitted 

28 percent of all U.S. GHGs in 2012 when considering all upstream and downstream emissions, 

and the transportation-related GHGs alone have grown 18 percent between 1990 and 2012.2

 

Mobile sources addressed in the recent endangerment finding under CAA Section 202(a) – 

highway vehicles including passenger cars, light-duty trucks, heavy-duty trucks, buses, and 

motorcycles – accounted for 24 percent of all U.S. GHGs in 2012.3

 Heavy-duty vehicles emit 

CO2, methane, nitrous oxide, and hydrofluorocarbons and are responsible for almost 22 percent 

of all mobile source GHGs (over 6 percent of all U.S. GHGs) and about 26 percent of CAA 

Section 202(a) mobile source GHGs. For heavy-duty vehicles in 2007, CO2 emissions 

represented nearly 97 percent of all GHG emissions (including HFCs).4

This chapter provides the anticipated emissions impacts from the proposed standards. In 

addition, the emissions impacts of Alternative 4 are presented because the agencies are carefully 

considering it along with the preferred alternative. The reductions in emissions are expected for 

carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and hydrofluorocarbons (HFCs). In 

addition to reducing the emissions of greenhouse gases, this program would also affect the 

emissions of “criteria” air pollutants and their precursors, including carbon monoxide (CO), fine 

particulate matter (PM2.5), oxides of sulfur (SOX), volatile organic compounds (VOC) and oxides 

of nitrogen (NOX), and several air toxics, such as benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde, and acrolein. 

The proposed standards will affect both diesel- and gasoline-fueled heavy-duty vehicles, 

as well as those running on natural gases. The analyses account for both vehicle emissions 

(“downstream” emissions) and emissions from fuel production and distribution (“upstream” 

emissions). The agencies conducted coordinated and complementary analyses by employing 

both DOT’s CAFE model and EPA’s Motor Vehicle Emission Simulator (MOVES2014)5

, 

relative to different reference cases (i.e., different baselines). The agencies used EPA’s MOVES 

model to estimate fuel consumption and emissions impacts for tractor-trailers (including the 

engines which power the vehicle), and vocational vehicles (including the engine which powers 

the vehicle). For heavy-duty pickups and vans, the agencies performed complementary analyses, 

using the CAFE model (“Method A”) and the MOVES model (“Method B”), to estimate fuel 

consumption and emissions from these vehicles. See Section 5.3 for additional details. The 

changes in upstream emissions result from decreased fuel consumption. The emission factors 

from GREET6

 were used to estimate the changes in upstream emissions. In some cases, the 

GREET values were modified or updated by the agencies to be consistent with the EPA’s 

National Emission Inventory (NEI) and emission factors from MOVES. 
5-2 

Table 5-1 through Table 5-3 summarize the impact of the proposed program on GHG 

emissions from the heavy-duty sector in calendar years 2025, 2035 and 2050, using Method A 

and B, relative to two reference cases – more dynamic and less dynamic. Table 5-4 through 

Table 5-6 summarize the projected fuel savings from the proposed program in calendar years 

2025, 2035 and 2050, using Method A and B, relative to the two reference cases. The 

comparable analyses for Alternative 4 are summarized in Table 5-7 through Table 5-12. 

Table 5-1 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 – 

Preferred Alternative vs. Alt 1b using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 27.2 86.9 123.0 

Upstream 9.3 29.7 42.0 

HFC 0.09 0.25 0.3 

Total 36.6 116.9 165.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-2 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 – 

Preferred Alternative vs. Alt 1a using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 28.1 94.6 134.9 

Upstream 9.6 32.3 46.1 

HFC 0.09 0.25 0.3 

Total 37.8 127.2 181.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-3 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 – 

Preferred Alternative vs. Alt 1a using Analysis Method B a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 27.4 94.7 136.5 

Upstream 9.3 32.2 46.5 

HFC 0.1 0.25 0.3 

Total 36.8 127.2 183.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-3 

Table 5-4 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1b 

using Analysis Method A a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 2.5 5.1% 7.6 14.3% 10.8 17.0% 

Gasoline 0.2 2.4% 0.9 11.0% 1.2 13.2% 

Total 2.7 4.7% 8.5 13.8% 12.0 16.5% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-5 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1a 

using Analysis Method A a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 2.5 5.2% 8.3 15.4% 11.9 18.4% 

Gasoline 0.2 2.6% 1.0 11.5% 1.3 13.7% 

Total 2.7 4.8% 9.3 14.8% 13.2 17.8% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-6 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1a 

using Analysis Method B a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 2.5 5.1% 8.5 15.6% 12.3 18.7% 

Gasoline 0.2 2.1% 0.8 10.4% 1.1 12.8% 

Total 2.7 4.7% 9.3 14.9% 13.4 18.0% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-7 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 –

Alternative 4 vs. Alt 1b using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 33.5 90.9 124.0 

Upstream 11.5 31.0 42.3 

HFC 0.09 0.25 0.3 

Total 45.1 122.2 166.6 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-4 

Table 5-8 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 – 

Alternative 4 vs. Alt 1a using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 34.6 98.7 136.0 

Upstream 11.8 33.7 46.5 

HFC 0.09 0.25 0.3 

Total 46.5 132.7 182.8 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-9 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025, 2035 and 2050 – 

Alternative 4 vs. Alt 1a using Analysis Method B a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ)

Downstream 33.7 98.3 136.9 

Upstream 11.5 33.4 46.6 

HFC 0.1 0.25 0.3 

Total 45.3 132.0 183.8 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-10 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1b using 

Analysis Method A a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 3.0 6.1% 7.9 14.8% 10.8 17.0% 

Gasoline 0.3 3.9% 1.0 12.1% 1.3 13.8% 

Total 3.3 5.8% 8.9 14.4% 12.1 16.6% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-11 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1a using 

Analysis Method A a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 3.1 6.3% 8.6 16.0% 12.0 18.5% 

Gasoline 0.3 4.3% 1.1 12.5% 1.3 14.3% 

Total 3.4 6.0% 9.7 15.5% 13.3 18.0% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-5 

Table 5-12 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1a using 

Analysis Method B a

CY2025 CY2035 CY2050 

Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction Billion 

Gallons 

% Reduction 

Diesel 3.1 6.2% 8.8 16.1% 12.3 18.7% 

Gasoline 0.3 3.4% 0.9 11.1% 1.1 12.9% 

Total 3.4 5.9% 9.7 15.5% 13.4 18.0% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

The non-GHG impacts of the proposed rulemaking are largely driven by three factors. 

The largest contributor is from the projected increased use of auxiliary power units (APUs), 

which provide power, heat and cooling for trucks during extended engine idling. Reduced 

emissions from upstream fuel production and distribution also contribute significantly to the 

emissions benefits. Emissions of certain pollutants, such as NOX and PM2.5 are further reduced 

through improved engine efficiency, aerodynamics and tire rolling resistance and absolute 

changes in average total running weight of the vehicles. To a smaller extent, a rebound of 

vehicle miles traveled (VMT) would increase the emissions of all pollutants proportional to the 

VMT rebound amount. The emissions impacts of non-GHGs on both downstream and upstream 

from the heavy-duty sector in calendar years 2025, 2035 and 2050 are summarized in Table 5-13 

through Table 5-15, using Method A and B, relative to the two reference cases. The comparable 

analyses for Alternative 4 are summarized in Table 5-16 through Table 5-18. 

Table 5-13 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Preferred Alternative vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 3% 25 13% 34 16% 

Acetaldehyde 672 10% 1,893 30% 2,682 36% 

Acrolein 97 10% 273 31% 387 37% 

Benzene 145 5% 421 18% 595 22% 

CO 30,282 3% 87,286 8% 123,876 10% 

Formaldehyde 2,119 11% 5,969 32% 8,460 37% 

NOX 101,916 7% 291,282 26% 413,501 31% 

PM2.5 376 1% 1,535 3% 2,199 4% 

SOX 6,213 5% 19,905 14% 28,101 17% 

VOC 16,227 6% 49,080 18% 69,525 22% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-6 

Table 5-14 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Preferred Alternative vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 3% 25 13% 35 16% 

Acetaldehyde 672 10% 1,891 30% 2,680 36% 

Acrolein 97 10% 273 31% 386 37% 

Benzene 145 5% 425 18% 603 22% 

CO 30,487 3% 88,724 8% 126,081 10% 

Formaldehyde 2,119 11% 5,969 32% 8,461 37% 

NOX 102,983 7% 299,911 26% 427,332 32% 

PM2.5 419 1% 1,910 4% 2,791 5% 

SOX 6,421 5% 21,672 15% 30,850 18% 

VOC 16,403 6% 50,812 19% 72,253 23% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-15 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Preferred Alternative vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 2.7% 25 15.1% 36 19.4% 

Acetaldehyde 674 10.1% 1,902 30.5% 2,697 36.0% 

Acrolein 97 9.8% 274 31.3% 388 36.9% 

Benzene 149 5.4% 445 18.8% 633 22.9% 

CO 29,622 1.9% 85,961 6.6% 122,659 8.4% 

Formaldehyde 2,121 11.4% 5,978 31.7% 8,475 37.0% 

NOX 102,502 7.2% 298,907 26.6% 426,610 32.1% 

SOX 386 0.6% 1,883 4.2% 2,815 5.4% 

PM2.5 6,070 4.9% 20,777 15.3% 30,000 18.4% 

VOC 16,724 5.6% 52,872 18.8% 75,521 22.7% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-7 

Table 5-16 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Alternative 4 vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 3% 25 13% 34 16% 

Acetaldehyde 673 10% 1,893 30% 2,682 36% 

Acrolein 97 10% 273 31% 387 37% 

Benzene 152 6% 426 18% 595 22% 

CO 31,383 3% 88,047 8% 124,137 10% 

Formaldehyde 2,123 11% 5,970 32% 8,460 37% 

NOX 105,693 7% 293,918 26% 413,967 31% 

PM2.5 639 1% 1,703 4% 2,237 4% 

SOX 7,682 6% 20,849 15% 28,385 17% 

VOC 18,006 6% 50,189 19% 69,796 22% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-17 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Alternative 4 vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 3% 25 13% 35 16% 

Acetaldehyde 672 10% 1,891 30% 2,679 36% 

Acrolein 97 10% 273 31% 386 37% 

Benzene 153 6% 430 18% 603 22% 

CO 31,637 3% 89,514 8% 126,360 10% 

Formaldehyde 2,123 11% 5,969 32% 8,460 37% 

NOX 106,822 7% 302,575 26% 427,805 32% 

PM2.5 689 1% 2,082 5% 2,833 5% 

SOX 7,941 6% 22,646 16% 31,151 18% 

VOC 18,222 6% 51,924 19% 72,509 23% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-8 

Table 5-18 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions – 

Alternative 4 vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene 9 2.8% 26 15.2% 36 19.4% 

Acetaldehyde 676 10.1% 1,903 30.6% 2,697 36.0% 

Acrolein 97 9.8% 274 31.3% 388 36.9% 

Benzene 157 5.7% 450 18.9% 634 22.9% 

CO 30,580 1.9% 86,526 6.6% 122,703 8.4% 

Formaldehyde 2,125 11.4% 5,980 31.7% 8,476 37.0% 

NOX 106,180 7.4% 301,339 26.8% 426,796 32.1% 

SOX 646 1.1% 2,036 4.6% 2,827 5.4% 

PM2.5 7,450 6.1% 21,550 15.9% 30,364 18.4% 

VOC 18,652 6.2% 53,966 19.2% 75,621 22.7% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.2 Introduction 

5.2.1 Downstream (Tailpipe) Emissions 

As described in more detail in this chapter, the downstream reductions in emissions due 

to the proposed program are anticipated to be achieved through improvements in engine 

efficiency, road load reduction, and APU use during extended idling, with the exception of 

PM2.5.

A Absolute reductions in tailpipe emissions are projected to grow over time as the fleet 

turns over to vehicles affected by the proposed standards, meaning that the emissions benefits of 

the program would continue to grow as older vehicles in the fleet are replaced by newer vehicles 

that emit less CO2. 

The effect of the regulations on the timing of fleet turnover and total VMT can have an 

impact on downstream GHG and other emissions, as discussed in Section IX of the preamble. If 

the regulations spur firms to increase their purchase of new vehicles before efficiency standards 

are in place (“pre-buy”) or to delay their purchases once the standards are in place then there 

would be a delay in achieving the full GHG and other emission reductions from improved fuel 

economy across the fleet. If the lower per-mile costs associated with higher fuel economy lead 

to an increase in VMT (the “rebound effect”), then the total emission reductions would also be 

reduced. Chapter 8 of the draft RIA provides more detail on how the rebound effect was 

calculated in the agencies’ analysis. The analysis discussed in this chapter incorporates the 

rebound effect into the estimates. However, the impacts of any delayed fleet turnover are not 

estimated. 

 A The projected increased use of APUs would lead to higher PM2.5 emissions since engines powering APUs are 

currently required to meet less stringent PM standards than on-road engines. 
5-9 

5.2.2 Upstream Emissions 

In addition to downstream emission reductions, reductions are expected in the emissions 

associated with the processes involved in getting fuel to the pump, including the extraction and 

transportation of crude oil, the production and distribution of finished gasoline and diesel, and 

the production and transportation of renewable fuels. Changes are anticipated in upstream 

emissions due to the expected reduction in the overall volume of gasoline and diesel consumed. 

Less fuel consumed means less fuel transported, less fuel refined, and less crude oil extracted 

and transported to refineries. Thus, there would be reductions in the emissions associated with 

each of these steps in the fuel production and distribution processes. In addition, any changes in 

downstream reductions associated with changes in fleet turnover, and VMT are reflected in a 

corresponding change in upstream emissions associated with fuel processing and distribution. 

The agencies recognize that the proposed standards could lower the world price of oil 

(the “monopsony” effect, further discussed in Chapter 8 of the draft RIA). Lowering oil prices 

could lead to an uptick in oil consumption globally, resulting in a corresponding increase in 

GHG emissions in other countries. This global increase in emissions could slightly offset some 

of the emission reductions achieved domestically as a result of the regulation. EPA does not 

provide quantitative estimates of the impact of the proposed regulation on global petroleum 

consumption and GHG emissions in this draft RIA. 

5.2.3 Global Warming Potentials 

Throughout this document, in order to refer to the four inventoried greenhouse gases on 

an equivalent basis, Global Warming Potentials (GWPs) are used. In simple terms, GWPs 

provide a common basis with which to combine several gases with different heat trapping 

abilities into a single inventory (Table 5-19). When expressed in CO2eq terms, each gas is 

weighted by its heat trapping ability relative to that of CO2. The GWPs used in this analysis are 

consistent with the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment 

Report (AR4) on a 100-year timescale.7 

Table 5-19 Global Warming Potentials of GHGs 

GAS GLOBAL WARMING POTENTIAL (CO2EQ) 

CO2 1 

CH4 25 

N2O 298 

HFC134a 1,430 

5.3 Program Analysis and Modeling Methods 

5.3.1 Models Used 

Different tools exist for estimating potential fuel consumption and emissions impacts 

associated with fuel efficiency and GHG emissions standards. One such tool is EPA’s official 

mobile source emissions inventory model named Motor Vehicle Emissions Simulator 

(MOVES).8

 The agencies used the most current version of the model, MOVES2014, to quantify 
5-10 

the impacts of these proposed standards on GHG emissions, fuel consumption, as well as criteria 

pollutants and air toxics emissions. In MOVES, vehicle types are categorized by their use and 

represented by combination tractors, single unit trucks, refuse trucks, motor homes, transit buses, 

intercity buses, school buses, and light commercial trucks. The agencies ran MOVES with user 

input databases that reflected the projected technological improvements resulting from the 

proposed rules, such as the improvements in engine and vehicle efficiency, aerodynamic drag, 

and tire rolling resistance. The changes made to the default MOVES database are described 

below in Section 5.3.2. All the input data, MOVES runspec files, and the scripts used for the 

analysis, as well as the version of MOVES used to generate the emissions inventories, can be 

found in the docket.9

Another such tool is DOT’s CAFE model. For this analysis, the model was reconfigured 

to use the work based attribute metric of “work factor” established in the Phase 1 rule for heavyduty pickups and vans, instead of the light-duty “footprint” attribute metric. The CAFE model 

takes user-specified inputs on, among other things, vehicles that will be produced in a given 

model year, technologies available to improve fuel efficiency on those vehicles, potential 

regulatory standards that would drive improvements in fuel efficiency, and economic 

assumptions. The CAFE model takes every vehicle in each manufacturer’s fleet and decides 

what technologies to add to those vehicles in order to allow each manufacturer to comply with 

the standards in the most cost-effective way and uses a representation of the HD pickup and van 

fleet that captures heterogeneity at the manufacturer, model year, and powertrain (and other 

technology) level. Based on the resulting improved vehicle fleet, the CAFE model then 

calculates total fuel consumption and GHG, criteria, and toxics emissions impacts based on those 

inputs, along with economic costs and benefits. The CAFE model is discussed in greater detail 

in Chapter 10 of the draft RIA. 

For this rule, the agencies conducted coordinated and complementary analyses by 

employing both DOT’s CAFE model and EPA’s MOVES model. These models were used to 

project the impacts resulting from the proposed standards on fuel consumption, GHG emissions, 

as well as criteria pollutants and air toxics emissions. As described in Section 5.3.2, the 

agencies used EPA’s MOVES model to estimate fuel consumption and emissions impacts for 

tractor-trailers (including the engines which power the vehicle), and vocational vehicles 

(including the engine which powers the vehicle). For heavy-duty pickups and vans, the agencies 

performed complementary analyses using the CAFE model (“Method A”) and the MOVES 

model (“Method B”) to estimate fuel consumption and emissions from these vehicles. For both 

methods, the agencies analyzed the impact of the proposed rules, relative to two different 

reference cases – less dynamic and more dynamic. The less dynamic baseline projects very little 

improvement in new vehicles in the absence of new Phase 2 standards. In contrast, the more 

dynamic baseline projects more improvements in vehicle fuel efficiency. The agencies 

considered both reference cases, reaching corroborative conclusions. The results for all of the 

regulatory alternatives relative to both reference cases, derived via the same methodologies 

discussed in this Chapter, are presented in Chapter 11 of the draft RIA and these different 

analyses all support the reasonableness of the proposed standards. 

For brevity, a subset of these analyses are presented in this section, and the reader is 

referred to both the Chapter 11 of the draft RIA and NHTSA’s DEIS Chapters 3 and 5 for 

complete sets of these analyses. In this Chapter, Method A is presented for both the proposed 
5-11 

standards (i.e., Alternative 3 – the agencies’ preferred alternative) and for the standards the 

agencies considered in Alternative 4. Method A is presented relative to both the more dynamic 

baseline (Alternative 1b) and the less dynamic baseline (Alternative 1a). Method B is presented 

also for the proposed standards and Alternative 4, but relative only to the less dynamic baseline. 

The agencies’ intention for presenting both of these complementary and coordinated analyses is 

to offer interested readers the opportunity to compare the regulatory alternatives considered for 

Phase 2 in both the context of our HD Phase 1 analytical approaches and our light-duty vehicle 

analytical approaches. The agencies view these analyses as corroborative and reinforcing: both 

support agencies’ conclusion that the proposed standards are appropriate and at the maximum 

feasible levels. 

Because reducing fuel consumption also affects emissions that occur as a result of fuel 

production and distribution (including renewable fuels), the agencies also calculated those 

“upstream” changes using the “downstream” fuel consumption reductions predicted by the 

MOVES model for vocational vehicles and tractor-trailers. As described earlier, for HD pickups 

and vans, parallel and complementary analyses of estimating the emissions from upstream 

processes were conducted using the fuel consumption estimates from both DOT’s CAFE model 

(Method A) and EPA’s MOVES model (Method B), relative to the two reference cases. Method 

A used the CAFE model to estimate vehicular fuel consumption and emissions impacts for HD 

pickups and vans and to calculate upstream impacts. For vocational vehicles and combination 

tractor-trailers, both Method A and Method B estimated the projected corresponding changes in 

upstream emissions using the same tools originally created for the Renewable Fuel Standard 2 

(RFS2) rulemaking analysis,10 used in the LD GHG rulemakings,11 HD GHG Phase 1,12 and 

updated for the current analysis. The estimate of emissions associated with production and 

distribution of gasoline and diesel from crude oil is based on emission factors in the “Greenhouse 

Gases, Regulated Emissions, and Energy Use in Transportation” model (GREET) developed by 

DOE's Argonne National Lab. In some cases, the GREET values were modified or updated by 

the agencies to be consistent with the National Emission Inventory (NEI) and emission factors 

from MOVES. Method B used the same tool described above to estimate the upstream impacts 

for HD pickups and vans. 

Updates and enhancements to the GREET model assumptions include updated crude oil 

and gasoline transport emission factors that account for recent EPA emission standards and 

modeling, such as accounting for impacts of fuel requirements on vapor emissions from storage 

and transport. In addition, GREET does not include air toxics. Thus, emission factors for the 

following air toxics were added: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and 

acrolein. These upstream toxics emission factors were calculated from the 2005 National 

Emissions Inventory (NEI), a risk and technology review for petroleum refineries, speciated 

emission profiles in EPA's SPECIATE database, or the Mobile Source Air Toxics rulemaking 

(MSAT) inventory for benzene; these pollutant tons were divided by refinery energy use or 

gasoline distribution quantities published by the DOE Energy Information Administration (EIA) 

to get emission factors in terms of grams per million BTU of finished gasoline and diesel. These 

updates are consistent with those used for the upstream analysis included in the LD GHG 

rulemaking and HD GHG Phase 1. The actual calculation of the emission inventory impacts of 

the decreased gasoline and diesel production is done in EPA's tool for upstream emission 

impacts.13 
5-12 

5.3.2 Calculation of Downstream Emissions 

5.3.2.1 Model inputs and Assumptions for the Less Dynamic Reference Case 

The less dynamic reference case (identified as Alternative 1a in Section X of the 

preamble and Chapter 11 of the draft RIA), a “no action” alternative, functions as one the 

baselines against which the impacts of the proposed standards can be evaluated and includes the 

impact of HD GHG Phase 1, but generally assumes that fuel efficiency and GHG emission 

standards are not improved beyond the required 2018 model year levels. However, the less 

dynamic reference case projects some improvements in the efficiency of the box trailers pulled 

by combination tractors due to increased penetration of aerodynamic technologies and low 

rolling resistance tires attributed to both EPA’s SmartWay Transport Partnership and California 

Air Resources Board’s Tractor-Trailer Greenhouse Gas regulation, as described in Section IV of 

the preamble. For other HD vehicle sectors, no market-driven improvement in fuel efficiency 

was assumed. For HD pickups and vans, the CAFE model was applied in a manner that assumes 

manufacturers would only add fuel-saving technology as needed to continue complying with 

Phase 1 standards. MOVES2014 defaults were used for all other parameters to estimate the less 

dynamic reference case emissions inventories. For the aerodynamic drag and tire rolling 

resistance coefficients of combination tractor-trailers and vocational vehicles, default MOVES 

values for each MOVES source/vehicle type were used that represent a fleet-wide adoption of 

HD GHG Phase 1. 

The less dynamic reference case assumed the MOVES2014 default vehicle population 

and vehicle miles traveled (VMT).14 The growth in vehicle populations and miles traveled in 

MOVES2014 is based on the relative annual VMT growth from AEO2014 Early Release for 

model years 2012 and later.15 For extended idling emission inventories, the MOVES2014 

default auxiliary power unit (APU) penetration rates were used. These rates assume that 30 

percent of all combination long-haul tractors model year 2010 and later use an APU during 

extended idling.B 

5.3.2.2 Model inputs and Assumptions for the More Dynamic Reference Case 

The more dynamic reference case (identified as Alternative 1b in Section X of the 

preamble and Chapter 11 of the draft RIA), also includes the impact of Phase 1 and generally 

assumes that fuel efficiency and GHG emission standards are not improved beyond the required 

2018 model year levels. However, for this case, the agencies assume market forces would lead 

to additional fuel efficiency improvements for tractors and trailers. These additional assumed 

improvements are described in Section X of the preamble. No additional fuel efficiency 

improvements due to market forces were assumed for vocational vehicles. For HD pickups and 

vans, the agencies applied the CAFE model using the input assumption that manufacturers 

having achieved compliance with Phase 1 standards would continue to apply technologies for 

 B The agencies assessed the current level of automatic engine shutdown and idle reduction technologies used by the 

tractor manufacturers to comply with the 2014 model year CO2 and fuel consumption standards. To date, the 

manufacturers are meeting the 2014 model year standards without the use of this technology. Therefore, the 

agencies are reverting the baseline APU adoption rate back to 30 percent, the value used in the HD GHG Phase 1 

baseline. 
5-13 

which increased purchase costs would be “paid back” through corresponding fuel savings within 

the first six months of vehicle operation. The agencies conducted the MOVES analysis of this 

case in the same manner as for the less dynamic reference case. 

5.3.2.3 Model Inputs and Assumptions for the Control Case 

The control case (identified as Alternative 3 in Chapter 11 of the draft RIA) represents 

the agencies’ proposed fuel efficiency and GHG standards for HD engines, HD pickup trucks 

and vans, Class 2b through Class 8 vocational vehicles, Class 7 and 8 combination tractors, and 

trailers. To account for improvements of engine and vehicle efficiency in vocational vehicles 

and combination tractor-trailers, EPA developed additional user input data for MOVES runs to 

estimate the control case inventories. 

The agencies used the percent reduction in aerodynamic drag and tire rolling resistance 

coefficients and absolute changes in average total running weight (gross combined weight) 

expected from the proposed rules to develop the road load inputs for the control case. For 

running emissions, the key concept underlying the definition of operating mode in MOVES is 

scaled tractive power (STP), vehicle speed and vehicle acceleration.16 STP represents the 

vehicle’s tractive power scaled by a constant factor. It is calculated using mass of the vehicle 

and road load factors that include tire rolling resistance, aerodynamic drag, and friction losses in 

the drivetrain. STP is estimated using the equation below: 

scale

ttttt

t f

amvCvBvAv

STP

 

32

 Equation 5-1 

Where: 

A = the rolling resistance coefficient [kWsec/m], 

B = the rotational resistance coefficient [kWsec2/m2], 

C = the aerodynamic drag coefficient [kWsec3/m3], 

m = mass of individual vehicle [metric ton], 

fscale = fixed mass factor, 

vt = instantaneous vehicle velocity at time t [m/s], 

at = instantaneous vehicle acceleration [m/s2

] 

The proposed improvements in road load factors would reduce the tractive power exerted 

by a vehicle to move itself and its cargo. The emissions emitted by heavy-duty trucks are a 

function of STP as determined from a variety of data sources. Thus, a reduction in road load 

factors are expected to result in reduced GHG and non-GHG emissions. The improvements in 

tire rolling resistance, aerodynamic drag, and absolute changes in average vehicle weight 
5-14 

expected from the technologies which could be used to meet the proposed standards were 

modified in the “sourceusetypephysics” table.C 

For vocational vehicles and tractor-trailers, the agencies also used the percent reduction 

in CO2 emissions expected from the powertrain and other vehicle technologies not accounted for 

in the aerodynamic drag and tire rolling resistance in the proposed rules to develop energy inputs 

for the control case runs. In contrast, for HD pickup trucks and vans, the proposed standards 

were evaluated only in terms of the total vehicle reductions in fuel use and CO2 emissions, since 

nearly all of these vehicles would be certified on a chassis dynamometer. Finally, EPA used an 

assumed percent penetration of APU use during extended idling, based on the expectation that 

manufacturers will use APUs to meet the vehicle GHG standard for combination long-haul 

tractors, as discussed in Section III.D of the preamble. 

5.3.2.3.1 Emission Rate and Road Load Inputs 

Both the stringency and the form of the proposed fuel consumption and CO2 emission 

standards vary by vehicle category. Accordingly, the modeling of the proposed standards in 

MOVES varies by the vehicle category. For the vocational vehicles and combination tractortrailers, EPA has analyzed the impacts of the proposed standards by evaluating the technologies 

applied to the energy rates as well as to the road load inputs. However, the impacts on the HD 

pickup trucks and vans were estimated only in terms of reduction in energy rates. 

5.3.2.3.1.1 Tractor-Trailers 

Similar to the approach used in the HD GHG Phase 1 analysis, EPA aggregated the nine 

tractor subcategories into the two MOVES combination tractor-trailer categories – short-haul and 

long-haul. The agencies used sales distribution data from the HD GHG Phase 1 analysis and 

determined the long-haul reductions in energy rates and road load factors, based on a sales mix 

assumption of 80 percent high roof, 15 percent mid roof, and 5 percent low roof sleeper cabs. 

The short-haul combination tractors were evaluated using a day cab sales distribution assumption 

of 7 percent Class 7 low roof, 10 percent Class 7 high roof, 40 percent Class 8 low roof, 35 

percent Class 8 high roof, and 8 percent vocational tractors, based on the information used in the 

HD GHG Phase 1 analysis. The details of the analyses aggregating the tractor subcategories into 

MOVES categories using the sales mix assumption described above can be found in the docket.17

The trailer category encompasses many types of trailers. As with the tractor category, 

EPA aggregated the trailer subcategories into two MOVES combination tractor-trailer 

categories. EPA used a combination of ACT Research’s 2013 factory shipment data18 for trailer 

distribution by type and “primary trip length” information from the U.S. Census’ 2002 Vehicle 

Inventory and Use Survey19 to distribute each trailer type into long- and short-haul categories. 

EPA applied the trailer market percentages as shown in Table 5-20 to determine the trailer 

impact on the MOVES long- and short-haul combination tractor-trailer categories. 

 C Class 2b and 3 trucks do not use the STP metric and are regulated, for non-GHG emissions, based on chassis 

testing (gram per mile basis) rather than engine testing (gram per brake horsepower-hour basis), therefore road load 

reductions are not necessarily expected to result in reduced non-GHG emissions. 
5-15 

Table 5-20 Aggregation of Trailer Types into MOVES Combination Tractor-Trailer Categories 

TRAILER TYPE Combination Long-Haul 

Tractor-Trailers 

Combination Short-Haul TractorTrailers 

Long Dry Van 55.5% 20.3% 

Short Dry Van 12.3% 20.2% 

Long Refrigerated Van 18.2% 2.6% 

Short Refrigerated Van 5.2% 3.8% 

Special Purpose Van 0.0% 6.4% 

Container Chassis 0.2% 1.8% 

Flatbed 6.9% 10.9% 

Tank 0.4% 1.5% 

Other On-Highway Trailers 1.2% 2.7% 

Off-Road Trailers 0.0% 29.9% 

Table 5-21 describes the improvements in the energy rate expected from the heavy-duty 

engine, transmission, and driveline technologies which could be applied to meet the proposed 

tractor standards. The percentage reductions from the reference case were applied to the default 

MOVES energy rates in the appropriate source bins within MOVES “emissionrate" table. 

Table 5-21 Estimated Reductions in Energy Rates for the Proposed Standards for Tractor-Trailers

VEHICLE TYPE FUEL MODEL YEARS REDUCTION FROM 

REFERENCE CASE 

Long-haul TractorTrailers Diesel 2018-2020 1.3% 

2021-2023 5.2% 

2024-2026 9.7% 

2027+ 10.4% 

Short-haul TractorTrailersD

Diesel 2018-2020 0.9% 

2021-2023 5.0% 

2024-2026 9.5% 

2027+ 10.4% 

Table 5-22 contains the improvements in tire rolling resistance, coefficient of drag, and 

weight reductions expected from the technologies which could be used to meet the proposed 

standards for combination tractor-trailers. The percentage reductions in tire rolling resistance 

and drag coefficients and the absolute changes in average vehicle weight were modified in the 

“sourceusetypephysics” table. 

 D Vocational tractors are included in the short-haul tractor segment. 
5-16 

Table 5-22 Estimated Reductions in Road Load Factors for the Proposed Standards for Tractor-Trailers 

VEHICLE TYPE MODEL 

YEARS 

REDUCTION IN 

TIRE ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)A

Combination Longhaul TractorTrailers 2018-2020 5.5% 5.1% -131 

2021-2023 9.8% 15.3% -199 

2024-2026 15.7% 20.5% -246 

2027+ 17.9% 26.9% -304 

Combination Shorthaul TractorTrailersE

2018-2020 4.0% 1.6% -41 

2021-2023 10.5% 9.3% -79 

2024-2026 13.9% 12.3% -100 

2027+ 17.6% 15.9% -127 

Note: 

a

 Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine 

improvements.

In addition, the projected use of auxiliary power units (APU) during extended idling, 

shown below in Table 5-23, was included in the modeling for the long-haul combination tractortrailers by modifying the “hotellingactivitydistribution” table in MOVES. 

Table 5-23 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers 

VEHICLE TYPE MODEL 

YEARS 

APU 

PENETRATION 

Combination 

Long-Haul 

Trucksa

2010-2020 30% 

2021-2023 80% 

2024+ 90% 

Note: 

a

 The assumed APU penetration remains constant for model years 

2024 and later. 

5.3.2.3.1.2 Vocational Vehicles 

Similar to the approach for tractor-trailers, EPA aggregated the nine vocational vehicle 

subcategories into each of the seven MOVES vehicle types.F The energy rate inputs were 

derived by applying the anticipated levels of engine, axle, transmission, and idle reduction 

technologies equally across all weight classes and vehicle types. Each of these technology 

packages is described in Chapter 2 of the draft RIA. The differences between gasoline and diesel 

vocational vehicles in energy rate reduction from the reference cases, shown in Table 5-24, are 

due to the differences in anticipated engine-level technology packages, as described in Chapter 2 

of the draft RIA. 

 E Vocational tractors are included in the short-haul tractor segment. F Seven MOVES vehicle types for vocational vehicles are intercity bus, transit bus, school bus, refuse truck, singleunit short-haul truck, single-unit long-haul truck, and motor home. 
5-17 

The percentage reductions from the reference case were applied to the default MOVES 

energy rates in the appropriate source bins within MOVES “emissionrate" table. 

Table 5-24 Estimated Reductions in Energy Rates for the Proposed Standards for Vocational Vehicles 

VEHICLE TYPE FUEL MODEL 

YEARS 

REDUCTION FROM 

REFERENCE CASE 

Single-Frame 

VocationalG

Diesel & CNG 2021-2023 5.3% 

2024-2026 8.9%

2027+ 13.3% 

Gasoline 2021-2023 3.3%

2024-2026 5.4% 

2027+ 10.3% 

The agencies used MOVES population data for new vehicles expected to be sold in 2018 

for each weight class, as well as assumptions about their distribution among the three new 

vocational vehicle duty cycles. This population allocation is shown in Table 5-25. 

Table 5-25 Vocational Vehicle Types and Population Allocation 

VEHICLE TYPE RURAL MULTI-PURPOSE URBAN 

Short Haul Straight Truck 35% 60% 5% 

Long Haul Straight Truck, Motor 

Home, Intercity Bus 

100% 0% 0% 

School Bus 0% 100% 0% 

Transit Bus 0% 75% 25% 

Refuse 0% 0% 100% 

All Class 4-5 23% 21% 2% 

All Class 6-7 15% 19% 1% 

All Class 8 7% 8% 4% 

Using these population distribution estimates and the technology application rates 

described in Chapter 2 of the draft RIA, EPA derived the levels of improvements in tire rolling 

resistance and weight reduction. 

Table 5-26 contains the improvements in tire rolling resistance, coefficient of drag, and 

weight reductions expected from the technologies which could be used to meet the proposed 

standards for vocational vehicles. The percentage reductions in tire rolling resistance and drag 

coefficients and the absolute changes in average vehicle weight were modified in the 

 G Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles with GVWR less than 14,000 lbs were modeled using the 

energy rate reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these 

vehicles choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups 

and vans). 
5-18 

“sourceusetypephysics” table in MOVES. The analyses used to develop the MOVES inputs for 

vocational vehicles, described above, can be found in the docket.20

Table 5-26 Estimated Reductions in Road Load Factors for the Proposed Standards for Vocational Vehicles 

VEHICLE 

TYPE 

MODEL 

YEARS 

REDUCTION IN TIRE 

ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG COEFFICIENT 

WEIGHT 

REDUCTION 

(LB) 

Intercity 

Buses 

2021-2023 6.5% 0% 0 

2024-2026 9.2% 0% 0 

2027+ 16.5% 0% 0 

Transit Buses 2021-2023 0% 0% 0 

2024-2026 2.9% 0% 0 

2027+ 3.0% 0% 0 

School Buses 2021-2023 0% 0% 0 

2024-2026 2.9% 0% 0 

2027+ 4.0% 0% 0 

Refuse Trucks 2021-2023 0% 0% 20 

2024-2026 2.9% 0% 20 

2027+ 3.0% 0% 25 

Single Unit 

Short-haul 

Trucks 

2021-2023 4.8% 0% 5.8 

2024-2026 8.3% 0% 5.8 

2027+ 13.0% 0% 7 

Single Unit 

Long-haul 

Trucks 

2021-2023 6.5% 0% 20 

2024-2026 9.2% 0% 20 

2027+ 16.5% 0% 25 

Motor Homes 2021-2023 3.0% 0% 0 

2024-2026 6.2% 0% 0 

2027+ 7.4% 0% 0 

5.3.2.3.1.3 Heavy-Duty Pickup Trucks and Vans 

As explained above, the agencies used both DOT’s CAFE model and EPA’s MOVES 

model, using analytical Method A and B, respectively, to project fuel consumption, GHG and 

non-GHG emissions impacts resulting from the proposed standards for HD pickups and vans, 

including downstream vehicular emissions as well as emissions from upstream processes related 

to fuel production, distribution, and delivery. 

5.3.2.3.1.3.1 Method A for HD Pickups and Vans 

For Method A, the CAFE model calculated fuel consumption rates, then calculated 

vehicular CO2 emissions from the fuel consumption based on fuel properties (density and carbon 

content). It also applies per-mile emission factors from MOVES to estimated VMT (for each 

regulatory alternative, adjusted to account for the rebound effect) in order to calculate vehicular 

CH4 and N2O emissions (as well, as discussed below, of non-GHG pollutants), and applies pergallon upstream emission factors from GREET in order to calculate upstream GHG (and nonGHG) emissions. 
5-19 

Consistent with HD GHG Phase 1 approach, the proposed standards for HD pickups and 

vans are established as a function of the vehicle work factor, a metric unique to this segment 

which is calculated based on the vehicle capabilities (i.e., payload, towing and four-wheel drive). 

As proposed, the work-factor-based standards would increase in stringency by 2.5 percent per 

year, starting in MY 2021 until they reach the final level of the proposed standards in MY 2027. 

The standards define targets specific to each vehicle model, but no vehicle is required to meet its 

target; instead, the production-weighted averages of the vehicle-specific targets define average 

fuel consumption and CO2 emission rates that a given manufacturer’s overall fleet of produced 

vehicles is required to achieve. The standards are specified separately for gasoline and diesel 

vehicles, and vary with work factor. Work factors could change, and today’s analysis assumes 

that some applications of mass reduction could enable increased work factor in cases where 

manufacturers could increase a vehicle’s rated payload and/or towing capacity. Therefore, 

average required levels will depend on the mix of vehicles and work factors of the vehicles 

produced for sale in the U.S., and since these can only be estimated at this time, average required 

and achieved fuel consumption and CO2 emission rates are subject to uncertainty. Between 

today’s notice and issuance of the ensuing final rule, the agencies intend to update the market 

forecast (and other inputs) used to analyze HD pickup and van standards, and expect that doing 

so will lead to different estimates of required and achieved fuel consumption and CO2 emission 

rates (as well as different estimates of impacts, costs, and benefits). 

The following four tables present stringency increases and estimated required and 

achieved fuel consumption and CO2 emission rates for the two No Action Alternatives 

(Alternative 1a and 1b) and the proposed standards defining the Preferred Alternative. 

Stringency increases are shown relative to standards applicable in model year 2018 (and through 

model year 2020). As mathematical functions, the standards themselves are not subject to 

uncertainty. By 2027, they are 16.2 percent more stringent (i.e., lower) than those applicable 

during 2018-2020. The DOT’s CAFE model estimate that, by model 2027, the proposed 

standards could reduce average required fuel consumption and CO2 emission rates to about 4.86 

gallons/100 miles and about 458 grams/mile, respectively. The model further estimate that 

average achieved fuel consumption and CO2 emission rates could correspondingly be reduced to 

about the same levels. If, as represented by Alternative 1b, manufacturers would, even absent 

today’s proposed standards, voluntarily make improvements that pay back within six months, 

these model year 2027 levels are about 13.5 percent lower than the agencies estimate could be 

achieved under the Phase 1 standards defining the No Action Alternative. If, as represented by 

Alternative 1a, manufacturers would, absent today’s proposed standards, only apply technology 

as required to achieve compliance, these model year 2027 levels are about 15 percent lower than 

the agencies estimate could be achieved under the Phase 1 standards. As indicated below, the 

agencies estimate that these improvements in fuel consumption and CO2 emission rates would 

build from model year to model year, beginning as soon as model year 2017 (insofar as 

manufacturers may make anticipatory improvements if warranted given planned produce 

cadence). 
5-20 

Table 5-27 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved Fuel 

Consumption Rates for Method A, Relative to Alternative 1b a

MODEL 

YEAR 

STRINGENCY 

(VS. 2018) 

AVE. REQUIRED FUEL CONS. 

(GAL./100 MI.)

AVE. ACHIEVED FUEL CONS. 

(GAL./100 MI.)

No Action Proposed Reduction No Action Proposed Reduction 

2014 MYs 2014-

2020 Subject to 

Phase 1 

Standards 

 6.41 6.41 0.0% 6.21 6.21 0.0% 

2015 6.41 6.41 0.0% 6.12 6.12 0.0% 

2016 6.27 6.27 0.0% 6.15 6.15 0.0% 

2017 6.11 6.11 0.0% 5.89 5.88 0.2% 

2018 5.80 5.80 0.0% 5.75 5.70 0.8% 

2019 5.78 5.78 0.0% 5.72 5.68 0.7% 

2020 5.78 5.78 0.0% 5.69 5.64 0.8% 

2021 2.5% 5.77 5.64 2.2% 5.63 5.42 3.8% 

2022 4.9% 5.77 5.50 4.7% 5.63 5.42 3.8% 

2023 7.3% 5.77 5.38 6.8% 5.63 5.28 6.3% 

2024 9.6% 5.77 5.25 9.0% 5.63 5.23 7.1% 

2025 11.9% 5.77 5.12 11.4% 5.63 4.99 11.5% 

2026 14.1% 5.77 4.98 13.7% 5.63 4.93 12.5% 

2027 16.2% 5.77 4.86 15.8% 5.62 4.86 13.7% 

2028* 16.2% 5.77 4.86 15.8% 5.62 4.86 13.7% 

2029* 16.2% 5.77 4.86 15.8% 5.62 4.85 13.7% 

2030* 16.2% 5.77 4.86 15.8% 5.62 4.85 13.7% 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

*Absent further action, standards assumed to continue unchanged after model year 2027. 
5-21 

Table 5-28 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved CO2

Emission Rates for Method A, Relative to Alternative 1b a

MODEL 

YEAR 

STRINGENCY 

(VS. 2018) 

AVE. REQUIRED CO2 RATE 

(G./MI.)

AVE. ACHIEVED CO2 RATE 

(G./MI.)

No Proposed Reduction No Proposed Reduction 

2014 MYs 2014-

2020 Subject to 

Phase 1 

Standards 

 602 602 0.0% 581 581 0.0% 

2015 608 608 0.0% 578 578 0.0% 

2016 593 593 0.0% 580 580 0.0% 

2017 578 578 0.0% 556 554 0.2% 

2018 548 548 0.0% 543 538 0.8% 

2019 545 545 0.0% 539 535 0.7% 

2020 545 545 0.0% 536 532 0.8% 

2021 2.5% 544 532 2.2% 530 510 3.8% 

2022 4.9% 544 519 4.7% 530 510 3.8% 

2023 7.3% 544 507 6.8% 530 496 6.4% 

2024 9.6% 544 495 9.1% 530 492 7.2% 

2025 11.9% 544 482 11.3% 530 470 11.3% 

2026 14.1% 544 470 13.6% 530 465 12.3% 

2027 16.2% 544 458 15.8% 529 458 13.4% 

2028* 16.2% 544 458 15.8% 529 458 13.4% 

2029* 16.2% 544 458 15.8% 529 458 13.5% 

2030* 16.2% 544 458 15.8% 529 458 13.5% 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

*Absent further action, standards assumed to continue unchanged after model year 2027. 
5-22 

Table 5-29 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved Fuel 

Consumption Rates for Method A, Relative to Alternative 1a a

MODEL 

YEAR 

STRINGENCY 

(VS. 2018) 

AVE. REQUIRED FUEL CONS. 

(GAL./100 MI.)

AVE. ACHIEVED FUEL CONS. 

(GAL./100 MI.)

No Action Proposed Reduction No Action Proposed Reduction

2014 MYs 2014-

2020 Subject to 

Phase 1 

Standards 

 6.41 6.41 0.0% 6.21 6.21 0.0% 

2015 6.41 6.41 0.0% 6.12 6.12 0.0% 

2016 6.27 6.27 0.0% 6.15 6.15 0.0% 

2017 6.11 6.11 0.0% 5.89 5.87 0.3% 

2018 5.80 5.80 -0.1%** 5.75 5.70 0.9% 

2019 5.78 5.78 0.0% 5.73 5.68 0.8% 

2020 5.78 5.78 0.0% 5.73 5.68 0.8% 

2021 2.5% 5.77 5.64 2.3% 5.72 5.44 4.8% 

2022 4.9% 5.77 5.50 4.7% 5.72 5.44 4.8% 

2023 7.3% 5.77 5.38 6.8% 5.72 5.29 7.6% 

2024 9.6% 5.77 5.25 9.1% 5.72 5.23 8.5% 

2025 11.9% 5.77 5.12 11.4% 5.72 4.98 12.9% 

2026 14.1% 5.77 4.98 13.7% 5.72 4.94 13.6% 

2027 16.2% 5.77 4.86 15.8% 5.72 4.87 14.9% 

2028* 16.2% 5.77 4.86 15.8% 5.72 4.87 14.9% 

2029* 16.2% 5.77 4.86 15.8% 5.72 4.86 15.0% 

2030* 16.2% 5.77 4.86 15.8% 5.72 4.86 15.0% 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

*Absent further action, standards assumed to continue unchanged after model year 2027. 

**Increased work factor for some vehicles produces a slight increase in average required fuel consumption. 
5-23 

Table 5-30 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved CO2

Emission Rates for Method A, Relative to Alternative 1a a

MODEL 

YEAR 

STRINGENCY 

(VS. 2018) 

AVE. REQUIRED CO2 RATE 

(G./MI.)

AVE. ACHIEVED CO2 RATE 

(G./MI.)

No Proposed Reduction No Proposed Reduction 

2014 MYs 2014-

2020 Subject to 

Phase 1 

Standards 

 602 602 0.0% 581 581 0.0% 

2015 608 608 0.0% 578 578 0.0% 

2016 593 593 0.0% 580 580 0.0% 

2017 578 578 0.0% 556 554 0.3% 

2018 548 548 -0.1%** 543 538 0.9% 

2019 545 546 -0.1%** 539 535 0.8% 

2020 545 545 -0.1%** 539 535 0.8% 

2021 2.5% 544 532 2.2% 538 512 4.9% 

2022 4.9% 544 519 4.7% 538 512 4.9% 

2023 7.3% 544 507 6.8% 538 497 7.7% 

2024 9.6% 544 495 9.1% 538 492 8.6% 

2025 11.9% 544 482 11.4% 538 470 12.7% 

2026 14.1% 544 470 13.6% 538 466 13.4% 

2027 16.2% 544 458 15.8% 538 459 14.7% 

2028* 16.2% 544 458 15.8% 538 459 14.7% 

2029* 16.2% 544 458 15.8% 538 459 14.8% 

2030* 16.2% 544 458 15.8% 538 459 14.8% 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

*Absent further action, standards assumed to continue unchanged after model year 2027. 

**Increased work factor for some vehicles produces a slight increase in the average required CO2 emission rate. 

While the above tables show the agencies’ estimates of average fuel consumption and 

CO2 emission rates manufacturers might achieve under today’s proposed standards, total U.S. 

fuel consumption and GHG emissions from HD pickups and vans will also depend on how many 

of these vehicles are produced, and how they are operated over their useful lives. Relevant to 

estimating these outcomes, the CAFE model applies vintage-specific estimates of vehicle 

survival and mileage accumulation, and adjusts the latter to account for the rebound effect. This 

impact of the rebound effect is specific to each model year (and, underlying, to each vehicle 

model in each model year), varying with changes in achieved fuel consumption rates. For 

additional details, see Chapter 2 of the draft RIA. 

5.3.2.3.1.3.2 Method B for HD Pickups and Vans 

For Method B, MOVES model was used to estimate fuel consumption and GHG 

emissions for HD pickups and vans. MOVES evaluated the proposed standards for HD pickup 

trucks and vans in terms of grams of CO2 per mile or gallons of fuel per 100 miles. Since nearly 

all HD pickup trucks and vans are certified on a chassis dynamometer, the CO2 reductions for 

these vehicles were not represented as engine and road load reduction components, but rather as 

total vehicle CO2 reductions. The stringency increases relative to the Phase 1 standards for HD 

pickup trucks and vans (Table 5-32) were modified in the “emissionrate” table in MOVES. 
5-24 

Table 5-31 Estimated Total Vehicle CO2 Reductions for the Proposed Standards and In-Use Emissions for 

HD Pickup Trucks and Vans in Method B a

VEHICLE TYPE FUEL MODEL YEAR CO2 REDUCTION 

FROM REFERENCE 

CASE 

HD Pickup Trucks 

and Vans 

Gasoline 

and Diesel 

2021 2.50% 

2022 4.94% 

2023 7.31% 

2024 9.63% 

2025 11.89% 

2026 14.09% 

2027+ 16.24% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble 

Section X.A.1

5.3.2.3.2 VMT Inputs 

The “HPMSVtype” table in MOVES was modified to reflect the VMT rebound (VMT 

rebound is described in more detail in Chapter 8.3 of the draft RIA). This table estimated VMT 

values for all calendar years. For the control case, the absolute VMT for vocational vehicle and 

combination tractor-trailer were increased from the reference cases by 1.83 percent, and 0.79 

percent, respectively, to reflect the VMT rebound. Since VMT is applied by calendar year and 

not by model year, post-processing of the results were performed to ensure that only the model 

years affected by the program experienced VMT rebound – the results from the reference cases 

were used in the control case inventories for model years not affected by the proposed rules. 

For HD pickups and vans, Method A used the CAFE model which simulates VMT in a 

dynamic fashion that responds to changes in vehicle fuel economy and fuel prices and adjusts the 

marginal VMT of each vehicle model, at every age (so in each calendar year). In general, the 

more stringent alternatives considered lead to larger improvements in fuel economy and, thus, a 

greater number of vehicle miles traveled as a result of the rebound effect. In the CAFE model, 

the rebound effect represents a symmetric driver of changes to VMT; if the per-mile price of 

driving declines relative to today (either from improvements in vehicle fuel economy or declines 

in fuel prices), VMT increases by the amount of the rebound effect, conversely, if the per-mile 

price of driving increases relative to today (due to increases in the price of fuel), VMT will 

decline by the amount of the rebound effect. In Method B, the VMT rebound effect was 

modeled using the MOVES model which assumed an increase in VMT from the reference levels 

by 1.18 percent. 

5.3.3 Calculation of Upstream Emissions 
5-25 

The term "upstream emissions" refers to air pollutant emissions generated from all crude 

oil extraction, transport, refining, and finished fuel transport, storage, and distribution; this 

includes all stages prior to the final filling of vehicle fuel tanks at retail service stations. 

Additionally, it includes the production of renewable fuels and transportation of such fuel, either 

separately or mixed with conventional fuels. 

As described in Section 5.3.1, the decreased volumes of the crude based fuels and the 

various crude production and transport emission factors from GREET were used to estimate the 

net impact of fuel use changes on upstream emissions. The analysis for this proposed 

rulemaking assumes that all changes in volumes of fuel used affect only gasoline and diesel, with 

no effects on use of ethanol, biodiesel or other renewable fuels. The production and transport of 

these renewable fuels, although unchanged in volume for this analysis, are still accounted for in 

the total inventory in this proposed rulemaking. Although impacts to agriculture related to 

renewable fuels and the associated transport of these feedstocks were originally included in the 

RFS2 rulemaking, the effects to these sectors from the proposed regulations would be minimal 

and have therefore excluded them from this analysis. 

The agencies recognize the unique GHG emission characteristics associated with 

biofuels, and specifically that in the context of biofuels, “upstream emissions” include not only 

GHG emissions, but also any net biological sequestration that takes place. When considered on 

a lifecycle basis (including both tailpipe and upstream emissions), the net GHG emission impact 

of individual biofuels can vary significantly from both petroleum-based fuels and from one 

biofuel to another. EPA’s Renewable Fuel Standard (RFS) program, as modified by EISA, 

examined these differences in lifecycle emissions in detail. For example, EPA found that with 

respect to aggregate lifecycle emissions including non-tailpipe GHG emissions (such as 

feedstock growth, transportation, fuel production, and land use), lifecycle GHG emissions in 

2022 for biodiesel from soy, using certain advanced production technologies, are about 50 

percent less than diesel from petroleum. 

Non-GHG fuel production and distribution emission impacts of the program were 

estimated in conjunction with the development of lifecycle GHG emission impacts, and the GHG 

emission inventories discussed above. The basic calculation is a function of fuel volumes in the 

analysis year and the emission factors associated with each process or subprocess. It relies 

partially on the GREET model, but takes advantage of additional information and models to 

significantly strengthen and expand on the GREET analysis, as discussed in Section 5.3.1. The 

details of the assumptions, data sources, and calculations that were used to estimate the emission 

impacts presented here can be found in the docket memo, “Calculation of Upstream Emissions 

for the GHG Vehicle Rule,” initially created for use in the LD GHG rulemaking.21 The agencies 

note that to the extent future policy decisions involve upstream emissions, the agencies will need 

to consider the unique emission characteristics associated with biofuels. More broadly, the 

agencies recognize that biofuels, including biodiesel, will play an important role in reducing the 

nation’s dependence on foreign oil, thereby increasing domestic energy security. The volumes 

of renewable fuels are defined by the RFS2 standards as well as the Annual RFS rulemakings, 

and are projected using AEO2014. The volumes of renewable fuel for these standards remain in 

place regardless of overall volume of fuel affected by this proposed rulemaking. Therefore, we 

have assumed that the effect of this proposal on biofuels agriculture and transportation of raw 

agricultural goods would be minimal and excluded it from this analysis. 
5-26 

As described earlier, the agencies estimated the impact of the proposed rules on upstream 

using the downstream fuel consumption reductions predicted by MOVES for vocational vehicles 

and tractor-trailers. For HD pickups and vans, parallel and complementary analyses of 

estimating the emissions from upstream processes were conducted using the fuel consumption 

estimates from DOT’s CAFE model and EPA’s MOVES model, using Method A and B, 

respectively. As noted previously, these analyses corroborate each other’s results. 

5.3.4 Calculation of HFC EmissionsH 

EPA is proposing new air conditioning (A/C) leakage standards for vocational vehicles to 

reduce HFC emissions. The Vintaging Model, developed by EPA Office of Atmospheric 

programs, produces HFC inventories for several categories of stationary and mobiles sources. 

However, it does not include air conditioning systems in medium and heavy-duty trucks within 

its inventory calculations. For this proposal, we conducted an analysis based on the inputs to the 

Vintaging Model and the inputs to the MOVES analysis discussed in Chapter 5.3.2 above. 

The general equation for calculating HFC emissions follows: 

HFC emissionsYear x = A/C SystemsYear x x Average Charge Size x HFC loss rate 

We determined the number of functioning A/C systems in each year based on the 

projected sales of vehicles, the fraction of vehicles with A/C systems, and the average lifetime of 

an A/C system. Sales were drawn from the MOVES analysis and we assumed that every vehicle 

had a functioning A/C system when sold. The Vintaging Model assumes that all light-duty 

passenger vehicle A/C systems (in the U.S.) last exactly 12 years.I In the absence of other 

information, we assumed that heavy-duty vehicles A/C systems last for the same period of time 

as light-duty vehicles. Light, medium and heavy-duty vehicles use largely the same components 

in their air conditioning systems (sometimes from the same suppliers), which would indicate 

similar periods of durability. 

The charge size was determined using the Minnesota refrigerant leakage database.22 EPA 

sorted the data based on A/C charge size and evaluated only the largest 25 percent of A/C 

systems to be more representative of HD systems. The average charge size is 1,025 grams of 

refrigerant. 

Due to the similarity in system design, we assumed that the light-duty vehicle emission 

rate in the Vintaging Model was applicable to the current analysis, as shown in Table 5-32. The 

Vintaging Model assumes that losses occur from three events: leak, service, and disposal. 

Although vehicle A/C systems are serviced during discrete events and not usually every year, 

emissions from those events are averaged over the lifetime of the A/C system in the Vintaging 

 H The U.S. has submitted a proposal to the Montreal Protocol which, if adopted, would phase-down production and 

consumption of HFCs. 

I

 This is in agreement with the IPCC report IPCC/TEAP 2005 Safeguarding the Ozone Layer and the Global 

Climate System – Issues Related to Hydrofluorocarbons and Perfluorocarbons, which indicates lifetimes 

(worldwide) of 9 to 12 years. 
5-27 

model. Leak and service emissions are considered “annual losses” and are applied every year; 

disposal is considered an “end of life loss” and is applied only once for each vintage of vehicles.J

 

Table 5-32 Annual In-use Vehicle HFC134a Emission Rate from Vintaging Model 

KIND OF LOSS LOSS FRACTION 

Leakage 8%

Maintenance /Servicing 10%

End of Life 43%

The Vintaging Model assumes that charge loss is replaced every year; i.e., assuming an 

18 percent rate of charge loss, a vehicle with a charge of 1,000 grams would lose a constant rate 

of 180 grams per year. While this loss rate is not representative of any single given vehicle, it is 

assumed accurate for the fleet as a whole. Other emissions, such as fugitive emissions at a 

production facility, leaks from cylinders in storage, etc., are not explicitly modeled, but such 

emissions are accounted for within the average annual loss rate. 

EPA’s analysis of the Minnesota database of MY 2010 vehicles suggests that many of the 

modeled vehicles likely contain some of the technology required to meet the leakage standard, 

and as a consequence are leaking less. We assume that these improvements are independent of 

EPA regulation, rather than a preemptive response to regulation. Consequently, this rulemaking 

does not take credit for these emission reductions. 

Based on the Minnesota database, we determined that it is possible to reduce the HFC 

emissions from these vehicles on average by 13 percent. EPA calculated this based on the 

assumption that vehicles currently in the fleet which meet the MY 2014 standard would not 

make any additional improvements to reduce leakage. We also assumed that the systems which 

currently have leakage rates above the standard will reduce their leakage to the level of the 

standard. We then applied the 13 percent reduction to the baseline 18 percent leakage rate to 

develop a 15.6 percent leakage rate for MY 2014 and later vehicles to determine the reduction in 

emission rate which should be credited to this rulemaking.K 

We calculated our emission reductions based on the difference between the baseline case 

of 2010 vehicle technology (discussed above) and the control scenario where the loss prevention 

technology has been applied to 100 percent of the new vocational vehicles starting in 2021 

model year, as would be required by the proposed standards. 

 J

 The U.S. EPA has reclamation requirements for refrigerants in place under Title VI of the Clean Air Act. K Using 18 percent as the base emission rate may overstate the net emission reductions. However, recent number 

from the ERG Report to CARB studying the leakage rate of heavy-duty vehicles are actually much larger (range of 

near 0 to 150 percent annually), and places an 18 percent annual loss rate well within the literature. However, (a) the 

net impact is very small, (b) these numbers have significant uncertainty, and (c) it is unclear what the appropriate 

modification would be. 
5-28 

Total HFC reductions are 177 metric tons over the MY 2021 baseline A/C system in 

2035 and 210 metric tons in 2050. This is equivalent to a reduction of 253,118 metric tons of 

CO2eq emissions in 2035; and 299,590 metric tons CO2eq in 2050.L 

EPA reviewed a study conducted by the Eastern Research Group (ERG) of R134a leaks 

in heavy-duty vehicles to California Air Resources Board.23 The study included a total of 70 

medium- and heavy-duty vehicles and off-road equipment; of which 18 of the samples were HD 

tractors ranging between 1990 and 2008 model years. The mobile air conditioning capacity in 

the tractors ranged between 1,080 grams to 1,950 grams. The study measured HFC leakage 

during sample times which ranged between 0.3 and 0.6 years. ERG then calculated an 

annualized in-use leakage rate with an assumed linear projection of measured leak rates to annual 

leak rates, which may be an over-estimate. The annualize leakage rate for tractors ranged 

between nearly 0 to nearly 1.5 grams leakage per gram of MAC capacity. These leakage rates 

did not include other leakage sources such as maintenance or end of life recovery. ERG found 

that the average of all MD and HD trucks and equipment which were 2006 MY or newer had an 

average leakage of 103 grams of R134a per year. Based on these results, the agency believes 

that our estimates for HFC reductions may understate the benefits of the proposed program. The 

agency will continue to analyze this and other studies that may be conducted in the future. 

5.4 Greenhouse Gas Emission and Fuel Consumption Impacts 

The following subsections summarize two slightly different analyses of the annual GHG 

emissions and fuel consumption reductions expected from the proposed standards, as well as the 

reductions in GHG emissions and fuel consumption expected over the lifetime of each heavyduty vehicle categories. In addition, because the agencies are carefully considering Alternative 4 

along with Alternative 3, the preferred alternative, the results from both are presented here for 

the reader’s reference. Section 5.4.1 shows the impacts of the proposed rules and Alternative 4 

on fuel consumption and GHG emissions using the MOVES model for tractor-trailers and 

vocational vehicles, and the DOT’s CAFE model for HD pickups and vans (Method A), relative 

to two different reference cases – less dynamic and more dynamic. Section 5.4.2 shows the 

impacts of the proposed standards and Alternative 4, relative to the less dynamic reference case 

only, using the MOVES model for all heavy-duty vehicle categories. 

5.4.1 Impacts of the Proposed Rules and Alternative 4 using Analysis Method A 

5.4.1.1 Calendar Year Analysis 

5.4.1.1.1 Downstream Impacts 

The following two tables summarize the agencies’ estimates of HD pickup and van fuel 

consumption and GHG emissions under the current and proposed standards defining the NoAction and Preferred alternatives, respectively. The first shows results assuming manufacturers 

would voluntarily make improvements that pay back within six months (i.e., Alternative 1b). 

The second shows results assuming manufacturers would only make improvements as needed to 

 L Using a Global Warming Potential of 1,430 for HFC-134a. 
5-29 

achieve compliance with standards (i.e., Alternative 1a). While underlying calculations are all 

performed for each calendar year during each vehicle’s useful life, presentation of outcomes on a 

model year basis aligns more clearly with consideration of cost impacts in each model year, and 

with consideration of standards specified on a model year basis. In addition, the agencies 

performed explicit analysis of manufacturers’ potential responses to HD pickup and van 

standards on a model year basis through 2030, and any longer-term costs presented in today’s 

notice represent extrapolation of these results absent any underlying analysis of longer-term 

technology prospects and manufacturers’ longer-term product offerings. 

Table 5-33 Estimated Fuel Consumption and GHG Emissions over Useful Life of HD Pickups and Vans 

Produced in Each Model year for Method A, Relative to Alternative 1b a

MODEL 

YEAR 

FUEL CONSUMPTION (B. GAL.)

OVER FLEET’S USEFUL LIFE 

GHG EMISSIONS (MMT CO2EQ)

OVER FLEET’S USEFUL LIFE 

No Action Proposed Reduction No Action Proposed Reduction 

2014 9.41 9.41 0.0% 115 115 0.0% 

2015 9.53 9.53 0.0% 117 117 0.0% 

2016 9.72 9.72 0.0% 119 119 0.0% 

2017 9.49 9.47 0.2% 116 116 0.2% 

2018 9.26 9.19 0.7% 113 113 0.7% 

2019 9.20 9.14 0.7% 113 112 0.7% 

2020 9.19 9.12 0.7% 112 112 0.7% 

2021 9.10 8.79 3.4% 111 107 3.4% 

2022 9.13 8.82 3.4% 112 108 3.4% 

2023 9.11 8.59 5.7% 111 105 5.7% 

2024 9.32 8.72 6.4% 114 107 6.4% 

2025 9.49 8.49 10.5% 116 104 10.4% 

2026 9.67 8.56 11.5% 118 105 11.3% 

2027 9.78 8.55 12.6% 120 105 12.3% 

2028 9.90 8.66 12.6% 121 106 12.3% 

2029 10.02 8.75 12.6% 122 107 12.4% 

2030 10.03 8.76 12.6% 123 107 12.4% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-30 

Table 5-34 Estimated Fuel Consumption and GHG Emissions over Useful Life of HD Pickups and Vans 

Produced in Each Model year for Method A, Relative to Alternative 1a a

MODEL 

YEAR 

FUEL CONSUMPTION (B. GAL.) 

OVER FLEET’S USEFUL LIFE 

GHG EMISSIONS (MMT CO2EQ) 

OVER FLEET’S USEFUL LIFE 

No Action Proposed Reduction No Action Proposed Reduction 

2014 9.41 9.41 0.0% 115 115 0.0% 

2015 9.53 9.53 0.0% 117 117 0.0% 

2016 9.72 9.72 0.0% 119 119 0.0% 

2017 9.49 9.46 0.3% 116 116 0.3% 

2018 9.27 9.19 0.8% 114 113 0.8% 

2019 9.20 9.14 0.7% 113 112 0.7% 

2020 9.25 9.18 0.7% 113 112 0.8% 

2021 9.23 8.82 4.4% 113 108 4.4% 

2022 9.26 8.85 4.4% 113 108 4.4% 

2023 9.23 8.60 6.9% 113 105 6.9% 

2024 9.45 8.72 7.7% 116 107 7.7% 

2025 9.62 8.48 11.8% 118 104 11.7% 

2026 9.81 8.58 12.5% 120 105 12.3% 

2027 9.93 8.57 13.7% 121 105 13.5% 

2028 10.05 8.68 13.7% 123 106 13.5% 

2029 10.17 8.77 13.7% 124 108 13.5% 

2030 10.18 8.78 13.7% 124 108 13.5% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

To more clearly communicate these trends visually, the following two charts present the 

above results graphically for Method A, relative to Alternative 1b. As shown, fuel consumption 

and GHG emissions follow parallel though not precisely identical paths. Though not presented, 

charts for analysis relative to Alternative 1a would appear sufficiently similar that differences 

between Alternative 1a and Alternative 1b remain best communicated by comparing values in 

the above tables. 
5-31 

Figure 5-1 Fuel Consumption (b. gal.) over Useful Life of HD Pickups and Vans Produced in Each Model 

Year for Method A 
5-32 

Figure 5-2 GHG Emissions (MMT CO2eq) over Useful Life of HD Pickups and Vans Produced in Each 

Model Year for Method A 
5-33 

Table 5-35 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1b using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT 

CO2EQ) 

N2O 

(MMT 

CO2EQ)A

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -2.4 0 0 -2.4 

Vocational -2.4 0 0 -2.4 

Tractor-Trailers -22.1 -0.4 0 -22.4 

Total -26.9 -0.4 0 -27.2 

2035 HD Pickups and 

Vans -9.4 0 0 -9.4 

Vocational -11.9 0 0 -11.9 

Tractor-Trailers -64.7 -1 0 -65.6 

Total -86.0 -1 0 -86.9 

2050 HD Pickups and 

Vans -11.8 0 0 -11.8 

Vocational -17.2 0 0 -17.2 

Tractor-Trailers -92.6 -1.4 0 -94.0 

Total -121.6 -1.4 0 -123.0 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-36 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1a using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT 

CO2EQ) 

N2O 

(MMT 

CO2EQ)A

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -2.9 0 0 -2.9 

Vocational -2.4 0 0 -2.4 

Tractor-Trailers -22.5 -0.4 0 -22.8 

Total -27.7 -0.4 0 -28.1 

2035 HD Pickups and 

Vans -10.5 0 0 -10.5 

Vocational -11.9 0 0 -11.9 

Tractor-Trailers -71.1 -1 0 -72.1 

Total -93.6 -1 0 -94.6 

2050 HD Pickups and 

Vans -13.1 0 0 -13.1 

Vocational -17.2 0 0 -17.2 

Tractor-Trailers -103.2 -1.4 0 -104.6 

Total -133.5 -1.4 0 -134.9 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-34 

Table 5-37 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Alternative 4 vs. Alt 1b using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT 

CO2EQ) 

N2O 

(MMT 

CO2EQ)A

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -3.3 0 0 -3.3 

Vocational -4.0 0 0 -4.0 

Tractor-Trailers -25.9 -0.4 0 -26.2 

Total -33.2 -0.4 0 -33.5 

2035 HD Pickups and 

Vans -10.3 0 0 -10.3 

Vocational -12.9 0 0 -12.9 

Tractor-Trailers -66.7 -1 0 -67.7 

Total -89.9 -1 0 -90.9 

2050 HD Pickups and 

Vans -12.6 0 0 -12.6 

Vocational -17.3 0 0 -17.3 

Tractor-Trailers -92.8 -1.4 0 -94.2 

Total -122.6 -1.4 0 -124.0 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-38 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Alternative 4 vs. Alt 1a using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT 

CO2EQ) 

N2O 

(MMT 

CO2EQ)A

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -4.0 0 0 -4.0 

Vocational -4.0 0 0 -4.0 

Tractor-Trailers -26.2 -0.4 0 -26.6 

Total -34.3 -0.4 0 -34.6 

2035 HD Pickups and 

Vans -11.6 0 0 -11.6 

Vocational -12.9 0 0 -12.9 

Tractor-Trailers -73.2 -1 0 -74.2 

Total -97.7 -1 0 -98.7 

2050 HD Pickups and 

Vans -14.0 0 0 -14.0 

Vocational -17.3 0 0 -17.3 

Tractor-Trailers -103.3 -1.4 0 -104.7 

Total -134.6 -1.4 0 -136.0 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-35 

Table 5-39 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category – 

Preferred Alternative vs. Alt 1b using Analysis Method A a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.1 0.1 

Vocational 0.2 0 

Tractor-Trailers 2.2 0 

Total 2.5 0.2 

2035 HD Pickups and Vans 0.3 0.7

Vocational 1.0 0.2 

Tractor-Trailers 6.3 0 

Total 7.6 0.9 

2050 HD Pickups and Vans 0.3 0.9

Vocational 1.4 0.3 

Tractor-Trailers 9.1 0 

Total 10.8 1.2 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-40 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category – 

Preferred Alternative vs. Alt 1a using Analysis Method A a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.1 0.2 

Vocational 0.2 0 

Tractor-Trailers 2.2 0 

Total 2.5 0.2 

2035 HD Pickups and Vans 0.3 0.8

Vocational 1.0 0.2 

Tractor-Trailers 7.0 0 

Total 8.3 1.0 

2050 HD Pickups and Vans 0.4 1.0

Vocational 1.4 0.3 

Tractor-Trailers 10.1 0 

Total 11.9 1.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-36 

Table 5-41 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category – 

Alternative 4 vs. Alt 1b using Analysis Method A a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.1 0.2 

Vocational 0.3 0.1 

Tractor-Trailers 2.5 0 

Total 3.0 0.3 

2035 HD Pickups and Vans 0.3 0.8

Vocational 1.0 0.2 

Tractor-Trailers 6.5 0 

Total 7.9 1.0 

2050 HD Pickups and Vans 0.4 1.0

Vocational 1.4 0.3 

Tractor-Trailers 9.1 0 

Total 10.8 1.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-42 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category – 

Alternative 4 vs. Alt 1a using Analysis Method A a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.2 0.3 

Vocational 0.3 0.1 

Tractor-Trailers 2.6 0 

Total 3.1 0.3 

2035 HD Pickups and Vans 0.4 0.8

Vocational 1.0 0.2 

Tractor-Trailers 7.2 0 

Total 8.6 1.1 

2050 HD Pickups and Vans 0.5 1.0

Vocational 1.4 0.3 

Tractor-Trailers 10.1 0 

Total 12.0 1.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.4.1.1.1 Upstream Impacts 
5-37 

Table 5-43 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1b using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ)A

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -0.6 -0.1 0 -0.7 

Vocational -0.7 -0.1 0 -0.8 

Tractor-Trailers -7.0 -0.7 0 -7.8 

Total -8.4 -0.9 -0.1 -9.3 

2035 HD Pickups and 

Vans -2.5 -0.4 -0.1 -2.9 

Vocational -3.6 -0.4 0 -4.0 

Tractor-Trailers -20.6 -2.1 -0.1 -22.8 

Total -26.6 -2.8 -0.2 -29.7 

2050 HD Pickups and 

Vans -3.1 -0.4 -0.1 -3.7 

Vocational -5.1 -0.6 0 -5.7 

Tractor-Trailers -29.5 -3.0 -0.1 -32.6 

Total -37.7 -4.0 -0.3 -42.0 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-44 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1a using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ)A

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -0.7 -0.1 0 -0.9 

Vocational -0.7 -0.1 0 -0.8 

Tractor-Trailers -7.1 -0.7 0 -7.9 

Total -8.6 -0.9 -0.1 -9.6 

2035 HD Pickups and 

Vans -2.8 -0.4 -0.1 -3.3 

Vocational -3.6 -0.4 0 -4.0 

Tractor-Trailers -22.6 -2.3 -0.1 -25.0 

Total -29.0 -3.1 -0.2 -32.3 

2050 HD Pickups and 

Vans -3.5 -0.5 -0.1 -4.1 

Vocational -5.1 -0.6 0 -5.7 

Tractor-Trailers -32.8 -3.3 -0.2 -36.3 

Total -41.4 -4.4 -0.3 -46.1 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-38 

Table 5-45 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Alternative 4 vs. Alt 1b using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ)A

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -0.9 -0.1 0 -1.0 

Vocational -1.2 -0.1 0 -1.3 

Tractor-Trailers -8.2 -0.8 0 -9.1 

Total -10.3 -1.1 -0.1 -11.5 

2035 HD Pickups and 

Vans -2.7 -0.4 -0.1 -3.2 

Vocational -3.8 -0.4 0 -4.3 

Tractor-Trailers -21.2 -2.2 -0.1 -23.5 

Total -27.8 -3.0 -0.2 -31.0 

2050 HD Pickups and 

Vans -3.3 -0.5 -0.1 -4.0 

Vocational -5.1 -0.6 0 -5.7 

Tractor-Trailers -29.5 -3.0 -0.1 -32.7 

Total -38.0 -4.0 -0.3 -42.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-46 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Alternative 4 vs. Alt 1a using Analysis Method A a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ)A

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans -1.0 -0.2 0 -1.2 

Vocational -1.2 -0.1 0 -1.3 

Tractor-Trailers -8.4 -0.8 0 -9.2 

Total -10.6 -1.1 -0.1 -11.8 

2035 HD Pickups and 

Vans -3.1 -0.4 -0.1 -3.6 

Vocational -3.8 -0.4 0 -4.3 

Tractor-Trailers -23.3 -2.4 -0.1 -25.8 

Total -30.2 -3.2 -0.2 -33.7 

2050 HD Pickups and 

Vans -3.7 -0.5 -0.1 -4.4 

Vocational -5.1 -0.6 0 -5.7 

Tractor-Trailers -32.9 -3.3 -0.2 -36.4 

Total -41.7 -4.4 -0.3 -46.5 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-39 

5.4.1.1.1 HFC Impacts 

The projected HFC emission reductions due to the proposed AC leakage standards are 

estimated to be 93,272 metric tons of CO2eq in 2025, 253,118 metric tons of CO2eq in 2035, and 

299,590 metric tons CO2eq in 2050. 

5.4.1.1.2 Total (Downstream + Upstream + HFC) Impacts 

Table 5-47 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Preferred 

Alternative vs. Alt 1b using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -27.2 -86.9 -123.0 

Upstream -9.3 -29.7 -42.0

HFC -0.09 -0.25 -0.3

Total -36.6 -116.9 -165.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-48 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Preferred 

Alternative vs. Alt 1a using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -28.1 -94.6 -134.9 

Upstream -9.6 -32.3 -46.1

HFC -0.09 -0.25 -0.3

Total -37.8 -127.2 -181.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-49 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. 

Alt 1b using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -33.5 -90.9 -124.0 

Upstream -11.5 -31.0 -42.3

HFC -0.09 -0.25 -0.3

Total -45.1 -122.2 -166.6

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-40 

Table 5-50 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. 

Alt 1a using Analysis Method A a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -34.6 -98.7 -136.0 

Upstream -11.8 -33.7 -46.5

HFC -0.09 -0.25 -0.3

Total -46.5 -132.7 -182.8

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.4.1.1 Model Year Lifetime Analysis 

Table 5-51 Lifetime GHG Reductions and Fuel Savings by Heavy-Duty Vehicle Category – Summary for 

Model Years 2018-2029 using Analysis Method A a

 ALTERNATIVE 3 

(PROPOSED) 

ALTERNATIVE 4 

NO-ACTION ALTERNATIVE 

(BASELINE) 

1b (More 

Dynamic) 

1a (Less 

Dynamic) 

1b (More 

Dynamic) 

1a (Less 

Dynamic) 

Fuel Savings (Billion Gallons) 72.2 76.7 81.9 86.7 

 HD Pickups and Vans 7.8 8.9 9.4 10.8 

 Vocational 8.3 8.3 10.9 10.9 

 Tractor/Trailers 56.1 59.5 61.6 65.0 

Total GHG Reductions (MMT CO2eq) 986.5 1,047.4 1,114.8 1,181.1 

 HD Pickups and Vans 94.8 108.5 113.7 132.8 

 Vocational 109.7 109.7 143.0 143.0 

 Tractor/Trailers 782.0 829.2 858.1 905.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

5.4.2 Impacts of the Proposed Rules and Alternative 4 using Analysis Method B 

5.4.2.1 Calendar Year Analysis 

5.4.2.1.1 Downstream Impacts 

After all the MOVES runs and post-processing was completed, the less dynamic 

reference (Alternative 1a) and control case (Alternative 3) inventories were totaled for all heavyduty vehicle types and emission processes to estimate total downstream GHG and fuel 

consumption impacts of the program. 

To estimate the fuel savings from the proposed rules, the total energy consumption for all 

HD segments was run as a surrogate in MOVES since fuel consumption is not directly modeled 

in MOVES. Then, the total energy consumption was converted to fuel consumption based on 
5-41 

fuel heating values assumed in the Renewable Fuels Standard rulemakingM and used in the 

development of MOVES emission and energy rates.N 

Table 5-52 and Table 5-53 summarize these downstream GHG impacts in calendar years 

2025, 2035, and 2050, relative to Alternative 1a, for the preferred alternative and Alternative 4, 

respectively. Table 5-54 and Table 5-55 show the estimated fuel savings from the preferred 

alternative and Alternative 4 in 2025, 2035, and 2050, relative to Alternative 1a. The reductions 

in CO2 emissions result from all heavy-duty vehicle categories (including the engines associated 

with tractor-trailer combinations and vocational vehicles) due to engine and vehicle 

improvements. N2O emissions show a very slight increase because of a rebound in vehicle miles 

traveled (VMT). However, since N2O is produced as a byproduct of fuel combustion, the 

increase in N2O emissions is expected to be more than offset by the improvements in fuel 

efficiency from the proposed rules.O The methane emissions decrease primarily due to 

differences in hydrocarbon emission characteristics between on-road diesel engines and APUs. 

The amount of methane emitted as a fraction of total hydrocarbons is expected to be significantly 

less for APUs than for diesel engines. Overall, downstream GHG emissions would be reduced 

significantly. In addition, substantial fuel savings would be achieved from improved fuel 

efficiency. All emissions impacts reflect the heavy-duty sector only, and do not include 

emissions from light-duty vehicles or any other vehicle sector. 

 M Renewable Fuels Standards assumptions of 115,000 BTU/gallon gasoline (E0) and 76,330 BTU/gallon ethanol 

(E100) were weighted 90% and 10%, respectively, for E10 and 85% and 15%, respectively, for E15 and converted 

to kJ at 1.055 kJ/BTU. The conversion factors are 117,245 kJ/gallon for gasoline blended with ten percent ethanol 

(E10) and 115,205 kJ/gallon for gasoline blended with fifteen percent ethanol (E15). 

N The conversion factor for diesel is 138,451 kJ/gallon. See MOVES2004 Energy and Emission Inputs. EPA420-P05-003, March 2005. http://www.epa.gov/otaq/models/ngm/420p05003.pdf 

O MOVES is not capable of modeling the changes in exhaust N2O emissions from the improvements in fuel 

efficiency. Due to this limitation, a conservative approach was taken to only model the VMT rebounds in estimating 

the emissions impact on N2O from the proposed rules, resulting in a slight increase in downstream N2O inventory. 
5-42 

Table 5-52 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1a using Analysis Method B a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ) 

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans 

-2.1 0.0003 0.0005 -2.1 

Vocational -2.4 0.0009 0.0007 -2.4 

Tractor-Trailers -22.5 -0.4 0.0006 -22.9 

Total -27.0 -0.4 0.002 -27.4 

2035 HD Pickups and 

Vans 

-10.6 0.0007 0.001 -10.6 

Vocational -11.9 0.002 0.002 -11.9 

Tractor-Trailers -71.2 -1.0 0.001 -72.2 

Total -93.7 -1.0 0.004 -94.7 

2050 HD Pickups and 

Vans 

-14.8 0.0009 0.001 -14.8 

Vocational -17.2 0.003 0.002 -17.2 

Tractor-Trailers -103.1 -1.4 0.002 -104.5 

Total -135.1 -1.4 0.005 -136.5 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-53 Annual Downstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category –Alternative 4 vs. Alt 1a using Analysis Method B a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ) 

TOTAL 

DOWNSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans 

-3.1 0.0003 0.0005 -3.1 

Vocational -4.0 0.0009 0.0007 -4.0 

Tractor-Trailers -26.2 -0.4 0.0006 -26.6 

Total -33.3 -0.4 0.002 -33.7 

2035 HD Pickups and 

Vans 

-11.2 0.0007 0.001 -11.2 

Vocational -12.9 0.0024 0.002 -12.9 

Tractor-Trailers -73.2 -1.0 0.001 -74.2 

Total -97.3 -1.0 0.004 -98.3 

2050 HD Pickups and 

Vans 

-14.9 0.0009 0.001 -14.9 

Vocational -17.3 0.003 0.002 -17.3 

Tractor-Trailers -103.3 -1.4 0.002 -104.7 

Total -135.5 -1.4 0.005 -136.9 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-43 

Table 5-54 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category – 

Preferred Alternative vs. Alt 1a using Analysis Method B a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.1 0.1 

Vocational 0.2 0.05 

Tractor-Trailers 2.2 0 

Total 2.5 0.2 

2035 HD Pickups and Vans 0.5 0.6

Vocational 1.0 0.2 

Tractor-Trailers 7.0 0 

Total 8.5 0.8 

2050 HD Pickups and Vans 0.8 0.8

Vocational 1.4 0.3 

Tractor-Trailers 10.1 0 

Total 12.3 1.1 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-55 Annual Fuel Savings in Calendar Years 2025, 2035 and 2050 by Heavy-Duty Vehicle Category –

Alternative 4 vs. Alt 1a using Analysis Method B a

CY VEHICLE CATEGORY DIESEL SAVINGS 

(BILLION GALLONS) 

GASOLINE SAVINGS 

(BILLION GALLONS) 

2025 HD Pickups and Vans 0.2 0.2 

Vocational 0.3 0.1 

Tractor-Trailers 2.6 0 

Total 3.1 0.3 

2035 HD Pickups and Vans 0.6 0.6 

Vocational 1.0 0.3 

Tractor-Trailers 7.2 0 

Total 8.8 0.9 

2050 HD Pickups and Vans 0.8 0.8 

Vocational 1.4 0.3 

Tractor-Trailers 10.1 0 

Total 12.3 1.1 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.4.2.1.2 Upstream Impacts 

The upstream GHG impacts of preferred alternative and Alternative 4 associated with the 

production and distribution of gasoline and diesel from crude oil, relative to Alternative 1a, are 

summarized in Table 5-56 and Table 5-57, for calendar years 2025, 2035, and 2050. These 

estimates show impacts for domestic emission reductions only. Additionally, since this 
5-44 

rulemaking is not expected to impact biofuel volumes mandated by the Annual Renewable Fuel 

Standards (RFS) regulations, the impacts on upstream emissions from changes in biofuel 

feedstock (i.e., agricultural sources such as fertilizer, fugitive dust, and livestock) are not 

included. The reductions in upstream GHGs are proportional to the amount of fuel saved. 

Table 5-56 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Preferred Alternative vs. Alt 1a using Analysis Method B a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ) 

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans 

-0.6 -0.1 -0.003 -0.7 

Vocational -0.7 -0.1 -0.004 -0.8 

Tractor-Trailers -7.1 -0.7 -0.03 -7.8 

Total -8.4 -0.9 -0.04 -9.3 

2035 HD Pickups and 

Vans 

-2.9 -0.3 -0.02 -3.2 

Vocational -3.6 -0.4 -0.02 -4.0 

Tractor-Trailers -22.6 -2.3 -0.1 -25.0 

Total -29.1 -3.0 -0.1 -32.2 

2050 HD Pickups and 

Vans 

-4.0 -0.5 -0.02 -4.5 

Vocational -5.1 -0.6 -0.03 -5.7 

Tractor-Trailers -32.8 -3.3 -0.2 -36.3 

Total -41.9 -4.4 -0.2 -46.5 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-45 

Table 5-57 Annual Upstream GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 by HeavyDuty Vehicle Category – Alternative 4 vs. Alt 1a using Analysis Method B a

CY VEHICLE 

CATEGORY 

CO2 

(MMT) 

CH4 

(MMT CO2EQ) 

N2O 

(MMT CO2EQ) 

TOTAL 

UPSTREAM 

(MMT CO2EQ) 

2025 HD Pickups and 

Vans 

-0.8 -0.1 -0.005 -1.0 

Vocational -1.2 -0.1 -0.01 -1.3 

Tractor-Trailers -8.4 -0.8 -0.04 -9.2 

Total -10.4 -1.0 -0.1 -11.5 

2035 HD Pickups and 

Vans 

-3.0 -0.4 -0.02 -3.4 

Vocational -3.8 -0.4 -0.02 -4.2 

Tractor-Trailers -23.3 -2.4 -0.1 -25.8 

Total -30.1 -3.2 -0.1 -33.4 

2050 HD Pickups and 

Vans 

-4.0 -0.5 -0.02 -4.5 

Vocational -5.1 -0.6 -0.03 -5.7 

Tractor-Trailers -32.9 -3.3 -0.2 -36.4 

Total -42.0 -4.4 -0.2 -46.6 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.4.2.1.3 HFC Impacts 

Based on projected HFC emission reductions due to the proposed AC leakage standards, 

EPA estimates the HFC reductions to be 93,272 metric tons of CO2eq in 2025, 253,118 metric 

tons of CO2eq in 2035, and 299,590 metric tons CO2eq in 2050. 

5.4.2.1.4 Total (Downstream + Upstream + HFC) Impacts 

The combined annual GHG emissions reductions of preferred alternative from 

downstream, upstream, and HFC, relative to Alternative 1a, are summarized in Table 5-58 for 

calendar years 2025, 2035 and 2050. The combined impact of Alternative 4 on total GHG 

emissions are shown in Table 5-59. Because of the differences in lead time, as expected, 

Alternative 4 shows greater annual GHG reductions in earlier years (i.e., calendar year 2025), but 

by 2050, the preferred alternative and Alternative 4 show the same magnitude of reductions in 

annual GHG emissions. 
5-46 

Table 5-58 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Preferred 

Alternative vs. Alt 1a using Analysis Method B a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -27.4 -94.7 -136.5 

Upstream -9.3 -32.2 -46.5

HFC -0.1 -0.25 -0.3

Total -36.8 -127.2 -183.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-59 Annual Total GHG Emissions Impacts in Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. 

Alt 1a using Analysis Method B a

CY 2025 (MMT CO2EQ) 2035 (MMT CO2EQ) 2050 (MMT CO2EQ) 

Downstream -33.7 -98.3 -136.9 

Upstream -11.5 -33.4 -46.6

HFC -0.1 -0.25 -0.3

Total -45.3 -132.0 -183.8

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Figure 5-3 graphically illustrates the total annual GHG trends for both Phase 1 and Phase 

2 proposal, using Method B, for calendar years from 2014 to 2050. The less dynamic baseline 

from Phase 2 proposal is assumed to be equivalent to the Phase 1 program. 
5-47 

Figure 5-3 Total Annual GHG Trends for Phase 1 and Phase 2 Proposal, using Analysis Method B 

5.4.2.2 Model Year Lifetime Analysis 

In addition to the annual GHG emissions and fuel consumption reductions expected from 

the proposed rules and Alternative 4, the combined (downstream and upstream) GHG and fuel 

consumption impacts over the model year lifetimes of the impacted vehicles sold in the 

regulatory timeframe were estimated. In contrast to the calendar year analysis, the model year 

lifetime analyses show the impacts of the program on each of these model year fleets over the 

course of their lifetimes. Table 5-60 shows the fleet-wide GHG reductions and fuel savings from 

the proposed rules and Alternative 4 through the lifetimeP

 of heavy-duty vehicles, relative to 

Alternative 1a. In addition, because the agencies are carefully considering Alternative 4 along 

with the preferred alternative, the lifetime GHG reductions and fuel savings of Alternative 4 are 

presented as well in Table 5-60. Compared to the preferred alternative, Alternative 4 shows 

greater lifetime GHG reductions and fuels savings by 12 percent and 13 percent, respectively. 

 P A lifetime of 30 years is assumed in MOVES. 
5-48 

Table 5-60 Lifetime GHG Reductions and Fuel Savings by Heavy-Duty Vehicle Category – Summary for 

Model Years 2018-2029 using Analysis Method B a

 ALTERNATIVE 3 

(PROPOSED) 

ALTERNATIVE 4 

NO-ACTION ALTERNATIVE (BASELINE) 1a (Less Dynamic) 1a (Less Dynamic) 

Fuel Savings (Billion Gallons) 75.8 85.4 

 HD Pickups and Vans 8.0 9.5 

 Vocational 8.3 10.9 

 Tractor/Trailers 59.5 65.0 

Total GHG Reductions (MMT CO2eq) 1,036.4 1,163.1 

 HD Pickups and Vans 97.5 114.8 

 Vocational 109.7 143.0 

 Tractor/Trailers 829.2 905.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

Furthermore, the combined lifetime GHG reductions and fuel savings of Phase 1 and 

proposed Phase 2 programs are presented in Table 5-62. To be consistent with the emissions 

modeling done for this proposed program, the lifetime GHG reductions and fuel savings from 

Phase 1 were estimated using the same modeling tools used in the proposed program. 

Table 5-61 Combined Lifetime GHG Reductions and Fuel Savings of Phase 1 and Proposed Phase 2 Program 

using Analysis Method B a

TOTAL GHG REDUCTIONS 

(MMT CO2EQ) 

FUEL SAVINGS 

(BILLION GALLONS) 

Phase 1 

 MY 2014-2018 313 23 

 MY 2019-2029 1,020 75 

Phase 2 - Proposed 

 MY 2018-2029 1,036 76 

Combined Total 2,369 174 

Note: 

a

 For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

5.5 Non-Greenhouse Gas Emission Impacts 

The proposed heavy-duty vehicle standards and Alternative 4 are expected to influence 

the emissions of criteria air pollutants and several air toxics. Similar to Section 5.4, the 

following subsections summarize two slightly different analyses of the annual non-GHG 

emissions reductions expected from the proposed standards. Section 5.5.1 shows the impacts of 

the proposed rules and Alternative 4 on non-GHG emissions using the analytical Method A, 

relative to two different reference cases – less dynamic and more dynamic. Section 5.5.2 shows 
5-49 

the impacts of the proposed standards and Alternative 4, relative to the less dynamic reference 

case only, using the MOVES model for all heavy-duty vehicle categories. 

5.5.1 Impacts of the Proposed Rules and Alternative 4 using Analysis Method A 

5.5.1.1 Calendar Year Analysis 

5.5.1.1.1 Downstream Impacts 

Table 5-62 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Preferred Alternative vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -3% -21 -12% -30 -16% 

Acetaldehyde -669 -10% -1,882 -31% -2,667 -36% 

Acrolein -97 -10% -272 -31% -385 -37% 

Benzene -123 -6% -347 -19% -490 -24% 

CO -26,485 -3% -75,199 -8% -106,756 -9% 

Formaldehyde -2,100 -12% -5,910 -32% -8,376 -37% 

NOX -92,444 -7% -260,949 -28% -370,663 -34% 

PM2.5 643 2% 1,722 8% 2,410 10% 

SOX -229 -4% -715 -13% -1,026 -15% 

VOC -13,161 -6% -38,051 -21% -54,139 -26% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-63 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Preferred Alternative vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -3% -21 -12% -30 -16% 

Acetaldehyde -669 -10% -1,880 -31% -2,664 -36% 

Acrolein -97 -10% -271 -31% -384 -37% 

Benzene -123 -6% -346 -19% -490 -24% 

CO -26,576 -3% -75,571 -8% -107,287 -9% 

Formaldehyde -2,100 -12% -5,904 -32% -8,369 -37% 

NOX -93,197 -8% -266,890 -29% -380,303 -35% 

PM2.5 632 2% 1,635 8% 2,267 9% 

SOX -232 -4% -776 -14% -1,125 -16% 

VOC -13,210 -6% -38,964 -22% -55,628 -26% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-50 

Table 5-64 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Alternative 4 vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -2% -21 -12% -30 -16% 

Acetaldehyde -669 -10% -1,882 -31% -2,667 -36% 

Acrolein -97 -10% -271 -31% -385 -37% 

Benzene -124 -6% -347 -19% -490 -24% 

CO -26,705 -3% -75,407 -8% -106,874 -9% 

Formaldehyde -2,100 -12% -5,908 -32% -8,375 -37% 

NOX -93,984 -8% -262,150 -28% -370,704 -34% 

PM2.5 619 2% 1,705 8% 2,412 10% 

SOX -280 -5% -742 -13% -1,029 -15% 

VOC -13,925 -7% -38,472 -22% -54,150 -26% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-65 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Alternative 4 vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -2% -21 -12% -29 -16% 

Acetaldehyde -668 -10% -1,880 -31% -2,664 -36% 

Acrolein -97 -10% -271 -31% -384 -37% 

Benzene -124 -6% -346 -19% -489 -24% 

CO -26,821 -3% -75,795 -8% -107,414 -9% 

Formaldehyde -2,099 -12% -5,902 -32% -8,367 -37% 

NOX -94,724 -8% -268,075 -29% -380,328 -35% 

PM2.5 609 2% 1,618 8% 2,269 9% 

SOX -282 -5% -803 -14% -1,127 -16% 

VOC -13,971 -7% -39,383 -22% -55,638 -26% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.5.1.1.2 Upstream Impacts 
5-51 

Table 5-66 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Preferred Alternative vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -5% -3 -14% -5 -17% 

Acetaldehyde -3 -3% -10 -11% -15 -13% 

Acrolein 0 -4% -1 -12% -2 -15% 

Benzene -21 -4% -74 -13% -104 -15% 

CO -3,798 -5% -12,087 -14% -17,120 -17% 

Formaldehyde -19 -5% -59 -14% -84 -17% 

NOX -9,472 -5% -30,333 -14% -42,839 -17% 

PM2.5 -1,019 -5% -3,257 -14% -4,609 -17% 

SOX -5,983 -5% -19,190 -14% -27,074 -17% 

VOC -3,066 -4% -11,029 -13% -15,386 -15% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-67 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Preferred Alternative vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -5% -4 -15% -5 -18% 

Acetaldehyde -3 -3% -11 -12% -16 -14% 

Acrolein 0 -4% -1 -13% -2 -15% 

Benzene -22 -4% -80 -14% -113 -16% 

CO -3,911 -5% -13,153 -15% -18,794 -18% 

Formaldehyde -19 -5% -65 -15% -92 -18% 

NOX -9,787 -5% -33,021 -15% -47,028 -18% 

PM2.5 -1,051 -5% -3,545 -15% -5,058 -18% 

SOX -6,189 -5% -20,896 -15% -29,726 -18% 

VOC -3,193 -4% -11,848 -13% -16,625 -16% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-52 

Table 5-68 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Alternative 4 vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -6% -3 -15% -5 -17% 

Acetaldehyde -4 -5% -11 -12% -15 -14% 

Acrolein -1 -5% -1 -13% -2 -15% 

Benzene -28 -5% -78 -13% -105 -16% 

CO -4,679 -6% -12,640 -15% -17,263 -17% 

Formaldehyde -23 -6% -62 -15% -85 -17% 

NOX -11,708 -6% -31,769 -15% -43,263 -17% 

PM2.5 -1,259 -6% -3,408 -15% -4,649 -17% 

SOX -7,402 -6% -20,107 -15% -27,356 -17% 

VOC -4,081 -5% -11,717 -13% -15,645 -15% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-69 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Alternative 4 vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -6% -4 -16% -5 -18% 

Acetaldehyde -4 -5% -12 -12% -16 -14% 

Acrolein -1 -5% -1 -13% -2 -16% 

Benzene -29 -5% -84 -14% -114 -17% 

CO -4,816 -6% -13,720 -16% -18,945 -18% 

Formaldehyde -24 -6% -67 -16% -93 -18% 

NOX -12,098 -6% -34,501 -16% -47,477 -18% 

PM2.5 -1,298 -6% -3,700 -16% -5,101 -18% 

SOX -7,658 -6% -21,843 -16% -30,024 -18% 

VOC -4,251 -5% -12,541 -14% -16,870 -16% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.5.1.1.3 Total Impacts 
5-53 

Table 5-70 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -3% -25 -13% -34 -16% 

Acetaldehyde -672 -10% -1,893 -30% -2,682 -36% 

Acrolein -97 -10% -273 -31% -387 -37% 

Benzene -145 -5% -421 -18% -595 -22% 

CO -30,282 -3% -87,286 -8% -123,876 -10% 

Formaldehyde -2,119 -11% -5,969 -32% -8,460 -37% 

NOX -101,916 -7% -291,282 -26% -413,501 -31% 

PM2.5 -376 -1% -1,535 -3% -2,199 -4% 

SOX -6,213 -5% -19,905 -14% -28,101 -17% 

VOC -16,227 -6% -49,080 -18% -69,525 -22% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-71 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -3% -25 -13% -35 -16% 

Acetaldehyde -672 -10% -1,891 -30% -2,680 -36% 

Acrolein -97 -10% -273 -31% -386 -37% 

Benzene -145 -5% -425 -18% -603 -22% 

CO -30,487 -3% -88,724 -8% -126,081 -10% 

Formaldehyde -2,119 -11% -5,969 -32% -8,461 -37% 

NOX -102,983 -7% -299,911 -26% -427,332 -32% 

PM2.5 -419 -1% -1,910 -4% -2,791 -5% 

SOX -6,421 -5% -21,672 -15% -30,850 -18% 

VOC -16,403 -6% -50,812 -19% -72,253 -23% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-54 

Table 5-72 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1b using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -3% -25 -13% -34 -16% 

Acetaldehyde -673 -10% -1,893 -30% -2,682 -36% 

Acrolein -97 -10% -273 -31% -387 -37% 

Benzene -152 -6% -426 -18% -595 -22% 

CO -31,383 -3% -88,047 -8% -124,137 -10% 

Formaldehyde -2,123 -11% -5,970 -32% -8,460 -37% 

NOX -105,693 -7% -293,918 -26% -413,967 -31% 

PM2.5 -639 -1% -1,703 -4% -2,237 -4% 

SOX -7,682 -6% -20,849 -15% -28,385 -17% 

VOC -18,006 -6% -50,189 -19% -69,796 -22% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 5-73 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1a using Analysis Method A a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -3% -25 -13% -35 -16% 

Acetaldehyde -672 -10% -1,891 -30% -2,679 -36% 

Acrolein -97 -10% -273 -31% -386 -37% 

Benzene -153 -6% -430 -18% -603 -22% 

CO -31,637 -3% -89,514 -8% -126,360 -10% 

Formaldehyde -2,123 -11% -5,969 -32% -8,460 -37% 

NOX -106,822 -7% -302,575 -26% -427,805 -32% 

PM2.5 -689 -1% -2,082 -5% -2,833 -5% 

SOX -7,941 -6% -22,646 -16% -31,151 -18% 

VOC -18,222 -6% -51,924 -19% -72,509 -23% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

 
5-55 

5.5.1.2 Model Year Lifetime Analysis 

Table 5-74 Lifetime Non-GHG Reductions by Heavy-Duty Vehicle Category – Summary for Model Years 

2018-2029 using Analysis Method A (US Short Tons) a

 ALTERNATIVE 3 

(PROPOSED) 

ALTERNATIVE 4 

NO-ACTION ALTERNATIVE 

(BASELINE) 

1b (More 

Dynamic) 

1a (Less 

Dynamic) 

1b (More 

Dynamic) 

1a (Less 

Dynamic) 

NOX 2,359,548 2,409,738 2,420,931 2,472,021 

 HD Pickups and Vans 24,663 27,772 30,213 34,222 

 Vocational 15,810 15,810 24,265 24,265 

 Tractor/Trailers 2,319,075 2,366,156 2,366,453 2,413,534 

PM2.5 13,496 15,706 17,524 19,839 

 HD Pickups and Vans 2,502 2,842 3,038 3,484 

 Vocational 2,509 2,509 3,421 3,421 

 Tractor/Trailers 8,485 10,355 11,065 12,934 

SOX 167,415 177,948 189,670 200,992 

 HD Pickups and Vans 19,221 21,813 23,395 26,776 

 Vocational 17,295 17,295 22,416 22,416 

 Tractor/Trailers 130,899 138,840 143,859 151,800 

Note: 

a

 For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

5.5.2 Impacts of the Proposed Rules and Alternative 4 using Analysis Method B 

5.5.2.1 Calendar Year Analysis 

5.5.2.1.1 Downstream Impacts 

After all the MOVES runsQ and post-processing were completed, the less dynamic 

reference (Alternative 1a) and control case (Alternative 3) inventories were aggregated for all 

vehicle types and emission processes to estimate the total downstream non-GHG impacts of the 

proposed program. Table 5-75 and Table 5-76 summarize these downstream non-GHG impacts 

of preferred alternative and Alternative 4 for calendar years 2025, 2035 and 2050, relative to 

Alternative 1a. The results are shown both in changes in absolute tons and in percent reductions 

from the less dynamic reference to alternatives for the heavy-duty sector. 

The agencies expect the proposed program to impact the downstream emissions of nonGHG pollutants. These pollutants include oxides of nitrogen (NOX), oxides of sulfur (SOX),

 Q For non-GHGs, MOVES was run only for January and July and the annual emissions were extrapolated by scaling 

up each month by a factor of 5.88 for all pollutants except particulate matter (PM). For PM, to offset the 

disproportionate effect of the cold temperature on January results, a scaling factor of 4.3 was applied to January and 

7.5 to July; these factors were determined based on analysis of annual PM emissions during modeling for the RFS2 

rule. Note that for GHGs, MOVES was run for all months. 
5-56 

volatile organic compounds (VOC), carbon monoxide (CO), fine particulate matter (PM2.5), and 

air toxics. The agencies are expecting reductions in downstream emissions of NOX, VOC, SOX, 

CO, and air toxics. Much of these estimated net reductions are a result of the agencies’ 

anticipation of increased use of auxiliary power units (APUs) in combination tractors during 

extended idling; APUs emit these pollutants at a lower rate than on-road engines during extended 

idle operation, with the exception of PM2.5. 

Additional reductions in tailpipe emissions of NOX and CO and refueling emissions of 

VOC would be achieved through improvements in engine efficiency and reduced road load 

(improved aerodynamics and tire rolling resistance), which reduces the amount of work required 

to travel a given distance and increases fuel economy. 

For vehicle types not affected by road load improvements, such as HD pickups and 

vansR, non-GHG emissions would increase very slightly due to VMT rebound. In addition, 

brake wear and tire wear emissions of PM2.5 would also increase very slightly due to VMT 

rebound. The agencies estimate that downstream emissions of SOX would be reduced, because 

they are roughly proportional to fuel consumption. Alternative 4 would have directionally 

similar effects as the preferred alternative. 

Table 5-75 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Preferred Alternative vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -2.6% -22 -15.1% -31 -19.6% 

Acetaldehyde -670 -10.3% -1,884 -31.0% -2,671 -36.5% 

Acrolein -97 -9.9% -272 -31.6% -385 -37.3% 

Benzene -125 -5.9% -353 -21.0% -501 -25.7% 

CO -25,824 -1.7% -72,960 -6.0% -103,887 -7.6% 

Formaldehyde -2,102 -11.5% -5,911 -32.1% -8,379 -37.5% 

NOX -93,220 -7.5% -267,125 -29.1% -380,721 -35.2% 

PM2.5 634 1.6% 1,631 7.6% 2,257 9.1% 

SOX -254 -4.8% -876 -15.0% -1,264 -18.1% 

VOC -13,440 -6.4% -40,148 -21.7% -57,308 -26.1% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

 R HD pickups and vans are subject to gram per mile (distance) emissions standards, as opposed to larger heavy-duty 

vehicles which are certified to a gram per brake horsepower (work) standard. 
5-57 

Table 5-76 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 

and 2050 – Alternative 4 vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -8 -2.6% -22 -15.1% -31 -19.6% 

Acetaldehyde -670 -10.3% -1,884 -31.0% -2,671 -36.5% 

Acrolein -97 -9.9% -272 -31.6% -385 -37.3% 

Benzene -126 -5.9% -354 -21.0% -501 -25.7% 

CO -25,919 -1.7% -73,041 -6.0% -103,891 -7.6% 

Formaldehyde -2,101 -11.5% -5,910 -32.1% -8,378 -37.5% 

NOX -94,787 -7.6% -268,373 -29.2% -380,810 -35.2% 

PM2.5 610 1.5% 1,611 7.5% 2,256 9.1% 

SOX -313 -5.9% -909 -15.6% -1,267 -18.1% 

VOC -14,310 -6.8% -40,640 -22.0% -57,348 -26.1% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

As shown in Table 5-77, a net increase in downstream PM2.5 emissions is expected in 

both 2035 and 2050. Although the improvements in engine efficiency and road load are 

expected to reduce tailpipe emissions of PM2.5, the projected increased use (Table 5-23) of APUs 

would lead to higher PM2.5 emissions that more than offset the reductions from the tailpipe, since 

engines powering APUs are currently required to meet less stringent PM standards than on-road 

engines. Therefore, EPA conducted an evaluation of a program that would reduce the 

unintended consequence of increase in PM2.5 emissions from increased APU use by fitting the 

APU with a diesel particulate filter or having the APU exhaust plumbed into the vehicle’s 

exhaust system upstream of the particulate matter aftertreatment device. Such program requiring 

additional PM2.5 controls on APU could significantly reduce PM2.5 emissions, as shown in Table 

5-77 below. For additional details, see Section III.C.3 of the preamble. 

Table 5-77 Projected Impact on PM2.5 Emissions of Further PM2.5 Control on APUs using Analysis Method 

Ba

CY PROPOSED 

PROGRAM 

INVENTORY 

WITHOUT 

FURTHER PM2.5

CONTROL ON 

APUS

PROPOSED 

PROGRAM 

INVENTORY 

WITH FURTHER 

PM2.5 CONTROL 

ON APUS 

NET IMPACT OF 

FURTHER PM2.5

CONTROL ON 

APUS 

2035 23,083 19,999 -3,084 

2050 26,932 22,588 -4,344 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble 

Section X.A.1
5-58 

It is worth noting that the emission reductions shown in Table 5-75 are not incremental to 

the emissions reductions projected in the Phase 1 rulemaking. This is because the agencies have 

revised their assumptions about the adoption rate of APUs. This proposal assumes that without 

the proposed Phase 2 program (i.e., in the Phase 2 reference case), the APU adoption rate will be 

30 percent for model years 2010 and later, which is the value used in the Phase 1 reference case. 

This decision was based on the agencies’ assessment of how the current level of automatic 

engine shutdown and idle reduction technologies are used by the tractor manufacturers to comply 

with the 2014 model year CO2 and fuel consumption standards. To date, the manufacturers are 

meeting the 2014 model year standards without the use of this technology. Compared to Phase 

1, the proposed program projects much delayed penetration of APUs starting in model year 2021 

( Figure 5-4). 

 Figure 5-4 Comparison of Assumed APU Use during Extended Idle in Phase 1 and Phase 2 

Considering the change in assumptions about APU use and the magnitude of impact of 

APUs on criteria emissions, EPA conducted an analysis estimating the combined emissions 

impacts of the Phase 1 and proposed Phase 2 programs for NOX, VOC, SOX and PM2.5 in 

calendar year 2050. The analysis estimated the combined Phase 1 and Phase 2 emissions 

impacts by comparing the Phase 2 control case inventories to the Phase 1 reference case 

inventories. To be consistent with the emissions modeling done for this proposed program, the 

emissions inventories for Phase 1 reference case were estimated using MOVES2014.S

 The 

results are shown in Table 5-78. The differences in downstream reduction estimates between 

 S The emissions modeling for Phase 1 was performed using MOVES2010a. 
5-59 

Phase 2 alone (Table 5-75) and combined Phase 1 and Phase 2 (Table 5-78) reflect the 

improvements in road loads from Phase 1. For NOX and PM2.5 only, we also estimated the 

combined Phase 1 and Phase 2 downstream and upstream emissions impacts for calendar year 

2025, and project that the two rules combined would reduce NOX by up to 120,000 tons and 

PM2.5 by up to 2,000 tons in that year. 

Table 5-78 Combined Phase 1 and Phase 2 Annual Downstream Reductions of Heavy-Duty Criteria 

Emissions in Calendar Year 2050 using Analysis Method B a

CY NOX VOC SOX PM2.5 

b

2050 403,915 69,415 2,111 -1,890

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 

b

 Negative reduction reflects an increase in emissions. 

5.5.2.1.2 Upstream Impacts 

The proposed program is projected to provide emissions reductions in all pollutants 

associated with upstream from production and distribution as the projected fuel savings reduce 

the demands for gasoline and diesel. Table 5-79 and Table 5-80 summarize the annual upstream 

reductions of preferred alternative and Alternative 4 for criteria pollutants and individual air 

toxic pollutants in calendar years 2025, 2035 and 2050, relative to Alternative 1a. The results 

are shown both in changes in absolute tons and in percent reductions from the less dynamic 

reference to alternatives for the heavy-duty sector. 

Table 5-79 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Preferred Alternative vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -5.0% -4 -15.3% -5 -18.4% 

Acetaldehyde -4 -3.0% -18 -11.9% -26 -14.6% 

Acrolein -0.5 -3.4% -2 -12.7% -3 -15.5% 

Benzene -24 -3.8% -92 -13.4% -132 -16.3% 

CO -3,798 -4.9% -13,001 -15.3% -18,772 -18.4% 

Formaldehyde -19 -4.7% -67 -14.9% -98 -18.0% 

NOX -9,282 -4.9% -31,782 -15.3% -45,888 -18.4% 

PM2.5 -1,020 -4.9% -3,514 -15.2% -5,072 -18.2% 

SOX -5,817 -4.9% -19,902 -15.3% -28,736 -18.4% 

VOC -3,283 -3.7% -12,724 -13.2% -18,214 -16.1% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
5-60 

Table 5-80 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025, 2035 and 

2050 – Alternative 4 vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -1 -6.1% -4 -15.9% -5 -18.4%

Acetaldehyde -6 -4.3% -20 -12.6% -26 -14.7% 

Acrolein -1 -4.7% -2 -13.3% -3 -15.5% 

Benzene -32 -5.0% -97 -14.0% -133 -16.3% 

CO -4,661 -6.1% -13,485 -15.9% -18,812 -18.4% 

Formaldehyde -24 -5.9% -70 -15.5% -97 -18.0% 

NOX -11,393 -6.1% -32,965 -15.9% -45,986 -18.4% 

PM2.5 -1,256 -6.0% -3,647 -15.7% -5,083 -18.3% 

SOX -7,137 -6.1% -20,641 -15.9% -28,797 -18.4% 

VOC -4,342 -4.9% -13,326 -13.8% -18,273 -16.1% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.5.2.1.3 Total Impacts 

As shown in Table 5-81 and Table 5-82, the agencies estimate that this proposed program 

and Alternative 4 would result in overall net reductions of NOX, VOC, SOX, CO, PM2.5, and air 

toxics emissions. The downstream increase in PM2.5 due to APU use is expected to be more than 

offset by reductions in PM2.5 from upstream.T The results are shown both in changes in absolute 

tons and in percent reductions from the less dynamic reference to the alternatives for the heavyduty sector. By 2050, the total impacts of the proposed program and Alternative 4 on criteria 

pollutants and air toxics are indistinguishable. 

 T Although a net reduction in PM2.5 is expected at the national level, it is unlikely that the geographic location of 

increases in downstream PM2.5 emissions will coincide with the location of decreases in upstream PM2.5 emissions. 

For the final rulemaking, a national-scale air quality modeling analysis will be performed to estimate the future year 

ambient PM2.5 concentrations for 2040. For further details, see Section VIII.D of this preamble and Chapter 8 of the 

draft RIA. 
5-61 

Table 5-81 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Preferred Alternative vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -2.7% -25 -15.1% -36 -19.4% 

Acetaldehyde -674 -10.1% -1,902 -30.5% -2,697 -36.0% 

Acrolein -97 -9.8% -274 -31.3% -388 -36.9% 

Benzene -149 -5.4% -445 -18.8% -633 -22.9% 

CO -29,622 -1.9% -85,961 -6.6% -122,659 -8.4% 

Formaldehyde -2,121 -11.4% -5,978 -31.7% -8,475 -37.0% 

NOX -102,502 -7.2% -298,907 -26.6% -426,610 -32.1% 

PM2.5 -386 -0.6% -1,883 -4.2% -2,815 -5.4% 

SOX -6,070 -4.9% -20,777 -15.3% -30,000 -18.4% 

VOC -16,724 -5.6% -52,872 -18.8% -75,521 -22.7% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 

Table 5-82 Annual Total Impacts (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions in 

Calendar Years 2025, 2035 and 2050 – Alternative 4 vs. Alt 1a using Analysis Method B a

POLLUTANT CY2025 CY2035 CY2050 

US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction US Short 

Tons 

% Reduction 

1,3-Butadiene -9 -2.8% -26 -15.2% -36 -19.4% 

Acetaldehyde -676 -10.1% -1,903 -30.6% -2,697 -36.0% 

Acrolein -97 -9.8% -274 -31.3% -388 -36.9% 

Benzene -157 -5.7% -450 -18.9% -634 -22.9% 

CO -30,580 -1.9% -86,526 -6.6% -122,703 -8.4% 

Formaldehyde -2,125 -11.4% -5,980 -31.7% -8,476 -37.0% 

NOX -106,180 -7.4% -301,339 -26.8% -426,796 -32.1% 

PM2.5 -646 -1.1% -2,036 -4.6% -2,827 -5.4% 

SOX -7,450 -6.1% -21,550 -15.9% -30,064 -18.4% 

VOC -18,652 -6.2% -53,966 -19.2% -75,621 -22.7% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

5.5.2.2 Model Year Lifetime Analysis 

In addition to the annual non-GHG emissions reductions expected from the proposed 

rules and Alternative 4, the combined (downstream and upstream) non-GHG impacts for the 

lifetime of the impacted vehicles were estimated by heavy-duty vehicle category. Table 5-83 
5-62 

shows the fleet-wide reductions of NOX, PM2.5 and SOX from the preferred alternative and 

Alternative 4, relative to Alternative 1a, through the lifetimeU of heavy-duty vehicles. 

Table 5-83 Lifetime Non-GHG Reductions by Heavy-Duty Vehicle Category – Summary for Model Years 

2018-2029 using Analysis Method B (US Short Tons) a

 ALTERNATIVE 3 

(PROPOSED) 

ALTERNATIVE 4 

NO-ACTION ALTERNATIVE 

(BASELINE) 1a (Less Dynamic) 1a (Less Dynamic) 

NOX 2,399,990 2,459,497 

 HD Pickups and Vans 18,024 21,698 

 Vocational 15,810 24,265 

 Tractor/Trailers 2,366,156 2,413,534 

PM2.5 15,206 19,151 

 HD Pickups and Vans 2,342 2,796 

 Vocational 2,509 3,421 

 Tractor/Trailers 10,355 12,934 

SOX 169,436 189,904 

 HD Pickups and Vans 13,301 15,688 

 Vocational 17,295 22,416 

 Tractor/Trailers 138,840 151,800 

Note: 

a

 For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

 

 U A lifetime of 30 years is assumed in MOVES. 
5-63 

References

1

 Intergovernmental Panel on Climate Change Working Group I. 2007. Climate Change 2007 – The Physical 

Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on 

Climate Change (IPCC). 

2

 U.S. EPA. 2014. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012. EPA 430-R-14-003. 

Available at

http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2014-Main-Text.pdf 

3

 U.S. EPA. 2009. Technical Support Document for Endangerment and Cause or Contribute 

Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. Washington, DC. pp. 180-194. Available 

at

http://epa.gov/climatechange/endangerment/downloads/Endangerment%20TSD.pdf 

4

 U.S. EPA. 2014. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012. EPA 430-R-14-003. 

Available at

http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2014-Main-Text.pdf 

5

 MOVES2014 homepage: http://www.epa.gov/otaq/models/moves/index.htm 6

 Argonne National Laboratory. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation 

(GREET) Model versions 1.8.c. http://www.transportation.anl.gov/modeling_simulation/GREET/. 7

 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) 

8

 MOVES2014 homepage: http://www.epa.gov/otaq/models/moves/index.htm 9

 Memorandum to the Docket “Runspecs, Model Inputs, MOVES Code and Database for HD GHG Phase 2 NPRM 

Emissions Modeling” Docket No. EPA-HQ-OAR-2014-0827 

10 U.S. EPA. Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program. Chapters 2 and 3. 

May 26, 2009. Docket No. EPA-HQ-OAR-2009-0472-0119 

11 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel 

Economy Standards (77 FR 62623, October 15, 2012) 

12 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and 

Vehicles (76 FR 57106, September 15, 2011) 

13 Memorandum to the Docket “Upstream Emissions Modeling Files for HDGHG Phase 2 NPRM” Docket No. 

EPA-HQ-OAR-2014-0827 

14 U.S. EPA. 2015. Population and Activity of On-road Vehicles in MOVES2014 – Draft Report” Docket No. EPAHQ-OAR-2014-0827. 

15 Annual Energy Outlook 2014. http://www.eia.doe.gov/oiaf/aeo/ 16 U.S. EPA. 2015. “Exhaust Emission Rates for Heavy-Duty On-road Vehicles in MOVES2014 – Draft Report” 

Docket No. EPA-HQ-OAR-2014-0827. 

17 Memorandum to the Docket “NPRM - Tractor-Trailer Inputs to MOVES” Docket No. EPA-HQ-OAR-2014-0827 18 ACT Research Co., LLC. U.S. Trailers Monthly Market Indicators. Available at www.actresearch.net/reports

Accessed 7/28/2014 

19 U.S. Census Bureau. 2002 Vehicle Inventory and Use Survey. Available at 

https://www.census.gov/svsd/www/vius/2002.html Accessed 6/30/2014 20 Memorandum to the Docket “NPRM - Vocational Inputs to MOVES” Docket No. EPA-HQ-OAR-2014-0827 

21 Craig Harvey, EPA, “Calculation of Upstream Emissions for the GHG Vehicle Rule.” 2009. Docket No. EPAHQ-OAR-2009-0472-0216 

22 The Minnesota refrigerant leakage data: http://www.pca.state.mn.us/climatechange/mobileair.html#leakdata 23 Eastern Research Group. “A Study of R134a Leaks in Heavy Duty Vehicles.” CARB Contract 06-342. Presented 

during CARB Seminar on January 6, 2011. 
6-1 

Chapter 6: Health and Environmental Impacts 

6.1 Health and Environmental Effects of Non-GHG Pollutants 

6.1.1 Health Effects Associated with Exposure to Non-GHG Pollutants 

In this section we will discuss the health effects associated with non-GHG pollutants, 

specifically: particulate matter, ozone, nitrogen oxides (NOX), sulfur oxides (SOX), carbon 

monoxide and air toxics. These pollutants will not be directly regulated by the standards, but the 

standards will affect emissions of these pollutants and precursors. 

6.1.1.1 Particulate Matter 

6.1.1.1.1 Background on Particulate Matter 

Particulate matter (PM) is a highly complex mixture of solid particles and liquid droplets 

distributed among numerous atmospheric gases which interact with solid and liquid phases. 

Particles range in size from those smaller than 1 nanometer (10-9 meter) to over 100 micrometer 

(μm, or 10-6 meter) in diameter (for reference, a typical strand of human hair is 70 μm in 

diameter and a grain of salt is about 100 μm). Atmospheric particles can be grouped into several 

classes according to their aerodynamic and physical sizes. Generally, the three broad classes of 

particles considered by EPA include ultrafine particles (UFP, aerodynamic diameter <0.1 μm), 

“fine” particles (PM2.5; particles with a nominal mean aerodynamic diameter less than or equal to 

2.5 μm), and “thoracic” particles (PM10; particles with a nominal mean aerodynamic diameter 

less than or equal to 10 μm). Particles that fall within the size range between PM2.5 and PM10, 

are referred to as “thoracic coarse particles” (PM10-2.5, particles with a nominal mean 

aerodynamic diameter less than or equal to 10 μm and greater than 2.5 μm). EPA currently has 

standards that regulate PM2.5 and PM10.

A 

Particles span many sizes and shapes and may 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. Particle concentration and composition varies by time of year and location, and in 

addition to differences in source emissions, 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. 

Fine particles are produced primarily by combustion processes and by transformations of 

gaseous emissions (e.g., sulfur oxides (SOX), nitrogen oxides (NOX) and volatile organic 

compounds (VOCs)) in the atmosphere. The chemical and physical properties of PM2.5 may 

 

A Regulatory definitions of PM size fractions, and information on reference and equivalent methods for measuring 

PM in ambient air, are provided in 40 CFR Parts 50, 53, and 58. With regard to national ambient air quality 

standards (NAAQS) which provide protection against health and welfare effects, the 24-hour PM10 standard 

provides protection against effects associated with short-term exposure to thoracic coarse particles (i.e., PM10-2.5). 
6-2 

vary greatly with time, region, meteorology, and source category. Thus, PM2.5 may include a 

complex mixture of different components including sulfates, nitrates, organic compounds, 

elemental carbon and metal compounds. These particles can remain in the atmosphere for days 

to weeks and travel through the atmosphere hundreds to thousands of kilometers.1

 

6.1.1.1.2 Health Effects of Particulate Matter 

Scientific studies show ambient PM is associated with a broad range of health effects. 

These health effects are discussed in detail in the December 2009 Integrated Science Assessment 

for Particulate Matter (PM ISA).2 The PM ISA summarizes health effects evidence associated 

with both short- and long-term exposures to PM2.5, PM10-2.5, and ultrafine particles. The PM ISA 

concludes that human exposures to ambient PM2.5 concentrations are associated with a number 

of adverse health effects and characterizes the weight of evidence for these health outcomes.B 

The discussion below highlights the PM ISA’s conclusions pertaining to health effects associated 

with both short- and long-term PM exposures. Further discussion of health effects associated 

with PM2.5 can also be found in the rulemaking documents for the most recent review of the PM 

NAAQS completed in 2012.3,4

EPA has concluded that a causal relationship exists between both long- and short-term 

exposures to PM2.5 and premature mortality and cardiovascular effects and a likely causal 

relationship exists between long- and short-term PM2.5 exposures and respiratory effects. 

Further, there is evidence suggestive of a causal relationship between long-term PM2.5 exposures 

and other health effects, including developmental and reproductive effects (e.g., low birth 

weight, infant mortality) and carcinogenic, mutagenic, and genotoxic effects (e.g., lung cancer 

mortality).C

As summarized in the Final PM NAAQS rule, and discussed extensively in the 2009 PM 

ISA, the available scientific evidence significantly strengthens the link between long- and shortterm exposure to PM2.5 and premature mortality, while providing indications that the magnitude 

of the PM2.5- mortality association with long-term exposures may be larger than previously 

estimated.5,6 The strongest evidence comes from recent studies investigating long-term exposure 

to PM2.5 and cardiovascular-related mortality. The evidence supporting a causal relationship 

between long-term PM2.5 exposure and mortality also includes consideration of new studies that 

demonstrated an improvement in community health following reductions in ambient fine 

particles.7

 

B The causal framework draws upon the assessment and integration of evidence from across epidemiological, 

controlled human exposure, and toxicological studies, and the related uncertainties that ultimately influence our 

understanding of the evidence. This framework employs a five-level hierarchy that classifies the overall weight of 

evidence and causality using the following categorizations: causal relationship, likely to be causal relationship, 

suggestive of a causal relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship 

(U.S. EPA. (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental 

Protection Agency, Washington, DC, EPA/600/R-08/139F, Table 1–3). 

C These causal inferences are based not only on the more expansive epidemiological evidence available in this 

review of the PM NAAQS but also reflect consideration of important progress that has been made to advance 

understanding of a number of potential biologic modes of action or pathways for PM-related cardiovascular and 

respiratory effects (U.S. EPA. (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-08/139F, Chapter 5). 
6-3 

Several studies evaluated in the 2009 PM ISA have examined the association between 

cardiovascular effects and long-term PM2.5 exposures in multi-city studies conducted in the U.S. 

and Europe. These studies have provided new evidence linking long-term exposure to PM2.5

with an array of cardiovascular effects such as heart attacks, congestive heart failure, stroke, and 

mortality. This evidence is coherent with studies of short-term exposure to PM2.5 that have 

observed associations with a continuum of effects ranging from subtle changes in indicators of 

cardiovascular health to serious clinical events, such as increased hospitalizations and emergency 

department visits due to cardiovascular disease and cardiovascular mortality.8

As detailed in the 2009 PM ISA, extended analyses of seminal epidemiological studies, 

as well as more recent epidemiological studies conducted in the U.S. and abroad, provide strong 

evidence of respiratory-related morbidity effects associated with long-term PM2.5 exposure. The 

strongest evidence for respiratory-related effects is from studies that evaluated decrements in 

lung function growth (in children), increased respiratory symptoms, and asthma development. 

The strongest evidence from short-term PM2.5 exposure studies has been observed for increased 

respiratory-related emergency department visits and hospital admissions for chronic obstructive 

pulmonary disease (COPD) and respiratory infections.9

The body of scientific evidence detailed in the 2009 PM ISA is still limited with respect 

to associations between long-term PM2.5 exposures and developmental and reproductive effects 

as well as cancer, mutagenic, and genotoxic effects. The strongest evidence for an association 

between PM2.5 and developmental and reproductive effects comes from epidemiological studies 

of low birth weight and infant mortality, especially due to respiratory causes during the postneonatal period (i.e., 1 month to 12 months of age). With regard to cancer effects, ‘‘[m]ultiple 

epidemiologic studies have shown a consistent positive association between PM2.5 and lung 

cancer mortality, but studies have generally not reported associations between PM2.5 and lung 

cancer incidence.’’10,11 

Specific groups within the general population are at increased risk for experiencing 

adverse health effects related to PM exposures.12,13,14,15 The evidence detailed in the 2009 PM 

ISA expands our understanding of previously identified at-risk populations and lifestages (i.e., 

children, older adults, and individuals with pre-existing heart and lung disease) and supports the 

identification of additional at-risk populations (e.g., persons with lower socioeconomic status, 

genetic differences). Additionally, there is emerging, though still limited, evidence for additional 

potentially at-risk populations and lifestages, such as those with diabetes, people who are obese, 

pregnant women, and the developing fetus.16

For PM10-2.5, the 2009 PM ISA concluded that available evidence was suggestive of a 

causal relationship between short-term exposures to PM10-2.5 and cardiovascular effects (e.g., 

hospital admissions and ED visits, changes in cardiovascular function), respiratory effects (e.g., 

ED visits and hospital admissions, increase in markers of pulmonary inflammation), and 

premature mortality. Data were inadequate to draw conclusions regarding the relationships 

between long-term exposure to PM10-2.5 and various health effects. 17,18,19

For ultrafine particles, the 2009 PM ISA concluded that the evidence was suggestive of a 

causal relationship between short-term exposures and cardiovascular effects, including changes 

in heart rhythm and vasomotor function (the ability of blood vessels to expand and contract). It 
6-4 

also concluded that there was evidence suggestive of a causal relationship between short-term 

exposure to ultrafine particles and respiratory effects, including lung function and pulmonary 

inflammation, with limited and inconsistent evidence for increases in ED visits and hospital 

admissions. Data were inadequate to draw conclusions regarding the relationship between shortterm exposure to ultrafine particle and additional health effects including premature mortality as 

well as long-term exposure to ultrafine particles and all health outcomes evaluated.20,21 

6.1.1.2 Ozone 

6.1.1.2.1 Background on Ozone 

Ground-level ozone pollution is typically formed through reactions involving VOCs and 

NOX in the lower atmosphere in the presence of sunlight. These pollutants, often referred to as 

ozone precursors, are emitted by many types of pollution sources such as highway and nonroad 

motor vehicles and engines, power plants, chemical plants, refineries, makers of consumer and 

commercial products, industrial facilities, and smaller area sources. 

The science of ozone formation, transport, and accumulation is complex. Ground-level 

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 occurs on a single high-temperature day. Ozone and its 

precursors can be transported hundreds of miles downwind of precursor emissions, 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. 

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO) 

with ozone, forming nitrogen dioxide (NO2). As the air moves downwind and the cycle 

continues, the NO2 forms additional ozone. The importance of this reaction depends, in part, on 

the relative concentrations of NOX, VOC, and ozone, all of which change with time and location. 

When NOX levels are relatively high and VOC levels 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 such 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. 
6-5 

6.1.1.2.2 Health Effects of Ozone 

This section provides a summary of the health effects associated with exposure to 

ambient concentrations of ozone.D The information in this section is based on the information 

and conclusions in the February 2013 Integrated Science Assessment for Ozone (Ozone ISA).22 

The Ozone ISA concludes that human exposures to ambient concentrations of ozone are 

associated with a number of adverse health effects and characterizes the weight of evidence for 

these health effects. E The discussion below highlights the Ozone ISA’s conclusions pertaining 

to health effects associated with both short-term and long-term periods of exposure to ozone. 

For short-term exposure to ozone, the Ozone ISA concludes that respiratory effects, 

including lung function decrements, pulmonary inflammation, exacerbation of asthma, 

respiratory-related hospital admissions, and mortality, are causally associated with ozone 

exposure. It also concludes that cardiovascular effects, including decreased cardiac function and 

increased vascular disease, and total mortality are likely to be causally associated with short-term 

exposure to ozone and that evidence is suggestive of a causal relationship between central 

nervous system effects and short-term exposure to ozone. 

For long-term exposure to ozone, the Ozone ISA concludes that respiratory effects, 

including new onset asthma, pulmonary inflammation and injury, are likely to be causally related 

with ozone exposure. The Ozone ISA characterizes the evidence as suggestive of a causal 

relationship for associations between long-term ozone exposure and cardiovascular effects, 

reproductive and developmental effects, central nervous system effects and total mortality. The 

evidence is inadequate to infer a causal relationship between chronic ozone exposure and 

increased risk of lung cancer. 

Finally, interindividual variation in human responses to ozone exposure can result in 

some groups being at increased risk for detrimental effects in response to exposure. The Ozone 

ISA identified several groups that are at increased risk for ozone-related health effects. These 

groups are people with asthma, children and older adults, individuals with reduced intake of 

certain nutrients (i.e., Vitamins C and E), outdoor workers, and individuals having certain 

genetic variants related to oxidative metabolism or inflammation. Ozone exposure during 

childhood can have lasting effects through adulthood. Such effects include altered function of 

the respiratory and immune systems. Children absorb higher doses (normalized to lung surface 

area) of ambient ozone, compared to adults, due to their increased time spent outdoors, higher 

ventilation rates relative to body size, and a tendency to breathe a greater fraction of air through 

the mouth. Children also have a higher asthma prevalence compared to adults. Additional 

children’s vulnerability and susceptibility factors are listed in Section XIV of the preamble. 

 

D 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 concentrations but also by the breathing route and rate. 

E The ISA evaluates evidence and draws conclusions on the causal relationship between relevant pollutant exposures 

and health effects, assigning one of five “weight of evidence” determinations: causal relationship, likely to be a 

causal relationship, suggestive of a causal relationship, inadequate to infer a causal relationship, and not likely to be 

a causal relationship. For more information on these levels of evidence, please refer to Table II in the Preamble of 

the ISA. 
6-6 

6.1.1.3 Nitrogen Oxides 

6.1.1.3.1 Background on Nitrogen Oxides 

Nitrogen dioxide (NO2) is a member of the nitrogen oxide (NOX) family of gases. Most 

NO2 is formed in the air through the oxidation of nitric oxide (NO) emitted when fuel is burned 

at a high temperature. NO2 and its gas phase oxidation products can dissolve in water droplets 

and further oxidize to form nitric acid which reacts with ammonia to form nitrates, which are 

important components of ambient PM. The health effects of ambient PM are discussed in 

Chapter 6.1.1.1.2. NOX along with VOCs are the two major precursors of ozone. The health 

effects of ozone are covered in Chapter 6.1.1.2.2. 

6.1.1.3.2 Health Effects of Nitrogen Oxides 

The most recent review of the health effects of oxides of nitrogen completed by EPA can 

be found in the 2008 Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Oxides of Nitrogen ISA).23 EPA concluded that the findings of epidemiological, controlled 

human exposure, and animal toxicological studies provided evidence that was sufficient to infer 

a likely causal relationship between respiratory effects and short-term NO2 exposure. The 2008 

ISA for Oxides of Nitrogen concluded that the strongest evidence for such a relationship comes 

from epidemiological studies of respiratory effects including increased respiratory symptoms, 

emergency department visits, and hospital admissions. Based on both short- and long-term 

exposure studies, the 2008 ISA for Oxides of Nitrogen concluded that individuals with 

preexisting pulmonary conditions (e.g., asthma or COPD), children, and older adults are 

potentially at greater risk of NO2-related respiratory effects. Based on findings from controlled 

human exposure studies, the 2008 ISA for Oxides of Nitrogen also drew two broad conclusions 

regarding airway responsiveness following NO2 exposure. First, the NOX ISA concluded that 

NO2 exposure may enhance the sensitivity to allergen-induced decrements in lung function and 

increase the allergen-induced airway inflammatory response following 30-minute exposures of 

asthmatic adults to NO2 concentrations as low as 260 ppb.24 Second, exposure to NO2 was found 

to enhance the inherent responsiveness of the airway to subsequent nonspecific challenges in 

controlled human exposure studies of healthy and asthmatic adults. Statistically significant 

increases in nonspecific airway responsiveness were reported for asthmatic adults following 30-

minute exposures to 200-300 ppb NO2 and following 1-hour exposures to 100 ppb NO2.

25 

Enhanced airway responsiveness could have important clinical implications for asthmatics since 

transient increases in airway responsiveness following NO2 exposure have the potential to 

increase symptoms and worsen asthma control. Together, the epidemiological and experimental 

data sets formed a plausible, consistent, and coherent description of a relationship between NO2

exposures and an array of adverse health effects that range from the onset of respiratory 

symptoms to hospital admissions and emergency department visits for respiratory causes, 

especially asthma.26 

In evaluating a broader range of health effects, the 2008 ISA for Oxides of Nitrogen 

concluded evidence was “suggestive but not sufficient to infer a causal relationship” between 

short-term NO2 exposure and premature mortality and between long-term NO2 exposure and 

respiratory effects. The latter was based largely on associations observed between long-term 

NO2 exposure and decreases in lung function growth in children. Furthermore, the 2008 ISA for 
6-7 

Oxides of Nitrogen concluded that evidence was “inadequate to infer the presence or absence of 

a causal relationship” between short-term NO2 exposure and cardiovascular effects as well as 

between long-term NO2 exposure and cardiovascular effects, reproductive and developmental 

effects, premature mortality, and cancer.27 The conclusions for these health effect categories 

were informed by uncertainties in the evidence base such as the independent effects of NO2

exposure within the broader mixture of traffic-related pollutants, limited evidence from 

experimental studies, and/or an overall limited literature base. 

6.1.1.4 Sulfur Oxides 

6.1.1.4.1 Background 

Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed from 

burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting 

metals from ore. SO2 andits gas phase oxidation products can dissolve in water droplets and 

further oxidize to form sulfuric acid which reacts with ammonia to form sulfates, which are 

important components of ambient PM. The health effects of ambient PM are discussed in 

Chapter 6.1.1.1.2. 

6.1.1.4.2 Health Effects of Sulfur Oxides 

This section provides an overview of the health effects associated with SO2. Additional 

information on the health effects of SO2 can be found in the 2008 Integrated Science Assessment 

for Sulfur Oxides – Health Criteria (SOX ISA).28 Following an extensive evaluation of health 

evidence from epidemiologic and laboratory studies, EPA has concluded that there is a causal 

relationship between respiratory health effects and short-term exposure to SO2. The immediate 

effect of SO2 on the respiratory system in humans is bronchoconstriction. Asthmatics are more 

sensitive to the effects of SO2 likely resulting from preexisting inflammation associated with this 

disease. In addition to those with asthma (both children and adults), potentially sensitive groups 

include all children and the elderly. In free-breathing laboratory studies involving controlled 

human exposures to SO2, respiratory effects have consistently been observed following 5-10 min 

exposures at SO2 concentrations ≥ 400 ppb in asthmatics engaged in moderate to heavy levels of 

exercise, with respiratory effects occurring at concentrations as low as 200 ppb in some 

asthmatics. A clear concentration-response relationship has been demonstrated in these studies 

following exposures to SO2 at concentrations between 200 and 1000 ppb, both in terms of 

increasing severity of respiratory symptoms and decrements in lung function, as well as the 

percentage of asthmatics adversely affected. 

In epidemiologic studies, respiratory effects have been observed in areas where the mean 

24-hour SO2 levels range from 1 to 30 ppb, with maximum 1 to 24-hour average SO2 values 

ranging from 12 to 75 ppb. Important new multicity studies and several other studies have found 

an association between 24-hour average ambient SO2 concentrations and respiratory symptoms 

in children, particularly those with asthma. Generally consistent associations also have been 

observed between ambient SO2 concentrations and emergency department visits and 

hospitalizations for all respiratory causes, particularly among children and older adults (≥ 65 

years), and for asthma. A limited subset of epidemiologic studies has examined potential 

confounding by copollutants using multipollutant regression models. These analyses indicate 
6-8 

that although copollutant adjustment has varying degrees of influence on the SO2 effect 

estimates, the effect of SO2 on respiratory health outcomes appears to be generally robust and 

independent of the effects of gaseous and particulate copollutants, suggesting that the observed 

effects of SO2 on respiratory endpoints occur independent of the effects of other ambient air 

pollutants. 

Consistent associations between short-term exposure to SO2 and mortality have been 

observed in epidemiologic studies, with larger effect estimates reported for respiratory mortality 

than for cardiovascular mortality. While this finding is consistent with the demonstrated effects 

of SO2 on respiratory morbidity, uncertainty remains with respect to the interpretation of these 

observed mortality associations due to potential confounding by various copollutants. Therefore, 

EPA has concluded that the overall evidence is suggestive of a causal relationship between shortterm exposure to SO2 and mortality. Significant associations between short-term exposure to 

SO2 and emergency department visits and hospital admissions for cardiovascular diseases have 

also been reported. However, these findings have been inconsistent across studies and do not 

provide adequate evidence to infer a causal relationship between SO2 exposure and 

cardiovascular morbidity. 

6.1.1.5 Carbon Monoxide 

6.1.1.5.1 Background 

Carbon monoxide (CO) is a colorless, odorless gas emitted from combustion processes. 

Nationally and, particularly in urban areas, the majority of CO emissions to ambient air come 

from mobile sources. 

6.1.1.5.2 Health Effects of Carbon Monoxide 

Information on the health effects of carbon monoxide (CO) can be found in the January 

2010 Integrated Science Assessment for Carbon Monoxide (CO ISA).29 The CO ISA concludes 

that ambient concentrations of CO are associated with a number of adverse health effects.F

 This 

section provides a summary of the health effects associated with exposure to ambient 

concentrations of CO.G 

Controlled human exposure studies of subjects with coronary artery disease show a 

decrease in the time to onset of exercise-induced angina (chest pain) and electrocardiogram 

changes following CO exposure. In addition, epidemiologic studies show associations between 

short-term CO exposure and cardiovascular morbidity, particularly increased emergency room 

visits and hospital admissions for coronary heart disease (including ischemic heart disease, 

 

F The ISA evaluates the health evidence associated with different health effects, assigning one of five “weight of 

evidence” determinations: causal relationship, likely to be a causal relationship, suggestive of a causal relationship, 

inadequate to infer a causal relationship, and not likely to be a causal relationship. For definitions of these levels of 

evidence, please refer to Section 1.6 of the ISA. 

G Personal exposure includes contributions from many sources, and in many different environments. Total personal 

exposure to CO includes both ambient and non-ambient components; and both components may contribute to 

adverse health effects. 
6-9 

myocardial infarction, and angina). Some epidemiologic evidence is also available for increased 

hospital admissions and emergency room visits for congestive heart failure and cardiovascular 

disease as a whole. The CO ISA concludes that a causal relationship is likely to exist between 

short-term exposures to CO and cardiovascular morbidity. It also concludes that available data 

are inadequate to conclude that a causal relationship exists between long-term exposures to CO 

and cardiovascular morbidity. 

Animal studies show various neurological effects with in-utero CO exposure. Controlled 

human exposure studies report central nervous system and behavioral effects following low-level 

CO exposures, although the findings have not been consistent across all studies. The CO ISA 

concludes the evidence is suggestive of a causal relationship with both short- and long-term 

exposure to CO and central nervous system effects. 

A number of studies cited in the CO ISA have evaluated the role of CO exposure in birth 

outcomes such as preterm birth or cardiac birth defects. The epidemiologic studies provide 

limited evidence of a CO-induced effect on preterm births and birth defects, with weak evidence 

for a decrease in birth weight. Animal toxicological studies have found perinatal CO exposure to 

affect birth weight, as well as other developmental outcomes. The CO ISA concludes the 

evidence is suggestive of a causal relationship between long-term exposures to CO and 

developmental effects and birth outcomes. 

Epidemiologic studies provide evidence of associations between ambient CO 

concentrations and respiratory morbidity such as changes in pulmonary function, respiratory 

symptoms, and hospital admissions. A limited number of epidemiologic studies considered 

copollutants such as ozone, SO2, and PM in two-pollutant models and found that CO risk 

estimates were generally robust, although this limited evidence makes it difficult to disentangle 

effects attributed to CO itself from those of the larger complex air pollution mixture. Controlled 

human exposure studies have not extensively evaluated the effect of CO on respiratory 

morbidity. Animal studies at levels of 50-100 ppm CO show preliminary evidence of altered 

pulmonary vascular remodeling and oxidative injury. The CO ISA concludes that the evidence 

is suggestive of a causal relationship between short-term CO exposure and respiratory morbidity, 

and inadequate to conclude that a causal relationship exists between long-term exposure and 

respiratory morbidity. 

Finally, the CO ISA concludes that the epidemiologic evidence is suggestive of a causal 

relationship between short-term concentrations of CO and mortality. Epidemiologic studies 

provide evidence of an association between short-term exposure to CO and mortality, but limited 

evidence is available to evaluate cause-specific mortality outcomes associated with CO exposure. 

In addition, the attenuation of CO risk estimates which was often observed in copollutant models 

contributes to the uncertainty as to whether CO is acting alone or as an indicator for other 

combustion-related pollutants. The CO ISA also concludes that there is not likely to be a causal 

relationship between relevant long-term exposures to CO and mortality. 
6-10 

6.1.1.6 Diesel Exhaust 

6.1.1.6.1 Background on Diesel Exhaust 

Diesel exhaust consists of a complex mixture composed 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 present in diesel exhaust consists mostly of fine particles (< 2.5 μm), of which 

a significant fraction is ultrafine particles (< 0.1 μm). These particles have a large surface area 

which makes them an excellent medium for adsorbing organics and their small size makes them 

highly respirable. Many of the organic compounds present in the gases and on the particles, such 

as polycyclic organic matter, 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). Also, there are emissions differences 

between on-road and nonroad engines because the nonroad engines are generally of older 

technology. After being emitted in the engine exhaust, 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. 

6.1.1.6.2 Health Effects of Diesel Exhaust 

In EPA’s 2002 Diesel Health Assessment Document (Diesel HAD), exposure to diesel 

exhaust was classified as likely to be carcinogenic to humans by inhalation from environmental 

exposures, in accordance with the revised draft 1996/1999 EPA cancer guidelines.30,31 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 U.S. 

Department of Health and Human Services) had made similar hazard classifications prior to 

2002. EPA also concluded in the 2002 Diesel HAD that it was not possible to calculate a cancer 

unit risk for diesel exhaust due to limitations in the exposure data for the occupational groups or 

the absence of a dose-response relationship. 

In the absence of a cancer unit risk, the Diesel HAD sought to provide additional insight 

into the significance of the diesel exhaust cancer hazard by estimating possible ranges of risk that 

might be present in the population. An exploratory analysis was used to characterize a range of 

possible lung cancer risk. The outcome was that environmental risks of cancer from long-term 

diesel exhaust exposures could plausibly range from as low as 10-5 to as high as 10-3. Because of 

uncertainties, the analysis acknowledged that the risks could be lower than 10-5, and a zero risk 

from diesel exhaust exposure could not be ruled out. 

Noncancer health effects of acute and chronic exposure to diesel exhaust emissions are 

also of concern to EPA. EPA derived a diesel exhaust reference concentration (RfC) from 

consideration of four well-conducted chronic rat inhalation studies showing adverse pulmonary 

effects. The RfC is 5 μg/m3

 for diesel exhaust measured as diesel particulate matter. This RfC 
6-11 

does not consider allergenic effects such as those associated with asthma or immunologic or the 

potential for cardiac effects. There was emerging evidence in 2002, discussed in the Diesel 

HAD, that exposure to diesel exhaust can exacerbate these effects, but the exposure-response 

data were lacking at that time to derive an RfC based on these then emerging considerations. 

The Diesel HAD states, “With [diesel particulate matter] being a ubiquitous component of 

ambient PM, there is an uncertainty about the adequacy of the existing [diesel exhaust] 

noncancer database to identify all of the pertinent [diesel exhaust]-caused noncancer health 

hazards.” The Diesel HAD also notes “that acute exposure to [diesel exhaust] has been 

associated with irritation of the eye, nose, and throat, respiratory symptoms (cough and phlegm), 

and neurophysiological symptoms such as headache, lightheadedness, nausea, vomiting, and 

numbness or tingling of the extremities.” The Diesel HAD noted that the cancer and noncancer 

hazard conclusions applied to the general use of diesel engines then on the market and as cleaner 

engines replace a substantial number of existing ones, the applicability of the conclusions would 

need to be reevaluated. 

It is important to note that the Diesel HAD also briefly summarizes health effects 

associated with ambient PM and discusses EPA’s then-annual PM2.5 NAAQS of 15 μg/m3. In 

2012, EPA revised the annual PM2.5 NAAQS to 12 μg/m3. There is a large and 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 noncancer health effects and premature mortality 

attributed to exposure to PM2.5. The contribution of diesel PM to total ambient PM varies in 

different regions of the country and also, within a region, from one area to another. The 

contribution can be high in near-roadway environments, for example, or in other locations where 

diesel engine use is concentrated. 

Since 2002, several new studies have been published which continue to report increased 

lung cancer risk with occupational exposure to diesel exhaust from older engines. Of particular 

note since 2011 are three new epidemiology studies which have examined lung cancer in 

occupational populations, for example, truck drivers, underground nonmetal miners and other 

diesel motor related occupations. These studies reported increased risk of lung cancer with 

exposure to diesel exhaust with evidence of positive exposure-response relationships to varying 

degrees.32,33,34 These newer studies (along with others that have appeared in the scientific 

literature) add to the evidence EPA evaluated in the 2002 Diesel HAD and further reinforces the 

concern that diesel exhaust exposure likely poses a lung cancer hazard. The findings from these 

newer studies do not necessarily apply to newer technology diesel engines since the newer 

engines have large reductions in the emission constituents compared to older technology diesel 

engines. 

In light of the growing body of scientific literature evaluating the health effects of 

exposure to diesel exhaust, in June 2012 the World Health Organization’s International Agency 

for Research on Cancer (IARC), a recognized international authority on the carcinogenic 

potential of chemicals and other agents, evaluated the full range of cancer related health effects 

data for diesel engine exhaust. IARC concluded that diesel exhaust should be regarded as 

“carcinogenic to humans.”35 This designation was an update from its 1988 evaluation that 

considered the evidence to be indicative of a “probable human carcinogen.” 
6-12 

6.1.1.7 Air Toxics 

Heavy-duty vehicle emissions contribute to ambient levels of air toxics known or 

suspected as human or animal carcinogens, or that have noncancer health effects. The 

population experiences an elevated risk of cancer and other noncancer health effects from 

exposure to the class of pollutants known collectively as “air toxics.”36 These compounds 

include, but are not limited to, benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, 

polycyclic organic matter, and naphthalene. These compounds were identified as national or 

regional risk drivers or contributors in the 2005 National-scale Air Toxics Assessment and have 

significant inventory contributions from mobile sources.37 

6.1.1.7.1 Health Effects of Benzene 

EPA’s IRIS database lists benzene as a known human carcinogen (causing leukemia) by 

all routes of exposure, and concludes 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.38,39,40 EPA states in its IRIS database that data indicate a causal 

relationship between benzene exposure and acute lymphocytic leukemia and suggest a 

relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic 

lymphocytic leukemia. EPA’s IRIS documentation for benzene also lists a range of 2.2 x 10-6 to 

7.8 x 10-6 as the unit risk estimate (URE) for benzene.H,41 The International Agency for 

Research on Carcinogens (IARC) has determined that benzene is a human carcinogen and the 

U.S. Department of Health and Human Services (DHHS) has characterized benzene as a known 

human carcinogen.42,43 

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.44,45 The most sensitive noncancer effect observed in humans, based on current data, is 

the depression of the absolute lymphocyte count in blood.46,47 EPA’s inhalation reference 

concentration (RfC) for benzene is 30 μg/m3. The RfC is based on suppressed absolute 

lymphocyte counts seen in humans under occupational exposure conditions. 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.48,49,50,51 EPA’s IRIS program has not yet evaluated these new data. EPA does not 

currently have an acute reference concentration for benzene. The Agency for Toxic Substances 

and Disease Registry (ATSDR) Minimal Risk Level (MRL) for acute exposure to benzene is 29 

μg/m3

 for 1-14 days exposure.52,I

6.1.1.7.2 Health Effects of 1,3-Butadiene 

EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.53,54 The 

IARC has determined that 1,3-butadiene is a human carcinogen and the U.S. DHHS has 

 

H A unit risk estimate is defined as the increase in the lifetime risk of an individual who is exposed for a lifetime to 1 

μg/m3 benzene in air. 

I

 A minimal risk level (MRL) is defined as an estimate of the daily human exposure to a hazardous substance that is 

likely to be without appreciable risk of adverse noncancer health effects over a specified duration of exposure. 
6-13 

characterized 1,3-butadiene as a known human carcinogen.55,56,57 There are numerous studies 

consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by 

experimental animals and humans. The specific mechanisms of 1,3-butadiene-induced 

carcinogenesis are unknown; however, the scientific evidence strongly suggests that the 

carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females 

may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure; 

there are insufficient data in humans from which to draw conclusions about sensitive 

subpopulations. The URE for 1,3-butadiene is 3 × 10-5 per μg/m3.

58 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.59 Based on this critical effect and the benchmark concentration methodology, an 

RfC for chronic health effects was calculated at 0.9 ppb (approximately 2 μg/m3

). 

6.1.1.7.3 Health Effects of Formaldehyde 

In 1991, EPA concluded that formaldehyde is a carcinogen based on nasal tumors in 

animal bioassays.60 An Inhalation URE for cancer and a Reference Dose for oral noncancer 

effects were developed by the agency and posted on the Integrated Risk Information System 

(IRIS) database. Since that time, the National Toxicology Program (NTP) and International 

Agency for Research on Cancer (IARC) have concluded that formaldehyde is a known human 

carcinogen.61,62,63 

The conclusions by IARC and NTP reflect the results of epidemiologic research 

published since 1991 in combination with previous animal, human and mechanistic evidence. 

Research conducted by the National Cancer Institute reported an increased risk of 

nasopharyngeal cancer and specific lymphohematopoietic malignancies among workers exposed 

to formaldehyde.64,65,66 A National Institute of Occupational Safety and Health study of garment 

workers also reported increased risk of death due to leukemia among workers exposed to 

formaldehyde.67 Extended follow-up of a cohort of British chemical workers did not report 

evidence of an increase in nasopharyngeal or lymphohematopoietic cancers, but a continuing 

statistically significant excess in lung cancers was reported.68 Finally, a study of embalmers 

reported formaldehyde exposures to be associated with an increased risk of myeloid leukemia 

but not brain cancer.69 

Health effects of formaldehyde in addition to cancer were reviewed by the Agency for 

Toxics Substances and Disease Registry in 199970 and supplemented in 2010,71 and by the World 

Health Organization.72 These organizations reviewed the scientific literature concerning health 

effects linked to formaldehyde exposure to evaluate hazards and dose response relationships and 

defined exposure concentrations for minimal risk levels (MRLs). The health endpoints reviewed 

included sensory irritation of eyes and respiratory tract, pulmonary function, nasal 

histopathology, and immune system effects. In addition, research on reproductive and 

developmental effects and neurological effects were discussed along with several studies that 

suggest that formaldehyde may increase the risk of asthma – particularly in the young. 

EPA released a draft Toxicological Review of Formaldehyde – Inhalation Assessment 

through the IRIS program for peer review by the National Research Council (NRC) and public 

comment in June 2010.73 The draft assessment reviewed more recent research from animal and 
6-14 

human studies on cancer and other health effects. The NRC released their review report in April 

201174 (http://www.nap.edu/catalog.php?record_id=13142). EPA is currently developing a new 

draft assessment in response to this review. 

6.1.1.7.4 Health Effects of 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.75 The URE in IRIS for acetaldehyde is 2.2 × 10-6 per μg/m3.

76 Acetaldehyde is 

reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 13th Report on 

Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC.77,78 

EPA is currently conducting a reassessment of cancer risk from inhalation exposure to 

acetaldehyde. 

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of 

the eyes, skin, and respiratory tract.79 In short-term (4 week) rat studies, degeneration of 

olfactory epithelium was observed at various concentration levels of acetaldehyde exposure.80,81 

Data from these studies were used by EPA to develop an inhalation reference concentration of 9 

μg/m3. Some asthmatics have been shown to be a sensitive subpopulation to decrements in 

functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde 

inhalation.82 The agency is currently conducting a reassessment of the health hazards from 

inhalation exposure to acetaldehyde. 

6.1.1.7.5 Health Effects of Acrolein 

EPA most recently evaluated the toxicological and health effects literature related to 

acrolein in 2003 and concluded 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.83 The IARC determined in 1995 that acrolein was not classifiable as to its 

carcinogenicity in humans.84 

Lesions to the lungs and upper respiratory tract of rats, rabbits, and hamsters have been 

observed after subchronic exposure to acrolein.85 The agency has developed an RfC for acrolein 

of 0.02 μg/m3 and an RfD of 0.5 μg/kg-day.86 EPA is considering updating the acrolein 

assessment with data that have become available since the 2003 assessment was completed. 

Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure 

resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. The intense 

irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who 

suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure.87 These 

data and additional studies regarding acute effects of human exposure to acrolein are 

summarized in EPA’s 2003 IRIS Human Health Assessment for acrolein.88 Studies in humans 

indicate that levels as low as 0.09 ppm (0.21 mg/m3

) for five minutes may elicit subjective 

complaints of eye irritation with increasing concentrations leading to more extensive eye, nose 

and respiratory symptoms. Acute exposures in animal studies report bronchial hyperresponsiveness. Based on animal data (more pronounced respiratory irritancy in mice with 
6-15 

allergic airway disease in comparison to non-diseased mice89) and demonstration of similar 

effects in humans (e.g., reduction in respiratory rate), individuals with compromised respiratory 

function (e.g., emphysema, asthma) are expected to be at increased risk of developing adverse 

responses to strong respiratory irritants such as acrolein. EPA does not currently have an acute 

reference concentration for acrolein. The available health effect reference values for acrolein 

have been summarized by EPA and include an ATSDR MRL for acute exposure to acrolein of 

7 μg/m3

 for 1-14 days exposure; and Reference Exposure Level (REL) values from the 

California Office of Environmental Health Hazard Assessment (OEHHA) for one-hour and 8-

hour exposures of 2.5 μg/m3

 and 0.7 μg/m3, respectively.90 

6.1.1.7.6 Health Effects of Polycyclic Organic Matter (POM) 

The term polycyclic organic matter (POM) defines a broad class of compounds that 

includes the polycyclic aromatic hydrocarbon compounds (PAHs). One of these compounds, 

naphthalene, is discussed separately below. POM compounds are formed primarily from 

combustion and are present in the atmosphere in gas and particulate form. Cancer is the major 

concern from exposure to POM. Epidemiologic studies have reported an increase in lung cancer 

in humans exposed to diesel exhaust, coke oven emissions, roofing tar emissions, and cigarette 

smoke; all of these mixtures contain POM compounds.9192 Animal studies have reported 

respiratory tract tumors from inhalation exposure to benzo[a]pyrene and alimentary tract and 

liver tumors from oral exposure to benzo[a]pyrene.93 In 1997 EPA classified seven PAHs 

(benzo[a]pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, 

dibenz[a,h]anthracene, and indeno[1,2,3-cd]pyrene) as Group B2, probable human carcinogens.94 

Since that time, 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 in preschool children (3 years 

of age).95,96 These and similar studies are being evaluated as a part of the ongoing IRIS 

assessment of health effects associated with exposure to benzo[a]pyrene. 

6.1.1.7.7 Health Effects of Naphthalene 

Naphthalene is found in small quantities in gasoline and diesel fuels. Naphthalene 

emissions have been measured in larger quantities in both gasoline and diesel exhaust compared 

with evaporative emissions from mobile sources, indicating it is primarily a product of 

combustion. Acute (short-term) exposure of humans to naphthalene by inhalation, ingestion, or 

dermal contact is associated with hemolytic anemia and damage to the liver and the nervous 

system.97 Chronic (long term) exposure of workers and rodents to naphthalene has been reported 

to cause cataracts and retinal damage.98 EPA released an external review draft of a reassessment 

of the inhalation carcinogenicity of naphthalene based on a number of recent animal 

carcinogenicity studies.99 The draft reassessment completed external peer review.100 Based on 

external peer review comments received, a revised draft assessment that considers all routes of 

exposure, as well as cancer and noncancer effects, is under development. The external review 

draft does not represent official agency opinion and was released solely for the purposes of 

external peer review and public comment. The National Toxicology Program listed naphthalene 

as "reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting 

clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice.101 
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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.102 

Naphthalene also causes a number of chronic non-cancer effects in animals, including 

abnormal cell changes and growth in respiratory and nasal tissues.103 The current EPA IRIS 

assessment includes noncancer data on hyperplasia and metaplasia in nasal tissue that form the 

basis of the inhalation RfC of 3 μg/m3

.

104 The ATSDR MRL for acute exposure to naphthalene 

is 0.6 mg/kg/day. 

6.1.1.7.8 Health Effects of Other Air Toxics 

In addition to the compounds described above, other compounds in gaseous hydrocarbon 

and PM emissions from vehicles will be affected by this proposal. Mobile source air toxic 

compounds that would potentially be impacted include ethylbenzene, propionaldehyde, toluene, 

and xylene. Information regarding the health effects of these compounds can be found in EPA’s 

IRIS database.105

6.1.1.8 Exposure and Health Effects Associated with Traffic 

In addition to health concerns resulting from specific air pollutants, a large number of 

studies have examined the health status of populations near major roadways. These studies 

frequently have employed exposure metrics that are not specific to individual pollutants, but 

rather reflect the large number of different pollutants found in elevation near major roads. 

In this section of the RIA, information on health effects associated with air quality near 

major roads or traffic in general is summarized. Generally, the section makes use of publications 

that systematically review literature on a given health topic. In particular, this section makes 

frequent reference of a report of by the Health Effects Institute (HEI) Panel on the Health Effects 

of Traffic-Related Air Pollution, published in 2010 as a review of relevant studies.J,106 Other 

systematic reviews of relevant literature are cited were appropriate. 

6.1.1.8.1 Populations near Major Roads 

Numerous studies have estimated the size and demographics of populations that live near 

major roads. Other studies have estimated the number of schools near major roads, and the 

populations of students in such schools. 

Every two years, the U.S. Census Bureau’s American Housing Survey (AHS) has 

reported whether housing units are within 300 feet of an “airport, railroad, or highway with four 

or more lanes.” The 2009 survey reports that over 22 million homes, or 17 percent of all housing 

 

J

 It should be noted that there are no peer reviewed EPA-authored reviews of traffic-related health studies. The HEI 

panel primarily used epidemiology studies for inferring whether there was sufficient evidence of a causal association 

exists between a particular health effect and traffic-related air pollution. In its weight-of-evidence determinations, 

the panel also placed “considerable weight” on controlled human exposure studies. However, it restricted 

consideration of other toxicological studies to whether or not the studies provided “general mechanistic support” for 

the inferences of causality made on the basis of epidemiology. 
6-17 

units in the U.S., were located in such areas. Assuming that populations and housing units are in 

the same locations, this corresponds to a population of more than 50 million U.S. residents in 

close proximity to high-traffic roadways or other transportation sources. According to the 

Central Intelligence Agency’s World Factbook, in 2010, the United States had 6,506,204 km or 

roadways, 224,792 km of railways, and 15,079 airports. As such, highways represent the 

overwhelming majority of transportation facilities described by this factor in the AHS. 

The AHS reports are published every two years, and until 2011 recorded whether homes 

were located near highways with four or more lanes, railroads, or airports. As such, trends in the 

AHS can be reported to describe whether a greater or lesser proportion of homes are located near 

major roads over time. Figure 6-1 depicts trends in the number and proportion of homes located 

near major transportation sources, which generally indicate large roadways. As the figure 

indicates, since 2005, there has been a substantial increase in the number and percentage of 

homes located near major transportation sources. As such, the population in close proximity to 

these sources, which may be affected by near-road air quality and health concerns, appears to 

have increased over time. 

Figure 6-1 Trends in Populations Near Large Highways, Railroads, and Airports 

Furthermore, according to data from the 2008 American Time Use Survey (ATUS), 

conducted by the Bureau of Labor Statistics (BTS), Americans spend more than an hour 

traveling each day, on average.107 Although the ATUS does not indicate their mode of travel, the 

majority of trips undertaken nationally is by motor vehicle.108 As such, daily travel activity 

brings nearly all residents into a high-exposure microenvironment for part of the day. 

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

10

12

14

16

18

20

22

24

1997 1999 2001 2003 2005 2007 2009

Percent Near Infrastructure

Million Homes

Year of American Housing Survey

Million Housing Units

Near 4+ Lane Highway, 

Railroad, or Airport
6-18 

6.1.1.8.2 Premature Mortality 

The HEI panel report concluded that evidence linking traffic-associated air pollution with 

premature mortality from all causes was “suggestive but not sufficient” to infer a causal 

relationship. This conclusion was based largely on several long-term studies that “qualitatively” 

examined whether or not someone was exposed to traffic-associated air pollution. In addition, 

based on several short-term studies of exposure, the panel concluded that there was “suggestive 

but not sufficient” evidence to infer a causal relation between traffic-related exposure and 

cardiovascular mortality. 

6.1.1.8.3 Cardiovascular Effects 

6.1.1.8.3.1 Cardiac Physiology 

Exposure to traffic-associated pollutants has been associated with changes in cardiac 

physiology, including cardiac function. One common measure of cardiac function is heart rate 

variability (HRV), an indicator of the heart’s ability to respond to variations in stress, reflecting 

the nervous system’s ability to regulate the heart.K Reduced HRV is associated with adverse 

cardiovascular events, such as myocardial infarction, in heart disease patients. The HEI panel 

concluded that available evidence provides evidence for a causal association between exposure 

to traffic-related pollutants and reduced control of HRV by the nervous system. Overall, the 

panel concluded that the evidence was “suggestive but not sufficient” to infer a causal relation 

between traffic-related pollutants and cardiac function. Studies suggest that the HRV changes 

from traffic-related air pollution result in changes to heart rhythms, which can lead to 

arrhythmia.109,110 

6.1.1.8.3.2 Heart Attack and Atherosclerosis 

The HEI panel concluded that epidemiologic evidence of the association between trafficrelated pollutants and heart attacks and atherosclerosis was “suggestive but not sufficient” to 

infer a causal association. In addition, the panel concluded that the toxicology studies they 

reviewed provided “suggestive evidence that exposure to traffic emissions, including ambient 

and laboratory-generated [PM] and diesel- and gasoline-engine exhaust, alters cardiovascular 

function.” The panel noted there are few studies of human volunteers exposed to real-world 

traffic mixture, which were not entirely consistent. The panel notes that the studies provide 

consistent evidence for exposure to PM and impaired cardiovascular responses. In addition to 

the HEI study, several other reviews of available evidence conclude that there is evidence 

supporting a causal association between traffic-related air pollution and cardiovascular 

disease.111

A number of mechanisms for cardiovascular disease are highlighted in the HEI and AHA 

report, including modified blood vessel endothelial function (e.g., the ability to dilate), 

atherosclerosis, and oxidative stress. The HEI review cites “two well executed studies” in which 

 

K The autonomic nervous system (ANS) consists of sympathetic and parasympathetic components. The sympathetic 

ANS signals body systems to “fight or flight.” The parasympathetic ANS signals the body to “rest and digest.” In 

general, HRV is indicative of parasympathetic control of the heart. 
6-19 

hospitalization for acute myocardial infarction (i.e., heart attack) were associated with traffic 

exposures and a prospective study finding higher rates of arterial hardening and coronary heart 

disease near traffic. 

6.1.1.8.4 Respiratory Effects 

6.1.1.8.4.1 Asthma 

Pediatric asthma and asthma symptoms are the effects that have been evaluated by the 

largest number of studies in the epidemiologic literature on the topic. In general, studies 

consistently show effects of residential or school exposure to traffic and asthma symptoms, and 

the effects are frequently statistically significant. Studies have employed both short-term and 

long-term exposure metrics, and a range of different respiratory measures. HEI Special Report 

17 (HEI Panel on the Health Effects of Traffic-Related Air Pollution, 2010) concluded that there 

is sufficient evidence for a causal association between exposure to traffic-related air pollution 

and exacerbation of asthma symptoms in children. 

While there is general consistency in studies examining asthma incidence in children, the 

available studies employ different definitions of asthma (e.g., self-reported vs. hospital records), 

methods of exposure assessment, and population age ranges. As such, the overall evidence, 

while supportive of an association between traffic exposure and new onset asthma, are less 

consistent than for asthma symptoms. The HEI report determined that evidence is between 

“sufficient” and “suggestive” of a causal relationship between exposure to traffic-related air 

pollution and incident (new onset) asthma in children (HEI Panel on the Health Effects of 

Traffic-Related Air Pollution, 2010). A recent meta-analysis of studies on incident asthma and 

air pollution in general, based on studies dominated by traffic-linked exposure metrics, also 

concluded that available evidence is consistent with HEI’s conclusion (Anderson et al., 2011). 

The study reported excess main risk estimates for different pollutants ranging from 7-16 percent 

per 10 g/m3

 of long-term exposure (random effects models). Other qualitative reviews (Salam 

et al., 2008; Braback and Forsberg, 2009) conclude that available evidence is consistent with the 

hypothesis that traffic-associated air pollutants are associated with incident asthma. 

6.1.1.8.4.2 Chronic Obstructive Pulmonary Disease (COPD) 

The HEI panel reviewed available studies examining COPD in the context of trafficassociated air pollution. Because of how the panel selected studies for inclusion in review, there 

were only two studies that they used to review the available evidence. Both studies reported 

some positive associations, but not for all traffic metrics. The small number of studies and lack 

of consistency across traffic metrics led the panel to conclude that there is insufficient evidence 

for traffic-associated air pollution causing COPD. 

6.1.1.8.4.3 Allergy 

There are numerous human and animal experimental studies that provides strongly 

suggestive evidence that traffic-related air pollutants can enhance allergic responses to common 

allergens.112,113,114 However, in its review of 16 epidemiologic studies that address traffic-related 

air pollution’s effect on allergies, the HEI expert panel (HEI, 2010) reported that only two such 

studies showed consistently positive associations. As a result, despite the strongly suggestive 
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experimental evidence, the panel concluded that there is “inadequate/insufficient” evidence of an 

association between allergy and traffic-associated air pollution. As noted above, the HEI panel 

considered toxicological studies only based on whether or not they provide mechanistic support 

for observations and inferences derived from epidemiology. 

6.1.1.8.4.4 Lung Function 

There are numerous measurements of breathing (spirometry) that indicate the presence or 

degree of airway disease, such as asthma and chronic obstructive pulmonary disease (COPD). 

Forced vital capacity (FVC) is measured when a patient maximally fills their lungs and then 

blows their hardest in completely exhaling. The peak expiratory flow (PEF) is the maximum air 

flow achievable during exhalation. The forced expiratory volume in the first second of 

exhalation is referred to as FEV1. FEV1 and PEF reflect the function of the large airways. FVC 

and FEV1, along with their ratio (FVC/FEV1) are used to classify airway obstruction in asthma 

and COPD. Measurements of air flow at various times during forced exhalation, such as 25 

percent, 50 percent, and 75 percent, are also used. The flow at 75 percent of forced exhalation 

(FEF75) reflects the status of small airways, which asthma and COPD affect. 

The HEI panel concluded that the available literature suggests that long-term exposure to 

traffic-related air pollution is associated with reduced lung function in adolescents and young 

adults and that lung function is lower in populations in areas with high traffic-related air 

pollutant levels. However, the panel noted the difficulty of disentangling traffic-specific 

exposures from urban air pollution in general. The studies reviewed that were more specifically 

oriented toward traffic were not consistent in their findings. As a result, the panel found that the 

evidence linking lung function and traffic exposure is “inadequate and insufficient” to infer a 

causal relationship. 

6.1.1.8.5 Reproductive and Developmental Effects 

Several studies have reported associations between traffic-related air pollution and 

adverse birth outcomes, such as preterm birth and low birth weight. At the time of the HEI 

review, the panel concluded that evidence for adverse birth outcomes being causally associated 

with traffic-related exposures was “inadequate and insufficient.” Only four studies met the 

panel’s inclusion criteria, and had limited geographic coverage. One study provided evidence of 

small but consistently increased risks using multiple exposure metrics. No studies were at the 

time available that examined traffic-specific exposures and congenital abnormalities. Since then, 

several studies investigating birth outcomes have been published, but no new systematic reviews. 

One new meta-analysis of air pollution and congenital abnormalities has been published, though 

none of the reviewed studies includes traffic-specific exposure information. 

The HEI panel also reviewed toxicological studies of traffic-related air pollutants and 

fertility. While numerous studies examining animal or human exposure and sperm count have 

been published, the panel concluded that the generally high exposure concentrations employed in 

the studies limited the applicability to typical ambient concentrations. Because there was no 

overlap in the effects studied by epidemiology and toxicology studies, no synthesis review of the 

combined literature was undertaken. 
6-21 

Since the HEI panel’s publication, a systematic review and meta-analysis of air pollution 

and congenital abnormalities was published.115 In that review, only one study directly included 

nearby traffic in its exposure analysis. As such, there are no systematic reviews that specifically 

address traffic’s impact on congenital abnormalities. 

6.1.1.8.6 Cancer 

6.1.1.8.6.1 Childhood Cancer 

Earlier this year, Boothe et al. (2014) published a systematic review and meta-analysis of 

studies of childhood leukemia risks associated for populations near major roads.116 The study 

concluded that childhood leukemia was positively associated with residential exposure during 

childhood, but not during the prenatal period. Other literature reviews have not concluded that 

available evidence supports an association between childhood leukemia and traffic 

exposure.117,118 For example, the HEI panel concluded that the available epidemiologic evidence 

was “inadequate and insufficient” to infer a causal relationship between traffic-related air 

pollution and childhood cancer. 

6.1.1.8.6.2 Adult Cancer 

Several studies have examined the risk of adult lung cancers in relation to exposure to 

traffic-related air pollutants. The HEI panel evaluated four such studies, and rated the available 

evidence as “inadequate and insufficient” to infer a causal relation for non-occupational lung 

cancer. 

6.1.1.8.7 Neurological Effects 

The HEI panel found that current toxicologic and epidemiologic literature on the 

neurotoxicity of traffic-related air pollution was inadequate for their evaluation. The panel noted 

that there were a number of toxicologic studies of traffic-associated pollutants, but found them to 

have diverse exposure protocols, animal models, and endpoints, making them unsuitable for 

systematic evaluation. 

6.1.2 Environmental Effects Associated with Exposure to Non-GHG Pollutants 

In this section we will discuss the environmental effects associated with non-GHG 

pollutants, specifically: particulate matter, ozone, NOX, SOX and air toxics. 

6.1.2.1 Visibility Degradation 

Visibility can be defined as the degree to which the atmosphere is transparent to visible 

light.119 Visibility impairment is caused by light scattering and absorption by suspended 

particles and gases. Fine particles with significant light-extinction efficiencies include sulfates, 

nitrates, organic carbon, elemental carbon, and soil.120 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 
6-22 

given to protecting visibility in these areas. For more information on visibility see the final 2009 

PM ISA.121 

The extent to which any amount of light extinction affects a person’s ability to view a 

scene depends on both scene and light characteristics. For example, the appearance of a nearby 

object (e.g., a building) is generally less sensitive to a change in light extinction than the 

appearance of a similar object at a greater distance. See Figure 6-2 for an illustration of the 

important factors affecting visibility. 

Figure 6-2 Important Factors Involved in Seeing a Scenic Vista (Malm, 1999) 

EPA is working to address visibility impairment. Reductions in air pollution from 

implementation of various programs associated with the Clean Air Act Amendments of 1990 

(CAAA) provisions have resulted in substantial improvements in visibility, and will continue to 

do so in the future. Because trends in haze are closely associated with trends in particulate 

sulfate and nitrate due to the simple relationship between their concentration and light extinction, 

visibility trends have improved as emissions of SO2 and NOX have decreased over time due to air 

pollution regulations such as the Acid Rain Program.122 

In the Clean Air Act Amendments of 1977, Congress recognized visibility’s value to 

society by establishing a national goal to protect national parks and wilderness areas from 

visibility impairment caused by manmade pollution.L In 1999, EPA finalized the regional haze 

 

L See Section 169(a) of the Clean Air Act. 
6-23 

program (64 FR 35714) to protect the visibility in Mandatory Class I Federal areas. There are 

156 national parks, forests and wilderness areas categorized as Mandatory Class I Federal areas 

(62 FR 38680-38681, July 18, 1997). These areas are defined in CAA Section 162 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. Figure 6-3 shows 

the location of the 156 Mandatory Class I Federal areas. 

Figure 6-3 Mandatory Class I Federal Areas in the U.S. 

EPA has also concluded that PM2.5 causes adverse effects on visibility in other areas that 

are not protected by the Regional Haze Rule, depending on PM2.5 concentrations and other 

factors such as dry chemical composition and relative humidity (i.e., an indicator of the water 

composition of the particles). EPA revised the PM2.5 standards in December 2012 and 

established a target level of protection that is expected to be met through attainment of the 

existing secondary standards for PM2.5. 

6.1.2.2 Visibility Monitoring 

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal 

land managers, and State organizations in the U.S., EPA has supported visibility monitoring in 

national parks and wilderness areas since 1988. The monitoring network was originally 

established at 20 sites, but it has now been expanded to 110 sites that represent all but one of the 

156 Mandatory Federal Class I areas across the country (see Figure 6-3). This long-term 

visibility monitoring network is known as IMPROVE (Interagency Monitoring of Protected 

Visual Environments).

IMPROVE provides direct measurement of fine particles that contribute to visibility 

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical and 
6-24 

scene measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5

mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon 

OC and EC), soil dust, and several other elements. Measurements for specific aerosol 

constituents are used to calculate "reconstructed" aerosol light extinction by multiplying the mass 

for each constituent by its empirically-derived scattering and/or absorption efficiency, with 

adjustment for the relative humidity. The IMPROVE program utilizes both an “original” and a 

“revised” reconstruction formula for this purpose, with the latter explicitly accounting for sea salt 

concentrations. Knowledge of the main constituents of a site's light extinction "budget" is 

critical for source apportionment and control strategy development. In addition to this indirect 

method of assessing light extinction, there are optical measurements which directly measure light 

extinction or its components. Such measurements are made principally with a nephelometer to 

measure light scattering, some sites also include an aethalometer for light absorption, or a few 

sites use a transmissometer, which measures total light extinction. Scene characteristics are 

typically recorded using digital or video photography and are used to determine the quality of 

visibility conditions (such as effects on color and contrast) associated with specific levels of light 

extinction as measured under both direct and aerosol-related methods. Directly measured light 

extinction is used under the IMPROVE protocol to cross check that the aerosol-derived light 

extinction levels are reasonable in establishing current visibility conditions. Aerosol-derived 

light extinction is used to document spatial and temporal trends and to determine how changes in 

atmospheric constituents would affect future visibility conditions.

Annual average visibility conditions (reflecting light extinction due to both anthropogenic 

and non-anthropogenic sources) vary regionally across the U.S. Visibility is typically worse in 

the summer months, and the rural East generally has higher levels of impairment than remote 

sites in the West. Figures 9-9 through 9-11 in the PM ISA detail the percent contributions to 

particulate light extinction for ammonium nitrate and sulfate, EC and OC, and coarse mass and 

fine soil, by season.123 

6.1.2.3 Plant and Ecosystem Effects of Ozone 

The welfare effects of ozone can be observed across a variety of scales, i.e. subcellular, 

cellular, leaf, whole plant, population and ecosystem. Ozone effects that begin at small spatial 

scales, such as the leaf of an individual plant, when they occur at sufficient magnitudes (or to a 

sufficient degree) can result in effects being propagated along a continuum to larger and larger 

spatial scales. For example, effects at the individual plant level, such as altered rates of leaf gas 

exchange, growth and reproduction can, when widespread, result in broad changes in 

ecosystems, such as productivity, carbon storage, water cycling, nutrient cycling, and community 

composition. 

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure.124 In those sensitive speciesM, effects from 

repeated exposure to ozone throughout the growing season of the plant tend to accumulate, so 

 

M 73 FR 16491 (March 27, 2008). Only a small percentage of all the plant species growing within the U.S. (over 

43,000 species have been catalogued in the USDA PLANTS database) have been studied with respect to ozone 

sensitivity. 
6-25 

that even low concentrations experienced for a longer duration have the potential to create 

chronic stress on vegetation.125,N Ozone damage to sensitive species includes impaired 

photosynthesis and visible injury to leaves. The impairment of photosynthesis, the process by 

which the plant makes carbohydrates (its source of energy and food), can lead to reduced crop 

yields, timber production, and plant productivity and growth. Impaired photosynthesis can also 

lead to a reduction in root growth and carbohydrate storage below ground, resulting in other, 

more subtle plant and ecosystems impacts.126 These latter impacts include increased 

susceptibility of plants to insect attack, disease, harsh weather, interspecies competition and 

overall decreased plant vigor. The adverse effects of ozone on areas with sensitive species could 

potentially lead to species shifts and loss from the affected ecosystemsO, resulting in a loss or 

reduction in associated ecosystem goods and services.127 Additionally, visible ozone injury to 

leaves can result in a loss of aesthetic value in areas of special scenic significance like national 

parks and wilderness areas and reduced use of sensitive ornamentals in landscaping.128 

The Integrated Science Assessment (ISA) for Ozone presents more detailed information 

on how ozone effects vegetation and ecosystems.129 The ISA concludes that ambient 

concentrations of ozone are associated with a number of adverse welfare effects and 

characterizes the weight of evidence for different effects associated with ozone.P The ISA 

concludes that visible foliar injury effects on vegetation, reduced vegetation growth, reduced 

productivity in terrestrial ecosystems, reduced yield and quality of agricultural crops, and 

alteration of below-ground biogeochemical cycles are causally associated with exposure to 

ozone. It also concludes that reduced carbon sequestration in terrestrial ecosystems, alteration of 

terrestrial ecosystem water cycling, and alteration of terrestrial community composition are 

likely to be causally associated with exposure to ozone. 

6.1.2.4 Particulate Matter Deposition 

Particulate matter contributes to adverse effects on vegetation and ecosystems, and to 

soiling and materials damage. These welfare effects result predominantly from exposure to 

excess amounts of specific chemical species, regardless of their source or predominant form 

(particle, gas or liquid). The following characterizations of the nature of these environmental 

effects are based on information contained in the 2009 PM ISA and the 2008 Integrated Science 

Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria (secondary NOX/SOX

ISA).130, 131 

 

N The concentration at which ozone levels overwhelm a plant’s ability to detoxify or compensate for oxidant 

exposure varies. Thus, whether a plant is classified as sensitive or tolerant depends in part on the exposure levels 

being considered. 

O Per footnote above, ozone impacts could be occurring in areas where plant species sensitive to ozone have not yet 

been studied or identified. 

P The Ozone ISA evaluates the evidence associated with different ozone related health and welfare effects, assigning 

one of five “weight of evidence” determinations: causal relationship, likely to be a causal relationship, suggestive of 

a causal relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship. For more 

information on these levels of evidence, please refer to Table II of the ISA. 
6-26 

6.1.2.4.1 Deposition of Nitrogen and Sulfur 

Nitrogen and sulfur interactions in the environment are highly complex as shown in 

Figure 6-4. Both nitrogen and sulfur are essential, and sometimes limiting, nutrients needed for 

growth and productivity of ecosystem components (e.g. algae, plants). In terrestrial and aquatic 

ecosystems excesses of nitrogen or sulfur can lead to acidification and nutrient enrichment.132 In 

addition, in aquatic ecosystems, sulfur deposition can increase mercury methylation. 

Figure 6-4 Nitrogen and Sulfur Cycling, and Interactions in the Environment 

Source: U.S. EPA, 2008c 

6.1.2.4.1.1 Ecological Effects of Acidification 

Deposition of nitrogen and sulfur can cause acidification, which alters biogeochemistry 

and affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Soil 

acidification is a natural process, but is often accelerated by acidifying deposition, which can 

decrease concentrations of exchangeable base cations in soils.133 Biological effects of 

acidification in terrestrial ecosystems are generally linked to aluminum toxicity and decreased 

ability of plant roots to take up base cations.134 Decreases in the acid neutralizing capacity and 

increases in inorganic aluminum concentration contribute to declines in zooplankton, macro 

invertebrates, and fish species richness in aquatic ecosystems.135

Geology (particularly surficial geology) is the principal factor governing the sensitivity of 

terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition.136

Geologic formations having low base cation supply generally underlie the watersheds of acidsensitive lakes and streams. Other factors contribute to the sensitivity of soils and surface waters 
6-27 

to acidifying deposition, including topography, soil chemistry, land use, and hydrologic flow 

path.137

6.1.2.4.1.1.1 Aquatic Acidification 

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at 

various trophic levels. These studies indicate that aquatic biota have been affected by 

acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems. Effects 

have been most clearly documented for fish, aquatic insects, other invertebrates, and algae. 

Biological effects are primarily attributable to a combination of low pH and high inorganic 

aluminum concentrations. Such conditions occur more frequently during rainfall and snowmelt 

that cause high flows of water and less commonly during low-flow conditions, except where 

chronic acidity conditions are severe. Biological effects of episodes include reduced fish 

condition factorA, changes in species composition and declines in aquatic species richness across 

multiple taxa, ecosystems and regions. 

Because acidification primarily affects the diversity and abundance of aquatic biota, it 

also affects the ecosystem services that are derived from the fish and other aquatic life found in 

these surface waters. In the northeastern United States, the surface waters affected by 

acidification are a source of food for some recreational and subsistence fishermen and for other 

consumers with particularly high rates of self-caught fish consumption, such as the Hmong and 

Chippewa ethnic groups.138,139

6.1.2.4.1.1.2 Terrestrial Acidification 

Acidifying deposition has altered major biogeochemical processes in the U.S. by 

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching from 

soil to drainage waters, depleting base cations (especially calcium and magnesium) from soils, 

and increasing the mobility of aluminum. Inorganic aluminum is toxic to some tree roots. Plants 

affected by high levels of aluminum from the soil often have reduced root growth, which restricts 

the ability of the plant to take up water and nutrients, especially calcium.140 These direct effects 

can, in turn, influence the response of these plants to climatic stresses such as droughts and cold 

temperatures. They can also influence the sensitivity of plants to other stresses, including insect 

pests and disease leading to increased mortality of canopy trees.141 In the U.S., terrestrial effects 

of acidification are best described for forested ecosystems (especially red spruce and sugar maple 

ecosystems) with additional information on other plant communities, including shrubs and 

lichen.142 

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing 

gradual losses of base cation nutrients from the soil due to accelerated leaching from acidifying 

deposition. This change in nutrient availability may reduce the quality of forest nutrition over 

the long term. Evidence suggests that red spruce and sugar maple in some areas in the eastern 

U.S. have experienced declining health because of this deposition. For red spruce, (Picea 

rubens) dieback or decline has been observed across high elevation landscapes of the 

northeastern U.S., and to a lesser extent, the southeastern U.S., and acidifying deposition has 

been implicated as a causal factor.143
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6.1.2.4.1.2 Ecological Effects from Nitrogen Enrichment 

6.1.2.4.1.2.1 Aquatic Enrichment 

Eutrophication in estuaries is associated with a range of adverse ecological effects 

including low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic 

vegetation (SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to 

fish and shellfish, which, in the short-term, can lead to episodic fish kills and, in the long-term, 

can damage overall growth in fish and shellfish populations. Low DO also degrades the 

aesthetic qualities of surface water. In addition to often being toxic to fish and shellfish, and 

leading to fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also be 

harmful to human health. SAV provides critical habitat for many aquatic species in estuaries 

and, in some instances, can also protect shorelines by reducing wave strength; therefore, declines 

in SAV due to nutrient enrichment are an important source of concern. Low water clarity is in 

part the result of accumulations of both algae and sediments in estuarine waters. In addition to 

contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of 

the estuarine environment. 

An assessment of estuaries nationwide by the National Oceanic and Atmospheric 

Administration (NOAA) concluded that 64 estuaries (out of 99 with available data) suffered 

from moderate or high levels of eutrophication due to excessive inputs of both N and 

phosphorus.144 For estuaries in the Mid-Atlantic region, the contribution of atmospheric 

deposition to total N loads is estimated to range between 10 percent and 58 percent.145 Estuaries 

in the eastern United States are an important source of food production, in particular fish and 

shellfish production. The estuaries are capable of supporting large stocks of resident commercial 

species, and they serve as the breeding grounds and interim habitat for several migratory species. 

Eutrophication in estuaries may also affect the demand for seafood after well-publicized toxic 

blooms, water-based recreation, and erosion protection provided by SAV.

6.1.2.4.1.2.2 Terrestrial Enrichment 

Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess of 

natural background levels, through either atmospheric deposition or direct application. 

Atmospheric N deposition is associated with changes in the types and number of species and 

biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as a 

result, it may take as much as 50 years or more to see changes in ecosystem conditions and 

indicators. One of the main provisioning services potentially affected by N deposition is grazing 

opportunities offered by grasslands for livestock production in the Central U.S. Although N 

deposition on these grasslands can offer supplementary nutritive value and promote overall grass 

production, there are concerns that fertilization may favor invasive grasses and shift the species 

composition away from native grasses. This process may ultimately reduce the productivity of 

grasslands for livestock production. 

Terrestrial enrichment also affects habitats, for example the Coastal Sage Scrub (CSS) 

and Mixed Conifer Forest (MCF) habitats which are an integral part of the California landscape. 

Together the ranges of these habitats include the densely populated and valuable coastline and 

the mountain areas. Numerous threatened and endangered species at both the state and federal 
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levels reside in CSS and MCF. Fire regulation is also an important regulating service that could 

be affected by nutrient enrichment of the CSS and MCF ecosystems by encouraging growth of 

more flammable grasses, increasing fuel loads, and altering the fire cycle. 

6.1.2.4.1.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide 

Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving 

adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO2 penetrates into 

leaves through the stomata, although there is evidence for limited pathways via the cuticle.146

Pollutants must be transported from the bulk air to the leaf boundary layer in order to get to the 

stomata. When the stomata are closed, as occurs under dark or drought conditions, resistance to 

gas uptake is very high and the plant has a very low degree of susceptibility to injury. In 

contrast, mosses and lichens do not have a protective cuticle barrier to gaseous pollutants or 

stomates and are generally more sensitive to gaseous sulfur and nitrogen than vascular plants.147

Acute foliar injury usually happens within hours of exposure, involves a rapid absorption of a 

toxic dose, and involves collapse or necrosis of plant tissues. Another type of visible injury is 

termed chronic injury and is usually a result of variable SO2 exposures over the growing season. 

Besides foliar injury, chronic exposure to low SO2 concentrations can result in reduced 

photosynthesis, growth, and yield of plants.148 These effects are cumulative over the season and 

are often not associated with visible foliar injury. As with foliar injury, these effects vary among 

species and growing environment. SO2 is also considered the primary factor causing the death of 

lichens in many urban and industrial areas.149

6.1.2.4.1.4 Mercury Methylation 

Mercury is a persistent, bioaccumulative toxic metal that is emitted in three forms: 

gaseous elemental Hg (Hg0), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP). 

Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils, 

after Hg has precipitated from the air and deposited into waterbodies or land. Once formed, 

MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger 

predatory fish may have MeHg concentrations many times, typically on the order of one million 

times, that of the concentrations in the freshwater body in which they live. The NOX SOX ISA—

Ecological Criteria concluded that evidence is sufficient to infer a causal relationship between 

sulfur deposition and increased mercury methylation in wetlands and aquatic environments.150

Specifically, there appears to be a relationship between SO4

2- deposition and mercury 

methylation; however, the rate of mercury methylation varies according to several spatial and 

biogeochemical factors whose influence has not been fully quantified. Therefore, the correlation 

between SO4

2- deposition and MeHg cannot yet be quantified for the purpose of interpolating the 

association across waterbodies or regions. Nevertheless, because changes in MeHg in 

ecosystems represent changes in significant human and ecological health risks, the association 

between sulfur and mercury cannot be neglected.151

6.1.2.4.2 Deposition of Metallic and Organic Constituents of PM 

Several significant ecological effects are associated with deposition of chemical 

constituents of ambient PM such as metals and organics.152 The trace metal constituents of PM 

include cadmium, copper, chromium, mercury, nickel, zinc, and lead. The organics include 
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persistent organic pollutants (POPs), polyaromatic hydrocarbons (PAHs) and polybromiated 

diphenyl ethers (PBDEs). Exposure to PM for direct effects occur via deposition (e.g., wet, dry 

or occult) to vegetation surfaces, while indirect effects occur via deposition to ecosystem soils or 

surface waters where the deposited constituents of PM then interacts with biological organisms. 

While both fine and coarse-mode particles may affect plants and other organisms, more often the 

chemical constituents drive the ecosystem response to PM.153 Ecological effects of PM include 

direct effects to metabolic processes of plant foliage; contribution to total metal loading resulting 

in alteration of soil biogeochemistry and microbiology, plant and animal growth and 

reproduction; and contribution to total organics loading resulting in bioaccumulation and 

biomagnification. 

Particulate matter can adversely impact plants and ecosystem services provided by plants 

by deposition to vegetative surfaces.154 Particulates deposited on the surfaces of leaves and 

needles can block light, altering the radiation received by the plant. PM deposition near sources 

of heavy deposition can obstruct stomata limiting gas exchange, damage leaf cuticles and 

increase plant temperatures.155 Plants growing on roadsides exhibit impact damage from nearroad PM deposition, having higher levels of organics and heavy metals, and accumulate salt from 

road de-icing during winter months.156 In addition, atmospheric PM can convert direct solar 

radiation to diffuse radiation, which is more uniformly distributed in a tree canopy, allowing 

radiation to reach lower leaves.157 Decreases in crop yields (a provisioning service) due to 

reductions in solar radiation have been attributed to regional scale air pollution in other counties 

with especially severe regional haze.158

In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil 

or foliage. Copper, zinc, and nickel have been shown to be directly toxic to vegetation under 

field conditions.159 The ability of vegetation to take up heavy metals is dependent upon the 

amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants from 

soils and vegetative surfaces can disrupt photosynthesis, alter pigments and mineral content, 

reduce plant vigor, decrease frost hardiness and impair root development. 

Particulate matter can also contain organic air toxic pollutants, including PAHs, which 

are a class of polycyclic organic matter (POM). PAHs can accumulate in sediments and 

bioaccumulate in freshwater, flora and fauna. The uptake of organics depends on the plant 

species, site of deposition, physical and chemical properties of the organic compound and 

prevailing environmental conditions.160 Different species can have different uptake rates of 

PAHs. For example, zucchini (Cucurbita pepo) accumulated significantly more PAHs than 

related plant species.161 PAHs can accumulate to high enough concentrations in some coastal 

environments to pose an environmental health threat that includes cancer in fish populations, 

toxicity to organisms living in the sediment and risks to those (e.g., migratory birds) that 

consume these organisms.162,163 Atmospheric deposition of particles is thought to be the major 

source of PAHs to the sediments of Lake Michigan, Chesapeake Bay, Tampa Bay and other 

coastal areas of the U.S.164

Contamination of plant leaves by heavy metals can lead to elevated concentrations in the 

soil. Trace metals absorbed into the plant, frequently bind to the leaf tissue, and then are lost 

when the leaf drops. As the fallen leaves decompose, the heavy metals are transferred into the 

soil.165,166 Many of the major indirect plant responses to PM deposition are chiefly soil-mediated 
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and depend on the chemical composition of individual components of deposited PM. Upon 

entering the soil environment, PM pollutants can alter ecological processes of energy flow and 

nutrient cycling, inhibit nutrient uptake to plants, change microbial community structure and, 

affect biodiversity. Accumulation of heavy metals in soils depends on factors such as local soil 

characteristics, geologic origin of parent soils, and metal bioavailability. Heavy metals, such as 

zinc, copper, and cadmium, and some pesticides can interfere with microorganisms that are 

responsible for decomposition of soil litter, an important regulating ecosystem service that serves 

as a source of soil nutrients.167 Surface litter decomposition is reduced in soils having high metal 

concentrations. Soil communities have associated bacteria, fungi, and invertebrates that are 

essential to soil nutrient cycling processes. Changes to the relative species abundance and 

community composition are associated with deposited PM to soil biota.168

Atmospheric deposition can be the primary source of some organics and metals to 

watersheds. Deposition of PM to surfaces in urban settings increases the metal and organic 

component of storm water runoff.169 This atmospherically-associated pollutant burden can then 

be toxic to aquatic biota. The contribution of atmospherically deposited PAHs to aquatic food 

webs was demonstrated in high elevation mountain lakes with no other anthropogenic 

contaminant sources.170 Metals associated with PM deposition limit phytoplankton growth, 

affecting aquatic trophic structure. Long-range atmospheric transport of 47 pesticides and 

degradation products to the snowpack in seven national parks in the Western U.S. was recently 

quantified indicating PM-associated contaminant inputs to receiving waters during spring 

snowmelt.171

The recently completed Western Airborne Contaminants Assessment Project (WACAP) 

is the most comprehensive database on contaminant transport and PM depositional effects on 

sensitive ecosystems in the Western U.S.172 In this project, the transport, fate, and ecological 

impacts of anthropogenic contaminants from atmospheric sources were assessed from 2002 to 

2007 in seven ecosystem components (air, snow, water, sediment, lichen, conifer needles and 

fish) in eight core national parks. The study concluded that bioaccumulation of semi-volatile 

organic compounds occurred throughout park ecosystems, an elevational gradient in PM 

deposition exists with greater accumulation in higher altitude areas, and contaminants 

accumulate in proximity to individual agriculture and industry sources, which is counter to the 

original working hypothesis that most of the contaminants would originate from Eastern Europe 

and Asia. 

6.1.2.4.3 Materials Damage and Soiling 

Building materials including metals, stones, cements, and paints undergo natural 

weathering processes from exposure to environmental elements (e.g., wind, moisture, 

temperature fluctuations, sunlight, etc.). Pollution can worsen and accelerate these effects. 

Deposition of PM is associated with both physical damage (materials damage effects) and 

impaired aesthetic qualities (soiling effects). Wet and dry deposition of PM can physically affect 

materials, adding to the effects of natural weathering processes, by potentially promoting or 

accelerating the corrosion of metals, by degrading paints and by deteriorating building materials 

such as stone, concrete and marble.173 The effects of PM are exacerbated by the presence of 

acidic gases and can be additive or synergistic due to the complex mixture of pollutants in the air 

and surface characteristics of the material. Acidic deposition has been shown to have an effect 
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on materials including zinc/galvanized steel and other metal, carbonate stone (as monuments and 

building facings), and surface coatings (paints).174 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. 

6.1.2.5 Environmental Effects of Air Toxics 

Emissions from producing, transporting and combusting fuel contribute to ambient levels 

of pollutants that contribute to adverse effects on vegetation. Volatile organic compounds 

(VOCs), some of which are considered air toxics, have long been suspected to play a role in 

vegetation damage.175 In laboratory experiments, a wide range of tolerance to VOCs has been 

observed.176 Decreases in harvested seed pod weight have been reported for the more sensitive 

plants, and some studies have reported effects on seed germination, flowering and fruit ripening. 

Effects of individual VOCs or their role in conjunction with other stressors (e.g., acidification, 

drought, temperature extremes) have not been well studied. In a recent study of a mixture of 

VOCs including ethanol and toluene on herbaceous plants, significant effects on seed production, 

leaf water content and photosynthetic efficiency were reported for some plant species.177

Research suggests an adverse impact of vehicle exhaust on plants, which has in some 

cases been attributed to aromatic compounds and in other cases to nitrogen oxides.178,179,180 The 

impacts of VOCs on plant reproduction may have long-term implications for biodiversity and 

survival of native species near major roadways. Most of the studies of the impacts of VOCs on 

vegetation have focused on short-term exposure and few studies have focused on long-term 

effects of VOCs on vegetation and the potential for metabolites of these compounds to affect 

herbivores or insects. 

6.2 Air Quality Impacts of Non-GHG Pollutants 

Chapter 5 of this draft RIA presents the projected emissions changes due to the proposal. 

Once the emissions changes are projected the next step is to look at how the ambient air quality 

would be impacted by those emissions changes. Although the purpose of this proposal is to 

address greenhouse gas emissions, this proposal would also impact emissions of criteria and 

hazardous air pollutants. No air quality modeling was done for this draft RIA to project the 

impacts of the proposal. Air quality modeling will be done for the final rulemaking, however, 

and those plans are discussed in Section 6.2.2. 

6.2.1 Current Concentrations of Non-GHG Pollutants 

6.2.1.1 Current Concentrations of Particulate Matter 

As described in Chapter 6.1.1.1, PM causes adverse health effects, and EPA has set 

national standards to provide requisite protection against those health effects. There are two 

primary NAAQS for PM2.5: an annual standard (12.0 μg/m3

) and a 24-hour standard (35 μg/m3) 

with a 98th percentile form, and two secondary NAAQS for PM2.5: an annual standard (15.0 

μg/m3

) and a 24-hour standard (35 μg/m3), likewise with a 98th percentile form. The initial PM2.5

standards were set in 1997 and revisions to the standards were finalized in 2006 and in December 

2012. The 2006 revision revised the level of the 24-hour standards from 65 μg/m3

 to 35 ug/m3, 
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and the December 2012 rule revised the level of the primary annual PM2.5 standard from 15.0 

μg/m3

 to 12.0 μg/m3.

181 

In 2005 EPA designated 39 nonattainment areas for the 1997 PM2.5 NAAQS (70 FR 

19844, April 14, 2005). As of July 2, 2014, over 47 million people lived in the 19 areas that are 

still designated as nonattainment for the 1997 annual PM2.5 NAAQS. These PM2.5 nonattainment 

areas are comprised of 105 full or partial counties. EPA anticipates making initial area 

designation decisions for the 2012 primary annual PM2.5 NAAQS in December 2014, with those 

designations likely becoming effective in early 2015.182 On November 13, 2009 and February 3, 

2011, EPA designated 32 nonattainment areas for the 2006 24-hour PM2.5 NAAQS (74 FR 

58688, November 13, 2009 and 76 FR 6056, February 3, 2011). As of July 2, 2014, 24 of these 

areas remain designated as nonattainment, and they are composed of 74 full or partial counties, 

with a population of over 43 million. In total, there are currently 33 PM2.5 nonattainment areas 

with a population of over 61 million people.Q 

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

into attainment in the future. Designated nonattainment areas not currently attaining the 1997 

annual PM2.5 NAAQS are required to attain the NAAQS by 2015 and will be required to 

maintain the 1997 annual PM2.5 NAAQS thereafter. The 2006 24-hour PM2.5 nonattainment 

areas are required to attain the 2006 24-hour PM2.5 NAAQS in the 2015 to 2019 time frame and 

will be required to maintain the 2006 24-hour PM2.5 NAAQS thereafter. Areas to be designated 

nonattainment for the 2012 primary annual PM2.5 NAAQS will likely be required to attain the 

2012 NAAQS in the 2021 to 2025 time frame. The heavy-duty vehicle standards proposed here 

first apply to model year 2021 vehicles. 

6.2.1.2 Current Concentrations of Ozone 

As described in Chapter 6.1.1.2.2, ozone causes adverse health effects, and EPA has set 

national ambient air quality standards to protect against those health effects. The primary and 

secondary NAAQS for ozone are 8-hour standards with a level of 0.075 ppm. The most recent 

revision to the ozone standards was in 2008; the previous 8-hour ozone standards, set in 1997, 

had a level of 0.08 ppm. In 2004, EPA designated nonattainment areas for the 1997 8-hour 

ozone NAAQS (69 FR 23858, April 30, 2004).R As of July 2, 2014, there were 37 8-hour ozone 

nonattainment areas for the 1997 ozone NAAQS, composed of 188 full or partial counties, with a 

total population of over 105 million. Nonattainment designations for the 2008 ozone standards 

were finalized on April 30, 2012 and May 31, 2012.183 As of July 2, 2014, there were 46 ozone 

nonattainment areas for the 2008 ozone NAAQS, composed of 227 full or partial counties, with a 

 

Q Data come from Summary Nonattainment Area Population Exposure Report, current as of July 2, 2014 at: 

http://www.epa.gov/oar/oaqps/greenbk/popexp.html and contained in Docket EPA-HQ-OAR-2014-0827. The 61 

million total is calculated by summing, without double counting, the 1997 and 2006 PM2.5 nonattainment 

populations contained in the Summary Nonattainment Area Population Exposure report 

(http://www.epa.gov/oar/oaqps/greenbk/popexp.html). If there is a population associated with both the 1997 and 

2006 nonattainment areas, and they are not the same, then the larger of the two populations is included in the sum. R 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. 
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population of over 123 million. As of July 2, 2014, over 134 million people are living in ozone 

nonattainment areas.S

 

States with ozone nonattainment areas are required to take action to bring those areas into 

attainment. The attainment date assigned to an ozone nonattainment area is based on the area’s 

classification. Areas with higher 3–year design values are classified at higher levels and subject 

to more stringent control requirements, but they are also given more time to attain the ozone 

NAAQS. Most ozone nonattainment areas for the 1997 8-hour ozone NAAQS were required to 

attain in the 2007 to 2013 time frame and then to maintain it thereafter.T The attainment dates 

for areas designated nonattainment for the 2008 8-hour ozone NAAQS are in the 2015 to 2032 

timeframe, depending on the severity of the problem in each area. In addition, EPA is currently 

working on a review of the ozone NAAQS.U If EPA revises the ozone standards pursuant to that 

review, the attainment dates associated with areas designated nonattainment for that NAAQS 

would be 5 or more years after the final rule is promulgated, depending on the severity of the 

problem in each area. The heavy-duty vehicle standards proposed here first apply to model year 

2021 vehicles. 

6.2.1.3 Current Concentrations of Nitrogen Oxides 

EPA most recently completed a review of the primary NAAQS for NO2 in January 2010. 

There are two primary NAAQS for NO2: an annual standard (53 ppb) and a 1-hour standard (100 

ppb). EPA promulgated area designations in the Federal Register on February 17, 2012. In this 

initial round of designations, all areas of the country were designated as 

“unclassifiable/attainment” for the 2010 NO2 NAAQS based on data from the existing air quality 

monitoring network. EPA and state agencies are working to establish an expanded network of 

NO2 monitors, expected to be deployed in the 2013-2017 time frame. Once three years of air 

quality data have been collected from the expanded network, EPA will be able to evaluate NO2

air quality in additional locations.184,185

6.2.1.4 Current Concentrations of Sulfur Oxides 

EPA most recently completed a review of the primary SO2 NAAQS in June 2010. The 

current primary NAAQS for SO2 is a 1-hour standard of 75 ppb. EPA finalized the initial area 

designations for 29 nonattainment areas in 16 states in a notice published in the Federal Register 

 

S The 134 million total is calculated by summing, without double counting, the 1997 and 2008 ozone nonattainment 

populations contained in the Summary Nonattainment Area Population Exposure report 

(http://www.epa.gov/oar/oaqps/greenbk/popexp.html). If there is a population associated with both the 1997 and 

2008 nonattainment areas, and they are not the same, then the larger of the two populations is included in the sum. T The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air Basin 8-

hour ozone nonattainment area are designated as Extreme and will have to attain before June 15, 2024. The 

Sacramento, Coachella Valley, Western Mojave and Houston 8-hour ozone nonattainment areas are designated as 

Severe and will have to attain by June 15, 2019. 

U On November 25, 2014 EPA proposed to update both the primary ozone standard, to protect public health, and the 

secondary standard, to protect the public welfare. Both standards would be 8-hour standards set within a range of 65 

to 70 parts per billion (ppb). EPA is seeking comment on levels for the health standard as low as 60 ppb. The agency 

will accept comments on all aspects of the proposal, including on retaining the existing standard. 

(http://www.epa.gov/glo/pdfs/20141125proposal.pdf) 
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on August 5, 2013. In this first round of designations, EPA only designated nonattainment areas 

that were violating the standard based on existing air quality monitoring data provided by the 

states. The agency did not have sufficient information to designate any area as “attainment” or 

make final decisions about areas for which additional modeling or monitoring is needed (78 FR 

47191, August 5, 2013). EPA anticipates designating areas for the revised SO2 standard in 

multiple rounds. 

6.2.1.5 Current Concentrations of Carbon Monoxide 

There are two NAAQS for CO: an 8-hour standard (9 ppm) and a 1-hour standard (35 

ppm). The primary NAAQS for CO were retained in August 2011. There are currently no CO 

nonattainment areas; as of September 27, 2010, all CO nonattainment areas were redesignated to 

maintenance areas. The designations were based on the existing community-wide monitoring 

network. EPA is making changes to the ambient air monitoring requirements for CO. The new 

requirements are expected to result in approximately 52 CO monitors operating near roads within 

52 urban areas by January 2015 (76 FR 54294, August 31, 2011). 

6.2.1.6 Current Concentrations of Diesel Exhaust PM 

Because DPM is part of overall ambient PM and cannot be easily distinguished from 

overall PM, we do not have direct measurements of DPM in the ambient air. DPM 

concentrations are estimated using ambient air quality modeling based on DPM emission 

inventories. DPM concentrations were recently estimated as part of the 2005 NATA.186 

Ambient impacts of mobile source emissions were predicted using the Assessment System for 

Population Exposure Nationwide (ASPEN) dispersion model. 

Concentrations of DPM were calculated at the census tract level in the 2005 NATA. 

Figure 6-5 below summarizes the distribution of ambient DPM concentrations at the national 

scale. Areas with high concentrations are clustered in the Northeast, Great Lake States, 

California, and the Gulf Coast States, and are also distributed throughout the rest of the U.S. 

Table 6-1 presents a distribution of ambient DPM concentrations around the country. The 

median DPM concentration calculated nationwide is 0.53 μg/m3

. Half of the DPM and diesel 

exhaust organic gases can be attributed to onroad diesels. 
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Figure 6-5 Estimated County Ambient Concentration of Diesel Particulate Matter 

Table 6-1 Distribution of Census Tract Ambient Concentrations of DPM at the National Scale in 2005 NATAa

 AMBIENT CONCENTRATION 

(μg/m3

) 

5th Percentile 0.03 

25th Percentile 0.17 

50th Percentile 0.53 

75th Percentile 1.22 

95th Percentile 2.91 

Onroad Contribution to Median Census Tract Concentrations 50% 

 Note: 

 a This table is generated from data contained in the diesel particulate matter Microsoft Access database file found in 

 the Tract-Level Pollutants section of the 2005 NATA webpage (http://www.epa.gov/ttn/atw/nata2005/tables.html). 
6-37 

6.2.1.7 Current Concentrations of Air Toxics 

The majority of Americans continue to be exposed to ambient concentrations of air toxics 

at levels which have the potential to cause adverse health effects.187 The levels of air toxics to 

which people are exposed vary depending on where people live and work and the kinds of 

activities in which they engage, as discussed in detail in EPA’s most recent Mobile Source Air 

Toxics (MSAT) Rule.188 In order to identify and prioritize air toxics, emission source types and 

locations which are of greatest potential concern, EPA conducts the National-Scale Air Toxics 

Assessment (NATA). The most recent NATA was conducted for calendar year 2005, and was 

released in March 2011.189 NATA for 2005 includes four steps: 

1) Compiling a national emissions inventory of air toxics emissions from outdoor 

sources 

2) Estimating ambient concentrations of air toxics across the United States 

3) Estimating population exposures across the United States 

4) Characterizing potential public health risk due to inhalation of air toxics including 

both cancer and noncancer effects 

According to the NATA for 2005, mobile sources were responsible for 43 percent of 

outdoor toxic emissions and over 50 percent of the cancer risk and noncancer hazard attributable 

to direct emissions from mobile and stationary sources.V,W,190 Mobile sources are also large 

contributors to precursor emissions which react to form secondary concentrations of air toxics. 

Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively assessed 

in the 2005 NATA, and mobile sources were responsible for over 40 percent of primary 

emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor 

emissions. Benzene is also a large contributor to cancer risk, and mobile sources account for 

over 70 percent of ambient exposure. Over the years, EPA has implemented a number of mobile 

source and fuel controls which have resulted in VOC reductions, which also reduced 

formaldehyde, benzene and other air toxic emissions. 

6.2.2 Impacts of Proposed Standards on Future Air Quality 

Air quality models use mathematical and numerical techniques to simulate the physical 

and chemical processes that affect air pollutants as they disperse and react in the atmosphere. 

Based on inputs of meteorological data and source information, these models are designed to 

characterize primary pollutants that are emitted directly into the atmosphere and secondary 

pollutants that are formed as a result of complex chemical reactions within the atmosphere. 

 

V NATA also includes estimates of risk attributable to background concentrations, which includes contributions 

from long-range transport, persistent air toxics, and natural sources; as well as secondary concentrations, where 

toxics are formed via secondary formation. Mobile sources substantially contribute to long-range transport and 

secondarily formed air toxics. 

W NATA relies on a Gaussian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to 

estimate toxic air pollutant concentrations. 
6-38 

Photochemical air quality models have become widely recognized and routinely utilized tools for 

regulatory analysis by assessing the effectiveness of control strategies. These models are applied 

at multiple spatial scales from local, regional, national, and global. 

Full-scale photochemical air quality modeling is necessary to accurately project levels of 

criteria and air toxic pollutants. For the final rulemaking, national-scale air quality modeling 

analyses will be performed to analyze the impacts of the standards on PM2.5, NO2, ozone, and 

selected air toxics (i.e., benzene, formaldehyde, acetaldehyde, naphthalene, acrolein and 1,3-

butadiene). The length of time needed to prepare the necessary emissions inventories, in 

addition to the processing time associated with the modeling itself, has precluded us from 

performing air quality modeling for this proposal. 

Section VIII of the preamble presents projections of the changes in criteria pollutant and 

air toxics emissions due to the proposed standards; the basis for those estimates is set out in 

Chapter 5 of the draft RIA. NHTSA also provides its projections in Chapter 4 of its DEIS. The 

atmospheric chemistry related to ambient concentrations of PM2.5, ozone and air toxics is very 

complex, and making predictions based solely on emissions changes is extremely difficult. 

However, based on the magnitude of the emissions changes predicted to result from the proposed 

standards, the agencies expect that there will be improvements in ambient air quality, pending a 

more comprehensive analysis for the final rulemaking. 

For the final rulemaking, the agencies intend to use a 2011-based Community Multi-scale 

Air Quality (CMAQ) modeling platform as the tool for the air quality modeling. The CMAQ 

modeling system is a comprehensive three-dimensional grid-based Eulerian air quality model 

designed to estimate the formation and fate of oxidant precursors, primary and secondary PM 

concentrations and deposition, and air toxics, over regional and urban spatial scales (e.g., over 

the contiguous U.S.).191,192,193 ,194 The CMAQ model is a well-known and well-established tool 

and is commonly used by EPA for regulatory analyses, and by States in developing attainment 

demonstrations for their State Implementation Plans.195 The CMAQ model version 5.0 was most 

recently peer-reviewed in September of 2011 for the U.S. EPA. 

CMAQ includes many science modules that simulate the emission, production, decay, 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the 

atmosphere. The agencies intend to use the most recent multi-pollutant CMAQ code available at 

the time of air quality modeling (CMAQ version 5.0.2; multipollutant versionX) which reflects 

updates to version 5.0 to improve the underlying science algorithms as well as include new 

diagnostic/scientific modules which are detailed at http://www.cmascenter.org.196,197,198 Figure 

6-6 shows the geographic extent of the modeling domain that will be used for air quality 

modeling in these analyses. The domain covers the 48 contiguous states along with the southern 

portions of Canada and the northern portions of Mexico. This modeling domain contains 25 

vertical layers with a top at about 17,600 meters, or 50 millibars (mb) and a horizontal resolution 

of 12 x 12 km. 

 

X CMAQ version 5.0.2 was released in April 2014. It is available from the Community Modeling and Analysis 

System (CMAS) website: http://www.cmascenter.org. 
6-39 

Figure 6-6 Map of the CMAQ 12-km US Modeling Domain 

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic 

sources, meteorological data, and initial and boundary conditions. The 2011 CMAQ 

meteorological inputs will be derived from Version 3.4 of the Weather Research Forecasting 

Model (WRF).199 These inputs included hourly-varying horizontal wind components (i.e., speed 

and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell 

in each vertical layer. Details of the annual 2011 meteorological model simulation and 

evaluation will be described in more detail within the final RIA, the technical support document 

for the final rulemaking air quality modeling, and NHTSA’s Final Environmental Impact 

Statement. 

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model200 (standard version 

8-03-02 with 8-02-01 chemistry. 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-5; additional information available at: 

http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-
6-40 

5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitudelongitude). The predictions were used to provide one-way dynamic boundary conditions at onehour intervals and an initial concentration field for the CMAQ simulations. A GEOS-Chem 

evaluation was conducted for the purpose of validating the 2011 GEOS-Chem simulation for 

predicting selected measurements relevant to their use as boundary conditions for CMAQ. This 

evaluation included using satellite retrievals paired with GEOS-Chem grid cells.201 More 

information is available about the GEOS-CHEM model and other applications using this tool at: 

http://www-as.harvard.edu/chemistry/trop/geos. 

6.3 Changes in Atmospheric CO2 Concentrations, Global Mean 

Temperature, Sea Level Rise, and Ocean pH Associated with the 

Program’s GHG Emissions Reductions 

6.3.1 Introduction 

The impact of GHG emissions on the climate has been reviewed in the 2009 

Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of 

the Clean Air Act, the 2012-2016 light-duty vehicle rulemaking, the 2014-2018 heavy-duty 

vehicle GHG rulemaking, and the 2017-2025 light-duty vehicle rulemaking. See 74 FR at 66496; 

75 FR at 25491; 76 FR at 57294; 77 FR at 62894. This section briefly discusses again some of 

the climate impact context for transportation emissions. 

Once emitted, GHGs that are the subject of this regulation can remain in the atmosphere 

for decades to millennia, meaning that 1) their concentrations become well-mixed throughout the 

global atmosphere regardless of emission origin, and 2) their effects on climate are long lasting. 

GHG emissions come mainly from the combustion of fossil fuels (coal, oil, and gas), with 

additional contributions from the clearing of forests, agricultural activities, cement production, 

and some industrial activities. Transportation activities, in aggregate, were the second largest 

contributor to total U.S. GHG emissions in 2010 (27 percent of total emissions).Y 

EPA Administrator relied on thorough and peer-reviewed assessments of climate change 

science prepared by the Intergovernmental Panel on Climate Change (“IPCC”), the United States 

Global Change Research Program (“USGCRP”), and the National Research Council of the 

National Academies (“NRC”) Z as the primary scientific and technical basis for the 

Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of 

the Clean Air Act (74 FR 66496, December 15, 2009). These assessments comprehensively 

address the scientific issues EPA Administrator had to examine, providing her both data and 

information on a wide range of issues pertinent to the Endangerment Finding. These 

assessments have been rigorously reviewed by the expert community, and also by United States 

government agencies and scientists, including by EPA itself. 

 

Y U.S. EPA (2012) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2010. EPA 430-R-12-001. 

Available at http://epa.gov/climatechange/emissions/downloads12/US-GHG-Inventory-2012-Main-Text.pdf Z For a complete list of core references from IPCC, USGCRP/CCSP, NRC and others relied upon for development 

of the TSD for EPA’s Endangerment and Cause or Contribute Findings see Section 1(b), specifically, Table 1.1 of 

the TSD. (Docket EPA-HQ-OAR-2010-0799)
6-41 

Based on these assessments, EPA Administrator determined that the emissions from new 

motor vehicles and engines contributes to elevated concentrations of greenhouse gases, that these 

greenhouse gases cause warming; that the recent warming has been attributed to the increase in 

greenhouse gases; and that warming of the climate endangers the public health and welfare of 

current and future generations. The D.C. Circuit has emphatically upheld the reasonableness of 

these findings. Coalition for Responsible Regulation v. EPA, 684 F. 3d 102, 121 (D.C. Cir. 

2012) upholding all of EPA’s findings and stating “EPA had before it substantial record evidence 

that anthropogenic emissions of greenhouse gases ‘very likely’ caused warming of the climate 

over the last several decades. EPA further had evidence of current and future effects of this 

warming on public health and welfare. Relying again upon substantial scientific evidence, EPA 

determined that anthropogenically induced climate change threatens both public health and 

public welfare. It found that extreme weather events, changes in air quality, increases in foodand water-borne pathogens, and increases in temperatures are likely to have adverse health 

effects. The record also supports EPA’s conclusion that climate change endangers human 

welfare by creating risk to food production and agriculture, forestry, energy, infrastructure, 

ecosystems, and wildlife. Substantial evidence further supported EPA’s conclusion that the 

warming resulting from the greenhouse gas emissions could be expected to create risks to water 

resources and in general to coastal areas as a result of expected increase in sea level.”) 

A number of major peer-reviewed scientific assessments have been released since the 

administrative record concerning the Endangerment Finding closed following EPA’s 2010 

Reconsideration Denial202. These assessments include the “Special Report on Managing the 

Risks of Extreme Events and Disasters to Advance Climate Change Adaptation”203, the 2013-14 

Fifth Assessment Report (AR5)204, the 2014 National Climate Assessment report205, the “Ocean 

Acidification: A National Strategy to Meet the Challenges of a Changing Ocean”206, “Report on 

Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to 

Millennia”207, “National Security Implications for U.S. Naval Forces” (National Security 

Implications)208, “Understanding Earth’s Deep Past: Lessons for Our Climate Future”209, “Sea 

Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and Future”210, 

“Climate and Social Stress: Implications for Security Analysis”211, and “Abrupt Impacts of 

Climate Change” (Abrupt Impacts) assessments212. 

EPA has reviewed these assessments and finds that in general, the improved 

understanding of the climate system they present are consistent with the assessments underlying 

the 2009 Endangerment Finding. 

The most recent assessments to be released were the IPCC AR5 assessments between 

September 2013 and April 2014, the NRC Abrupt Impacts assessment in December of 2013, and 

the U.S. National Climate Assessment in May of 2014. The NRC Abrupt Impacts report 

examines the potential for tipping points, thresholds beyond which major and rapid changes 

occur in the Earth’s climate system or other systems impacted by the climate. The Abrupt 

Impacts report did find less cause for concern than some previous assessments regarding some 

abrupt events within the next century such as disruption of the Atlantic Meridional Overturning 

Circulation (AMOC) and sudden releases of high-latitude methane from hydrates and 

permafrost, but found that the potential for abrupt changes in ecosystems, weather and climate 

extremes, and groundwater supplies critical for agriculture now seem more likely, severe, and 

imminent. The assessment found that some abrupt changes were already underway (Arctic sea 
6-42 

ice retreat and increases in extinction risk due to the speed of climate change), but cautioned that 

even abrupt changes such as the AMOC disruption that are not expected in this century can have 

severe impacts when they happen. 

The IPCC AR5 assessments are also generally consistent with the underlying science 

supporting the 2009 Endangerment Finding. For example, confidence in attributing recent 

warming to human causes has increased: the IPCC stated that it is extremely likely (>95 percent 

confidence) that human influences have been the dominant cause of recent warming. Moreover, 

the IPCC found that the last 30 years were likely (>66 percent confidence) the warmest 30 year 

period in the Northern Hemisphere of the past 1400 years, that the rate of ice loss of worldwide 

glaciers and the Greenland and Antarctic ice sheets has likely increased, that there is medium 

confidence that the recent summer sea ice retreat in the Arctic is larger than has been in 1450 

years, and that concentrations of carbon dioxide and several other of the major greenhouse gases 

are higher than they have been in at least 800,000 years. Climate-change induced impacts have 

been observed in changing precipitation patterns, melting snow and ice, species migration, 

negative impacts on crops, increased heat and decreased cold mortality, and altered ranges for 

water-borne illnesses and disease vectors. Additional risks from future changes include death, 

injury, and disrupted livelihoods in coastal zones and regions vulnerable to inland flooding, food 

insecurity linked to warming, drought, and flooding, especially for poor populations, reduced 

access to drinking and irrigation water for those with minimal capital in semi-arid regions, and 

decreased biodiversity in marine ecosystems, especially in the Arctic and tropics, with 

implications for coastal livelihoods. The IPCC determined that “[c]ontinued emissions of 

greenhouse gases will cause further warming and changes in all components of the climate 

system. Limiting climate change will require substantial and sustained reductions of greenhouse 

gases emissions.” 

Finally, the recently released National Climate Assessment stated, “Climate change is 

already affecting the American people in far reaching ways. Certain types of extreme weather 

events with links to climate change have become more frequent and/or intense, including 

prolonged periods of heat, heavy downpours, and, in some regions, floods and droughts. In 

addition, warming is causing sea level to rise and glaciers and Arctic sea ice to melt, and oceans 

are becoming more acidic as they absorb carbon dioxide. These and other aspects of climate 

change are disrupting people’s lives and damaging some sectors of our economy.” 

Assessments from these bodies represent the current state of knowledge, 

comprehensively cover and synthesize thousands of individual studies to obtain the majority 

conclusions from the body of scientific literature and undergo a rigorous and exacting standard 

of review by the peer expert community and U.S. government. 

Based on modeling analysis performed by EPA, reductions in CO2 and other GHG 

emissions associated with this proposed rule will affect future climate change. Since GHGs are 

well-mixed in the atmosphere and have long atmospheric lifetimes, changes in GHG emissions 

will affect atmospheric concentrations of greenhouse gases and future climate for decades to 

millennia, depending on the gas. This section provides estimates of the projected change in 

atmospheric CO2 concentrations based on the emission reductions estimated for this proposed 

rule, compared to the reference case. In addition, this section analyzes the response to the 

changes in GHG concentrations of the following climate-related variables: global mean 
6-43 

temperature, sea level rise, and ocean pH. See Chapter 5 in this RIA for the estimated net GHG 

emissions reductions over time. 

6.3.2 Projected Change in Atmospheric CO2 Concentrations, Global Mean 

Surface Temperature and Sea Level Rise 

To assess the impact of the emissions reductions from the proposed alternative, EPA 

estimated changes in projected atmospheric CO2 concentrations, global mean surface 

temperature and sea-level rise to 2100 using the GCAM (Global Change Assessment Model, 

formerly MiniCAM), integrated assessment modelAA,213 coupled with the MAGICC (Model for 

the Assessment of Greenhouse-gas Induced Climate Change) simple climate model.BB,214,215

GCAM was used to create the globally and temporally consistent set of climate relevant 

emissions required for running MAGICC. MAGICC was then used to estimate the projected 

change in relevant climate variables over time. Given the magnitude of the estimated emissions 

reductions associated with the proposal, a simple climate model such as MAGICC is appropriate 

for estimating the atmospheric and climate response. 

6.3.2.1 Methodology 

Emissions reductions associated with this proposal were evaluated with respect to a 

baseline reference case. An emissions scenario was developed by applying the estimated 

emissions reductions from the proposal’s proposed alternative relative to the baseline to the 

GCAM reference (no climate policy) scenario (used as the basis for the Representative 

Concentration Pathway RCP4.5).216 Specifically, the annual CO2, N2O, CH4, NOX and SO2

emissions reductions estimated from this proposed rule were applied as net reductions to the 

GCAM global baseline net emissions for each substance. The emissions reductions past 2050 

for all emissions were scaled with total U.S. road transportation fuel consumption from the 

GCAM reference scenario. This was chosen as a simple scale factor given that both direct and 

upstream emissions changes are included in the emissions reduction scenario provided. Road 

transport fuel consumption past 2050 does not change significantly and thus emissions 

reductions remain relatively constant from 2050 through 2100. 

The GCAM reference scenario217 depicts a world in which global population reaches a 

maximum of more than 9 billion in 2065 and then declines to 8.7 billion in 2100 while global 

 

AA GCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use that 

considers the sources of emissions of a suite of greenhouse gases (GHG's), emitted in 14 globally disaggregated 

regions, the fate of emissions to the atmosphere, and the consequences of changing concentrations of greenhouse 

related gases for climate change. GCAM begins with a representation of demographic and economic developments 

in each region and combines these with assumptions about technology development to describe an internally 

consistent representation of energy, agriculture, land-use, and economic developments that in turn shape global 

emissions. 

BB MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single 

framework. The framework allows the user to determine changes in greenhouse-gas concentrations, global-mean 

surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane 

(CH4), nitrous oxide (N2O), reactive gases (CO, NOX, VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and 

sulfur dioxide (SO2). MAGICC emulates the global-mean temperature responses of more sophisticated coupled 

Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. 
6-44 

GDP grows by an order of magnitude and global energy consumption triples. The reference 

scenario includes no explicit policies to limit carbon emissions, and therefore fossil fuels 

continue to dominate global energy consumption, despite substantial growth in nuclear and 

renewable energy. Atmospheric CO2 concentrations rise throughout the century and reach 760 to 

820 ppmv by 2100, depending on climatic parameters, with total radiative forcing increasing 

more than 5 Watts per square meter (W/m2

) above 1990 levels by 2100. Forest land declines in 

the reference scenario to accommodate increases in land use for food and bioenergy crops. Even 

with the assumed agricultural productivity increases, the amount of land devoted to crops 

increases in the first half of the century due to increases in population and income (higher 

income drives increases in land-intensive meat consumption). After 2050 the rate of growth in 

food demand slows, in part due to declining population. As a result the amount of cropland and 

also land use change (LUC) emissions decline as agricultural crop productivity continues to 

increase. 

The GCAM reference scenario uses non-CO2 and pollutant emissions implemented as 

described in Smith and Wigley (2006); land-use change emissions as described in Wise et al. 

(2009); and updated base-year estimates of global GHG emissions. This scenario was created as 

part of the Climate Change Science Program (CCSP) effort to develop a set of long-term global 

emissions scenarios that incorporate an update of economic and technology data and utilize 

improved scenario development tools compared to the IPCC Special Report on Emissions 

Scenarios (SRES) (IPCC 2000). 

Using MAGICC 5.3 v2,218 the change in atmospheric CO2 concentrations, global mean 

temperature, and sea level were projected at five-year time steps to 2100 for both the reference 

(no climate policy) scenario and the emissions reduction scenario specific to the proposed 

alternative of this proposal. To capture some of the uncertainty in the climate system, the 

changes in projected atmospheric CO2 concentrations, global mean temperature and sea level 

were estimated across a range of plausible climate sensitivities, 1.5°C to 6.0°C.CC The range as 

illustrated in Chapter 10, Box 10.2, Figure 2 of the IPCC’s Working Group I is approximately 

consistent with the 10-90 percent probability distribution of the individual cumulative 

distributions of climate sensitivity.219 Other uncertainties, such as uncertainties regarding the 

carbon cycle, ocean heat uptake, or aerosol forcing, were not addressed. 

MAGICC calculates the forcing response at the global scale from changes in atmospheric 

concentrations of CO2, CH4, N2O, HFCs, and tropospheric ozone. It also includes the effects of 

temperature changes on stratospheric ozone and the effects of CH4 emissions on stratospheric 

water vapor. Changes in CH4, NOX, VOC, and CO emissions affect both O3 concentrations and 

CH4 concentrations. MAGICC includes the relative climate forcing effects of changes in sulfate 

concentrations due to changing SO2 emissions, including both the direct effect of sulfate particles 

 

CC In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global 

surface temperature following a doubling of the atmospheric equivalent carbon dioxide concentration. The most 

recent IPCC AR5 assessment states that climate sensitivity is “likely” to be in the range of 1.5°C to 4.5°C, 

“extremely unlikely” to be less than 1°C, and “very unlikely” to be greater than 6 °C .” Intergovernmental Panel on 

Climate Change (IPCC). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I 

to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 

Cambridge, United Kingdom and New York, NY, USA. 
6-45 

and the indirect effects related to cloud interactions. However, MAGICC does not calculate the 

effect of changes in concentrations of other aerosols such as nitrates, black carbon, or organic 

carbon, making the assumption that the sulfate cooling effect is a proxy for the sum of all the 

aerosol effects. Therefore, the climate effects of changes in PM2.5 emissions and precursors 

(besides SO2) presented in Chapter 5 were not included in the calculations in this chapter. 

MAGICC also calculates all climate effects at the global scale. This global scale captures the 

climate effects of the long-lived, well-mixed greenhouse gases, but does not address the fact that 

short-lived climate forcers such as aerosols and ozone can have effects that vary with location 

and timing of emissions. Black carbon in particular is known to cause a positive forcing or 

warming effect by absorbing incoming solar radiation, but there are uncertainties about the 

magnitude of that warming effect and the interaction of black carbon (and other co-emitted 

aerosol species) with clouds. See 77 FR 38890, 38991-993 (June 29, 2012). While black carbon 

is likely to be an important contributor to climate change, it would be premature to include 

quantification of black carbon climate impacts in an analysis of the proposal’s standards at this 

time. See generally, EPA, Response to Comments to the Endangerment Finding Vol. 9 Section 

9.1.6.1, the discussion of black carbon in the endangerment finding at 74 FR at 66520, EPA’s 

discussion in the recent proposal to revise the PM NAAQS (77 FR at 38991-993), and the 

recently published EPA Report to Congress on Black Carbon. Additionally, the magnitude of 

PM2.5 emissions changes (and therefore, black carbon emission changes) related to these 

standards are small in comparison to the changes in the pollutants which have been included in 

the MAGICC model simulations. 

To compute the changes in atmospheric CO2 concentration, global mean temperature, and 

sea level rise specifically attributable to the impacts of the proposal, the difference in emissions 

between the proposal and the baseline scenario was subtracted from the GCAM reference 

emissions scenario. As a result of the proposal’s emissions reductions from the proposed 

alternative relative to the baseline case, by 2100 the concentration of atmospheric CO2 is 

projected to be reduced by approximately 1.1 to 1.2 parts per million by volume (ppmv), the 

global mean temperature is projected to be reduced by approximately 0.0026 to 0.0065°C, and 

global mean sea level rise is projected to be reduced by approximately 0.023 to 0.057 cm. For 

sea level rise, the calculations in MAGICC do not include the possible effects of accelerated ice 

flow in Greenland and/or Antarctica; including these effects would show correspondingly larger 

benefits of mitigation. 

Figure 6-7 provides the results over time for the estimated reductions in atmospheric CO2

concentration associated with the proposal compared to the baseline scenario. Figure 6-8 

provides the estimated change in projected global mean temperatures associated with the 

proposal. Figure 6-9 provides the estimated reductions in global mean sea level rise associated 

with the proposal. The range of reductions in global mean temperature and sea level rise due to 

uncertainty in climate sensitivity is larger than that for CO2 concentrations because CO2

concentrations are only weakly coupled to climate sensitivity through the dependence on 

temperature of the rate of ocean absorption of CO2, whereas the magnitude of temperature 

change response to CO2 changes (and therefore sea level rise) is more tightly coupled to climate 

sensitivity in the MAGICC model. 
6-46 

Figure 6-7 Estimated Projected Reductions in Atmospheric CO2 Concentrations (parts per million by 

volume) from the Baseline for the Proposed Alternative of the Heavy-Duty Proposal (climate sensitivity (CS) 

cases ranging from 1.5-6°C) 

Figure 6-8 Estimated Projected Reductions in Global Mean Surface Temperatures from the Baseline for the 

Proposed Alternative of the Heavy-Duty Proposal (climate sensitivity (CS) cases ranging from 1.5-6°C) 

‐1.4

‐1.2

‐1.0

‐0.8

‐0.6

‐0.4

‐0.2

0.0

2000 2020 2040 2060 2080 2100

ppmv

Change in CO2 Concentration

(Primary Alternative ‐ Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0

‐0.007

‐0.006

‐0.005

‐0.004

‐0.003

‐0.002

‐0.001

0.000

2000 2020 2040 2060 2080 2100

degrees

C

Change in Global Mean Temperature

(Primary Alternative ‐ Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0
6-47 

Figure 6-9 Estimated Projected Reductions in Global Mean Sea Level Rise from the Baseline for the 

Proposed Alternative of the Heavy-Duty Proposal (climate sensitivity (CS) cases ranging from 1.5-6°C) 

The results in Figure 6-8 and Figure 6-9 show reductions in the projected global mean 

temperature and sea level respectively, across all climate sensitivities. The projected reductions 

are small relative to the change in temperature (1.8 – 4.8 oC) and sea level rise (23 – 56 cm) from 

1990 to 2100 from the MAGICC simulations for the GCAM reference case. However, this is to 

be expected given the magnitude of emissions reductions expected from the proposal in the 

context of global emissions. These reductions are quantifiable, directionally consistent, and will 

contribute to reducing the risks associated with climate change. Notably, these effects are 

occurring everywhere around the globe, so benefits that appear to be marginal for any one 

location, such as a reduction in sea level rise of half a millimeter, can be sizable when the effects 

are summed along thousands of miles of coastline. Climate change is a global phenomenon and 

EPA recognizes that this one national action alone will not prevent it; EPA notes this would be 

true for any given GHG mitigation action when taken alone or when considered in isolation. 

EPA also notes that a substantial portion of CO2 emitted into the atmosphere is not removed by 

natural processes for millennia, and therefore each unit of CO2 not emitted into the atmosphere 

due to this rule avoids essentially permanent climate change on centennial time scales. Again, it 

should be noted that the calculations in MAGICC do not include the possible effects of 

accelerated ice flow in Greenland and/or Antarctica: the recent NRC report estimated a likely sea 

level increase for the A1B SRES scenario of 0.5 to 1.0 meters, almost double the estimate from 

MAGICC, so projected reductions in sea level rise may be similarly underestimated.220 If other 

uncertainties besides climate sensitivity were included in the analysis, the resulting ranges of 

projected changes would likely be slightly larger. 

6.3.3 Estimated Projected Change in Ocean pH 

For this proposal, EPA analyzes another key climate-related variable and calculates 

projected change in ocean pH for tropical waters. For this analysis, changes in ocean pH are 

related to the change in the atmospheric concentration of carbon dioxide (CO2) resulting from the 

emissions reductions associated with the proposed alternative. EPA used the program developed 

‐0.06

‐0.05

‐0.04

‐0.03

‐0.02

‐0.01

0.00

2000 2020 2040 2060 2080 2100

cm

Change in Global Mean Sea Level Rise

(Primary Alternative ‐ Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0
6-48 

for CO2 System Calculations CO2SYS,221 version 1.05, a program which performs calculations 

relating parameters of the carbon dioxide (CO2) system in seawater. The program was 

developed by Ernie Lewis at Brookhaven National Laboratory and Doug Wallace at the Institut 

für Meereskunde in Germany, supported by the U.S. Department of Energy, Office of Biological 

and Environmental Research, under Contract No. DE-ACO2-76CH00016. 

The CO2SYS program uses two of the four measurable parameters of the CO2 system 

[total alkalinity (TA), total inorganic CO2 (TC), pH, and either fugacity (fCO2) or partial 

pressure of CO2 (pCO2)] to calculate the other two parameters given a specific set of input 

conditions (temperature and pressure) and output conditions chosen by the user. EPA utilized 

the Excel version (Pierrot et al. 2006)222 of the program to compute pH for three scenarios: the 

baseline scenario at a climate sensitivity of 3 degrees for which the CO2 concentrations was 

calculated to be 784.87 in 2100, the proposed alternative relative to the baseline with a CO2

concentration of 784.11, and a calculation for 1990 with a CO2 concentration of 353.63. 

Using the set of seawater parameters detailed below, EPA calculated pH levels for the 

three scenarios. The pH of the emissions standards relative to the baseline scenario pH was 

+0.0004 units (more basic). For comparison, the difference between the baseline scenario in 

2100 and the pH in 1990 was -0.30 pH units (more acidic). 

The CO2SYS program required the input of a number of variables and constants for each 

scenario for calculating the result for both the reference case and the proposal’s emissions 

reduction baseline cases. EPA used the following inputs, with justification and references for 

these inputs provided in brackets: 

1) Input mode: Single-input 

2) Choice of constants: Mehrbach et al. (1973)223, refit by Dickson and Millero (1987)224 

3) Choice of fCO2 or pCO2: pCO2 

4) Choice of KSO4: Dickson (1990)225 Choice of KSO4: Dickson (1990)226 

5) Choice of pH scale: Total scale Choice of pH scale: Total scale 

6) [B]T value: Uppstrom, 1974 

The program provides several choices of constants for saltwater that are needed for the 

calculations. EPA calculated pH values using all choices and found that in all cases the choice 

had an indistinguishable effect on the results. In addition, EPA ran the model using a variety of 

other required input values to test whether the model was sensitive to these inputs. EPA found 

the model was not sensitive to these inputs in terms of the incremental change in pH calculated 

for each climate sensitivity case. The input values are derived from certified reference materials 

of sterilized natural sea water (Dickson, 2003, 2005, and 2009).227 Based on the projected 

atmospheric CO2 concentration reductions that would result from this proposal’s baseline case 

(1.2 ppmv for a climate sensitivity of 3.0), the modeling program calculates an increase in ocean 

pH of approximately 0.0006 pH units in 2100. Thus, this analysis indicates the projected 

decrease in atmospheric CO2 concentrations from the proposed alternative yields an increase in 

ocean pH. Table 6-2 contains the projected changes in ocean pH based the change in 

atmospheric CO2 concentrations which were derived from the MAGICC modeling.
6-49 

Table 6-2 Impact of the Proposal’s GHG Emissions Reductions on Ocean pH 

CLIMATE 

SENSITIVITY 

DIFFERENCE 

IN CO2

A

YEAR PROJECTED 

CHANGE 

3.0 -1.2 ppmv 2100 0.0006 

Note: 

a

 Represents the change in atmospheric CO2 concentrations in 2100 based on the difference 

from the proposed alternative relative to the base case from the GCAM reference scenario 

used in the MAGICC modeling. 

6.3.4 Summary of Climate Analyses 

EPA’s analysis of the impact of the proposal’s emissions reductions on global climate 

conditions is intended to quantify these potential reductions using the best available science. 

While EPA’s modeling results of the impact of this proposal alone show small differences in 

climate effects (CO2 concentration, global mean temperature, sea level rise, and ocean pH), in 

comparison to the total projected changes, they yield results that are repeatable and directionally 

consistent within the modeling frameworks used. The results are summarized in Table 6-3, 

Impact of GHG Emissions Reductions on Projected Changes in Global Climate Associated with 

the Proposal. 

These projected reductions are proportionally representative of changes to U.S. GHG 

emissions in the transportation sector. While not formally estimated for this proposal, a 

reduction in projected global mean temperature and sea level rise implies a reduction in the risks 

associated with climate change. The figures for these variables illustrate that across a range of 

climate sensitivities projected global mean temperature and sea level rise increase less in the 

proposed alternative scenario than in the reference (no climate policy) case. The benefits of 

GHG emissions reductions can be characterized both qualitatively and quantitatively, some of 

which can be monetized (see Chapter 9). There are substantial uncertainties in modeling the 

global risks of climate change, which complicates quantification and cost-benefits assessments. 

Changes in climate variables are a meaningful proxy for changes in the risk of all potential 

impacts--including those that can be monetized, and those that have not been monetized but can 

be quantified in physical terms (e.g., water availability), as well as those that have not yet been 

quantified or are extremely difficult to quantify (e.g., forest disturbance and catastrophic events 

such as collapse of large ice sheets and subsequent sea level rise).

Table 6-3 Impact of GHG Emissions Reductions on Projected Changes in Global Climate Associated with the 

Proposal (Based on a Range of Climate Sensitivities from 1.5-6°C) 

VARIABLE UNITS YEAR PROJECTED CHANGE 

Atmospheric CO2

Concentration

ppmv 2100 -1.1 to -1.2 

Global Mean Surface 

Temperature

oC 2100 -0.0026 to -0.0065 

Sea Level Rise cm 2100 -0.023 to -0.057 

Ocean pH pH units 2100 +0.0006a

Note: 

a The value for projected change in ocean pH is based on a climate sensitivity of 3.0. 
6-50 

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7-1 

Chapter 7: Vehicle-Related Costs, Fuel Savings & 

Maintenance Costs 

 In this chapter, the agencies present our estimates of the vehicle -related costs associated 

with the proposed standards along with corresponding fuel savings and maintenance costs. For 

this rule, the agencies conducted coordinated and complementary analyses using two analytical 

methods for the heavy-duty pick up and van segment by employing both DOT’s CAFE model 

and EPA’s MOVES model. The agencies used EPA’s MOVES model to estimate fuel 

consumption and emissions impacts for tractor-trailers (including the engine that powers the 

tractor), and vocational vehicles (including the engine that powers the vehicle). Additional 

calculations were performed to determine corresponding monetized program costs and benefits. 

For heavy-duty pickups and vans, the agencies performed complementary analyses, which we 

refer to as “Method A” and “Method B”. In Method A, the CAFE model was used to project a 

pathway the industry could use to comply with each regulatory alternative and the estimated 

effects on fuel consumption, emissions, benefits and costs. In Method B, the CAFE model was 

used to project a pathway the industry could use to comply with each regulatory alternative, 

along with resultant impacts on per vehicle costs, and the MOVES model was used to calculate 

corresponding changes in total fuel consumption and annual emissions. Additional calculations 

were performed to determine corresponding monetized program costs and benefits. NHTSA 

considered Method A as its central analysis and Method B as a supplemental analysis. EPA 

considered the results of both methods. The agencies concluded that both methods led the 

agencies to the same conclusions and the same selection of the proposed standards. Throughout 

this Chapter and in later chapters presenting program-related costs and benefits, engine costs are 

included along with vehicle-related costs. 

7.1 Vehicle Costs, Fuel Savings and Maintenance Costs vs. the Dynamic 

Baseline and Using Method A 

The agencies joint analysis of the potential costs of the proposed standards combines 

DOT CAFE model calculations of HD pickup and van costs with EPA MOVES modeling of 

vocational vehicle, tractor and trailer fuel consumption along with EPA analysis of vocational 

vehicle, tractor and trailer costs. The analysis includes costs for fuel-saving technology that 

manufacturers could add in response to the proposed standards, EPA estimates of the additional 

compliance and R&D costs for vocational vehicles and combination tractor trailers, and some 

additional maintenance costs. 

7.1.1 Vehicle Program Costs 

In this section, the agencies present our estimate of the vehicle- -related costs associated 

with the proposed program versus Alternative 1b using the CAFE model analysis of HD pickups 

and vans. The presentation here summarizes the costs associated with new technology the 

agencies estimate manufacturers could add to meet the proposed GHG and fuel consumption 

standards. The analysis summarized here provides our estimate of incremental costs on a per 

vehicle basis and on a MY lifetime basis. In Chapter 7.2, where the agencies present the Method 

B analysis, the analogous information is presented along with costs on an annual, or calendar 
7-2 

year, basis for all segments. For details behind the cost estimates associated with individual 

technologies, the reader is directed to Sections III through VI of the proposed preamble and to 

Chapter 2 of the draft RIA. 

Note that all discounted costs presented in this chapter, whether in the Calendar Year (or 

annual) analysis or the Model Year Lifetime analysis, are discounted back to 2015 at the 

discount rate shown in the table(s). 

7.1.1.1 Technology Costs 

For the HD pickup trucks and vans, the agencies have used a methodology consistent 

with that used for our recent 2017-2025 light-duty joint rulemaking since most of the 

technologies expected for HD pickup trucks and vans are consistent with those expected for the 

larger light-duty trucks. The cost estimates presented in the recent light-duty joint rulemaking 

were then scaled upward to account for the larger weight, towing capacity, and work demands of 

the trucks in these heavier classes. For details on that scaling process and the resultant costs for 

individual technologies, the reader is directed to Chapter 2.6 and 2.12 of this draft RIA. Note 

also that all cost estimates have been updated to 2012 dollars for this analysis while the 2017-

2025 light-duty joint rulemaking was presented in 2010 dollars.1

 

For vocational vehicles, tractors and trailers, consistent with the Phase 1 rule, the 

agencies have estimated costs using a different methodology than that employed in the recent 

light-duty joint rulemaking establishing fuel economy and GHG standards. In the recent lightduty joint rulemaking, all fixed costs were included in the hardware costs via an indirect cost 

multiplier. As such, the hardware costs presented in that analysis included both the actual 

hardware and the associated fixed costs. For the vocational, tractor and trailer segments in this 

analysis, some of the fixed costs are estimated separately and are presented separately from the 

technology costs. As noted above, all costs are presented in 2012 dollars. 

The estimates of vehicle costs are generated relative to two unique “no action” baselines. 

The first of these (alternative 1a, presented below in Chapter 7.2) representing generally flat fuel 

consumption improvements, or a fleet of vehicles meeting the Phase 1 heavy-duty requirements. 

The second of these (alternative 1b and presented here) representing dynamic fuel consumption 

improvements, or a fleet of vehicles with improving fuel consumption despite the lack of 

regulatory drivers. See Section X of the preamble and Chapter 11 of this draft RIA for more 

detail on these two baselines. As such, costs to comply with the Phase 1 standards are not 

included in the estimates here. In fact, in the methodology used for vocational vehicles, tractors 

and trailers, there are cases where Phase 1 technologies are being removed in favor of Phase 2 

technologies – that is, the technology basis for the Phase 2 proposed standards involves 

removing certain of the Phase 1 technologies. In those cases, savings are associated with the 

removal of the Phase 1 technology. The details of which technologies and where such savings 

occur are presented in Chapter 2.12 of the draft RIA. 

For HD pickups and vans, as described in Chapter 2 of this draft RIA, the agencies used 

NHTSA’s CAFE model to estimate the cost per vehicle associated with the proposed (and 
7-3 

possible alternative) standards.A That model has the capability to look ahead at future standards 

when making determinations of how vehicles should be changed to comply. It does this because 

redesign cycles do not always line up well with regulatory implementation schedules, so a 

manufacturer may choose to redesign a vehicle in MY2018 in preparation for upcoming 

MY2021-2025 standards if that particular vehicle is not scheduled for another redesign until, say, 

MY2026. The result being new technology costs in years prior to implementation of the 

standards. The CAFE model’s output would show such costs occurring in years prior to 

MY2021. On the other hand, the CAFE model also estimates the potential that credits generated 

in earlier model years might be carried forward (i.e., “banked”) and then used in later model 

years, potentially reducing costs in some model years covered by the analysis. 

Table 7-1 presents the average incremental technology costs per vehicle for the proposed 

program relative to alternative 1b. These tables include both engine and vehicle technologies. 

For HD pickups and vans, costs begin with new standards in MY2018, as technology is utilized 

in vehicles with early redesign cycles. The costs jump in MY2021 as more complex 

technologies are utilized, then generally increase through the remainder of the analysis period. 

For vocational vehicles, the costs begin in MY2021, then decrease slightly through MY2023, 

with an increase in MY2024, decreasing slightly through MY2026, and followed by a large 

increase in costs from MY2027 until the end of the analysis period. For tractor/trailers, the costs 

begin in MY2018 as trailers begin adding new technology to meet the 2018 trailer standards. 

Costs then increase in MY2021 as the tractor standards begin through 2027. After 2027, costs 

begin to decrease due to learning effects. All costs shown in the table represent the weighted 

average cost of all vehicles within the category shown in the heading. 

 

 

A The CAFE model also provides a full benefit-cost analysis associated with proposed standards, and the agencies 

have used this analysis as part of Method A to provide estimates of the costs and benefits of today’s proposed 

standards. The full benefit-cost analysis for Method A is presented in Chapters 9 and 10 of this draft RIA. The full 

benefit-cost analysis for Method B is presented in Chapter 8 of this draft RIA. 
7-4 

Table 7-1 Estimated Technology Costs per Vehicle for the Preferred Alternative versus the Dynamic Baseline 

and using Method A (2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS 

2018 $98 $0 $639 

2019 $92 $0 $548 

2020 $95 $0 $478 

2021 $493 $1,152 $7,445 

2022 $485 $1,128 $7,273 

2023 $766 $1,037 $6,790 

2024 $896 $1,766 $10,690 

2025 $1,149 $1,731 $10,476 

2026 $1,248 $1,695 $10,206 

2027 $1,366 $3,381 $12,487 

2028 $1,356 $3,323 $12,292 

2029 $1,357 $3,271 $12,178 

2030 $1,348 $3,222 $12,070 

2050 $1,348 $3,232 $11,981 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section 

I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic 

baseline, 1b, please see Preamble Section X.A.1

As noted in the text, MYs 2018-2020 include costs for trailers only, and in MYs 

2021 and later the costs include both tractor and trailer costs. Detailed 

technology and package costs for all segments can be found in Chapter 2 of this 

draft RIA (notably, see Sections 2.12 and 2.13). 

Table 7-2 presents the model year lifetime costs for new technology discounted at 3 

percent using Method A. And Table 7-3 presents the model year lifetime costs for new 

technology discounted at 7 percent using Method A.
7-5 

Table 7-2 Discounted MY Lifetime New Technology Costs of the Preferred Alternative 

Vs. the Dynamic Baseline and using Method A 

 (3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $61 $0 $104 $165 

2019 $55 $0 $99 $154 

2020 $55 $0 $95 $150 

2021 $280 $488 $1,013 $1,781 

2022 $269 $468 $976 $1,713 

2023 $412 $421 $898 $1,731 

2024 $479 $719 $1,412 $2,610 

2025 $608 $705 $1,396 $2,709 

2026 $654 $689 $1,369 $2,712 

2027 $704 $1,363 $1,676 $3,743 

2028 $688 $1,321 $1,642 $3,651 

2029 $677 $1,279 $1,618 $3,574 

Sum $4,942 $7,452 $12,299 $24,693 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble 

Section X.A.1

Table 7-3 Discounted MY Lifetime New Technology Costs of the Preferred Alternative 

Vs. the Dynamic Baseline and using Method A 

 (7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $54 $0 $91 $145 

2019 $47 $0 $84 $131 

2020 $46 $0 $77 $123 

2021 $223 $381 $791 $1,395 

2022 $206 $352 $734 $1,292 

2023 $304 $305 $650 $1,259 

2024 $340 $501 $984 $1,825 

2025 $415 $473 $936 $1,824 

2026 $430 $445 $884 $1,759 

2027 $446 $847 $1,041 $2,334 

2028 $419 $790 $982 $2,191 

2029 $397 $736 $932 $2,065 

Sum $3,327 $4,829 $8,186 $16,342 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-6 

7.1.1.2 Compliance Costs 

As noted above, some fixed costs were estimated separately from the hardware costs. As 

such, not all fixed costs are included in the tables presented in Section 7.1.1.1. The agencies 

have estimated additional and/or new compliance costs associated with the proposed standards. 

Normally, compliance program costs would be considered part of the indirect costs and, 

therefore, would be accounted for via the markup applied to direct manufacturing costs. 

However, since the agencies are proposing new compliance elements that were not present 

during development of the indirect cost markups used in this analysis, additional compliance 

program costs are being accounted for via a separate “line-item” here. Note that, for HD pickups 

and vans, compliance elements were present during development of the indirect cost markups 

used; as such, these costs are already included as part of the technology costs described above. 

There are three elements to the compliance costs estimated in this analysis. The first is 

for construction of new, or upgrades to existing, test facilities for conducting powertrain testing. 

The second costs are for conducting the powertrain tests themselves. And the third is for 

reporting of compliance data to EPA and NHTSA. We estimated these latter costs in the Phase 1 

rule as $0.24 million, $0.9 million and $1.1 million for HD pickups and vans, vocational and 

tractors, respectively, for a total of $2.3 million per year (2009$).2

 All of these are industrywide, annual costs. 

We have estimated new reporting costs in this Phase 2 proposal associated with new 

powertrain testing within the vocational vehicle program, the increased level of reporting in the 

tractor program and an all new compliance program where none has existed to date within the 

trailer program. We have estimated those costs at $95,000 and $240,000 for vocational and 

tractor programs, and at $1.2 million in the trailer program, all in 2012$. All of these are 

industry-wide, annual costs. 

For powertrain testing facility upgrades and construction, we have estimated that 6 

manufacturers would upgrade and 5 would construct new facilities at an upgrade cost of $1.2 

million and a new construction cost of $1.9 million, all in 2012$. The result being an industrywide (but vocational program only) cost of $16.6 million (2012$). This cost would occur once 

which we have attributed to CY2021, what would be the first year of the Phase 2 vocational 

standards.B

Lastly, the vocational program is also estimated to incur costs associated with conducting 

powertrain testing. We have estimated the cost of testing at $69,000 per test and expect three 

large manufacturers to conduct 20 tests/year for a total of $4.1 million/year.C

 

B Note that, in alternative 2, we expect no manufacturers would conduct powertrain testing so none would construct 

or upgrade facilities. 

C Note that, in alternative 2, we expect no powertrain testing so no testing costs; in alternative 4, we expect 4 large 

manufacturers to conduct the testing for an annual cost of $5.5 million. 
7-7 

Table 7-4 and Table 7-5 present the MY lifetime costs for new compliance program 

elements at 3 percent and 7 percent, respectively. 

Table 7-4 Discounted MY Lifetime Compliance Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 

2019 $0.0 $0.0 $0.0 

2020 $0.0 $0.0 $0.0 

2021 $13.7 $1.2 $14.9 

2022 $3.4 $1.1 $4.5 

2023 $3.3 $1.1 $4.4 

2024 $3.2 $1.1 $4.3 

2025 $3.1 $1.0 $4.1 

2026 $3.0 $1.0 $4.0 

2027 $2.9 $1.0 $3.9 

2028 $2.8 $1.0 $3.8 

2029 $2.8 $0.9 $3.7 

Sum $38.3 $9.4 $47.7 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; 

for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, 

please see Preamble Section X.A.1
7-8 

Table 7-5 Discounted MY Lifetime Compliance Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 

2019 $0.0 $0.0 $0.0 

2020 $0.0 $0.0 $0.0 

2021 $10.7 $0.9 $11.6 

2022 $2.5 $0.9 $3.4 

2023 $2.4 $0.8 $3.2 

2024 $2.2 $0.7 $3.0 

2025 $2.1 $0.7 $2.8 

2026 $1.9 $0.7 $2.6 

2027 $1.8 $0.6 $2.4 

2028 $1.7 $0.6 $2.3 

2029 $1.6 $0.5 $2.1 

Sum $27.0 $6.4 $33.4 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; 

for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, 

please see Preamble Section X.A.1

7.1.1.3 Research & Development Costs 

Much like the compliance program costs described above, EPA has estimated additional 

engine, vocational vehicle and tractor R&D associated with the proposed standards that is not 

accounted for via the indirect cost markups used in this analysis for those segments. The 

necessary R&D for HD pickups and vans is covered by the indirect costs included as part of the 

technology costs described above. In the Phase 1 rule, the agencies estimated the engine R&D 

costs at $6.8 million (2009$) per engine class per manufacturer per year for five years. In this 

Phase 2 analysis, EPA has estimated this same level of R&D and has assumed 12 heavy-heavy 

and 12 medium-heavy HD engine R&D programs would be conducted for a total of $214 

million/year (2012$). In this analysis, EPA has assumed those costs would occur annually for 4 

years, MYs 2021-2024. The total being $857 million (2012$) over 4 years (by comparison, the 

Phase 1 rule estimated a total of $852 million (2009$) over 5 years). To this, EPA has estimated 

an additional $6 million/year spent by vocational vehicle manufacturers and $20 million/year 

spent by tractor manufacturers. In the end, EPA is estimating a total of $961 million in R&D 

spending above and beyond the level included in the markups used to estimate indirect costs for 

these segments. Under alternative 4, due to the accelerated implementation of technology, EPA 

has estimated even more R&D—an additional $6.5 million per year or $26 million total spent by 

vocational vehicle and tractor manufacturers. Importantly, EPA estimates that these costs would 

occur during normal R&D and vehicle design cycles for both alternatives 3 and 4. EPA has not 

included any additional R&D would be spent by trailer manufacturers since our cost estimates 
7-9 

include R&D conducted by trailer parts suppliers which are subsequently included in the prices 

charged by those suppliers to the trailer manufacturer. Additionally, the markups we have 

applied to cover indirect costs (see Chapter 2.12 of this draft RIA) include costs associated with 

R&D incurred by the trailer manufacturer. 

Table 7-6 through Table 7-7 present the model year lifetime R&D costs discounted at 3 

percent and 7 percent, respectively. 

Table 7-6 Discounted MY Lifetime R&D Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 

2019 $0.0 $0.0 $0.0 

2020 $0.0 $0.0 $0.0 

2021 $93.4 $104.9 $198.3 

2022 $90.6 $101.9 $192.5 

2023 $88.0 $98.9 $186.9 

2024 $85.4 $96.0 $181.5 

2025 $0.0 $0.0 $0.0 

2026 $0.0 $0.0 $0.0 

2027 $0.0 $0.0 $0.0 

2028 $0.0 $0.0 $0.0 

2029 $0.0 $0.0 $0.0 

Sum $357.5 $401.7 $759.2 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; 

for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, 

please see Preamble Section X.A.1
7-10 

Table 7-7 Discounted MY Lifetime R&D Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 

2019 $0.0 $0.0 $0.0 

2020 $0.0 $0.0 $0.0 

2021 $72.9 $81.9 $154.9 

2022 $68.1 $76.6 $144.7 

2023 $63.7 $71.6 $135.3 

2024 $59.5 $66.9 $126.4 

2025 $0.0 $0.0 $0.0 

2026 $0.0 $0.0 $0.0 

2027 $0.0 $0.0 $0.0 

2028 $0.0 $0.0 $0.0 

2029 $0.0 $0.0 $0.0 

Sum $264.3 $297.0 $561.3 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; 

for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, 

please see Preamble Section X.A.1

7.1.1.4 Summary of Vehicle-Related Costs of the Proposed Program using Method A 

Table 7-8 presents the model year lifetime costs associated with the preferred alternative 

discounted at 3 percent relative to the dynamic baseline and using Method A. Table 7-9 presents 

the model year lifetime costs associated with the preferred alternative discounted at 7 percent 

relative to the dynamic baseline and using Method A. 
7-11 

Table 7-8 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $61 $0 $104 $165 

2019 $55 $0 $99 $154 

2020 $55 $0 $95 $150 

2021 $280 $595 $1,119 $1,994 

2022 $269 $563 $1,079 $1,911 

2023 $412 $512 $998 $1,922 

2024 $479 $807 $1,509 $2,795 

2025 $608 $708 $1,397 $2,713 

2026 $654 $692 $1,370 $2,716 

2027 $704 $1,366 $1,677 $3,747 

2028 $688 $1,324 $1,643 $3,655 

2029 $677 $1,282 $1,619 $3,578 

Sum $4,942 $7,848 $12,710 $25,500 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-9 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $54 $0 $91 $145 

2019 $47 $0 $84 $131 

2020 $46 $0 $77 $123 

2021 $223 $465 $874 $1,562 

2022 $206 $423 $811 $1,440 

2023 $304 $371 $722 $1,397 

2024 $340 $562 $1,051 $1,953 

2025 $415 $475 $937 $1,827 

2026 $430 $446 $885 $1,761 

2027 $446 $849 $1,042 $2,337 

2028 $419 $792 $983 $2,194 

2029 $397 $738 $932 $2,067 

Sum $3,327 $5,121 $8,489 $16,937 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-12 

7.1.2 Changes in Fuel Consumption and Savings 

7.1.2.1 Changes in Fuel Consumption 

The proposed standards would result in significant improvements in the fuel efficiency of 

affected vehicles. Drivers of those vehicles would see corresponding savings associated with 

reduced fuel expenditures. The agencies have estimated the impacts on fuel consumption for the 

proposed standards. More detail behind these changes in fuel consumption is presented in 

Chapter 5 and Chapter 10 of this draft RIA. The expected impacts on fuel consumption are 

shown in Table 7-10 as reductions from the dynamic baseline reference case (i.e., positive values 

represent fewer gallons consumed) and using Method A. The gallons shown in this table include 

any increased consumption resulting from the rebound effect. 

Table 7-10 MY Lifetime Fuel Consumption Reductions due to the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(Million Gallons) a

GASOLINE REDUCTIONS b DIESEL REDUCTIONS 

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

2018 44 0 0 44 25 0 934 959

2019 41 0 0 41 20 0 870 890

2020 42 0 0 42 20 0 840 860

2021 172 54 0 226 136 340 4,091 4567

2022 173 54 0 227 136 340 4,057 4533

2023 265 54 0 319 246 340 3,958 4544

2024 306 127 0 433 283 705 6,289 7277

2025 678 130 0 808 309 722 6,330 7361

2026 782 133 0 915 315 738 6,359 7412

2027 884 297 0 1181 323 1,193 7,350 8866

2028 894 300 0 1194 326 1,210 7,417 8953

2029 910 303 0 1213 330 1,222 7,563 9115

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the 

less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b Gasoline reductions include reductions in Ethanol85.

7.1.2.2 Changes in Fuel Expenditures 

Using the fuel consumption reductions presented above, the agencies have calculated the fuel 

expenditure changes associated with the proposed standards, subcategory by subcategory. To do 

this, reduced fuel consumption is multiplied in each year by the corresponding estimated average 

fuel price in that year, using the reference case fuel prices from AEO 2014. For the Method A 

analysis, the AEO 2014 early release reference case was used for the vocational vehicles and 

tractor/trailers; the AEO 2014 final release reference case was used for HD pickups and vans. 

As the AEO fuel price projections go through 2040 and not beyond, fuel prices beyond 2040 

were set equal to the 2040 values for vocational vehicles and tractor/trailers. For the Method A 
7-13 

HD pickups and vans, the retail price of gasoline was projected to rise at 0.2 percent per year for 

gasoline and 0.7 percent for diesel. These estimates do not account for the significant 

uncertainty in future fuel prices; the monetized fuel savings would be understated if actual fuel 

prices are higher (or overstated if fuel prices are lower) than estimated. The Annual Energy 

Outlook (AEO) is a standard reference used by NHTSA and EPA and many other government 

agencies to estimate the projected price of fuel. This has been done using both the pre-tax and 

post-tax fuel prices. Since the post-tax fuel prices are the prices paid at fuel pumps, the fuel 

expenditure changes calculated using these prices represent the changes fuel purchasers would 

see. The pre-tax fuel savings are those that society would see. Assuming no change in fuel tax 

rates, the difference between these two columns represents the reduction in fuel tax revenues that 

would be received by state and federal governments. The MY lifetime fuel savings for the 

preferred alternative relative to the dynamic baseline and using Method A are shown in Table 

7-11 using a 3 percent discount rate and in Note: a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-12 using a 7 percent discount rate. Note that in Chapters 8 and 11 of this draft 

RIA, the overall benefits and costs of the rulemaking are presented and only the pre-tax fuel 

expenditure impacts are presented there. 

Table 7-11 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(3% Discount Rate, Millions of 2012$) a

REDUCED FUEL EXPENDITURES – 

RETAIL 

REDUCED FUEL EXPENDITURES – 

UNTAXED 

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

2018 $181 $0 $2,744 $2,925 $161 $0 $2,435 $2,596 

2019 $157 $0 $2,515 $2,672 $140 $0 $2,238 $2,378 

2020 $160 $0 $2,386 $2,547 $143 $0 $2,130 $2,273 

2021 $794 $1,082 $11,422 $13,297 $711 $966 $10,222 $11,898 

2022 $782 $1,064 $11,139 $12,984 $701 $952 $9,991 $11,645 

2023 $1,281 $1,044 $10,675 $13,000 $1,151 $937 $9,596 $11,684 

2024 $1,446 $2,156 $16,663 $20,264 $1,303 $1,937 $15,008 $18,248 

2025 $2,294 $2,167 $16,460 $20,922 $2,066 $1,951 $14,855 $18,872 

2026 $2,494 $2,172 $16,217 $20,883 $2,247 $1,959 $14,663 $18,870 

2027 $2,686 $3,617 $18,381 $24,684 $2,423 $3,267 $16,650 $22,340 

2028 $2,664 $3,591 $18,164 $24,419 $2,407 $3,250 $16,482 $22,138 

2029 $2,654 $3,555 $18,144 $24,352 $2,402 $3,222 $16,491 $22,115 

Sum $17,592 $20,446 $144,911 $182,949 $15,854 $18,442 $130,760 $165,056 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-14 

Table 7-12 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(7% Discount Rate, Millions of 2012$) a

REDUCED FUEL EXPENDITURES – 

RETAIL

REDUCED FUEL EXPENDITURES – 

UNTAXED

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

2018 $130 $0 $1,954 $2,084 $116 $0 $1,729 $1,845

2019 $109 $0 $1,721 $1,830 $97 $0 $1,528 $1,625

2020 $107 $0 $1,566 $1,673 $95 $0 $1,395 $1,490

2021 $510 $689 $7,197 $8,395 $455 $614 $6,426 $7,495

2022 $483 $653 $6,766 $7,902 $433 $583 $6,056 $7,072

2023 $762 $617 $6,249 $7,628 $684 $553 $5,605 $6,842

2024 $828 $1,227 $9,401 $11,457 $745 $1,101 $8,451 $10,297

2025 $1,265 $1,188 $8,945 $11,399 $1,138 $1,068 $8,058 $10,264

2026 $1,324 $1,147 $8,487 $10,957 $1,191 $1,033 $7,661 $9,884

2027 $1,373 $1,840 $9,264 $12,476 $1,236 $1,660 $8,378 $11,274

2028 $1,311 $1,758 $8,810 $11,879 $1,183 $1,589 $7,982 $10,753

2029 $1,257 $1,676 $8,472 $11,406 $1,136 $1,517 $7,690 $10,343

Sum $9,459 $10,796 $78,832 $99,087 $8,508 $9,717 $70,958 $89,184

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

7.1.3 Maintenance Costs 

The agencies have estimated increased maintenance costs associated with installation of 

lower rolling resistance tires. We expect that, when replaced, the lower rolling resistance tires 

would be replaced by equivalent performing tires throughout the vehicle lifetime. As such, the 

incremental increases in costs for lower rolling resistance tires would be incurred throughout the 

vehicle lifetime at intervals consistent with current tire replacement intervals. Those intervals 

are difficult to quantify given the variety of vehicles and operating modes within the HD 

industry. For HD pickups and vans, we have chosen a tire replacement interval of 40,000 miles. 

We have done the same for all vocational vehicles which is probably overly conservative as 

more frequent intervals results in higher maintenance costs. For tractors and trailers, we have 

used a maintenance interval of 200,000 miles. The presence of tire inflation management 

systems, and the increased use of those systems expected due to this proposed rule, should serve 

to improve tire maintenance intervals. 

In evaluating maintenance costs associated with the proposal relative to the less dynamic 

baseline, EPA has used the maintenance intervals noted above, the MOVES policy case VMT, 

and the MOVES population of specific MY vehicles in future calendar years to estimate the 

increased maintenance costs associated with the proposal, again for each subcategory. Note that, 

in the context of the benefit-cost analysis, EPA has estimated maintenance costs using the policy 

case VMT which, by definition, includes rebound VMT (see Section IX of the preamble and 
7-15 

Chapter 8 of this draft RIA for a discussion of rebound VMT). In evaluating maintenance costs 

associated with the proposal relative to Alternative 1b, NHTSA has used, for HD pickups and 

vans, the integrated analysis performed using the CAFE modeling system. For vocational 

vehicles, tractors and trailers, NHTSA has used the MOVES-based approach outlined above. 

Table 7-13 shows the incremental increased costs associated with lower rolling resistance 

tires for HD pickups and vans, vocational vehicles, tractors, trailers and tractor/trailers relative to 

the dynamic baseline. 

Table 7-13 Increased Maintenance Costs at Maintenance Intervals Associated with the Preferred Alternative 

Vs. The Dynamic Baseline using Method A 

(2012$/event) a,b

MODEL 

YEAR 

HD 

PICKUPS & 

VANS 

VOCATIONAL TRACTOR TRAILER TRACTOR/ 

TRAILER 

2018 $0.00 $0.00 $0.00 $79.92 $67.16

2019 $0.00 $0.00 $0.00 $77.77 $65.36

2020 $0.00 $0.00 $0.00 $75.69 $63.60

2021 $1.72 $6.99 $33.16 $78.81 $99.38

2022 $3.45 $6.91 $32.53 $75.98 $96.39

2023 $5.17 $8.12 $31.66 $71.17 $91.46

2024 $6.90 $17.66 $33.50 $73.97 $95.66

2025 $8.62 $17.47 $32.92 $72.02 $93.43

2026 $8.62 $13.50 $30.01 $70.72 $89.44

2027 $8.62 $21.34 $32.27 $69.44 $90.62

2028 $8.62 $20.26 $31.52 $68.18 $88.81

2029 & later $8.62 $15.99 $31.22 $66.27 $86.91

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The maintenance category includes only the incremental expenditure required to assure that low rolling resistance, 

rather than conventional, tires are used throughout the useful life of the vehicle. The agencies request comment and 

information on other relevant maintenance costs

Table 7-14 presents the model year lifetime in-use maintenance costs—versus the 

dynamic baseline and using Method A— discounted at 3 percent. Table 7-15 presents the model 

year lifetime in-use maintenance costs—versus the dynamic baseline and using Method A—

discounted at 7 percent. 
7-16 

Table 7-14 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

 (3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $51.2 $51.2

2019 $0.0 $0.0 $49.2 $49.2

2020 $0.0 $0.0 $47.5 $47.5

2021 $3.3 $14.1 $72.6 $90.0

2022 $6.5 $13.5 $69.0 $88.9

2023 $9.4 $15.4 $64.0 $88.8

2024 $12.3 $33.3 $66.6 $112.2

2025 $15.1 $32.7 $65.1 $112.9

2026 $15.0 $25.0 $62.1 $102.1

2027 $14.7 $38.9 $62.3 $115.9

2028 $14.4 $36.2 $60.2 $110.7

2029 $14.0 $27.9 $57.9 $99.8

Sum $104.6 $237.0 $727.6 $1,069.2

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-15 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative 

Vs. The Dynamic Baseline and using Method A 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $35.8 $35.8

2019 $0.0 $0.0 $33.2 $33.2

2020 $0.0 $0.0 $30.9 $30.9

2021 $2.1 $8.9 $45.5 $56.6

2022 $3.9 $8.3 $41.7 $53.9

2023 $5.5 $9.1 $37.3 $51.9

2024 $6.9 $18.9 $37.4 $63.2

2025 $8.3 $17.8 $35.2 $61.3

2026 $7.9 $13.1 $32.4 $53.4

2027 $7.4 $19.7 $31.3 $58.5

2028 $7.0 $17.7 $29.2 $53.9

2029 $6.6 $13.1 $27.1 $46.8

Sum $55.7 $126.6 $417.2 $599.5

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-17 

7.1.4 Analysis of Payback Periods 

An important metric to vehicle purchasers is the payback period that can be expected on 

any new purchase. In other words, there is greater willingness to pay for new technology if that 

new technology “pays back” within an acceptable period of time. We make no effort to define 

the acceptable period of time here, but seek to estimate the payback period for others to make the 

decision themselves. We define the payback period as the point at which reduced fuel 

expenditures outpace increased vehicle costs. For example, a new MY2027 HD pickup truck is 

estimated to cost roughly $1,400 more (on average, in 2012$, and relative to the reference case 

vehicle) due to the addition of new fuel consumption improving and GHG reducing technology. 

This new technology would result in lower fuel consumption and, therefore, reduced fuel 

expenditures. But how many months or years would pass before the reduced fuel expenditures 

would surpass the increased costs? 

To estimate the costs, we have considered not only the cost of the new technology, but 

also the taxes paid on the incrementally higher purchase expense, the slightly higher insurance 

expenses on the slightly higher value vehicle, the increased finance cost, and the increased 

maintenance costs associated with the new technology. Taxes and fees paid were estimated as 

5.46 percent of the final MSRP. Financing was estimated to be 15.32 percent of final MSRP, 

and for insurance costs, the model uses an estimate of 19.23 percent of the final MSRP of a 

vehicle as the cost of insurance. For maintenance costs, the results shown in Table 7-16 express 

the average incremental maintenance costs associated with new technology added to an average 

HD pickup or van which drives an average amount of miles each year. These calculations do not 

represent specific vehicle classes or specific use cases so should not be seen as being applicable 

to any particular individual’s situation. However, the payback periods do provide a general 

sense, on average, of what sort of payback periods are likely at a national, societal perspective. 

Table 7-16 presents the discounted annual increased vehicle costs and fuel expenditure 

impacts associated with owning a new MY2027 HD pickup or van using both 3 percent and 7 

percent discount rates. The results in this table use Method A. As shown in the table, the 

payback for HD pickups and vans occurs late in the 3rd year of ownership (the year in which 

cumulative expenditures become negative) using a 3 percent discount rate and in the early part of 

the 4th year using a 7 percent discount rate. For other classes of vehicles, including vehicle 

types such as refuse trucks and transit buses, refer to the Method B analysis of payback periods 

presented in Chapter 7.2.4. 
7-18 

Table 7-16 Discounted Owner Expenditures & Payback Period for MY2027 HD Pickups & Vans under the 

Preferred Alternative Vs. The Dynamic Baseline and using Method A 

 3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $1,855 $0 -$522 $1,333 $1,779 $0 -$501 $1,278 

2 $0 $0 -$513 $820 $0 $0 -$471 $806 

3 $0 $0 -$467 $353 $0 $0 -$411 $395 

4 $0 $0 -$429 -$76 $0 $0 -$363 $32 

5 $0 $0 -$408 -$484 $0 $0 -$330 -$298 

6 $0 $0 -$369 -$852 $0 $0 -$286 -$584 

7 $0 $0 -$325 -$1,177 $0 $0 -$242 -$826 

8 $0 $0 -$282 -$1,459 $0 $0 -$201 -$1,027 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.

7.2Vehicle Costs, Fuel Savings and Maintenance Costs vs. the Less 

Dynamic Baseline and using Method B 

As noted in the introduction to Chapter 7.1, the agencies joint analysis of the potential 

costs of the proposed standards combines EPA MOVES modeling of vocational vehicle, tractor 

and trailer fuel consumption, EPA analysis of vocational vehicle, tractor and trailer costs, along 

with DOT CAFE model calculations of HD pickup and van costs per vehicle. The analysis 

includes costs for fuel-saving technology that manufacturers could add in response to the 

proposed standards, EPA estimates of the additional compliance and R&D costs for vocational 

vehicles and combination tractor trailers, and some additional maintenance costs.3

7.2.1 Vehicle Program Costs 

In this section, the agencies present our estimate of the vehicle-related costs associated 

with the preferred alternative (Alternative 3) versus the less dynamic baseline (Alternative 1a) 

using the MOVES analysis of HD pickups and vans as well as vocational vehicle, tractors and 

trailers. The presentation here summarizes the costs associated with new technology the 

agencies estimate manufacturers could add to meet the proposed GHG and fuel consumption 

standards. The analysis summarized here provides our estimate of incremental costs on a per 

vehicle basis, on a MY lifetime basis and on an annual basis. For details behind the cost 

estimates associated with individual technologies, the reader is directed to Sections III through 

VI of the proposed preamble and to Chapter 2 of the draft RIA. The analysis here also includes a 

look at payback periods—the time at which cumulative fuel savings outweigh increased costs 

associated with new, more fuel efficient vehicles. And finally, the analysis here includes a look 

at the cost per ton of GHG emissions reduced by the addition of new technology. 
7-19 

Note that all discounted costs presented in this chapter, whether in the Calendar Year (or 

annual) analysis or the Model Year Lifetime analysis, are discounted back to 2015 at the 

discount rate shown in the table(s). 

7.2.1.1 Technology Costs 

For the HD pickups and vans, the agencies have used a methodology consistent with that 

used for our recent 2017-2025 light-duty joint rulemaking since most of the technologies 

expected for HD pickups and vans are consistent with those expected for the larger light-duty 

trucks. The cost estimates presented in the recent light-duty joint rulemaking were then scaled 

upward to account for the larger weight, towing capacity, and work demands of the trucks in 

these heavier classes. For details on that scaling process and the resultant costs for individual 

technologies, the reader is directed to Chapter 2.12 of this draft RIA. Note also that all cost 

estimates have been updated to 2012 dollars for this analysis while the 2017-2025 light-duty 

joint rulemaking was presented in 2010 dollars.4

 

For vocational vehicles, tractors and trailers, consistent with the Phase 1 rule, the 

agencies have estimated costs using a different methodology than that employed in the recent 

light-duty joint rulemaking establishing fuel economy and GHG standards. In the recent lightduty joint rulemaking, all fixed costs were included in the hardware costs via an indirect cost 

multiplier. As such, the hardware costs presented in that analysis included both the actual 

hardware and the associated fixed costs. For the vocational, tractor and trailer segments in this 

analysis, some of the fixed costs are estimated separately and are presented separately from the 

technology costs. As noted above, all costs are presented in 2012 dollars. 

The estimates of vehicle costs are generated relative to two unique “no action” baselines. 

The first of these (alternative 1a, presented here) representing generally flat or less dynamic fuel 

consumption improvements, or a fleet of vehicles meeting the Phase 1 heavy-duty requirements. 

The second of these (alternative 1b and presented in detail in Chapter 7.1) representing dynamic 

fuel consumption improvements, or a fleet of vehicles with improving fuel consumption despite 

the lack of regulatory drivers. See Section X of the preamble and Chapter 11 of this draft RIA 

for more detail on these two baselines. As such, costs to comply with the Phase 1 standards are 

not included in the estimates here. In fact, in the methodology used for vocational vehicles, 

tractors and trailers, there are cases where Phase 1 technologies are being removed in favor of 

Phase 2 technologies – that is, the technology basis for the Phase 2 proposed standards involves 

removing certain of the Phase 1 technologies. In those cases, savings are associated with the 

removal of the Phase 1 technology. The details of which technologies and where such savings 

occur are presented in Chapter 2.12 of the draft RIA. 

For HD pickups and vans, as described in Chapter 2 of this draft RIA, the agencies used 

NHTSA’s CAFE model to estimate the cost per vehicle associated with the preferred and 

possible alternative standards.D That model has the capability to look ahead at future standards 

 

D The CAFE model also provides a full benefit-cost analysis associated with the HD pickup and van portion of the 

proposed and alternative standards, and the agencies have used this analysis as part of Method A to provide 

estimates of the costs and benefits of today’s proposed standards. The full benefit-cost analysis for Method A is 
7-20 

when making determinations of how vehicles should be changed to comply. It does this because 

redesign cycles do not always line up well with regulatory implementation schedules, so a 

manufacturer may choose to redesign a vehicle in MY2018 in preparation for upcoming 

MY2021-2025 standards if that particular vehicle is not scheduled for another redesign until, say, 

MY2026. The result being new technology costs in years prior to implementation of the 

standards. The CAFE model’s output would show such costs occurring in years prior to 

MY2021. On the other hand, the CAFE model also estimates the potential that credits generated 

in earlier model years might be carried forward (i.e., “banked”) and then used in later model 

years, potentially reducing costs in some model years covered by the analysis. In Table 7-17, 

EPA has taken those early costs and spread them over the years 2021 through 2026 so that those 

costs can be fully realized while showing them occurring during the expected years of 

implementation. 

Table 7-17 presents the average incremental technology costs per vehicle for the 

preferred alternative relative to the less dynamic baseline and using Method B (the MOVES 

analysis for all vehicle categories). These tables include both engine and vehicle technologies. 

For HD pickups and vans, costs begin with new standards in MY2021 then generally increase 

through MY2027 after which time they begin to decrease as vehicles continue to meet the 

MY2027 standards at ever decreasing cost due to learning effects. The trend is similar for 

vocational vehicles. For tractor/trailers, the costs begin in MY2018 as trailers begin adding new 

technology to meet the 2018 trailer standards. Costs then increase in MY2021 as the tractor 

standards begin through 2027. After 2027, costs begin to decrease due to learning effects. All 

costs shown in the table represent the weighted average cost of all vehicles within the category 

shown in the heading. 

 

presented in Chapters 9 and 10 of this draft RIA. The full benefit-cost analysis for Method B is presented in Chapter 

8 of this draft RIA. 
7-21 

Table 7-17 Estimated Technology Costs per Vehicle for the Preferred Alternative versus the Less Dynamic 

Baseline and using Method B (2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS 

2018 $0 $0 $639 

2019 $0 $0 $613 

2020 $0 $0 $592 

2021 $558 $1,152 $7,606 

2022 $551 $1,128 $7,482 

2023 $834 $1,037 $7,016 

2024 $991 $1,766 $10,947 

2025 $1,204 $1,731 $10,763 

2026 $1,267 $1,695 $10,513 

2027 $1,342 $3,381 $12,849 

2028 $1,332 $3,323 $12,655 

2029 $1,334 $3,271 $12,548 

2030 $1,324 $3,222 $12,439 

2050 $1,324 $3,232 $12,415 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section 

I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline,

1b, please see Preamble Section X.A.1

As noted in the text, MYs 2018-2020 include costs for trailers only, and in MYs 

2021 and later the costs include both tractor and trailer costs, inclusive of 

engine-related costs. Detailed technology and package costs for all segments can 

be found in Chapter 2 of this draft RIA (notably, see Sections 2.12 and 2.13). 

Also, for HD pickups and vans, EPA has taken early costs and spread them over 

the years 2021 through 2026 so that those costs can be fully realized while 

showing them occurring during the expected years of implementation. 

Table 7-18 presents the annual costs—versus the less dynamic baseline and using Method 

B—for new engine- and vehicle-related technology along with net present values at 3 percent 

and 7 percent. Table 7-19 presents the model year lifetime costs—versus the less dynamic 

baseline and using Method B—for new technology discounted at 3 percent. Table 7-20 presents 

the model year lifetime costs—versus the less dynamic baseline and using Method B—for new 

technology discounted at 7 percent. 
7-22 

Table 7-18 Annual Technology Costs and Net Present Values Associated with the Preferred Alternative vs. 

the Less Dynamic Baseline and using Method B 

($Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $116 $116

2019 $0 $0 $113 $113

2020 $0 $0 $112 $112

2021 $328 $591 $1,254 $2,173

2022 $324 $585 $1,252 $2,161

2023 $491 $541 $1,192 $2,224

2024 $590 $951 $1,913 $3,455

2025 $731 $961 $1,955 $3,647

2026 $788 $967 $1,980 $3,736

2027 $844 $1,972 $2,493 $5,309

2028 $847 $1,969 $2,519 $5,334

2029 $854 $1,963 $2,559 $5,376

2030 $855 $1,956 $2,589 $5,399

2035 $892 $2,076 $2,888 $5,856

2040 $935 $2,204 $3,177 $6,316

2050 $1,024 $2,414 $3,548 $6,987

NPV, 3% $13,475 $29,183 $43,268 $85,926

NPV, 7% $6,461 $13,502 $20,553 $40,516

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-23 

Table 7-19 Discounted MY Lifetime New Technology Costs of the Preferred Alternative 

Vs. the Less Dynamic Baseline and using Method B 

 (3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $104 $104

2019 $0 $0 $99 $99

2020 $0 $0 $95 $95

2021 $271 $488 $1,035 $1,794

2022 $260 $468 $1,003 $1,731

2023 $382 $421 $927 $1,730

2024 $446 $719 $1,445 $2,610

2025 $536 $705 $1,434 $2,674

2026 $561 $689 $1,410 $2,660

2027 $583 $1,363 $1,723 $3,670

2028 $568 $1,321 $1,690 $3,580

2029 $556 $1,279 $1,667 $3,502

Sum $4,164 $7,452 $12,632 $24,248

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-20 Discounted MY Lifetime New Technology Costs of the Preferred Alternative 

Vs. the Less Dynamic Baseline and using Method B 

 (7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $91 $91

2019 $0 $0 $84 $84

2020 $0 $0 $77 $77

2021 $212 $381 $808 $1,401

2022 $195 $352 $754 $1,302

2023 $276 $305 $671 $1,252

2024 $311 $501 $1,007 $1,818

2025 $360 $473 $961 $1,793

2026 $362 $445 $910 $1,717

2027 $363 $847 $1,071 $2,280

2028 $340 $790 $1,011 $2,141

2029 $320 $736 $960 $2,017

Sum $2,738 $4,829 $8,405 $15,973

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-24 

7.2.1.2 Compliance Costs 

As noted above, some fixed costs were estimated separately from the hardware costs. As 

such, not all fixed costs are included in the tables presented in Section 7.2.1.1. The agencies 

have estimated additional and/or new compliance costs associated with the proposed standards. 

Normally, compliance program costs would be considered part of the indirect costs and, 

therefore, would be accounted for via the markup applied to direct manufacturing costs. 

However, since the agencies are proposing new compliance elements that were not present 

during development of the indirect cost markups used in this analysis, additional compliance 

program costs are being accounted for via a separate “line-item” here. Note that, for HD pickups 

and vans, compliance elements were present during development of the indirect cost markups 

used; as such, these costs are already included as part of the technology costs described above. 

There are three elements to the compliance costs estimated in this analysis. The first is 

for construction of new, or upgrades to existing, test facilities for conducting powertrain testing. 

The second costs are for conducting the powertrain tests themselves. And the third is for 

reporting of compliance data to EPA and NHTSA. We estimated these latter costs in the Phase 1 

rule as $0.24 million, $0.9 million and $1.1 million for HD pickups and vans, vocational and 

tractors, respectively, for a total of $2.3 million per year (2009$).5

 All of these are industrywide, annual costs. 

We have estimated new reporting costs in this Phase 2 proposal associated with new 

powertrain testing within the vocational vehicle program, the increased level of reporting in the 

tractor program and an all new compliance program where none has existed to date within the 

trailer program. We have estimated those costs at $95,000 and $240,000 for vocational and 

tractor programs, and at $1.2 million in the trailer program, all in 2012$. All of these are 

industry-wide, annual costs. 

For powertrain testing facility upgrades and construction, we have estimated that 6 

manufacturers would upgrade and 5 would construct new facilities at an upgrade cost of $1.2 

million and a new construction cost of $1.9 million, all in 2012$. The result being an industrywide (but vocational program only) cost of $16.6 million (2012$). This cost would occur once 

which we have attributed to CY2021, what would be the first year of the Phase 2 vocational 

standards.E

Lastly, the vocational program is also estimated to incur costs associated with conducting 

powertrain testing. We have estimated the cost of testing at $69,000 per test and expect three 

large manufacturers to conduct 20 tests/year for a total of $4.1 million/year.F

 

E Note that, in alternative 2, we expect no manufacturers would conduct powertrain testing so none would construct 

or upgrade facilities. 

F Note that, in alternative 2, we expect no powertrain testing so no testing costs; in alternative 4, we expect 4 large 

manufacturers to conduct the testing for an annual cost of $5.5 million. 
7-25 

Table 7-21 through Note: a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-23 present the annual costs for new compliance program elements along with net 

present values at 3 percent and 7 percent, and the model year lifetime compliance costs 

discounted at 3 percent and 7 percent, respectively. 

Table 7-21 Annual Compliance Costs and Net Present Values Associated with the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

($Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $16.6 $1.4 $18.1

2022 $0.0 $4.2 $1.4 $5.7

2023 $0.0 $4.2 $1.4 $5.7

2024 $0.0 $4.2 $1.4 $5.7

2025 $0.0 $4.2 $1.4 $5.7

2026 $0.0 $4.2 $1.4 $5.7

2027 $0.0 $4.2 $1.4 $5.7

2028 $0.0 $4.2 $1.4 $5.7

2029 $0.0 $4.2 $1.4 $5.7

2030 $0.0 $4.2 $1.4 $5.7

2035 $0.0 $4.2 $1.4 $5.7

2040 $0.0 $4.2 $1.4 $5.7

2050 $0.0 $4.2 $1.4 $5.7

NPV, 3% $0.0 $80.8 $23.7 $104.4

NPV, 7% $0.0 $44.2 $12.1 $56.4

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-26 

Table 7-22 Discounted MY Lifetime Compliance Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $13.7 $1.2 $14.9

2022 $0.0 $3.4 $1.1 $4.5

2023 $0.0 $3.3 $1.1 $4.4

2024 $0.0 $3.2 $1.1 $4.3

2025 $0.0 $3.1 $1.0 $4.1

2026 $0.0 $3.0 $1.0 $4.0

2027 $0.0 $2.9 $1.0 $3.9

2028 $0.0 $2.8 $1.0 $3.8

2029 $0.0 $2.8 $0.9 $3.7

Sum $0.0 $38.3 $9.4 $47.7

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-23 Discounted MY Lifetime Compliance Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $10.7 $0.9 $11.6

2022 $0.0 $2.5 $0.9 $3.4

2023 $0.0 $2.4 $0.8 $3.2

2024 $0.0 $2.2 $0.7 $3.0

2025 $0.0 $2.1 $0.7 $2.8

2026 $0.0 $1.9 $0.7 $2.6

2027 $0.0 $1.8 $0.6 $2.4

2028 $0.0 $1.7 $0.6 $2.3

2029 $0.0 $1.6 $0.5 $2.1

Sum $0.0 $27.0 $6.4 $33.4

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-27 

7.2.1.3 Research & Development Costs 

Much like the compliance program costs described above, EPA has estimated additional 

engine, vocational vehicle and tractor R&D associated with the proposed standards that is not 

accounted for via the indirect cost markups used in this analysis for those segments. The 

necessary R&D for HD pickups and vans is covered by the indirect costs included as part of the 

technology costs described above. In the Phase 1 rule, the agencies estimated the engine R&D 

costs at $6.8 million (2009$) per engine class per manufacturer per year for five years. In this 

Phase 2 analysis, EPA has estimated this same level of R&D and has assumed 12 heavy-heavy 

and 12 medium-heavy HD engine R&D programs would be conducted for a total of $214 

million/year (2012$). In this analysis, EPA has assumed those costs would occur annually for 4 

years, MYs 2021-2024. The total being $857 million (2012$) over 4 years (by comparison, the 

Phase 1 rule estimated a total of $852 million (2009$) over 5 years). To this, EPA has estimated 

an additional $6 million/year spent by vocational vehicle manufacturers and $20 million/year 

spent by tractor manufacturers. In the end, EPA is estimating a total of $961 million in R&D 

spending above and beyond the level included in the markups used to estimate indirect costs for 

these segments. Under alternative 4, due to the accelerated implementation of technology, EPA 

has estimated even more R&D—an additional $6.5 million per year or $26 million total spent by 

vocational vehicle and tractor manufacturers. Importantly, EPA estimates that these costs would 

occur during normal R&D and vehicle design cycles for both alternatives 3 and 4. EPA has not 

included any additional R&D would be spent by trailer manufacturers since our cost estimates 

include R&D conducted by trailer parts suppliers which are subsequently included in the prices 

charged by those suppliers to the trailer manufacturer. Additionally, the markups we have 

applied to cover indirect costs (see Chapter 2.12 of this draft RIA) include costs associated with 

R&D incurred by the trailer manufacturer. 

Table 7-24 through Table 7-26 present the annual costs for R&D spending along with net 

present values at 3 percent and 7 percent, and the model year lifetime R&D costs discounted at 3 

percent and 7 percent, respectively. 
7-28 

Table 7-24 Annual R&D Costs and Net Present Values Associated with the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

($Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $113.1 $127.1 $240.3

2022 $0.0 $113.1 $127.1 $240.3

2023 $0.0 $113.1 $127.1 $240.3

2024 $0.0 $113.1 $127.1 $240.3

2025 $0.0 $0.0 $0.0 $0.0

2026 $0.0 $0.0 $0.0 $0.0

2027 $0.0 $0.0 $0.0 $0.0

2028 $0.0 $0.0 $0.0 $0.0

2029 $0.0 $0.0 $0.0 $0.0

2030 $0.0 $0.0 $0.0 $0.0

2035 $0.0 $0.0 $0.0 $0.0

2040 $0.0 $0.0 $0.0 $0.0

2050 $0.0 $0.0 $0.0 $0.0

NPV, 3% $0.0 $357.5 $401.7 $759.2

NPV, 7% $0.0 $264.3 $297.0 $561.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-29 

Table 7-25 Discounted MY Lifetime R&D Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $93.4 $104.9 $198.3

2022 $0.0 $90.6 $101.9 $192.5

2023 $0.0 $88.0 $98.9 $186.9

2024 $0.0 $85.4 $96.0 $181.5

2025 $0.0 $0.0 $0.0 $0.0

2026 $0.0 $0.0 $0.0 $0.0

2027 $0.0 $0.0 $0.0 $0.0

2028 $0.0 $0.0 $0.0 $0.0

2029 $0.0 $0.0 $0.0 $0.0

Sum $0.0 $357.5 $401.7 $759.2

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-26 Discounted MY Lifetime R&D Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.0 $72.9 $81.9 $154.9

2022 $0.0 $68.1 $76.6 $144.7

2023 $0.0 $63.7 $71.6 $135.3

2024 $0.0 $59.5 $66.9 $126.4

2025 $0.0 $0.0 $0.0 $0.0

2026 $0.0 $0.0 $0.0 $0.0

2027 $0.0 $0.0 $0.0 $0.0

2028 $0.0 $0.0 $0.0 $0.0

2029 $0.0 $0.0 $0.0 $0.0

Sum $0.0 $264.3 $297.0 $561.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-30 

7.2.1.4 Summary of Vehicle-Related Costs of the Program using Method B 

Table 7-27 presents the annual new vehicle costs (including engine-related costs) 

associated with the preferred alternative for HD pickups and vans, vocational vehicles, and 

tractor and trailer programs along with net present values at 3 percent and 7 percent. This table 

presents costs relative to the less dynamic baseline and using the MOVES analysis of all vehicle 

categories (Method B). Table 7-28 presents the model year lifetime costs associated with the 

preferred alternative discounted at 3 percent relative to the less dynamic baseline and using 

Method B. Table 7-29 presents the model year lifetime costs associated with the preferred 

alternative discounted at 7 percent relative to the less dynamic baseline and using Method B. 

Table 7-27 Annual Vehicle-Related Costs and Net Present Values Associated with the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

($Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $116 $116

2019 $0 $0 $113 $113

2020 $0 $0 $112 $112

2021 $328 $721 $1,382 $2,432

2022 $324 $702 $1,381 $2,407

2023 $491 $659 $1,320 $2,470

2024 $590 $1,069 $2,042 $3,701

2025 $731 $965 $1,956 $3,653

2026 $788 $972 $1,982 $3,742

2027 $844 $1,976 $2,495 $5,315

2028 $847 $1,973 $2,520 $5,340

2029 $854 $1,967 $2,560 $5,381

2030 $855 $1,960 $2,591 $5,405

2035 $892 $2,080 $2,890 $5,862

2040 $935 $2,209 $3,179 $6,322

2050 $1,024 $2,418 $3,550 $6,992

NPV, 3% $13,475 $29,621 $43,693 $86,789

NPV, 7% $6,461 $13,810 $20,862 $41,133

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-31 

Table 7-28 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $104 $104

2019 $0 $0 $99 $99

2020 $0 $0 $95 $95

2021 $271 $595 $1,141 $2,007

2022 $260 $563 $1,106 $1,928

2023 $382 $512 $1,027 $1,921

2024 $446 $807 $1,542 $2,795

2025 $536 $708 $1,435 $2,678

2026 $561 $692 $1,411 $2,664

2027 $583 $1,366 $1,724 $3,673

2028 $568 $1,324 $1,691 $3,583

2029 $556 $1,282 $1,668 $3,506

Sum $4,164 $7,848 $13,044 $25,055

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-29 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0 $0 $91 $91

2019 $0 $0 $84 $84

2020 $0 $0 $77 $77

2021 $212 $465 $891 $1,567

2022 $195 $423 $832 $1,450

2023 $276 $371 $743 $1,390

2024 $311 $562 $1,074 $1,947

2025 $360 $475 $962 $1,796

2026 $362 $446 $911 $1,719

2027 $363 $849 $1,072 $2,283

2028 $340 $792 $1,012 $2,143

2029 $320 $738 $960 $2,019

Sum $2,738 $5,121 $8,709 $16,568

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-32 

7.2.2 Changes in Fuel Consumption and Savings 

7.2.2.1 Changes in Fuel Consumption 

The proposed standards would result in significant improvements in the fuel efficiency of 

affected vehicles. Drivers of those vehicles would see corresponding savings associated with 

reduced fuel expenditures. The agencies have estimated the impacts on fuel consumption for the 

proposed standards. More detail behind these changes in fuel consumption is presented in 

Chapter 5 of this draft RIA. The expected impacts on fuel consumption are shown in Table 7-30 

as reductions from the less dynamic baseline reference case (i.e., positive values represent fewer 

gallons consumed) and using the MOVES analysis of all vehicle categories (Method B). The 

gallons shown in this table include any increased consumption resulting from the rebound effect. 

Table 7-30 Annual Fuel Consumption Reductions due to the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(Million Gallons) a

 GASOLINE REDUCTIONS DIESEL REDUCTIONS 

CALENDAR 

YEAR 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

2018 0 0 0 0 0 0 74 74

2019 0 0 0 0 0 0 150 150

2020 0 0 0 0 0 0 227 227

2021 5 5 0 10 5 29 489 523

2022 19 10 0 29 20 58 817 894

2023 42 15 0 57 43 86 1,147 1,276

2024 73 26 0 99 76 146 1,674 1,895

2025 113 37 0 151 117 205 2,202 2,523

2026 161 48 0 210 167 263 2,722 3,152

2027 217 74 0 291 224 357 3,308 3,890

2028 270 99 0 369 280 449 3,871 4,600

2029 321 123 0 445 333 538 4,407 5,278

2030 370 147 0 516 383 622 4,918 5,924

2035 561 240 0 801 579 962 6,975 8,517

2040 674 294 0 968 690 1,169 8,349 10,209

2050 783 343 0 1,127 803 1,390 10,117 12,310

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-33 

Table 7-31 MY Lifetime Fuel Consumption Reductions due to the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(Million Gallons) a

 GASOLINE REDUCTIONS DIESEL REDUCTIONS 

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

2018 0 0 0 0 0 0 754 754

2019 0 0 0 0 0 0 745 745

2020 0 0 0 0 0 0 738 738

2021 58 54 0 113 59 340 4,025 4,424

2022 162 54 0 216 164 340 4,064 4,568

2023 263 54 0 317 267 340 4,096 4,703

2024 366 127 0 493 373 705 6,551 7,628

2025 472 130 0 602 481 722 6,763 7,967

2026 582 133 0 714 593 738 6,958 8,289

2027 685 297 0 982 699 1,193 8,092 9,984

2028 692 300 0 992 707 1,210 8,265 10,181

2029 696 303 0 999 712 1,222 8,427 10,360

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

7.2.2.2 Changes in Fuel Expenditures 

Using the fuel consumption reductions presented above, the agencies have calculated the fuel 

expenditure changes associated with the proposed standards, subcategory by subcategory. To do 

this, reduced fuel consumption is multiplied in each year by the corresponding estimated average 

fuel price in that year, using the reference case fuel prices from AEO 2014. As the AEO fuel 

price projections go through 2040 and not beyond, fuel prices beyond 2040 were set equal to the 

2040 values. These estimates do not account for the significant uncertainty in future fuel prices; 

the monetized fuel savings would be understated if actual fuel prices are higher (or overstated if 

fuel prices are lower) than estimated. The Annual Energy Outlook (AEO) is a standard reference 

used by NHTSA and EPA and many other government agencies to estimate the projected price 

of fuel. This has been done using both the pre-tax and post-tax fuel prices. Since the post-tax 

fuel prices are the prices paid at fuel pumps, the fuel expenditure changes calculated using these 

prices represent the changes fuel purchasers would see. The pre-tax fuel savings are those that 

society would see. Assuming no change in fuel tax rates, the difference between these two 

columns represents the reduction in fuel tax revenues that would be received by state and federal 

governments, or about $240 million in 2021 and $5.2 billion by 2050 as shown in Table 7-32. 

Table 7-33 presents the model year lifetime fuel savings—versus the less dynamic baseline and 

using Method B—discounted at 3 percent. Note: a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-34 

Table 7-34 presents the model year lifetime costs fuel savings—versus the less dynamic baseline 

and using Method B—discounted at 7 percent. Note that in Chapters 8 and 11 of this draft RIA, 

the overall benefits and costs of the rulemaking are presented and only the pre-tax fuel 

expenditure impacts are presented there. 

Table 7-32 Annual Reductions in Fuel Expenditures and Net Present Values due to the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(Millions of 2012$) a

 REDUCED FUEL EXPENDITURES – 

RETAIL 

REDUCED FUEL EXPENDITURES – 

UNTAXED 

CALENDAR 

YEAR 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/

TRAILERS SUM 

HD 

PICKUPS 

& VANS 

VOC TRACTOR/ 

TRAILERS SUM 

2018 $0 $0 $261 $261 $0 $0 $227 $227

2019 $0 $0 $540 $540 $0 $0 $472 $472

2020 $0 $0 $834 $834 $0 $0 $731 $731

2021 $36 $124 $1,830 $1,989 $31 $109 $1,610 $1,750

2022 $137 $251 $3,117 $3,505 $120 $222 $2,753 $3,095

2023 $303 $381 $4,435 $5,119 $267 $337 $3,928 $4,532

2024 $536 $656 $6,558 $7,750 $474 $581 $5,824 $6,879

2025 $838 $937 $8,756 $10,531 $742 $833 $7,797 $9,372

2026 $1,206 $1,218 $10,953 $13,378 $1,071 $1,085 $9,777 $11,934

2027 $1,643 $1,708 $13,508 $16,859 $1,462 $1,525 $12,089 $15,076

2028 $2,062 $2,182 $15,937 $20,181 $1,837 $1,952 $14,289 $18,079

2029 $2,478 $2,658 $18,349 $23,485 $2,213 $2,383 $16,487 $21,083

2030 $2,877 $3,120 $20,677 $26,675 $2,574 $2,803 $18,616 $23,993

2035 $4,633 $5,175 $31,160 $40,968 $4,190 $4,698 $28,354 $37,242

2040 $5,896 $6,681 $39,501 $52,078 $5,385 $6,122 $36,276 $47,783

2050 $6,859 $7,917 $47,862 $62,638 $6,264 $7,255 $43,955 $57,474

NPV, 3% $59,038 $66,542 $418,711 $544,290 $53,537 $60,566 $381,492 $495,595

NPV, 7% $24,187 $27,169 $176,228 $227,584 $21,881 $24,670 $160,096 $206,646

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-35 

Table 7-33 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(3% Discount Rate, Millions of 2012$) a

REDUCED FUEL EXPENDITURES – 

RETAIL 

REDUCED FUEL EXPENDITURES – 

UNTAXED 

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

2018 $0 $0 $2,183 $2,183 $0 $0 $1,937 $1,937

2019 $0 $0 $2,123 $2,123 $0 $0 $1,890 $1,890

2020 $0 $0 $2,066 $2,066 $0 $0 $1,844 $1,844

2021 $296 $1,066 $11,074 $12,436 $263 $952 $9,911 $11,126

2022 $813 $1,048 $10,995 $12,856 $724 $938 $9,862 $11,525

2023 $1,296 $1,029 $10,888 $13,212 $1,157 $923 $9,787 $11,867

2024 $1,774 $2,124 $17,103 $21,001 $1,587 $1,909 $15,405 $18,901

2025 $2,246 $2,136 $17,330 $21,712 $2,013 $1,923 $15,640 $19,577

2026 $2,712 $2,140 $17,490 $22,342 $2,436 $1,931 $15,814 $20,180

2027 $3,135 $3,564 $19,944 $26,643 $2,821 $3,220 $18,065 $24,106

2028 $3,104 $3,539 $19,949 $26,592 $2,798 $3,202 $18,101 $24,101

2029 $3,064 $3,503 $19,925 $26,493 $2,767 $3,175 $18,109 $24,052

Sum $18,440 $20,149 $151,070 $189,659 $16,567 $18,173 $136,364 $171,105

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-34 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(7% Discount Rate, Millions of 2012$) a

REDUCED FUEL EXPENDITURES – 

RETAIL

REDUCED FUEL EXPENDITURES – 

UNTAXED

MODEL 

YEAR 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

HD 

PICKUPS 

& VANS 

VOC 

TRACTO

R/ 

TRAILER

S 

SUM 

2018 $0 $0 $1,529 $1,529 $0 $0 $1,352 $1,352

2019 $0 $0 $1,428 $1,428 $0 $0 $1,267 $1,267

2020 $0 $0 $1,331 $1,331 $0 $0 $1,185 $1,185

2021 $186 $666 $6,848 $7,701 $165 $594 $6,115 $6,874

2022 $492 $631 $6,555 $7,678 $437 $564 $5,867 $6,869

2023 $755 $597 $6,255 $7,607 $673 $535 $5,611 $6,819

2024 $995 $1,187 $9,472 $11,654 $889 $1,065 $8,515 $10,469

2025 $1,213 $1,149 $9,244 $11,607 $1,086 $1,033 $8,328 $10,447

2026 $1,410 $1,109 $8,986 $11,505 $1,265 $999 $8,111 $10,374

2027 $1,570 $1,780 $9,868 $13,218 $1,411 $1,605 $8,924 $11,940

2028 $1,496 $1,701 $9,500 $12,697 $1,347 $1,537 $8,607 $11,490

2029 $1,423 $1,621 $9,135 $12,179 $1,283 $1,468 $8,291 $11,041

Sum $9,540 $10,442 $80,151 $100,134 $8,555 $9,399 $72,174 $90,128

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-36 

7.2.3 Maintenance Costs 

The agencies have estimated increased maintenance costs associated with installation of 

lower rolling resistance tires. We expect that, when replaced, the lower rolling resistance tires 

would be replaced by equivalent performing tires throughout the vehicle lifetime. As such, the 

incremental increases in costs for lower rolling resistance tires would be incurred throughout the 

vehicle lifetime at intervals consistent with current tire replacement intervals. Those intervals 

are difficult to quantify given the variety of vehicles and operating modes within the HD 

industry. For HD pickups and vans, we have chosen a tire replacement interval of 40,000 miles. 

We have done the same for all vocational vehicles which is probably overly conservative as 

more frequent intervals results in higher maintenance costs. For tractors and trailers, we have 

used a maintenance interval of 200,000 miles. The presence of tire inflation management 

systems, and the increased use of those systems expected due to this proposed rule, should serve 

to improve tire maintenance intervals. 

In evaluating maintenance costs associated with the proposal relative to the less dynamic 

baseline, EPA has used the maintenance intervals noted above, the MOVES policy case VMT, 

and the MOVES population of specific MY vehicles in future calendar years to estimate the 

increased maintenance costs associated with the proposal, again for each subcategory. Note that, 

in the context of the benefit-cost analysis, EPA has estimated maintenance costs using the policy 

case VMT which, by definition, includes rebound VMT (see Section IX of the preamble and 

Chapter 8 of this draft RIA for a discussion of rebound VMT). In contrast, in the context of the 

payback analysis discussed below, EPA estimates maintenance costs using the reference case 

VMT which, by definition, excludes rebound VMT. EPA does this for reasons explained in the 

payback discussion presented in Chapter 0 of this draft RIA. 

Table 7-35 shows the incremental increased costs associated with lower rolling resistance 

tires for HD pickups and vans, vocational vehicles, tractors, trailers and tractor/trailers relative to 

the less dynamic baseline and using Method B. Table 7-36 shows the lifetime maintenance 

intervals for MY2018 through MY2029. 
7-37 

Table 7-35 Increased Maintenance Costs at Maintenance Intervals Associated with the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(2012$/event) a

MODEL 

YEAR 

HD 

PICKUPS & 

VANS 

VOCATIONAL TRACTOR TRAILER TRACTOR/ 

TRAILER 

2018 $0.00 $0.00 $0.00 $79.92 $67.16

2019 $0.00 $0.00 $0.00 $77.77 $65.36

2020 $0.00 $0.00 $0.00 $75.69 $63.60

2021 $1.72 $6.99 $33.16 $78.81 $99.38

2022 $3.45 $6.91 $32.53 $76.74 $97.02

2023 $5.17 $8.12 $31.66 $71.17 $91.46

2024 $6.90 $17.66 $33.50 $73.97 $95.66

2025 $8.62 $17.47 $32.92 $72.02 $93.43

2026 $8.62 $13.50 $30.01 $70.72 $89.44

2027 $8.62 $21.34 $32.27 $69.44 $90.62

2028 $8.62 $20.26 $31.52 $68.85 $89.37

2029 & later $8.62 $15.99 $31.22 $68.27 $88.59

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-36 Lifetime Maintenance Intervals for the Indicated Model Years 

Vs. The Less Dynamic Baseline and using Method B a, b 

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILER 

2018 7.19 7.89 7.74 

2019 7.18 7.85 7.74 

2020 7.16 7.82 7.73 

2021 7.24 7.93 7.74 

2022 7.23 7.90 7.74 

2023 7.23 7.88 7.74 

2024 7.23 7.87 7.75 

2025 7.23 7.86 7.77 

2026 7.25 7.87 7.80 

2027 7.28 7.88 7.85 

2028 7.32 7.90 7.90 

2029 & later 7.37 7.93 7.97 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section 

I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic 

baseline, 1b, please see Preamble Section X.A.1 b

 Includes rebound vehicle miles traveled (VMT). 
7-38 

Table 7-37 presents the annual in-use maintenance costs associated with the preferred 

alternative along with net present values at 3 percent and 7 percent. This table presents costs 

relative to the less dynamic baseline and using the MOVES analysis for all vehicle categories 

(Method B). Table 7-38 presents the model year lifetime in-use maintenance costs—versus the 

less dynamic baseline and using Method B— discounted at 3 percent. Table 7-39 presents the 

model year lifetime in-use maintenance costs—versus the less dynamic baseline and using 

Method B—discounted at 7 percent. 

Table 7-37 Annual Increased Maintenance Costs and Net Present Values Associated with the Preferred 

Alternative Vs. The Less Dynamic Baseline and using Method B 

 ($Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $5.5 $5.5

2019 $0.0 $0.0 $11.0 $11.0

2020 $0.0 $0.0 $16.5 $16.5

2021 $0.4 $1.8 $25.4 $27.6

2022 $1.3 $3.5 $33.8 $38.6

2023 $2.5 $5.6 $41.5 $49.6

2024 $4.2 $10.2 $49.3 $63.8

2025 $6.3 $14.7 $56.8 $77.8

2026 $8.4 $18.1 $63.4 $90.0

2027 $10.4 $23.6 $69.8 $103.7

2028 $12.3 $28.5 $75.5 $116.3

2029 $14.1 $32.0 $80.7 $126.8

2030 $14.1 $32.0 $80.7 $126.8

2035 $14.1 $32.0 $80.7 $126.8

2040 $14.1 $32.0 $80.7 $126.8

2050 $14.1 $32.0 $80.7 $126.8

NPV, 3% $183.5 $417.8 $1,194.7 $1,796.0

NPV, 7% $83.7 $191.1 $585.4 $860.2

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-39 

Table 7-38 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

 (3% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $51.2 $51.2

2019 $0.0 $0.0 $49.2 $49.2

2020 $0.0 $0.0 $47.5 $47.5

2021 $3.3 $14.1 $72.6 $90.0

2022 $6.5 $13.5 $69.4 $89.4

2023 $9.4 $15.4 $64.0 $88.8

2024 $12.3 $33.3 $66.6 $112.2

2025 $15.1 $32.7 $65.1 $112.9

2026 $15.0 $25.0 $62.1 $102.1

2027 $14.7 $38.9 $62.3 $115.9

2028 $14.4 $36.2 $60.6 $111.1

2029 $14.0 $27.9 $59.1 $101.0

Sum $104.6 $237.0 $729.5 $1,071.2

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-39 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

(7% Discount Rate, $Millions of 2012$) a

MODEL 

YEAR 

HD PICKUPS 

& VANS VOCATIONAL TRACTOR/ TRAILERS SUM 

2018 $0.0 $0.0 $35.8 $35.8

2019 $0.0 $0.0 $33.2 $33.2

2020 $0.0 $0.0 $30.9 $30.9

2021 $2.1 $8.9 $45.5 $56.6

2022 $3.9 $8.3 $42.0 $54.2

2023 $5.5 $9.1 $37.3 $51.9

2024 $6.9 $18.9 $37.4 $63.2

2025 $8.3 $17.8 $35.2 $61.3

2026 $7.9 $13.1 $32.4 $53.4

2027 $7.4 $19.7 $31.3 $58.5

2028 $7.0 $17.7 $29.4 $54.0

2029 $6.6 $13.1 $27.6 $47.3

Sum $55.7 $126.6 $418.2 $600.5

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-40 

7.2.4 Analysis of Payback Periods 

An important metric to vehicle purchasers is the payback period that can be expected on 

any new purchase. In other words, there is greater willingness to pay for new technology if that 

new technology “pays back” within an acceptable period of time. We make no effort to define 

the acceptable period of time here, but seek to estimate the payback period for others to make the 

decision themselves. We define the payback period as the point at which reduced fuel 

expenditures outpace increased vehicle costs. For example, a new MY2027 tractor with trailer is 

estimated to cost roughly $12,850 more (on average, including an “average” trailer, in 2012$, 

and relative to the reference case vehicle) due to the addition of new GHG reducing/fuel 

consumption improving technology. This new technology would result in lower fuel 

consumption and, therefore, reduced fuel expenditures. But how many months or years would 

pass before the reduced fuel expenditures would surpass the increased costs? 

To estimate the costs, we have considered not only the cost of the new technology, but 

also the taxes paid on the incrementally higher purchase expense, the slightly higher insurance 

expenses on the slightly higher value vehicle, and the increased maintenance costs associated 

with the new technology. Taxes paid were estimated as 6 percent sales tax in all regulated 

sectors and a 12 percent excise tax applicable in the tractor/trailer and vocational sectors. As 

such, the vehicle costs presented here are slightly higher than those presented elsewhere in this 

draft RIA. For insurance costs, we have estimated the collision insurance to be 2 percent of the 

purchase price of a vehicle consistent with the approach taken in our 2017-2025 light-duty 

GHG/CAFE rule.6

 Therefore, increased insurance costs would equal 2 percent of the increased 

technology costs, and would be incurred every year going forward. But, since collision 

insurance is tied to vehicle value, we have also included a depreciation rate consisting of 

straight-line depreciation of 3 percent each year through the 25th year of ownership at which time 

we have flat-lined the depreciation and held vehicle value constant (see Table 7-59 in Chapter 

7.3, below). For maintenance costs, we have used the same method described above (see 

Chapter 0 of this draft RIA) except that we have used reference case VMT for the calculation 

rather than policy case VMT (i.e., we exclude rebound miles here) because typical payback 

considerations generally do not account for possible increased miles driven. Also, here we use 

retail fuel prices since those are the prices paid by owners of these vehicles. 

We have conducted this payback analysis for HD pickups and vans, vocational vehicles 

and for tractor/trailers (including the engines used in each of these subcategories). All 

calculations are for the average vehicle, or average tractor/trailer combination, that drives the 

average number of miles each year. The calculations do not represent specific vehicle classes or 

specific use cases so should not be seen as being applicable to any particular individual’s 

situation. However, the payback periods do provide a general sense, on average, of what sort of 

payback periods are likely at a national, societal perspective. 

 Table 7-40 presents the discounted annual increased vehicle costs and fuel expenditure 

impacts associated with owning a new MY2027 HD pickup or van using both 3 percent and 7 

percent discount rates. The results in this table use Method B. As shown in the table, the 

payback for HD pickups and vans occurs late in the 3rd year of ownership (the year in which 

cumulative expenditures become negative) using a 3 percent discount rate and in the early part of 

the 3rd year using a 7 percent discount rate. 
7-41 

Table 7-40 Discounted Owner Expenditures & Payback Period for MY2027 HD Pickups & Vans under the 

Preferred Alternative Vs. The Less Dynamic Baseline and using Method B 

 3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $1,587 $4 -$759 $832 $1,558 $3 -$745 $817 

2 $25 $3 -$734 $126 $23 $3 -$694 $150 

3 $23 $3 -$714 -$561 $21 $3 -$649 -$476 

4 $22 $3 -$693 -$1,229 $19 $3 -$606 -$1,060 

5 $20 $3 -$651 -$1,857 $17 $2 -$549 -$1,590 

6 $19 $3 -$611 -$2,446 $15 $2 -$496 -$2,067 

7 $18 $2 -$571 -$2,997 $14 $2 -$446 -$2,497 

8 $16 $2 -$536 -$3,514 $12 $2 -$403 -$2,886 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.

Table 7-41 and Table 7-42 show the same information for a MY2027 vocational vehicle 

and a tractor/trailer, respectively. As shown, payback for vocational vehicles occurs in the 5th

year of ownership (using 3 percent discounting and in the 6th year using 7 percent discounting) 

while payback for tractor/trailers occurs early in the 2nd year of ownership. 

Table 7-41 Discounted Owner Expenditures & Payback Period for MY2027Vocational Vehicles under the 

Preferred Alternative Vs. The Less Dynamic Baseline and using Method B 

 3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $3,998 $10 -$965 $3,043 $3,924 $10 -$947 $2,987 

2 $63 $9 -$937 $2,178 $59 $9 -$885 $2,169 

3 $59 $9 -$914 $1,331 $53 $8 -$832 $1,399 

4 $55 $9 -$891 $504 $48 $8 -$780 $675 

5 $51 $8 -$829 -$265 $43 $7 -$699 $27 

6 $48 $7 -$771 -$981 $39 $6 -$625 -$554 

7 $45 $7 -$716 -$1,645 $35 $5 -$559 -$1,073 

8 $42 $6 -$667 -$2,264 $31 $5 -$501 -$1,538 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
7-42 

Table 7-42 Discounted Owner Expenditures & Payback Period for MY2027Tractor/Trailers under the 

Preferred Alternative Vs. The Less Dynamic Baseline and using Method B 

 3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insuranceb 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $15,194 $48 -$14,649 $593 $14,914 $47 -$14,379 $582 

2 $238 $46 -$14,204 -$13,327 $225 $43 -$13,421 -$12,571 

3 $223 $44 -$13,809 -$26,869 $203 $40 -$12,561 -$24,889 

4 $209 $42 -$13,416 -$40,034 $183 $37 -$11,746 -$36,415 

5 $195 $39 -$12,391 -$52,191 $164 $33 -$10,443 -$46,661 

6 $182 $35 -$11,411 -$63,385 $148 $29 -$9,258 -$55,743 

7 $170 $32 -$10,511 -$73,694 $133 $25 -$8,209 -$63,794 

8 $158 $29 -$9,704 -$83,211 $119 $22 -$7,295 -$70,949 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.

The fuel expenditure column uses retail fuel prices specific to gasoline and diesel fuel as 

projected in AEO2014 Early Release. This payback analysis does not include other private 

impacts, such as reduced refueling events, or other societal impacts, such as the potential 

rebound miles driven or the value of driving those rebound miles, or noise, congestion and 

accidents. It also does not include societal impacts such as co-pollutant environmental benefits 

or benefits associated with reduced GHG emissions. We use retail fuel prices and exclude these 

other private and social impacts because the focus is meant to be on those factors that buyers 

think about most while considering a new vehicle purchase and those factors that result in more 

or fewer dollars in their pockets. 

In an effort to provide further information on payback, we have also looked at the 

payback periods for more specific vehicle subcategories. For example, while the tractor/trailer 

payback shown in Table 7-42 occurs early in the 2nd year, the payback for a Class 8 sleeper cab 

would occur within the first year of ownership as shown in Table 7-43. 
7-43 

Table 7-43 Discounted Owner Expenditures & Payback Period for MY2027 Sleeper Cab with Trailer under 

the Preferred Alternative Vs. The Less Dynamic Baseline and using Method B 

 3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $16,541 $60 -$20,087 -$3,486 $16,236 $59 -$19,717 -$3,422 

2 $259 $58 -$19,477 -$22,646 $244 $55 -$18,403 -$21,526 

3 $243 $56 -$18,936 -$41,285 $221 $50 -$17,224 -$38,479 

4 $227 $53 -$18,396 -$59,401 $199 $47 -$16,107 -$54,341 

5 $212 $49 -$17,053 -$76,193 $179 $41 -$14,373 -$68,494 

6 $198 $45 -$15,768 -$91,718 $161 $36 -$12,793 -$81,090 

7 $185 $41 -$14,591 -$106,083 $144 $32 -$11,395 -$92,309 

8 $172 $37 -$13,538 -$119,412 $129 $28 -$10,178 -$102,330 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.

Given the variety in the vocational market, the subcategory analysis becomes more 

interesting. For example, Table 7-44 shows the payback for an intercity bus. Table 7-45 shows 

the same information for a transit bus, while Table 7-46 shows this information for a school bus. 

These tables highlight how much the payback period can vary depending on the level of 

technology cost and fuel consumption improvement versus the number of miles driven. The 

high VMT intercity bus (~80,000 miles/year) and transit bus (~60,000 miles/year) payback in the 

1st and 3rd year, respectively, despite first year costs exceeding $4,000 and $8,000, respectively. 

By contrast, the lower VMT school bus (~13,000 miles/year) pays back in the 11th year (or 14th

year with 7 percent discounting) despite first year costs under $6,000. 

Table 7-44 Discounted Owner Expenditures & Payback Period for MY2027 Intercity Bus under the 

Preferred Alternative Vs. The Less Dynamic Baseline and using Method B 

3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures

Technology 

cost, taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures

1 $4,863 $57 -$8,388 -$3,469 $4,773 $56 -$8,234 -$3,405

2 $76 $55 -$8,203 -$11,541 $72 $52 -$7,751 -$11,032

3 $71 $53 -$8,046 -$19,462 $65 $48 -$7,318 -$18,237

4 $67 $52 -$7,881 -$27,225 $58 $45 -$6,901 -$25,034

5 $62 $50 -$7,734 -$34,846 $53 $42 -$6,518 -$31,457

6 $58 $49 -$7,593 -$42,332 $47 $39 -$6,160 -$37,530

7 $54 $47 -$7,459 -$49,689 $42 $37 -$5,826 -$43,277

8 $51 $46 -$7,357 -$56,950 $38 $34 -$5,531 -$48,735

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
7-44 

Table 7-45 Discounted Owner Expenditures & Payback Period for MY2027Transit Bus under the Preferred 

Alternative Vs. The Less Dynamic Baseline and using Method B 

3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $8,718 $45 -$4,328 $4,435 $8,558 $44 -$4,248 $4,353 

2 $136 $42 -$4,098 $516 $129 $40 -$3,872 $650 

3 $128 $40 -$3,892 -$3,209 $116 $36 -$3,540 -$2,738 

4 $120 $37 -$3,689 -$6,741 $105 $33 -$3,230 -$5,830 

5 $112 $35 -$3,503 -$10,097 $94 $29 -$2,953 -$8,659 

6 $104 $33 -$3,330 -$13,290 $85 $27 -$2,702 -$11,249 

7 $97 $31 -$3,170 -$16,332 $76 $24 -$2,476 -$13,625 

8 $91 $29 -$3,026 -$19,238 $68 $22 -$2,275 -$15,810 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates. 

Table 7-46 Discounted Owner Expenditures & Payback Period for MY2027School Bus under the Preferred 

Alternative Vs. The Less Dynamic Baseline and using Method B 

3% and 7% Discount Rates (2012$) a

Age 

3% Discount Rate 7% Discount Rate 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

Technology 

cost, 

taxes, 

insurance b 

Maintenance 

expenditures 

Fuel 

expenditures c 

Cumulative 

expenditures 

1 $5,687 $7 -$675 $5,019 $5,582 $7 -$662 $4,926 

2 $89 $7 -$660 $4,455 $84 $6 -$623 $4,393 

3 $83 $6 -$647 $3,897 $76 $6 -$589 $3,886 

4 $78 $6 -$634 $3,348 $68 $6 -$555 $3,405 

5 $73 $6 -$622 $2,805 $62 $5 -$524 $2,948 

6 $68 $6 -$611 $2,268 $55 $5 -$495 $2,512 

7 $63 $6 -$600 $1,737 $50 $4 -$469 $2,098 

8 $59 $6 -$592 $1,210 $44 $4 -$445 $1,701 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b 6% sales tax and 12% excise tax; insurance estimates are described in text.

c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.

We could present tables for each MOVES subcategory, but since all are calculated using 

the same methodology, the detailed tables seem unnecessary. Instead, we provide Table 7-47 

which summarizes the payback period for each MOVES subcategory at both 3 percent and 7 

percent discount rates and for each fuel type. 
7-45 

Table 7-47 Payback Periods Associated with the Preferred Alternative 

Vs. The Less Dynamic Baseline and using Method B 

for MY2027 Vehicle Subcategories at 3% and 7% Discount Rates 

Payback occurs in Year Shown a

Subcategory 3% Discount Rate 7% Discount Rate 

Gasoline Diesel All Gasoline Diesel All 

HD Pickups & Vans (MY2027) 3 2 3 3 2 3 

Vocational (MY2027 for each) 

Intercity bus N/A 1 1 N/A 1 1 

Transit bus 3 3 3 3 3 3 

School bus 13 11 11 18 13 14 

Refuse truck N/A 4 4 N/A 4 4 

Single unit short haul 4 5 5 4 5 5 

Single unit long haul N/A 3 3 N/A 3 3 

Motor home >23 >23 >23 >23 >23 >23

All 5 5 5 5 6 6 

Tractor/Trailer (MY2027 for each) 

Combination short haul N/A 2 2 N/A 2 2 

Combination long haul N/A 1 1 N/A 1 1 

All N/A 2 2 N/A 2 2 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

N/A denotes no such vehicles in this segment. 

7.2.5 Cost per Ton of CO2 Equivalent Reduced vs. the Less Dynamic Baseline 

and using Method B 

The agencies have calculated the cost per ton of GHG (CO2-equivalent, or CO2eq) 

reductions associated with this rulemaking using the costs presented in Chapter 7.2.1 and 

7.2.2, and the GHG emissions reductions described in Chapter 5 of this draft RIA. These 

costs per ton-reduction values are presented in Table 7-48 through Notes: a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 GHG reductions include CO2 and CO2 equivalents of CH4, N2O 

and HFCs. 

Table 7-51 for HD pickups & vans, vocational vehicles, tractor/trailers and all segments, 

respectively. The cost per metric ton of GHG emissions reductions in 2050 represents the longterm cost per ton of the emissions reduced. The agencies have also calculated the cost per metric 

ton of GHG emission reductions including the savings associated with reduced fuel 

consumption. 

The calculations presented here include all engine-related costs but do not include 

benefits associated with the preferred alternative such as those associated with criteria pollutant 

reductions or energy security benefits (discussed in Chapter 8 of this draft RIA). By including 
7-46 

the fuel savings, the cost per ton-reduction is less than $0 since the estimated value of fuel 

savings outweighs the program costs. 

Table 7-48 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the Preferred Alternative 

 Vs. The Less Dynamic Baseline and using Method B 

HD Pickups and Vans only (dollar values are 2012$) a

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2021 $0.3 $0.0 0 $2,600 $2,400 

2024 $0.6 $0.5 2 $330 $67 

2027 $0.9 $1.5 5 $160 -$110 

2030 $0.9 $2.6 9 $95 -$190 

2035 $0.9 $4.2 14 $65 -$240 

2040 $0.9 $5.4 17 $57 -$270 

2050 $1.0 $6.3 19 $54 -$270 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 GHG reductions include CO2 and CO2 equivalents of CH4, N2O 

and HFCs. 

Table 7-49 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the Preferred Alternative 

 Vs. The Less Dynamic Baseline and using Method B 

Vocational Vehicles only (dollar values are 2012$) a

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2021 $0.7 $0.1 0 $1,500 $1,300 

2024 $1.1 $0.6 2 $460 $210 

2027 $2.0 $1.5 6 $340 $81 

2030 $2.0 $2.8 10 $190 -$78 

2035 $2.1 $4.7 16 $130 -$160 

2040 $2.2 $6.1 20 $110 -$200 

2050 $2.5 $7.3 23 $110 -$210 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 
7-47 

Preamble Section X.A.1 GHG reductions include CO2 and CO2 equivalents of CH4, N2O 

and HFCs. 

Table 7-50 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the Preferred Alternative 

 Vs. The Less Dynamic Baseline and using Method B 

Tractor/Trailers only (dollar values are 2012$) a

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2021 $1.4 $1.6 7 $210 -$30 

2024 $2.1 $5.8 23 $90 -$160 

2027 $2.6 $12.1 46 $56 -$210 

2030 $2.7 $18.6 69 $39 -$230 

2035 $3.0 $28.4 97 $31 -$260 

2040 $3.3 $36.3 116 $28 -$280 

2050 $3.6 $44.0 141 $26 -$290 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 GHG reductions include CO2 and CO2 equivalents of CH4, N2O 

and HFCs. 

Table 7-51 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the Preferred Alternative 

 Vs. The Less Dynamic Baseline and using Method B 

All Vehicle Segments (dollar values are 2012$) a

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2021 $2.5 $1.7 7 $330 $96 

2024 $3.8 $6.9 28 $140 -$110 

2027 $5.4 $15.1 57 $94 -$170 

2030 $5.5 $24.0 88 $63 -$210 

2035 $6.0 $37.2 127 $47 -$250 

2040 $6.4 $47.8 152 $42 -$270 

2050 $7.1 $57.5 183 $39 -$270 

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1 GHG reductions include CO2 and CO2 equivalents of CH4, N2O 

and HFCs. 

For comparison, Table 7-52 through Table 7-55 show the same information as it was 

presented in Chapter 7 of the final RIA for the Phase 1 HD rule.7

 
7-48 

Table 7-52 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the HD Phase 1 Final Rule 

 HD Pickups and Vans only (dollar values are 2009$) 

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2020 $0,8 $0.9 3 $240 -$30 

2030 $0.9 $3.0 10 $90 -$200 

2040 $1.0 $4.3 14 $70 -$240 

2050 $1.2 $5.5 16 $80 -$270 

Table 7-53 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the HD Phase 1 Final Rule 

Vocational Vehicles only (dollar values are 2009$) 

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2020 $0.2 $1.1 4 $50 -$210 

2030 $0.2 $2.4 9 $20 -$250 

2040 $0.3 $3.5 12 $30 -$270 

2050 $0.4 $4.7 14 $30 -$310 

Table 7-54 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the HD Phase 1 Final Rule 

Tractor/Trailers only (dollar values are 2009$) 

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2020 $1.0 $7.7 32 $30 -$210 

2030 $1.1 $15.3 57 $20 -$250 

2040 $1.4 $20.2 68 $20 -$280 

2050 $1.8 $26.4 78 $20 -$320 

Table 7-55 Annual Cost per Metric Ton of CO2eq Emissions Reduced in the HD Phase 1 Final Rule 

All Vehicle Segments (dollar values are 2009$) 

Calendar 

Year 

Vehicle & 

Maintenance 

Costs 

($Billions)

Fuel 

Savings 

($Billions) 

GHG 

Reduced 

(MMT)

$/metric 

ton w/o 

fuel 

$/metric 

ton w/ fuel

2020 $2.0 $9.6 39 $50 -$190 

2030 $2.2 $20.6 76 $30 -$240 

2040 $2.7 $28.0 94 $30 -$270 

2050 $3.3 $36.5 108 $30 -$310 
7-49 

7.3Key Parameters Used in the Estimation of Costs and Fuel Savings 

This section presents some of the parameters used in generating expenditure impacts 

associated with the program. Table 7-56 presents estimated sales of complying vehicles by 

calendar year. Table 7-57 presents AEO 2014 early release reference case fuel prices. Note that 

AEO projects fuel prices out to 2040. Table 7-58 presents AEO 2014 final reference case fuel 

prices which are used in Method A for analysis of HD pickups and vans. For that analysis, the 

retail (post-tax) prices are increased for each year after 2040 by 0.2 percent for gasoline and 0.7 

percent for diesel. For years beyond 2040, EPA has kept fuel prices at the 2040 level rather than 

growing those fuel prices at a rate consistent with years prior to 2040. Table 7-59 shows the 

depreciation rates used in the payback period analysis presented in Chapter 0. Table 7-60 

through Table 7-62 show the policy and reference case VMT values used in MOVES modeling. 
7-50 

Table 7-56 Estimated Calendar Year Sales by Vehicle Type using Method B a, b

Calendar Year HD Pickup & 

Vans 

Vocational 

Vehicles Tractors Semi-trailers 

2018 601,428 508,986 152,323 181,264 

2019 592,824 508,189 155,452 184,988 

2020 588,718 511,308 159,221 189,473 

2021 588,166 513,187 161,198 191,826 

2022 588,277 518,192 163,719 194,826 

2023 588,468 522,188 166,094 197,652 

2024 595,880 538,609 171,770 204,406 

2025 607,607 555,144 178,532 212,453 

2026 622,297 570,707 185,102 220,271 

2027 629,008 583,170 190,777 227,025 

2028 635,564 592,507 195,631 232,801 

2029 640,235 600,238 200,434 238,516 

2030 645,344 606,945 204,610 243,486 

2031 650,635 613,681 208,775 248,442 

2032 656,130 620,670 213,612 254,198 

2033 660,463 625,768 217,695 259,057 

2034 665,671 633,244 222,677 264,986 

2035 673,795 643,148 228,335 271,719 

2036 680,860 651,588 233,285 277,609 

2037 688,683 660,629 238,296 283,572 

2038 697,131 670,643 243,855 290,187 

2039 702,434 676,957 247,759 294,833 

2040 706,025 682,097 251,554 299,349 

2041 711,730 682,945 247,952 295,063 

2042 718,283 689,604 251,409 299,177 

2043 724,911 696,394 254,917 303,351 

2044 731,657 703,351 258,472 307,582 

2045 738,470 710,389 262,079 311,874 

2046 745,352 717,507 265,734 316,223 

2047 752,303 724,711 269,440 320,634 

2048 759,313 731,984 273,199 325,107 

2049 766,385 739,323 277,009 329,641 

2050 773,523 746,732 280,872 334,238 

Notes: 

a

 Sales are estimated using population data contained in MOVES. See Chapter 5 of this draft RIA for a 

description of the MOVES modeling done in support of this proposal. 

b

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the 

less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-51 

Table 7-57 AEO 2014 Early Release Reference Case Fuel Prices (2012$/gallon) 

 Pre-Tax Post-Tax 

Calendar 

Year Gasoline Diesel Gasoline Diesel 

2018 $2.61 $3.07 $3.02 $3.53 

2019 $2.62 $3.15 $3.03 $3.61 

2020 $2.67 $3.22 $3.08 $3.67 

2021 $2.71 $3.29 $3.12 $3.74 

2022 $2.77 $3.37 $3.17 $3.82 

2023 $2.82 $3.43 $3.22 $3.87 

2024 $2.86 $3.48 $3.26 $3.92 

2025 $2.89 $3.54 $3.29 $3.98 

2026 $2.92 $3.59 $3.32 $4.02 

2027 $2.96 $3.65 $3.36 $4.08 

2028 $2.98 $3.69 $3.37 $4.12 

2029 $3.01 $3.74 $3.40 $4.16 

2030 $3.04 $3.79 $3.43 $4.20 

2031 $3.08 $3.84 $3.46 $4.25 

2032 $3.12 $3.89 $3.50 $4.30 

2033 $3.16 $3.95 $3.54 $4.36 

2034 $3.24 $4.02 $3.61 $4.43 

2035 $3.27 $4.06 $3.65 $4.47 

2036 $3.32 $4.11 $3.69 $4.51 

2037 $3.36 $4.15 $3.73 $4.54 

2038 $3.40 $4.19 $3.77 $4.58 

2039 $3.47 $4.26 $3.83 $4.65 

2040 $3.54 $4.34 $3.90 $4.73 

2041 $3.54 $4.34 $3.90 $4.73 

2042 $3.54 $4.34 $3.90 $4.73 

2043 $3.54 $4.34 $3.90 $4.73 

2044 $3.54 $4.34 $3.90 $4.73 

2045 $3.54 $4.34 $3.90 $4.73 

2046 $3.54 $4.34 $3.90 $4.73 

2047 $3.54 $4.34 $3.90 $4.73 

2048 $3.54 $4.34 $3.90 $4.73 

2049 $3.54 $4.34 $3.90 $4.73 

2050 $3.54 $4.34 $3.90 $4.73 
7-52 

Table 7-58 AEO 2014 Final Reference Case Fuel Prices Used in Method A Analysis for HD Pickups and 

Vans (2012$/gallon) 

 Pre-Tax Post-Tax 

Calendar 

Year Gasoline Diesel Gasoline Diesel 

2018 $2.63 $3.10 $3.02 $3.53 

2019 $2.64 $3.19 $3.03 $3.61 

2020 $2.69 $3.25 $3.08 $3.67 

2021 $2.74 $3.32 $3.12 $3.74 

2022 $2.79 $3.41 $3.17 $3.82 

2023 $2.84 $3.46 $3.22 $3.87 

2024 $2.88 $3.51 $3.26 $3.92 

2025 $2.92 $3.58 $3.29 $3.98 

2026 $2.95 $3.62 $3.32 $4.02 

2027 $2.99 $3.68 $3.36 $4.08 

2028 $3.00 $3.73 $3.37 $4.12 

2029 $3.03 $3.77 $3.40 $4.16 

2030 $3.07 $3.81 $3.43 $4.20 

2031 $3.10 $3.87 $3.46 $4.25 

2032 $3.14 $3.92 $3.50 $4.30 

2033 $3.18 $3.98 $3.54 $4.36 

2034 $3.27 $4.06 $3.62 $4.43 

2035 $3.30 $4.10 $3.65 $4.47 

2036 $3.34 $4.14 $3.69 $4.51 

2037 $3.38 $4.18 $3.73 $4.54 

2038 $3.43 $4.22 $3.77 $4.58 

2039 $3.49 $4.29 $3.83 $4.65 

2040 $3.56 $4.38 $3.90 $4.73 

2041 $3.57 $4.41 $3.91 $4.76 

2042 $3.58 $4.44 $3.92 $4.80 

2043 $3.59 $4.48 $3.93 $4.83 

2044 $3.59 $4.51 $3.93 $4.86 

2045 $3.60 $4.54 $3.94 $4.90 

2046 $3.61 $4.58 $3.95 $4.93 

2047 $3.62 $4.61 $3.96 $4.97 

2048 $3.63 $4.65 $3.97 $5.00 

2049 $3.63 $4.68 $3.97 $5.04 

2050 $3.64 $4.72 $3.98 $5.07 
7-53 

Table 7-59 Depreciation Schedule used in Payback Analysis for Method B a

Age Depreciation 

0 0% 

1 3% 

2 7% 

3 10% 

4 13% 

5 17% 

6 20% 

7 23% 

8 27% 

9 30% 

10 33% 

11 37% 

12 40% 

13 43% 

14 47% 

15 50% 

16 53% 

17 57% 

18 60% 

19 63% 

20 67% 

21 70% 

22 73% 

23 77% 

24 80% 

25 83% 

26 83% 

27 83% 

28 83% 

29 83% 

30 83% 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-54 

Table 7-60 Reference Case and Policy Case Vehicle Miles Traveled (VMT) 

For the Preferred Alternative relative to the Less Dynamic Baseline using Method B 

Gasoline & Diesel Fueled 

HD Pickups and Vans a

Model Year Reference case Policy Case Rebound VMT 

2018 115,829,944,373 115,829,944,373 0 

2019 113,678,239,954 113,678,239,954 0 

2020 112,489,612,844 112,489,612,844 0 

2021 111,945,040,889 113,266,026,805 1,320,985,916 

2022 111,677,755,878 112,995,512,359 1,317,756,481 

2023 111,450,161,386 112,765,269,187 1,315,107,801 

2024 112,709,425,730 114,039,351,705 1,329,925,975 

2025 114,748,238,188 116,102,258,642 1,354,020,454 

2026 117,275,813,888 118,659,694,470 1,383,880,582 

2027 118,386,726,732 119,783,672,794 1,396,946,063 

2028 119,534,232,399 120,944,733,432 1,410,501,033 

2029 120,302,585,242 121,722,263,013 1,419,677,771 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 7-61 Reference Case and Policy Case Vehicle Miles Traveled (VMT) 

For the Preferred Alternative relative to the Less Dynamic Baseline using Method B 

Gasoline & Diesel Fueled 

Vocational Vehicles a

Model Year Reference case Policy Case Rebound VMT 

2018 118,450,357,579 118,450,357,579 0 

2019 116,749,449,396 116,749,449,396 0 

2020 116,127,003,082 116,127,003,082 0 

2021 115,248,392,233 117,357,427,520 2,109,035,287 

2022 115,272,965,194 117,382,447,329 2,109,482,135 

2023 115,175,262,339 117,282,914,481 2,107,652,142 

2024 117,884,442,538 120,041,702,114 2,157,259,576 

2025 120,740,410,310 122,949,989,775 2,209,579,465 

2026 123,305,465,962 125,561,901,338 2,256,435,376 

2027 125,295,415,305 127,588,325,885 2,292,910,580 

2028 126,961,637,088 129,285,065,704 2,323,428,616 

2029 128,161,021,270 130,506,385,592 2,345,364,323 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-55 

Table 7-62 Reference Case and Policy Case Vehicle Miles Traveled (VMT) 

For the Preferred Alternative relative to the Less Dynamic Baseline using Method B 

Gasoline & Diesel Fueled 

Tractor/Trailer a

Model Year Reference case Policy Case Rebound VMT 

2018 205,633,186,090 206,868,892,110 1,235,706,020 

2019 208,901,077,400 210,286,134,790 1,385,057,390 

2020 212,879,741,190 214,425,143,290 1,545,402,100 

2021 214,192,002,054 215,883,987,009 1,691,984,954 

2022 216,140,641,720 217,848,264,966 1,707,623,246 

2023 217,755,803,295 219,476,052,250 1,720,248,954 

2024 223,503,856,041 225,269,560,066 1,765,704,025 

2025 230,652,945,572 232,475,299,910 1,822,354,338 

2026 237,271,789,952 239,146,338,666 1,874,548,714 

2027 242,810,503,298 244,728,847,129 1,918,343,832 

2028 247,759,768,627 249,716,945,459 1,957,176,832 

2029 252,434,280,992 254,428,339,577 1,994,058,585 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
7-56 

References 

1

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final 

Rulemaking 75 Fed. Reg. 25323 (May 7, 2010). 

2

 Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Mediumand Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis, EPA-420-R-11-901, August 2011. 

3

 All of EPA’s calculations of costs and benefits (monetized) along with paybacks can be found in the docket as 

“GHGHD2 NPRM Package Costs.xlsb,” “GHGHD2 NPRM BCA.xlsb,” and “GHGHD2 NPRM Payback.xlsb.” 

4

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final 

Rulemaking 75 Fed. Reg. 25323 (May 7, 2010). 

5

 Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Mediumand Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis, EPA-420-R-11-901, August 2011. 

6

 Final Rulemaking for 2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average 

Fuel Economy Standards, Regulatory Impact Analysis, EPA-420-R-12-016, August 2012. 7

 Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Mediumand Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis, EPA-420-R-11-901, August 2011. 
8-1 

Chapter 8. Economic and Other Impacts 

8.1 Framework for Benefits and Costs 

This Chapter presents the costs, benefits and other economic impacts of the proposed 

Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standards 

and EPA’s proposed GHG standards would both be in effect, and each would lead to average 

fuel efficiency increases and GHG emission reductions. 

The net benefits of the proposed Phase 2 standards consist of the effects of the program 

on: 

 the vehicle program costs (costs of complying with the vehicle CO2 and fuel consumption 

standards), 

 changes in fuel expenditures associated with reduced fuel use resulting from more 

efficient vehicles and increased fuel use associated with the “rebound” effect, both of 

which result from the program, 

 the economic value of reductions in GHGs, 

 the economic value of reductions in other non-GHG pollutants, 

 costs associated with increases in noise, congestion, and accidents resulting from 

increased vehicle use, 

 savings in drivers’ time from less frequent refueling, 

 benefits of increased vehicle use associated with the “rebound” effect, 

 the economic value of improvements in U.S. energy security. 

The benefits and costs of these rules are analyzed using 3 percent and 7 percent discount 

rates, consistent with current OMB guidance.A These rates are intended to represent consumers’ 

preference for current over future consumption (3 percent), and the real rate of return on private 

investment (7 percent) which indicates the opportunity cost of capital. However, neither of these 

rates necessarily represents the discount rate that individual decision-makers use. 

The program may also have other economic effects that are not included here. In 

particular, as discussed in Chapter 2 of the draft RIA, the technology cost estimates developed 

here take into account the costs to hold other vehicle attributes, such as size and performance, 

constant. With these assumptions, and because welfare losses represent monetary estimates of 

how much buyers would have to be compensated to be made as well off as they would have been 

in the absence of this regulation,B price increases for new vehicles measure the welfare losses to 

 

A The range of Social Cost of Carbon (SCC) values uses several discount rates because the literature shows that the 

SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate 

rate to use in an intergenerational context (where costs and benefits are incurred by different generations). Refer to 

Section F.1 for more information. 

B This approach describes the economic concept of compensating variation, a payment of money after a change that 

would make a consumer as well off after the change as before it. A related concept, equivalent variation, estimates 
8-2 

the vehicle buyers.C If the full technology cost gets passed along to the buyer as an increase in 

price, the technology cost thus measures the primary welfare loss of the standards, including 

impacts on buyers. Increasing fuel efficiency would have to lead to other changes in the vehicles 

that buyers find undesirable for there to be additional welfare losses that are not included in the 

technology costs. 

As the 2012-2016 and 2017-2025 light-duty GHG/CAFE rules discussed, if other vehicle 

attributes are not held constant, then the technology cost estimates do not capture the losses to 

vehicle buyers associated with these changes.1 The light-duty rules also discussed other potential 

issues that could affect the calculation of the welfare impacts of these types of changes, such as 

aspects of buyers’ behavior that might affect the demand for technology investments, uncertainty 

in buyers’ investment horizons, and the rate at which truck owners trade off higher vehicle 

purchase price against future fuel savings. The agencies seek comments, including supporting 

data and quantitative analyses, of any additional impacts of the proposed standards on vehicle 

attributes and performance, or other potential aspects that could positively or negatively affect 

the welfare implications of this proposed rulemaking. 

Where possible, we identify the uncertain aspects of these economic impacts and attempt 

to quantify them (e.g., sensitivity ranges associated with quantified and monetized GHG impacts; 

range of dollar-per-ton values to monetize non-GHG health benefits; uncertainty with respect to 

learning and markups). For HD pickups and vans, the agencies explicitly analyzed the 

uncertainty surrounding its estimates of the economic impacts from requiring higher fuel 

efficiency in Chapter 7. The agencies have also examined the sensitivity of our estimates of 

savings in fuel expenditures to alternative assumptions about future fuel prices; results of this 

sensitivity analysis can be found in Chapter 8.12 of this draft RIA. NHTSA’s draft EIS also 

characterizes the uncertainty in economic impacts associated with the HD national program. For 

other impacts, however, there is inadequate information to inform a thorough, quantitative 

assessment of uncertainty. EPA and NHTSA continue to work toward developing a 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key elements of 

its analyses and we will continue to work to refine these uncertainty analyses in the future as 

time and resources permit. 

This and other chapters of the draft RIA address Section 317 of the Clean Air Act on 

economic analysis. Chapter 8.11 addresses Section 321 of the Clean Air Act on employment 

analysis. The total monetized benefits and costs of the program are summarized in Section 8.10 

for the preferred alternative and in Chapter 9 for all alternatives. 

 

the income change that would be an alternative to the change taking place. The difference between them is whether 

the consumer’s point of reference is her welfare before the change (compensating variation) or after the change 

(equivalent variation). In practice, these two measures are typically very close together. C Indeed, it is likely to be an overestimate of the loss to the consumer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, 

unless there are other changes to the vehicle due to the fuel efficiency improvements that make the vehicle less 

desirable to consumers. 
8-3 

8.2 Conceptual Framework for Evaluating Impacts 

The HD Phase 2 proposed standards would implement both the 2007 Energy 

Independence and Security Act requirement that NHTSA establish fuel efficiency standards for 

medium- and heavy-duty vehicles and the Clean Air Act requirement that EPA adopt 

technology-based standards to control pollutant emissions from motor vehicles and engines 

contributing to air pollution that endangers public health and welfare. NHTSA’s statutory 

mandate is intended to further the agency’s long-standing goals of reducing U.S. consumption 

and imports of petroleum energy to improve the nation’s energy security. 

From an economics perspective, government actions to improve our nation’s energy 

security and to protect our nation from the potential threats of climate change address 

“externalities,” or economic consequences of decisions by individuals and businesses that extend 

beyond those who make these decisions. For example, users of transportation fuels increase the 

entire U.S. economy’s risk of having to make costly adjustments due to rapid increases in oil 

prices, but these users generally do not consider such costs when they decide to consume more 

fuel. 

Similarly, consuming transportation fuel also increases emissions of greenhouse gases 

and other more localized air pollutants that occur when fuel is refined, distributed, and 

consumed. Some of these emissions increase the likelihood and severity of potential climaterelated economic damages, and others cause economic damages by adversely affecting human 

health. The need to address these external costs and other adverse effects provides a wellestablished economic rationale that supports the statutory direction given to government agencies 

to establish regulatory programs that reduce the magnitude of these adverse effects at reasonable 

costs. 

The proposed Phase 2 standards would require manufacturers of new heavy-duty 

vehicles, including trailers (HDVs), to improve the fuel efficiency of the products that they 

produce. As HDV users purchase and operate these new vehicles, they would consume 

significantly less fuel, in turn reducing U.S. petroleum consumption and imports as well as 

emissions of GHGs and other air pollutants. Thus as a consequence of the agencies’ efforts to 

meet NHTSA statutory obligations to improve U.S. energy security and EPA’s obligation to 

issue standards “to regulate emissions of the deleterious pollutant... from motor vehicles” that 

endangers public health and welfare,2

 the proposed fuel efficiency and GHG emission standards 

would also reduce HDV operators’ outlays for fuel purchases. These fuel savings are one 

measure of the proposed rule’s effectiveness in promoting NHTSA’s statutory goal of conserving 

energy, as well as EPA’s obligation to assess the cost of standards under section 202 (a) (1) and 

(2) of the Clean Air Act. Although these savings are not the agencies’ primary motivation for 

adopting higher fuel efficiency standards, these substantial fuel savings represent significant 

additional economic benefits of this proposal. 

Potential savings in fuel costs would appear to offer HDV buyers strong incentives to pay 

higher prices for vehicles that feature technology or equipment that reduces fuel consumption. 

These potential savings would also appear to offer HDV manufacturers similarly strong 

incentives to produce more fuel-efficient vehicles. Economic theory suggests that interactions 

between vehicle buyers and sellers in a normally-functioning competitive market would lead 
8-4 

HDV manufacturers to incorporate all technologies that contribute to lower net costs into the 

vehicles they offer, and buyers to purchase them willingly. Nevertheless, many readily available 

technologies that appear to offer cost-effective increases in HDV fuel efficiency (when evaluated 

over their expected lifetimes using conventional discount rates) have not been widely adopted, 

despite their potential to repay buyers’ initial investments rapidly. 

This economic situation is commonly known as the “energy efficiency gap” or “energy 

paradox.” This situation is perhaps more challenging to understand with respect to the heavyduty sector versus the light-duty vehicle sector. Unlike light-duty vehicles – which are purchased 

and used mainly by individuals and households – the vast majority of HDVs are purchased and 

operated by profit-seeking businesses for which fuel costs represent a substantial operating 

expense. Nevertheless, on the basis of evidence reviewed below, the agencies believe that a 

significant number of fuel efficiency improving technologies would remain far less widely 

adopted in the absence of these proposed standards. 

Economic research offers several possible explanations for why the prospect of these 

apparent savings might not lead HDV manufacturers and buyers to adopt technologies that 

would be expected to reduce HDV operating costs. Some of these explanations involve failures 

of the HDV market for reasons other than the externalities caused by producing and consuming 

fuel. These include situations where information about the performance of fuel economy 

technologies is incomplete, costly to obtain, or available only to one party to a transaction (or 

“asymmetrical”), as well as behavioral rigidities in either the HDV manufacturing or HDVoperating industries, such as standardized or inflexibly administered operating procedures, or 

requirements of other regulations on HDVs. Other explanations for the limited use of apparently 

cost-effective technologies that do not involve market failures include HDV operators’ concerns 

about the performance, reliability, or maintenance requirements of new technology under the 

demands of everyday use, uncertainty about the fuel savings they will actually realize, and 

questions about possible effects on carrying capacity or other aspects of HDVs’ utility. 

In the HD Phase 1 rulemaking (which, in contrast to these proposed standards, did not 

apply to trailers), the agencies raised five hypotheses that might explain this energy efficiency 

gap or paradox: 

 Imperfect information in the new vehicle market: information available to prospective 

buyers about the effectiveness of some fuel-saving technologies for new vehicles may 

be inadequate or unreliable. If reliable information on their effectiveness in reducing 

fuel consumption is unavailable or difficult to obtain, HDV buyers will 

understandably be reluctant to pay higher prices to purchase vehicles equipped with 

unproven technologies. 

 Imperfect information in the resale market: buyers in the used vehicle market may not 

be willing to pay adequate premiums for more fuel efficient vehicles when they are 

offered for resale to ensure that buyers of new vehicles can recover the remaining 

value of their original investment in higher fuel efficiency. The prospect of an 

inadequate return on their original owners’ investments in higher fuel efficiency may 

contribute to the short payback periods that buyers of new vehicles appear to 

demand.3

 
8-5 

 Principal-agent problems causing split incentives: an HDV buyer may not be directly 

responsible for its future fuel costs, or the individual who will be responsible for fuel 

costs may not participate in the HDV purchase decision. In these cases, the signal to 

invest in higher fuel efficiency normally provided by savings in fuel costs may not be 

transmitted effectively to HDV buyers, and the incentives of HDV buyers and fuel 

buyers will diverge, or be “split.” The trailers towed by heavy-duty tractors, which 

are typically not supplied by the tractor manufacturer or seller, present an obvious 

potential situation of split incentives that was not addressed in the HD Phase 1 

rulemaking, but it may apply in this rulemaking. If there is inadequate pass-through 

of price signals from trailer users to their buyers, then low adoption of fuel-saving 

technologies may result. 

 Uncertainty about future fuel cost savings: HDV buyers may be uncertain about 

future fuel prices, or about maintenance costs and reliability of some fuel efficiency 

technologies. Buyers may react to this uncertainty by implicitly discounting potential 

future savings at rates above discount rates used in this analysis. In contrast, the costs 

of fuel-saving or maintenance-reducing technologies are immediate and thus not 

subject to discounting. In this situation, potential variability about buyers’ expected 

returns on capital investments to achieve higher fuel efficiency may shorten the 

payback period – the time required to repay those investments – they demand in order 

to make them. 

 Adjustment and transactions costs: potential resistance to new technologies – 

stemming, for example, from drivers’ reluctance or slowness to adjust to changes in 

the way vehicles operate – may slow or inhibit new technology adoption. If a 

conservative approach to new technologies leads HDV buyers to adopt them slowly, 

then successful new technologies would be adopted over time without market 

intervention, but only with potentially significant delays in achieving the fuel saving, 

environmental, and energy security benefits they offer. There also may be costs 

associated with training drivers to realize potential fuel savings enabled by new 

technologies, or with accelerating fleet operators’ scheduled fleet turnover and 

replacement to hasten their acquisition of vehicles equipped with these technologies. 

Some of these explanations imply failures in the private market for fuel-saving 

technology beyond the externalities caused by producing and consuming fuel, while others 

suggest that complications in valuing or adapting to technologies that reduce fuel consumption 

may partly explain buyers’ hesitance to purchase more fuel-efficient vehicles. In either case, 

adopting this proposed rule would provide regulatory certainty and thus generate important 

economic benefits in addition to reducing externalities. 

Since the HD Phase 1 rulemaking, new research has provided further insight into 

potential barriers to adoption of fuel-saving technologies. Several studies utilized focus groups 

and interviews involving small numbers of participants, who were people with time and 

inclination to join such studies, rather than selected at random.4

 As a result, the information 

from these groups is not necessarily representative of the industry as a whole. While these 

studies cannot provide conclusive evidence about how all HDV buyers make their decisions, 

they do describe issues that arise for those that participated. 
8-6 

One common theme that emerges from these studies is the inability of HDV buyers to 

obtain reliable information about the fuel savings, reliability, and maintenance costs of 

technologies that improve fuel efficiency. In many product markets, such as consumer 

electronics, credible reviews and tests of product performance are readily available to potential 

buyers. In the trucking industry, however, the performance of fuel-saving technology is likely to 

depend on many firm-specific attributes, including the intensity of HDV use, the typical distance 

and routing of HDV trips, driver characteristics, road conditions, regional geography and traffic 

patterns. 

As a result, businesses that operate HDVs have strong preferences for testing fuel-saving 

technologies “in-house” because they are concerned that their patterns of vehicle use may lead to 

different results from those reported in published information. Businesses with less capability to 

do in-house testing often seek information from peers, yet often remain skeptical of its 

applicability due to differences in the nature of their operations. One source of imperfect 

information is the lack of availability of certain technologies from preferred suppliers. HDV 

buyers often prefer to have technology or equipment installed by their favored original 

equipment manufacturers. However, some technologies may not be available through these 

preferred sources, or may be available only as after-market installations from third parties 

(Aarnink et al. 2012, Roeth et al. 2013). 

Although these studies appear to show that information in the new HDV market is often 

limited or viewed as unreliable, the evidence for imperfect information in the market for used 

HDVs is mixed. On the one hand, some studies noted that fuel-saving technology is often not 

valued or demanded in the used vehicle market, because of imperfect information about its 

benefits, or greater mistrust of its performance among buyers in the used vehicle market than 

among buyers of new vehicles. The lack of demand might also be due to the intended use of the 

used HDV, which may not require or reward the presence of certain fuel-saving technologies. In 

other cases, however, fuel-saving technology can lead to a premium in the used market, as for 

instance to meet the more stringent requirements for HDVs operating in California. 

All of the recent research identifies split incentives, or principal-agent problems, as a 

potential barrier to technology adoption. These occur when those responsible for investment 

decisions are different from the main beneficiaries of the technology. For instance, businesses 

that own and lease trailers to HDV operators may not have an incentive to invest in trailerspecific fuel-saving technology, since they do not collect the savings from the lower fuel costs 

that result. Vernon and Meier (2012) estimate that 23 percent of trailers may be exposed to this 

kind of principal-agent problem, although they do not quantify its financial significance.5

Split incentives can also exist when the HDV driver is not responsible for paying fuel 

costs. Some technologies require additional effort, training, or changes in driving behavior to 

achieve their promised fuel savings; drivers who do not pay for fuel may be reluctant to 

undertake those changes, thus reducing the fuel-saving benefits from the perspective of the 

individual or company paying for the fuel. For instance, drivers might not consistently deploy 
8-7 

boat-tails equipped on trailers to improve vehicle aerodynamics.D Vernon and Meier also 

calculate that 91 percent of HDV fuel use is subject to this form of principal-agent problem, 

although they do not estimate how much it might reduce fuel savings to those who are paying for 

the fuel. 

The studies based on focus groups and interviews (Klemick et al. 2013, Aarnink et al. 

2012, Roeth et al. 2013) provide mixed evidence on the severity of the split-incentive problem. 

Focus groups often do identify diverging incentives between drivers and the decision-makers 

responsible for purchasing vehicles, and economics literature recognizes that this split incentive 

can be a barrier to adopting new technology. Aarnink et al. (2012) and Roeth et al. (2013) cite 

examples of split incentives involving trailers and fuel surcharges, although the latter also cites 

other examples where these same issues do not lead to split incentives. 

In an effort to minimize problems that can arise form split incentives, many businesses 

that operate HDVs also train drivers in the use of specific technologies or to modify their driving 

behavior in order to improve fuel efficiency, while some also offer financial incentives to their 

drivers to conserve fuel. All of these options can help to reduce the split incentive problem, 

although they may not be effective where it arises from different ownership of combination 

tractors and trailers. 

Uncertainty about future costs for fuel and maintenance, or about the reliability of new 

technology, also appears to be a significant obstacle that can slow the adoption of fuel-saving 

technologies. These examples illustrate the problem of uncertain or unreliable information about 

the actual performance of fuel efficiency technology discussed above. In addition, businesses 

that operate HDVs may be concerned about how reliable new technologies will prove to be on 

the road, and whether significant additional maintenance costs or equipment malfunctions that 

result in costly downtime could occur. Roeth et al. (2013) and Klemick et al. (2013) both 

document the short payback periods that HDV buyers require on their investments -- usually 

about 2 years -- which may be partly attributable to these uncertainties. 

These studies also provide some support for the view that adjustment and transactions 

costs may impede HDV buyers from investing in higher fuel efficiency. As discussed above, 

several studies note that HDV buyers are less likely to select new technology when it is not 

available from their preferred manufacturers. Some technologies are only available as aftermarket additions, which can add other costs to adopting them. 

Some studies also cite driver acceptance of new equipment or technologies as a barrier to 

their adoption. HDV driver turnover is high in the U.S., and businesses that operate HDVs are 

concerned about retaining their best drivers. Therefore, they may avoid technologies that require 

significant new training or adjustments in driver behavior. For some technologies that can be 

used to meet the proposed standards, such as automatic tire inflation systems, training costs are 

likely to be minimal. Other technologies such as stop-start systems, however, may require 

 

D Some boat-tails are being developed with technology to open them automatically when the trailer reaches a 

suitable speed, to reduce this problem. 
8-8 

drivers to adjust their expectations about vehicle operation, and it is difficult for the agencies to 

anticipate how drivers will respond to such changes.E 

In addition to these factors, the studies considered other possible explanations for HDV 

buyers’ apparent reluctance or slowness to invest in fuel-saving equipment or technology. 

Financial constraints – access to lending sources willing to finance purchases of more expensive 

vehicles – do not appear to be a problem for the medium- and large-sized businesses 

participating in Klemick et al.’s (2013) study. However, Roeth et al. (2013) noted that access to 

capital can be a significant challenge to smaller or independent businesses, and that price is 

always a concern to buyers. In general, businesses that operate HDVs face a range of competing 

uses for available capital other than investing in fuel-saving technologies, and may assign higher 

priority to these other uses, even when investing in higher fuel efficiency HDVs appears to 

promise adequate financial returns. 

Other potentially important barriers to the adoption of measures that improve fuel 

efficiency may arise from “network externalities,” where the benefits to new users of a 

technology depend on how many others have already adopted it. One example where network 

externalities seem likely to arise is the market for natural gas-fueled HDVs: the limited 

availability of refueling stations may reduce potential buyers’ willingness to purchase natural 

gas-fueled HDVs, while the small number of such HDVs in-use does not provide sufficient 

economic incentive to construct more natural gas refueling stations. 

Some businesses that operate HDVs may also be concerned about the difficulty in 

locating repair facilities or replacement parts, such as single-wide tires, wherever their vehicles 

operate. When a technology has been widely adopted, then it is likely to be serviceable even in 

remote or rural places, but until it becomes widely available, its early adopters may face 

difficulties with repairs or replacements. By accelerating the widespread adoption of these 

technologies, the proposed standards may assist in overcoming these difficulties. 

As discussed previously, the lack of availability of fuel-saving technologies from 

preferred manufactures can also be a significant barrier to adoption (Roeth et al. 2013). 

Manufacturers may be hesitant to offer technologies for which there is not strong demand, 

especially if the technologies require significant research and development expenses and other 

costs of bringing the technology to a market of uncertain demand. 

Roeth et al. (2013) also noted that it can take years, and sometimes as much as a decade, 

for a specific technology to become available from all manufacturers. Many manufacturers 

prefer to observe the market and follow other manufacturers rather than be the first to market 

with a specific technology. The “first-mover disadvantage” has been recognized in other 

research where the “first-mover” pays a higher proportion of the costs of developing technology, 

but loses the long-term advantage when other businesses follow quickly.6

 In this way, there may 

 

E The distinction between simply requiring drivers (or mechanics) to adjust their expectations and compromises in 

vehicle performance or utility is subtle. While the former may not impose significant compliance costs in the long 

run, the latter would represent additional economic costs of complying with the standard. 
8-9 

be barriers to innovation on the supply side that result in lower adoption rates of fuel-efficiency 

technology than would be optimal. 

In summary, the agencies recognize that businesses that operate HDVs are under 

competitive pressure to reduce operating costs, which should compel HDV buyers to identify and 

rapidly adopt cost-effective fuel-saving technologies. Outlays for labor and fuel generally 

constitute the two largest shares of HDV operating costs, depending on the price of fuel, distance 

traveled, type of HDV, and commodity transported (if any), so businesses that operate HDVs 

face strong incentives to reduce these costs.7,8

However, the short payback periods that buyers of new HDVs appear to require suggest 

that some combination of uncertainty about future cost savings, transactions costs, and 

imperfectly functioning markets impedes this process. Markets for both new and used HDVs 

may face these problems, although it is difficult to assess empirically the degree to which they 

actually do. Even if the benefits from widespread adoption of fuel-saving technologies exceed 

their costs, their use may remain limited or spread slowly because their early adopters bear a 

disproportionate share of those costs. In this case, the proposed standards may help to overcome 

such barriers by ensuring that these measures would be widely adopted. 

Providing information about fuel-saving technologies, offering incentives for their 

adoption, and sharing HDV operators’ real-world experiences with their performance through 

voluntary programs such as EPA’s SmartWay Transport Partnership should assist in the adoption 

of new cost-saving technologies. Nevertheless, other barriers that impede the diffusion of new 

technologies are likely to remain. Buyers who are willing to experiment with new technologies 

expect to find cost savings, but those savings may be difficult to verify or replicate. As noted 

previously, because benefits from employing these technologies are likely to vary with the 

characteristics of individual routes and traffic patterns, buyers of new HDVs may find it difficult 

to identify or verify the effects of fuel-saving technologies in their operations. Risk-averse 

buyers may also avoid new technologies out of concerns over the possibility of inadequate 

returns on their investments, or with other possible adverse impacts. 

Some HDV manufacturers may delay in investing in the development and production of 

new technologies, instead waiting for other manufacturers to bear the risks of those investments 

first. Competitive pressures in the HDV freight transport industry can provide a strong incentive 

to reduce fuel consumption and improve environmental performance. However, not every HDV 

operator has the requisite ability or interest to access and utilize the technical information, or the 

resources necessary to evaluate this information within the context of his or her own operations. 

As discussed previously, whether the technologies available to improve HDVs’ fuel 

efficiency would be adopted widely in the absence of the program is challenging to assess. To 

the extent that these technologies would be adopted in its absence, neither their costs nor their 

benefits would be attributed to the program. To account for this possibility, the agencies 

analyzed the proposed standards and the regulatory alternatives against two reference cases, or 

baselines, as described in Section X. 

The first case uses a baseline that projects some improvement in fuel efficiency for new 

trailers, but no improvement in fuel efficiency for other vehicle segments in the absence of new 
8-10 

Phase 2 standards. This first case is referred to as the less dynamic baseline, or Alternative 1a. 

The second case uses a baseline that projects some improvement in vehicle fuel efficiency for 

tractors, trailers, pickup trucks, and vans but not for vocational vehicles. This second case is 

referred to as the more dynamic baseline, or Alternative 1b. 

The agencies will continue to explore reasons for the slow adoption of readily available 

and apparently cost-effective technologies for improving fuel efficiency. We also will review 

any comments we receive on our hypotheses about its causes, as well as data or other 

information that can inform our understanding of why this situation seems to persist. 

8.3 Analysis of the Rebound Effect 

The “rebound effect” has been defined a number of ways in the literature, and one 

common definition states that the rebound effect is the increase in demand for an energy service 

when the cost of the energy service is reduced due to efficiency improvements.9,10,11 In the 

context of heavy-duty vehicles (HDVs), this can be interpreted as an increase in HDV fuel 

consumption resulting from more intensive vehicle use in response to increased vehicle fuel 

efficiency.F Although much of this vehicle use increase is likely to take the form of increases in 

the number of miles vehicles are driven, it can also take the form of increases in the loaded 

weight at which vehicles operate or changes in traffic and road conditions vehicles encounter as 

operators alter their routes and schedules in response to improved fuel efficiency. Because this 

more intensive use consumes fuel and generates emissions, it reduces the fuel savings and 

avoided emissions that would otherwise be expected to result from the increases in fuel 

efficiency this rulemaking proposes. 

 Unlike the light-duty vehicle (LDV) rebound effect, the HDV rebound effect has not 

been extensively studied. According to a 2010 HDV report published by the National Research 

Council of the National Academies (NRC)12, it is “not possible to provide a confident measure of 

the rebound effect,” yet NRC concluded that a HDV rebound effect probably exists and that, 

“estimates of fuel savings from regulatory standards will be somewhat misestimated if the 

rebound effect is not considered.” Although we believe the HDV rebound effect needs to be 

studied in more detail, we have nevertheless attempted to capture its potential effect in our 

analysis of these proposed rules, rather than to await further study. We have elected to do so 

because the magnitude of the rebound effect is an important determinant of the actual fuel 

savings and emission reductions that are likely to result from adopting stricter fuel efficiency and 

GHG emission standards. 

In our analysis and discussion below, we focus on one widely-used metric to estimate the 

rebound effect associated with all types of more intensive vehicle use, the increase in vehicle 

miles traveled (VMT) that results from improved fuel efficiency. VMT can often provide a 

reasonable approximation for all types of more intensive vehicle use. For simplicity, we refer to 

 

F We discuss other potential rebound effects in section 8.3.3, such as the indirect and economy-wide rebound effects. 

Note also that there is more than one way to measure HDV energy services and vehicle use. The agencies’ analyses 

use VMT as a measure (as discussed below); other potential measures include ton-miles, cube-miles, and fuel 

consumption. 
8-11 

this as “the VMT rebound effect” or “VMT rebound” throughout this section, although we 

acknowledge that it is an approximation to the rebound effect associated with all types of more 

intensive vehicle use. The agencies use our VMT rebound estimates to generate VMT inputs 

that are then entered into the EPA MOVES national emissions inventory model and the Volpe 

Center’s HD CAFE model. Both of these models use these inputs along with many others to 

generate projected emissions and fuel consumption changes resulting from each of the regulatory 

alternatives analyzed. 

Using VMT rebound to approximate the fuel consumption impact from all types of more 

intensive vehicle use may not be completely accurate. Many factors other than distance traveled 

– for example, a vehicle’s loaded weight – play a role in determining its fuel consumption, so it 

is also important to consider how changes in these factors are correlated with variation in vehicle 

miles traveled. Empirical estimates of the effect of weight on HDV fuel consumption vary, but 

universally show that loaded weight has some effect on fuel consumption that is independent of 

distance traveled. Therefore, the product of vehicle payload and miles traveled, which typically 

is expressed in units of “ton-miles” or “ton-kilometers”, has also been considered as a metric to 

approximate the rebound effect. Because this metric’s value depends on both payload and 

distance, it is important to note that changes in these two variables can have different impacts on 

HDV fuel consumption. This is because the fuel consumed by HDV freight transport is 

determined by several vehicle attributes including engine and accessory efficiencies, 

aerodynamic characteristics, tire rolling resistance and total vehicle mass—including payload 

carried, if any. 

Other factors such as vehicle route and traffic patterns can also affect how each of these 

vehicle attributes contributes to the overall fuel consumption of a vehicle. While it seems 

intuitive that if all of these other conditions remain constant, a vehicle driving the same route and 

distance twice will consume twice as much fuel as driving that same route once. However, 

because of the other vehicle attributes, it is less intuitive how a change in vehicle payload would 

affect vehicle fuel consumption. 

Because the factors influencing HDV VMT rebound are generally different from those 

affecting LDV VMT rebound, much of the research on the LDV sector is likely to not apply to 

the HDV sector. For example, the owners and operators of LDVs may respond to the costs and 

benefits associated with changes in their personal vehicle’s fuel efficiency very differently than a 

HDV fleet owner or operator would view the costs and benefits (e.g., profits, offering more 

competitive prices for services) associated with changes in their HDVs’ fuel efficiency. To the 

extent the response differs, such differences may be smaller for HD pickups and vans, which 

share some similarities with LDVs. As discussed in the 2010 NRC HD report, one difference 

from the LDV case is that when calculating the change in HDV costs that causes the rebound 

effect, it is more important to consider all components of HDV operating costs. The costs of 

labor and fuel generally constitute the two largest shares of HDV operating costs, depending on 

the price of petroleum, distance traveled, type of vehicle, and commodity transported (if 

any).13,14 Equipment depreciation costs associated with the purchase or lease of an HDV are 

another significant component of total operating costs (Figure 8-1). Even when HDV purchases 

involve upfront, one-time payments, HDV operators must recover the depreciation in the value 

of their vehicles resulting from their use, so this is likely to be considered as an operating cost 

they will attempt to pass on to final consumers of HDV operator services. 
8-12 

Figure 8-1 Average Truck Operation Costs 

Estimates of the impact of fuel efficiency standards on HDV VMT, and hence fuel 

consumption, should account for changes in all of these components of HDV operating costs. 

The higher the net savings in total operating costs is, the higher the expected rebound effect 

would be. Conversely, if higher HDV purchase costs outweigh future cost savings and total 

operating costs increase, HDV costs could rise, which would likely result in a decrease in HDV 

VMT. In theory, other cost changes resulting from any requirement to achieve higher fuel 

efficiency, such as changes in maintenance costs or insurance rates, should also be taken into 

account, although information on these elements of HDV operating costs is extremely limited. 

In this analysis, the agencies adapt estimates of the VMT rebound effect to project the response 

of HDV use to the estimated changes in total operating costs that result from the proposed Phase 

2 standards. 

Since businesses are profit-driven, one would expect their decisions to be based on the 

costs and benefits of different operating decisions, both in the near-term and long-term. 

Specifically, one would expect commercial HDV operators to take into account changes in 

overall operating costs per mile when making decisions about HDV use and setting rates they 
8-13 

charge for their services. If demand for those services is sensitive to the rates HDV operators 

charge, HDV VMT could change in response to the effect of higher fuel efficiency on the rates 

HDV operators charge. If demand for HDV services is insensitive to price (e.g., due to lack of 

good substitutes), however, or if changes in HDV operating costs due to the proposed standards 

are not passed on to final consumers of HDV operator services, the proposed standards may have 

a limited impact on HDV VMT. 

The following sections describe the factors affecting the magnitude of HDV VMT 

rebound; review the econometric and other evidence related to HDV VMT rebound; and 

summarize how we estimated the HDV rebound effect for this proposal. 

8.3.1 Factors Affecting the Magnitude of HDV VMT Rebound 

The magnitude and timing of HDV VMT rebound result from the interaction of many 

different factors.15 Fuel savings resulting from fuel efficiency standards may cause HDV 

operators and their customers to change their patterns of HDV use and fuel consumption in a 

variety of ways. For example, HDV operators may pass on the fuel cost savings to their 

customers by decreasing prices for shipping products or providing services, which in turn could 

stimulate more demand for those products and services (e.g., increases in freight output), and 

result in higher VMT. As discussed later in this section, HDV VMT rebound estimates 

determined via other proxy elasticities vary widely, but in no case has there been an estimate that 

fully offsets the fuel saved due to efficiency improvements (i.e., no rebound effect greater than or 

equal to 100 percent). 

If fuel cost savings are passed on to the HDV operators’ customers (e.g., logistics 

businesses, manufacturers, retailers, municipalities, utilities consumers), those customers might 

reorganize their logistics and distribution networks over time to take advantage of lower 

operating costs. For example, customers might order more frequent shipments or choose 

products that entail longer shipping distances, while freight carriers might divert some shipments 

to trucks from other shipping modes such as rail, barge or air. In addition, customers might 

choose to reduce their number of warehouses, reduce shipment rates or make smaller but more 

frequent shipments, all of which could lead to an increase in HDV VMT. Ultimately, fuel cost 

savings could ripple through the entire economy, thus increasing demand for goods and services 

shipped by trucks, and therefore increase HDV VMT due to increased gross domestic product 

(GDP). 

Conversely, if fuel efficiency standards lead to net increases in the total costs of HDV 

operation because fuel cost savings do not fully offset the increase in HDV purchase prices and 

associated depreciation costs, then the price of HDV services could rise. This is likely to spur a 

decrease in HDV VMT, and perhaps a shift to alternative shipping modes. These effects could 

also ripple through the economy and affect GDP. Note, however, that we project fuel cost 

savings will offset technology costs in our analysis supporting our proposed standards. 

It is also important to note that any increase in VMT on HDVs impacted by our proposed 

standards may be offset, to some extent, by a decrease in VMT on older HDVs. This may occur 

if lower fuel costs resulting from our standards cause multi-vehicle fleet operators to shift VMT 
8-14 

to newer, more efficient HDVs in their fleet or cause operators with newer, more efficient HDVs 

to be more successful at winning contracts than operators with older HDVs. 

Also, as discussed in Chapter 8.3.3 of this Draft RIA, the magnitude of the rebound effect 

is likely to be influenced by the extent of any market failures that affect the demand for more 

fuel efficient HDVs, as well as by HDV operators’ responses to their perception of the tradeoff 

between higher upfront HDV purchase costs versus lower but uncertain future expenditures on 

fuel. 

8.3.2 Econometric and Other Evidence Related to HDV VMT Rebound 

As discussed above, HDV VMT rebound is defined as the change in HDV VMT that 

occurs in response to an increase in HDV fuel efficiency. We are not aware of any studies that 

directly estimate this elasticityG for the U.S. This section discusses econometric analyses of 

other related elasticities that could potentially be used as a proxy for measuring HDV VMT 

rebound, as well as other analyses that may provide insight into the magnitude of HDV VMT 

rebound. 

One of the challenges to developing robust econometric analyses of HDV VMT rebound 

in the U.S. is data limitations. For example, the main source of time-series HDV fuel efficiency 

data in the U.S. is derived from aggregate fuel consumption and HDV VMT data. This may 

introduce interdependence or “simultaneity” between measures of HDV VMT and HDV fuel 

efficiency, because estimates of HDV fuel efficiency are derived partly from HDV VMT. This 

mutual interdependence makes it difficult to isolate the causal effect of HDV fuel efficiency on 

HDV VMT and to measure the response of HDV VMT to changes in HDV fuel efficiency. 

Data on other important determinants of HDV VMT, such as freight shipping rates, 

shipment sizes, HDV payloads, and congestion levels on key HDV routes is also limited, of 

questionable reliability, or unavailable. Additionally, data on HDVs and their use is usually only 

available at an aggregate level, making it difficult to evaluate potential differences in 

determinants of VMT for different types of HDV operations (e.g., long-haul freight vs. regional 

delivery operations) or vehicle sub-classes (e.g., utility vehicles vs. school buses). 

Another challenge inherent in using econometric techniques to measure the response of 

HDV VMT to HDV fuel efficiency is developing model specifications that incorporate the 

mathematical form and range of explanatory variables necessary to produce reliable estimates of 

HDV VMT rebound. Many different factors can influence HDV VMT, and the complex 

relationships among those factors should be considered when measuring the rebound effect.16 

In practice, however, most studies have employed simplified models. Many use price 

variables (e.g., price per gallon of fuel, or fuel cost per mile driven) and some measure of 

 

G Elasticity is the measurement of how responsive an economic variable is to a change in another. For example:

price elasticity of demand is a measure used in economics to show the responsiveness, or elasticity, of the quantity 

demanded of a good or service to a change in its price. More precisely, it gives the percentage change in quantity 

demanded in response to a one percent change in price.
8-15 

aggregate economic activity, such as GDP. However, some of these studies exclude potentially 

important variables such as the amount of road capacity (which affects travel speeds and may be 

related to other important characteristics of highway infrastructure), or the price or availability of 

competing forms of freight transport such as rail or barge (i.e., characteristics of the overall 

freight transport network). 

8.3.2.1 Fuel Price and Fuel Cost Elasticities 

This sub-section reviews econometric analyses of the change in HDV use (measured in 

VMT, ton-mile, or fuel consumption) in response to changes in fuel price ($/gallon) or fuel cost 

($/mile or $/ton-mile). The studies presented below attempt to estimate these elasticities in the 

HDV sector using varying approaches and data sources. 

Gately (1990) employed an econometric analysis of U.S. data for the years 1966 – 1988 

to examine the relationship between HDV VMT and average fuel cost per mile, real Gross 

National Product (GNP), and variables capturing the effects of fuel shortages in 1974 and 1979.17 

The study found no statistically significant relationship between HDV VMT and fuel cost per 

mile. Gately’s estimates of the elasticity of HDV VMT with respect to fuel cost per mile were -

0.035 with and -0.029 without the fuel shortage variables, but both estimates had large standard 

errors.  However, Gately’s study was beset by numerous statistical problems, which raise serious 

questions about the reliability of its results.H 

More recently, Matos and Silva (2011) analyzed road freight transportation sector data 

for the years 1987 – 2006 in Portugal to identify the determinants of demand for HDV freight 

transportation.18 Using a reduced-form equation relating HDV use (measured in ton-km) to 

economic activity (GDP) and the energy cost of HDV use (measured in fuel cost per ton-km 

carried), these authors estimated the elasticity of HDV ton-km with respect to energy costs to be 

-0.241. An important strength of Matos and Silva’s study is that it also estimated this same 

elasticity using a procedure that accounted for the effect of potential mutual causality between 

HDV ton-km and energy costs, and arrived at an identical value. 

Differences between HDV use and the level of highway service in Portugal and in the 

U.S. might limit the applicability of Matos and Silva’s result to the U.S. The volume and mix of 

commodities could differ between the two nations, as could the levels of congestion on their 

respective highway networks, transport distances, the extent of intermodal competition, and the 

characteristics of HDVs themselves. HDVs also operate over a more limited highway network 

in Portugal than in the United States. Unfortunately, it is difficult to anticipate how these 

differences might cause Matos and Silva’s elasticity estimates to differ from what we might find 

in the U.S. Finally, their analysis focused on HDV freight transport and did not consider non-

 

H The most important of these problems – similar historical time trends in the model’s dependent variable and the 

measures used to explain its historical variation – can lead to “spurious regressions,” or the appearance of behavioral 

relationships that are simply artifacts of the similarity (or correlation) in historical trends among the model’s 

variables. 
8-16 

freight uses of HDVs, which somewhat limits its usefulness in the analysis of this proposed 

rulemaking. 

De Borger and Mulalic (2012) examined the determinants of fuel use in the Denmark 

HDV freight transport sector for the years 1980 – 2007. The authors developed a system of 

equations that capture linkages among the demand for HDV freight transport, HDV fleet 

characteristics, and HDV fuel consumption.19 As De Borger and Mulalic state, “we precisely 

define and estimate a rebound effect of improvements in fuel efficiency in the trucking industry: 

behavioral adjustments in the industry imply that an exogenous improvement in fuel efficiency 

reduces fuel use less than proportionately. Our best estimate of this effect is approximately 10 

percent in the short run and 17 percent in the long run, so that a 1 percent improvement in fuel 

efficiency reduces fuel use by 0.90 percent (short-run) to 0.83 percent (long-run).” 

While De Borger and Mulalic capture a number of important responses that contribute to 

the rebound effect, some caution is appropriate when using their results to estimate the VMT 

rebound effect for this proposal. Like the Matos and Silva study, this study examined HDV 

activity in another country, Denmark, which has a less-developed highway system, lower levels 

of freight railroad service than the U.S., and is also likely to have a different composition of 

freight shipping activity. Although the effect of some of these differences is unclear, greater 

competition from rail shipping in the U.S. and the resulting potential for lower trucking costs to 

divert some rail freight to truck could cause the VMT rebound effect to be larger in the U.S. than 

De Borger and Mulalic’s estimate for Denmark. 

On the other hand, if freight networks are denser and commodity types are more 

homogenous in Denmark than the U.S., then shippers may have wider freight trucking options. 

If this is the case, shippers in Denmark might be more sensitive to changes in freight costs, 

which could cause the rebound effect in Denmark to be larger than the U.S. Like the Matos and 

Silva study, this analysis also focuses on freight trucking and does not consider non-freight 

HDVs (e.g. vocational vehicles). We have been unable to identify adequate data to employ De 

Borger and Mulalic’s model for the U.S. (mainly because time-series data on freight carriage by 

trucks, driver wages, and vehicle prices in the U.S. are limited). 

The Volpe National Transportation Systems Center previously has developed a series of 

travel forecasting models for the Federal Highway Administration (FHWA).20 Work conducted 

by the Volpe Center during 2009-2011 to develop the original version of FHWA’s forecasting 

model was presented in the Regulatory Impact Analysis for the HD GHG Phase 1 rule (see Table 

9-2 in that document: “Range of Rebound Effect Estimates from NHTSA Econometric 

Analysis”).21 In the analysis for the Phase 1 rule, Volpe estimated both state-level and national 

aggregate models to forecast HDV single unit and combination truck VMT that included fuel 

cost per mile as an explanatory variable. This analysis used data from 1970 – 2008 for its 
8-17 

national aggregate model, and data for the 50 individual states from 1994 – 2008 for its statelevel model.I, J 

Volpe analysts tested a large number of different specifications for its national and state 

level models that incorporated the effects of factors such as aggregate economic activity and its 

composition, the volume of U.S. exports and imports, and factors affecting the cost of producing 

trucking services (e.g., driver wage rates, truck purchase prices, and fuel costs), and the extent 

and capacity of the U.S. and states’ highway networks. Table 8-1 summarizes Volpe’s Phase 1 

estimates of the elasticity of truck VMT with respect to fuel cost per mile.K As it indicates, these 

estimates vary widely, and the estimates based on state-level and national data differ 

substantially. 

Table 8-1 Summary of Volpe Center Estimates of Elasticity of Truck VMT with Respect to Fuel Cost per 

Mile 

Truck Type National Data State Data

Short Run Long Run Short Run Long Run 

Single Unit 13-22% 28-45% 3-8% 12-21% 

Combination N/A 12-14% N/A 4-5% 

Volpe staff conducted additional analysis of the models that yielded the estimates of the 

elasticity of truck VMT with respect to fuel cost per mile reported in Table 8-1, using updated 

information on fuel costs and other variables appearing in these models, together with revised 

historical data on truck VMT provided by DOT’s Federal Highway Administration. The newlyavailable data, statistical procedures employed in conducting this additional analysis, and its 

results are summarized in materials that can be found in the docket for this rulemaking. This 

new Volpe analysis was not available at the time the agencies selected the values of the rebound 

effect for this proposal, but the agencies will consider this work and any other new work that 

becomes available in the final rule. 

Finally, EPA has contracted with Energy and Environmental Research Associates 

(EERA), LLC to analyze the HDV rebound effect for regulatory assessment purposes. Excerpts 

 

I

 Combination trucks are defined as “all [Class 7/8] trucks designed to be used in combination with one or more 

trailers with a gross vehicle weight rating over 26,000 lbs.” (AFDC, 2014; ORNL, 2013c). Single-unit trucks are 

defined as “single frame trucks that have 2–axles and at least 6 tires or a gross vehicle weight rating exceeding 

10,000 lbs.” (FHWA, 2013). 

J

 The national-level and functional class VMT forecasting models utilize aggregate time-series data for the nation as 

a whole, so that only a single measure of each variable is available during each time period (i.e., year). In contrast, 

the state-level VMT models have an additional data dimension, since both their dependent variable (VMT) and most 

explanatory variables have 51 separate observations available for each time period (one for each of the 50 states as 

well as Washington, DC). In this context, the states represent a “cross-section,” and a continuous annual sequence of 

these cross-sections is available. 

K One drawback of the fuel cost measure employed in Volpe’s models is that it is based on estimates of fuel 

economy derived from truck VMT and fuel consumption, which introduces the potential for mutual causality (or 

“simultaneity”) between VMT and the fuel cost measure and makes the effect of the latter difficult to isolate. This 

may cause their estimates of the sensitivity of truck VMT to fuel costs to be inaccurate, although the direction of any 

resulting bias is difficult to anticipate. 
8-18 

of EERA’s initial report to EPA are included in the docket and contain detailed qualitative 

discussions of the rebound effect as well as data sources that could be used in quantitative 

analysis.22 EERA also conducted follow-on quantitative analyses focused on estimating the 

impact of fuel prices on VMT and fuel consumption. We have included a working paper in the 

docket on this work.23 Note that EERA’s working paper was not available at the time the 

agencies conducted the analysis of the rebound effect for this proposal, but the agencies will 

consider this work and any other work in the final rule. 

There are reasons to be cautious about interpreting the elasticities from the studies 

reviewed in this section as a measure of VMT rebound resulting from our proposed standards. 

For example, vehicle capacity and loaded weight can vary dynamically in the HDV sector – 

possibly in response to changes in fuel price and fuel efficiency – and data on these measures are 

limited. This makes it difficult to confidently infer a direct relationship between trucking output 

(e.g., ton-miles carried) and VMT assuming a constant average payload. 

In addition, fuel cost per mile – calculated by multiplying fuel price per gallon by fuel 

efficiency in gallons per mile – and fuel price may be imprecise proxies for an improvement in 

fuel efficiency, because the response of VMT to these variables may differ. For example, if 

truck operators are more attentive to variation in fuel prices than to changes in fuel efficiency, 

then fuel price or fuel cost elasticities may overstate the true magnitude of the rebound effect. 

Similarly, there is some evidence in the literature that demand for crude petroleum and 

refined fuels is more responsive to increases than to decreases in their prices, although this 

research is not specific to the HDV sector.24 Since improved fuel efficiency typically causes fuel 

costs for HDVs to fall (and assuming fuel costs are not fully offset by increases in vehicle 

purchase prices), fuel price or cost elasticities derived from historical periods when fuel prices 

were increasing or fuel efficiency was declining may also overstate the magnitude of the rebound 

effect. An additional unknown is that HDV operators may factor fuel prices and fuel costs into 

their decision-making about rates to charge for their service differently from the way they 

incorporate initial vehicle purchase costs. 

Despite these limitations, elasticities with respect to fuel price and fuel cost can provide 

some insight into the magnitude of the HDV VMT rebound effect. 

8.3.2.2 Freight Price Elasticities 

Freight price elasticities measure the percent change in demand for freight in response to 

a percent change in freight prices, controlling for other variables that may influence freight 

demand such as GDP, the extent that goods are traded internationally, and road supply and 

capacity. This type of elasticity is only applicable to the HDV subcategory of freight trucks (i.e., 

combination tractors and vocational vehicles that transport freight). One desirable attribute of 

such measures for purposes of this analysis is that they show the response of freight trucking 
8-19 

activity to changes to trucking rates, including changes that result from fuel cost savings as well 

as increases in HDV technology costs.L 

Freight price elasticities, however, are imperfect proxies for the rebound effect in freight 

trucks for a number of reasons.25 For example, in order to apply these elasticities we must 

assume that our proposed rule’s impact on fuel and vehicle costs is fully reflected in freight rates. 

This may not be the case if truck operators adjust their profit margins or other operational 

practices (e.g., loading practices, truck driver’s wages) instead of freight rates. It is not well 

understood how trucking firms respond to different types of cost changes (e.g., changes to fuel 

costs versus labor costs). 

Freight price elasticity estimates in the literature typically measure freight activity in tons 

or ton-miles, rather than VMT. As discussed in the previous section, average truck capacity and 

payload in the HDV sector varies dynamically – possibly in response to changes in fuel price and 

fuel efficiency – and data on these measures are limited. This makes it difficult to confidently 

infer a direct relationship between ton-miles and VMT by assuming a constant average payload. 

Inferring a direct relationship between tons and VMT is even less straightforward. Additionally, 

there are significant limitations on national freight rate and freight truck ton-mile data in the 

U.S., making it difficult to confidently measure the impact of a change in freight rates on tonmiles.26 

 

L Note however that a percent change in freight activity in response to a percent change in freight rates should 

theoretically be larger than a percent change in freight activity in response to a percent change in fuel efficiency 

because fuel efficiency only impacts a portion of freight operating costs (e.g., fuel and vehicle costs, but not likely 

driver wages or highway tolls). 
8-20 

Table 8-2 An Illustration of the Impact of Various Factors on the Elasticity of Demand for HDV Freight Services With Respe

Services 

FACTOR OF VARIABILITY (1) 

SOURCE(2) LEAST 

ELASTIC (3) 

FACTORS OF VARIABILITY FOR LEAST ELASTIC VALUE (4) 

MOST ELASTIC (5) 

FACTORS OF VARIABILITY FOR MOST ELASTIC VALUE (6) 

REGION (7) 

Commodity shipped 

Abdelwahab (1998) -0.75 Construction -1.40 Textile products US A 

Friedlaender & Spady (1980) -1.00 Food Products -3.55 Electrical machinery USA Oum et al (1990) -0.41 Metallic products -1.07 Fuel oil Canada Winston (1981) -0.14 Lumber, wood and 

Furniture 

-2.96 Transport Equipment USA 

Friedlaender & Spady (1980) -0.15 Wood/wood products -5.06 Electrical machinery Southern 

USA 

Friedlaender and Spady (1981) 

-0.59 Petroleum products -1.72 Wood USA 

Campisi and Gastaldi (1996) -0.27 Petroleum products -1.37 Minerals Italy Li et al. (2011) -1.09 Other -1.30 Nature resource USA, Italy 

& India 

Bonilla (2008) -0.43 Oil and Coal -1.75 Building materials Denmark 

Distance shipped 

Beuthe et al. (2001) -1.06 <300 km -1.31 >300 km Belgium 

Winston (1981) -0.34 <900 miles (average) -1.56 >900 miles (average) USA Christidis and Leduc (2009) -0.21 < 800 km -1.15 > 1500 km EU 

Competing mode 

Rich et al (2011) -0.08 All O-D pairs -0.11 O-D Pairs w/alternatives Scandinavia 

Demand Measure 

Beuthe et al. (2001) -0.58 Tonnes -1.06 tonne-km Belgium 

Li et al. (2011) -1.02 Tonnes -1.30 tonne-km USA, Italy 

& India 

Region Abdelwahab (1998) -0.80 USA -2.18 Southwestern USA Various U.S. 

Friedlaender & Spady (1980) -1.66 Mountain-Pacific USA -5.06 Southern USA Various U.S. Li et al. (2011) -0.86 Canada -1.96 Australia Various 

Model Form Oum et al. (1992) -0.69 Translog 1.34 Log-linear Various 

Source: J.J. Winebrake et al./Energy Policy 48 (2012) 252-259
8-21 

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8-22 

Finally, freight price elasticity estimates in the literature vary significantly based on 

commodity type, length of haul, region, availability of alternative modes (discussed further in 

Section 8.3.2.3 below), and functional form of the model (i.e., log-linear, linear, translog) 

making it difficult to confidently apply any single estimate reported in the literature to 

nationwide freight activity (Table 8-2). For example, elasticity estimates for longer trips tend to 

be larger in magnitude than those for shorter trips, while demand to ship bulk commodities tends 

to be less elastic than for non-bulk commodities. 

Although these factors explain some of the differences among reported estimates, much 

of the observed variation cannot be explained quantitatively. For example, one study that 

controlled for mode, commodity class, demand elasticity measure (i.e., tons or ton-miles), model 

estimation form, country, and temporal nature of data only accounted for about half of the 

observed variation.27 

8.3.2.3 Mode Shift Case Study 

Although the total demand for freight transport is generally determined by economic 

activity, there is often the choice of shipping freight on modes other than HDVs. This is because 

the United States has extensive rail, waterway, pipeline, and air transport networks in addition to 

an extensive highway network; these networks often closely parallel each other and are often 

viable choices for freight transport for many long-distance shipping routes within the continental 

U.S. If rates for one mode decline, demand for that mode is likely to increase, and some of this 

new demand could represent shifts from other modes.M The “cross-price elasticity of demand,” 

which measures the percentage change in demand for shipping by another mode (e.g., rail) given 

a percentage change in the price of HDV freight transport services, provides a measure of the 

importance of such mode shifting. Aggregate estimates of cross-price elasticities vary widely28, 

and there is no general consensus on the most appropriate value to use for analytical purposes. 

When considering intermodal shift, one of the most relevant kinds of shipments are those 

that are competitive between rail and HDV modes. These trips generally include long-haul 

shipments greater than 500 miles, which weigh between 50,000 and 80,000 pounds (the legal 

road limit in many states). Special kinds of cargo like coal and short-haul deliveries are of less 

interest because they are generally not economically transferable between HDV and rail modes, 

so they would not be expected to shift modes except under an extreme price change. However, 

to the best of our knowledge, the total amount of freight that could potentially be subject to mode 

shifting has not been studied extensively. 

In order to explore the potential for HDV fuel efficiency standards to produce economic 

conditions that favor a mode shift from rail to HDVs, EPA commissioned GIFT Solutions, LLC 

to perform case studies on the HD GHG Phase 1 rule using a number of data sources, including 

the Commodity Flow Survey, interviews with trucking firms, and the Geospatial Intermodal 

 

M Rail lines in parts of the U.S. are thought to be currently oversubscribed. If that is the case, and new freight 

demand is already being satisfied by trucks, then this would limit the potential for intermodal freight shifts between 

trucks and rail as the result of this proposed rule. 
8-23 

Freight Transportation (GIFT) model developed by Winebrake and Corbett, which includes 

information on infrastructure and other route characteristics in the U.S.29,30 

A central assumption in the case studies was that economic conditions would favor a shift 

from rail to HDVs if either the price per ton-mile to ship a commodity by HDV, or the price to 

ship a given quantity of a commodity by HDV, became lower relative to rail transport options 

post-regulation. The results of the case studies indicate that the HD Phase 1 rule would not seem 

to create obvious economic conditions that lead to a mode shift from rail to truck, but there are a 

number of limitations and caveats to this analysis, which are discussed in the final report to EPA 

by GIFT.31,32 For example, even if trucking did not become less expensive than rail postregulation, a relative decrease in the truck versus rail rates might be enough to produce a shift, 

given that other factors could influence shippers’ decisions on modal choice. The study did not, 

however, consider these other factors such as time-of-delivery and modal capacity. As another 

example, the analysis assumes all fuel cost savings and incremental vehicle costs from the HD 

Phase 1 rule would be passed on to shippers via changes in freight rates, even though the analysis 

found some evidence that this might not occur (in two cases, the charges for shipping a truckload 

over a given route and distance were the same despite differences in payloads that should have 

been reflected in their fuel costs). Given these limitations, more work is needed in this area to 

explore the potential for mode shift in response to HD fuel efficiency standards. 

8.3.2.4 Case Study Using Freight Price Elasticities 

Cambridge Systematics, Inc. (CSI) employed a case study approach using freight price 

elasticity estimates in the literature to show several examples of the magnitude of the HDV 

rebound effect.33 In their unpublished paper commissioned by the National Research Council of 

the National Academies in support of its 2010 HDV report, CSI estimated the effect on HDV 

VMT from a net decrease in operating costs associated with fuel efficiency improvements, using 

two different technology cost and fuel savings scenarios for Class 8 combination tractors. 

Scenario 1 increased average fuel efficiency of the tractor from 5.59 miles per gallon to 6.8 miles 

per gallon, with an additional cost of $22,930 for purchasing the improved tractor. Scenario 2 

increased the average fuel efficiency to 9.1 miles per gallon, at an incremental cost of $71,630 

per tractor. Both of these scenarios were based on the technologies and targets from a report 

authored by the Northeast States Center for a Clean Air Future (NESCCAF) and International 

Council on Clean Transportation (ICCT).34 

The CSI estimates were based on a range of direct (or “own-price”) freight elasticities (-

0.5 to -1.5)35 and cross-price freight elasticities (0.35 to 0.59) 36 obtained from the literature.37 In 

their calculations, CSI assumed 142,706 million miles of tractor VMT and 1,852 billion tonmiles were affected. The tractor VMT was based on the Bureau of Transportation Statistics’ 

(BTS) estimate of highway miles for combination tractors in 2006, and the rail ton-miles were 

based on the BTS estimate of total railroad miles during 2006. This assumption is likely to 

overstate the rebound effect, since not all freight shipments occur on routes where tractors and 

rail service shipments compete directly. Nevertheless, this assumption appears to be reasonable 

in the absence of more detailed information on the percentage of total miles and ton-miles that 

are subject to potential mode shifting. 
8-24 

For CSI’s calculations, all costs except fuel costs and vehicle costs were taken from a 

2008 ATRI study.38 It is not clear from the report how the new vehicle costs were incorporated 

into CSI’s calculations of per-mile tractor operating costs. For example, neither the ATRI report 

nor the CSI report discusses assumptions about depreciation, useful lifetimes of tractors, and the 

opportunity cost of capital. 

Based on these two scenarios, CSI estimated the change in tractor VMT in response to a 

net decrease in operating costs (i.e., accounting for fuel cost and changes in tractor purchase 

costs) associated with fuel efficiency improvement of 11-31 percent for Scenario 1 and 5-16 

percent for Scenario 2, without accounting for any fuel savings from reduced rail service. When 

the fuel savings from reduced rail usage were included in the calculations, they estimated the 

change in tractor VMT in response to a net decrease in operating costs associated with fuel 

efficiency improvement would be 9-30 percent for Scenario 1, and 3-15 percent for Scenario 2. 

Note that these estimates reflect changes to tractor VMT with respect to total operating 

costs, so they should theoretically be larger than a percent change in tractor VMT with respect to 

a percent change in fuel efficiency because fuel efficiency only impacts a portion of truck 

operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls). 

CSI included caveats associated with these calculations. For example, their report states 

that freight price elasticity estimates derived from the literature are “heavily reliant on factors 

including the type of demand measures analyzed (vehicle-miles of travel, ton-miles, or tons), 

geography, trip lengths, markets served, and commodities transported.” These factors can 

increase variability in the results. Also, estimates in CSI’s study have the limitation of using 

freight price elasticities to estimate the HDV rebound effect discussed previously in Section 

IV.D.2.b. 

8.3.2.5 Simulation Model Study Using Freight Price Elasticities 

Guerrero (2014) constructs a freight simulation model of the California trucking sector to 

measure the impact of fuel saving investments and fleet management on GHG emissions.39

Rather than estimating these impacts using econometric analysis of raw data, the study uses 

values from the existing literature. Guerrero determines that “...improving the performance of 

trucking also increases the number of trips demanded because the market price also decreases. 

This ‘rebound’ effect offsets around 40-50 percent of these vehicle efficiency emission 

reductions, with 9-14 percent of the effect coming from increased pavement deterioration and 

31-36 percent coming from increased fuel combustion.” Note that to the extent that trip lengths 

also vary in response to improvements in HDV fuel efficiency, changes in the number of HDV 

trips may not exactly reflect changes in the total number of miles the vehicles are operated. 

However, these findings are based on freight price elasticities, which – as we discuss in 

Section IV.D.2.b and in the context of the CSI study above – have significant limitations. The 

study also simulates only one state’s freight network (California), which may not be a good 

representation of national activity. 
8-25 

8.3.3 How the Agencies Estimated the HDV Rebound Effect for this Proposal 

8.3.3.1 Values Used in the Phase 1 Analysis 

At the time the agencies conducted their analysis of the Phase 1 fuel efficiency and GHG 

emissions standards, the only evidence on the HDV rebound effect were the previously described 

studies from CSI and the Volpe Center.N On the basis of this evidence, the agencies chose 

rebound effects of 15 percent for vocational vehicles and 5 percent for combination tractors, both 

of which were toward the lower end of the range of values from these studies. The agencies 

found no evidence on the rebound effect for HD pickup trucks and vans, but concluded it would 

be inappropriate to use the values selected for vocational vehicles or combination tractors for 

those vehicles. Because the usage patterns of HD pickup trucks and vans can more closely 

resemble those of large light-duty vehicles, the agencies used the 10 percent rebound effect we 

had employed in our most recent light-duty rulemaking to analyze the Phase 1 standards for 2b/3 

vehicles. 

8.3.3.2 How the Agencies Analyzed VMT Rebound in this Proposal 

After considering the new evidence that has become available since the HD Phase 1 final 

rule, the agencies elected to continue using the rebound effect estimates we used previously in 

the HD Phase 1 rule in our analysis of Phase 2 proposed standards. In arriving at this decision, 

the agencies considered the shortcomings and limitations of the newly-available studies 

described previously, particularly the limited applicability of the two published studies using 

data from European nations to the U.S. context. After weighing these attributes of the more 

recent studies, the agencies concluded that we had insufficient evidence to justify revising the 

rebound effect values that were used in the Phase 1 analysis. 

In our assessment, we do not differentiate between short-run and long-run rebound 

effects, although these effects may differ. The vocational and combination truck estimates are 

based on the Volpe Center analysis presented in the HD Phase 1 rule and the case study from 

CSI. As with the HD Phase 1 rule, we did not find any literature specifically examining the HD 

pickup and truck sector. Since these vehicles are used for very different purposes than 

combination tractors and vocational vehicles, and they are more similar in use to large light-duty 

vehicles, we have chosen the light-duty rebound effect of 10 percent used in the final rule 

establishing fuel economy and GHG standards for MYs 2017-2025 light-duty vehicles in our 

analysis of HD pickup trucks and vans. 

While for this proposal, the agencies have selected to use these rebound effect values, we 

acknowledge the literature shows a wide range of rebound effect estimates. Therefore, we will 

review and consider revising these estimates in the final rule, taking into consideration all 

available data and analysis, including submissions from public commenters and new research on 

the rebound effect. 

 

N The Gately study was also available, however, the agencies were not aware of the work at the time. 
8-26 

It should be noted that the rebound estimates we have selected for our analysis represent 

the VMT impact from our proposed standards with respect to changes in the fuel cost per mile 

driven. As described previously, the HDV rebound effect should ideally be a measure of the 

change in fuel consumed with respect to the change in overall operating costs due to a change in 

HDV fuel efficiency. Such a measure would incorporate all impacts from our proposal, 

including those from incremental increases in vehicle prices that reflect costs for improving their 

fuel efficiency. Therefore, VMT rebound estimates with respect to fuel costs per mile must be 

“scaled” to apply to total operating costs, by dividing them by the fraction of total operating 

costs accounted for by fuel. 

The agencies scaled the VMT rebound calculations to total operating costs using the most 

recent information from the American Transportation Research Institute (ATRI).40 ATRI 

estimates that the average motor carrier cost per mile is $1.633 for 2012. Other elements of the 

total costs are listed below in Table 8-3. 

Table 8-3 Elements of the Operating Costs per Mile

OPERATING COST PER MILE ATRI 

Fuel Cost $0.641 

New Vehicle Cost $0.174 

Maintenance & Repair Cost $0.138 

All Other (labor, insurance, etc.) $0.680 

Total Motor Carrier Costs $1.633 

The agencies made simplifying assumptions in the VMT rebound analysis for this 

proposal, similar to the approach taken during the development of the HD GHG Phase 1 final 

rule. However, for the HD Phase 2 final rulemaking, we plan to use a more comprehensive 

approach. Due to timing constraints during the development of this proposal, the agencies did 

not have the technology package costs for each of the alternatives prior to the need to conduct 

the inventory analysis, except for the pickup truck and van category in analysis Method A. 

Therefore, the same "overall" VMT rebound values were used for Alternatives 2 through 5 (as 

discussed in Chapter 8.3.3 of this Draft RIA and analyzed in Chapter 6 of the Draft RIA), despite 

the fact that each alternative results in a different change in incremental technology and fuel 

costs. For the final rulemaking, we plan to determine VMT rebound separately for each HDV 

category and for each alternative. Tables 64 through 66 in Chapter 7 of the Draft RIA present 

VMT rebound for each HDV sector that we estimated for the preferred alternative. These VMT 

impacts are reflected in the estimates of total fuel savings and reductions in emissions of GHG 

and other air pollutants presented in Section VI and VII of this preamble for all categories. 

For the purposes of this proposal, we made several additional simplifying assumptions 

when applying the overall rebound effect to each class of truck. For example, we assumed that 

per mile vehicle costs were based on the new vehicle cost (e.g., $125,000 for the reference case 

Class 8 combination tractor, $40,000 for the reference case HD pickups, and $70,000 for the 

vocational vehicles) 41 divided by the total lifetime number of expected vehicle miles (e.g., 1.53 

million miles for a Class 8 combination tractor, 265,869 miles for 2b/3 trucks, and 306,457 miles 

for vocational vehicles). 42 We recognize that this calculation implicitly assumes that truck 

depreciation is strictly a function of usage, and that it does not take into account the opportunity 

cost of alternative uses of capital. As a result, the new vehicle cost per mile assumptions used in 
8-27 

these calculations represent a smaller percentage of total operating costs compared to the ATRI 

and CSI examples. 

The proposal assumes an “average” incremental technology cost for the alternatives, as 

shown in Table 8-4. The technology cost of the combination tractor category is based on the HD 

GHG Phase 1 technology package cost, plus $2,368 for the trailer technology cost.43 The 

technology cost for HD pickup and vans is also based on the HD GHG Phase 1 technology 

package cost.44 The agencies developed a unique vocational vehicle technology cost estimate 

because the HD GHG Phase 1 only represents the impact of tire and engine technologies. 

Table 8-4 Technology Costs Used to Determine the Rebound Effect of Each Alternative

VEHICLE CATEGORY TECHNOLOGY COST 

Combination Tractors $8,372 

HD Pickup & Vans $985 

Vocational Vehicles $1,000 

The fuel costs per mile in the analysis were calculated using EIA’s Annual Energy 

Outlook 2014’s projections for diesel fuel price.45 The average fuel economy for each category 

was determined using MOVES2014. The combination tractor-trailer fuel economy used was 

6.03 mpg, the vocational vehicle category was 9.84 mpg, and the HD pickup category was 13 

mpg. The technology effectiveness of the alternatives in the proposal was assumed to be 20 

percent for combination tractors and 15 percent for HD pickups and vocational vehicles. 

The operating costs calculated based on all of these inputs are shown below in Table 8-5. 

Table 8-5 Operating Costs for the Reference and Alternatives

OPERATING COST PER MILE REFERENCE CASE ALTERNATIVES

Fuel Cost $0.620 $0.517 

New Vehicle Cost $0.082 $0.087 

Maintenance & Repair Cost $0.138 $0.138 

All Other (labor, insurance, etc.) $0.680 $0.680 

Total Motor Carrier Costs $1.520 $1.422 

Other simplifying assumptions include the use of an average cost rather than a marginal 

cost. Some trucking firms may use a marginal cost to determine whether to increase their fuel 

usage, however we do not have any data on when firms might use a marginal cost calculation 

rather than an average cost calculation. Although using a marginal cost might be more 

appropriate for calculating the rebound effect, we do not have a methodology for calculating the 

marginal cost.46 

In the costs and benefits summarized in preamble Section IX.K, we have not explicitly 

taken into account any potential fuel savings or GHG emission reductions from the rail, air or 

water-borne shipping sectors due to mode shifting because estimates of this effect seem too 

speculative at this time. Likewise we have not taken into account any fuel savings or GHG 

emissions reductions from the potential shift in VMT from older HDVs to newer, more efficient 

HDVs. As discussed in the preamble at Section IX.E, we have found limited evidence of the 
8-28 

impact of HDV fuel efficiency standards on mode shifting and no evidence on shifting activity 

away from older HDVs to newer HDVs. 

In addition, we have not attempted to capture the extent of how current market failures 

might impact the rebound effect. The direction and magnitude of the rebound effect in the HD 

truck market are expected to vary depending on the existence and types of market failures 

affecting the fuel efficiency of the trucking fleet. If firms are already accurately accounting for 

the costs and benefits of these technologies and fuel savings, then these regulations would 

increase their net costs, because trucks would already include all cost-effective fuel saving 

technologies. As a result, the rebound effect would actually be negative and truck VMT would 

decrease as a result of these regulations. 

However, if firms are not optimizing their behavior today due to factors such as lack of 

reliable information (see preamble Section IX.A or RIA Chapter 8.2 for further discussion), it is 

more likely that truck VMT would increase. If firms recognize their lower net costs as a result of 

these regulations and pass those costs along to their customers, then the rebound effect would 

increase truck VMT. This response assumes that trucking rates include both truck purchase costs 

and fuel costs, and that the truck purchase costs included in the rates spread those costs over the 

full expected lifetime of the trucks. If those costs are spread over a shorter period, as the 

expected short payback period implies, then those purchase costs will inhibit reduction of freight 

rates, and to the extent that they do so the rebound effect will be proportionally smaller. 

As discussed in more detail in preamble Section IX.A and RIA Chapter 8.2, if there are 

market failures such as split incentives, estimating the rebound effect may depend on the nature 

of the failures. For example, if the original purchaser cannot fully recoup the higher upfront 

costs through fuel savings before selling the vehicle nor pass those costs onto the resale buyer, 

the firm would be expected to raise shipping rates. A firm purchasing the truck second-hand 

might lower shipping rates if the firm recognizes the cost savings after operating the vehicle, 

leading to an increase in VMT. Similarly, if there are split incentives and the vehicle buyer is 

not the same entity that purchases the fuel, than there would theoretically be a positive rebound 

effect. In this scenario, fuel savings would lower the net costs to the fuel purchaser, which 

would result in a larger increase in truck VMT. 

Note that while we focus on the VMT rebound effect in our analysis of this proposed 

rule, there are at least two other types of rebound effects discussed in the economics literature. 

In addition to VMT rebound effects, there are “indirect” rebound effects, which refers to the 

purchase of other goods or services (that consume energy) with the costs savings from energy 

efficiency improvements; and “economy-wide” rebound effects, which refers to the increased 

demand for energy throughout the economy in response to the reduced market price of energy 

that happens as a result of energy efficiency improvements.

Research on indirect and economy-wide rebound effects is nascent, and we have not 

identified any that attempts to quantify indirect or economy-wide rebound effects for HDVs. In 

particular, the agencies are not aware of any data to indicate that the magnitude of indirect or 
8-29 

economy-wide rebound effects, if any, would be significant for this proposed rule.O Therefore, 

we rely the same analysis of vehicle miles traveled to estimate the rebound effect in this proposal 

that we did for the HD Phase 1 rule, where we attempted to quantify only rebound effects from 

our rule that impact HDV VMT. We will review any comments received as well as any new 

work in this area that helps to assess and quantify different rebound effects that could result from 

improvements in HDV efficiency, including different types of more intensive truck usage that 

affect fuel consumption but not VMT such as loaded weight, truck routing, and scheduling. 

In order to test the effect of alternative assumptions about the rebound effect, NHTSA 

examined the sensitivity of its estimates of benefits and costs of the Phase 2 Preferred 

Alternative for HD pickups and vans to alternative assumptions about the rebound effect. While 

the main analysis for pickups and vans assumes a 10 percent rebound effect, the sensitivity 

analysis estimates the benefits and costs of the proposed standards under the assumptions of 5, 

15, and 20 percent rebound effects. 

Alternative values of the rebound effect change the estimates of benefits and costs from 

the proposed standards in three ways. First, higher values of the rebound effect increase the 

amount of additional VMT that results from improved fuel efficiency; this increases costs 

associated with additional congestion, accidents, and noise, thus increasing total costs associated 

with the proposed standards. Conversely, smaller values of the rebound effect reduce costs from 

additional congestion, accidents, and noise, so they reduce total costs of the proposed standards. 

Second, larger increases in VMT associated with higher values of the rebound effect reduce the 

value of fuel savings and related benefits (such as reductions in GHG emissions) by 

progressively larger amounts, while smaller values of the rebound effect cause smaller 

reductions in these benefits. At the same time, however, a higher rebound effect generates larger 

benefits from increased vehicle use, while a smaller rebound effect reduces these benefits 

compared to the base case. Thus the impact of alternative values of the rebound effect on total 

benefits from the proposed standards depends on the exact magnitudes of these latter two effects. 

On balance, these three effects can cause net benefits to increase or decrease for alternative 

values of the rebound effect. 

 

O One entity sought reconsideration of the Phase 1 rule on the grounds that indirect rebound effects had not been 

considered by the agencies and could negate all of the benefits of the standards. This assertion rested on an 

unsupported affidavit lacking any peer review or other indicia of objectivity. This affidavit cited only one published 

study. The study cited did not deal with vehicle efficiency, has methodological limitations (many of them 

acknowledged), and otherwise was not pertinent. EPA and NHTSA thus declined to reconsider the Phase 1 rule 

based on these speculative assertions. See generally 77 FR 51703-04 (Aug. 27, 2012) and 77 FR 51502-03 (Aug. 

24, 2012). 
8-30 

Table 8-6 Sensitivity of Preferred Alternative Impacts under Different Assumptions about Rebound Effect 

for Pickups and Vans, using 3% Discount Rate

HD PICKUPS AND VANS REBOUND EFFECT 

Main 

Analysis 

Sensitivity Cases Using 

Alternative Rebound 

Assumptions 

10 % 5 % 15 % 20 % 

Fuel Reductions (Billion Gallons) 7.8 8.2 7.5 7.1 

GHG Reductions (MMT CO2 eq) 94.1 95.7 87.2 83.0 

Total Costs ($ billion) 5.5 5.0 6.5 7.2 

Total Benefits ($ billion) 23.5 23.0 22.9 22.8 

Net Benefits ($ billion) 18.0 18.0 16.4 15.5 

Table 8-6 summarizes the impact of these alternative assumptions on fuel and GHG 

emissions savings, total costs, total benefits, and net benefits. As it indicates, using a 5 percent 

value for the rebound effect reduces benefits and costs of the proposed standards by identical 

amounts, leaving net benefits unaffected. Values of the rebound effect above 10 percent increase 

costs and reduce benefits from their values in the main analysis, thus reducing net benefits of the 

proposed standards. Nevertheless, the preferred alternative has significant net benefits under 

each alternative assumption about the magnitude of the rebound effect for HD pickups and vans. 

Thus, these alternative values of the rebound effect would not have affected the agencies’ 

selection of the preferred alternative, as that selection is based on NHTSA’s assessment of the 

maximum feasible fuel efficiency standards and EPA’s selection of appropriate GHG standards. 

8.4 Impact on Class Shifting, Fleet Turnover, and Sales 

The agencies considered two additional potential indirect effects which may lead to 

unintended consequences of the program to improve the fuel efficiency and reduce GHG 

emissions from HD trucks. The next sections cover the agencies’ qualitative discussions on 

potential class shifting and fleet turnover effects. 

8.4.1 Class Shifting 

Heavy-duty vehicles are typically configured and purchased to perform a function. For 

example, a concrete mixer truck is purchased to transport concrete, a combination tractor is 

purchased to move freight with the use of a trailer, and a Class 3 pickup truck could be 

purchased by a landscape company to pull a trailer carrying lawnmowers. The purchaser makes 

decisions based on many attributes of the vehicle, including the gross vehicle weight rating of the 

vehicle, which in part determines the amount of freight or equipment that can be carried. If the 

proposed Phase 2 standards impact either the performance of the vehicle or the marginal cost of 

the vehicle relative to the other vehicle classes, then consumers may choose to purchase a 

different vehicle, resulting in the unintended consequence of increased fuel consumption and 

GHG emissions in-use. 
8-31 

The agencies, along with the NAS panel, found that there is little or no literature which 

evaluates class shifting between trucks.47 NHTSA and EPA qualitatively evaluated the proposed 

rules in light of potential class shifting. The agencies looked at four potential cases of shifting: - 

from light-duty pickup trucks to heavy-duty pickup trucks; from sleeper cabs to day cabs; from 

combination tractors to vocational vehicles; and within vocational vehicles. 

Light-duty pickup trucks, those with a GVWR of less than 8,500 pounds, are currently 

regulated under the existing GHG/CAFE Phase 1 program and will meet GHG/CAFE Phase 2 

emission standards beginning in 2017. The increased stringency of the light-duty 2017-2025 

MY vehicle rule has led some to speculate that vehicle consumers may choose to purchase 

heavy-duty pickup trucks that are currently regulated under the HD Phase 1 program if the cost 

of the light-duty regulation is high relative to the cost to buy the larger heavy-duty pickup trucks. 

Since fuel consumption and GHG emissions rise significantly with vehicle mass, a shift from 

light-duty trucks to heavy-duty trucks would likely lead to higher fuel consumption and GHG 

emissions, an untended consequence of the regulations. Given the significant price premium of a 

heavy-duty truck (often five to ten thousand dollars more than a light-duty pickup), we believe 

that such a class shift would be unlikely even absent this program. These proposed rules would 

continue to diminish any incentive for such a class shift because they would narrow the GHG 

and fuel efficiency performance gap between light-duty and heavy-duty pickup trucks. The 

proposed regulations for the HD pickup trucks, and similarly for vans, are based on similar 

technologies and therefore reflect a similar expected increase in cost when compared to the lightduty GHG regulation. Hence, the combination of the two regulations provides little incentive for 

a shift from light-duty trucks to HD trucks. To the extent that our proposed regulation of heavyduty pickups and vans could conceivably encourage a class shift towards lighter pickups, this 

unintended consequence would in fact be expected to lead to lower fuel consumption and GHG 

emissions as the smaller light-duty pickups have significantly better fuel economy ratings than 

heavy-duty pickup trucks. 

The projected cost increases for this proposed action differ between Class 8 day cabs and 

Class 8 sleeper cabs, reflecting our expectation that compliance with the proposed standards 

would lead truck consumers to specify sleeper cabs equipped with APUs while day cab 

consumers would not. Since Class 8 day cab and sleeper cab trucks perform essentially the same 

function when hauling a trailer, this raises the possibility that the higher cost for an APU 

equipped sleeper cab could lead to a shift from sleeper cab to day cab trucks. We do not believe 

that such an intended consequence would occur for the following reasons. The addition of a 

sleeper berth to a tractor cab is not a consumer-selectable attribute in quite the same way as other 

vehicle features. The sleeper cab provides a utility that long-distance trucking fleets need to 

conduct their operations -- an on-board sleeping berth that lets a driver comply with federallymandated rest periods, as required by the Department of Transportation Federal Motor Carrier 

Safety Administration's hours-of-service regulations. The cost of sleeper trucks is already higher 

than the cost of day cabs, yet the fleets that need this utility purchase them.48 A day cab simply 

cannot provide this utility with a single driver. The need for this utility would not be changed 
8-32 

even if the additional costs to reduce greenhouse gas emissions from sleeper cabs exceed those 

reducing greenhouse gas emissions from day cabs.P

 

A trucking fleet could instead decide to put its drivers in hotels in lieu of using sleeper 

berths, and switch to day cabs. However, this is unlikely to occur in any great number, since the 

added cost for the hotel stays would far overwhelm differences in the marginal cost between day 

and sleeper cabs. Even if some fleets do opt to buy hotel rooms and switch to day cabs, they 

would be highly unlikely to purchase a day cab that was aerodynamically worse than the sleeper 

cab they replaced, since the need for features optimized for long-distance hauling would not have 

changed. So in practice, there would likely be little difference to the environment for any 

switching that might occur. Further, while our projected costs assume the purchase of an APU 

for compliance, in fact our regulatory structure would allow compliance using a near zero cost 

software utility that eliminates tractor idling after five minutes. Using this compliance approach, 

the cost difference between a Class 8 sleeper cab and day cab due to our proposed regulations is 

small. We are proposing this alternative compliance approach reflecting that some sleeper cabs 

are used in team driving situations where one driver sleeps while the other drives. In that 

situation, an APU is unnecessary since the tractor is continually being driven when occupied. 

When it is parked, it would automatically eliminate any additional idling through the shutdown 

software. If trucking businesses choose this option, then costs based on purchase of APUs may 

overestimate the costs of this program to this sector. 

Class shifting from combination tractors to vocational vehicles may occur if a customer 

deems the additional marginal cost of tractors due to the regulation to be greater than the utility 

provided by the tractor. The agencies initially considered this issue when deciding whether to 

include Class 7 tractors with the Class 8 tractors or regulate them as vocational vehicles. The 

agencies’ evaluation of the combined vehicle weight rating of the Class 7 shows that if these 

vehicles were treated significantly differently from the Class 8 tractors, then they could be easily 

substituted for Class 8 tractors. Therefore, the agencies are proposing to continue to include both 

classes in the tractor category. The agencies believe that a shift from tractors to vocational 

vehicles would be limited because of the ability of tractors to pick up and drop off trailers at 

locations which cannot be done by vocational vehicles. 

The agencies do not envision that the proposed regulatory program would cause class 

shifting within the vocational vehicle class. The marginal cost difference due to the regulation of 

vocational vehicles is minimal. The cost of LRR tires on a per tire basis is the same for all 

vocational vehicles so the only difference in marginal cost of the vehicles is due to the number of 

axles. The agencies believe that the utility gained from the additional load carrying capability of 

the additional axle would outweigh the additional cost for heavier vehicles.Q 

In conclusion, NHTSA and EPA believe that the proposed regulatory structure for HD 

trucks would not significantly change the current competitive and market factors that determine 

purchaser preferences among truck types. Furthermore, even if a small amount of shifting would 

 

P The average marginal cost difference between sleeper cabs and day cabs in the proposal is roughly $2,500. 

Q The proposed rule projects the difference in costs between the HHD and MHD vocational vehicle technologies is 

approximately $30. 
8-33 

occur, any resulting GHG impacts would likely to be negligible because any vehicle class that 

sees an uptick in sales is also being regulated for fuel efficiency. Therefore, the agencies did not 

include an impact of class shifting on the vehicle populations used to assess the benefits of the 

proposed program. 

8.4.2 Fleet Turnover and Sales Effects 

A regulation that affects the cost to purchase and/or operate trucks could affect whether a 

consumer decides to purchase a new truck and the timing of that purchase. The term pre-buy 

refers to the idea that truck purchases may occur earlier than otherwise planned to avoid the 

additional costs associated with a new regulatory requirement. Slower fleet turnover, or lowbuys, may occur when owners opt to keep their existing truck rather than purchase a new truck 

due to the incremental cost of the regulation. 

The 2010 NAS HD Report discussed the topics associated with HD truck fleet turnover. 

NAS noted that there is some empirical evidence of pre-buy behavior in response to the 2004 and 

2007 heavy-duty engine emission standards, with larger impacts occurring in response to higher 

costs.49 However, those regulations increased upfront costs to firms without any offsetting future 

cost savings from reduced fuel purchases. In summary, NAS stated that 

...during periods of stable or growing demand in the freight sector, pre-buy behavior may 

have significant impact on purchase patterns, especially for larger fleets with better 

access to capital and financing. Under these same conditions, smaller operators may 

simply elect to keep their current equipment on the road longer, all the more likely given 

continued improvements in diesel engine durability over time. On the other hand, to the 

extent that fuel economy improvements can offset incremental purchase costs, these 

impacts will be lessened. Nevertheless, when it comes to efficiency investments, most 

heavy-duty fleet operators require relatively quick payback periods, on the order of two 

to three years.50 

The proposed regulations are projected to return fuel savings to the truck owners that 

offset the cost of the regulation within a few years. The effects of the regulation on purchasing 

behavior and sales will depend on the nature of the market failures and the extent to which firms 

consider the projected future fuel savings in their purchasing decisions. 

If trucking firms account for the rapid payback, they are unlikely to strategically 

accelerate or delay their purchase plans at additional cost in capital to avoid a regulation that will 

lower their overall operating costs. As discussed in Chapter 8.2, this scenario may occur if this 

proposed program reduces uncertainty about fuel-saving technologies. More reliable 

information about ways to reduce fuel consumption allows truck purchasers to evaluate better the 

benefits and costs of additional fuel savings, primarily in the original vehicle market, but 

possibly in the resale market as well. In addition, the proposed standards are expected to lead 

manufacturers to install more fuel-saving technologies and promote their purchase; the increased 

availability and promotion may encourage sales. 

Other market failures may leave open the possibility of some pre-buy or delayed 

purchasing behavior. Firms may not consider the full value of the future fuel savings for several 
8-34 

reasons. For instance, truck purchasers may not want to invest in fuel efficiency because of 

uncertainty about fuel prices. Another explanation is that the resale market may not fully 

recognize the value of fuel savings, due to lack of trust of new technologies or changes in the 

uses of the vehicles. Lack of coordination (also called split incentives—see Chapter 8.2) 

between truck purchasers (who may emphasize the up-front costs of the trucks) and truck 

operators, who would like the fuel savings, can also lead to pre-buy or delayed purchasing 

behavior. If these market failures prevent firms from fully internalizing fuel savings when 

deciding on vehicle purchases, then pre-buy and delayed purchase could occur and could result 

in a slight decrease in the GHG benefits of the regulation. 

Thus, whether pre-buy or delayed purchase is likely to play a significant role in the truck 

market depends on the specific behaviors of purchasers in that market. Without additional 

information about which scenario is more likely to be prevalent, the agencies are not projecting a 

change in fleet turnover characteristics due to this regulation. 

Whether vehicle sales appear to be affected by the HD Phase 1 standards could provide 

some insight into the impacts of the proposed standards. At the time of this NPRM, sales data 

are not yet available for 2014 model year, the first year of the Phase 1 standards. In addition, any 

trends in sales are likely to be affected by macroeconomic conditions, which have been 

recovering since 2009-2010. As a result, it is unlikely to be possible, even when vehicle sales 

data are available, to separate the effects of the existing standards from other confounding 

factors. 

8.5 Monetized GHG Impacts 

8.5.1 Monetized CO2 Impacts - Social Cost of Carbon 

We estimate the global social benefits of CO2 emission reductions expected from the 

Proposed HD Phase 2 program using the social cost of carbon (SC-CO2) estimates presented in 

the 2013 Technical Support Document: Technical Update of the Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866 (2013 SCC TSD). We refer to these 

estimates, which were developed by the U.S., as “SC-CO2 estimates.” The SC-CO2 is a metric 

that estimates the monetary value of impacts associated with marginal changes in CO2 emissions 

in a given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is used in regulatory impact analyses to quantify the benefits of reducing CO2

emissions, or the disbenefit from increasing emissions. 

The SC-CO2 estimates used in this analysis were developed over many years, based on 

the best science available, and with input from the public. EPA and other federal agencies have 

considered the extensive public comments on ways to improve SC-CO2 estimation received via 

the notice and comment period that was part of numerous rulemakings since 2006. In addition, 

OMB’s Office of Information and Regulatory Affairs sought public comment on the approach 

used to develop the SC-CO2 estimates. The comment period ended on February 26, 2014, and 

OMB is reviewing the comments received. An interagency process that included EPA and other 

executive branch entities used three integrated assessment models (IAMs) to develop SC-CO2
8-35 

estimates and selected four global values for use in regulatory analyses. The SC-CO2 estimates 

were first released in February 2010 and updated in 2013 using new versions of each IAM. 

The SC-CO2 estimates represent global measures because of the distinctive nature of the 

climate change problem. The climate change problem is highly unusual in at least two respects. 

First, emissions of most GHGs contribute to damages around the world even when they are 

emitted in the United States. The SC-CO2 must therefore incorporate the full (global) damages 

caused by GHG emissions in order to address the global nature of the problem. Second, climate 

change presents a problem that the United States alone cannot solve. The US now operates in a 

global, highly interconnected economy such that impacts on the other side of the world now 

affect our economy. Climate damages in other countries can affect the U.S. economy; climateexacerbated conflict can require military expenditures by the U.S. All of this means that the true 

cost of climate change to U.S. is much larger than impacts that simply occur in the U.S. A global 

number is the economically appropriate reference point for collective actions to reduce climate 

change. 

A key objective in the development of the SC-CO2 estimates was to enable a consistent 

exploration of three IAMs (DICE, FUND, and PAGE) while respecting the different approaches 

to quantifying damages taken by the key modelers in the field. The selection of the three input 

parameters (equilibrium climate sensitivity, reference socioeconomic scenarios, discount rate) 

was based on an extensive review of the literature. Specifically, a probability distribution for 

climate sensitivity was specified as an input into all three models. In addition, the interagency 

group used a range of scenarios for the socio-economic parameters and a range of values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments. In DICE, these parameters are handled deterministically and 

represented by fixed constants; in PAGE, most parameters are represented by probability 

distributions. FUND was also run in a mode in which parameters were treated probabilistically. 

The use of three models and these input parameters allowed for exploration of important 

uncertainties in the way climate damages are estimated, including equilibrium climate 

sensitivity, reference socioeconomic and emission trajectories, and discount rate. As stated in 

the 2010 SCC TSD, however, key uncertainties remain as the existing models are imperfect and 

incomplete. See the 2010 SCC TSD for a complete discussion of the methods used to develop 

the estimates and the key uncertainties, and the 2013 SCC TSD for the updated estimates. 

Notably, the 2013 process did not revisit the 2010 interagency modeling decisions (e.g., 

with regard to the equilibrium climate sensitivity, reference case socioeconomic and emission 

scenarios, or discount rates). Rather, improvements in the way damages are modeled are 

confined to those that have been incorporated into the latest versions of the models by the 

developers themselves and used for analyses in peer-reviewed publications. The model updates 

that are relevant to the SC-CO2 estimates include: an explicit representation of sea level rise 

damages in the DICE and PAGE models; updated adaptation assumptions, revisions to ensure 

damages are constrained by GDP, updated regional scaling of damages, and a revised treatment 

of potentially abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in 

the DICE model; and updated damage functions for sea level rise impacts, the agricultural sector, 

and reduced space heating requirements, as well as changes to the transient response of 

temperature to the buildup of GHG concentrations and the inclusion of indirect effects of 

methane emissions in the FUND model. The 2013 SCC TSD provides complete details. 
8-36 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A report from the National 

Academies of Science (NRC, 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

The 2010 SCC TSD noted a number of limitations to the SC-CO2 analysis, including the 

incomplete way in which the IAMs capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. Current IAMs do not 

assign value to all of the important physical, ecological, and economic impacts of climate change 

recognized in the climate change literature due to a lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. The limited amount of research linking climate impacts to economic 

damages makes the modeling exercise even more difficult. These individual limitations do not 

all work in the same direction in terms of their influence on the SC-CO2 estimates, though taken 

together they suggest that the SC-CO2 estimates are likely conservative. In particular, the IPCC 

Fourth Assessment Report (2007) concluded that “It is very likely that [SC-CO2 estimates] 

underestimate the damage costs because they cannot include many non-quantifiable impacts.” 

Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 reductions to inform benefit-cost analysis. 

The new versions of the models used to estimate the values presented below offer some 

improvements in these areas, although further work is warranted. Accordingly, EPA and other 

agencies continue to engage in research on modeling and valuation of climate impacts with the 

goal to improve these estimates. The EPA and other federal agencies have considered the 

extensive public comments on ways to improve SC-CO2 estimation received via the notice and 

comment periods that were part of numerous rulemakings. In addition, OMB’s Office of 

Information and Regulatory Affairs sought public comment on the approach used to develop the 

SC-CO2 estimates (78 FR 70586; November 26, 2013). The comment period ended on February 

26, 2014, and OMB is reviewing the comments received. OMB also responded in January 2014 

to concerns submitted in a Request for Correction on the SCC TSDs.R

The four SC-CO2 estimates, updated in 2013, are as follows: $13, $46, $69, and $140 per 

metric ton of CO2 emissions in the year 2020 (2012$).S Table 8-7 presents the SC-CO2 estimates 

in selected years, rounded to two significant digits. The first three values are based on the 

average SC-CO2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-

 

R OMB’s 1/24/14 response to the petition is available at 

https://www.whitehouse.gov/sites/default/files/omb/inforeg/ssc-rfc-under-iqa-response.pdf S The SC-CO2 values have been rounded to two significant digits. Unrounded numbers from the 2013 SCC TSD 

were adjusted to 2012$ and used to calculate the CO2 benefits. 
8-37 

CO2 estimates for several discount rates are included because the literature shows that the SCCO2 is quite sensitive to assumptions about the discount rate, and because no consensus exists on 

the appropriate rate to use in an intergenerational context (where costs and benefits are incurred 

by different generations). The fourth value is the 95th percentile of the SC-CO2 from all three 

models at a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SC-CO2 distribution (representing less likely, 

but potentially catastrophic, outcomes). The SC-CO2 increases over time because future 

emissions are expected to produce larger incremental damages as economies grow and physical 

and economic systems become more stressed in response to greater climate change. 

Table 8-7 Social Cost of CO2, 2012 – 2050a (in 2012$ per Metric Ton) 

CALENDAR 

YEAR 

DISCOUNT RATE AND STATISTIC 

5% Average 3% Average 2.5% Average 3% 

95th percentile 

2012 $12 $37 $58 $100

2015 $12 $40 $61 $120

2020 $13 $46 $69 $140

2025 $15 $51 $74 $150

2030 $17 $56 $81 $170

2035 $20 $60 $86 $190

2040 $23 $66 $93 $210

2045 $26 $71 $99 $220

2050 $28 $77 $100 $240

Note: 

a

 The SC-CO2 values are dollar-year and emissions-year specific and have been rounded to two 

significant digits. Unrounded numbers from the 2013 SCC TSD were adjusted to 2012$ and used 

to calculate the CO2 benefits. 

Applying the global SC-CO2 estimates, shown in Table 8-7, to the estimated reductions 

in domestic CO2 emissions for the proposed program, we estimate the dollar value of the climate 

related benefits for each analysis year. In order to calculate the dollar value for emission 

reductions, the SC-CO2 estimate for each emissions year would be applied to changes in CO2

emissions for that year, and then discounted back to the analysis year using the same discount 

rate used to estimate the SC-CO2. For internal consistency, the annual benefits are discounted 

back to net present value terms using the same discount rate as each SC-CO2 estimate (i.e. 5 

percent, 3 percent, and 2.5 percent) rather than the discount rates of 3 percent and 7 percent used 

to derive the net present value of other streams of costs and benefits of the proposed rule.T The 

SC-CO2 estimates are presented in and the associated CO2 benefit estimates for each calendar 

year are shown in Table 8-8. 

 

T See more discussion on the appropriate discounting of climate benefits using SC-CO2 in the 2010 SCC TSD. Other 

benefits and costs of proposed regulations unrelated to CO2 emissions are discounted at the 3% and 7% rates 

specified in OMB guidance for regulatory analysis. 
8-38 

Table 8-8 Annual Upstream and Downstream CO2 Benefits and Net Present Values for the Given SC-CO2

Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B,a,b (Millions 

of 2012$) 

CALEND

AR 

YEAR 

5% 

(AVERAGE SC-CO2

= 

$12 IN 2012) 

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012) 

2.5% 

(AVERAGE SC-CO2

= 

$58 IN 2012) 

3% 

(95TH PERCENTILE = 

$105 IN 2012) 

2018 $13 $43 $65 $130

2019 $26 $91 $130 $270

2020 $40 $140 $210 $420

2021 $92 $330 $500 $1,000

2022 $170 $590 $880 $1,800

2023 $250 $860 $1,300 $2,600

2024 $400 $1,300 $1,900 $4,000

2025 $540 $1,800 $2,600 $5,500

2026 $720 $2,300 $3,400 $7,000

2027 $890 $2,900 $4,200 $8,900

2028 $1,100 $3,500 $5,100 $11,000

2029 $1,300 $4,200 $5,900 $13,000

2030 $1,500 $4,800 $6,900 $15,000

2035 $2,500 $7,400 $11,000 $23,000

2040 $3,300 $9,700 $14,000 $30,000

2050 $5,000 $14,000 $19,000 $42,000

NPVb $22,000 $100,000 $160,000 $320,000

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.

We also conducted a separate analysis of the CO2 benefits over the model year lifetimes 

of vehicles sold in the regulatory timeframe. In contrast to the calendar year analysis, the model 

year lifetime analysis shows the impacts of the program on each of these MY fleets over the 

course of their lifetimes. Full details of the inputs to this analysis can be found in RIA chapter 5. 

The CO2 benefits in the context of this MY lifetime analysis are shown in Table 8-9 for each of 

the four different social cost of carbon values. The CO2 benefits shown for each model year 

represent the net present value of the benefits in each year in the model year life discounted back 

to the first year of the model year. The same discount rate used to discount the value of damages 

from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate the net present value 

of SCC for internal consistency. 
8-39 

Table 8-9 Discounted Model Year Lifetime Upstream & Downstream CO2 Benefits for the Given SC-CO2

Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 

2012$)a,b

MODEL 

YEAR 

5% 

(AVERAGE SC-CO2 = 

$12 IN 2012) 

3% 

(AVERAGE SC-CO2 = 

$37 IN 2012) 

2.5% 

(AVERAGE SC-CO2 = 

$58 IN 2012) 

3% 

(95TH PERCENTILE =

$105 IN 2012) 

2018 $93 $380 $580 $1,100

2019 $90 $370 $570 $1,100

2020 $87 $360 $560 $1,100

2021 $520 $2,200 $3,400 $6,600

2022 $540 $2,300 $3,500 $6,900

2023 $550 $2,300 $3,600 $7,200

2024 $870 $3,700 $5,800 $11,000

2025 $900 $3,900 $6,100 $12,000

2026 $920 $4,000 $6,300 $12,000

2027 $1,100 $4,800 $7,600 $15,000

2028 $1,100 $4,800 $7,600 $15,000

2029 $1,100 $4,900 $7,700 $15,000

Sum $7,800 $34,000 $53,000 $100,000

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail. 

8.5.2 Sensitivity Analysis - Monetized Non-CO2 Impacts 

One limitation of the primary benefits analysis is that it does not include the valuation of 

non-CO2 GHG impacts (CH4, N2O, HFC-134a). Specifically, the IWG did not estimate the 

social costs of non-CO2 GHG emissions using an approach analogous to the one used to estimate 

the SC-CO2. However, EPA recognizes that non-CO2 GHG impacts associated with this 

rulemaking (e.g., net reductions in CH4, N2O, HFC-134a) would provide benefits to society. To 

understand the potential implication of omitting these benefits, EPA has conducted sensitivity 

analysis using two approaches: 1) an approximation approach based on global warming potential 

(GWP) gas comparison metrics that has been used in previous rulemakings, and 2) a set of 

recently published SC-CH4 and SC-N2O estimates that are consistent with the modeling 

assumptions underlying the SC-CO2 estimates (Marten et al. 2014). This section describes both 

approaches and presents estimates of the non-CO2 benefits of the proposed rulemaking. Other 

unquantified non-CO2 benefits are discussed in this section as well. 

8.5.2.1 Non-CO2 GHG Benefits Based on the GWP Approximation Approach 

In the absence of directly modeled estimates, one potential method for approximating the 

value of marginal non-CO2 GHG emission reductions is to convert non-CO2 emissions 

reductions to CO2-equivalents that may then be valued using the SC-CO2. Conversion to CO2-

equivalents is typically based on the global warming potentials (GWPs) for the non-CO2 gases. 
8-40 

This approach, henceforth referred to as the “GWP approach,” has been used in sensitivity 

analyses to estimate the non-CO2 benefits in previous EPA rulemakings (see US EPA 2012, 

2013).51 EPA has not presented these estimates in a main benefit-cost analysis due to the 

limitations associated with using the GWP approach to value changes in non-CO2 GHG 

emissions, and considered the GWP approach as an interim method of analysis until social cost 

estimates for non-CO2 GHGs, consistent with the SC-CO2 estimates, were developed. 

The GWP is a simple, transparent, and well-established metric for assessing the relative 

impacts of non-CO2 emissions compared to CO2 on a purely physical basis. However, as 

discussed both in the 2010 SCC TSD and previous rulemakings (e.g., US EPA 2012, 2013), the 

GWP approximation approach to measuring non-CO2 GHG benefits has several welldocumented limitations (e.g., Reilly and Richards 199352; Schmalensee 199353; Fankhauser 

199454; Marten and Newbold 201255). Gas comparison metrics, such as the GWP, are designed 

to measure the impact of non-CO2 GHG emissions relative to CO2 at a specific point along the 

pathway from emissions to monetized damages (depicted in Figure 8-2), and this point may 

differ across measures. The GWP measures the cumulative radiative forcing from a perturbation 

of a non-CO2 GHG relative to a perturbation of CO2 over a fixed time horizon. The GWP and 

other gas comparison metrics are not ideally suited for use in benefit-cost analyses to 

approximate the social cost of non-CO2 GHGs because they ignore important nonlinear 

relationships beyond radiative forcing in the chain between emissions and damages. These can 

become relevant because gases have different lifetimes and the SC-CO2 takes into account the 

fact that marginal damages from an increase in temperature are a function of existing 

temperature levels. Another limitation of gas comparison metrics for this purpose is that some 

environmental and socioeconomic impacts are not linked to all of the gases under consideration 

and will therefore be incorrectly allocated. For example, the economic impacts associated with 

increased agricultural productivity due to higher atmospheric CO2 concentrations included in the 

SC-CO2 would be incorrectly allocated to CH4 emissions with the GWP-based valuation 

approach. 

Figure 8-2 Path from GHG Emissions to Monetized Damages (Source: Marten et al., 2014) 

Furthermore, the assumptions made in estimating the GWP are not consistent with the 

assumptions underlying SC-CO2 estimates in general, and the SC-CO2 estimates more 

specifically. For example the 100 year time horizon usually used in estimating the GWP is less 

than the 300 year horizon used in developing the SC-CO2 estimates. The GWP approach also 

treats all impacts within the time horizon equally, independent of the time at which they occur. 

This is inconsistent with the role of discounting in economic analysis, which accounts for a basic 

preference for earlier over later gains in utility, the small but positive probability of a large 

global catastrophe (e.g., large asteroid collision, super volcanic eruption, pandemic), and 

expectations regarding future levels of economic growth. In the case of CH4, which has a 

relatively short lifetime compared to CO2, the temporal independence of the GWP could lead the 
8-41 

GWP approach to underestimate the SC-CH4 with a larger downward bias under higher discount 

rates (Marten and Newbold 2012).U 

Similar to the approach used in the RIA of the Final Rulemaking for 2017-2025 LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards (US EPA, 2013), EPA applies the GWP approach to estimate the benefits associated 

with reductions of CH4, N2O and HFCs in each calendar year. Under the GWP Approach, EPA 

converted CH4, N2O, and HFC-134a to CO2 equivalents using the AR4 100-year GWP for each 

gas: CH4 (25), N2O (298), and HFC-134a (1,430).56 These CO2-equivalent emission reductions 

are multiplied by the SC-CO2 estimate corresponding to each year of emission reductions. As 

with the calculation of annual benefits of CO2 emission reductions, the annual benefits of nonCO2 emission reductions based on the GWP approach are discounted back to net present value 

terms using the same discount rate as each SC-CO2 estimate. The estimated non-CO2 GHG 

benefits using the GWP approach are presented in Table 8-10 through Table 8-11. The total net 

present value of the GHG benefits for this proposed rulemaking would increase by about $760 

million to $11 billion (2012$), depending on discount rate, or roughly 3 percent if these non-CO2

estimates were included. 

 

U We note that the truncation of the time period in the GWP calculation could lead to an overestimate of SC-CH4 for 

near term perturbation years in cases where the SC-CO2 is based on a sufficiently low or steeply declining discount 

rate. 
8-42 

Table 8-10 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SCCO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the 

GWP Approach (Millions of 2012$) a,b

CALENDA

R 

YEAR 

5% 

(AVERAGE SC-CO2

= 

$12 IN 2012) 

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012) 

2.5% 

(AVERAGE SC-CO2

= 

$58 IN 2012) 

3% 

(95TH PERCENTILE 

= 

$105 IN 2012) 

2018 $0.3 $1.1 $1.6 $3.2

2019 $0.6 $2.2 $3.3 $6.6

2020 $1.0 $3.5 $5.2 $10

2021 $3.1 $11 $17 $33

2022 $6.0 $20 $30 $62

2023 $8.8 $30 $45 $93

2024 $14 $46 $68 $140

2025 $19 $62 $91 $190

2026 $25 $79 $120 $240

2027 $30 $99 $140 $300

2028 $36 $120 $170 $360

2029 $43 $140 $200 $420

2030 $49 $160 $230 $480

2035 $82 $240 $350 $760

2040 $110 $320 $440 $990

2050 $160 $440 $600 $1,400

NPVb $730 $3,400 $5,400 $11,000

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.
8-43 

Table 8-11 Annual Upstream and Downstream N2O GHG Benefits and Net Present Values for the Given SCCO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using 

the GWP Approach (Millions of 2012$) a,b

CALENDA

R 

YEAR 

5% 

(AVERAGE SC-CO2

= 

$12 IN 2012) 

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012) 

2.5% 

(AVERAGE SC-CO2

= 

$58 IN 2012) 

3% 

(95TH PERCENTILE 

= 

$105 IN 2012) 

2018 $0.0 $0.0 $0.1 $0.2

2019 $0.0 $0.1 $0.2 $0.3

2020 $0.0 $0.2 $0.2 $0.5

2021 $0.1 $0.4 $0.5 $1.1

2022 $0.2 $0.6 $1.0 $1.9

2023 $0.3 $0.9 $1.4 $2.8

2024 $0.4 $1.4 $2.1 $4.4

2025 $0.6 $2.0 $2.9 $6.0

2026 $0.8 $2.6 $3.7 $7.8

2027 $1.0 $3.2 $4.7 $10

2028 $1.2 $3.9 $5.7 $12

2029 $1.5 $4.6 $6.6 $14

2030 $1.6 $5.3 $7.7 $16

2035 $2.8 $8.3 $12 $26

2040 $3.8 $11 $15 $34

2050 $5.6 $15 $21 $47

NPVb $25 $120 $180 $360

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.
8-44 

Table 8-12 Annual Upstream and Downstream HFC-134a GHG Benefits and Net Present Values for the 

Given SC-CO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method 

B, using the GWP Approach (Millions of 2012$) a,b

CALENDA

R 

YEAR 

5% 

(AVERAGE SC-CO2

= 

$12 IN 2012) 

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012) 

2.5% 

(AVERAGE SC-CO2

= 

$58 IN 2012) 

3% 

(95TH PERCENTILE 

= 

$105 IN 2012) 

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.2 $0.8 $1.3 $2.6

2022 $0.5 $1.7 $2.6 $5.3

2023 $0.8 $2.7 $4.0 $8.1

2024 $1.1 $3.7 $5.4 $11

2025 $1.4 $4.7 $6.9 $14

2026 $1.8 $5.9 $8.6 $18

2027 $2.2 $7.1 $10 $22

2028 $2.5 $8.3 $12 $25

2029 $3.0 $10 $14 $29

2030 $3.4 $11 $16 $34

2035 $5.2 $15 $22 $48

2040 $6.1 $18 $25 $56

2050 $8.4 $23 $31 $71

NPVb $44 $200 $320 $630

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.

8.5.2.2 Non-CO2 GHG Benefits Based on Directly Modeled Estimates 

Several researchers have directly estimated the social cost of non-CO2 emissions using 

integrated assessment models (IAMs), though the number of such estimates is small compared to 

the large number of SC-CO2 estimates available in the literature. As discussed in previous RIAs 

(e.g., EPA 2012), there is considerable variation among these published estimates in the models 

and input assumptions they employ. These studies differ in the emission perturbation year, 

employ a wide range of constant and variable discount rate specifications, and consider a range 

of baseline socioeconomic and emissions scenarios that have been developed over the last 20 

years. However, none of the other published estimates of the social cost of non-CO2 GHG are 

consistent with the SC-CO2 estimates, and most are likely underestimates due to changes in the 

underlying science since their publication. 

Recently, a paper by Marten et al. (2014) provided the first set of published SC-CH4 and 

SC-N2O estimates that are consistent with the modeling assumptions underlying the SC-CO2.

57

Specifically, the estimation approach of Marten et al. (2014) used the same set of three IAMs, 

five socioeconomic-emissions scenarios, equilibrium climate sensitivity distribution, and three 
8-45 

constant discount rates used to develop the SC-CO2 estimates. Marten et al. also used the same 

aggregation method as the SC-CO2 to distill the 45 distribution of the SC-CH4 and SC-N2O 

produced for each emissions year into four estimates: the mean across all models and scenarios 

using a 2.5 percent, 3 percent, and 5 percent discount rate, and the 95th percentile of the pooled 

estimates from all models and scenarios using a 3 percent discount rate. Marten et al. used 

lifetimes and radiative efficiencies for CH4 and N2O based on the IPCC AR4 values. The 

authors also adjusted the CH4 radiative efficiency for CH4 to account for additional radiative 

effects due to increases in tropospheric ozone and stratospheric water vapor resulting from 

methane emissions, using the same adjustment used by in IPCC AR4 for calculating GWP 

values. Using this approach, Marten et al. (2014) finds that the GWP approach provides 

conservative estimates for the benefits of marginal reductions in CH4 and N2O emissions. 

The resulting SC-CH4 and SC-N2O estimates are presented in Table 8-13. More detailed 

results and a comparison to other published estimates can be found in Marten et al. (2014). The 

tables do not include HFC-134a because EPA is unaware of analogous estimates. 

Table 8-13 Social Cost of CH4 and N2O, 2012 – 2050a [2012$ per metric ton] 

(Source: Marten et al. (2014)) 

YEAR SC-CH4 SC-N2O 

5% 

Average 

3% 

Average 

2.5% 

Average 

3% 

95th percentile 

5% 

Average 

3% 

Average 

2.5% 

Average 

3% 

95th

percentile 

2012 $440 $1000 $1400 $2800 $4000 $14000 $20000 $37000

2015 500 1200 1500 3100 4400 15000 22000 39000

2020 590 1300 1700 3500 5200 16000 24000 44000

2025 710 1500 19000 4100 6000 18000 27000 50000

2030 840 1700 2300 4600 7000 20000 29000 55000

2035 990 2000 2500 5400 8100 23000 32000 61000

2040 1200 2300 2800 6000 9300 25000 35000 67000

2045 1300 2500 3100 6800 11000 27000 38000 73000

2050 1500 2700 3300 7400 12000 29000 41000 80000

Note: 

a

 The values are emissions-year specific and have been rounded to two significant digits, as shown in Marten et al. 

(2014). These rounded numbers were used to calculate the GHG benefits.

The application of directly modeled estimates from Marten et al. (2014) to benefit-cost 

analysis of a regulatory action is analogous to the use of the SC-CO2 estimates. Specifically, the 

SC-CH4 and SC-N2O estimates in Table 8-13 are used to monetize the benefits of changes in 

CH4 and N2O emissions expected as a result of the proposed rulemaking. Forecast changes in 

CH4 and N2O emissions in a given year resulting from the regulatory action are multiplied by the 

SC-CH4 and SC-N2O estimate for that year, respectively. To obtain a present value estimate, the 

monetized stream of future non-CO2 benefits are discounted back to the analysis year using the 

same discount rate used to estimate the social cost of the non-CO2 GHG emission changes. 

The CH4 and N2O benefits based on Marten et al. (2014) are presented for each calendar 

year in Table 8-14. Including these benefits would increase the total net present value of the 

GHG benefits for this proposed rulemaking by about $1.5 billion to $12 billion (2012$), or 

roughly 4 percent to 7 percent, depending on discount rate. 
8-46 

Table 8-14 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SCCH4 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the 

Directly Modeled Approach, Calendar Year Analysis (Millions of 2012$) a,b

CALENDAR 

YEAR 

5% 

(AVERAGE SC-CH4

= 

$440 IN 2012) 

3% 

(AVERAGE SC-CH4

= 

$1000 IN 2012) 

2.5% 

(AVERAGE SC-CH4

= 

$1400 IN 2012) 

3% 

(95TH PERCENTILE 

= 

$2800 IN 2012) 

2018 $0.6 $1.3 $1.6 $3.3

2019 $1.1 $2.6 $3.4 $6.8

2020 $1.8 $3.9 $5.2 $10

2021 $5.8 $13 $17 $35

2022 $11 $24 $31 $65

2023 $17 $35 $49 $97

2024 $26 $56 $72 $150

2025 $35 $74 $95 $200

2026 $46 $99 $130 $260

2027 $57 $120 $150 $320

2028 $69 $140 $190 $390

2029 $82 $170 $220 $460

2030 $95 $190 $260 $520

2035 $160 $330 $400 $870

2040 $230 $430 $540 $1,200

2050 $350 $620 $770 $1,700

NPVb $1,500 $4,600 $6,400 $12,000

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-CH4 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CH4 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CH4

for internal consistency. 
8-47 

Table 8-15 Annual Upstream and Downstream N2O GHG Benefits and Net Present Values for the Given SCN2O Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using 

the Directly Modeled Approach, Calendar Year Analysis (Millions of 2012$) a,b

CALENDAR 

YEAR 

5% 

(AVERAGE SC-N2O 

= 

$4000 IN 2012) 

3% 

(AVERAGE SC-N2O = 

$14000 IN 2012) 

2.5% 

(AVERAGE SC-N2O = 

$20000 IN 2012) 

3% 

(95TH PERCENTILE =

$37000 IN 2012) 

2018 $0.0 $0.1 $0.1 $0.2

2019 $0.0 $0.1 $0.2 $0.3

2020 $0.1 $0.2 $0.3 $0.5

2021 $0.1 $0.4 $0.6 $1.2

2022 $0.3 $0.8 $1.1 $2.1

2023 $0.4 $1.1 $1.7 $3.1

2024 $0.6 $1.8 $2.5 $4.7

2025 $0.8 $2.4 $3.5 $6.5

2026 $1.0 $3.0 $4.5 $8.4

2027 $1.3 $4.0 $5.8 $11

2028 $1.6 $4.8 $6.9 $13

2029 $1.9 $5.8 $8.2 $15

2030 $2.2 $6.5 $9.3 $18

2035 $3.7 $10 $15 $28

2040 $5.2 $14 $19 $37

2050 $7.9 $20 $27 $53

NPVb $34 $150 $230 $400

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The SC-N2O values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (N2O at 5, 3, and 2.5 percent) is used to calculate discounted values of N2O for 

internal consistency. 

As illustrated above, compared to the use of directly modeled estimates the GWP-based 

approximation approach underestimates the climate benefits of the CH4 emission reductions by 

12 percent to 52 percent, and the climate benefits of N2O reductions by 10 percent to 26 percent, 

depending on the discount rate assumption.

8.5.2.3 Additional non-CO2 GHGs Co-Benefits 

In determining the relative social costs of the different gases, the Marten et al. (2014) 

analysis accounts for differences in lifetime and radiative efficiency between the non-CO2 GHGs 

and CO2. The analysis also accounts for radiative forcing resulting from methane’s effects on 

tropospheric ozone and stratospheric water vapor, and for at least some of the fertilization effects 

of elevated carbon dioxide concentrations. However, there exist several other differences 

between these gases that have not yet been captured in this analysis, namely the non-radiative 

effects of methane-driven elevated tropospheric ozone levels on human health, agriculture, and 

ecosystems, and the effects of carbon dioxide on ocean acidification. Inclusion of these 

additional non-radiative effects would potentially change both the absolute and relative value of 

the various gases. 
8-48 

Of these effects, the human health effect of elevated tropospheric ozone levels resulting 

from methane emissions is the closest to being monetized in a way that would be comparable to 

the SCC. Premature ozone-related cardiopulmonary deaths resulting from global increases in 

tropospheric ozone concentrations produced by the methane oxidation process have been the 

focus of a number of studies over the past decade (e.g., West et al. 200658). Recent studies have 

produced an estimate of a monetized benefit of methane emissions reductions, with results on the 

order of $1000 per metric ton of CH4 emissions reduced (Anenberg et al. 201259; Shindell et al. 

201260), an estimate similar in magnitude to the climate benefits of CH4 reductions estimated by 

the Marten et al. or GWP methods. However, though EPA is continuing to monitor this area of 

research as it evolves, EPA is not applying them for benefit estimates at this time. 

8.6 Quantified and Monetized Non-GHG Health and Environmental Impacts 

This section analyzes the economic benefits from reductions in health and environmental 

impacts resulting from non-GHG emission reductions that can be expected to occur as a result of 

the proposed Phase 2 standards. CO2 emissions are predominantly the byproduct of fossil fuel 

combustion processes that also produce criteria and hazardous air pollutant emissions. The 

vehicles that are subject to the proposed standards are also significant sources of mobile source 

air pollution such as direct PM, NOX, VOCs and air toxics. The proposed standards would affect 

exhaust emissions of these pollutants from vehicles and would also affect emissions from 

upstream sources that occur during the refining and distribution of fuel. Changes in ambient 

concentrations of ozone, PM2.5, and air toxics that would result from the proposed standards are 

expected to affect human health by reducing premature deaths and other serious human health 

effects, as well as other important improvements in public health and welfare. 

It is important to quantify the health and environmental impacts associated with the 

proposed standards because a failure to adequately consider these ancillary impacts could lead to 

an incorrect assessment of their costs and benefits. Moreover, the health and other impacts of 

exposure to criteria air pollutants and airborne toxics tend to occur in the near term, while most 

effects from reduced climate change are likely to occur only over a time frame of several decades 

or longer. 

EPA typically quantifies and monetizes the health and environmental impacts related to 

both PM and ozone in its regulatory impact analyses (RIAs) when possible. However, EPA was 

unable to do so in time for this proposal. EPA attempts to make emissions and air quality 

modeling decisions early in the analytical process so that we can complete the photochemical air 

quality modeling and use that data to inform the health and environmental impacts analysis. 

Time constraints precluded the agency from completing this work in time for the proposal. 

Instead, EPA has applied PM-related benefits per-ton values to its estimated emission reductions 

as an interim approach to estimating the PM-related benefits of the proposal.61,V EPA also 

 

V See also: http://www.epa.gov/airquality/benmap/sabpt.html. The current values available on the webpage have 

been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated 

values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf

(accessed September 9, 2014). 
8-49 

characterizes the health and environmental impacts that will be quantified and monetized for the 

final rulemaking. 

This section is split into two sub-sections: the first presents the benefits-per-ton values 

used to monetize the benefits from reducing population exposure to PM associated with the 

proposed standards; the second explains what PM- and ozone-related health and environmental 

impacts EPA will quantify and monetize in the analysis for the final rule. EPA bases its analyses 

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

of the monetary values of public health and welfare improvements, and is generally consistent 

with benefits analyses performed for the analysis of the final Tier 3 Vehicle Rule,62 the final 

2012 PM NAAQS Revision,63 and the final 2017-2025 Light Duty Vehicle GHG Rule.64 

Though EPA is characterizing the changes in emissions associated with toxic pollutants, 

we are not able to quantify or monetize the human health effects associated with air toxic 

pollutants for either the proposal or the final rule analyses (see Chapter 8.6.2.3 for more 

information). Please refer to Chapter 5 for more information about the air toxics emissions 

impacts associated with the proposed standards. 

8.6.1 Economic Value of Reductions in Criteria Pollutants 

As described in Chapter 5, the proposed standards would reduce emissions of several 

criteria and toxic pollutants and their precursors. In this analysis, EPA estimates the economic 

value of the human health benefits associated with the resulting reductions in PM2.5 exposure. 

Due to analytical limitations with the benefit per-ton method, this analysis does not estimate 

benefits resulting from reductions in population exposure to other criteria pollutants such as 

ozone.W Furthermore, the benefits per-ton method, like all air quality impact analyses, does not 

monetize all of the potential health and welfare effects associated with reduced concentrations of 

PM2.5. 

This analysis uses estimates of the benefits from reducing the incidence of the specific 

PM2.5-related health impacts described below. These estimates, which are expressed per ton of 

PM2.5-related emissions eliminated by the proposed rule, represent the total monetized value of 

human health benefits (including reduction in both premature mortality and premature 

morbidity) from reducing each ton of directly emitted PM2.5, or its precursors (SO2 and NOx), 

from a specified source. Ideally, the human health benefits would be estimated based on changes 

in ambient PM2.5 as determined by full-scale air quality modeling. However, the length of time 

needed to prepare the necessary emissions inventories, in addition to the processing time 

associated with the modeling itself, has precluded us from performing air quality modeling for 

this proposal. We will conduct this modeling for the final rule. 

The dollar-per-ton estimates used in this analysis are provided in Table 8-16. As the 

table indicates, these values differ among pollutants, and also depend on their original source, 

 

W The air quality modeling that underlies the PM-related benefit per ton values also produced estimates of ozone 

levels attributable to each sector. However, the complex non-linear chemistry governing ozone formation prevented 

EPA from developing a complementary array of ozone benefit per ton values. This limitation notwithstanding, we 

anticipate that the ozone-related benefits associated with reducing emissions of NOX and VOC could be substantial. 
8-50 

because emissions from different sources can result in different degrees of population exposure 

and resulting health impacts. In the summary of costs and benefits, Chapter 8.10, EPA presents 

the monetized value of PM-related improvements associated with the proposal. 

Table 8-16 Benefits-per-ton Values (thousands, 2012$) a

YEARC ON-ROAD MOBILE SOURCES UPSTREAM SOURCESD 

Direct PM2.5 SO2 NOX Direct PM2.5 SO2 NOX

Estimated Using a 3 Percent Discount Rateb

2016 $380-$850 $20-$45 $7.7-$18 $330-$750 $69-$160 $6.8-$16 

2020 $400-$910 $22-$49 $8.1-$18 $350-$790 $75-$170 $7.4-$17 

2025 $440-$1,000 $24-$55 $8.8-$20 $390-$870 $83-$190 $8.1-$18 

2030 $480-$1,100 $27-$61 $9.6-$22 $420-$950 $91-$200 $8.7-$20 

Estimated Using a 7 Percent Discount Rateb

2016 $340-$770 $18-$41 $6.9-$16 $290-$670 $63-$140 $6.2-$14 

2020 $370-$820 $20-$44 $7.4-$17 $320-$720 $67-$150 $6.6-$15 

2025 $400-$910 $22-$49 $8.0-$18 $350-$790 $75-$170 $7.3-$17 

2030 $430-$980 $24-$55 $8.6-$20 $380-$850 $81-$180 $7.9-$18 

Notes: 

a The benefit-per-ton estimates presented in this table are based on a range of premature mortality estimates derived 

from the ACS study (Krewski et al., 2009) and the Six-Cities study (Lepeule et al., 2012). b

 The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the 

valuation of premature mortality to account for a twenty-year segmented cessation lag. c

 Benefit-per-ton values were estimated for the years 2016, 2020, 2025 and 2030. We hold values constant for 

intervening years (e.g., the 2016 values are assumed to apply to years 2017-2019; 2020 values for years 2021-2024; 

2030 values for years 2031 and beyond). 

d

 We assume for the purpose of this analysis that “upstream emissions” are most closely associated with refinery 

sector benefit per-ton values. The majority of upstream emission reductions associated with the NPRM are related 

to domestic onsite refinery emissions and domestic crude production. While upstream emissions also include 

storage and transport sources, as well as upstream refinery sources, we have chosen to simply apply the refinery 

values. Full-scale air quality modeling, and the associated benefits analysis, will include upstream emissions from 

all sources in the FRM. 

The benefit per-ton technique has been used in previous analyses, including EPA’s 2017-

2025 Light-Duty Vehicle Greenhouse Gas Rule,65 the Reciprocating Internal Combustion Engine 

rules,66,67 and the Residential Wood Heaters NSPS.68 Table 8-17 shows the quantified PM2.5-

related co-benefits captured in those benefit per-ton estimates, as well as unquantified effects the 

benefits per-ton estimates are unable to capture. 

Table 8-17 Human Health and Welfare Effects of PM2.5 

POLLUTANT QUANTIFIED AND MONETIZED 

IN PRIMARY ESTIMATES 

UNQUANTIFIED EFFECTS 

CHANGES IN: 

PM2.5 Adult premature mortality 

Acute bronchitis 

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) 

Infant mortality 

Chronic and subchronic bronchitis cases 

Strokes and cerebrovascular disease 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

Visibility 

Household soiling 
8-51 

Consistent with the cost-benefit analysis that accompanied the 2012 PM NAAQS 

revision, the benefits estimates utilize the concentration-response functions as reported in the 

epidemiology literature. X,69 To calculate the total monetized impacts associated with quantified 

health impacts, EPA applies values derived from a number of sources. For premature mortality, 

EPA applies a value of a statistical life (VSL) derived from the mortality valuation literature. 

For certain health impacts, such as respiratory-related ailments, EPA applies willingness-to-pay 

estimates derived from the valuation literature. For the remaining health impacts, EPA applies 

values derived from current cost-of-illness and/or wage estimates. 

Readers interested in reviewing the complete methodology for creating the benefit-perton estimates used in this analysis can consult EPA’s “Technical Support Document: Estimating 

the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors.”Y Readers can also refer to 

Fann et al. (2012)70 for a detailed description of the benefit-per-ton methodology. 

As described in the documentation for the benefit per-ton estimates cited above, national 

per-ton estimates were developed for selected pollutant/source category combinations. The perton values calculated therefore apply only to tons reduced from those specific pollutant/source 

combinations (e.g., NO2 emitted from on-road mobile sources; direct PM emitted from electricity 

generating units). Our estimate of PM2.5 benefits is therefore based on the total direct PM2.5 and 

PM-related precursor emissions controlled by sector and multiplied by each per-ton value. 

As Table 8-17indicates, EPA projects that the per-ton values for reducing emissions of 

non-GHG pollutants from both vehicle use and upstream sources such as fuel refineries.Z These 

projected increases reflect rising income levels, which increase affected individuals’ willingness 

to pay for reduced exposure to health threats from air pollution.AA They also reflect future 

population growth and increased life expectancy, which expands the size of the population 

exposed to air pollution in both urban and rural areas, especially among older age groups with 

the highest mortality risk.BB 

 

X Although we summarize the main issues in this chapter, we encourage interested readers to see the benefits chapter 

of the RIA that accompanied the PM NAAQS for a more detailed description of recent changes to the quantification 

and monetization of PM benefits. Note that the cost-benefit analysis was prepared solely for purposes of fulfilling 

analysis requirements under Executive Order 12866 and was not considered, or otherwise played any part, in the 

decision to revise the PM NAAQS. 

Y For more information regarding the updated values, see: 

http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed 

September 9, 2014). 

Z As we discuss in the emissions chapter (Chapter 5), the rule would yield emission reductions from upstream 

refining and fuel distribution due to decreased petroleum consumption. 

AA The issue is discussed in more detail in the 2012 PM NAAQS RIA, Section 5.6.8. See U.S. Environmental 

Protection Agency. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality 

Standards for Particulate Matter, Health and Environmental Impacts Division, Office of Air Quality Planning and 

Standards, EPA-452-R-12-005, December 2012. Available on the internet: 

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf BB For more information about EPA’s population projections, please refer to the following: 

http://www.epa.gov/air/benmap/models/BenMAPManualAppendicesAugust2010.pdf (See Appendix K) 
8-52 

The benefit-per-ton estimates are subject to a number of assumptions and uncertainties: 

 The benefit-per-ton estimates used here reflect specific geographic patterns of 

emissions reductions and specific air quality and benefits modeling assumptions 

associated with the derivation of those estimates (see the TSD describing the 

calculation of the national benefit-per-ton estimates). 71,CC Consequently, these 

estimates may not reflect local variability in population density, meteorology, 

exposure, baseline health incidence rates, or other local factors associated with the 

current analysis. Therefore, use of these benefit-per-ton values to estimate nonGHG benefits may lead to higher or lower benefit estimates than if these benefits 

were calculated based on direct air quality modeling. EPA will conduct full-scale 

air quality modeling for the final rulemaking in an effort to capture this 

variability. 

 This analysis assumes that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality. This is an 

important assumption, because PM2.5 produced via transported precursors emitted 

from stationary sources may differ significantly from direct PM2.5 released from 

diesel engines and other industrial sources. The PM ISA, which was twice 

reviewed by SAB-CASAC, concluded that “many constituents of PM2.5 can be 

linked with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to 

specific outcomes”.72 PM composition and the size distribution of those particles 

vary within and between areas due to source characteristics. Any specific location 

could have higher or lower contributions of certain PM species and other 

pollutants than the national average, meaning potential regional differences in 

health impact of given control strategies. Depending on the toxicity of each PM 

species reduced by the proposed standards, assuming equal toxicity could over or 

underestimate benefits. 

 This analysis assumes that the health impact function for fine particles is linear 

within the range of ambient concentrations under consideration. Thus, the 

estimates include health benefits from reducing fine particles in areas with varied 

concentrations of PM2.5, including regions that are in attainment with the fine 

particle standard. The direction of bias that assuming linear-no threshold model 

or alternative model introduces depends upon the “true” functional from of the 

relationship and the specific assumptions and data in a particular analysis. For 

example, if the true function identifies a threshold below which health effects do 

not occur, benefits may be overestimated if a substantial portion of those benefits 

were estimated to occur below that threshold. Alternately, if a substantial portion 

of the benefits occurred above that threshold, the benefits may be underestimated 

because an assumed linear no-threshold function may not reflect the steeper slope 

 

CC See also: http://www.epa.gov/airquality/benmap/sabpt.html. The current values available on the webpage have 

been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated 

values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf

(accessed September 9, 2014). 
8-53 

above that threshold to account for all health effects occurring above that 

threshold. 

 There are several health benefit categories that EPA was unable to quantify due to 

limitations associated with using benefits-per-ton estimates, several of which 

could be substantial. Because the NOX and VOC emission reductions associated 

with this proposal are also precursors to ozone, reductions in NOX and VOC 

would also reduce ozone formation and the health effects associated with ozone 

exposure. Unfortunately, ozone-related benefits-per-ton estimates do not exist 

due to issues associated with the complexity of the atmospheric air chemistry and 

nonlinearities associated with ozone formation. The PM-related benefits-per-ton 

estimates also do not include any human welfare or ecological benefits. Please 

refer to Chapter 8.6.2for a description of the agency’s plan to quantify and 

monetize the PM- and ozone-related health impacts for the FRM and a description 

of the unquantified co-pollutant benefits associated with this rulemaking. 

 There are many uncertainties associated with the health impact functions that 

underlie the benefits-per-ton estimates. These include: within-study variability 

(the precision with which a given study estimates the relationship between air 

quality changes and health effects); across-study variation (different published 

studies of the same pollutant/health effect relationship typically do not report 

identical findings and in some instances the differences are substantial); the 

application of concentration-response functions nationwide (does not account for 

any relationship between region and health effect, to the extent that such a 

relationship exists); extrapolation of impact functions across population (we 

assumed that certain health impact functions applied to age ranges broader than 

that considered in the original epidemiological study); and various uncertainties in 

the concentration-response function, including causality and thresholds. These 

uncertainties may under- or over-estimate benefits. 

 EPA has investigated methods to characterize uncertainty in the relationship 

between PM2.5 exposure and premature mortality. EPA’s final PM2.5 NAAQS 

analysis provides a more complete picture about the overall uncertainty in PM2.5

benefits estimates. For more information, please consult the PM2.5 NAAQS 

RIA.73

 The benefit-per-ton unit values used in this analysis incorporate projections of key 

variables, including atmospheric conditions, source level emissions, population, 

health baselines, incomes, and technology. These projections introduce some 

uncertainties to the benefit per ton estimates. 

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as there may be localized 

impacts associated with the proposed rulemaking. Additionally, the atmospheric chemistry 

related to ambient concentrations of PM2.5, ozone and air toxics is very complex. Full-scale 

photochemical modeling is therefore necessary to provide the needed spatial and temporal detail 

to more completely and accurately estimate the changes in ambient levels of these pollutants and 

their associated health and welfare impacts. As discussed above, timing constraints precluded 

EPA from conducting a full-scale photochemical air quality modeling analysis in time for the 
8-54 

NPRM. For the final rule, however, a national-scale air quality modeling analysis will be 

performed to analyze the impacts of the standards on PM2.5, ozone, and selected air toxics. The 

benefits analysis plan for the final rulemaking is discussed in the next section. 

8.6.2 Human Health and Environmental Benefits for the Final Rule 

8.6.2.1 Human Health and Environmental Impacts 

As noted above, to model the ozone and PM air quality benefits of the final standards, 

EPA plans to use the Community Multiscale Air Quality (CMAQ) model (see Chapter 6 for a 

description of the CMAQ model). The modeled ambient air quality data will serve as an input to 

the Environmental Benefits Mapping and Analysis Program (BenMAP).DD BenMAP is a 

computer program developed by EPA that integrates a number of the modeling elements used in 

previous RIAs (e.g., interpolation functions, population projections, health impact functions, 

valuation functions, analysis and pooling methods) to translate modeled air concentration 

estimates into health effects incidence estimates and monetized benefits estimates. 

Table 8-18 lists the PM- and ozone-related benefits categories we will use to quantify the 

non-GHG incidence impacts associated with the final standards. Table 8-18 also lists non-GHGrelated endpoints we are currently unable to quantify and/or monetize. 

Table 8-18 Estimated Quantified and Unquantified Health Effects 

BENEFITS 

CATEGORY 

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED 

EFFECT HAS 

BEEN 

MONETIZED 

MORE 

INFORMATION 

Improved Human Health 

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age 

>30) 

  PM NAAQS RIA, 

Section 5.6 

Infant mortality (age <1)   PM NAAQS RIA, 

Section 5.6 

Non-fatal heart attacks (age > 18)   PM NAAQS RIA, 

Section 5.6 

Hospital admissions—respiratory (all 

ages)

  PM NAAQS RIA, 

Section 5.6

Hospital admissions—cardiovascular 

(age >20) 

  PM NAAQS RIA, 

Section 5.6 

Emergency department visits for 

asthma (all ages) 

  PM NAAQS RIA, 

Section 5.6 

Acute bronchitis (age 8–12)   PM NAAQS RIA, 

Section 5.6 

Lower respiratory symptoms (age 7–

14) 

  PM NAAQS RIA, 

Section 5.6 

 

DD Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 
8-55 

BENEFITS 

CATEGORY 

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED 

EFFECT HAS 

BEEN 

MONETIZED 

MORE 

INFORMATION 

Upper respiratory symptoms 

(asthmatics age 9–11) 

  PM NAAQS RIA, 

Section 5.6 

Asthma exacerbation (asthmatics age 

6–18) 

  PM NAAQS RIA, 

Section 5.6 

Lost work days (age 18–65)   PM NAAQS RIA, 

Section 5.6 

Minor restricted-activity days (age 

18–65) 

  PM NAAQS RIA, 

Section 5.6 

Chronic Bronchitis (age >26) — — PM NAAQS RIA, 

Section 5.6c 

Emergency department visits for 

cardiovascular effects (all ages) 

— — PM NAAQS RIA, 

Section 5.6c 

Strokes and cerebrovascular disease 

(age 50–79) 

— — PM NAAQS RIA, 

Section 5.6c 

Other cardiovascular effects (e.g., 

other ages) 

— — PM ISAa

Other respiratory effects (e.g., 

pulmonary function, non-asthma ER 

visits, non-bronchitis chronic 

diseases, other ages and populations) 

— — PM ISAa

Reproductive and developmental 

effects (e.g., low birth weight, preterm births, etc.) 

— — PM ISAa,b

Cancer, mutagenicity, and 

genotoxicity effects 

— — PM ISAa,b

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to 

ozone 

Premature mortality based on shortterm study estimates (all ages) 

  Ozone ISA 

Premature mortality based on longterm study estimates (age 30–99) — — Ozone ISAc

Hospital admissions—respiratory 

causes (age > 65) 

  Ozone ISA 

Hospital admissions—respiratory 

causes (age <2) 

  Ozone ISA 

Emergency department visits for 

asthma (all ages) 

  Ozone ISA 

Minor restricted-activity days (age 

18–65) 

  Ozone ISA 

School absence days (age 5–17)   Ozone ISA 

Decreased outdoor worker 

productivity (age 18–65) 

  Ozone ISA 

Other respiratory effects (e.g., 

premature aging of lungs) 

— — Ozone ISAa

Cardiovascular and nervous system 

effects 

— — Ozone ISAb

Reproductive and developmental 

effects 

— — Ozone ISAb

Reduced 

incidence of 

morbidity from 

exposure to air 

toxics 

Cancer (benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood 

components (benzene) 

— — IRISa,b
8-56 

BENEFITS 

CATEGORY 

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED 

EFFECT HAS 

BEEN 

MONETIZED 

MORE 

INFORMATION 

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 nonasthmatics (formaldehyde) 

Irritation of the eyes, skin, and 

respiratory tract (acetaldehyde) 

Upper respiratory tract irritation and 

congestion (acrolein) 

Notes: 

a We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. b We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. c We assess these benefits qualitatively due to time and resource limitations for this analysis. 

Table 8-19 lists the specific PM- and ozone-related health effect exposure-response functions we 

will use to quantify the non-GHG incidence impacts associated with the final standards. 

Table 8-19 Health Impact Functions Used in BenMAP to Estimate Impacts of PM2.5 and Ozone Reductions 

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Premature Mortality 

Premature mortality – 

daily time series 

O3 Multi-city 

Bell et al (2004) (NMMAPS study)74 – Nonaccidental 

Huang et al (2005)75 - Cardiopulmonary 

Schwartz (2005)76 – Non-accidental 

Meta-analyses: 

Bell et al (2005)77 – All cause 

Ito et al (2005)78 – Non-accidental 

Levy et al (2005)79 – All cause 

All ages 

Premature mortality —

cohort study, all-cause 

PM2.5 Krewski et al. (2009)80

Lepeule et al. (2012)81

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)82 >24 years 

Premature mortality — 

all-cause 

PM2.5 Woodruff et al. (1997)83 Infant (<1 year) 
8-57 

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Chronic Illness 

Nonfatal heart attacks PM2.5 Peters et al. (2001)84

Pooled estimate:

 Pope et al. (2006)85

 Sullivan et al. (2005)86

 Zanobetti et al. (2009)87

 Zanobetti and Schwartz (2006)88

Adults (>18 years) 

Hospital Admissions 

Respiratory 

O3 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)89

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)90,91

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)92

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

Burnett et al. (2001)93 <2 years 

PM2.5 Pooled estimate: 

Zanobetti et al. (2009)—ICD 460-519 (All 

respiratory) 

Kloog et al. (2012)—ICD 460-519 (All 

Respiratory)94 

>64 years 

PM2.5 Moolgavkar (2000)—ICD 490–496 (Chronic 

lung disease)95

18–64 years 

PM2.5 Pooled estimate: 

Babin et al. (2007)—ICD 493 (asthma)96

Sheppard (2003)—ICD 493 (asthma)97

<18 years 

Cardiovascular PM2.5 Pooled estimate: Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular) 

Peng et al. (2009)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)98

Peng et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)99

Bell et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)100

>64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER 

visits 

O3 Pooled estimate: 

Peel et al (2005)101

Wilson et al (2005)102

All ages 

All ages 

Asthma-related ER 

visits (cont’d) 

PM2.5 Pooled estimate: 

Mar et al. (2010)103

Slaughter et al. (2005)104

Glad et al. (2012)105

All ages 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)106 8–12 years 
8-58 

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)107 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)108 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)109 (cough, wheeze and 

shortness of breath) 

Mar et al. (2004) (cough, shortness of breath) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987)110 18–65 years 

School absence days 

O3 

Pooled estimate: 

Gilliland et al. (2001)111

Chen et al. (2000)112

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs) 

O3 Ostro and Rothschild (1989)113 18–65 years 

PM2.5 Ostro and Rothschild (1989) 18–65 years 

Notes: 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC (2002). b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice 

from the National Research Council and EPA SAB-HES, we have calculated reductions in school absences for all 

school-aged children based on the biological similarity between children aged 5 to 17. 

8.6.2.2 Monetized Estimates of Impacts of Reductions in Co-Pollutants 

Table 8-20 presents the monetary values we will apply to changes in the incidence of 

health and welfare effects associated with reductions in non-GHG pollutants that will occur 

when these GHG control strategies are finalized. 
8-59 

Table 8-20 Valuation Metrics Used in BenMAP to Estimate Monetary Co-Benefits 

ENDPOINT VALUATION METHOD VALUATION (2011$)

Premature mortality Assumed Mean VSL $8,300,000 

 

Myocardial Infarctions, Nonfatal Medical Costs Over 5 Years. Varies by age and 

discount rate. Russell (1998)114

--- 

Medical Costs Over 5 Years. Varies by age and 

discount rate. Wittels (1990)115

--- 

Hospital Admissions 

 Respiratory, Age 65+ COI: Medical Costs + Wage Lost $37,000 

 Respiratory, Ages 0-2 COI: Medical Costs $13,000 

 Chronic Obstructive Pulmonary 

Disease (COPD) 

COI: Medical Costs + Wage Lost $22,000 

 Asthma COI: Medical Costs + Wage Lost $17,000 

 Cardiovascular COI: Medical Costs + Wage Lost (18-64) $43,000 

COI: Medical Costs + Wage Lost (65-99) $42,000 

 ER Visits, Asthma COI: Average of Smith et al. (1997)116 $440 

and Standford et al. (1999)117

Other Health Endpoints 

 Acute Bronchitis WTP: 6 Day Illness, CV Studies $470 

 Upper Respiratory Symptoms WTP: 1 Day, CV Studies $32 

 Lower Respiratory Symptoms WTP: 1 Day, CV Studies $21 

 Asthma Exacerbation WTP: Bad Asthma Day, Rowe and Chestnut 

(1986) 118

$57 

 Work Loss Days Median Daily Wage, County-Specific (median 

= $150) 

--- 

 Minor Restricted Activity Days WTP: 1 Day, CV Studies $66 

 School Absence Days Median Daily Wage, Women 25+ $98 

Source: Dollar amounts for each valuation method were extracted from BenMAP and adjusted to year 2011 

dollars (from year 2000 dollars) using the Consumer Price Urban Index (CPI-U). For endpoints valued using 

measures of VSL, WTP, or are wage-based, we use the CPI-U for “all items”: 224.939 (2011) and 172.2 (2000). 

For endpoints valued using a Cost-of-Illness measure, we use the CPI-U for “medical care”: 375.613 (2009) and 

260.8 (2000). 

8.6.2.3 Other Unquantified Health and Environmental Impacts 

In addition to the co-pollutant health and environmental impacts we plan to quantify for 

the analysis of the HD GHG standards, there are a number of other health and human welfare 

endpoints that we will not be able to quantify because of current limitations in the methods or 

available data. These impacts are associated with emissions of air toxics (including benzene, 

1,3-butadiene, formaldehyde, acetaldehyde, acrolein, and ethanol), ambient ozone, and ambient 

PM2.5 exposures. For example, 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). In 

addition, we are currently unable to quantify a number of known welfare effects, including 

reduced acid and particulate deposition damage to cultural monuments and other materials, and 

environmental benefits due to reductions of impacts of eutrophication in coastal areas. 

Although there will be impacts associated with air toxic pollutant emission changes that 

result from this action, we do not attempt to monetize those impacts. This is primarily because 
8-60 

currently available tools and methods to assess air toxics risk from mobile sources at the national 

scale are not adequate for extrapolation to incidence estimations or benefits assessment. The 

best suite of tools and methods currently available for assessment at the national scale are those 

used in the National-Scale Air Toxics Assessment (NATA). EPA’s Science Advisory Board 

specifically commented in their review of the 1996 NATA that these tools were not yet ready for 

use in a national-scale benefits analysis, because they did not consider the full distribution of 

exposure and risk, or address sub-chronic health effects.119 While EPA has since improved these 

tools, there remain critical limitations for estimating incidence and assessing benefits of reducing 

mobile source air toxics. 

As part of the second prospective analysis of the benefits and costs of the Clean Air 

Act,120 EPA conducted a case study analysis of the health effects associated with reducing 

exposure to benzene in Houston from implementation of the Clean Air Act. While reviewing the 

draft report, EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the 

challenges for assessing progress in health improvement as a result of reductions in emissions of 

hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response functions, 

uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk 

estimates to low doses and the challenges of tracking health progress for diseases, such as 

cancer, that have long latency periods.”121 EPA continues to work to address these limitations; 

however, we will not have the methods and tools available for national-scale application in time 

for the analysis of the final action.EE 

8.7 Additional Impacts 

8.7.1 Cost of Noise, Congestion, and Accidents 

Section 8.3 discusses the likely sign of the rebound effect. If net operating costs of the 

vehicle decline, then we expect a positive rebound effect. Increased vehicle use associated with 

a positive rebound effect also contributes to increased traffic congestion, motor vehicle 

accidents, and highway noise. Depending on how the additional travel is distributed throughout 

the day and on where it takes place, additional vehicle use can contribute to traffic congestion 

and delays by increasing traffic volumes on facilities that are already heavily traveled during 

peak periods. These added delays impose higher costs on drivers and other vehicle occupants in 

the form of increased travel time and operating expenses. Because drivers do not take these 

added costs into account in deciding when and where to travel, they must be accounted for 

separately as a cost of the added driving associated with the rebound effect. 

EPA and NHTSA rely on estimates of congestion, accident, and noise costs caused by 

pickup trucks and vans, single unit trucks, buses, and combination tractors developed by the 

 

EE In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials. 
8-61 

Federal Highway Administration to estimate the increased external costs caused by added 

driving due to the rebound effect.122 The FHWA estimates are intended to measure the increases 

in costs from added congestion, property damages and injuries in traffic accidents, and noise 

levels caused by various classes of trucks that are borne by persons other than their drivers (or 

“marginal” external costs). EPA and NHTSA employed estimates from this source previously in 

the analysis accompanying the light-duty 2012-2016 vehicle rulemaking. The agencies continue 

to find them appropriate for this analysis after reviewing the procedures used by FHWA to 

develop them and considering other available estimates of these values. 

FHWA’s congestion cost estimates for trucks, which are weighted averages based on the 

estimated fractions of peak and off-peak freeway travel for each class of trucks, already account 

for the fact that trucks make up a smaller fraction of peak period traffic on congested roads 

because they try to avoid peak periods when possible. FHWA’s congestion cost estimates focus 

on freeways because non-freeway effects are less serious due to lower traffic volumes and 

opportunities to re-route around the congestion. The agencies, however, applied the congestion 

cost to the overall VMT increase, though the fraction of VMT on each road type used in MOVES 

range from 27 to 29 percent of the vehicle miles on freeways for vocational vehicles and 53 

percent for combination tractors. The results of this analysis potentially overestimate the 

congestions costs associated with increased truck use, and thus lead to a conservative estimate of 

benefits. 

EPA and NHTSA estimated the costs of additional vocational vehicle travel using a 

weighted average of 15 percent of the FHWA estimate for bus costs and 85 percent of the 

FHWA estimate for single unit truck costs to reflect the make-up of this segment. The low, mid, 

and high cost estimates from FHWA updated to 2012 dollars are included in Table 8-21. 

Table 8-21 Low-Mid-High Cost Estimates (2012$/mile) 

NOISE

High Middle Low

Pickup Truck, Van $0.002 $0.001 $0.000 

Vocational Vehicle $0.024 $0.009 $0.003 

Combination Tractor $0.054 $0.021 $0.006 

Accidents

High Middle Low

Pickup Truck, Van $0.086 $0.028 $0.015 

Vocational Vehicle $0.050 $0.017 $0.008 

Combination Tractor $0.073 $0.023 $0.011 

Congestion

High Middle Low

Pickup Truck, Van $0.151 $0.051 $0.014 

Vocational Vehicle $0.344 $0.117 $0.031 

Combination Tractor $0.331 $0.113 $0.030 

The agencies are using FHWA’s “Middle” estimates for marginal congestion, accident, 

and noise costs caused by increased travel from trucks.123 This approach is consistent with the 

methodology used in the HD GHG Phase 1 rule and both LD GHG rules. These costs are 

multiplied by the annual increases in vehicle miles travelled from the rebound effect to yield the 
8-62 

estimated increases in congestion, accident, and noise externality costs during each future year. 

The results are shown in Table 8-22 through Table 8-24. 

Table 8-22 Annual Costs & Net Present Values Associated with Increased Noise, Accidents and Congestion 

for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $0 $0

2019 $0 $0 $0 $0

2020 $0 $0 $0 $0

2021 $9 $26 $82 $117

2022 $18 $51 $103 $172

2023 $27 $76 $123 $226

2024 $35 $102 $141 $279

2025 $44 $127 $159 $330

2026 $52 $151 $177 $379

2027 $59 $173 $192 $425

2028 $66 $194 $207 $467

2029 $72 $213 $221 $506

2030 $78 $230 $234 $542

2035 $100 $294 $282 $676

2040 $111 $330 $317 $758

2050 $126 $375 $370 $871

NPV, 3% $1,335 $3,934 $4,066 $9,334

NPV, 7% $593 $1,743 $1,867 $4,202

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 
8-63 

Table 8-23 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and 

Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (3% 

discount rate, Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $132 $132

2019 $0 $0 $146 $146

2020 $0 $0 $162 $162

2021 $71 $203 $176 $450

2022 $69 $197 $173 $438

2023 $66 $191 $169 $427

2024 $65 $190 $168 $424

2025 $64 $189 $169 $422

2026 $64 $188 $168 $420

2027 $63 $185 $167 $415

2028 $61 $182 $166 $409

2029 $60 $178 $164 $402

Sum $583 $1,704 $1,959 $4,247

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-24 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and 

Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (7% 

discount rate, Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $85 $85

2019 $0 $0 $94 $94

2020 $0 $0 $103 $103

2021 $45 $129 $110 $284

2022 $42 $120 $104 $266

2023 $39 $112 $98 $250

2024 $37 $108 $94 $239

2025 $35 $103 $91 $229

2026 $33 $98 $88 $219

2027 $32 $93 $84 $209

2028 $30 $88 $80 $198

2029 $28 $83 $76 $187

Sum $320 $935 $1,106 $2,362

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

8.7.2 Benefits of Reduced Refueling Time 

Reducing the fuel consumption of heavy-duty trucks will either increase their driving 

range before they require refueling, or lead truck manufacturers to offer, and truck purchasers to 

buy, smaller fuel tanks. Keeping the fuel tank the same size will allow truck operators to reduce 

the frequency with which drivers typically refuel their vehicles, by extending the upper limit on 
8-64 

the distance they can travel before requiring refueling. Alternatively, if truck purchasers and 

manufacturers respond to improved fuel economy by reducing the size of fuel tanks, the smaller 

tank will require less time to fill during each refueling stop. 

Because refueling time represents a time cost of truck operation, these time savings 

should be incorporated into truck purchasers’ decisions about how much fuel-saving technology 

they purchase as part of their choices of new vehicles. The savings calculated here thus raise the 

same questions discussed in preamble Section IX VIII.A and RIA Chapter 8.2: does the 

apparent existence of these savings reflect failures in the market for fuel economy, or does it 

reflect costs that are not addressed in this analysis? The response to these questions could vary 

across truck segment. 

No direct estimates of the value of extended vehicle range or reduced fuel tank size are 

readily available. Instead, this analysis calculates the reduction in the annual amount of time a 

driver of each type of truck would spend filling its fuel tank; this reduced time could result either 

from fewer refueling events, if new trucks’ fuel tanks stay the same size, or from less time spent 

filling the tank during each refueling stop, if new trucks’ fuels tank are made proportionately 

smaller. As discussed in Section 8.3 in this RIA, the average number of miles each type of 

vehicle is driven annually would likely increase under the regulation, as truck operators respond 

to lower fuel expenditures (the “rebound effect”). The estimates of refueling time with the 

proposal in effect allow for this increase in truck use. However, the estimate of the rebound 

effect does not account for any reduction in net operating costs from lower refueling time. 

Because the rebound effect should measure the change in VMT with respect to the net change in 

overall operating costs, refueling time costs would ideally factor into this calculation. The effect 

of this omission is expected to be minor because refueling time savings are generally small 

relative to the value of reduced fuel expenditures. 

The savings in refueling time are calculated as the total amount of time the driver of a 

typical truck in each class would save each year as a consequence of pumping less fuel into the 

vehicle’s tank. The calculation also includes a fixed time per refill event of 3.5 minutes which 

would not occur as frequently due to the fewer number of refills. 

The calculation uses the reduced number of gallons consumed by truck type and divides 

that value by the tank volume and refill amount to get the number of refills, then multiplies that 

by the time per refill to determine the number of hours saved in a given year. The calculation 

then applies DOT-recommended values of travel time savings to convert the resulting time 

savings to their economic value. The input metrics used in the analysis are included in Table 

8-25. The equation for the calculation is shown below: 

௬௣௢௟௜௖݈ܽܩ െ௥௘௙௘௥௘௡௖௘ ݈ܽܩቆ ൌ ݐ݂݁݊݁݅ܤ	݈݁݅݊݃ݑܴ݂݁

݂݈݈݁݅ݎ	ݎ݁݌	݈ܽܩ ቇൈ൬ ݂݈݈݁݅ݎ	ݎ݁݌	݈ܽܩ$ ቆ ൈ ൰݂݈݈݁݅ݎ	ݎ݁݌	݅݉݁ݐ ൅ ݁ݐܽݎ	݁ݏ݁݊݌ݏ݀݅	݈݁ݑܨ

ቇݎ݄

௟௔௕௢௥

The annual impacts associated with reduced refueling time are shown in Table 8-26 and the MY 

lifetime impacts are shown in Table 8-27 a

 For an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1

Table 8-27and Table 8-28. 
8-65 

Table 8-25 Inputs to Calculate Refueling Time Savings 

HD PICKUP AND VAN VOCATIONAL 

VEHICLE 

TRACTOR 

Fuel Dispensing Rate 

(gallon/minute)124

10 10 20 

Refueling fixed time 

(minutes/refill)125

3.5 3.5 3.5 

Tank volume (gallons)a 30 40 200 

Refill amount 

(%volume/refill) a

60% 75% 75% 

Resultant time/refill 

(minutes/refill) 

5.3 6.5 11.0 

Wage rate 

(2012$/hr)126,b

$27.22 31.01 28.56 

Notes: 

a

 HD pickup and van values based on a NHTSA survey, other are estimated. 

b

 A wage growth rate of 1.2% has been assumed for future years. 

Table 8-26 Annual Refueling Benefits and Net Present Values for the Preferred Alternative Relative to the 

Less Dynamic Baseline and using Method B 

(Dollar Values in Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUP AND VANS VOCATIONAL TRACTOR/TRAILER SUM OF 

Hours saved BENEFITS 

(thousands) 

Benefits Hours saved 

(thousands) 

Benefits Hours saved 

(thousands) 

Benefits 

2018 0 $0 0 $0 90 $3 $3

2019 0 $0 0 $0 183 $6 $6

2020 0 $0 0 $0 278 $9 $9

2021 51 $2 122 $4 598 $19 $25

2022 192 $6 244 $9 999 $32 $47

2023 419 $13 365 $13 1,401 $46 $72

2024 732 $23 620 $22 2,046 $67 $113

2025 1,130 $36 874 $32 2,691 $90 $157

2026 1,611 $52 1,124 $41 3,327 $112 $205

2027 2,164 $70 1,558 $58 4,043 $138 $266

2028 2,699 $89 1,979 $74 4,731 $164 $327

2029 3,211 $107 2,387 $91 5,387 $188 $386

2030 3,694 $125 2,778 $107 6,011 $213 $444

2035 5,595 $200 4,343 $177 8,526 $320 $698

2040 6,695 $254 5,286 $229 10,205 $407 $890

2050 7,788 $334 6,260 $305 12,365 $556 $1,195

NPV, 3% $2,627 $2,347 $4,436 $9,410

NPV, 7% $1,067 $950 $1,851 $3,868

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
8-66 

Table 8-27 Discounted Model Year Lifetime Refueling Benefits at 3% for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP 

 AND VANS 

VOCATIONAL TRACTOR/ 

TRAILER 

SUM 

2018 $0 $0 $23 $23 

2019 $0 $0 $22 $22 

2020 $0 $0 $21 $21 

2021 $13 $36 $114 $163 

2022 $35 $36 $113 $184 

2023 $56 $35 $112 $203 

2024 $77 $72 $176 $325 

2025 $97 $73 $179 $349 

2026 $118 $73 $181 $372 

2027 $136 $123 $207 $466 

2028 $135 $122 $207 $465 

2029 $134 $121 $208 $463 

Sum $801 $691 $1,563 $3,055 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-28 Discounted Model Year Lifetime Refueling Benefits at 7% for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP 

 AND VANS 

VOCATIONAL TRACTOR/ 

TRAILER 

SUM 

2018 $0 $0 $16 $16 

2019 $0 $0 $15 $15 

2020 $0 $0 $14 $14 

2021 $8 $23 $71 $101 

2022 $21 $21 $67 $110 

2023 $33 $20 $64 $117 

2024 $43 $40 $97 $181 

2025 $52 $39 $95 $187 

2026 $61 $38 $93 $191 

2027 $68 $61 $102 $231 

2028 $65 $59 $98 $222 

2029 $62 $56 $95 $213 

Sum $413 $357 $827 $1,597 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

8.7.3 Benefits of Increased Travel Associated with Rebound Driving 

The increase in travel associated with the rebound effect produces additional benefits to 

vehicle owners and operators, which reflect the value of the added (or more desirable) social and 

economic opportunities that become accessible with additional travel. The analysis estimates the 

economic benefits from increased rebound-effect driving as the sum of fuel expenditures 

incurred plus the vehicle owner/operator surplus from the additional accessibility it provides. As 
8-67 

evidenced by the fact that vehicles make more frequent or longer trips when the cost of driving 

declines, the benefits from this added travel exceed added expenditures for the fuel consumed. 

Note that the amount by which the benefits from this increased driving exceed its increased fuel 

costs measures the net benefits from the additional travel, usually referred to as increased 

consumer surplus or, in this case, increased owner/operator surplus. The equation for the 

calculation of the total travel benefit is shown below: 

$ ቆ ௗሻ௡௨௥௘௕௢ܶܯܸሺ ൌ ݐ݂݁݊݁݅ܤ	݈݁ݒܽݎܶ

݉݅ቇ

௣௢௟௜௖௬

൅	൬1

2

൰ ሺܸܯܶ௢௕௘௥௨௡ௗሻ ൥ቆ $

݈݉݅݁ቇ

௥௘௙௘௥௘௡௖௘

െ	ቆ $

݈݉݅݁ቇ

௣௢௟௜௖௬൩

The agencies’ analysis estimates the economic value of the increased owner/operator 

surplus provided by added driving using the conventional approximation, which is one half of 

the product of the decline in vehicle operating costs per vehicle-mile and the resulting increase in 

the annual number of miles driven. Because it depends on the extent of improvement in fuel 

economy, the value of benefits from increased vehicle use changes by model year and varies 

among alternative standards. Under even those alternatives that would impose the highest 

standards, however, the magnitude of the surplus from additional vehicle use represents a small 

fraction of this benefit. The benefits are shown in Table 8-29 through Table 8-31. 

Table 8-29 Annual Value of Increased Travel and Net Present Values at 3% and 7% Discount Rates for the 

Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

CALENDAR 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $0 $0

2019 $0 $0 $0 $0

2020 $0 $0 $0 $0

2021 $32 $68 $345 $445

2022 $64 $138 $434 $636

2023 $96 $208 $517 $821

2024 $127 $279 $595 $1,001

2025 $157 $349 $672 $1,179

2026 $186 $416 $744 $1,346

2027 $213 $481 $813 $1,506

2028 $237 $538 $872 $1,647

2029 $261 $593 $929 $1,783

2030 $282 $644 $983 $1,909

2035 $372 $855 $1,217 $2,445

2040 $437 $1,008 $1,428 $2,873

2050 $489 $1,141 $1,656 $3,286

NPV, 3% $5,021 $11,518 $17,700 $34,240

NPV, 7% $2,209 $5,045 $8,062 $15,316

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
8-68 

Table 8-30 Discounted Model Year Lifetime Value of Increased Travel for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (3% discount rate, Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $554 $554

2019 $0 $0 $618 $618

2020 $0 $0 $686 $686

2021 $252 $548 $711 $1,510

2022 $244 $539 $706 $1,488

2023 $236 $529 $698 $1,463

2024 $232 $522 $680 $1,434

2025 $229 $524 $689 $1,442

2026 $227 $525 $695 $1,447

2027 $223 $511 $688 $1,421

2028 $220 $507 $688 $1,415

2029 $218 $502 $686 $1,406

Sum $2,080 $4,706 $8,098 $14,884

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-31 Discounted Model Year Lifetime Value of Increased Travel for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (7% discount rate, Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP AND 

VANS 

VOCATIONAL TRACTOR/TRAILER SUM 

2018 $0 $0 $353 $353

2019 $0 $0 $390 $390

2020 $0 $0 $429 $429

2021 $158 $343 $441 $942

2022 $148 $325 $422 $894

2023 $138 $307 $402 $847

2024 $130 $292 $377 $799

2025 $124 $282 $368 $774

2026 $118 $272 $358 $748

2027 $111 $255 $341 $708

2028 $106 $244 $328 $678

2029 $101 $232 $315 $649

Sum $1,134 $2,553 $4,524 $8,211

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

8.8 The Effect of Safety Standards and Voluntary Safety Improvements on Vehicle 

Weight 

Safety standards developed by NHTSA in previous rulemakings may make compliance 

with the fuel efficiency and CO2 emissions standards more difficult or may reduce the projected 

benefits of the program. The primary way that safety regulations can impact fuel efficiency and 
8-69 

CO2 emissions is through increased vehicle weight, which reduces the fuel efficiency (and thus 

increases the CO2 emissions) of the vehicle. Using MY 2010 as a baseline, this section discusses 

the effects of other government regulations on MYs 2014-2016 medium and heavy-duty vehicle 

fuel efficiency and CO2 emissions. At this time, no known safety standards will affect new 

models in MY 2017 or 2018. NHTSA’s estimates are based on cost and weight tear-down 

studies of a few vehicles and cannot possibly cover all the variations in the manufacturers’ fleets. 

NHTSA also requested, and various manufacturers provided, confidential estimates of increases 

in weight resulting from safety improvements. Those increases are shown in subsequent tables. 

We have broken down our analysis of the impact of safety standards that might affect the 

MYs 2014-2016 fleets into three parts: 1) those NHTSA final rules with known effective dates, 

2) proposed rules or soon-to-be proposed rules by NHTSA with or without final effective dates, 

and 3) currently voluntary safety improvements planned by the manufacturers. 

8.8.1 Weight Impacts of Required Safety Standards 

NHTSA has undertaken several rulemakings in which several standards would become 

effective for medium- and heavy-duty (MD/HD) vehicles between MY 2014 and MY 2016. We 

will examine the potential impact on MD/HD vehicle weights for MYs 2014-2016 using MY 

2010 as a baseline. 

1. FMVSS 119, Heavy Truck Tires Endurance and High Speed Tests 

2. FMVSS 121, Air Brake Systems Stopping Distance 

3. FMVSS 214, Motor Coach Lap/Shoulder Belts 

4. MD/HD Vehicle Electronic Stability Control Systems 

8.8.1.1 FMVSS 119, Heavy Truck Tires Endurance and High Speed Tests 

NHTSA tentatively determined that the FMVSS No. 119 performance tests developed in 

1973 should be updated to reflect the increased operational speeds and duration of truck tires in 

commercial service. A Notice of Proposed Rulemaking (NPRM) was issued December 7, 2010 

(75 FR 60036). It proposed to increase significantly the stringency of the endurance test and to 

add a new high speed test. The data in the large truck crash causation study (LTCCS) that 

preceded that NPRM found that J and L load range tires were having proportionately more 

problems than the other sizes and the agency’s test results indicate that H, J, and L load range 

tires are more likely to fail the proposed requirements among the targeted F, G, H, J and L load 

range tires.127 To address these problems, the H and J load range tires could potentially use 

improved rubber compounds, which would add no weight to the tires, to reduce heat retention 

and improve the durability of the tires. The L load range tires, in contrast, appear to need to use 

high tensile strength steel chords in the tire bead, carcass and belt areas, which would enable a 

weight reduction with no strength penalties. Thus, if the update to FMVSS No. 119 was 

finalized, we anticipate no change in weight for H and J load range tires and a small reduction in 

weight for L load range tires. This proposal could become a final rule with an effective date of 

MY 2016. 
8-70 

8.8.1.2 FMVSS No. 121, Airbrake Systems Stopping Distance 

FMVSS No. 121 contains performance and equipment requirements for braking systems 

on vehicles with air brake systems. The most recent major final rule affecting FMVSS No. 121 

was published on July 27, 2009, and became effective on November 24, 2009 (MY 2009). The 

final rule requires the vast majority of new heavy truck tractors (approximately 99 percent of the 

fleet) to achieve a 30 percent reduction in stopping distance compared to currently required 

levels. Three-axle tractors with a gross vehicle weight rating (GVWR) of 59,600 pounds or less 

must meet the reduced stopping distance requirements by August 1, 2011 (MY 2011), while twoaxle tractors and tractors with a GVWR above 59,600 pounds must meet the reduced stopping 

distance requirements by the later date of August 1, 2013 (MY 2013). NHTSA determined that 

there are several brake systems that can meet the requirements established in the final rule, 

including installation of larger S-cam drum brakes or disc brake systems at all positions, or 

hybrid disc and larger rear S-cam drum brake systems. 

According to data provided by a manufacturer (Bendix) in response to the NPRM, the 

heaviest drum brakes weigh more than the lightest disc brakes, while the heaviest disc brakes 

weigh more than the lightest drum brakes. For a three-axle tractor equipped with all disc brakes, 

then, the total weight could increase by 212 pounds or could decrease by 134 pounds compared 

to an all-drum-braked tractor, depending on which disc or drum brakes are used for comparison. 

The improved brakes may add a small amount of weight to the affected vehicles for MYs 2014-

2016, resulting in a slight increase in fuel consumption. 

8.8.1.3 FMVSS No. 208, Motor coach Lap/Shoulder Belts 

NHTSA is proposing lap/shoulder belts for all motorcoach seats. About 2,000 

motorcoaches are sold per year in the United States. Based on preliminary results from the 

agency’s cost/weight teardown studies of motor coach seats,128 NHTSA estimates that the weight 

added by 3-point lap/shoulder belts ranges from 5.96 to 9.95 pounds per 2-person seat. This is 

the weight only of the seat belt assembly itself, and does not include changing the design of the 

seat, reinforcing the floor, walls or other areas of the motor coach. Few current production 

motor coaches have been installed with lap/shoulder belts on their seats, and the number of 

vehicles with these belts already installed could be negligible. Assuming a 54 passenger motor 

coach, the added weight for the 3-point lap/shoulder belt assembly would be in the range of 161 

to 269 pounds (27 * (5.96 to 9.95)) per vehicle. This proposal could become a final rule with an 

effective date of MY 2016. 

8.8.2 Electronic Stability Control Systems (ESC) for Medium- and Heavy-Duty 

(MD/HD) Vehicles 

The purpose of an ESC system for MD/HD vehicles is to reduce crashes caused by 

rollover or by directional loss-of-control. ESC monitors a vehicle’s rollover threshold and lateral 

stability using vehicle speed, wheel speed, steering wheel angle, lateral acceleration, side slip 

and yaw rate data and upon sensing an impending rollover or loss of directional control situation 

automatically reduces engine throttle and applies braking forces to individual wheels or sets of 

wheel to slow the vehicle down and regain directional control. ESC is not currently required in 

MD/HD vehicles, but could be proposed to be required in these vehicles by NHTSA. FMVSS 
8-71 

No. 105, Hydraulic and electric brake systems, requires multipurpose passenger vehicles, trucks 

and buses with a GVWR greater than 4,536 kg (10,000 pounds) to be equipped with an antilock 

brake system (ABS). All MD/HD vehicles having a GVWR of more than 10,000 pounds, are 

required to have ABS installed by that standard. 

In addition to the existing ABS functionality, ESC requires sensors including a yaw rate 

sensor, lateral acceleration sensor, steering angle sensor and brake pressure sensor along with a 

brake solenoid valve. According to data provided by Meritor WABCO, the weight of an ESC 

system for the model 4S4M tractor is estimated to be around 55.5 pounds, and the weight of the 

ABS only is estimated to be 45.5 pounds. Thus, we estimate the added weight for the ESC for 

the vehicle to be 10 (55.5 – 45.5) pounds. 

8.8.3 Summary – Overview of Anticipated Weight Increases 

Table 8-32 summarizes estimates made by NHTSA regarding the weight added by the 

above discussed standards or likely rulemakings. NHTSA estimates that weight additions 

required by final rules and likely NHTSA regulations effective in MY 2016 compared to the MY 

2010 fleet will increase motor coach vehicle weight by 171-279 pounds and will increase other 

heavy-duty truck weights by 10 pounds. 

Table 8-32 Weight Additions Due to Final Rules or Likely NHTSA Regulations: Comparing MY 2016 to the 

MY 2010 Baseline Fleet 

STANDARD NUMBER ADDED WEIGHT IN 

POUNDS 

 MD/HD VEHICLE 

ADDED WEIGHT IN 

KILOGRAMS 

MD/HD VEHICLE 

119 0 0 

121 0a 0a

208 

Motor coaches only 

161-269 73-122 

MD/HD Vehicle Electronic Stability 

Control Systems 

10 4.5 

Total 

Motor coaches 

171- 279 77.5-126.5 

Total 

All other MD/HD vehicles 

10 4.5 

Note: 

NHTSA’s final rule on Air Brakes, docket NHTSA-2009-0083, dated July 27, 2009, concluded that 

a small amount of weight would be added to the brake systems but a weight value was not 

provided. 

8.8.4 Effects of Vehicle Mass Reduction on Safety 

NHTSA and EPA have been considering the effect of vehicle weight on vehicle safety for 

the past several years in the context of our joint rulemaking for light-duty vehicle CAFE and 

GHG standards, consistent with NHTSA’s long-standing consideration of safety effects in setting 

CAFE standards. Combining all modes of impact, the latest analysis by NHTSA for the MYs 

2012-2016 final rule129 found that reducing the weight of the heavier light trucks (LT > 3,870) 

had a positive overall effect on safety, reducing societal fatalities. 
8-72 

In the context of the current rulemaking for HD fuel consumption and GHG standards, 

one would expect that reducing the weight of medium-duty trucks similarly would, if anything, 

have a positive impact on safety. However, given the large difference in weight between lightduty vehicles and medium-duty trucks, and even larger difference between light-duty vehicles 

and heavy-duty vehicles with loads, the agencies believe that the impact of weight reductions of 

medium- and heavy-duty trucks would not have a noticeable impact on safety for any of these 

classes of vehicles. 

However, the agencies recognize that it is important to conduct further study and research 

into the interaction of mass, size and safety to assist future rulemakings, and we expect that the 

collaborative interagency work currently on-going to address this issue for the light-duty vehicle 

context may also be able to inform our evaluation of safety effects for the final HD vehicle rule. 

We intend to continue monitoring this issue going forward, and may take steps in a future 

rulemaking if it appears that the MD/HD fuel efficiency and GHG standards have unforeseen 

safety consequences. The American Chemistry Council stated in comments to the agencies that 

plastics and plastic composite materials provide a new way to lighten vehicles while maintaining 

passenger safety. They added that properties of plastics including strength to weight ratio, 

energy absorption, and flexible design make these materials well suited for the manufacture of 

medium- and heavy-duty vehicles. They submitted supporting analyses with their comments. 

The National School Transportation Association stated that added structural integrity 

requirements increase weight of school buses, and thus decrease fuel economy. They asked that 

if there are safety and fuel economy trade-offs manufacturers should be able to receive a waiver 

from the regulation requirements. Since no weight reduction is required for school buses – or 

any other vocational vehicle – the agencies do not believe this is an issue with the current 

regulation. 

8.9 Petroleum, Energy and National Security impact 

8.9.1 Energy Security Impacts 

The Phase 2 standards are designed to require improvements in the fuel efficiency of 

medium- and heavy-duty vehicles and, thereby, reduce fuel consumption and GHG emissions. 

In turn, the Phase 2 standards help to reduce U.S. petroleum imports. A reduction of U.S. 

petroleum imports reduces both financial and strategic risks caused by potential sudden 

disruptions in the supply of imported petroleum to the U.S. This reduction in risk increases U.S. 

energy security. This section summarizes the agency’s estimates of U.S. oil import reductions 

and energy security benefits of the proposed Phase 2 standards. Additional discussion of this 

issue can be found in Section IX.H of the preamble. 

The U.S., as a large oil importer and oil consumer, is economically vulnerable to 

outcomes in a volatile global oil market that relies on oil supplies from potentially unstable 

sources. Much of the world’s oil and gas supplies are located in countries facing social, 

economic, and demographic challenges, thus making them vulnerable to potential local 

instability. In 2010, just over 40 percent of world oil supply came from OPEC (e.g., 

Organization of Petroleum Exporting Countries) nations and the Annual Energy Outlook 2014 
8-73 

(Early Release) projects that this share will rise steadily. In the AEO 2014 (Early Release) 

projections, OPEC nations supply over 44 percent by 2040.FF 

Approximately 31 percent of global supply is from Middle East and North African 

countries alone, a share that is expected to grow.GG Measured in terms of the share of world oil 

resources or the share of global oil export supply, rather than oil production, the concentration of 

global petroleum resources in OPEC nations is even larger. As another measure of 

concentration, of the 137 countries/principalities that export either crude or product, the top 12 

have recently accounted for over 55 percent of exports.130 Eight of these countries are members 

of OPEC, and a ninth is Russia.HH In a market where even a 1-2 percent supply loss raises prices 

noticeably, and where a 10 percent supply loss could lead to an unprecedented price shock, this 

regional concentration is of concern.II Historically, the countries of the Middle East have been 

the source of eight of the ten major world oil disruptions, with the ninth originating in 

Venezuela, an OPEC country, and the tenth being Hurricanes Katrina and Rita.JJ 

One impact of the Proposed Phase 2 HD National Program is that it promotes more 

efficient use of transportation fuels in the U.S. The result is that it reduces U.S. oil consumption 

and imports, which reduces both financial and strategic risks associated with a potential 

disruption in supply or a spike in the cost of a particular energy source. This reduction in risks 

increases U.S. energy security. For this rule, an “oil premium” approach is utilized that 

identifies those energy security related economic costs which are not reflected in the market 

price of oil, and which are expected to change in response to an incremental change in the level 

of U.S. oil imports. 

8.9.2 Impact on U.S. Petroleum Imports 

U.S. energy security is broadly defined as the continued availability of energy sources at 

an acceptable price. Most discussion of U.S. energy security revolves around the topic of the 

 

FF The agencies used the AEO 2014 (Early Release) since this version of AEO was available at the time that fuel 

savings from the rule were being estimated. The AEO 2014 (Early Release) and the AEO 2014 have very similar 

energy market and economic projections. For example, world oil prices are the same between the two forecasts. GG Middle East and North African oil supply share reaches 36 percent in 2040 in the AEO (Early Release) Reference 

Case. 

HH The other three are Norway, Canada, and the EU, an exporter of product. II For example, the 2005 Hurricanes Katrina/Rita and the 2011 Libyan conflict both led to a 1.8 percent reduction in 

global crude supply. While the price impact of the latter is not easily distinguished given the rapidly rising postrecession prices, the former event was associated with a 10-15 percent world oil price increase. There are a range of 

smaller events with smaller but noticeable impacts. Somewhat larger events, such as the 2002/3 Venezuelan Strike 

and the War in Iraq, corresponded to about a 2.9 percent sustained loss of supply, and was associated with a 28 

percent world oil price increase. (Compiled from EIA oil price data, IEA2012 [IEA Response System for Oil Supply 

Emergencies] (http://www.iea.org/publications/freepublications/publication/EPPD_Brochure_English_2012_02.pdf) 

See table on P. 11. and Hamilton 2011 "Historical Oil Shocks,"(http://econweb.ucsd.edu/~jhamilto/oil_history.pdf) 

in *Routledge Handbook of Major Events in Economic History*, pp. 239-265, edited by Randall E. Parker and 

Robert Whaples, New York: Routledge Taylor and Francis Group, 2013). 

JJ The events IEA categorized as oil supply disruptions all had a gross peak oil supply loss of at least 1.5 million 

barrels a day as a result of wars, revolutions, embargoes or strikes involving major oil exporting nations or from 

major storm events or disasters (like the double Hurricane Katrina/Rita) affecting oil producing/processing regions. 

IEA 2011 “IEA Response System for Oil Supply Emergencies.” 
8-74 

economic costs of U.S. dependence on oil imports. However, it is not imports alone, but both 

imports and consumption of petroleum from all sources, and their role in economic activity, that 

expose the U.S. to risk from price shocks in the world oil price. The relative significance of 

petroleum consumption and import levels for the macroeconomic disturbances that follow from 

oil price shocks is not fully understood. Recognizing that changing petroleum consumption will 

change U.S. imports, this assessment of oil costs focuses on those incremental social costs that 

follow from the resulting changes in imports, employing the usual oil import premium measure. 

The agencies will review any comments we receive on how to incorporate the impacts of 

changes in oil consumption, rather than imports exclusively, into our energy security analysis. 

The U.S.’s energy security problem is that the U.S. relies on imported oil from potentially 

unstable sources. In addition, oil exporters have the ability to raise the price of oil by exerting 

monopoly power through the formation of a cartel, the Organization of Petroleum Exporting 

Countries (OPEC). These factors contribute to the vulnerability of the U.S. economy to episodic 

oil supply shocks and price spikes. In 2012, U.S. net expenditures for imports of crude oil and 

petroleum products were $290 billion, and total consumption expenditure was $634 billion (in 

2012$) (see Figure 8-3).131 Import costs have declined since 2011 but total oil expenditures 

(domestic and imported) remain near historical highs, at roughly triple the real oil costs 

experienced by the U.S. from 1986 to 2002. 

Figure 8-3 U.S. Expenditures on Crude Oil from 1970 through 2015132

The agencies used EPA’s MOVES model to estimate the reductions in U.S. fuel 

consumption due to this proposed rule for vocational vehicles and tractors. For HD pickups and 

vans, the agencies used both DOT’s CAFE model and EPA’s MOVES model to estimate the fuel 

0

100

200

300

400

500

600

700

800

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

$Bill/yr, 2012$

Year

U.S. Expenditures on Crude Oil

Domestic

Imported
8-75 

consumption impacts. (Detailed explanations of the MOVES and CAFE models can be found in 

Chapters 5 and 10 of the draft RIA. See IX.C of the preamble for estimates of reduced fuel 

consumption from the proposed rule). Based on a detailed analysis of differences in U.S. fuel 

consumption, petroleum imports, and imports of petroleum products, the agencies estimate that 

approximately 90 percent of the reduction in fuel consumption resulting from adopting improved 

GHG emissions standards and fuel efficiency standards is likely to be reflected in reduced U.S. 

imports of crude oil and net imported petroleum products.KK Thus, on balance, each gallon of 

fuel saved as a consequence of the HD GHG and fuel efficiency standards is anticipated to 

reduce total U.S. imports of petroleum by 0.90 gallons.LL Based upon the fuel savings estimated 

by the MOVES/CAFE models and the 90 percent oil import factor, the reduction in U.S. oil 

imports from this rule are estimated for the years 2020, 2025, 2030, 2040, and 2050 (in millions 

of barrels per day (MMBD)) in Table 8-27 below. For comparison purposes, Table 8-27 also 

shows U.S. imports of crude oil in 2020, 2025, 2030 and 2040 as projected by DOE in the 

Annual Energy Outlook 2014 (Early Release) Reference Case. U.S. Gross Domestic Product 

(GDP) is projected to grow by roughly 59 percent between 2020 – 2040 in the AEO 2014 (Early 

Release) projections. 

 

KK We looked at changes in crude oil imports and net petroleum products in the Reference Case in comparison to 

two cases from the AEO 2014. The two cases are the Low Demand and Low VMT cases. See the spreadsheet 

“Impacts on Fuel Demands and ImportsJan9.xlsx” comparing the AEO 2014 Reference Case to the Low Demand 

Case. See the spreadsheet “Impact of Fuel Demand and Impacts January20VMT.xlsl” for a comparison of AEO 

2014 Reference Case and the Low VMT Case. We also considered a paper entitled “Effect of a U.S. Demand 

Reduction on Imports and Domestic Supply Levels” by Paul Leiby, 4/16/2013. This paper suggests that “Given a 

particular reduction in oil demand stemming from a policy or significant technology change, the fraction of oil use 

savings that shows up as reduced U.S. imports, rather than reduced U.S. supply, is actually quite close to 90 percent, 

and probably close to 95 percent”. 

LL The NHTSA analysis uses a slightly different value that was estimated using unique runs of the National Energy 

Modeling System (NEMS) that forms the foundation of the Annual Energy Outlook. NHTSA ran a version of 

NEMS from 2012 (which would have been used in the 2013 AEO) and computed the change in imports of 

petroleum products with and without the Phase 1 MDHD program to estimate the relationship between changes in 

fuel consumption and oil imports. The analysis found that reducing gasoline consumption by 1 gallon reduces 

imports of refined gasoline by 0.06 gallons and domestic refining from imported crude by 0.94 gallons. Similarly, 

one gallon of diesel saved by the Phase 1 rule was estimated to reduce imports of refined diesel by 0.26 gallons and 

domestic refining of imported crude by 0.74 gallons. The agencies will update this analysis for the Final Rule using 

the model associated with AEO2014, modeling the Phase 2 Preferred Alternative explicitly. 
8-76 

Table 8-33 Projected U.S. Imports of Crude Oil and U.S. Oil Import Reductions in 2020, 2025, 2030, 2040 

and 2050 for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B 

(Millions of barrels per day (MMBD)) a

YEAR U.S. OIL 

IMPORTS 

REDUCTIONS FROM 

PROPOSED HD RULE 

2020 4.94 0.01 

2025 5.07 0.16 

2030 5.38 0.37 

2040 6.0 0.65 

2050 X 0.78 

Notes: 

a

 For an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Preamble 

Section X.A.1

X – The AEO 2014 (Early Release) only projects energy market 

and economic trends through 2040. 

8.9.3 Methodology Used to Estimate U.S. Energy Security Benefits 

In order to understand the energy security implications of reducing U.S. oil imports, EPA 

has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for 

evaluating the social costs and energy security implications of oil use. The energy security 

estimates provided below are based upon a methodology developed in a peer-reviewed study 

entitled, “The Energy Security Benefits of Reduced Oil Use, 2006-2015,” completed in March 

2008. This ORNL study is an updated version of the approach used for estimating the energy 

security benefits of U.S. oil import reductions developed in a 1997 ORNL Report.

133 For EPA 

and NHTSA rulemakings, the ORNL methodology is updated periodically to account for 

forecasts of future energy market and economic trends reported in the U.S. Energy Information 

Administration’s Annual Energy Outlook. 

As part of the process for developing the ORNL energy security estimates, EPA 

sponsored an independent, expert peer review of the 2008 ORNL study.134 In addition, EPA 

worked with ORNL to address comments raised in the peer review and to develop estimates of 

the energy security benefits associated with a reduction in U.S. oil imports. In response to peer 

reviewer comments, ORNL modified its model by changing several key parameters involving 

OPEC supply behavior, the responsiveness of oil demand and supply to a change in the world oil 

price, and the responsiveness of U.S. economic output to a change in the world oil price. 

When conducting this analysis, ORNL considered the full cost of importing petroleum 

into the U.S. The full economic cost is defined to include two components in addition to the 

purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting from 

the effect of U.S. demand on the world oil price (i.e., the “demand” or “monopsony” costs); and 

(2) the risk of reductions in U.S. economic output and disruption to the U.S. economy caused by 

sudden disruptions in the supply of imported oil to the U.S. (i.e., macroeconomic 

disruption/adjustment costs). 
8-77 

The literature on the energy security for the last two decades has routinely combined the 

monopsony and the macroeconomic disruption components when calculating the total value of 

the energy security premium. However, in the context of using a global value for the Social Cost 

of Carbon (SCC) the question arises: how should the energy security premium be used when 

some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are 

calculated from a global perspective? Monopsony benefits represent avoided payments by U.S. 

consumers to oil producers that result from a decrease in the world oil price as the U.S. decreases 

its demand for oil. Although there is clearly an overall benefit to the U.S. when considered from 

a domestic perspective, the decrease in price due to decreased demand in the U.S. also represents 

a loss to oil producing countries, one of which is the United States. Given the redistributive 

nature of this monopsony effect from a global perspective, and the fact that an increasing fraction 

of it represents a transfer from U.S. consumers and producers, it is excluded in the energy 

security benefits calculations for this proposed rule. 

In contrast, the other portion of the energy security premium, the avoided U.S. 

macroeconomic disruption and adjustment cost that arises from reductions in U.S. petroleum 

imports, does not have offsetting impacts outside of the U.S., and, thus, is included in the energy 

security benefits estimated for this proposed rule. To summarize, the agencies have included 

only the avoided macroeconomic disruption portion of the energy security benefits to estimate 

the monetary value of the total energy security benefits of this proposed rule. 

For this rulemaking, ORNL updated the energy security premiums by incorporating the 

most recent oil price forecast and energy market trends, particularly regional oil supplies and 

demands, from the AEO 2014 (Early Release) into its model.135 Table 8-28 provides estimates 

for energy security premiums for the years 2020, 2025, 2030 and 2040,MM as well as a 

breakdown of the components of the energy security premiums for each year. The components 

of the energy security premiums and their values are discussed below. 

Table 8-34 Energy Security Premiums in 2020, 2025, 2030 and 2040 (20012$/Barrel)* 

YEAR 

(RANGE) 

MONOPSONY 

(RANGE) 

AVOIDED MACROECONOMIC 

DISRUPTION/ADJUSTMENT 

COSTS 

(RANGE) 

TOTAL MID-POINT 

(RANGE) 

2020 $4.91 

(1.63 – 9.15) 

$6.35 

(3.07 – 10.15) 

$11.25 

(6.67 – 16.53) 

2025 $5.46 

(1.81 – 10.47) 

$7.29 

(3.57 – 11.67) 

$12.75 

(7.58 – 18.65) 

2030 $6.04 

(2.00 – 11.67) 

$8.39 

(4.12 – 13.41) 

$14.43 

(8.54 – 21.13) 

2040 $7.17 

(2.32 – 14.03) 

$10.74 

(5.36 – 17.22) 

$17.91 

(10.52 – 26.14) 

Note: 

 *Top values in each cell are the midpoints, the values in parentheses are the 90 percent confidence intervals. 

 

MM AEO 2014 (Early Release) forecasts energy market trends and values only to 2040. The post-2040 energy 

security premium values are assumed to be equal to the 2040 estimate. 
8-78 

8.9.3.1 Effect of Oil Use on the Long-Run Oil Price 

The first component of the full economic costs of importing petroleum into the U.S. 

follows from the effect of U.S. import demand on the world oil price over the long-run. Because 

the U.S. is a sufficiently large purchaser of global oil supplies, its purchases can affect the world 

oil price. This monopsony power means that increases in U.S. petroleum demand can cause the 

world price of crude oil to rise, and conversely, that reduced U.S. petroleum demand can reduce 

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to 

improvements in the fuel efficiency of medium- and heavy-duty vehicles is the potential 

decrease in the crude oil price paid for all crude oil purchased. 

The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $100 per barrel, its total daily bill for oil 

imports is one billion dollars. If a 10 percent decrease in U.S. imports to 9 million barrels per day 

causes the world oil price to drop to $99 per barrel, the daily U.S. oil import bill drops to $891 

million (9 million barrels times $99 per barrel). While the world oil price only declines $1, the 

resulting decrease in oil purchase payments of $109 million per day (one billion dollars minus 

$891 million) is equivalent to an incremental benefit of $109 per barrel of oil imports reduced, or 

$10 more than the newly-decreased world price of $99 per barrel. This additional $10 per barrel 

“import cost premium” represents the incremental external benefits to the U.S. for avoided 

import costs beyond the price paid for oil purchases. This additional benefit from import 

reduction arises only to the extent that a reduction in U.S. oil imports affects the world oil price. 

ORNL estimates this component of the energy security benefit in 2020 to be $4.91/barrel, with a 

range of $1.63/barrel to $9.15/barrel of imported oil reduced. 

A variety of oil market and economic factors have contributed to lowering the estimated 

monopsony premium compared to monopsony premiums cited in recent EPA/NHTSA 

rulemakings. Three principal factors contribute to lowering the monopsony premium: lower 

world oil prices, lower U.S. oil imports and less responsiveness of world oil prices to changes in 

U.S. oil demand. For example, between 2012 (using the AEO 2012 (Early Release)) and 2014 

(using the AEO 2014 (Early Release)), there has been a general downward revision in world oil 

price projections in the near term (e.g. 19 percent in 2020) and a sharp reduction in projected 

U.S. oil imports in the near term, due to increased U.S. supply (i.e., a 41 percent reduction in 

U.S. oil imports by 2017 and a 36 percent reduction in 2020). Over the longer term, oil’s share 

of total U.S. imports is projected to gradually increase after 2020 but still remain 27 percent 

below the AEO 2012 (Early Release) projected level in 2035. 

Another factor influencing the monopsony premium is that U.S. demand on the global oil 

market is projected to decline, suggesting diminished overall influence and some reduction in the 

influence of U.S. oil demand on the world price of oil. Outside of the U.S., projected OPEC 

supply remains roughly steady as a share of world oil supply compared to the AEO 2012 (Early 

Release). OPEC’s share of world oil supply outside of the U.S. actually increases slightly. Since 

OPEC supply is estimated to be more price sensitive than non-OPEC supply, this means that 

under AEO 2014 (Early Release) world oil supply is slightly more responsive to changes in U.S. 

oil demand. Together, these factors suggest that changes in U.S. oil import reductions have a 

somewhat smaller effect on the long-run world oil price than changes based the 2012 estimates. 
8-79 

These changes in oil price and import levels lower the monopsony portion of energy 

security premium since this portion of the security premium is related to the change in total U.S. 

oil import costs that is achieved by a marginal reduction in U.S oil imports. Since both the price 

and the quantity of oil imports are lower, the monopsony premium component is 46 – 57 percent 

lower over the years 2017–2025 than the estimates based upon the AEO 2012 (Early Release) 

projections. 

There is disagreement in the literature about the magnitude of the monopsony 

component, and its relevance for policy analysis. Brown and Huntington (2013)136, for example, 

argue that the United States’ refusal to exercise its market power to reduce the world oil price 

does not represent a proper externality, and that the monopsony component should not be 

considered in calculations of the energy security externality. However, they also note in their 

earlier discussion paper (Brown and Huntington 2010)137 that this is a departure from the 

traditional energy security literature, which includes sustained wealth transfers associated with 

stable but higher-price oil markets. On the other hand, Greene (2010)138 and others in prior 

literature (e.g., Toman 1993)139 have emphasized that the monopsony cost component is policyrelevant because the world oil market is non-competitive and strongly influenced by cartelized 

and government-controlled supply decisions. Thus, while sometimes couched as an externality, 

Greene notes that the monopsony component is best viewed as stemming from a completely 

different market failure than an externality (Ledyard 2008)140, yet still implying marginal social 

costs to importers. 

There is also a question about the ability of gradual, long-term reductions, such as those 

resulting from this proposed rule, to reduce the world oil price in the presence of OPEC’s 

monopoly power. OPEC is currently the world’s marginal petroleum supplier, and could 

conceivably respond to gradual reductions in U.S. demand with gradual reductions in supply 

over the course of several years as the fuel savings resulting from this rule grow. However, if 

OPEC opts for a long-term strategy to preserve its market share, rather than maintain a particular 

price level (as they have done recently in response to increasing U.S. petroleum production), 

reduced demand would create downward pressure on the global price. The Oak Ridge analysis 

assumes that OPEC does respond to demand reductions over the long run, but there is still a price 

effect in the model. Under the mid-case behavioral assumption used in the premium 

calculations, OPEC responds by gradually reducing supply to maintain market share (consistent 

with the long-term self-interested strategy suggested by Gately (2004, 2007)).141 

It is important to note that the decrease in global petroleum prices resulting from this 

rulemaking could spur increased consumption of petroleum in other sectors and countries, 

leading to a modest uptick in GHG emissions outside of the United States. This increase in 

global fuel consumption could offset some portion of the GHG reduction benefits associated 

with this proposed rule. The agency have not quantified this increase in global GHG emissions. 

We will review any comments we receive, as well as data sources and methodologies for how 

global rebound effects may be quantified. 

8.9.3.2 Macroeconomic Disruption Adjustment Costs 

The second component of the oil import premium, “avoided macroeconomic 

disruption/adjustment costs,” arises from the effect of oil imports on the expected cost of supply 
8-80 

disruptions and accompanying price increases. A sudden increase in oil prices triggered by a 

disruption in world oil supplies has two main effects: (1) it increases the costs of oil imports in 

the short-run, and (2) it can lead to macroeconomic contraction, dislocation and Gross Domestic 

Product (GDP) losses. For example, ORNL estimates the combine value of these two factors to 

be $6.34/barrel when U.S. oil imports are reduced in 2020, with a range from $3.07/barrel to 

$10.15/barrel of imported oil reduced. 

There are two main effects of macroeconomic disruption/adjustment costs. The first is 

the aggregate effect of the short-run price increase from an oil shock. The oil price shock results 

in a combination of real resource shortages, costly short-run shifts in energy supply, behavioral 

and demand adjustments by energy users, and other response costs. Unlike pure transfers, the 

root cause of the disruption price increase is a real resource supply reduction due, for example, to 

disaster or war. Regions where supplies are disrupted, such as the U.S., suffer high costs. 

Businesses’ and households’ emergency responses to supply disruptions and rapid price 

increases consume real economic resources. 

When households and businesses make decisions related to their oil consumption, such as 

whether to invest in fuel-saving technologies or use futures markets, they are unlikely to account 

for the effect of their petroleum consumption on the magnitude of costs that supply interruptions 

and accompanying price shocks impose on others. As a consequence, the U.S. economy as a 

whole will not make sufficient use of these mechanisms to insulate itself from the real costs of 

rapid increases in energy prices and outlays that usually accompany oil supply interruptions. 

Therefore, the ORNL estimate of avoided macroeconomic disruption/adjustment costs that the 

agencies use to value energy security benefits includes the increased oil import costs stemming 

from oil price shocks that are unanticipated and not internalized by advance actions of U.S. 

consumers and businesses. This aggregate output effect will last as long as the oil price is 

elevated. It depends on the extent and duration of any disruption in the world supply of oil, since 

these factors determine the magnitude of the resulting increases in prices for petroleum products, 

as well as how rapidly these prices return to their pre-disruption level. 

The second main effect of macroeconomic disruption/adjustment costs is the 

macroeconomic losses due to “allocative” losses. These are the costs of temporary dislocation 

and underutilization of available resources due to the oil shock, such as labor unemployment and 

idle plant capacity. Because supply disruptions and resulting price increases occur suddenly, 

empirical evidence shows they impose additional costs on businesses and households that must 

adjust their use of petroleum and other productive factors more rapidly than if the same price 

increase had occurred gradually. Dislocational effects include the unemployment of workers and 

other resources during the time needed for their intersectoral or interregional reallocation, and 

pauses in capital investment due to uncertainty. These adjustments temporarily reduce the level 

of economic output that can be achieved even below the “potential” output level that would 

ultimately be reached once the economy’s adaptation to higher petroleum prices is complete. 

The additional costs imposed on businesses and households for making these adjustments reflect 

their limited ability to adjust prices, output levels, and their use of energy, labor and other inputs 

quickly and smoothly in response to rapid changes in prices for petroleum products. 

Since future disruptions in foreign oil supplies are an uncertain prospect, each of the 

disruption cost components must be weighted by the probability that the supply of petroleum to 
8-81 

the U.S. will actually be disrupted. Thus, the “expected value” of these costs – the product of the 

probability that a supply disruption will occur and the sum of costs from reduced economic 

output and the economy’s abrupt adjustment to sharply higher petroleum prices – is the relevant 

measure of their magnitude. Further, when assessing the energy security value of a policy to 

reduce oil use, it is only the change in the expected costs of disruption that results from the 

policy that is relevant. The expected costs of disruption may change from lowering the normal 

(i.e., pre-disruption) level of domestic petroleum use and imports, from any induced alteration in 

the likelihood or size of disruption, or from altering the short-run flexibility (e.g., elasticity) of 

petroleum use. 

With updated oil market and economic factors, the avoided macroeconomic disruption 

component of the energy security premiums is slightly lower in comparison to avoided 

macroeconomic disruption premiums used in previous rulemakings. There are several reasons 

why the avoided macroeconomic disruption premiums change only moderately. One reason is 

that the macroeconomic sensitivity to oil price shocks is assumed unchanged in recent years 

since U.S. oil consumption levels and the value share of oil in the U.S. economy remain at high 

levels. For example, Figure 8-4 below shows that under AEO 2014 (Early Release), projected 

U.S. real annual oil expenditures continue to rise after 2015 to over $800 billion (2012$) by 

2030. The value share of oil use in the U.S. economy remains between three and four percent, 

well above the levels observed from 1985 to 2005. A second factor is that oil disruption risks are 

little changed. The two factors influencing disruption risks are the probability of global supply 

interruptions and the world oil supply share from OPEC. Both factors are not significantly 

different from previous forecasts of oil market trends. 

Factors that contribute to moderately lowering the avoided macroeconomic disruption 

component are lower projected GDP, moderately lower oil prices and slightly smaller price 

increases during prospective shocks. For example, oil price levels are 5 – 19 percent lower over 

the 2020 – 2035 period, and the likely increase in oil prices in the event of an oil shock are 

somewhat smaller, given small increases in the responsiveness of oil supply to changes in the 

world price of oil. Overall, the avoided macroeconomic disruption component estimates for the 

oil security premiums are 2 – 19 percent lower over the period from 2020-2035 based upon 

different projected oil market and economic trends in the AEO 2014 (Early Release) compared to 

the AEO 2012 (Early Release). 

The energy security costs estimated here follow the oil security premium framework, 

which is well established in the energy economics literature. The oil import premium gained 

attention as a guiding concept for energy policy around the time of the second and third major 

post-war oil shocks (Bohi and Montgomery 1982, EMF 1982).

142 Plummer (1982)143 provided 

valuable discussion of many of the key issues related to the oil import premium as well as the 

analogous oil stockpiling premium. Bohi and Montgomery (1982)144 detailed the theoretical 

foundations of the oil import premium established many of the critical analytic relationships 

through their thoughtful analysis. Hogan (1981)145 and Broadman and Hogan (1986, 1988)146

revised and extended the established analytical framework to estimate optimal oil import premia 

with a more detailed accounting of macroeconomic effects. 

Since the original work on energy security was undertaken in the 1980’s, there have been 

several reviews on this topic. For example, Leiby, Jones, Curlee and Lee (1997)147 provided an 
8-82 

extended review of the literature and issues regarding the estimation of the premium. Parry and 

Darmstadter (2004)148 also provided an overview of extant oil security premium estimates and 

estimated of some premium components. 

The recent economics literature on whether oil shocks are a threat to economic stability 

that they once were is mixed. Some of the current literature asserts that the macroeconomic 

component of the energy security externality is small. For example, the National Research 

Council (2009) argued that the non-environmental externalities associated with dependence on 

foreign oil are small, and potentially trivial.149 Analyses by Nordhaus (2007) and Blanchard and 

Gali (2010) question the impact of more recent oil price shocks on the economy.150 They were 

motivated by attempts to explain why the economy actually expanded immediately after the last 

shocks, and why there was no evidence of higher energy prices being passed on through higher 

wage inflation. Using different methodologies, they conclude that the economy has largely 

gotten over its concern with dramatic swings in oil prices. 

One reason, according to Nordhaus, is that monetary policy has become more 

accommodating to the price impacts of oil shocks. Another is that consumers have simply 

decided that such movements are temporary, and have noted that price impacts are not passed on 

as inflation in other parts of the economy. He also notes that real changes to productivity due to 

oil price increases are incredibly modest,NN and that the general direction of the economy matters 

a great deal regarding how the economy responds to a shock. Estimates of the impact of a price 

shock on aggregate demand are insignificantly different from zero. 

Blanchard and Gali (2010) contend that improvements in monetary policy (as noted 

above), more flexible labor markets, and lessening of energy intensity in the economy, combined 

with an absence of concurrent shocks, all contributed to lessen the impact of oil shocks after 

1980. They find “... the effects of oil price shocks have changed over time, with steadily smaller 

effects on prices and wages, as well as on output and employment.”151 In a comment at the 

chapter’s end, this work is summarized as follows: “The message of this chapter is thus 

optimistic in that it suggests a transformation in U.S. institutions has inoculated the economy 

against the responses that we saw in the past.” 

At the same time, the implications of the “Shale Oil Revolution” are now being felt in the 

international markets, with current prices at four year lows. Analysts generally attribute this 

result in part to the significant increase in supply resulting from U.S. production, which has put 

liquid petroleum production on par with Saudi Arabia. The price decline is also attributed to the 

sustained reductions in U.S. consumption and global demand growth from fuel efficiency 

policies and high oil prices. The resulting decrease in foreign imports, down to about one-third 

of domestic consumption (from 60 percent in 2005, for example152), effectively permits U.S. 

 

NN In fact, “... energy-price changes have no effect on multifactor productivity and very little effect on labor 

productivity.” Page 19. He calculates the productivity effect of a doubling of oil prices as a decrease of 0.11 percent 

for one year and 0.04 percent a year for ten years. Page 5. (The doubling reflects the historical experience of the 

post-war shocks, as described in Table 7.1 in Blanchard and Gali, p. 380.) 
8-83 

supply to act as a buffer against artificial or other supply restrictions (the latter due to conflict or 

natural disaster, for example). 

However, other papers suggest that oil shocks, particularly sudden supply shocks, remain 

a concern. Both Blanchard and Gali’s and Nordhaus work were based on data and analysis 

through 2006, ending with a period of strong global economic growth and growing global oil 

demand. The Nordhaus work particularly stressed the effects of the price increase from 2002-

2006 that were comparatively gradual (about half the growth rate of the 1973 event and one-third 

that of the 1990 event). The Nordhaus study emphasizes the robustness of the U.S. economy 

during a time period through 2006. This time period was just before rapid further increases in 

the price of oil and other commodities with oil prices more-than-doubling to over $130/barrel by 

mid-2008, only to drop after the onset of the largest recession since the Great Depression. 

Hamilton (2012) reviewed the empirical literature on oil shocks and suggested that the 

results are mixed, noting that some work (e.g. Rasmussen and Roitman (2011) finds less 

evidence for economic effects of oil shocks, or declining effects of shocks (Blanchard and Gali 

2010), while other work continues to find evidence regarding the economic importance of oil 

shocks. For example, Baumeister and Peersman (2011) found that an oil price increase of a 

given size seems to have a decreasing effect over time, but noted that the declining priceelasticity of demand meant that a given physical disruption had a bigger effect on price and 

turned out to have a similar effect on output as in the earlier data.”153 Hamilton observes that “a 

negative effect of oil prices on real output has also been reported for a number of other countries, 

particularly when nonlinear functional forms have been employed” (citing as recent examples 

Kim 2012, Engemann, Kliesen, and Owyang 2011 and Daniel, et. al. 2011). Alternatively, rather 

than a declining effect, Ramey and Vine (2010) found “remarkable stability in the response of 

aggregate real variables to oil shocks once we account for the extra costs imposed on the 

economy in the 1970s by price controls and a complex system of entitlements that led to some 

rationing and shortages.”154

Some of the recent literature on oil price shocks has emphasized that economic impacts 

depend on the nature of the oil shock, with differences between price increases caused by sudden 

supply loss and those caused by rapidly growing demand. Most recent analyses of oil price 

shocks have confirmed that “demand-driven” oil price shocks have greater effects on oil prices 

and tend to have positive effects on the economy while “supply-driven” oil shocks still have 

negative economic impacts (Baumeister, Peersman and Robays, 2010). A recent paper by Kilian 

and Vigfusson (2014), for example, assigned a more prominent role to the effects of price 

increases that are unusual, in the sense of being beyond range of recent experience. Kilian and 

Vigfussen also conclude that the difference in response to oil shocks may well stem from the 

different effects of demand- and supply-based price increases: “One explanation is that oil price 

shocks are associated with a range of oil demand and oil supply shocks, some of which stimulate 

the U.S. economy in the short run and some of which slow down U.S. growth (see Kilian 2009a). 

How recessionary the response to an oil price shock is thus depends on the average composition 

of oil demand and oil supply shocks over the sample period.” 

The general conclusion that oil supply-driven shocks reduce economic output is also 
8-84 

reached in a recently published paper by Cashin et al. (2014) for 38 countries from 1979-2011. 

“The results indicate that the economic consequences of a supply-driven oil-price shock are very 

different from those of an oil-demand shock driven by global economic activity, and vary for oilimporting countries compared to energy exporters,” and “oil importers [including the U.S.] 

typically face a long-lived fall in economic activity in response to a supply-driven surge in oil 

prices” but almost all countries see an increase in real output for an oil-demand disturbance. 

Note that the energy security premium calculation in this analysis is based on price shocks from 

potential future supply events only. 

Despite continuing uncertainty about oil market behavior and outcomes and the 

sensitivity of the U.S. economy to oil shocks, it is generally agreed that it is beneficial to reduce 

petroleum fuel consumption from an energy security standpoint. Reducing fuel consumption 

reduces the amount of domestic economic activity associated with a commodity whose price 

depends on volatile international markets. Also, reducing U.S. oil import levels reduces the 

likelihood and significance of supply disruptions. 

Figure 8-4 Projected and Historical U.S. Expenditures, and Expenditure Share, on Crude Oil155

8.9.3.3 Cost of Existing U.S. Energy Security Policies 

The last often-identified component of the full economic costs of U.S. oil imports are the 

costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples 

are maintaining the Strategic Petroleum Reserve (SPR) and maintaining a military presence to 

help secure a stable oil supply from potentially vulnerable regions of the world. The SPR is the 

largest stockpile of government-owned emergency crude oil in the world. Established in the 

aftermath of the 1973/74 oil embargo, the SPR provides the U.S. with a response option should a 

0%

1%

2%

3%

4%

5%

6%

7%

0

100

200

300

400

500

600

700

800

900

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

Expenditure as Share of GDP

$Bill/yr, 2012$

Year

Projected and Historical U.S. Expenditures, 

and Expenditure Share, on Crude Oil

Domestic

Imported

US Oil Expenditures as Share of GDP
8-85 

disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet 

part of its International Energy Agency obligation to maintain emergency oil stocks, and it 

provides a national defense fuel reserve. While the costs for building and maintaining the SPR 

are more clearly related to U.S. oil use and imports, historically these costs have not varied in 

response to changes in U.S. oil import levels. Thus, while the effect of the SPR in moderating 

price shocks is factored into the ORNL analysis, the cost of maintaining the SPR is excluded. 

U.S. military costs are excluded from the analysis performed by ORNL because their 

attribution to particular missions or activities is difficult, and because it is not clear that these 

outlays would decline in response to incremental reductions in U.S. oil imports. Most military 

forces serve a broad range of security and foreign policy objectives. Attempts to attribute some 

share of U.S. military costs to oil imports are further challenged by the need to estimate how 

those costs might vary with incremental variations in U.S. oil imports. 

8.9.4 Energy Security Benefits of this Program 

Using the ORNL “oil premium” methodology, updating world oil price values and 

energy trends using AEO 2014 (Early Release) and using the estimated fuel savings from the 

proposed rule estimated from the MOVES/CAFE models, the agencies has calculated the energy 

security benefits of this proposed rule for different classes for medium- and heavy-duty vehicles 

for the various years up to 2050.OO Since the agencies are taking a global perspective with 

respect to valuing greenhouse gas benefits from the rule, only the avoided macroeconomic 

adjustment/disruption portion of the energy security premium is used in the energy security 

benefits estimates present below. These results are shown below in Table 8-35. Table 8-36 and 

Table 8-37 show discounted model year lifetime energy security benefits for different classes of 

heavy-duty vehicles using a three and seven percent discount rate. 

 

OO In order to determine the energy security benefits beyond 2040, we use the 2040 energy security premium 

multiplied by the estimate fuel savings from the proposed rule. Since the AEO 2014 (Early Release) only goes to 

2040, we only calculate energy security premiums to 2040. 
8-86 

Table 8-35 Annual U.S. Energy Security Benefits and Net Present Values at 3% and 7% Discount Rates for 

the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (in Millions of 2012$) a

CALENDAR 

YEAR 

HD 

PICKUP 

& 

VANS 

VOCATIONAL TRACTOR/ 

TRAILER 

SUM 

2018 $0 $0 $10 $10 

2019 $0 $0 $20 $20 

2020 $0 $0 $31 $31 

2021 $3 $6 $69 $77 

2022 $10 $12 $118 $140 

2023 $23 $19 $170 $211 

2024 $41 $33 $255 $328 

2025 $65 $47 $344 $456 

2026 $95 $63 $438 $596 

2027 $131 $91 $548 $770 

2028 $167 $120 $660 $947 

2029 $204 $150 $772 $1,126 

2030 $241 $180 $884 $1,306 

2035 $413 $322 $1,421 $2,156 

2040 $556 $443 $1,922 $2,920 

2050 $647 $522 $2,328 $3,498 

NPV, 3% $5,356 $4,209 $19,383 $28,947 

NPV, 7% $2,163 $1,689 $8,005 $11,857 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-36 Discounted Model Year Lifetime Energy Security Benefits at a 3% Discount Rate for the 

Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

MODEL 

YEAR 

HD PICKUP 

 AND VANS 

VOCATIONAL TRACTOR/ 

TRAILER 

SUM 

2018 $0 $0 $86 $86 

2019 $0 $0 $85 $85 

2020 $0 $0 $84 $84 

2021 $24 $55 $455 $534 

2022 $66 $54 $458 $579 

2023 $107 $54 $460 $621 

2024 $148 $116 $732 $996 

2025 $190 $118 $751 $1,060 

2026 $233 $120 $768 $1,121 

2027 $272 $216 $887 $1,375 

2028 $273 $217 $898 $1,388 

2029 $272 $217 $907 $1,397 

Sum $1,587 $1,168 $6,571 $9,325 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1
8-87 

Table 8-37 Discounted Model Year Lifetime Energy Security Benefits due to the Preferred Alternative at a 

7% Discount Rates for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B 

using the MOVES Analysis of HD Pickups and Vans and Relative to the Less Dynamic Baseline (Millions of 

2012$) a

MODEL 

YEAR 

HD PICKUP 

 AND VANS 

VOCATIONAL TRACTOR/ 

TRAILER 

SUM 

2018 $0 $0 $60 $60 

2019 $0 $0 $56 $56 

2020 $0 $0 $53 $53 

2021 $15 $34 $277 $326 

2022 $40 $32 $269 $341 

2023 $62 $31 $260 $353 

2024 $82 $64 $400 $546 

2025 $102 $63 $395 $560 

2026 $120 $62 $390 $571 

2027 $135 $107 $434 $676 

2028 $130 $104 $423 $657 

2029 $125 $100 $412 $637 

Sum $810 $597 $3,430 $4,837 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

8.10 Summary of Benefits and Costs 

This section presents the costs, benefits, and other economic impacts of the proposed 

Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standards 

and EPA’s proposed GHG standards would both be in effect, and would jointly lead to increased 

fuel efficiency and reductions in GHG and non-GHG emissions. The individual categories of 

benefits and costs presented in the tables below include: 

• the vehicle program costs (costs of complying with the vehicle CO2 and fuel 

consumption standards), 

• changes in fuel expenditures associated with reduced fuel use by more efficient vehicles 

and increased fuel use associated with the “rebound” effect, both of which result from the 

program, 

• the global economic value of reductions in GHGs, 

• the economic value of reductions in non-GHG pollutants, 

• costs associated with increases in noise, congestion, and accidents resulting from 

increased vehicle use, 

• savings in drivers’ time from less frequent refueling, 

• benefits of increased vehicle use associated with the “rebound” effect, and 

• the economic value of improvements in U.S. energy security impacts. 

For a discussion of the cost of ownership and the agencies’ payback analysis of vehicles 

covered by this proposal, please see Chapter 7 of this draft RIA. 
8-88 

The agencies conducted coordinated and complementary analyses using two analytical 

methods referred to as Method A and Method B. For an explanation of these methods, please 

see Section I.D for the preamble. And as discussed in preamble Section X.A.1, the agencies 

present estimates of benefits and costs that are measured against two different assumptions about 

improvements in fuel efficiency that might occur in the absence of the Phase 2 standards. The 

first case (Alternative 1a) uses a baseline that projects very little improvement in new vehicles in 

the absence of new Phase 2 standards, and the second (Alternative 1b) uses a more dynamic 

baseline that projects more significant improvements in vehicle fuel efficiency. 

Table 8-38 shows benefits and costs for the proposed standards from the perspective of a 

program designed to improve the nation’s energy security and conserve energy by improving 

fuel efficiency. From this viewpoint, technology costs occur when the vehicle is purchased. 

Fuel savings are counted as benefits that occur over the lifetimes of the vehicles produced during 

the model years subject to the Phase 2 standards as they consume less fuel. The table shows that 

benefits far outweigh the costs, and the preferred alternative is anticipated to result in large net 

benefits to the U.S economy. 

Table 8-38 Lifetime Benefits & Costs of the Preferred Alternative for Model Years 2018 - 2029 Vehicles 

Using Analysis Method A (Billions of 2012$ discounted at 3% and 7%) 

CATEGORY BASELINE 1A BASELINE 1B 

3% 7% 3% 7% 

Vehicle Program: Technology and 

Indirect Costs, Normal Profit on 

Additional Investments 

25.4 17.1 25.0 16.8 

Additional Routine Maintenance 1.1 0.6 1.0 0.6 

Congestion, Accidents, and Noise 

from Increased Vehicle Use 

4.7 2.8 4.5 2.6 

Total Costs 31.1 20.5 30.5 20.0 

Fuel Savings (valued at pre-tax prices) 175.1 94.2 165.1 89.2 

Savings from Less Frequent Refueling 3.1 1.6 2.9 1.5 

Economic Benefits from Additional 

Vehicle Use 

15.1 8.4 14.7 8.2 

Reduced Climate Damages from GHG 

Emissions a

34.9 34.9 32.9 32.9 

Reduced Health Damages from NonGHG Emissions 38.8 20.7 37.2 20.0 

Increased U.S. Energy Security 8.9 4.7 8.1 4.3 

Total Benefits 276 165 261 156 

Net Benefits 245 144 231 136 

Note: 

a

 Benefits and net benefits use the 3 percent average global SCC value applied only to CO2 

emissions; GHG reductions include CO2, CH4, N2O and HFC reductions, and include benefits to 
8-89 

other nations as well as the U.S. See Draft RIA Chapter 8.5 and Preamble Section IX.G for further 

discussion. 

Table 8-39, Table 8-40 and Table 8-41 report benefits and cost from the perspective of 

reducing GHG. Table 8-39 shows the annual impacts and net benefits of the preferred 

alternative for selected future years, together with the net present values of cumulative annual 

impacts from 2018 through 2050, discounted at 3 percent and 7 percent rates. Table 8-40 and 

Table 8-41 show the discounted lifetime costs and benefits for each model year affected by the 

Phase 2 standards at 3 percent and 7 percent discount rates, respectively. 

Table 8-39 Annual Benefits & Costs and Net Present Values for the Preferred Alternative Relative to the 

Less Dynamic Baseline and using Method B 

(Billions of 2012$)a,b,c 

2018 2021 2024 2030 2035 2040 2050 NPV, 

3% 

NPV, 

7% 

Vehicle 

program

-$0.1 -$2.4 -$3.7 -$5.4 -$5.9 -$6.3 -$7.0 -$86.8 -$41.1

Maintenance $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$1.8 -$0.9

Pre-tax Fuel $0.2 $1.7 $6.9 $24.0 $37.2 $47.8 $57.5 $495.6 $206.7

Energy 

security 

$0.0 $0.1 $0.3 $1.3 $2.2 $2.9 $3.5 $28.9 $11.9

Accidents/ 

Congestion/ 

Noise 

$0.0 -$0.1 -$0.3 -$0.5 -$0.7 -$0.8 -$0.9 -$9.3 -$4.2

Refueling $0.0 $0.0 $0.1 $0.4 $0.7 $0.9 $1.2 $9.4 $3.9

Travel value $0.0 $0.4 $1.0 $1.9 $2.4 $2.9 $3.3 $34.2 $15.3

Non-GHG $0.0 to 

$0.1 

$0.4 to 

$0.9 

$1.0 to 

$2.4 

$3.3 to 

$8.3 

$4.8 to 

$12.1 

$5.7 to 

$14.3 

$7.0 to 

$17.5 

$69.7 to 

$157.0 

$26.6 to 

$60.4

SCC 

SC-CO2; 5% 

avg 

$0.0 $0.1 $0.4 $1.5 $2.5 $3.3 $5.0 $22.1 $22.1

SC-CO2; 3% 

avg 

$0.0 $0.3 $1.3 $4.8 $7.4 $9.7 $13.6 $103.1 $103.1

SC-CO2; 

2.5% avg 

$0.1 $0.5 $1.9 $6.9 $10.6 $13.7 $18.5 $164.1 $164.1

SC-CO2; 3% 

95th 

$0.1 $1.0 $4.0 $14.6 $23.2 $30.3 $42.0 $320.5 $320.5

Net benefits d 

SC-CO2; 5% 

avg 

$0.2 $0.4 $6.4 $28.8 $46.8 $60.6 $74.6 $605.8 $257.1

SC-CO2; 3% 

avg 

$0.2 $0.7 $7.3 $32.1 $51.7 $66.9 $83.2 $686.8 $338.1

SC-CO2; 

2.5% avg 

$0.2 $0.8 $7.9 $34.2 $54.9 $70.9 $88.2 $747.8 $399.1

SC-CO2; 3% 

95th 

$0.3 $1.3 $10.0 $41.9 $67.5 $87.6 $111.7 $904.1 $555.5

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
8-90 

b

 Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail. c

 Section 8.5 of the draft RIA notes that SC-CO2 increases over time. Corresponding to the years in this table (2020-

2050), the SC-CO2 estimates range as follows: for Average SC-CO2 at 5%: $7-$16; for Average SC-CO2 at 3%: 

$27-$46; for Average SC-CO2 at 2.5%: $43-$67; and for 95th percentile SC-CO2 at 3%: $83-$140. Section VIII.F 

also presents these SC-CO2 estimates. d

 Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value. 

The table shows the benefits of reduced CO2 emissions—and consequently the annual 

quantified benefits (i.e., total benefits)—for each of four SC-CO2 values estimated by the 

interagency working group. As discussed in Section 8.5, there are some limitations to the SCCO2 analysis, including the incomplete way in which the integrated assessment models capture 

catastrophic and non-catastrophic impacts, their incomplete treatment of adaptation and 

technological change, uncertainty in the extrapolation of damages to high temperatures, and 

assumptions regarding risk aversion. 

In addition, these monetized GHG benefits exclude the value of reductions in non-CO2

GHG emissions (CH4, N2O, HFC) expected under this program. Although EPA has not 

monetized the benefits of reductions in non-CO2 GHGs in this Section 8.10, the value of these 

reductions should not be interpreted as zero. The reader is referred to Section 8.5.2 of this draft 

RIA to see the value of those monetized benefits. Also, note that the net reductions in non-CO2

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F of the 

preamble. 

The agencies have also conducted a separate analysis of the total benefits over the model 

year lifetimes of 2018 through 2029 model year vehicles. In contrast to the calendar year 

analysis presented in Table 8-39, the model year lifetime analysis shows the impacts of the 

program on vehicles produced during each of the affected model years over the course of their 

expected lifetimes. The net societal benefits over the full lifetimes of vehicles produced during 

each of the model years are shown in Table 8-40 and Table 8-41 at both 3 percent and 7 percent 

discount rates, respectively. 
8-91 

Table 8-40 Discounted Model Year Lifetime Impacts for the Preferred Alternative Relative to the Less 

Dynamic Baseline and using Method B 

 (Billions of 2012$; 3% Discount Rate) a,b,c

 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 SUM 

Vehicle 

Program 

-$0.1 -$0.1 -$0.1 -$2.0 -$1.9 -$1.9 -$2.8 -$2.7 -$2.7 -$3.7 -$3.6 -$3.5 -$25.1

Maintenance -$0.1 $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$1.1

Pre-tax Fuel $1.9 $1.9 $1.8 $11.1 $11.5 $11.9 $18.9 $19.6 $20.2 $24.1 $24.1 $24.1 $171.1

Energy 

Security 

$0.1 $0.1 $0.1 $0.5 $0.6 $0.6 $1.0 $1.1 $1.1 $1.4 $1.4 $1.4 $9.3

Accidents, 

Noise, 

Congestion 

-$0.1 -$0.1 -$0.2 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$4.2

Refueling $0.0 $0.0 $0.0 $0.2 $0.2 $0.2 $0.3 $0.3 $0.4 $0.5 $0.5 $0.5 $3.1

Travel value $0.6 $0.6 $0.7 $1.5 $1.5 $1.5 $1.4 $1.4 $1.4 $1.4 $1.4 $1.4 $14.9

Non-GHG $0.2 

to 

$0.5 

$0.2 

to 

$0.4 

$0.2 

to 

$0.4 

$2.0 

to 

$4.5 

$2.0 

to 

$4.5 

$2.0 

to 

$4.5 

$2.9 

to 

$6.6 

$3.0 

to 

$6.8 

$2.6 

to 

$5.9 

$3.1 

to 

$6.9 

$3.1 

to 

$6.9 

$3.1 

to 

$7.0 

$24.4 

to 

$55.0

SCC 

SC-CO2; 5% 

avg 

$0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8

SC-CO2; 3% 

avg 

$0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0

SC-CO2; 

2.5% avg 

$0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4

SC-CO2; 3% 

95th 

$1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0

Net benefits d 

SC-CO2; 5% 

avg 

$2.8 $2.7 $2.7 $14.6 $15.1 $15.5 $23.9 $25.0 $25.1 $29.2 $29.4 $29.4 $215.5

SC-CO2; 3% 

avg 

$3.0 $3.0 $3.0 $16.2 $16.8 $17.3 $26.8 $28.0 $28.2 $33.0 $33.1 $33.2 $241.7

SC-CO2; 

2.5% avg 

$3.2 $3.2 $3.2 $17.4 $18.1 $18.6 $28.9 $30.2 $30.5 $35.7 $35.9 $36.0 $261.1

SC-CO2; 3% 

95th 

$3.8 $3.8 $3.7 $20.7 $21.5 $22.1 $34.5 $36.0 $36.6 $43.1 $43.3 $43.5 $312.7

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this program (see draft RIA Chapter 8.5). Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero. 

c Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail. d

 Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value. 
8-92 

Table 8-41 Discounted Model Year Lifetime Impacts for the Preferred Alternative Relative to the Less 

Dynamic Baseline and using Method B (Billions of 2012$; 7% Discount Rate) a,b,c

 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 SUM 

Vehicle 

Program 

-$0.1 -$0.1 -$0.1 -$1.6 -$1.4 -$1.4 -$1.9 -$1.8 -$1.7 -$2.3 -$2.1 -$2.0 -$16.6

Maintenance $0.0 $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 $0.0 -$0.6

Pre-tax Fuel $1.4 $1.3 $1.2 $6.9 $6.9 $6.8 $10.5 $10.4 $10.4 $11.9 $11.5 $11.0 $90.1

Energy 

Security 

$0.1 $0.1 $0.1 $0.3 $0.3 $0.4 $0.5 $0.6 $0.6 $0.7 $0.7 $0.6 $4.8

Accidents, 

Noise, 

Congestion 

-$0.1 -$0.1 -$0.1 -$0.3 -$0.3 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$2.4

Refueling $0.0 $0.0 $0.0 $0.1 $0.1 $0.1 $0.2 $0.2 $0.2 $0.2 $0.2 $0.2 $1.6

Travel value $0.4 $0.4 $0.4 $0.9 $0.9 $0.8 $0.8 $0.8 $0.7 $0.7 $0.7 $0.6 $8.2

Non-GHG $0.1 

to 

$0.3 

$0.1 

to 

$0.3 

$0.1 

to 

$0.3 

$1.1 

to 

$2.5 

$1.1 

to 

$2.4 

$1.0 

to 

$2.3 

$1.4 

to 

$3.3 

$1.4 

to 

$3.2 

$1.2 

to 

$2.7 

$1.3 

to 

$3.0 

$1.3 

to 

$2.9 

$1.3 

to 

$2.8 

$11.5 

to 

$26.0

SCC 

SC-CO2; 5% 

avg 

$0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8

SC-CO2; 3% 

avg 

$0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0

SC-CO2; 

2.5% avg 

$0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4

SC-CO2; 3% 

95th 

$1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0

Net benefits d 

SC-CO2; 5% 

avg 

$1.9 $1.8 $1.7 $8.7 $8.7 $8.7 $13.0 $13.1 $12.7 $14.3 $13.8 $13.4 $111.8

SC-CO2; 3% 

avg 

$2.2 $2.1 $2.0 $10.3 $10.4 $10.5 $15.8 $16.1 $15.8 $18.0 $17.6 $17.2 $138.0

SC-CO2; 

2.5% avg 

$2.4 $2.3 $2.2 $11.5 $11.7 $11.8 $17.9 $18.3 $18.1 $20.7 $20.4 $20.0 $157.4

SC-CO2; 3% 

95th 

$2.9 $2.8 $2.8 $14.8 $15.1 $15.3 $23.6 $24.2 $24.2 $28.1 $27.8 $27.4 $209.0

Notes: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 b The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this program (see draft RIA Chapter 8.5). Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero. 

c Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail. d

 Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value. 
8-93 

8.11Employment Impacts 

8.11.1 Introduction 

Executive Order 13563 (January 18, 2011) directs federal agencies to consider regulatory 

impacts on, among other criteria, job creation.156 According to the Executive Order “Our 

regulatory system must protect public health, welfare, safety, and our environment while 

promoting economic growth, innovation, competitiveness, and job creation. It must be based on 

the best available science.” Analysis of employment impacts of a regulation is not part of a 

standard benefit-cost analysis (except to the extent that labor costs contribute to costs). 

Employment impacts of federal rules are of general interest, however, and have been particularly 

so, historically, in the auto sector during periods of challenging labor market conditions. For this 

reason, we are describing the connections of these proposed standards to employment in the 

regulated sector, the motor vehicle manufacturing sector, as well as the motor vehicle body and 

trailer and motor vehicle parts manufacturing sectors. 

The overall effect of the proposed rules on motor vehicle sector employment depends on 

the relative magnitude of output and substitution effects, described below. Because we do not 

have quantitative estimates of the output effect, and only a partial estimate of the substitution 

effect, we cannot reach a quantitative estimate of the overall employment effects of the proposed 

rules on motor vehicle sector employment or even whether the total effect will be positive or 

negative. 

According to the U.S. Bureau of Labor Statistics, in 2014, about 850,000 people in the 

U.S. were employed in the Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, 

and 3363),157 the directly regulated sector. The employment effects of these proposed rules are 

expected to expand beyond the regulated sector. Though some of the parts used to achieve the 

proposed standards are likely to be built by motor vehicle manufacturers (including trailer 

manufacturers) themselves, the motor vehicle parts manufacturing sector also plays a significant 

role in providing those parts, and will also be affected by changes in vehicle sales. Changes in 

truck sales, discussed in Chapter8.4.2, could also affect employment for truck and trailer 

vendors. As discussed in Chapter 7.2, this proposed rule is expected to reduce the amount of fuel 

these vehicles use, and thus affect the petroleum refinery and supply industries as well. Finally, 

since the net reduction in cost associated with these proposed rules is expected to lead to lower 

transportation and shipping costs, in a competitive market a substantial portion of those cost 

savings will be passed along to consumers, who then will have additional discretionary income 

(how much of the cost is passed along to consumers depends on market structure and the relative 

price elasticities). The proposed rules are not expected to have any notable inflationary or 

recessionary effect. 

The employment effects of environmental regulation are difficult to disentangle from 

other economic changes and business decisions that affect employment, over time and across 

regions and industries. In light of these difficulties, we lean on economic theory to provide a 

constructive framework for approaching these assessments and for better understanding the 

inherent complexities in such assessments. Neoclassical microeconomic theory describes how 

profit-maximizing firms adjust their use of productive inputs in response to changes in their 

economic conditions.158 Berman and Bui (2001, pp. 274-75) model two components that drive 
8-94 

changes in firm-level labor demand: output effects and substitution effects. 159,PP Regulation can 

affect the profit-maximizing quantity of output by changing the marginal cost of production. If 

regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to 

a decrease in the quantity demanded, and resulting in a decrease in production. The output effect 

describes how, holding labor intensity constant, a decrease in production causes a decrease in labor 

demand. As noted by Berman and Bui, although many assume that regulation increases marginal 

cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more 

efficient equipment that lowers marginal production costs, or it may induce use of technologies that 

may prove popular with buyers or provide positive network externalities (see Chapter 8.2 for 

discussion of this effect). In such a case, output could increase.

The substitution effect describes how, holding output constant, regulation affects labor 

intensity of production. Although increased environmental regulation may increase use of pollution 

control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. 

For example, equipment inspection requirements, specialized waste handling, or pollution 

technologies that alter the production process may affect the number of workers necessary to produce 

a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on 

pollution control equipment and expenditures required by the regulation and the corresponding 

change in labor intensity of production. 

In summary, as output and substitution effects may be positive or negative, theory alone 

cannot predict the direction of the net effect of regulation on labor demand at the level of the 

regulated firm. Operating within the bounds of standard economic theory, empirical estimation of 

net employment effects on regulated firms is possible when data and methods of sufficient detail and 

quality are available. The literature, however, illustrates difficulties with empirical estimation. For 

example, studies sometimes rely on confidential plant-level employment data from the U.S. Census 

Bureau, possibly combined with pollution abatement expenditure data that are too dated to be 

reliably informative. In addition, the most commonly used empirical methods do not permit 

estimation of net effects. 

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not necessarily 

represent impacts for the sector as a whole. The approach must be modified when applied at the 

industry level. 

At the industry level, labor demand is more responsive if: (1) the price elasticity of demand 

for the product is high, (2) other factors of production can be easily substituted for labor, (3) the 

supply of other factors is highly elastic, or (4) labor costs are a large share of total production 

costs.160 For example, if all firms in an industry are faced with the same regulatory compliance costs 

and product demand is inelastic, then industry output may not change much, and output of individual 

firms may change slightly.161 In this case, the output effect may be small, while the substitution 

effect depends on input substitutability. Suppose, for example, that new equipment for fuel efficiency 

 

PP Berman and Bui also discuss a third component, the impact of regulation on factor prices, but conclude that this 

effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer 

and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 

2) a cost effect; and 3) a factor-shift effect. 
8-95 

improvements requires labor to install and operate. In this case, the substitution effect may be 

positive, and with a small output effect, the total effect may be positive. As with potential effects for 

an individual firm, theory cannot determine the sign or magnitude of industry-level regulatory effects 

on labor demand. Determining these signs and magnitudes requires additional sector-specific 

empirical study. For environmental rules, much of the data needed for these empirical studies is not 

publicly available, would require significant time and resources in order to access confidential U.S. 

Census data for research, and also would not be necessary for other components of a typical RIA. 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors. For example, the proposed standards are expected to 

increase demand for fuel-saving technologies. This increased demand may increase revenue and 

employment in the firms providing these technologies. At the same time, the regulated industry is 

purchasing the equipment, and these costs may impact labor demand at regulated firms. Therefore, it 

is important to consider the net effect of compliance actions on employment across multiple sectors 

or industries. 

If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net employment.QQ Instead, labor would primarily 

be reallocated from one productive use to another, and net national employment effects from 

environmental regulation would be small and transitory (e.g., as workers move from one job to 

another).162

Affected sectors may experience transitory effects as workers change jobs. Some workers 

may retrain or relocate in anticipation of new requirements or require time to search for new jobs, 

while shortages in some sectors or regions could bid up wages to attract workers. These adjustment 

costs can lead to local labor disruptions. Although the net change in the national workforce is 

expected to be small, localized reductions in employment may adversely impact individuals and 

communities just as localized increases may have positive impacts. 

If the economy is operating at less than full employment, economic theory does not clearly 

indicate the direction or magnitude of the net impact of environmental regulation on employment; it 

could cause either a short-run net increase or short-run net decrease.163 An important research 

question is how to accommodate unemployment as a structural feature in economic models. This 

feature may be important in assessing large-scale regulatory impacts on employment.164 

Environmental regulation may also affect labor supply. In particular, pollution and other 

environmental risks may impact labor productivity or employees’ ability to work.165 While the 

theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is 

more difficult to study empirically. There is a small emerging literature described in the next section 

that uses detailed labor and environmental data to assess these impacts. 

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

employment changes (both positive and negative) in the regulated sector and elsewhere. Labor 

 

QQ Full employment is a conceptual target for the economy where everyone who wants to work and is available to 

do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 
8-96 

demand impacts for regulated firms, and also for the regulated industry, can be decomposed into 

output and substitution effects which may be either negative or positive. Estimation of net 

employment effects for regulated sectors is possible when data of sufficient detail and quality are 

available. Finally, economic theory suggests that labor supply effects are also possible. In the next 

section, we discuss the empirical literature. 

8.11.1.1 Current State of Knowledge Based on the Peer-Reviewed Literature 

In the labor economics literature there is an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the above theoretical framework.166

This work focuses primarily on the effects of employment policies, e.g. labor taxes, minimum 

wage, etc.167 In contrast, the peer-reviewed empirical literature specifically estimating 

employment effects of environmental regulations is very limited. Several empirical studies, 

including Berman and Bui (2001),168 Morgenstern, Pizer and Shih (2002),169 Gray et al 

(2014),170 and Ferris, Shadbegian and Wolverton (2014)171 suggest that net employment impacts 

may be zero or slightly positive but small even in the regulated sector. Other research suggests 

that more highly regulated counties may generate fewer jobs than less regulated ones.172 

However, since these latter studies compare more regulated to less regulated counties, they 

overstate the net national impact of regulation to the extent that regulation causes plants to locate 

in one area of the country rather than another. List et al. (2003)173 find some evidence that this 

type of geographic relocation may be occurring. Overall, the peer-reviewed literature does not 

contain evidence that environmental regulation has a large impact on net employment (either 

negative or positive) in the long run across the whole economy. 

Analytic challenges make it very difficult to accurately produce net employment 

estimates for the whole economy that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the macro-economy. 

Quantitative estimates are further complicated by the fact that macroeconomic models often have 

very little sectoral detail and usually assume that the economy is at full employment. EPA is 

currently in the process of seeking input from an independent expert panel on modeling 

economy-wide impacts, including employment effects. For more information, see: 

https://federalregister.gov/a/2014-02471. 

8.11.2 Employment Impacts in the Motor Vehicle and Parts Manufacturing Sector 

This chapter describes changes in employment in the motor vehicle, trailer, and parts 

(hence, motor vehicle) manufacturing sectors due to these proposed rules. We focus on the 

motor vehicle manufacturing sector because it is directly regulated, and because it is likely to 

bear a substantial share of changes in employment due to these proposed rules. We include 

discussion of effects on the parts manufacturing sector, because the motor vehicle manufacturing 

sector can either produce parts internally or buy them from an external supplier, and we do not 

have estimates of the likely breakdown of effort between the two sectors. 

We follow the theoretical structure of Berman and Bui 174 of the impacts of regulation in 

employment in the regulated sectors. In Berman and Bui’s (2001, p. 274-75) theoretical model, 

as described above, the change in a firm’s labor demand arising from a change in regulation is 
8-97 

decomposed into two main components: output and substitution effects.RR As the output and 

substitution effects may be both positive, both negative, or some combination, standard 

neoclassical theory alone does not point to a definitive net effect of regulation on labor demand 

at regulated firms. 

Following the Berman and Bui framework for the impacts of regulation on employment 

in the regulated sector, we consider two effects for the motor vehicle sector: the output effect 

and the substitution effect. 

8.11.2.1 The Output Effect 

If truck or trailer sales increase, then more people will be required to assemble trucks, 

trailers, and their components. If truck or trailer sales decrease, employment associated with 

these activities will decrease. The effects of this proposed rulemaking on HD vehicle sales thus 

depend on the perceived desirability of the new vehicles. On one hand, this proposed 

rulemaking will increase truck and trailer costs; by itself, this effect would reduce truck and 

trailer sales. In addition, while decreases in truck performance would also decrease sales, this 

program is not expected to have any negative effect on truck performance. On the other hand, 

this proposed rulemaking will reduce the fuel costs of operating the trucks; by itself, this effect 

would increase truck sales, especially if potential buyers have an expectation of higher fuel 

prices. The agencies have not made an estimate of the potential change in truck or trailer sales. 

However, as discussed in Chapter 8.3, the agencies have estimated an increase in vehicle miles 

traveled (i.e., VMT rebound) due to the reduced operating costs of trucks meeting these proposed 

standards. Since increased VMT is most likely to be met with more drivers and more trucks, our 

projection of VMT rebound is suggestive of an increase in vehicle sales and truck driver 

employment (recognizing that these increases may be partially offset by a decrease in 

manufacturing and sales for equipment of other modes of transportation such as rail cars or 

barges). 

8.11.2.2 The Substitution Effect 

The output effect, above, measures the effect due to new truck and trailer sales only. The 

substitution effect includes the impacts due to the changes in technologies needed for vehicles to 

meet the proposed standards, separate from the effect on output (that is, as though holding output 

constant). This effect includes both changes in employment due to incorporation of abatement 

technologies and overall changes in the labor intensity of manufacturing. We present estimates 

for this effect to provide a sense of the order of magnitude of expected impacts on employment, 

 

RR The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) the demand effect; 2) 

the cost effect; and 3) the factor-shift effect. See Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. 

“Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR). 
8-98 

which we expect to be small in the automotive sector, and to repeat that regulations may have 

positive as well as negative effects on employment. 

One way to estimate this effect, given the cost estimates for complying with the proposed 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios for 

quite specific sectors of the economy: for instance, it is possible to estimate the average number 

of workers in the motor vehicle body and trailer manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly 

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, or when manufacturing processes change 

sufficiently that labor intensity changes. For instance, the ratio for the motor vehicle 

manufacturing sector represents the ratio for all vehicle manufacturing, not just for emissions 

reductions associated with compliance activities. In addition, these estimates do not include 

changes in sectors that supply these sectors, such as steel or electronics producers. They thus 

may best be viewed as the effects on employment in the motor vehicle sector due to the changes 

in expenditures in that sector, rather than as an assessment of all employment changes due to 

these changes in expenditures. In addition, this approach estimates the effects of increased 

expenditures while holding constant the labor intensity of manufacturing; it does not take into 

account changes in labor intensity due to changes in the nature of production. This latter effect 

could either increase or decrease the employment impacts estimated here.SS

Some of the costs of these proposed rules will be spent directly in the motor vehicle 

manufacturing sector, but it is also likely that some of the costs will be spent in the motor vehicle 

body and trailer and motor vehicle parts manufacturing sectors. The analysis here draws on 

estimates of workers per $1 million of expenditures for each of these sectors. 

There are several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),175 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The values considered here are for Motor Vehicle 

Manufacturing (NAICS 3361), Motor Vehicle Body and Trailer Manufacturing (NAICS 3362), 

and Motor Vehicle Parts Manufacturing (NAICS 3363) for 2012. 

The Census Bureau provides the Annual Survey of Manufacturers176 (ASM), a subset of 

the Economic Census, based on a sample of establishments; though the Census itself is more 

complete, it is conducted only every 5 years, while the ASM is annual. Both include more 

sectoral detail than the BLS ERM: for instance, while the ERM includes the Motor Vehicle 

Manufacturing sector, the ASM and Economic Census have detail at the 6-digit NAICS code 

level (e.g., light truck and utility vehicle manufacturing). While the ERM provides direct 

 

SS As noted above, Morgenstern et al. (2002) separate the effect of holding output constant into two effects: the cost 

effect, which holds labor intensity constant, and the factor shift effect, which estimates those changes in labor 

intensity. 
8-99 

estimates of employees/$1 million in expenditures, the ASM and Economic Census separately 

provide number of employees and value of shipments; the direct employment estimates here are 

the ratio of those values. At this time, the Economic Census values for 2012 (the most recent 

year) are not fully available; we therefore do not report them, and instead provide the 2011 ASM 

results (the most recent available). The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Light Truck and Utility Vehicle Manufacturing (NAICS 336112), Heavy Duty 

Truck Manufacturing (33612), Motor Vehicle Body and Trailer Manufacturing (3362), and 

Motor Vehicle Parts Manufacturing (NAICS 3363). The values used here are adjusted to remove 

the employment effects of imports through use of a ratio of domestic production to domestic 

sales of 0.78.TT 

Table 8-42 provides the values, either given (BLS) or calculated (ASM) for employment 

per $1 million of expenditures, all adjusted to 2012 dollars using the Bureau of Economic 

Analysis’s Implicit GDP Price Deflators. Although the ASM appears to provide slightly higher 

values than the ERM, the different data sources provide similar patterns for the estimates for the 

sectors. Body and trailer manufacturing and parts manufacturing appear to be more laborintensive than vehicle manufacturing; light truck and utility vehicle manufacturing appears to be 

less, and heavy duty truck manufacturing appears to be more, labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8-42 Employment per $1 Million Expenditures (2012$) in the Motor Vehicle Manufacturing Sectora

SOURCE SECTOR RATIO OF 

WORKERS PER 

$1 MILLION 

EXPENDITURE

S 

RATIO OF WORKERS 

PER $1 MILLION 

EXPENDITURES, 

ADJUSTED FOR 

DOMESTIC VS. 

FOREIGN 

PRODUCTION 

BLS ERM Motor vehicle mfg (3361) 0.460 0.355 

BLS ERM Motor vehicle body & trailer mfg (3362) 1.450 1.153 

BLS ERM Motor vehicle parts mfg (3363) 1.950 1.590 

ASM Motor vehicle mfg (3361) 0.538 0.414 

ASM Light truck & utility vehicle mfg 

(336112) 

0.443 0.341 

ASM Heavy duty truck mfg (33612) 0.832 0.641 

ASM Motor vehicle body & trailer mfg (3362) 2.797 2.156 

ASM Motor Vehicle Parts Mfg (3363) 1.635 1.260 

Note: 

aBLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix, 2012 values. 

ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures, 2011 values. 

Over time, the amount of labor needed in the motor vehicle industry has changed: 

automation and improved methods have led to significant productivity increases. The BLS 

 

TT To estimate the proportion of domestic production affected by the change in sales, we use data from Ward’s 

Automotive Group for total truck production in the U.S. compared to total truck sales in the U.S. For the period 

2004-2013, the proportion is 78 percent (Docket EPA-HQ-OAR-), ranging from 68 percent (2009) to 83 percent 

(2012) over that time. 
8-100 

ERM, for instance, provided estimates that, in 1993, 1.52 workers in the Motor Vehicle 

Manufacturing sector were needed per $1 million, but only 0.53 workers by 2012 (in 2005$).177 

Because the ERM is available annually for 1993-2012, we used these data to estimate 

productivity improvements over time. We regressed logged ERM values on a year trend for the 

Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor 

Vehicle Parts Manufacturing sectors. We used this approach because the coefficient describing 

the relationship between time and productivity is a direct measure of the average percent change 

in productivity per year. The results suggest a 5.1 percent per year productivity improvement in 

the Motor Vehicle Manufacturing Sector, and a 4.7 percent per year improvement in the Motor 

Vehicle Parts Manufacturing Sector. The Motor Vehicle Body and Trailer Manufacturing Sector 

results were more complex: the workers/$1 million values before 2009 are substantially higher 

(averaging 5.90 in 2005$) than those in 2009 and after (averaging 2.45 in 2005$); we used 

dummy variables to account for this shift, and estimate productivity gains of 0.4 percent per year 

before 2009, and 22 percent after. This dramatic difference may suggest taking care when 

relying on the data for this sector. As discussed further below, we only report maximum and 

minimum employment impacts, and the Motor Vehicle Body and Trailer Manufacturing 

estimates provide the minimum values; they may therefore create greater uncertainty about the 

lower bound of the substitution-effect employment. 

We then used the regression results to project the number of workers per $1 million 

through 2027. We calculated separate sets of projections (adjusted to 2012$) for both the BLS 

ERM data as well as the ASM for all three sectors discussed above. The BLS ERM projections 

were calculated directly from the fitted regression equations since the regressions themselves 

used ERM data. For the ASM projections, we used the ERM’s ratio of the projected value in 

each future year to the projected value in 2011 (the base year for the ASM) to determine how 

many workers will be needed per $1 million of 2012$. In other words, we apply the projected 

productivity growth estimated using the ERM data to the ASM numbers. 

Finally, to simplify the presentation and give a range of estimates, we compared the 

projected employment among the 3 sectors for the ERM and ASM, and we provide only the 

maximum and minimum employment effects estimated for the ERM and the ASM. We provide 

the range rather than a point estimate because of the inherent difficulties in estimating 

employment impacts; the range gives an estimate of the expected magnitude. The details of the 

calculations may be found in the docket. The ERM estimate in the Motor Vehicle Parts 

Manufacturing Sector are consistently the maximum values. The ERM estimate in the Motor 

Vehicle Body and Trailer Manufacturing Sector are the minimum values for all years but 2018-

2019, where the ASM value for the Light Truck and Utility Vehicle Manufacturing Sector 

(336112) provide the minimum values. 

Chapter 7 of the draft RIA discusses the vehicle cost estimates developed for these 

proposed rules. The final step in estimating employment impacts is to multiply costs (in $ 

millions) by workers per $1 million in costs, to estimate employment impacts in the regulated 

and parts manufacturing sectors. Increased costs of vehicles and parts would, by itself, and 

holding labor intensity constant, be expected to increase employment between 2018 and 2027 

from none to a few thousand jobs each year. 
8-101 

While we estimate employment impacts, measured in job-years, beginning with program 

implementation, some of these employment gains may occur earlier as motor vehicle 

manufacturers and parts suppliers hire staff in anticipation of compliance with the standards. A 

job-year is a way to calculate the amount of work needed to complete a specific task. For 

example, a job-year is one year of work for one person. 

Table 8-43 Employment Effects due to Increased Costs of Vehicles and Parts (Substitution Effect), in Jobyears 

YEAR COSTS 

(MILLIONS OF 

2012$) 

MINIMUM EMPLOYMENT 

DUE TO SUBSTITUTION 

EFFECT (ERM ESTIMATES, 

EXPENDITURES IN THE 

PARTS SECTORa

) 

MAXIMUM EMPLOYMENT DUE 

TO SUBSTITUTION EFFECT (ERM 

ESTIMATES, EXPENDITURES IN 

THE BODY AND TRAILER MFG 

SECTOR) 

2018 $ 116 0 100 

2019 $ 113 0 100 

2020 $ 112 0 100 

2021 $ 2,173 300 2,300 

2022 $ 2,161 300 2,200 

2023 $ 2,224 200 2,100 

2024 $ 3,455 300 3,200 

2025 $ 3,647 200 3,200 

2026 $ 3,736 200 3,100 

2027 $ 5,309 200 4,200 

Note:

a For 2018 and 2019, the minimum employment effects are associated with the ASM’s Light Truck and Utility 

Vehicle Manufacturing sector.

8.11.2.3 Summary of Employment Effects in the Motor Vehicle Sector 

The overall effect of these proposed rules on motor vehicle sector employment depends 

on the relative magnitude of the output effect and the substitution effect. Because we do not 

have quantitative estimates of the output effect, and only a partial estimate of the substitution 

effect, we cannot reach a quantitative estimate of the overall employment effects of these 

proposed rules on motor vehicle sector employment or even whether the total effect will be 

positive or negative. 

The proposed standards are not expected to provide incentives for manufacturers to shift 

employment between domestic and foreign production. This is because the proposed standards 

will apply to vehicles sold in the U.S. regardless of where they are produced. If foreign 

manufacturers already have increased expertise in satisfying the requirements of the standards, 

there may be some initial incentive for foreign production, but the opportunity for domestic 

manufacturers to sell in other markets might increase. To the extent that the requirements of 

these proposed rules might lead to installation and use of technologies that other countries may 

seek now or in the future, developing this capacity for domestic production now may provide 

some additional ability to serve those markets. 

Some vehicle parts are made in-house and would be included directly in the regulated 

sector. Others are made by independent suppliers and are not directly regulated, but they will be 

affected by the rules as well. The parts manufacturing sector will be involved primarily in 
8-102 

providing “add-on” parts, or components for replacement parts built internally. If demand for 

these parts increases due to the increased use of these parts, employment effects in this sector are 

expected to be positive. If the demand effect in the regulated sectors is significantly negative 

enough, it is possible that demand for other parts may decrease. As noted, the agencies do not 

predict a direction for the demand effect. 

8.11.3 Employment Impacts in Other Affected Sectors 

8.11.3.1 Transport and Shipping Sectors 

Although not directly regulated by these proposed rules, employment effects in the 

transport and shipping sector are likely to result from these regulations. If the overall cost of 

shipping a ton of freight decreases because of increased fuel efficiency (taking into account the 

increase in upfront purchasing costs), in a perfectly competitive industry these costs savings, 

depending on the relative elasticities of supply and demand, will be passed along to customers. 

With lower prices, demand for shipping would lead to an increase in demand for truck shipping 

services (consistent with the VMT rebound effect analysis) and therefore an increase in 

employment in the truck shipping sector. In addition, if the relative cost of shipping freight via 

trucks becomes cheaper than shipping by other modes (e.g., rail or barge), then employment in 

the truck transport industry is likely to increase. If the trucking industry is more labor intensive 

than other modes, we would expect this effect to lead to an overall increase in employment in the 

transport and shipping sectors.178,179 Such a shift would, however, be at the expense of 

employment in the sectors that are losing business to trucking. The first effect – a gain due to 

lower shipping costs – is likely to lead to a net increase in employment. The second effect, due 

to mode-shifting, may increase employment in trucking, but decrease employment in other 

shipping sectors (e.g., rail or barge), with the net effects dependent on the labor-intensity of the 

sectors and the volumes. 

8.11.3.2 Fuel Suppliers 

In addition to the effects on the trucking industry and related truck parts sector, these 

proposed rules will result in reductions in fuel use that lower GHG emissions. Fuel saving, 

principally reductions in liquid fuels such as diesel and gasoline, will affect employment in the 

fuel suppliers industry sectors, principally the Petroleum Refinery sector. 

Chapter 7.2 of this draft RIA provides estimates of the effects of these proposed 

standards on expected fuel consumption. While reduced fuel consumption represents savings for 

purchasers of fuel, it also represents a loss in value of output for the petroleum refinery industry, 

which will result in reduced sectoral employment. Because this sector is material-intensive, the 

employment effect is not expected to be large.UU

 

UU In the 2012 BLS ERM cited above, the Petroleum and Coal Products Manufacturing sector has a ratio of workers 

per $1 million of 0.242, lower than all but two of the 181 sectors with non-zero employment per $1 million. 
8-103 

8.11.3.3 Fuel Savings 

As a result of this proposed rulemaking, it is anticipated that trucking firms will 

experience fuel savings. Fuel savings lower the costs of transportation goods and services. In a 

competitive market, some of the fuel savings that initially accrue to trucking firms are likely to 

be passed along as lower transportation costs that, in turn, could result in lower prices for final 

goods and services. Some of the savings might also be retained by firms for investments or for 

distributions to firm owners. Again, how much accrues to customers versus firm owners will 

depend on the relative elasticities of supply and demand. Regardless, the savings will accrue to 

some segment of consumers: either owners of trucking firms or the general public, and the effect 

will be increased spending by consumers in other sectors of the economy, creating jobs in a 

diverse set of sectors, including retail and service industries. 

As described in Chapter 7.2, the value of fuel savings from this proposed rulemaking is 

projected to be $15.1 billion (2012$) in 2027, according to Table 7-19. If all those savings are 

spent, the fuel savings will stimulate increased employment in the economy through those 

expenditures. If the fuel savings accrue primarily to firm owners, they may either reinvest the 

money or take it as profit. Reinvesting the money in firm operations could increase employment 

directly. If they take the money as profit, to the extent that these owners are wealthier than the 

general public, they may spend less of the savings, and the resulting employment impacts would 

be smaller than if the savings went to the public. Thus, while fuel savings are expected to 

decrease employment in the refinery sector, they are expected to increase employment through 

increased consumer expenditures. 

8.11.4 Summary of Employment Impacts 

The primary employment effects of these rules are expected to be found throughout 

several key sectors: truck and engine manufacturers, the trucking industry, truck parts 

manufacturing, fuel production, and consumers. These rules initially take effect in model year 

2018, a time period sufficiently far in the future that the unemployment rate at that time is 

unknowable. In an economy with full employment, the primary employment effect of a 

rulemaking is likely to be to move employment from one sector to another, rather than to 

increase or decrease employment. For that reason, we focus our partial quantitative analysis on 

employment in the regulated sector, to examine the impacts on that sector directly. We discuss 

the likely direction of other impacts in the regulated sector as well as in other directly related 

sectors, but we do not quantify those impacts, because they are more difficult to quantify with 

reasonable accuracy, particularly so far into the future. 

For the regulated sector, we have not quantified the output effect. The substitution effect 

is associated with potential increased employment from none to a few thousand jobs per year 

between 2018 and 2027, depending on the share of employment impacts in the affected sectors 

(Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor 

Vehicle Parts Manufacturing). These estimates do not include potential changes, either greater 

or less, in labor intensity of production. As mentioned above, some of these job gains may occur 

earlier as auto manufacturers and parts suppliers hire staff to prepare to comply with the 

standard. 
8-104 

Lower prices for shipping are expected to lead to an increase in demand for truck 

shipping services and, therefore, an increase in employment in that sector, though this effect may 

be offset somewhat by changes in employment in other shipping sectors. Reduced fuel 

production implies less employment in the fuel provision sectors. Finally, any net cost savings 

would be expected to be passed along to some segment of consumers: either the general public or 

the owners of trucking firms, who are expected then to increase employment through their 

expenditures. Under conditions of full employment, any changes in employment levels in the 

regulated sector due to this program are mostly expected to be offset by changes in employment 

in other sectors. 

8.12 Oil Price Sensitivity Analysis using Method B 

In this section, EPA presents a sensitivity analysis examining the impact on net benefits 

using AEO’s “low oil price” and “high oil price” cases. The sensitivity analysis is based on the 

preferred alternative relative to the less dynamic baseline as the “primary” case using Method B. 

Fuel price changes were not used as an input to technology application rates (i.e., a constant 

$/vehicle has been used throughout this sensitivity analysis). The primary analysis (presented 

earlier in this chapter) uses the AEO reference case oil prices. The primary case and both high 

and low oil price case $/gallon values are shown in Table 8-44. 

Table 8-44 AEO Fuel Prices in the Low Oil Price Case, our Primary Analysis Case, and the AEO High Oil 

Price Case (2012$) 

YEAR RETAIL UNTAXED 

Diesel Gasoline Diesel Gasoline 

Low Primary High Low Primary High Low Primary High Low Primary High 

2018 $2.94 $3.53 $4.89 $2.54 $3.02 $4.10 $2.48 $3.07 $4.43 $2.13 $2.61 $3.68

2019 $2.95 $3.61 $4.94 $2.54 $3.03 $4.13 $2.49 $3.15 $4.49 $2.13 $2.62 $3.71

2020 $2.96 $3.67 $4.99 $2.55 $3.08 $4.16 $2.51 $3.22 $4.54 $2.15 $2.67 $3.74

2021 $3.00 $3.74 $5.03 $2.59 $3.12 $4.17 $2.55 $3.29 $4.59 $2.19 $2.71 $3.76

2022 $3.01 $3.82 $5.08 $2.59 $3.17 $4.18 $2.56 $3.37 $4.64 $2.19 $2.77 $3.77

2023 $3.01 $3.87 $5.10 $2.57 $3.22 $4.19 $2.57 $3.43 $4.67 $2.18 $2.82 $3.78

2024 $3.02 $3.92 $5.14 $2.56 $3.26 $4.21 $2.58 $3.48 $4.70 $2.16 $2.86 $3.80

2025 $3.03 $3.98 $5.17 $2.55 $3.29 $4.23 $2.59 $3.54 $4.74 $2.16 $2.89 $3.82

2026 $3.04 $4.02 $5.24 $2.55 $3.32 $4.26 $2.60 $3.59 $4.81 $2.16 $2.92 $3.86

2027 $3.03 $4.08 $5.30 $2.54 $3.36 $4.31 $2.60 $3.65 $4.88 $2.15 $2.96 $3.91

2028 $3.03 $4.12 $5.39 $2.54 $3.37 $4.38 $2.61 $3.69 $4.97 $2.16 $2.98 $3.98

2029 $3.04 $4.16 $5.47 $2.54 $3.40 $4.43 $2.61 $3.74 $5.06 $2.16 $3.01 $4.03

2030 $3.06 $4.20 $5.51 $2.55 $3.43 $4.45 $2.64 $3.79 $5.09 $2.17 $3.04 $4.06

2031 $3.06 $4.25 $5.58 $2.55 $3.46 $4.51 $2.64 $3.84 $5.17 $2.17 $3.08 $4.12

2032 $3.06 $4.30 $5.66 $2.56 $3.50 $4.57 $2.64 $3.89 $5.25 $2.18 $3.12 $4.18

2033 $3.07 $4.36 $5.72 $2.57 $3.54 $4.62 $2.66 $3.95 $5.32 $2.20 $3.16 $4.24

2034 $3.08 $4.43 $5.77 $2.58 $3.61 $4.66 $2.67 $4.02 $5.37 $2.21 $3.24 $4.28

2035 $3.08 $4.47 $5.83 $2.58 $3.65 $4.71 $2.67 $4.06 $5.44 $2.22 $3.27 $4.33

2036 $3.09 $4.51 $5.89 $2.59 $3.69 $4.77 $2.68 $4.11 $5.50 $2.23 $3.32 $4.39

2037 $3.09 $4.54 $5.96 $2.59 $3.73 $4.81 $2.69 $4.15 $5.57 $2.23 $3.36 $4.44

2038 $3.10 $4.58 $6.03 $2.60 $3.77 $4.87 $2.70 $4.19 $5.65 $2.24 $3.40 $4.49

2039 $3.11 $4.65 $6.12 $2.61 $3.83 $4.95 $2.71 $4.26 $5.74 $2.25 $3.47 $4.57

2040 $3.11 $4.73 $6.23 $2.61 $3.90 $5.04 $2.72 $4.34 $5.85 $2.26 $3.54 $4.67

Note: 
8-105 

Our Primary case values are the AEO reference case values and are taken from AEO2014 Early Release; other 

values from AEO2014. 

The impacts of using the low and high oil price cases on our estimated fuel savings and 

net benefits are shown in Table 8-45. 

Table 8-45 MY2018-2029 Lifetime Sensitivity on Net Benefits using AEO2014 Low and High Oil Price Cases 

for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Billions of 2012$) a

LOW OIL 

PRICE 

CASE 

PRIMARY 

CASE 

HIGH OIL 

PRICE 

CASE 

Vehicle program -$25 -$25 -$25

Maintenance -$1.1 -$1.1 -$1.1

Fuel $117 $171 $230

Benefits $93 $97 $101

Net benefits $184 $242 $305

Note: 

a

 For an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1
8-106 

1

 Environmental Protection Agency and Department of Transportation, “Light-Duty Vehicle Greenhouse Gas 

Emissions Standards and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 75(88) (May 7, 

2010), especially Sections III.H.1 (pp. 25510-25513) and IV.G.6 (pp. 25651-25657); Environmental Protection 

Agency and Department of Transportation, ”2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas 

Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 77(199) (October 15, 

2012), especially Sections III.H.1 (pp. 62913-62919) and IV.G.5.a (pp. 63102-63104). 

2 State of Massachusetts v. EPA, 549 U.S. at 533. 3

 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles,” (hereafter, “NAS 2010”). Washington, D.C. The National Academies 

Press. Available electronically from the National Academies Press Website at 

http://www.nap.edu/catalog.php?record_id=12845 (accessed September 10, 2010). 4

 Klemick, Heather, Elizabeth Kopits, Keith Sargent, and Ann Wolverton (2014). “Heavy-Duty Trucking and the 

Energy Efficiency Paradox.” US EPA NCEE Working Paper Series. Working Paper 14-02; Roeth, Mike, Dave 

Kircher, Joel Smith, and Rob Swim (2013). “Barriers to the Increased Adoption of Fuel Efficiency Technologies in 

the North American On-Road Freight Sector.” NACFE report for the International Council on Clean Transportation; 

Aarnink, Sanne, Jasper Faber, and Eelco den Boer (2012). “Market Barriers to Increased Efficiency in the European 

On-road Freight Sector.” CE Delft report for the International Council on Clean Transportation. 5

 Vernon, David and Alan Meier (2012). “Identification and quantification of principal-agent problems affecting 

energy efficiency investments and use decisions in the trucking industry.” Energy Policy, 49(C), pp. 266-273 

6 Blumstein, Carl and Margaret Taylor (2013). “Rethinking the Energy-Efficiency Gap: Producers, Intermediaries, 

and Innovation,” Energy Institute at Haas Working Paper 243, University of California at Berkeley; Tirole, Jean 

(1998). The Theory of Industrial Organization. Cambridge, MA: MIT Press, pp.400, 402. This first-mover 

disadvantage must large enough to overcome the incentive normally offered by the potential to for first movers to 

earn unusually high (but temporary) profit levels. 

7

 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, September 2013). 8

 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report-acgoperatingcost2005-2005-e-2-1727.htm, accessed on July 16, 2010. 9

 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

on-road freight transportation. Energy Policy 48, 252-259. 

10 Greene, D.L., Kahn, J.R., Gibson, R.C., 1999, “Fuel economy rebound effect for U.S. household vehicles”, The 

Energy Journal, 20. 

11 For a discussion of the wide range of definitions found in the literature, see Appendix D: Discrepancy in Rebound 

Effect Definitions, in EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, 

Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR-2014-

0827). See also Greening, L.A., Greene, D.L., Difiglio, C., 2000, “Energy efficiency and consumption — the 

rebound effect — a survey”, Energy Policy, 28, 389-401. 

12 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles,” Washington, D.C. The National Academies Press. Available electronically 

from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845 (last accessed 

September 10, 2010). 

13 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, September 2013. 14 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report-acgoperatingcost2005-2005-e-2-1727.htm, accessed on July 16, 2010. 15 These factors are discussed more fully in a report to EPA from EERA, which illustrates in a series of diagrams the 

complex system of decisions and decision-makers that could influence the magnitude and timing of the rebound 

effect. See Sections 2.2.2, 2.2.3, 2.2.4, and 2.3 in EERA (2014), “Research to Inform Analysis of the Heavy-Duty 

Vehicle Rebound Effect”, Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 16 A useful framework for understanding how various responses interact to determine the rebound effect is presented 

in Section 2 and Appendix B of De Borger, B. and Mulalic, I. (2012), “The determinants of fuel use in the trucking 

industry – volume, fleet characteristics and the rebound effect”, Transportation Policy, Volume 24, pp. 284–295. 

References 
8-107 

See also Section 3.4 of EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, 

Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 

17 Gately, D., The U.S. Demand for Highway Travel and Motor Fuels, The Energy Journal, Volume 11, No. 3, July 

1990, pp.59-73. 

18 Matos, F. J. F., and Silva, F. J. F., “The Rebound Effect on Road Freight Transport: Empirical Evidence from 

Portugal,” Energy Policy, 39, 2011, pp. 2833–2841. 

19 De Borger, B. and Mulalic, I., “The determinates of fuel use in the trucking industry – volume, fleet 

characteristics and the rebound effect”, Transportation Policy, Volume 24, November 2012, pp. 284–295. 20 FHWA Travel Analysis Framework Development of VMT Forecasting Models for Use by the Federal Highway 

Administration May 12, 2014 http://www.fhwa.dot.gov/policyinformation/tables/vmt/vmt_model_dev.pdf. Volpe’s 

work was advised by a panel of approximately 20 experts in the measurement, analysis, and forecasting of travel, 

including academic researchers, transportation consultants, and members of local, state, and federal government 

transportation agencies. It was also summarized in the paper “Developing a Multi-Level Vehicle Miles of Travel 

Forecasting Model,” November, 2011, which was presented to the Transportation Research Board’s 91st Annual 

Meeting in January, 2012. 

21 EPA/NHTSA, August 2011. Chapter 9.3.3, Final Rulemaking to Establish Greenhouse gas Emission Standards & 

Fuel Efficiency Standards for Medium-and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis. EPA420-R-11-901. (http://www.epa.gov/otaq/climate/documents/420r11901.pdf) 22 EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, Excerpts of Draft Final 

Report of Phase 1 under EPA contract EP-C-13-025. 

23 EERA (2015), “Working Paper on Fuel Price Elasticities for Heavy Duty Vehicles”, Draft Final Report of Phase 2 

under EPA contract EP-C-11-046. 

24 Gately, D. 1993. The Imperfect Price-Reversibility of World Oil Demand. The Energy Journal, International 

Association for Energy Economics, vol. 14 (4), pp. 163-182; Dargay, J.M., Gately, D. 1997. The demand for 

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2012. Is the price effect on fuel consumption symmetric? Some evidence from an empirical study. Energy Policy, 

vol. 41, pp. 59-65. 

25 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

on-road freight transportation. Energy Policy 48, 252-259. 

26 See, for example, Appendix E in EERA (2014), “Research to Inform Analysis of the Heavy-Duty Vehicle 

Rebound Effect”, Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 

27 Li, Z., D.A. Hensher, and J.M. Rose, Identifying sources of systematic variation in direct price elasticities from 

revealed preference studies of inter-city freight demand. Transport Policy, 2011. 28 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

on-road freight transportation. Energy Policy 48, 252-259. 

29 Winebrake, James and James J. Corbett (2010). “Improving the Energy Efficiency and Environmental 

Performance of Goods Movement,” in Sperling, Daniel and James S. Cannon (2010) Climate and Transportation 

Solutions: Findings from the 2009 Asilomar Conference on Transportation and Energy Policy. See 

http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf 30 Winebrake, J. J.; Corbett, J. J.; Falzarano, A.; Hawker, J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing 

Energy, Environmental, and Economic Tradeoffs in Intermodal Freight Transportation, Journal of the Air & Waste 

Management Association, 58(8), 2008 (Docket ID: EPA-HQ-OAR-2010-0162-0008). 31 See GIFT Solutions, LLC, “Potential for Mode Shift due to Heavy Duty Vehicle Fuel Efficiency Improvements”. 

February, 2012. 

32 Winebrake, James, J. Corbett, J. Silberman, E. Erin, & B. Comer, 2012. Potential for Mode Shift due to Heavy 

Duty Vehicle Fuel Efficiency Improvements: A Case Study Approach. GIFT Solutions, LLC. 

33 Cambridge Systematics, Inc., Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: 

Commissioned Paper on Indirect Costs and Alternative Approaches, 2009. 

34 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International 

Council on Clean Transportation, Reducing Heavy-Duty Long Haul Truck Fuel Consumption and CO2 Emissions, 

September 2009. See http://www.nescaum.org/documents/heavy-duty-truck-ghg_report_final-200910.pdf 35 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review,” Transport Reviews Volume 24, 3, 

pp. 261-274, 2004. 

36 Based upon a study for the National Cooperative Highway Research Program by Cambridge Systematics, Inc., 

Characteristics and Changes in Freight Transportation Demand: A Guidebook for Planners and Policy Analysts 

Phase II Report, National Cooperative Highway Research Program Project 8-30, June 1995. 
8-108 

37 The own (i.e., self) price elasticity provides a measure for describing how the volume of truck shipping (demand) 

changes with its price while the cross-price elasticity provides a measure for describing how the volume of rail 

shipping changes with truck price. In general, an elasticity describes the percent change in one variable (e.g. demand 

for trucking) in response to a percent-change in another (e.g. price of truck operations). 

38 American Transportation Research Institute, “An Analysis of the Operational Costs of Trucking”, October 2008. 

39 Guerrero, Sebastian. Modeling fuel saving investments and fleet management in the trucking industry: The impact 

of shipment performance on GHG emissions. Transportation Research Part E, May 2014. 

40 American Transportation Research Institute. An Analysis of the Operational Costs of Trucking: A 2013 Update. 

2013. 

41 The new vocational vehicle cost assumed in the VMT rebound analysis is $70,000 based on the average of the 

vocational vehicle prices listed in the ICF cost report, July 2010, pages 16-17. The combination tractor-trailer costs 

are $100,000 based on the average price of day cabs and sleeper cabs and $25,000 for the trailer as listed in the ICF 

cost report pages 3 and 16. Docket Identification Number EPA-HQ-OAR-2010-0162-0044. 

42 U.S. EPA. Motor Vehicle Emission Simulator (MOVES2014). Last accessed on September 11, 2014 at 

http://www.epa.gov/otaq/models/moves/. 

43 EPA/NHTSA, August 2011. Page 7-3, Final Rulemaking to Establish Greenhouse gas Emissions Standards & 

Fuel Efficiency Standards for Medium-and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis. EPA420-R-11-901. (http://www.epa.gov/otaq/climate/documents/420r11901.pdf) 44 EPA/NHTSA, August 2011. Page 7-3, Final Rulemaking to Establish Greenhouse gas Emissions Standards & 

Fuel Efficiency Standards for Medium-and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis. EPA420-R-11-901. (http://www.epa.gov/otaq/climate/documents/420r11901.pdf) 45 U.S. Energy Information Administration. Annual Energy Outlook 2014. Table 12: Petroleum Product Prices. 

Last accessed on September 11, 2014 at http://www.eia.gov/forecasts/aeo/tables_ref.cfm. 46 Memo from Energy and Environmental Research Associates, LLC Regarding HDV Rebound Effect, dated June 8, 

2011. 

47 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles,” Washington, D.C., The National Academies Press. Available electronically 

from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845, page 152. 48 A baseline tractor price of a new day cab is $89,500 versus $113,000 for a new sleeper cab based on information 

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155 Historical data are from EIA Annual Energy Review, various editions. For data since 2011 and projected data: 

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20 (Macroeconomic Indicators,” (file “aeotab_20.xlsx”). 156 http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf. 157 U.S. Department of Labor, Bureau of Labor Statistics. “Automotive Industry; Employment, Earnings, and 

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161 This discussion draws from Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR), p. 

293. 

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166 Hamermesh (1993), Labor Demand (Princeton, NJ: Princeton University Press), Chapter 2 (Docket EPA-HQOAR-2011-0135). 167 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy 

(Addison Wesley Longman, Inc.), Chapter 4 (Docket EPA-HQ-OAR-),. 168Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South 

Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR-2011-0135). 
8-114 

169Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the Environment: An IndustryLevel Perspective.” Journal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQOAR-2011-0135-0057). 170 Gray, Wayne B., Ronald J. Shadbegian, Chunbei Wang, and Merve Meral. “Do EPA Regulations Affect Labor 

Demand? Evidence from the Pulp and Paper Industry.” Journal of Environmental Economics and Management: 68, 

2014, 188-202. 

171 Ferris, Ann, Ronald J. Shadbegian and Ann Wolverton. “The Effect of Environmental Regulation on Power 

Sector Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the Association of Environmental 

and Resource Economists. (Forthcoming 2014). 

172 Greenstone, M. (2002). “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 

1970 and 1977 Clean Air Act Amendments and the Census of Manufactures,” Journal of Political Economy 110(6): 

1175-1219 (Docket EPA-HQ-OAR-2011-0135); Walker, Reed. (2011).“Environmental Regulation and Labor 

Reallocation." American Economic Review: Papers and Proceedings 101(3): 442-447 (Docket EPA-HQ-OAR2011-0135). 173List, J. A., D. L. Millimet, P. G. Fredriksson, and W. W. McHone (2003). “Effects of Environmental Regulations 

on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics 

and Statistics 85(4): 944-952 (Docket EPA-HQ-OAR-2011-0135). 174Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South 

Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR-2011-0135). 175 http://www.bls.gov/emp/ep_data_emp_requirements.htm. 176 http://www.census.gov/manufacturing/asm/index.html. 177 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 81 (Motor Vehicle 

Manufacturing), 82 (Motor Vehicle Body and Trailer Manufacturing), and 83 (Motor Vehicle Parts Manufacturing) 

from “Chain-weighted (2005 dollars) real domestic employment requirements tables.” 

178 American Transportation Research Institute, “An Analysis of the Operational Costs of Trucking: 2011 Update.” 

See http://www.atri-online.org/research/results/Op_Costs_2011_Update_one_page_summary.pdf. 

179 Association of American Railroads, “All Inclusive Index and Rail Adjustment Factor.” June 3, 2011. See

http://www.aar.org/~/media/aar/RailCostIndexes/AAR-RCAF-2011-Q3.ashx 
9-1 

Chapter 9. Safety Impacts 

9.1 Summary of Supporting HD Vehicle Safety Research 

NHTSA and EPA considered the potential safety impact of technologies that improve HD 

vehicle fuel efficiency and GHG emissions as part of the assessment of regulatory alternatives. 

The safety assessment of the technologies in this proposal was informed by two NAS reports, an 

analysis of safety effects of HD pickups and vans using estimates from the DOT report on the 

effect of mass reduction and vehicle size on safety, and agency-sponsored safety testing and 

research. A summary of the literature and work considered by the agencies follows. 

9.2 National Academy of Sciences HD Phase 1 and Phase 2 Reports 

As required by EISA, the National Research Council has conducted two studies of the 

technologies and approaches for reducing the fuel consumption of medium- and heavy-duty 

vehicles. The first was documented in a report issued in 2010, “Technologies and Approaches to 

Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles’’ (“NAS Report”).A The 

second was documented in a report issued in 2014, “Reducing the Fuel Consumption and 

Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two-First Report” 

(“NAS HD Phase 2 First Report”).B While the reports primarily focused on reducing vehicle 

fuel consumption and emissions through technology application, and examined potential 

regulatory frameworks, both reports additionally contain findings and recommendations on 

safety. In developing this proposal, the agencies carefully considered both of the reports’ 

findings related to safety. Some of the reports’ key findings related to safety follow. 

NAS commented that idle reduction strategies can be sophisticated to provide for the 

safety of the driver in hot and cold weather. The agencies considered this comment in our 

approach for idle reduction technologies (e.g., APUs, diesel fired heaters, and battery powered 

units with automatic engine shutoff (AES)) and allow override provisions, as discussed in 

Preamble Section III. Override of the automatic engine shutoff (AES) feature is allowed if the 

external ambient temperature reaches a level below which or above which the cabin temperature 

cannot be maintained within reasonable heat or cold exposure threshold limit values for the 

health and safety of the operator (not merely comfort). 

 

A Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles.” Washington, D.C., The National Academies Press. Available electronically 

from the National Academy Press Website at http://www.nap.edu/catalog/12845/technologies-and-approaches-toreducing-the-fuel-consumption-of-medium--and-heavy-duty-vehicles (last accessed June 4, 2015). B Transportation Research Board 2014. “Reducing the Fuel Consumption and Greenhouse Gas Emissions of 

Medium- and Heavy-Duty Vehicles, Phase Two.” Washington, D.C., The National Academies Press. Available 

electronically from the National Academy Press Website http://www.nap.edu/catalog/18736/reducing-the-fuelconsumption-and-greenhouse-gas-emissions-of-medium--and-heavy-duty-vehicles-phase-two (last accessed June 4, 

2015). 
9-2 

NAS commented extensively on the recent emergence of natural gas (NG) as a viable 

technology option for commercial vehicles, but alluded to the existence of uncertainties 

regarding its safety. The committee found that while the public crash databases do not contain 

information on vehicle fuel type, the existing information indicates that the crash-related safety 

risk for NG storage on vehicles does not appear to be appreciably different from diesel fuel risks. 

The committee also found that while there are two existing SAE-recommended practice 

standards for NG-powered HD vehicles, the industry could benefit from best practice directives 

to minimize crash risks for NG fuel tanks, such as on shielding to prevent punctures during 

crashes. As a final point, NAS stated that manufacturers and operators have a great incentive to 

prevent possible NG leakage from a vehicle fuel system because it would be a significant safety 

concern and reduce vehicle range. No recommendations were made for additional Federal safety 

regulations for these vehicles. In response, the agencies have reviewed and discuss the existing 

NG vehicle standards and best practices cited by NAS in Section XI. 

In the NAS Committee’s Phase 1 report, the Committee commented that aerodynamic 

fairings detaching from trucks on the road was a potential safety issue. However, the Phase 2 

interim report stated that “Anecdotal information gained during the observations of on-road 

trailers indicates a few skirts badly damaged or missing from one side. The skirt manufacturers 

report no safety concerns (such as side skirts falling off) and little maintenance needed.” 

The NAS report also identified the link between tire inflation and condition and vehicle 

stopping distance and handling, which impacts overall safety. The committee found that tire 

pressure monitoring systems and automatic tire inflation systems are being adopted by fleets at 

an increasing rate. However, the committee noted that there are no standards for performance, 

display, and system validation. The committee recommended that NHTSA issue a white paper 

on the minimum performance of tire pressure systems from a safety perspective. 

The agencies considered the safety findings in both NAS reports in developing this 

proposal and conducted additional research on safety to further examine information and 

findings of the reports. 

9.3 DOT CAFE Model HD Pickup and Van Safety Analysis 

This analysis considered the potential effects on crash safety of the technologies 

manufacturers may apply to their HD pickups and vans to meet each of the regulatory 

alternatives evaluated. NHTSA research has shown that vehicle mass reduction affects overall 

societal fatalities associated with crashes and, most relevant to this proposal, that mass reduction 

in heavier light- and medium-duty vehicles has an overall beneficial effect on societal fatalities. 

Reducing the mass of a heavier vehicle involved in a crash with another vehicle(s) makes it less 

likely that there will be fatalities among the occupants of the other vehicles. In addition to the 

effects of mass reduction, the analysis anticipates that the proposed standards, by reducing the 

cost of driving HD pickups and vans, would lead to increased travel by these vehicles and, 

therefore, more crashes involving these vehicles. The Method A analysis considers overall 

impacts from both of these factors, using a methodology similar to NHTSA’s analyses for the 

MYs 2017 – 2025 CAFE and GHG emission standards. 
9-3 

The Method A analysis includes estimates of the extent to which HD pickups and vans 

produced during MYs 2014-2030 may be involved in fatal crashes, considering the mass, 

survival, and mileage accumulation of these vehicles, taking into account changes in mass and 

mileage accumulation under each regulatory alternative. These calculations make use of the 

same coefficients applied to light trucks in the MYs 2017-2025 CAFE rulemaking analysis. As 

discussed above, vehicle miles traveled may increase due to the fuel economy rebound effect, 

resulting from improvements in vehicle fuel efficiency and cost of fuel, as well as the assumed 

future growth in average vehicle use. Increases in total lifetime mileage increase exposure to 

vehicle crashes, including those that result in fatalities. Consequently, the modeling system 

computes total fatalities attributed to vehicle use for vehicles of a given model year based on 

safety class and weight threshold. These calculations also include a term that accounts for the 

fact that vehicles involved in future crashes will be certified to more stringent safety standards 

than those involved with past crashes upon which the base rates of involvement in fatal crashes 

were estimated. Since the use of mass reducing technology is present within the model, safety 

impacts may also be observed whenever a vehicle’s base weight decreases. Thus, in addition to 

computing total fatalities related to vehicle use, the modeling system also estimates changes in 

fatalities due to reduction in a vehicle’s curb weight. 

The total fatalities attributed to vehicle use and vehicle weight change for vehicles of a 

given model year are then summed. Lastly, total fatalities occurring within the industry in a 

given model year are accumulated across all vehicles. In addition to using inputs to estimate the 

future involvement of modeled vehicles in crashes involving fatalities, the model also applies 

inputs defining other accident-related externalities estimated on a dollar per mile basis. For 

vehicles above 4,594 pounds—i.e., the majority of the HD pickup and van fleet—mass reduction 

is estimated to reduce the net incidence of highway fatalities by 0.34 percent per 100 pounds of 

removed curb weight. For the few HD pickups and vans below 4,594 pounds, mass reduction is 

estimated to increase the net incidence of highway fatalities by 0.52 percent per 100 pounds. 

Because there are many more HD pickups and vans above 4,594 pounds than below 4,594 

pounds, the overall effect of mass reduction in the segment is estimated to reduce the incidence 

of highway fatalities. The estimated increase in vehicle miles traveled due to the fuel economy 

rebound effect is estimated to increase exposure to vehicle crashes and offset these reductions. 

9.4 Volpe Research on MD/HD Fuel Efficiency Technologies 

The 2010 NAS Report recommended that NHTSA perform a thorough safety analysis to 

identify and evaluate potential safety issues with fuel efficiency-improving technologies. The 

Department of Transportation Volpe Center’s 2015 report titled “Review and Analysis of 

Potential Safety Impacts of and Regulatory Barriers to Fuel Efficiency Technologies and 

Alternative Fuels in Medium- and Heavy-Duty Vehicles” C summarizes research and analysis 

findings on potential safety issues associated with both the diverse alternative fuels (natural gasCNG and LNG, propane, biodiesel, and power train electrification), and the specific FE 

 

C Brecher, A., Epstein, A. K., & Breck, A. (2015, June). “Review and analysis of potential safety impacts of and 

regulatory barriers to fuel efficiency technologies and alternative fuels in medium- and heavy-duty vehicles.” 

(Report No. DOT HS 812 159). Washington, DC: National Highway Traffic Safety Administration. 
9-4 

technologies recently adopted by the MD/HDV fleets. These include Intelligent Transportation 

Systems (ITS) and telematics, speed limiters, idle reduction devices, tire technologies (singlewide tires, and tire pressure monitoring systems-TPMS and Automated Tire Inflation SystemsATIS), aerodynamic components, vehicle light-weighting materials, and Long Combination 

Vehicles (LCVs). 

Chapter 1 provides an overview of the study’s rationale, background, and key objective, 

namely, to identify the technical and operational/behavioral safety benefits and disbenefits of 

MD/HDVs equipped with FE technologies and using emerging alternative fuels (AFs). Recent 

MD/HDV national fleet crash safety statistical averages are also provided for context, although 

no information exists in crash reports relating to specific vehicle FE technologies and fuels. 

(NHTSA/FARS and FMCSA/CSA databases do not include detailed information on vehicle fuel 

economy technologies, since the state crash report forms are not coded down to an individual 

fuel economy technology level). 

Chapters 2 and 3 are organized by clusters of functionally-related FE technologies for 

vehicles and trailers (e.g., tire systems, ITS, light-weighting materials, and aerodynamic systems) 

and alternative fuels, which are described and their respective associated potential safety issues 

are discussed. Chapter 2 summarizes the findings from a comprehensive review of available 

technical and trade literature and Internet sources regarding the benefits, potential safety hazards, 

and the applicable safety regulations and standards for deployed FE technologies and alternative 

fuels. Chapter 2 safety-relevant fuel-specific findings include: 

• Both CNG- and LNG-powered vehicles present potential hazards, and call for wellknown engineering and process controls to assure safe operability and 

crashworthiness. However, based on the reported incident rates of NGVs and the 

experiences of adopting fleets, it appears that NGVs can be operated at least as safely 

as diesel MD/HDVs. 

• There are no safety contraindications to the large scale fleet adoption of CNG or LNG 

fueled heavy duty trucks and buses, and there is ample experience with the safe 

operation of large public transit fleets. Voluntary industry standards and best 

practices suffice for safety assurance, though improved training of CMV operators 

and maintenance staff in natural gas safety of equipment and operating procedures is 

needed. 

• Observing CNG and LNG fuel system and maintenance facility standards, coupled 

with sound design, manufacture, and inspection of natural gas storage tanks will 

further reduce the potential for leaks, tank ruptures, fires, and explosions. 

• Biodiesel blends used as drop-in fuels have presented some operational safety 

concerns dependent on blending fraction, such as material compatibility, bio-fouling 

sludge accumulation, or cold-weather gelling. However, best practices for biodiesel 

storage, and improved gaskets and seals that are biodiesel resistant, combined with 
9-5 

regular maintenance and leak inspection schedules for the fuel lines and components 

enable the safe use of biodiesel in newer MD/HDVs 

• Propane (LPG, or autogas) presents well-known hazards including ignition (due to 

leaks or crash) that are preventable by using Overfill Prevention Devices (OPDs), 

which supplement the automatic stop-fill system on the fueling station side, and 

pressure release devices (PRDs). Established best practices and safety codes (e.g., 

NFPA) have proven that propane fueled MD/HDVs can be as operationally safe as 

the conventionally-fueled counterparts. 

• As the market penetration of hybrid and electric drivetrain accelerates, and as the 

capacity and reliability of lithium ion batteries used in Rechargeable Energy Storage 

Systems (RESS) improve, associated potential safety hazards (e.g., electrocution from 

stranded energy, thermal runaway leading to battery fire) have become well 

understood, preventable, and manageable. Existing and emerging industry technical 

and safety voluntary standards, applicable NHTSA regulations and guidance, and the 

growing experience with the operation of hybrid and electric MD/HDVs will enable 

the safe operation and large-scale adoption of safer and more efficient power-train 

electrification technologies. 

The safety findings from literature review pertaining to the specific FE technologies 

implemented to date in the MD/HDV fleet include: 

• Telematics—integrating on-board sensors, video, and audio alerts for MD/HDV 

drivers—offer potential improvements in both driver safety performance and fuel 

efficiency. Both camera and non-camera based telematics setups are currently 

integrated with available crash avoidance systems (such as ESC, RSC, LDWS, etc.) 

and appear to be well accepted by MD/HDV fleet drivers. 

• Both experience abroad and the cited US studies of trucks equipped with active speed 

limiters indicated a safety benefit, as measured by up to 50 percent reduced crash 

rates, in addition to fuel savings and other benefits, with good CMV driver 

acceptance. Any negative aspects were small and avoidable if all the speed limitation 

devices were set to the same speed, so there would be less need for overtaking at 

highway speeds. 

• No literature reports of adverse safety impacts were found regarding implementation 

of on-board idle-reduction technologies in MD/HDVs (such as automatic start-stop, 

direct-fired heaters, and APUs). 
9-6 

• There was no clear consensus from the literature regarding the relative crash rates and 

highway safety impacts of LCVs, due to lack of sufficient data and controls and 

inconsistent study methodologies. Recent safety evaluations of LCVs and ongoing 

MAP-21 mandated studies will clarify and quantify this issue. 

• Tire technologies for FE (including ATIS, TPMS, LRR and single-wide tires) 

literature raised potential safety concerns regarding lower stability or loss of control, 

e.g., when tire pressure is uneven or a single wide tire blows out on the highway. 

However, systems such as automated tire monitoring systems and stability enhancing 

electronic systems (ABS, ESC, RSC) may compensate and mitigate any adverse 

safety impacts. 

• Aerodynamic technologies that offer significant fuel savings have raised potential 

concerns about vehicle damage or injury in case of detached fairings or skirts, 

although there were no documented incidents of this type in the literature. 

• Some light weighting materials may pose some fire safety and crashworthiness 

hazards, depending on their performance in structural or other vehicle subsystem 

applications (chassis, power-train, and crash box or safety cage). Some composites 

(fiberglass, plastics, CFRC, foams) may become brittle on impact or due to 

weathering from UV exposure or extreme cold. Industry has developed advanced, 

high performance lightweight material options tailored to their automotive 

applications, e.g., thermoplastics resistant to UV and weathering. No examples of 

such lightweight material failures on MD/HDVs were identified in the literature. 

Chapter 3 provides complementary inputs on the potential safety issues associated with 

FE technologies and alternative fuels obtained from Subject Matter Experts (SMEs). The broad 

cross-section of SMEs consulted had experience with the operation of “green” truck and bus 

fleets, were Federal program managers, or were industry developers of FE systems for 

MD/HDVs. Safety concerns raised by the SMEs can be prevented or mitigated by complying 

with applicable regulations and safety standards and best practices, and are being addressed by 

evolving technologies, such as electronic collision prevention devices. Although SMEs raised 

some safety concerns, their experience indicates that system- or fuel-specific hazards can be 

prevented or mitigated by observing applicable industry standards, and by training managers, 

operators and maintenance staff in safety best practices. Specific safety concerns raised by 

SMEs based on their experience included: 

• Alternative fuels did not raise major safety concerns, but generally required better 

education and training of staff and operators. There was a concern expressed 

regarding high pressure (4000 psi) CNG cylinders that could potentially explode in a 

crash scenario or if otherwise ruptured. However, aging CNG fuel tank safety can be 

assured by enforcing regulations such as FMVSS No. 304, and by periodic inspection 
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and end-of-life disposal and replacement. A propane truck fleet manager stated that 

the fuel was as safe as or safer than gasoline, and reported no safety issues with the 

company’s propane, nor with hybrid gasoline-electric trucks. OEMs of drivetrain 

hybridization and electrification systems, including advanced Lithium Ion batteries 

for RESS, indicated that they undergo multiple safety tests and are designed with failsafes for various misuse and abuse scenarios. Integration of hybrid components 

downstream by bodybuilders in retrofits, as opposed to new vehicles, was deemed a 

potential safety risk. Another potential safety concern raised was the uncertain 

battery lifetime due to variability of climate, duty-cycles, and aging. Without stateof-charge indicators, this could conceivably leave vehicles underpowered or stranded 

if the battery degrades and is not serviced or replaced in a timely manner. 

• ITS and telematics raised no safety concerns; on the contrary, fleet managers stated 

that “efficient drivers are safer drivers.” Monitoring and recording of driver behavior, 

combined with coaching, appeared to reduce distracted and aggressive driving and 

provided significant FE and safety benefits. 

• A wide-base single tire safety concern was the decrease in tire redundancy in case of 

a tire blowout at highway speeds. For LRRs, a concern was that they could 

negatively affect truck stopping distance and stability control. 

• A speed-limiter safety concern was related to scenarios when such trucks pass other 

vehicles on the highway instead of staying in the right-hand lane behind other 

vehicles. By combining speed limiters with driver training programs, overall truck 

safety could actually improve, as shown by international practice. 

• Aerodynamic systems’ safety performance to date was satisfactory, with no instances 

of on-road detaching. However, covering underside or other components with 

aerodynamic fairings can make them harder to inspect, such as worn lugs, CNG relief 

valve shrouds, wheel covers, and certain fairings. Drivers and inspectors need to be 

able to see through wheel covers and to be able to access lug nuts through them. 

These covers must also be durable to withstand frequent road abuse. 

• For lightweighting materials, the safety concern raised was lower crashworthiness 

(debonding or brittle fracture on impact) and the potential for decreased survivability 

in vehicle fires depending on the specific material choice and its application. 

The key finding from the literature review and SME interviews is that there appear to be 

no major safety hazards preventing the adoption of FE technologies, or the increased use of 

alternative fuels and vehicle electrification. In view of the scarcity of hard data currently 

available on actual highway crashes that can be directly or causally attributed to adoption of FE 
9-8 

technologies and/or alternative fuels by MD/HDVs, and the limited experience with commercial 

truck and transit bus fleets operations equipped with these technologies, it was not possible to 

perform a quantitative, probabilistic risk assessment, or even a semi-quantitative preliminary 

hazard analysis (PHA). 

Chapter 4 employs a deterministic scenario-based hazard analysis of potential crash or 

other safety concerns identified from the literature review or raised by subject matter experts 

(SMEs) interviewed (e.g., interfaces with charging or refueling infrastructure). For each specific 

hazard scenario discussed, the recommended prevention or mitigation options, including 

compliance with applicable NHTSA or FMCSA regulations, and voluntary industry standards 

and best practices are identified, along with FE technology or fuel-specific operator training. 

SMEs safety concerns identified in Sec 3.3 were complemented with actual incidents, and 

developed into the hazard scenarios analyzed in Chapter 4. 

The scenario-based deterministic hazard analysis reflected not only the literature findings 

and SMEs’ safety concerns, but also real truck or bus mishaps that have occurred in the past. 

Key hazard analysis scenarios included: CNG-fueled truck and bus vehicle fires or explosions 

due to tank rupture, when pressurized fuel tanks were degraded due to aging or when PRDs 

failed; LNG truck crashes leading to fires, or LNG refueling-related mishaps; the flammability or 

brittle fracture issues related to lightweighting materials in crashes; reduced safety performance 

for either LRR or wide-base tires; highway pile-ups when LCVs attempt to pass at highway 

speeds; aerodynamic components detaching while the vehicle traveled on a busy highway or 

urban roadway; and fires resulting in overheated lithium ion batteries in electric or hybrid buses. 

These hypothetical worst case scenarios appear to be preventable or able to be mitigated by 

observing safety regulations and voluntary standards, or with engineering and operational best 

practices. 

Chapter 5 reviews and discusses the existing federal and state regulatory framework for 

safely operating MD/HDVs equipped with FE technologies or powered by alternative fuels. The 

review identifies potential regulatory barriers to their large-scale deployment in the national fleet 

that could delay achievement of desired fuel consumption and environmental benefits, while 

ensuring equal or better safety performance. 

Chapter 6 summarizes the major findings and recommendations of this preliminary safety 

analysis of fuel efficiency technologies and alternative fuels adopted by MD/HDVs. The 

scenario-based hazard analysis, based on the literature review and experts’ inputs, indicates that 

MD/HDVs equipped with advanced FE technologies and/or using alternative fuels have 

manageable potentially adverse safety impacts. The findings suggest that the potential safety 

hazards identified during operation, maintenance, and crash scenarios can be prevented or 

mitigated by complying with safety regulations and voluntary standards and industry best 

practices. The study also did not identify any major regulatory barriers to rapid adoption of FE 

technologies and alternative fuels by the MD/HDV fleet. 
9-9 

9.5 Oak Ridge National Laboratory (ORNL) Research on Low Rolling 

Resistance Truck Tires 

DOT’s Federal Motor Carrier Safety Administration and NHTSA sponsored a test 

program conducted by Oak Ridge National Laboratory to explore the effects of tire rolling 

resistance levels on Class 8 tractor-trailer stopping distance performance over a range of loading 

and surface conditions.D The objective was to determine whether there is a relationship between 

tire rolling resistance and stopping distance for vehicles of this type. The overall results of this 

research suggest that tire rolling resistance is not a reliable indicator of Class 8 tractor-trailer 

stopping distance. The correlation coefficients (R2 values) for linear regressions of wet and dry 

stopping distance versus overall vehicle rolling resistance values did not meet the minimum 

threshold for statistical significance for any of the test conditions. Correlation between CRR and 

stopping distance was found to be negligible for the dry tests for both loading conditions. While 

correlation was higher for the wet testing (showing a slight trend in which lower CRRs 

correspond to longer stopping distances), it still did not meet the minimum threshold for 

statistical significance. In terms of compliance with Federal safety standards, it was found that 

the stopping distance performance of the vehicle with the four tire sets studied in this research 

(with estimated tractor CRRs which varied by 33 percent), were well under the FMVSS No. 121 

stopping distance requirements. 

9.6 Additional Safety Considerations 

The agencies’ considered the Organic Rankine Cycle waste heat recovery (WHR) as a 

fuel saving technology in the rulemaking timeframe. The basic approach of these systems is to 

use engine waste heat from multiple sources to evaporate a working fluid through a heat 

exchanger, which is then passed through a turbine or equivalent expander to create mechanical or 

electrical power. The working fluid is then condensed as it passes through a heat exchanger and 

returns to back to the fluid tank, and pulled back to the flow circuit through a pump to continue 

the cycle. Despite the promising performance of pre-prototype WHR systems, manufacturers 

have not yet arrived at a consensus on which working fluid(s) to be used in WHR systems to 

balance concerns regarding performance, global warming potential (GWP), and safety. Current 

working fluids have a high GWP (conventional refrigerant), are expensive (low GWP 

refrigerant), are hazardous (ammonia, etc.), are flammable (ethanol/methanol), or can freeze 

(water). One of the challenges is determining how to seal the working fluid properly under the 

vacuum condition with high temperature to avoid safety issues for flammable/hazardous working 

fluids. Because of these challenges, choosing a working fluid will be an important factor for 

system safety, efficiency, and overall production viability. The agencies believe manufacturers 

will require additional time and development effort to assure that a working fluid that is both 

appropriate, given the noted challenges, and has a low GWP for use in waste heat recovery 

systems. Based on this and other factors, the analysis for the Preferred Alternative assumes that 

WHR would not achieve a significant market penetration for diesel tractor engines (i.e., greater 

than 5 percent) until 2027, which would provide time for these considerations to be addressed. 

 

D Lascurain, M.B. (2015, June). “Effects of tire rolling resistance levels on Class 8 tractor trailer stopping distance 

performance.” Washington, DC: National Highway Traffic Safety Administration. 
9-10 

The agencies assume no use of this technology in the HD pickups and vans and vocational 

vehicle segments. 

9.7 The Agencies’ Assessment of Potential Safety Impacts 

NHTSA and EPA considered the potential safety impact of technologies that improve HD 

vehicle fuel efficiency and GHG emissions as part of the assessment of regulatory alternatives. 

The safety assessment of the technologies in this proposal was informed by two NAS reports, an 

analysis of safety effects of HD pickups and vans using estimates from the DOT report on the 

effect of mass reduction and vehicle size on safety, and agency-sponsored safety testing and 

research. The agencies considered safety from the perspective of both direct effects and indirect 

effects. 

In terms of direct effects on vehicle safety, research from NAS and Volpe, and direct 

testing of technologies like the ORNL tire work, indicate that there are no major safety hazards 

associated with the adoption of technologies that improve HD vehicle fuel efficiency and GHG 

emissions or the increased use of alternative fuels and vehicle electrification. The findings 

suggest that the potential safety hazards identified during operation, maintenance, and crash 

scenarios can be prevented or mitigated by complying with safety regulations, voluntary 

standards and industry best practices. Tire testing showed tire rolling resistance did not impact 

of Class 8 tractor-trailer stopping distance for the tires tested. Also, because the majority of HD 

pickup and van fleet are above 4,594 pounds, the vehicle mass reduction in HD pickup and vans 

is estimated to reduce the net incidence of highway fatalities. Taken together, these studies 

suggest that the fuel efficiency improving technologies assessed in the studies can be 

implemented with no degradation in overall safety. 

However, analysis anticipates that the indirect effect of the proposed standards, by 

reducing the operating costs, would lead to increased travel by tractor-trailers and HD pickups 

and vans and, therefore, more crashes involving these vehicles. 
10-1 

Chapter 10: CAFE Model for HD Pickups and Vans 

For this rule, the agencies conducted coordinated and complementary analyses using two 

analytical methods for the heavy-duty pick up and van segment by employing both DOT’s CAFE 

model and EPA’s MOVES model. For heavy-duty pickups and vans, the agencies performed 

complementary analyses, which we refer to as “Method A” and “Method B”. In Method A, the 

CAFE model was used to project a pathway the industry could use to comply with each 

regulatory alternative and the estimated effects on fuel consumption, emissions, benefits and 

costs. In Method B, the CAFE model was used to project a pathway the industry could use to 

comply with each regulatory alternative, along with resultant impacts on per vehicle costs, and 

the MOVES model was used to calculate corresponding changes in total fuel consumption and 

annual emissions. Additional calculations were performed to determine corresponding 

monetized program costs and benefits. NHTSA considered Method A as its central analysis and 

Method B as a supplemental analysis. EPA considered the results of both methods. The 

agencies concluded that both methods led the agencies to the same conclusions and the same 

selection of the proposed standards. See Section VII of the preamble for additional discussion of 

these two methods. 

In this chapter, the CAFE modeling system is described and used to analyze technology 

use and per-vehicle costs under each regulatory alternative, including the no action alternative 

(which reflects continuation of previously-promulgated standards). However, this model is more 

comprehensive and also projects other impacts. NHTSA addresses these other impacts in the 

Draft EIS and these are also presented here.1

2.1 HD Pickup and Van Fleet 

2.1.1 Why did the Agencies Develop the Analysis Fleet? 

The modeling system relies on many inputs, including an analysis fleet. In order to 

estimate the impacts of potential standards, it is necessary to estimate the composition of the 

future vehicle fleet. Doing so enables estimation of the extent to which each manufacturer may 

need to add technology in response to a given series of attribute-based standards, accounting for 

the mix and fuel consumption of vehicles in each manufacturer’s regulated fleet. The agencies 

create an analysis fleet in order to track the volumes and types of fuel economy-improving and 

CO2-reducing technologies that are already present in the existing vehicle fleet. This aspect of 

the analysis fleet helps to keep the CAFE model from adding technologies to vehicles that 

already have these technologies, which would result in “double counting” of technologies’ costs 

and benefits. An additional step involved projecting the fleet sales into MYs 2019-2030. This 

represents the fleet volumes that the agencies believe would exist in MYs 2019-2030. The 

following presents an overview of the information and methods applied to develop the analysis 

fleet, and some basic characteristics of that fleet. Details appear in the input file. 

 1

 EPA uses its MOVES model to project these other impacts as discussed in Chapters 5 through 8 of this draft RIA. 

Note that the results of both modeling approaches corroborate the results of the overall analysis. 
10-2 

2.1.2 How the MY2014 Based Analysis Fleet Was Developed? 

Most of the information about the vehicles that make up the 2014 analysis fleet was 

gathered from the 2014 Pre-Model Year Reports submitted to EPA by the manufacturers under 

Phase 1 of Fuel Efficiency and GHG Emission Program for Medium- and Heavy-Duty Trucks, 

MYs 2014-2018. 

The major manufactures of class 2b and class 3 trucks (Chrysler, Ford and GM) were 

asked to voluntarily submit updates to their Pre-Model Year Reports. Updated data were 

provided by Chrysler and GM. These updated data were used in constructing the analysis fleet 

for these manufacturers. 

The agencies agreed to treat this information as Confidential Business Information (CBI) 

until the publication of the NPRM. This information can be made public at this time because by 

now all MY2014 vehicle models have been produced, which makes data about them essentially 

public information. 

These data (by individual vehicle configuration produced in MY2014) include: Projected 

Production Volume/MY2014 Sales, Drive Type, Axle Ratio, Work Factor, Curb Weight2

, Test 

Weight3

, GVWR, GCWR, Fuel Consumption (gal/100 mile)4, engine type (gasoline or diesel), 

engine displacement, transmission type and number of gears5. 

The column “Engine” of the Pre-Model Year report for each OEM was copied to the 

column “Engine Code” of the vehicle sheet of the CAFE model market data input file.6 Values 

of “Engine” were changed to Engine Codes for use in the CAFE model. The codes indicated on 

the vehicle sheet map the detailed engine data on the engine sheet to the appropriate vehicle on 

the vehicle sheet of the CAFE model input file. 

The column “Trans Class” of the Pre-Model Year report for each OEM was copied to the 

column “Transmission Code” of the vehicle sheet of the market data input file. Values of 

“Trans Class” were changed to Transmission Codes for use in the CAFE model. The codes 

indicated on the vehicle sheet map the detailed transmission data on the transmission sheet to the 

appropriate vehicle on the vehicle sheet of the CAFE model input file. 

 2

 GM did not provide curb weight in its submittal. GM did provide “Payload.” Curb weight for GM vehicles was 

calculated as GVWR – Payload. 

3

 Chrysler and GM did not provide test weights in their submittals. Test weights were calculated as the average of 

GVWR and curb weight rounded up to the nearest 100 lbs. 

4

 These values were converted to mile/gal for use in the Volpe model. In their supplemental data submission GM 

provided the data as mpg in its report column “Fuel Consumption Performance”. In its supplemental data 

submission Fiat provided “Fuel Economy on Primary Fuel (Unadjusted combined CO2 g/mi).” These values were 

converted to mpg using the factors 8,887 gCO2/gal for gas engines and 10.180 gCO2/gal for diesel engines. 5

 GM did not provide transmission data in its submittal. Specific transmissions associated with each of GM’s trucks 

were identified using information from GM’s websites. 6

 The GM data was an exception. In its case the column “Disp” of the Pre-Model Year report was copied to the 

column “Engine Code.” 
10-3 

In addition to information about each vehicle, the agencies need additional information 

about the fuel economy-improving/CO2-reducing technologies already on those vehicles in order 

to assess how much and which technologies to apply to determine a path toward future 

compliance. Thus, the agencies augmented this information with publicly-available data that 

includes more complete technology descriptions. Specific engines and transmissions associated 

with each manufacturer’s trucks were identified using their respective internet sites. Detailed 

technical data on individual engines and transmissions indicated on the engine sheet and 

transmission sheet of the CAFE model input file were then obtained from manufacturer internet 

sites, spec sheets and product literature, Ward’s Automotive Group and other commercial 

internet sites such as cars.com, edmunds.com, and motortrend.com.7

“Fuel Economy on Secondary Fuel” was calculated as E85 = .74 gasoline fuel economy, 

or B20 = .98 diesel fuel economy. These values were duplicated in the columns “Fuel Economy 

(Ethanol-85)” and “Fuel Economy (Biodiesel-20)” of the CAFE market data input file. 

Values in the columns “Fuel Share (Gasoline)”, “Fuel Share (Ethanol-85)”, “Fuel Share 

(Diesel),” and “Fuel Share (Biodiesel-20)” are Volpe assumptions. 

The CAFE model also requires that values of Origin, Regulatory Class, Technology 

Class, Safety Class, and Seating (Max) be present in the file in order for the model to run. 

Placeholder values were added in these columns. 

In addition to the data taken from the OEM Pre Model Year submittals, NHTSA added 

additional data for use by the CAFE model. These included Platform, Refresh Years, Redesign 

Years, MSRP, Style, Structure and Fuel Capacity.8

MSRP was obtained from web2carz.com and the OEM web sites. 

Fuel capacity was obtained from OEM spec sheets and product literature. 

 7

 In their data update Chrysler provided much of the detailed engine data utilized in the Volpe model input file. 

These included Fuel Cycle, Fuel Delivery System, Aspiration, Cylinders, Valves/Cylinder, Deactivation, 

Displacement, Compression Ratio, Max. Horsepower, Max. Horsepower RPM, Max. Torque, and Max. Torque 

RPM. These were copied directly to the engine tab of the Volpe model input file. 

GM provided similar engine data including Engine Oil Viscosity Fuel Cycle, Fuel Delivery System, Aspiration, 

Cylinders, Valves/Cylinder, Valvetrain Design, Valve Actuation/Timing, Valve Lift, Deactivation, Displacement, 

Compression Ratio, Max. Horsepower, Max. Horsepower RPM, Max. Torque, and Max. Torque RPM. These were 

copied directly to the engine tab of the Volpe model input file. 

8

 Daimler, Fiat, GM and Nissan provided Truck Line Name (nameplate) information that could be used to 

distinguish individual vehicles and their associated characteristics. This level of disaggregation was needed in order 

to get data on fuel capacity and MSRP for individual vehicles from the OEM web sites or other commercial 

automotive web sites. Volpe created nameplates to distinguish vehicle types in order to get data on fuel capacity 

and MSRP for Ford. A comparison of the curb weights and GVWR in the Ford data with those in their product spec 

sheets (while controlling for drive and engine type) allowed us to make an educated guess as to which of the pickups 

were for example, 250/350series, regular/crew cab, short box/long box, SRW/DRW. These guesses appear in the 

column ”Probable Name Plate” of the Volpe input file and are used in the assignment of appropriate values of the 

various data inputs. 
10-4 

The Structure values (Ladder, Unibody) used by the CAFE model were added. These 

were determined from OEM product literature and the automotive press. It should be noted that 

the new vans such as the Transit in fact utilize a ladder/unibody structure. Ford product 

literature uses the term “Uniladder” to describe the structure. Vans based on this structure are 

noted in the Vehicle Notes column of the NHTSA input file. 

Style values used by the CAFE model were also added: Chassis Cab, Cutaway, Pickup 

and Van.

2.1.2.1 Vehicle Redesign Schedules & Platforms 

2.1.2.2 Pickup Trucks 

Product cadence in the Class 2b and 3 pickup market has historically ranged from 7-9 

years between major redesigns. However, due to increasing competitive pressures and consumer 

demands the agency anticipates that manufacturers will generally shift to shorter design cycles 

resembling those of the light duty market. Pickup truck manufacturers in the Class 2b and 3 

segments are shown to adopt redesign cycles of six years, allowing two redesigns prior to the end 

of the proposed regulatory period in 2027. 

2.1.2.2.1 Ford 

In the 2b/3 pickup truck market, Ford produces the F250, F350 and F450, currently based 

on the P3 platform. These models adopted the Super Duty moniker in 1999, and began using 

architecture and product cadence distinct from the F150 light-duty pickup models. The first full 

redesign of these models occurred in 2008, with smaller redesigns in 2005 and 2011. 

The agencies estimate that the next major redesign of Ford’s 2b/3 products will occur in 

or about 2017, trailing Ford’s announced update of a redesigned F150 in its light-duty pickup 

portfolio, with a more rapid product cadence leading to a subsequent redesign in 2023 and 

refreshes in 2020 and 2029. 

2.1.2.2.2 General Motors 

General Motors HD pickup trucks, the Silverado and Sierra HD series, are based on the 

GMT910 platform and were introduced as a 2007 model. GM has announced a redesigned HD 

pickup for the 2015 model year. The agencies estimate that, like Ford, GM will adopt an 

approximate six-year product cadence in the HD truck market, with redesigns in 2015 and 2021. 

2.1.2.2.3 Fiat (Ram) 

The current Ram HD models, on the D2/DJ platform, are anticipated for a major redesign 

in the 2018 model year, and the agencies estimate that the product will adopt a similar, shorter 

life cycle of six years, with a subsequent redesign in the 2024 model year. 
10-5 

2.1.2.3 Vans 

The 2b/3 van market has changed markedly from five years ago. Ford, Nissan, Ram and 

Daimler have adopted vans of “Euro Van” appearance, and in many cases now use smaller 

turbocharged gasoline or diesel engines in the place of larger, naturally-aspirated V8s. The 2014 

Model Year used in this analysis represents a period where most manufacturers, with the 

exception of General Motors, have recently introduced a completely redesigned product after 

many years. The van segment has historically been one of the slowest to be redesigned of any 

product segment, with some products going two decades or more between redesigns. 

Due to new entrants in the field and increased competition, the agencies anticipate that 

most manufacturers will increase the pace of product redesigns in the van segment, but that they 

will continue to trail other segments. The cycle time used in this analysis is approximately ten 

years between major redesigns, allowing manufacturers’ only one major redesign during the 

regulatory period. 

2.1.2.3.1 General Motors 

The GM Savana/Chevrolet Express, built on the GMT600 platform, has been produced 

since 1996 with a facelift in 2003. The van is currently due for a redesign, and while it is 

unknown when this will occur, the agencies anticipate a major redesign due to strong 

competitive pressure from other manufacturers will occur in or about 2017, with no further 

redesigns occurring until after 2025. 

2.1.2.3.2 Ford 

2014 marks the first year in more than three decades that Ford has used a completely new 

platform for its vans. The Transit replaces the Econoline except in Chassis Cab or cutaway 

configurations. The agencies anticipate that Ford will gradually shift production volume to the 

Transit, and will not redesign the Transit until 2025, with one intermediate product freshening. 

2.1.2.3.3 Fiat (Ram) 

The product cycle of the van from the Ram brand has less of a historical precedent. Fiat 

currently offers the Promaster (a variant of the Ducato van sold in other markets). Previously 

Chrysler sold Sprinter vans in an agreement with Daimler from 2003, and had previously 

manufactured its own full-sized van. 

The Promaster has just been introduced to the US market, and the agencies anticipate that 

Fiat will offer a refreshed version in 2020 prior to a full redesign in 2025. 

2.1.2.3.4 Nissan 

The Nissan NV launched for the 2012 model using the F-alpha platform shared with the 

light-duty Nissan Titan pickup truck. Trade publications and internet sources suggest the nextgeneration Nissan Titan could debut in model year 2016, and the agencies anticipate that the NV 
10-6 

van may adopt some of the features and components of the Titan for a mid-cycle freshening of 

the NV, with a full redesign in 2021. 

2.1.2.3.5 Daimler 

Daimler introduced its current Sprinter van for the 2007 model year on the NCV3 

platform. U.S. models received an update across 2014 and 2015, with rear wheel drive models 

arriving one year ahead of AWD models. The agencies anticipate that Daimler will redesign the 

Sprinter for 2017 with a subsequent freshening in model year 2021. 

2.1.2.4 Sales Volume Forecast 

Since each manufacturer’s required average fuel consumption and GHG levels are salesweighted averages of the fuel economy/GHG targets across all model offerings, sales volumes 

play a critical role in estimating that burden. The CAFE model requires a forecast of sales 

volumes, at the vehicle model-variant level, in order to simulate the technology application 

necessary for a manufacturer to achieve compliance in each model year for which outcomes are 

simulated. 

For today’s analysis, the agencies relied on the MY 2014 pre-model-year compliance 

submissions from manufacturers to provide sales volumes at the model level based on the level 

of disaggregation in which the models appear in the compliance data. However, the agencies 

only use these reported volumes without adjustment for MY 2014. For all future model years, 

we combine the manufacturer submissions with sales projections from the 2014 Annual Energy 

Outlook Reference Case and IHS Automotive to determine model variant level sales volumes in 

future years.9

 Figure 10-1 shows the projected sales volumes by class that appear in the 2014 

Annual Energy Outlook as a result of a collection of assumptions about economic conditions, 

demand for commercial miles traveled, and technology migration from light-duty pickup trucks 

in response to the concurrent light-duty CAFE/GHG standards. 

For this analysis, the agencies have limited this analysis fleet to class 2b and 3 HD 

pickups and vans. However, especially considering interactions between the light-duty and HD 

pickup and van fleets (e.g., MDPVs being included in the light-duty fleet), the agencies could 

also evaluate the potential to analyze the fleets in an integrated fashion. 

 9

 Tables from AEO’s forecast are available at http://www.eia.gov/oiaf/aeo/tablebrowser/. The agencies also made 

use of the IHS Automotive Light Vehicle Production Forecast (August 2014). 
10-7 

Figure 10-1 AEO2014 Sales Projections for 2b/3 Vehicles 

The projection of total sales volumes for the Class 2b and 3 market segment was based on 

the total volumes in the 2014 AEO Reference Case. For the purposes of this analysis, the 

AEO2014 calendar year volumes have been used to represent the corresponding model-year 

volumes. While AEO2014 provides enough resolution in its projections to separate the volumes 

for the Class 2b and 3 segments (see Figure 10-1), the agencies deferred to the vehicle 

manufacturers and chose to rely on the relative shares present in the pre-model-year compliance 

data. 

The relative sales share by vehicle type (van or pickup truck, in this case) was derived 

from a sales forecast that the agencies purchased from IHS Automotive, and applied to the total 

volumes in the AEO2014 projection. Table 10-1 shows the implied shares of the total new 2b/3 

vehicle market broken down by manufacturer and vehicle type. 
10-8 

Table 10-1 IHS Automotive Market Share Forecast for 2b/3 Vehicles

 MODEL YEAR MARKET SHARE 

Manufacturer Style 2015 2016 2017 2018 2019 2020 2021 

Daimler Van 3% 3% 3% 3% 3% 3% 3%

Fiat Van 2% 2% 2% 2% 2% 2% 3%

Ford Van 16% 17% 17% 17% 18% 18% 18%

General Motors Van 12% 12% 11% 12% 13% 13% 13%

Nissan Van 2% 2% 2% 2% 2% 2% 2%

 

Daimler Pickup 0% 0% 0% 0% 0% 0% 0% 

Fiat Pickup 14% 14% 14% 14% 11% 12% 12% 

Ford Pickup 28% 27% 30% 30% 30% 27% 26% 

General Motors Pickup 23% 23% 21% 21% 21% 22% 23% 

Nissan Pickup 0% 0% 0% 0% 0% 0% 0% 

Within those broadly defined market shares, volumes at the manufacturer/model-variant 

level were constructed by applying the model-variant’s share of manufacturer sales in the premodel-year compliance data for the relevant vehicle style, and multiplied by the total volume 

estimated for that manufacturer and that style. 

After building out a set of initial future sales volumes based on the sources described 

above, the agencies attempted to incorporate new information about changes in sales mix that 

would not be captured by either the existing sales forecasts or the simulated technology changes 

in vehicle platforms. In particular, Ford has announced intentions to phase out their existing 

Econoline vans, gradually shifting volumes to the new Transit platform for some model variants 

(notably chassis cabs and cutaways variants) and eliminating offerings outright for complete 

Econoline vans as early as model year 2015. In the case of complete Econoline vans, the 

volumes for those vehicles were allocated to MY2015 Transit vehicles based on assumptions 

about likely production splits for the powertrains of the new Transit platform. The volumes for 

complete Econoline vans were shifted at ratios of 50 percent, 35 percent, and 15 percent for 3.7 

L, 3.5 L Eco-boost, and 3.2 L diesel, respectively. Within each powertrain, sales were allocated 

based on the percentage shares present in the pre-model-year compliance data. The chassis cab 

and cutaway variants of the Econoline were phased out linearly between MY2015 and MY2020, 

at which time the Econolines cease to exist in any form and all corresponding volume resides 

with the Transits. 

2.1.2.5 Selected Characteristics of the MY2014 Based Analysis Fleet 

The tables below summarize some of the characteristics of the MY2014 based analysis 

fleet for Class 2b and Class 3 trucks. 

Table 10-2 shows production by manufacturer and indicates that Ford is dominant with 

52 percent of this market. 
10-9 

Table 10-2 Estimated MY2014 Production by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler 25,327 4.0% 

Fiat 138,902 21.8% 

Ford 330,919 51.9% 

General Motors 129,435 20.3% 

Nissan 13,526 2.1% 

Total 638,109 100.0% 

Table 10-3 shows production by class with 80 percent of production in class 2b, those 

trucks with a GVW between 8,501 and 10,000 lbs. 

Table 10-3 Estimated MY2014 Production by Class 

GVW CLASS PRODUCTION PERCENT 

2b (8,501-10,000 lbs.) 506,989 79.5% 

3 (10,001-14,000 lbs.) 131,120 20.5% 

Total 638,109 100.0% 

Table 10-4 shows production by style or body type. Pickup trucks make up 52 percent of 

production and vans 42 percent of production. 

Table 10-4 Estimated MY2014 Production by Vehicle Style 

STYLE PRODUCTION PERCENT 

Chassis Cab 19,724 3.1% 

Cutaway 20,539 3.2% 

Pickup 333,100 52.2% 

Van 264,746 41.5% 

Total 638,109 100.0% 

Table 10-5 shows production by engine type. Diesel powered trucks make up a 

significant share (40 percent) of this market in comparison to light duty vehicles. 
10-10 

Table 10-5 Estimated MY2014 Production by Engine Type 

ENGINE TYPE PRODUCTION PERCENT

Diesel 252,744 39.6% 

Gasoline 105,604 16.5% 

FFV 279,761 43.8% 

Total 638,109 100.0% 

Table 10-6 shows production by drive type with an almost equal division between two 

wheel drive (55 percent) and four wheel drive (45 percent). 

Table 10-6 Estimated MY2014 Production by Drive 

DRIVE PRODUCTION PERCENT

4WD 286,122 44.8% 

FWD 23,309 3.7% 

RWD 328,678 51.5% 

Total 638,109 100.0% 

The following tables show some of the characteristics of the baseline analysis fleet at the 

manufacturer level. Table 10-7 and Table 10-8 show production by manufacturer for class 2b 

and class 3 trucks respectively. As noted above Ford is the dominant manufacturer with 52 

percent of the market in both class 2b and class 3 trucks. While Fiat and General Motors have 

comparable shares of the class 2b market (20 percent and 22 percent respectively), Fiat (at 31 

percent) has a significantly larger share of the class 3 market than General Motors (at 13 

percent). 

Table 10-7 Estimated MY2014 Production Class 2b by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler 19,556 3.9% 

Fiat 98,722 19.5% 

Ford 262,687 51.8% 

General Motors 112,498 22.2% 

Nissan 13,526 2.7% 

Total 506,989 100.0% 
10-11 

Table 10-8 Estimated MY2014 Production Class 3 by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler 5,771 4.4% 

Fiat 40,180 30.6% 

Ford 68,232 52.0% 

General Motors 16,937 12.9% 

Nissan - 0.0% 

Total 131,120 100.0% 

As noted above pickup trucks were the dominant body style in Class 2b and 3 trucks. 

Table 10-9 shows pickup truck production by manufacturer. Only three manufactures share this 

market with Ford the leader at 43 percent, followed by Fiat at 35 percent and General Motors at 

22 percent. 

Table 10-9 Estimated MY2014 Production Pickups by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler - 0.0% 

Fiat 115,593 34.7% 

Ford 142,580 42.8% 

General Motors 74,927 22.5% 

Nissan - 0.0% 

Total 333,100 100.0% 

All five manufactures share the Class 2b and 3 van market. Table 10-10 shows van 

production by manufacturer. Ford is again dominant with 57 percent of the market followed by 

General Motors at 21 percent with the remainder divided among Fiat, Daimler and Nissan. 

Table 10-10 Estimated MY2014 Production Vans by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler 21,900 8.3% 

Fiat 23,309 8.8% 

Ford 151,503 57.2% 

General Motors 54,508 20.6% 

Nissan 13,526 5.1% 

Total 264,746 100.0% 

Table 10-11 and 

Table 10-12 give an indication of the significance of diesel powered trucks in the class 2b 

and 3 market. Table 10-11shows the distribution of diesel trucks by manufacturer. Ford is the 

leader at 40 percent followed by Fiat at 34 percent. 
10-12 

Table 10-12 shows diesel production as a percent of total production for each 

manufacturer. At either end of the spectrum are Nissan at 0 percent and Daimler at 100 percent. 

Of the producers with significant market share Fiat leads with 62 percent of its production in 

diesels, followed by General Motors at 32 percent and Ford at 30 percent. 

Table 10-11 Estimated MY2014 Production Diesel Powered Trucks by Manufacturer 

MANUFACTURER PRODUCTION PERCENT 

Daimler 25,327 10.0% 

Fiat 86,124 34.1% 

Ford 100,208 39.6% 

General Motors 41,085 16.3% 

Nissan - 0.0% 

Total 252,744 100.0% 

Table 10-12 Estimated MY2014 Diesel Penetration by Manufacturer 

MANUFACTURER DIESEL 

PRODUCTION 

TOTAL 

PRODUCTION 

PERCENT 

DIESEL 

Daimler 25,327 25,327 100.0% 

Fiat 86,124 138,902 62.0% 

Ford 100,208 330,919 30.3% 

General Motors 41,085 129,435 31.7% 

Nissan - 13,526 0.0% 

Total 252,744 638,109 39.6% 

The resultant analysis fleet is provided in detail at NHTSA’s web site, along with all 

other inputs to and outputs from today’s analysis. 

2.2 CAFE Model Analysis of Regulatory Alternatives for HD Pickups and 

Vans 

EPA and NHTSA have evaluated a range of potential regulatory alternatives since we are 

considering the proposal of new class 2b and 3 pickup and van fuel consumption and GHG 

standards to follow those already established through model year 2018. The agencies estimated 

the extent to which manufacturers might add fuel-saving (and, therefore, CO2-reducing) 

technologies under each regulatory alternative, including the no-action alternative defined by 

Phase 1 standards. NHTSA also used the CAFE model to estimate the extent to which this 

additional technology would incrementally (compared to the no-action alternative) impact costs 

to manufacturers and vehicle buyers, reduce fuel consumption and greenhouse gas emissions, 

provide economic benefits and reduce costs to vehicle owners and society. The remainder of this 

section presents the regulatory alternatives the agencies have considered, summarizes the 

analysis, and explains the selection of the preferred alternative defined by today’s proposed 

standards. 
10-13 

As discussed above, the agencies are proposing standards defined by fuel consumption 

and GHG targets that continue through model year 2020 unchanged from model year 2018, and 

then increase in stringency at an annual rate of 2.5 percent through model year 2027. In addition 

to this regulatory alternative, the agencies also considered a no-action alternative under which 

standards remain unchanged after model year 2018, as well as three other alternatives, defined by 

annual stringency increases of (1) 2.0 percent, (2), 3.5 percent, and (3) 4.0 percent during model 

years 2021-2025. For each of the “action alternatives” (i.e., those involving stringency increases 

beyond the no-action alternative), the annual stringency increases are applied as follows: an 

annual stringency increase of r is applied by multiplying the model year 2020 target functions 

(identical to those applicable to model year 2018) by 1 – r to define the model year 2021 target 

functions, multiplying the model year 2021 target functions by 1 – r to define the model year 

2022 target functions, continuing through 2025 for all alternatives except for the preferred 

Alternative 3 which extends through 2027. In summary, the agencies have considered the 

following five regulatory alternatives: 

REGULATORY 

ALTERNATIVE 

ANNUAL STRINGENCY INCREASE 

2019-2020 2021-2025 2026-2027 

1: No Action None None None 

2: 2.0%/y None 2.0% None 

3: 2.5%/y None 2.5% 2.5% 

4: 3.5%/y None 3.5% None 

5: 4.0%/y None 4.0% None 

2.2.1 Evaluation of the Regulatory Alternatives. 

To conduct an analysis of potential standards for HD pickups and vans, the agencies have 

applied DOT’s Corporate Average Fuel Economy (CAFE) Compliance and Effects Modeling 

System (sometimes referred to as “the CAFE model” or “the Volpe model”), which DOT’s 

Volpe National Transportation Systems Center (Volpe Center) developed, maintains, and applies 

to support NHTSA CAFE analyses and rulemakings. DOT developed the model in 2002 to 

support the 2003 issuance of CAFE standards for MYs 2005-2007 light trucks. DOT has since 

significantly expanded and refined the model, and has applied the model to support every 

ensuing CAFE rulemaking: 

 2006: MYs 2008-2011 light trucks 

 2008: MYs 2011-2015 passenger cars and light trucks (final rule prepared but withheld) 

 2009: MY 2011 passenger cars and light trucks 

 2010: MYs 2012-2016 passenger cars and light trucks (joint rulemaking with EPA) 

 2012: MYs 2017-2021 passenger cars and light trucks (joint rulemaking with EPA) 

Past analyses conducted using the CAFE model have been subjected to extensive and 

detailed review and comment, much of which has informed the model’s expansion and 

refinement. NHTSA’s use of the model was considered and supported in 2007 litigation (CBD 

v. NHTSA), and the model has been subjected to formal peer review and review by the General 

Accounting Office (GAO) and National Research Council (NRC). NHTSA makes public the 

model, source code, and—except insofar as doing so would compromise confidential business 
10-14 

information (CBI) manufacturers have provided to NHTSA—all model inputs and outputs 

underlying published rulemaking analyses.10

Although the CAFE model can also be used for more aggregated analysis (e.g., involving 

“representative vehicles”, single-year snapshots, etc.), NHTSA designed the model with a view 

toward (a) detailed simulation of manufacturers’ potential actions given a defined set of 

standards, followed by (b) calculation of resultant impacts and economic costs and benefits. The 

model is intended to describe actions manufacturers could take in light of defined standards and 

other input assumptions and estimates, not to predict actions manufacturers will take. 

As a starting point, the model makes use of an input file defining the analysis fleet—that 

is, a set of specific vehicle models (e.g., Toyota Tacoma) and model configurations (e.g., Toyota 

Tacoma with 4.0-liter V6 engine, 4WD, and 5-speed manual transmission) estimated or assumed 

to be produced by each manufacturer in each model year to be included in the analysis. The 

analysis fleet includes key engineering attributes (e.g., curb weight, payload and towing 

capacities, dimensions, presence of various fuel-saving technologies) of each vehicle model, 

engine, and transmissions, along with estimates or assumptions of future production volumes. It 

also specifies the extent to which specific vehicle models share engines, transmissions, and 

vehicle platforms, and describes each manufacturer’s estimated or assumed product cadence (i.e., 

timing for freshening and redesigning different vehicles and platforms). This input file also 

specifies a payback period used to estimate the potential that each manufacturer might apply 

technology to improve fuel economy beyond levels required by standards. 

A second input file to the model contains a variety of contextual estimates and 

assumptions. Some of these inputs, such as future fuel prices and vehicle survival and mileage 

accumulation (versus vehicle age), are relevant to estimating manufacturers’ potential 

application of fuel-saving technologies. Some others, such as fuel density and carbon content, 

vehicular and upstream emission factors, the social cost of carbon dioxide emissions, and the 

discount rate, are relevant to calculating physical and economic impacts of manufacturers’ 

application of fuel-saving technologies. 

A third input file contains estimates and assumptions regarding the future applicability, 

availability, efficacy, and cost of various fuel-saving technologies. Efficacy is expressed in 

terms of the percentage reduction in fuel consumption, cost is expressed in dollars, and both 

efficacy and cost are expressed on an incremental basis (i.e., estimates for more advanced 

technologies are specified as increments beyond less advanced technologies). The input file also 

includes “synergy factors” used to make adjustments accounting for the potential that some 

combinations of technologies may result fuel savings or costs different from those indicated by 

incremental values. 

Finally, a fourth model input file specifies standards to be evaluated. Standards are 

defined on year-by-year basis separately for each regulatory class (passenger cars, light trucks, 

and heavy-duty pickups and vans). Regulatory alternatives are specified as discrete scenarios, 

 10 Analyses can be found at http://www.nhtsa.gov/fuel-economy
10-15 

with one scenario defining the no-action alternative or “baseline”, all other scenarios defining 

regulatory alternatives to be evaluated relative to that no-action alternative. 

Given these inputs, the model estimates each manufacturer’s potential year-by-year 

application of fuel-saving technologies to each engine, transmission, and vehicle. Subject to a 

range of engineering and planning-related constraints (e.g., secondary axle disconnect can’t be 

applied to 2-wheel drive vehicles, many major technologies can only be applied practicably as 

part of a vehicle redesign, and applied technologies carry forward between model years), the 

model attempts to apply technology to each manufacturers’ fleet in a manner that minimizes 

“effective costs” (accounting, in particular, for technology costs and avoided fuel outlays), 

continuing to add improvements as long as doing so would help toward compliance with 

specified standards or would produce fuel savings that “pay back” at least as quickly as specified 

in the input file mentioned above. 

Having estimated the extent to which each manufacturer might add fuel-saving 

technologies under each specified regulatory alternative, the model calculates a range of physical 

impacts, such as changes in highway travel (i.e., VMT), changes in fleetwide fuel consumption, 

changes in highway fatalities, and changes in vehicular and upstream greenhouse gas and criteria 

pollutant emissions. The model also applies a variety of input estimates and assumptions to 

calculate economic costs and benefits to vehicle owners and society, based on these physical 

impacts. 

This analysis reflects several changes made to the model since 2012, when NHTSA used 

the model to estimate the effects, costs, and benefits of final CAFE standards for light-duty 

vehicles produced during MYs 2017-2021, and augural standards for MYs 2022-2025. Some of 

these changes specifically enable analysis of potential fuel consumption standards (and, hence, 

related CO2 emissions standards harmonized with fuel consumption standards) for heavy-duty 

pickups and vans; other changes implement more general improvements to the model. Key 

changes include the following: 

 Expansion and restructuring of model inputs, compliance calculations, and reporting to 

accommodate standards for heavy-duty pickups and vans, including attribute-based 

standards involving targets that vary with “work factor”. 

 Explicit calculation of test weight, taking into account test weight “bins” and differences 

in the definition of test weight for light-duty vehicles (curb weight plus 300 pound) and 

heavy-duty pickups and vans (average of GVWR and curb weight). 

 Procedures to estimate increases in payload when curb weight is reduced, increases in 

towing capacity if GVWR is reduced, and calculation procedures to correspondingly 

update calculated work factors. 

 Expansion of model inputs, procedures, and outputs to accommodate technologies not 

included in prior analyses. 

 Changes to the algorithm used to apply technologies, enabling more explicit accounting 

for shared vehicle platforms and adoption and “inheritance” of major engine changes. 
10-16 

 Expansion of the Monte Carlo simulation procedures used to perform probabilistic 

uncertainty analysis. 

These changes are reflected in updated model documentation available at NHTSA’s web 

site, the documentation also providing more information about the model’s purpose, scope, 

structure, design, inputs, operation, and outputs 

10.1.1.1 Accounting for Product Cadence 

Past comments on the CAFE model have stressed the importance of product cadence—

i.e., the development and periodic redesign and freshening of vehicles—in terms of involving 

technical, financial, and other practical constraints on applying new technologies, and DOT has 

steadily made changes to the model with a view toward accounting for these considerations. For 

example, early versions of the model added explicit “carrying forward” of applied technologies 

between model years, subsequent versions applied assumptions that most technologies would be 

applied when vehicles are freshened or redesigned, and more recent versions applied 

assumptions that manufacturers would sometimes apply technology earlier than “necessary” in 

order to facilitate compliance with standards in ensuing model years. Thus, for example, if a 

manufacturer is expected to redesign many of its products in model years 2018 and 2023, and the 

standard’s stringency increases significantly in model year 2021, the CAFE model will estimate 

the potential that the manufacturer will add more technology than necessary for compliance in 

MY 2018, in order to carry those product changes forward through the next redesign and 

contribute to compliance with the MY 2021 standard. 

The model also accommodates estimates of overall limits (expressed as “phase-in caps” 

in model inputs) on the rates at which manufacturers’ may practicably add technology to their 

respective fleets. So, for example, even if a manufacturer is expected to redesign half of its 

production in MY 2016, if the manufacturer is not already producing any strong hybrid electric 

vehicles (SHEVs), a phase-in cap can be specified in order to assume that manufacturer will stop 

applying SHEVs in MY 2016 once it has done so to at least 3 percent of its production in that 

model year. 

After the light-duty rulemaking analysis accompanying the 2012 final rule regarding 

post-2016 CAFE standards and related GHG emissions standards, DOT staff began work on 

CAFE model changes expected to better reflect additional considerations involved with product 

planning and cadence. These changes, summarized below, interact with preexisting model 

characteristics discussed above. 

10.1.1.2 Platforms & Technology 

The term “platform” is used loosely in industry, but generally refers to a common 

structure shared by a group of vehicle variants. The degree of commonality varies, with some 

platform variants exhibiting traditional “badge engineering” where two products are 

differentiated by little more than insignias, while other platforms be used to produce a broad 

suite of vehicles that bear little outer resemblance to one another. 
10-17 

Given the degree of commonality between variants of a single platform, manufacturers 

do not have complete freedom to apply technology to a vehicle: while some technologies (e.g. 

low rolling resistance tires) are very nearly “bolt-on” technologies, others involve substantial 

changes to the structure and design of the vehicle, and therefore necessarily are constant between 

vehicles that share a common platform. DOT staff has, therefore, modified the CAFE model 

such that all mass reduction and aero technologies are forced to be constant between variants of a 

platform. 

Within the analysis fleet, each vehicle is associated with a specific platform. As the 

CAFE model applies technology, it first defines a platform “leader” as the vehicle variant of a 

platform with the highest technology utilization vehicle of mass reduction and aerodynamic 

technologies. As the vehicle applies technologies, it effectively harmonizes to the highest 

common denominator of the platform. If there is a tie, the CAFE model begins applying 

aerodynamic and mass reduction technology to the vehicle with the lowest average sales across 

all available model years. If there remains a tie, the model begins by choosing the vehicle with 

the highest average MSRP across all available model years. The model follows this formulation 

due to previous market trends suggesting that many technologies begin deployment at the highend, low-volume end of the market as manufacturers build their confidence and capability in a 

technology, and later expand the technology across more mainstream product lines. 

In the HD pickup and van market, there is a relatively small amount of diversity in 

platforms produced by manufacturers: typically 1-2 truck platforms and 1-2 van platforms. 

However, accounting for platforms will take on greater significance in future analyses involving 

the light-duty fleet, and the agency requests comments on the general use of platforms within 

CAFE rulemaking. 

10.1.1.3 Engine and Transmission Inheritance 

In practice, manufacturers are limited in the number of engines and transmissions that 

they produce. Typically a manufacturer produces a number of engines—perhaps six or eight 

engines for a large manufacturer—and tunes them for slight variants in output for a variety of car 

and truck applications. Manufacturers limit complexity in their engine portfolio for much the 

same reason as they limit complexity in vehicle variants: they face engineering manpower 

limitations, and supplier, production and service costs that scale with the number of parts 

produced. 

In previous usage of the CAFE model, engines and transmissions in individual models 

were allowed relative freedom in technology application, potentially leading to solutions that 

would, if followed, involve unaccounted-for costs associated with increased complexity in the 

product portfolio. The lack of a constraint in this area allowed the model to apply different 

levels of technology to the engine in each vehicle at the time of redesign or refresh, independent 

of what was done to other vehicles using a previously identical engine. 

In the current version of the CAFE model, engines and transmissions that are shared 

between vehicles must apply the same levels of technology in all technologies dictated by engine 

or transmission inheritance. This forced adoption is referred to as “engine inheritance” in the 

model documentation. 
10-18 

As with platform-shared technologies, the model first chooses an “engine leader” among 

vehicles sharing the same engine. The leader is selected first by the vehicle with the lowest 

average sales across all available model years. If there is a tie, the vehicle with the highest 

average MSRP across model years is chosen. The model applies the same logic with respect to 

the application of transmission changes. As with platforms, this is driven by the concept that 

vehicle manufacturers typically deploy new technologies in small numbers prior to deploying 

widely across their product lines. 

10.1.1.4 Interactions between Regulatory Classes 

Like earlier versions, the current CAFE model provides for integrated analysis spanning 

different regulatory classes, accounting both for standards that apply separately to different 

classes and for interactions between regulatory classes. Light vehicle CAFE standards are 

specified separately for passenger cars and light trucks. However, there is considerable sharing 

between these two regulatory classes. Some specific engines and transmissions are used in both 

passenger cars and light trucks, and some vehicle platforms span these regulatory classes. For 

example, some sport-utility vehicles are offered in 2WD versions classified as passenger cars and 

4WD versions classified as light trucks. Integrated analysis of manufacturers’ passenger car and 

light truck fleets provides the ability to account for such sharing and reduce the likelihood of 

finding solutions that could involve impractical levels of complexity in manufacturers’ product 

lines. In addition, integrated analysis provides the ability to simulate the potential that 

manufactures could earn CAFE credits by over complying with one standard and use those 

credits toward compliance with the other standard (i.e., to simulate credit transfers between 

regulatory classes). 

HD pickups and vans are regulated separately from light-duty vehicles. While 

manufacturers cannot transfer credits between light-duty and MDHD classes, there is some 

sharing of engineering and technology between light-duty vehicles and HD pickups and vans. 

For example, some passenger vans with GVWR over 8,500 pounds are classified as mediumduty passenger vehicles (MDPVs) and thus included in manufacturers’ light-duty truck fleets, 

while cargo vans sharing the same nameplate are classified as HD vans. 

While this analysis examines the HD pickup and van fleet in isolation, as a basis for 

analysis supporting the planned final rule, the agencies intend to develop an overall analysis fleet 

spanning both the light-duty and HD pickup and van fleets. Doing so could show some 

technology “spilling over” to HD pickups and vans due, for example, to the application of 

technology in response to current light-duty standards. More generally, modeling the two fleets 

together should tend to more realistically limit the scope and complexity of estimated 

compliance pathways. 

NHTSA anticipates that the impact of modeling a combined fleet will primarily arise 

from engine-transmission inheritance. While platform sharing between the light-duty and MD 

pickup and van fleets is relatively small (MDPVs aside), there are a number of instances of 

engine and transmission sharing across the two fleets. When the fleets are modeled together, the 

agencies anticipate that engine inheritance will be implemented across the combined fleet, and 

therefore only one engine-transmission leader can be defined across the combined fleet. As with 
10-19 

the fleets separately, all vehicles using a shared engine/transmission would automatically adopt 

technologies adopted by the engine-transmission leader. 

10.1.1.5 Phase-In Caps 

The CAFE model retains the ability to use phase-in caps (specified in model inputs) as 

proxies for a variety of practical restrictions on technology application. Unlike vehicle-specific 

restrictions related to redesign, refreshes or platforms/engines, phase-in caps constrain 

technology application at the vehicle manufacturer level. They are intended to reflect a 

manufacturer's overall resource capacity available for implementing new technologies (such as 

engineering and development personnel and financial resources), thereby ensuring that resource 

capacity is accounted for in the modeling process. 

In previous CAFE rulemakings, redesign/refresh schedules and phase-in caps were the 

primary mechanisms to reflect an OEM's limited pool of available resources during the 

rulemaking time frame and the years leading up to the rulemaking time frame, especially in years 

where many models may be scheduled for refresh or redesign. The newly-introduced 

representation platform-, engine-, and transmission-related considerations discussed above 

augment the model’s preexisting representation of redesign cycles and accommodation of phasein caps. Considering these new constraints, inputs for today’s analysis de-emphasize reliance on 

phase-in caps. 

In this application of the CAFE model, phase-in caps are used only for the most advanced 

technologies included in the analysis, i.e., SHEVs and lean-burn GDI engines, considering that 

these technologies are most likely to involve implementation costs and risks not otherwise 

accounted for in corresponding input estimates of technology cost. For these two technologies, 

the agencies have applied caps that begin at 3 percent (i.e., 3 percent of the manufacturer’s 

production) in MY 2017, increase at 3 percent annually during the ensuing nine years (reaching 

30 percent in the MY 2026), and subsequently increasing at 5 percent annually for four years 

(reaching 50 percent in MY 2030). Note that the agencies did not feel that lean-burn engines 

were feasible in the timeframe of this rulemaking, so decided to reject any model runs where 

they were selected. Due to the cost ineffectiveness of this technology, it was never chosen. 

10.1.1.6 Impact of Vehicle Technology Application Requirements 

Compared to prior analyses of light-duty standards, these model changes, along with 

characteristics of the HD pickup and van fleet result in some changes in the broad characteristics 

of the model’s application of technology to manufacturers’ fleets. First, since the number of HD 

pickup and van platforms in a portfolio is typically small, compliance with standards may appear 

especially “lumpy” (compared to previous applications of the CAFE model to the more highly 

segmented light-duty fleet), with significant over compliance when widespread redesigns 

precede stringency increases, and/or significant application of carried-forward (aka “banked”) 

credits. 

Second, since the use of phase-in caps has been de-emphasized and manufacturer 

technology deployment remains tied strongly to estimated product redesign and freshening 
10-20 

schedules, technology penetration rates may jump more quickly as manufacturers apply 

technology to high-volume products in their portfolio. 

By design, restrictions that enforce commonality of mass reduction and aerodynamic 

technologies on variants of a platform, and those that enforce engine inheritance, will result in 

fewer vehicle-technology combinations in a manufacturer’s future modeled fleet. These 

restrictions are expected to more accurately capture the true costs associated with producing and 

maintaining a product portfolio. 

10.1.1.7 Example of Technology Application Estimated using Current 

Model and Inputs 

The example presented below illustrates how some of aspects of the current model and 

inputs impact estimation of technology application by a manufacturer within the context of a 

specified set of standards, focusing here on the model’s estimate of GM’s technology application 

under the 4.0 percent/y regulatory alternative (Alternative 5). Overall results for GM and other 

manufacturers are summarized below, after discussion of the analysis fleet used for today’s 

analysis. Results for GM clearly reflect the analysis fleet’s inclusion of just one HD pickup 

platform with redesigns estimated to occur in MYs 2021 and 2026 and one HD van platform 

with a redesign estimated to occur in MY 2020. The analysis suggests that GM could take some 

advantage of credit carry-forward provisions (e.g., to cover a shortfall projected to occur in MY 

2016, when the HD pickup and van fleet first includes some vehicles not previously subject to 

chassis dynamometer testing), but that GM could need to significantly over comply with 

standards during MYs 2021-2024 in order take full advantage of the estimated MY 2021 HD 

pickup redesign and thereby carry forward enough technology to remain in compliance through 

MY 2030 (the last model year included in today’s analysis). The results also reflect that credits 

earned during MY 2021-2024 expire before MY 2030, and that the Express/Savana vans inherit 

a new transmission in MY 2027. 
10-21 

Figure 10-2 Example of technology application during redesigns 

Specific steps estimated to be taken when these platforms are redesigned and freshened 

are as follows: 

MY 2017 Express/Savana Redesign 

 4.8 liter gasoline engine: replace with smaller turbocharged direct injection engine 

 6.0 liter gasoline engine: add lower-friction lubricants, engine friction reduction, cylinder 

deactivation, variable valve actuation 

 All Express/Savana vans: apply 5 percent mass reduction, aerodynamic improvements, 

electric power steering, improved accessories, integrated starter/generators, and low-drag 

brakes 

 For Express/Savana vans, vs. MY 2014 

o additional $3,425-$4,473 

o avoided 0.94-2.05 gal./100 mi. 

MY2018 Sierra/Silverado Freshening 
10-22 

 6.0 liter gasoline engine: apply lower-friction lubricants and engine friction reduction 

 All Sierra/Silverado pickups: apply 5 percent mass reduction, aerodynamic 

improvements, and electric power steering, and improved accessories 

 For Sierra/Silverado pickups, vs. MY 2014 

o additional $383-$643 

o avoided 0.15-0.50 gal./100 mi. 

MY 2020 Express/Savana Freshening 

 Carry forward changes from MY 2017 (and through 2018-2019) 

 All Express/Savana vans: apply reduced rolling resistance tires 

 For Express/Savana vans, vs. MY 2014 

o additional $3,128-$4,033 

o avoided 1.01-2.11 gal./100 mi. 

MY 2021 Sierra/Silverado Redesign 

 Carry forward changes from MY 2018 (and through 2019-2020) 

 6.0 liter gasoline engine: apply cylinder deactivation and variable valve actuation 

 6.6 liter diesel engine: engine friction reduction and improved turbocharging 

 All Sierra/Silverado pickups: apply 8-speed automatic transmission, 10 percent mass 

reduction, further aerodynamic improvements, low-drag brakes, secondary axle 

disconnect (on all 4WD units), low rolling resistance tires 

 76 percent of Sierra/Silverado pickups: apply integrated starter-generators 

 24 percent of Sierra/Silverado pickups: apply strong hybrid-electric systems (37k units) 

 For Sierra/Silverado pickups, vs. MY 2014 

o additional $3,197-$5,805 

o avoided 1.12-3.09 gal./100 mi. 

MY 2027 Express/Savana Redesign 

 Carry forward changes from MY 2020 (and through 2021-2026) 

 All Express/Savana vans: apply 8-speed transmission (inherited from 2021 

Sierra/Silverado) 

 1.4 percent of Express/Savana vans: apply strong hybrid-electric systems (1.4k units) 

 For Express/Savana vans, vs. MY 2014 

o additional $3,086-$5,226 

o avoided 1.03-3.29 gal./100 mi. 
10-23 

As discussed above, these results provide an estimate, based on analysis inputs, of one 

way GM could add fuel-saving technologies to its HD pickups and vans under one of the 

regulatory alternatives considered here, and are not a prediction of what GM would do under this 

regulatory alternative. In addition, it should be recognized that specific results vary among 

manufacturers and among regulatory alternatives (and under different analytical inputs). Still, 

the example should serve to illustrate how various inputs can impact results given the CAFE 

model’s approach to estimating how fuel-saving technologies might be added to manufacturers’ 

fleets. 

10.1.1.8 Accounting for Test Weight, Payload, and Towing Capacity 

As mentioned above, NHTSA has also revised the CAFE model to explicitly account for 

the regulatory “binning” of test weights used to certify light-duty fuel economy and HD pickup 

and van fuel consumption for purposes of evaluating fleet-level compliance with fuel economy 

and fuel consumption standards. For HD pickups and vans, test weight (TW) is based on 

adjusted loaded vehicle weight (ALVW), which is defined as the average of gross vehicle weight 

rating (GVWR) and curb weight (CW).11 TW values are then rounded, resulting in TW “bins”: 

ALVW ≤ 4,000 lb.: TW rounded to nearest 125 lb. 

4000 lb. < ALVW ≤ 5,500 lb.: TW rounded to nearest 250 lb. 

ALVW > 5,500 lb.: TW rounded to nearest 500 lb. 

This “binning” of TW is relevant to calculation of fuel consumption reductions 

accompanying mass reduction. Model inputs for mass reduction (as an applied technology) are 

expressed in terms of a percentage reduction of curb weight and an accompanying estimate of 

the percentage reduction in fuel consumption, setting aside rounding of test weight. Therefore, 

to account for rounding of test weight, NHTSA has modified these calculations as follows: 

∆ܥܨ௢௥௨௡ௗ௘ௗ_்ௐ ൌ ∆ܹܶ ൈ

∆ܥܨ௨௡௥௢௨௡ௗ௘ௗ_்ௐ

ܹܥ∆

Where: 

∆CW = % change in curb weight (from model input), 

∆FCunrounded_TW = % change in fuel consumption (from model input), without TW 

rounding, 

∆TW = % change in test weight (calculated), and 

∆FCrounded_TW = % change in fuel consumption (calculated), with TW rounding. 

 11 Or, equivalently, CW + 1⁄2 payload, where payload = GVWR – CW. 
10-24 

As a result, some applications of vehicle mass reduction will produce no compliance 

benefit at all, in cases where the changes in ALVW are too small to change test weight when 

rounding is taken into account. On the other hand, some other applications of vehicle mass 

reduction will produce significantly more compliance benefit than when rounding is not taken 

into account, in cases where even small changes in ALVW are sufficient to cause vehicles’ test 

weights to increase by, e.g., 500 pounds when rounding is accounted for. Model outputs now 

include initial and final TW, GVWR, and GCWR (and, as before, CW) for each vehicle model in 

each model year, and the agencies invite comment on the extent to which these changes to 

account explicitly for changes in TW are likely to produce more realistic estimates of the 

compliance impacts of reductions in vehicle mass. 

In addition, considering that the regulatory alternatives in the agencies’ analysis all 

involve attribute-based standards in which underlying fuel consumption targets vary with “work 

factor” (defined by the agencies as the sum of three quarters of payload, one quarter of towing 

capacity, and 500 lb. for vehicles with 4WD), NHTSA has modified the CAFE model to apply 

inputs defining shares of curb weight reduction to be “returned” to payload and shares of GVWR 

reduction to be returned to towing capacity. The standards’ dependence on work factor provides 

some incentive to increase payload and towing capacity, both of which are buyer-facing 

measures of vehicle utility. In the agencies’ judgment, this provides reason to assume that if 

vehicle mass is reduced, manufacturers are likely to “return” some of the change to payload 

and/or towing capacity. For this analysis, the agencies have applied the following assumptions: 

GVWR will be reduced by half the amount by which curb weight is reduced. In other 

words, 50 percent of the curb weight reduction will be returned to payload. 

GCWR will not be reduced. In other words, 100 percent of any GVWR reduction will be 

returned to towing capacity. 

GVWR/CW and GCWR/GVWR will not increase beyond levels observed among the 

majority of similar vehicles (or, for outlier vehicles, initial values): 

MAXIMUM RATIOS ASSUMED ENABLED BY MASS 

REDUCTION 

Group GVWR/CW GCWR/GVWR 

unibody 1.75 1.50 

gasoline pickups > 13k GVWR 2.00 1.50 

other gasoline pickups 1.75 2.25 

diesel SRW pickups 1.75 2.50 

All other 1.75 2.25 

The first of two of these inputs are specified along with standards for each regulatory 

alternative, and the GVWR/CW and GCWR/GVWR “caps” are specified separately for each 

vehicle model in the analysis fleet. 

In addition, DOT has changed the model to prevent HD pickup and van GVWR from 

falling below 8,500 pounds when mass reduction is applied (because doing so would cause 

vehicles to be reclassified as light-duty vehicles), and to treat any additional mass for hybrid 
10-25 

electric vehicles as reducing payload by the same amount (e.g., if adding a strong HEV package 

to a vehicle involves a 350 pound penalty, GVWR is assumed to remain unchanged, such that 

payload is also reduced by 350 pounds). 

The agencies invite comment on these methods for estimating how changes in vehicle 

mass may impact fuel consumption, GVWR, and GCWR, and on corresponding inputs to today’s 

analysis. 

2.2.2 What Impacts Did the Agencies’ Analysis Show for Different 

Regulatory Alternatives 

10.1.1.9 Industry Impacts 

As discussed above, the agencies’ analysis fleet provides a starting point for estimating 

the extent to which manufacturers might add fuel-saving (and, therefore, CO2-avoiding) 

technologies under various regulatory alternatives, including the no-action alternative that 

defines a baseline relative to which to measure estimated impacts of new standards. The analysis 

fleet is a forward-looking projection of production of new HD pickups and vans, holding vehicle 

characteristics (e.g., technology content and fuel consumption levels) constant at model year 

2014 levels, and adjusting production volumes based on recent DOE and commercially-available 

forecasts. This analysis fleet includes some significant changes relative to fleet information 

underlying analysis supporting the establishment of Phase 1 standards applicable starting in 

model year 2014; in particular, the analysis fleet includes some new HD vans (e.g., Ford’s 

Transit and Fiat/Chrysler’s Promaster) that are considerable more fuel-efficient than HD vans 

these manufacturers have previously produced for the U.S. market. 

While the proposed standards are scheduled to begin in model year 2021, the 

requirements they define are likely to influence planning decisions made by manufacturers 

several years before they begin, as illustrated by example above. This is true in light-duty 

planning, but accentuated by the comparatively long redesign cycles and small number of models 

and platforms offered for sale in the 2b/3 market segment. Additionally, manufacturers will 

respond to the cost and efficacy of available fuel consumption improvements, the price of fuel, 

and the requirements of the Phase 1 standards that specify maximum allowable average fuel 

consumption improvements and GHG levels for MY2014-MY2018 vehicles (the final standard 

for MY2018 is held constant for model years 2019 and 2020). The forward-looking nature of 

product plans that determine which vehicle models will be offered in the model years affected by 

the proposed standards lead to additional technology application to vehicles in the analysis fleet 

that occurs in the years prior to the start of the proposed standards. From the industry 

perspective, this means that manufacturers will incur costs to comply with the proposed 

standards in the baseline and that the total cost of the proposed regulations will include some 

costs that occur prior to their start, and represent incremental changes over a world in which 

manufacturers will have already modified their vehicle offerings compared to today. 
10-26 

Table 10-13 MY2021 Baseline Costs for Manufacturers in 2b/3 Market Segment in the Dynamic Baseline, or 

Alternative 1b 

MANUFACTURER AVERAGE 

TECHNOLOGY 

COST ($)

TOTAL COST 

INCREASE ($M) 

Chrysler/Fiat 275 27 

Daimler 18 0 

Ford 258 78 

General Motors 782 191 

Nissan 282 3 

Industry 442 300 

As Table 10-13 shows, the industry as a whole is expected to add about $440 of new 

technology to each new vehicle model by 2021 under the no-action alternative defined by the 

Phase 1 standards. Reflecting differences in projected product offerings in the analysis fleet, 

some manufacturers (notably Daimler) are significantly less constrained by the Phase 1 standards 

than others and face lower cost increases as a result. General Motors (GM) shows the largest 

increase in average vehicle cost, but results for GM’s closest competitors (Ford and 

Chrysler/Fiat) do not include the costs of their recent van redesigns, which are already present in 

the analysis fleet (discussed in greater detail below). 

The above results reflect the assumption that manufacturers having achieved compliance 

with standards might act as if buyers are willing to pay for further fuel consumption 

improvements that “pay back” within 6 months. It is also possible that manufacturers will 

choose not to migrate cost-effective technologies to the 2b/3 market segment from similar 

vehicles in the light-duty market. To examine this possibility, all regulatory alternatives were 

also using the DOT CAFE model (Method A) with a 0-month payback period in lieu of the 6-

month payback period discussed above. (A sensitivity analysis using Method A, discussed 

below, also explores longer payback periods, as well as the combined effect of payback period 

and fuel price on vehicle design decisions.) Resultant technology costs in model year 2021 

results for the no-action alternative, summarized below, are quite similar to those shown above 

for the 6-month payback period: 

Table 10-14 MY2021 Baseline Costs for HD Pickups and Vans in the Flat Baseline, or Alternative 1a 

MANUFACTURER AVERAGE 

TECHNOLOGY COST 

($) 

TOTAL COST 

INCREASE ($M) 

Chrysler/Fiat 268 27 

Daimler 0 0 

Ford 248 75 

General Motors 767 188 

Nissan 257 3 

Industry 431 292 

The results below represent the impacts of other regulatory alternatives, including those 

defined by the proposed standards, as incremental changes over the baseline, where the baseline 

is defined as the state of the world in the absence of the proposed regulatory action. Large-scale, 
10-27 

macroeconomic conditions like fuel prices are constant across all alternatives, including the 

baseline, as are the fuel economy improvements under the no-action alternative defined by the 

Phase 1 MDHD rulemaking that covers model years 2014 – 2018 and is constant from model 

year 2018 through 2020. In the baseline scenario, the Phase 1 standards are assumed to remain 

in place and at 2018 levels throughout the analysis (i.e. MY 2030). The only difference between 

the definitions of the alternatives is the stringency of the proposed standards for MYs 2021 – 

2025, and all of the differences in outcomes across alternatives are attributable to differences in 

the standards. 

The standards vary in stringency across regulatory alternatives (1 – 5), but as discussed 

above, all of the standards are based on the curve developed in the Phase 1 standards that relate 

fuel economy and GHG emissions to a vehicle’s work factor. The alternatives considered here 

represent different rates of annual increase in the curve defined for model year 2018, growing 

from a 0 percent annual increase (Alternative 1, the baseline or “no-action” alternative) up to a 4 

percent annual increase (Alternative 5). Table 10-15 shows a summary of outcomes by 

alternative incremental to the baseline (Alternative 1b) for Model Year 203012, with the 

exception of technology penetration rates, which are absolute. 

The technologies applied by the CAFE model have been grouped (in most cases) to give 

readers a general sense of which types of technology are applied more frequently than others, 

and are more likely to be offered in MY2030 2b/3 vehicles. The summaries of technology 

penetration are also intended to reflect the relationship between technology application and cost 

increases across the alternatives. The table rows present the degree to which specific 

technologies will be present in new class 2b and class 3 vehicles in 2030, and correspond to: 

variable valve timing (VVT) and/or variable valve lift (VVL), cylinder deactivation, direct 

injection, engine turbocharging, 8-speed automatic transmissions, electric power-steering and 

accessory improvements, micro-hybridization (which reduces engine idle, but does not assist 

propulsion), full hybridization (integrated starter generator or strong hybrid that assists 

propulsion and recaptures braking energy), and aerodynamic improvements to the vehicle shape. 

In addition to the technologies in the following tables, there are some lower-complexity 

technologies that have high market penetration across all the alternatives and manufacturers; low 

rolling-resistance tires, low friction lubricants, and reduced engine friction, for example. 

 12 The CAFE model estimates that redesign schedules will “straddle” model year 2025, the latest year for which the 

agencies are proposing increases in the stringency of fuel consumption and GHG standards. Considering also that 

today’s analysis estimates some earning and application of “carried forward” compliance credits, the model was run 

extending the analysis through model year 2030. 
10-28 

Table 10-15 Summary of HD Pickup and Van Alternatives’ Impact on Industry versus the Dynamic 

Baseline, Alternative 1b

ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase Through MY 2025 2027 2025 2025 

Total Stringency Increase 9.6% 16.2% 16.3% 18.5% 

Average Fuel Economy (miles per gallon) 

Required 19.04 20.57 20.57 21.14 

Achieved 19.14 20.61 20.83 21.27 

Average Fuel Consumption (gallons /100 mi.) 

Required 5.25 4.86 4.86 4.73 

Achieved 5.22 4.85 4.80 4.70 

Average Greenhouse Gas Emissions (g/mi) 

Required 495 458 458 446 

Achieved 491 458 453 444 

Technology Penetration (%) 

VVT and/or VVL 46 46 46 46 

Cylinder Deac. 29 21 21 21 

Direct Injection 17 25 31 32 

Turbocharging 55 63 63 63 

8-Speed AT 67 96 96 97 

EPS, Accessories 54 80 79 79 

Stop Start 0 0 10 13 

Hybridizationa 0 8 35 51 

Aero. Improvements 36 78 78 78 

Mass Reduction (vs. No-Action) 

CW (lb.) 239 243 325 313 

CW (%) 3.7 3.7 5.0 4.8 

Technology Cost (vs. No-Action) 

Average ($) b 578 1,348 1,655 2,080 

Total ($m) c 437 1,019 1,251 1,572 

Payback period (m) c 25 31 34 38 

Notes: 

a Includes mild hybrids (ISG) and strong HEVs. 

b Values used in Methods A & B 

c

 Values used in Method A, calculated using a 3% discount rate. 

In general, the standards cause manufacturers to produce HD pickups and vans that are 

lighter, more aerodynamic, and more technologically complex across all the alternatives. As 

Table 10-15 shows, there is a major difference between the relatively small increases in required 

fuel economy and average incremental technology cost between the alternatives, suggesting that 

the challenge of improving fuel consumption and CO2 emissions accelerates as stringency 

increases (i.e., that there may be a “knee” in the dependence of the challenge and on the 

stringency). Despite the fact that the required average fuel consumption level only changes by 

about 3 percent between Alternative 4 and Alternative 5, average technology cost increases by 
10-29 

more than 25 percent. These differences help illustrate the clustered character of this market 

segment. 

The contrast between alternatives 3 and 4 is even more prominent, with an identical 

required fuel economy improvement leading to price increases greater than 20 percent based on 

the more rapid rate of increase and shorter time span of Alternative 4, which achieves all of its 

increases by MY 2025 while Alternative 3 continues to increase at a slower rate until MY 2027. 

Despite these differences, the increase in average payback period when moving from Alternative 

3 to Alternative 4 to Alternative 5 is fairly constant at around an additional three months for each 

jump in stringency. 

Manufacturers offer few models, typically only a pickup truck and/or a cargo van, and 

while there are a large number of variants of each model, the degree of component sharing across 

the variants can make diversified technology application either economically impractical or 

impossible. This forces manufacturers to apply some technologies more broadly in order to 

achieve compliance than they might do in other market segments (passenger cars, for example). 

This difference between broad and narrow application – where some technologies must be 

applied to entire platforms, while some can be applied to individual model variants – also 

explains why certain technology penetration rates decrease between alternatives of increasing 

stringency (cylinder deactivation or mass reductions in Table 10-15, for example). For those 

cases, narrowly applying a more advanced (and costly) technology can be a more cost effective 

path to compliance and lead to reductions in the amount of lower-complexity technology that is 

applied. 

One driver of the change in technology cost between Alternative 3 and Alternative 4 is 

the amount of hybridization resulting from the implementation of the standards. While only 

about 8 percent full hybridization (defined as either integrated starter-generator or strong hybrid) 

is expected to be required to comply with Alternative 3, the higher rate of increase and 

compressed schedule moving from Alternative 3 and Alternative 4 is enough to increase the 

percentage of the fleet adopting full hybridization to 35 percent. To the extent that 

manufacturers are concerned about introducing hybrid vehicles in the 2b and 3 market, it is 

worth noting that new vehicles subject to Alternative 3 achieves the same fuel economy as new 

vehicles subject to Alternative 4, with less hybridization required to achieve the improvement. 

The alternatives also lead to important differences in outcomes at the manufacturer level, 

both from the industry average and from each other. General Motors, Ford, and Chrysler (Fiat), 

are expected to have approximately 95 percent of the 2b/3 new vehicle market during the years 

that the proposed standards are in effect. Due to their importance to this market and the 

similarities between their model offerings, these three manufacturers are discussed together and a 

summary of the way each is impacted by the standards appears below in Table 10-16, Table 

10-17, and Table 10-18 for General Motors, Ford, and Chrysler/Fiat, respectively. 
10-30 

Table 10-16 Summary of Impacts on General Motors by 2030 in the HD Pickup and Van Market versus the 

Dynamic Baseline, Alternative 1b

ANNUAL 

STRINGENCY 

INCREASE 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Average Fuel Economy (miles per gallon) 

Required 18.38 19.96 20 20.53 

Achieved 18.43 19.95 20.24 20.51 

Average Fuel Consumption (gallons /100 mi.) 

Required 5.44 5.01 5 4.87 

Achieved 5.42 5.01 4.94 4.87 

Average Greenhouse Gas Emissions (g/mi) 

Required 507 467 467 455 

Achieved 505 468 461 455 

Technology Penetration (%) 

VVT and/or VVL 64 64 64 64 

Cylinder Deac. 47 47 47 47 

Direct Injection 18 18 36 36 

Turbocharging 53 53 53 53 

8-Speed AT 36 100 100 100 

EPS, Accessories 100 100 100 100 

Stop Start 0 0 2 0 

Hybridizationc 0 19 79 100 

Aero. Improvements 100 100 100 100 

Mass Reduction (vs. No-Action) 

CW (lb.) 325 161 158 164 

CW (%) 5.3 2.6 2.6 2.7 

Technology Cost (vs. No-Action) 

Average ($) a 785 1,706 2,244 2,736 

Total ($m, 

undiscounted) b 

214 465 611 746 

Notes: 

a Values used in Methods A & B 

b Values used in Method A, calculated at a 3% discount rate 

c Includes mild hybrids (ISG) and strong HEVs. 
10-31 

Table 10-17 Summary of Impacts on Ford by 2030 in the HD Pickup and Van Market versus the Dynamic 

Baseline, Alternative 1b 

ANNUAL 

STRINGENCY 

INCREASE 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Average Fuel Economy (miles per gallon) 

Required 19.42 20.96 20.92 21.51 

Achieved 19.5 21.04 21.28 21.8 

Average Fuel Consumption (gallons /100 mi.) 

Required 5.15 4.77 4.78 4.65 

Achieved 5.13 4.75 4.70 4.59 

Average Greenhouse Gas Emissions (g/mi) 

Required 485 449 450 438 

Achieved 482 447 443 433 

Technology Penetration (%) 

VVT and/or VVL 34 34 34 34 

Cylinder Deac. 18 0 0 0 

Direct Injection 16 34 34 34 

Turbocharging 51 69 69 69 

8-Speed AT 100 100 100 100 

EPS, Accessories 41 62 59 59 

Stop Start 0 0 20 29 

Hybridizationc 0 2 14 30 

Aero. Improvements 0 59 59 59 

Mass Reduction (vs. No-Action) 

CW (lb.) 210 202 379 356 

CW (%) 3.2 3 5.7 5.3 

Technology Cost (vs. No-Action) 

Average ($) a 506 1,110 1,353 1,801 

Total ($m, 

undiscounted) b

170 372 454 604 

Notes: 

a Values used in Methods A & B 

b Values used in Method A, calculated at a 3% discount rate 

c Includes mild hybrids (ISG) and strong HEVs. 
10-32 

Table 10-18 Summary of Impacts on Fiat/Chrysler by 2030 in the HD Pickup and Van Market versus the 

Dynamic Baseline, Alternative 1b 

ANNUAL 

STRINGENCY 

INCREASE 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Average Fuel Economy (miles per gallon) 

Required 18.73 20.08 20.12 20.70 

Achieved 18.83 20.06 20.10 20.70 

Average Fuel Consumption (gallons /100 mi.) 

Required 5.34 4.98 4.97 4.83 

Achieved 5.31 4.99 4.97 4.83 

Average Greenhouse Gas Emissions (g/mi) 

Required 515 480 479 466 

Achieved 512 481 480 467 

Technology Penetration (%) 

VVT and/or VVL 40 40 40 40 

Cylinder Deac. 23 23 23 23 

Direct Injection 17 17 17 17 

Turbocharging 74 74 74 74 

8-Speed AT 65 88 88 88 

EPS, Accessories 0 100 100 100 

Stop-Start 0 0 0 0 

Hybridizationc 0 3 3 10 

Aero. Improvements 0 100 100 100 

Mass Reduction (vs. No-Action) 

CW (lb.) 196 649 648 617 

CW (%) 2.8 9.1 9.1 8.7 

Technology Cost (vs. No-Action) 

Average ($) a 434 1,469 1,486 1,700 

Total ($m, 

undiscounted) b

48 163 164 188 

Notes: 

a Values used in Methods A & B 

b Values used in Method A, calculated at a 3% discount rate 

c Includes mild hybrids (ISG) and strong HEVs. 

The fuel consumption and GHG standards require manufacturers to achieve an average 

level of compliance, represented by a sales-weighted average across the specific targets of all 

vehicles offered for sale in a given model year, such that each manufacturer will have a unique 
10-33 

required consumption/emissions level determined by the composition of its fleet, as illustrated 

above. However, there are more interesting differences than the small differences in required 

fuel economy levels among manufacturers. In particular, the average incremental technology 

cost increases with the stringency of the alternative for each manufacturer, but the size of the 

cost increase from one alternative to the next varies widely among them, with General Motors in 

particular showing considerably larger increases in cost than the other two manufacturers moving 

both from Alternative 2 to Alternative 3, and again moving from Alternative 3 to Alternative 4. 

The simulation results show all three manufacturers facing large cost increases when the 

proposed standards move from 2.5 percent annual increases over the period from MY 2021 – 

2027 to 3.5 percent annual increases from MY 2021 - 2025, but General Motors has the largest at 

75 percent more than the industry average price increase for Alternative 4. GM also faces higher 

cost increases in Alternative 2, 2 about 50 percent more than either Ford or Fiat/Chrysler. And 

for the most stringent alternative considered, General Motors would face average cost increases 

of more than $2,700, in addition to the more than $700 increase in the baseline – approaching 

nearly $3,500 per vehicle over today’s prices. 

Technology choices also differ by manufacturer, and some of those decisions are directly 

responsible for the largest cost discrepancies. For example, GM is estimated to engage in the 

least amount of mass reduction among the Big 3 after Phase 1, and much less than Chrysler/Fiat, 

but reduces average vehicle mass by over 300 pounds in the baseline – suggesting that some of 

GM’s easiest Phase 1 compliance opportunities can be found in lightweighting technologies. 

Similarly, Chrysler/Fiat applies less hybridization than the others, and much less than General 

Motors, which is simulated to have hybrids (either integrated starter generator or full hybrid 

system) on much of its fleet by 2030, nearly 20 percent of which will be strong hybrids, in 

Alternative 4 and the strong hybrid share decreases to about 18 percent in Alternative 5, as some 

lower level technologies are applied more broadly. Because the analysis applies the same 

technology inputs and the same logic for selecting among available opportunities to apply 

technology, the unique situation of each manufacturer determined which technology path was the 

most cost-effective. 

In order to understand the differences in incremental technology costs and fuel economy 

achievement across manufacturers in this market segment, it is important to understand the 

differences in their starting position relative to the proposed standards. One important factor, 

made more obvious in the following figures, is the difference between the fuel economy and 

performance of the recently redesigned vans offered by Fiat/Chrysler and Ford (the Promaster 

and Transit, respectively), and the more traditionally-styled vans that continue to be offered by 

General Motors (the Express/Savannah). In MY 2014, Ford began the phase-out of the 

Econoline van platform, moving those volumes to the Euro-style Transit vans (discussed in more 

detail in Section 2.1.2). The Transit platform represents a significant improvement over the 

existing Econoline platform from the perspective of fuel economy, and for the purpose of 

complying with the standards, the relationship between the Transit’s work factor and fuel 

economy is a more favorable one than the Econoline vans it replaces. Since the redesign of van 

offerings from both Chrysler/Fiat and Ford occur in (or prior to) the 2014 model year, the costs, 

fuel consumption improvements, and reductions of vehicle mass associated with those redesigns 

are included in the analysis fleet, meaning they are not carried as part of the compliance 

modeling exercise. By contrast, General Motors is simulated to redesign their van offerings after 
10-34 

2014, such that there is a greater potential for these vehicles to incur additional costs attributable 

to new standards, unlike the costs associated with the recent redesigns of their competitors. The 

inclusion of these new Ford and Chrysler/Fiat products in the analysis fleet is the primary driver 

of the cost discrepancy between GM and its competitors in both the baseline and Alternative 2, 

when Ford and Chrysler/Fiat have to apply considerably less technology to achieve compliance. 

Figure 10-3 and Figure 10-4 show the relationship between work factor and fuel 

economy for the model variants offered by GM, Ford and Chrysler/Fiat for gasoline (Figure 

10-3Error! Reference source not found.) and diesel (Figure 10-4) vehicles based on product 

information the manufacturers supplied to the agencies. In the figures, vans are represented by 

crosses and pickup trucks by circles, with a different color corresponding to each member of the 

Big 3 (blue, green, and orange for Fiat/Chrysler, Ford, and General Motors, respectively). 

In Figure 10-3, the field of green crosses in the upper left shows the impact of the Transit 

vans on Ford’s product mix. While Chrysler/Fiat has a single van below that cloud, the gasolinepowered Promaster, both have real separation from GM’s van offerings which are generally 

higher in work factor and considerably lower in fuel economy. Ford has a cluster of gasoline 

pickup truck variants with among the highest work factors and lowest fuel economies, but 

another cluster of pickup trucks with work factors between 4500 and 6000 and higher fuel 

economy values than nearly all competitors’ pickup trucks in that range. 

The curves provide a sliding a scale of fuel economy targets for vehicle models based on 

work factor, but the changes in scale are not sufficient to overcome GM’s poor starting position 

in fuel economy relative to its peers. As Figure 10-4 shows, this pattern is even stronger in the 

diesel market, where both GM and Chrysler/Fiat have multiple offerings above 20 MPG. These 

high MPG models are all vans and, as noted above, GM’s van offerings would need changes in 

order to provide fuel economy competing with offerings of GM’s peers – even in the absence of 

regulatory pressure. 
10-35 

Figure 10-3 Comparison of Fuel Economy and Work Factor for Gasoline Vehicles in the Analysis Fleet 

2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500

Work Factor

10

11

12

13

14

15

16

17

18

19

20

21

22

Fuel Economy on Gasoline

Manufacturer

Fiat

Ford

General Motors

Bosdy Style

Pickup

Van
10-36 

Figure 10-4 Comparison of Fuel Economy and Work Factor for Diesel Vehicles in the Analysis Fleet 

In the context of an averaging such as under the fuel consumption and GHG standards, 

individual model offerings mean little without considering the sales volumes associated with 

those models. Figure 10-5 shows two pictures of empirical cumulative distributions, curves that 

show the sales weighted mix of work factor (on the left) and fuel economy (on the right) 

increasing from zero percent of sales to 100 percent of sales. At any given point in the curve, the 

value on the (vertical) y-axis shows the percentage of total sales that are less than or equal to the 

work factor (or fuel economy) at that point. 

1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10000 10500

Work Factor

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Fuel Economy on Diesel

Manufacturer

Fiat

Ford

General Motors

Bosdy Style

Pickup

Van
10-37 

Figure 10-5 Comparing sales mix for Chrysler/Fiat, GM, and Ford 

Since larger work factors correspond to lower fuel economy targets, the standards 

provide an incentive for a manufacturer to have more of its production closer to the right side of 

the work factor graph to reduce its required average fuel economy level (i.e., under the standards, 

to increase its required average fuel consumption and GHG levels), although this incentive is 

offset by the tendency of fuel consumption to increase as vehicle payload and towing capacity 

increase, and when 4WD is added. Figure 10-5 shows Chrysler/Fiat with the most favorable 

position, from the perspective of work factor, followed by GM. Although Ford’s sales mix of 

work factors is the least favorable, Ford’s sales mix generally has the highest fuel economy of 

the Big 3. As the graph on the right in Figure 10-5 shows, Ford has the most favorable sales mix 

of high fuel economy vehicles, and generally outperforms both other manufacturers at each fuel 

economy level. The sales distribution of fuel economies highlights the other important reason 

that GM appears to face much higher costs than their competitors in this segment: not only does 

GM have consistently lower fuel economy than Ford and Chrysler in their MY2014 vehicle fleet, 

they also have no models (at any significant level of sales) achieving more than 18 MPG, while 

both Ford and Chrysler/Fiat have about 40 percent and 10 percent of sales, respectively, at higher 

levels of fuel economy. Some of this discrepancy could be explained by measurement error in 

the pre-model-year compliance data; GM’s final submission for model year 2014 may contain 

higher fuel economies as a result of more direct vehicle testing, or a different mix of final sales 

volumes that makes their starting position more favorable. The agencies intend to update the 

data in the analysis fleet before issuing the final rule. 

The remaining 5 percent of the 2b/3 market is attributed to two manufacturers, Daimler 

and Nissan, which, unlike the other manufacturers in this market segment, only produce vans. 
10-38 

The vans offered by both manufacturers currently utilize two engines and two transmissions, 

although both Nissan engines are gasoline engines and both Daimler engines are diesels. Despite 

the logical grouping, these two manufacturers are impacted much differently by the proposed 

standards. For the least stringent alternative considered, Daimler adds no technology and incurs 

no incremental cost in order to comply with the standards. At stringency increases greater than 

or equal to 3.5 percent per year, Daimler only really improves some of their transmissions of its 

Sprinter vans. By contrast, Nissan’s starting position is much weaker and their compliance costs 

closer to the industry average in Table 10-15. This difference could increase if the analysis fleet 

supporting the final rule includes forthcoming Nissan HD pickups. 
10-39 

Table 10-19 Summary of Impacts on Daimler by 2030 in the HD Pickup and Van Market versus the 

Dynamic Baseline, Alternative 1b 

ANNUAL 

STRINGENCY 

INCREASE 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Average Fuel Economy (miles per gallon) 

Required 23.36 25.19 25.25 25.91 

Achieved 25.23 25.79 25.79 26.53 

Average Fuel Consumption (gallons /100 mi.) 

Required 4.28 3.97 3.96 3.86 

Achieved 3.96 3.88 3.88 3.77 

Average Greenhouse Gas Emissions (g/mi) 

Required 436 404 404 393 

Achieved 404 395 395 384 

Technology Penetration (%) 

VVT and/or VVL 0 0 0 0 

Cylinder Deac. 0 0 0 0 

Direct Injection 0 0 0 0 

Turbocharging 44 44 44 44 

8-Speed AT 0 44 44 100 

EPS, Accessories 0 0 0 0 

Stop-Start 0 0 0 0 

Hybridizationc 0 0 0 0 

Aero. Improvements 0 0 0 0 

Mass Reduction (vs. No-Action) 

CW (lb.) 0 0 0 0 

CW (%) 0 0 0 0 

Technology Cost (vs. No-Action) 

Average ($) a 0 165 165 374 

Total ($m, 

undiscounted) b 

0 4 4 9 

Notes: 

a Values used in Methods A & B 

b Values used in Method A, calculated at a 3% discount rate 

c Includes mild hybrids (ISG) and strong HEVs. 
10-40 

Table 10-20 Summary of Impacts on Nissan by 2030 in the HD Pickup and Van Market versus the Dynamic 

Baseline, Alternative 1b 

ANNUAL 

STRINGENCY 

INCREASE 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Average Fuel Economy (miles per gallon) 

Required 19.64 21.19 20.92 21.46 

Achieved 19.84 21.17 21.19 21.51 

Average Fuel Consumption (gallons /100 mi.) 

Required 5.09 44.72 4.78 4.66 

Achieved 5.04 4.72 4.72 4.65 

Average Greenhouse Gas Emissions (g/mi) 

Required 452 419 425 414 

Achieved 448 419 419 413 

Technology Penetration (%) 

VVT and/or VVL 100 100 100 100 

Cylinder Deac. 49 49 49 49 

Direct Injection 51 51 51 100 

Turbocharging 51 51 51 50 

8-Speed AT 0 51 51 51 

EPS, Accessories 0 100 100 100 

Stop-Start 0 0 0 0 

Hybridizationc 0 0 0 28 

Aero. Improvements 0 100 100 100 

Mass Reduction (vs. No-Action) 

CW (lb.) 0 0 307 303 

CW (%) 0 0 5 4.9 

Technology Cost (vs. No-Action) 

Average ($) a 378 1,150 1,347 1,935 

Total ($m, 

undiscounted) b 

5 15.1 17.7 25.4 

Notes: 

a Values used in Methods A & B 

b Values used in Method A, calculated at a 3% discount rate 

c Includes mild hybrids (ISG) and strong HEVs. 
10-41 

As Table 10-19 and Table 10-20 show, Nissan applies more technology than Daimler in 

the less stringent alternatives and significantly more technology with increasing stringency. The 

Euro-style Sprinter vans that comprise all of Daimler’s model offerings in this segment put 

Daimler in a favorable position. However, those vans are already advanced – containing 

downsized diesel engines and advanced aerodynamic profiles. Much like the Ford Transit vans, 

the recent improvements to the Sprinter vans occurred outside the scope of the compliance 

modeling so the costs of the improvements are not captured in the analysis. 

Although Daimler’s required fuel economy level is much higher than Nissan’s (in miles 

per gallon), Nissan starts from a much weaker position than Daimler and must incorporate 

additional engine, transmission, platform-level technologies (e.g. mass reduction and 

aerodynamic improvements) in order to achieve compliance. In fact, more than 25 percent of 

Nissan’s van offerings become are projected to contain integrated starter generators by 2030 in 

Alternative 5. 

10.1.1.10 Estimated Consumer Impacts 

The consumer impacts of the rule are more straightforward. Table 10-21 shows the 

impact on the average consumer who buys a new class 2b or 3 vehicle in model year 2030. All 

dollar values are discounted at a rate of 7 percent per year from the time of purchase (except the 

average price increase, which occurs at the time of purchase). The additional costs associated 

with increases in taxes, registration fees, and financing costs are also captured in the table. 
10-42 

Table 10-21 Summary of Individual Consumer Impacts in MY 2030 in the HD Pickup and Van2b3 Market 

Segment using Method A and versus the Dynamic Baseline, Alternative 1b a

ANNUAL 

STRINGENCY 

INCREASE 

INCREASES 

2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Value of Lifetime Fuel Savings (discounted 2012 dollars) 

Pretax 2,068 3,924 4,180 4,676 

Tax 210 409 438 491 

Total 2,278 4,334 4,618 5,168 

Economic Benefits (discounted 2012 dollars) 

Mobility Benefit 244 437 472 525 

Avoided Refueling 

Time 

86 164 172 193 

New Vehicle Purchase (vs. No-Action Alternative) 

Avg. Cost Increase 

($) 

578 1,348 1,655 2,080 

Avg. Payback (years) 2.5 3 3.4 3.9 

Additional costs ($) 120 280 344 432 

Net Lifetime Consumer Benefits (discounted $) 

Total Net Benefits 1,910 3,307 3,263 3,374 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

As expected, a consumer’s lifetime fuel savings increase monotonically across the 

alternatives. The mobility benefit in Table 10-21 refers to the value of additional miles that an 

individual consumer travels as a result of reduced per-mile travel costs. The additional miles 

result in additional fuel consumption and represent foregone fuel savings, but are valued by 

consumers at the cost of the additional fuel plus the consumer surplus (a measure of the increase 

in welfare that consumers achieve by having more mobility). The refueling benefit measures the 

value of time saved through reduced refueling events, the result of improved fuel economy and 

range in vehicles that have been modified in response to the standards. 

There are some limitations to using payback period as a measure, as it accounts for fuel 

expenditures and incremental costs associated with taxes, registration fees and financing, and 

increased maintenance costs, but not the cost of potential repairs or replacements, which may or 

may not be more expensive with more advanced technology. 
10-43 

Overall, the average consumer is likely to see discounted lifetime benefits that are 

multiples of the price increases faced when purchasing the new vehicle in MY 2030. In 

particular, the net present value of future benefits at the time of purchase are estimated to be 3.5, 

3.0, 2.2, and 1.8 times the price increase of the average new MY2030 vehicle for Alternatives 2-

5, respectively. As the table above illustrates, the preferred alternative has the highest ratio of 

discounted future consumer benefits to consumer costs. 

10.1.1.11 Social and Environmental Impacts 

Social benefits increase with the increasing stringency of the alternatives. As in the 

consumer analysis, the net benefits continue to increase with increasing stringency – suggesting 

that benefits are still increasing faster than costs for even the most stringent alternative. 

Table 10-22 Summary of Total Social Costs and Benefits Through MY2029 in the HD Pickup and Van 

Market Segment using Method A and versus the Dynamic Baseline, Alternative 1b a

ALTERNATIVE 2 3 4 5

Annual Stringency 

Increase 

2.0% 2.5% 3.5% 4.0% 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Fuel Purchases ($billion) 

Pretax Savings 9.6 15.9 19.1 22.2 

Fuel Externalities ($billion) 

Energy Security 0.5 0.9 1.1 1.3 

CO2 emissionsb 1.9 3.2 3.8 4.4 

VMT-Related Externalities ($billion) 

Driving Surplus 1.1 1.8 2.1 2.4 

Refueling Surplus 0.4 0.7 0.8 0.9 

Congestion -0.2 -0.4 -0.4 -0.5 

Accidents -0.1 -0.2 -0.2 -0.3 

Noise 0 0 0 0 

Fatalities 0.1 -0.2 -0.2 -0.5 

Criteria Emissions 0.6 1.1 1.3 1.6 

Technology Costs vs. No-Action ($billion) 

Incremental Cost 2.5 5.0 7.2 9.7 

Additional Costs 0.5 1.0 1.5 2.0 

Benefit Cost Summary ($billion) 

Total Social Cost 3.3 6.8 9.5 13.0 

Total Social Benefit 13.9 22.7 27.4 31.7 

Net Social Benefit 10.6 15.9 17.9 18.7 

*

All dollar values are discounted at a rate of 3 percent per year from the time of 

purchase. 

Notes: 
10-44 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

b Using the 3% average social cost of CO2 value. There are four distinct social cost of 

CO2 values presented in the Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866 (2010 and 2013). The CO2

emissions presented here would be valued lower with one of those other three values 

and higher at the other two values. 

Table 10-22 provides a summary of benefits and costs, cumulative from MY2015 – 

MY2029 (although the early years of the series have nearly zero incremental costs and benefits 

over the baseline), for each alternative. In the social perspective, fuel savings are considered net 

of fuel taxes, which are a transfer from purchasers of fuel to society at large. The energy security 

component represents the risk premium associated with exposure to oil price spikes and the 

economic consequences of adapting to them. This externality is monetized on a per-gallon basis, 

just as the social cost of carbon is used in this analysis. Just as the previous two externalities are 

caused by fuel consumption, others are caused by travel itself. The additional VMT resulting 

from the increase in travel demand that occurs when the price of driving decreases (i.e. the 

rebound effect), not only leads to increased mobility, but also to increases in congestion, noise, 

accidents, and per-mile emissions of criteria pollutants like carbon monoxide and diesel 

particulates. Although increases in VMT lead to increases in tailpipe emissions of criteria 

pollutants, the proposed regulations decrease overall consumption enough that the emissions 

reductions associated with the remainder of the fuel cycle (extraction, refining, transportation 

and distribution) are large enough to create a net reduction in the emissions of criteria 

pollutants.13 A full presentation of the costs and benefits, and the considerations that have gone 

into each cost and benefit category—such as how energy security premiums were developed, 

how the social costs of carbon and co-pollutant benefits were developed, etc.—is presented in 

Section IX of the preamble and in Chapters 7 and 8 of this draft RIA for each regulated segment 

(engines, HD pickups and vans, vocational vehicles, tractors and trailers). 

Another side effect of increased VMT is the likely increase in traffic fatalities, which is a 

function of the total vehicle travel in each year. As 

Table 10-22 illustrates, the positive social cost associated with traffic fatalities is the 

result of an additional -10 (implying that Alternative 2 actually leads to a reduction in fatalities 

over the baseline, due to the application of mass reduction technologies), 35, 36, and 66 fatalities 

for Alternatives 2-5, respectively. To put those numbers in context, the baseline contains nearly 

25,000 fatalities attributable to 2b/3 vehicles over the same period. The incremental fatalities 

associated with the alternatives translate to less than -0.4, 0.1, 0.1, and 0.3 percent increases over 

the MY2015-2029 baseline, respectively. 

The CAFE model was used to estimate the emissions impacts of the various alternatives 

that are the result of lower fuel consumption, but increased vehicle miles traveled for vehicle 

 13 For a more detailed discussion of the results from the CAFE Model on the proposed heavy duty pickups and vans 

regulation’s impact on emissions of CO2 and criteria pollutants, see NHTSA’s accompanying Draft Environmental 

Impact Statement. 
10-45 

produced in model years subject to the standards in the alternatives. Criteria pollutants are 

largely the result of vehicle use, and accrue on a per-mile-of-travel basis, but the alternatives still 

generally lead to emissions reductions. Although vehicle use increases under each of the 

alternatives, upstream emissions associated with fuel refining, transportation and distribution are 

reduced for each gallon of fuel saved and that savings is larger than the incremental increase in 

emissions associated with increased travel. The net of the two factors is a savings of criteria (and 

other) pollutant emissions. 

Table 10-23 Summary of Environmental Impacts Through MY2029 in the HD Pickup and Van Market 

Segment, using Method A and versus the Dynamic Baseline, Alternative 1b a

ANNUAL STRINGENCY 

INCREASE 

2.0% 2.5% 3.5% 4.0% 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Greenhouse Gas Emissions vs. No-Action Alternative 

CO2 (MMT) 54 91 110 127 

CH4 and N2O (tons) 65,600 111,400 133,700 155,300 

Other Emissions vs. No-Action Alternative (tons) 

CO 10,400 20,700 25,800 30,400 

VOC and NOX 23,800 43,600 53,500 62,200 

PM 1,470 2,550 3,090 3,590 

SO2 11,400 19,900 24,100 28,000 

Air Toxics 44 47 49 55 

Diesel PM10 2,470 4,350 5,300 6,160 

Other Emissions vs. No-Action Alternative (% reduction) 

CO 0.1 0.3 0.4 0.4 

VOC and NOX 1.1 2.1 2.6 3.0 

PM 1.7 3.0 3.6 4.2 

SO2 2.9 5.1 6.2 7.2 

Air Toxics 0.1 0.1 0.1 0.2 

Diesel PM10 2.7 4.8 5.9 6.8 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

In addition to comparing environmental impacts of the alternatives against a dynamic 

baseline that shows some improvement over time, compared to today’s fleet, even in the absence 

of the alternatives, the environmental impacts from the Method A analysis were compared 

against a flat baseline. This other comparison is summarized below, but both comparisons are 

discussed in greater detail in the Draft EIS. 
10-46 

Table 10-24 Summary of Environmental Impacts Through MY2029 in the HD Pickup and Van Market 

Segment, using Method A and versus the Flat Baseline, Alternative 1a a

ANNUAL STRINGENCY 

INCREASE 

2.0% 2.5% 3.5% 4.0% 

Stringency Increase 

Through MY 

2025 2027 2025 2025 

Greenhouse Gas Emissions vs. No-Action Alternative 

CO2 (MMT) 66 105 127 142 

CH4 and N2O (tons) 79,700 127,400 154,800 172,800 

Other Emissions vs. No-Action Alternative (tons) 

CO 11,630 22,160 28,030 32,370 

VOC and NOX 28,280 48,770 60,180 68,050 

PM 1,780 2,900 3,550 3,980 

SO2 13,780 22,580 27,660 31,020 

Air Toxics 60 65 72 73 

Diesel PM10 2,980 4,930 6,060 6,810 

Other Emissions vs. No-Action Alternative (% reduction) 

CO 0.2 0.3 0.4 0.4 

VOC and NOX 1.4 2.3 2.9 3.3 

PM 2.1 3.4 4.2 4.7 

SO2 3.5 5.7 7.0 7.9 

Air Toxics 0.2 0.2 0.2 0.2 

Diesel PM10 3.3 5.4 6.7 7.5 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation 

of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

10.1.1.12 Sensitivity Analysis to Different Inputs to the CAFE Model 

This section describes some of the principal sensitivity results, obtained by running the 

various scenarios describing the policy alternatives with alternative inputs. OMB Circular A-4 

indicates that “it is usually necessary to provide a sensitivity analysis to reveal whether, and to 

what extent, the results of the analysis are sensitive to plausible changes in the main assumptions 

and numeric inputs.”14 Considering this guidance, a number of sensitivity analyses were 

performed using analysis Method A to examine important assumptions and inputs, including the 

following: 

1. Payback Period: In addition to the 0 and 6 month payback periods discussed above, also 

evaluated cases involving payback periods of 12, 18, and 24 months. 

2. Fuel Prices: Evaluated cases involving fuel prices from the AEO 2014 low and high oil 

price scenarios. (See AEO-Low and AEO-High in the tables.) 

3. Fuel Prices and Payback Period: Evaluated one side case involving a 0 month payback 

period combined with fuel prices from the AEO 2014 low oil price scenario, and one side 

case with a 24 month payback period combined with fuel prices from the AEO 2014 high 

oil price scenario. 

 14 Available at http://www.whitehouse.gov/omb/circulars_a004_a-4/. 
10-47 

4. Benefits to Vehicle Buyers: The main Method A analysis assumes there is no loss in 

value to owner/operators resulting from vehicles that have an increase in price and higher 

fuel economy. NHTSA performed this sensitivity analysis assuming that there is a 25, or 

50 percent loss in value to owner/operators – equivalent to the assumption that 

owner/operators will only value the calculated benefits they will achieve at 75, or 50 

percent, respectively, of the main analysis estimates. (These are labeled as 

75pctOwner/operatorBenefit and 50pctOwner/operatorBenefit.) 

5. Value of Avoided GHG Emissions: Evaluated side cases involving lower and higher 

valuation of avoided CO2 emissions, expressed as the social cost of carbon (SCC). 

6. Rebound Effect: Evaluated side cases involving rebound effect values of 5 percent, 15 

percent, and 20 percent. (These are labeled as 05PctReboundEffect, 15PctReboundEffect 

and 20PctReboundEffect.) 

7. RPE-based Markup: Evaluated a side case using a retail price equivalent (RPE) markup 

factor of 1.5 for non-electrification technologies, which is consistent with the NAS 

estimation for technologies manufactured by suppliers, and a RPE markup factor of 1.33 

for electrification technologies (mild and strong HEV). 

8. ICM-based Post-Warranty Repair Costs: NHTSA evaluated a side case that scaled the 

frequency of repair by vehicle survival rates, assumes that per-vehicle repair costs during 

the post-warranty period are the same as in the in-warranty period, and that repair costs 

are proportional to incremental direct costs (therefore vehicles with additional 

components will have increased repair costs). 

9. Mass-Safety Effect: Evaluated side cases with the mass-safety impact coefficient at the 

values defining the 5th and 95th percent points of the confidence interval estimated in the 

underlying statistical analysis. (These are labeled MassFatalityCoeff05pct and 

MassFatalityCoeff95pct.) 

10. Strong HEVs: Evaluated a side case in which strong HEVs were excluded from the set of 

technology estimated to be available for HD pickups and vans through model year 2030. 

An additional “no strong HEV” case was run where all GM gasoline-engine vans were 

allowed to have turbo-downsized engines to provide a lower-cost option for compliance. 

These cases were all run for both 0-month and 6-month payback periods. 

11. Diesel Downsizing: Evaluated a side case in which downsizing of diesel engines was 

estimated to be more widely available to HD pickups and vans. 

12. Technology Effectiveness: Evaluated side cases involving inputs reflecting lower and 

higher impacts of technologies on fuel consumption. 

13. Technology Direct Costs: Evaluated side cases involving inputs reflecting lower and 

higher direct incremental costs for fuel-saving technologies. 

14. Fleet Mix: Evaluated a side case in which the shares of individual vehicle models and 

configurations were kept constant at estimated current levels. 

Table 10-25 below summarizes key metrics for each of the cases included in the 

sensitivity analysis using Method A for the proposed alternative. The table reflects the percent 

change in the metrics (columns), relative to the main analysis, the proposed alternative 3. For 

each sensitivity run, the change in the metric can we described as the difference between the 

baseline and the preferred alternative for the sensitivity case, minus the difference between the 

preferred alternative and the baseline in the main analysis, divided by the difference between the 

preferred alternative and the baseline in the main analysis. Or, 
10-48 

	 ൌ ݅ܿݎݐ݁ܯ	ܾ݈ܶܽ݁

∆஺௟௧	௦௘௡	௖௔௦௘ െ ∆஺௟௧	௠௔௜௡	௥௨௡

∆஺௟௧	௠௔௜௡	௥௨௡

	∙ 	100

Each metric represents the sum of the impact of the preferred alternative over the model 

years 2018 – 2029, and the percent changes in the table represent percent changes to those sums. 

Table 10-25 Sensitivity Analysis Results from CAFE Model for the Proposed Standards in the HD Pickup 

and Van Market Segment using Method A and versus the Dynamic Baseline, Alternative 1b (Cells are 

percent change from base case) a 

Sensitivity Case Fuel 

Savings 

(gallons)

CO2 

savings 

(MMT)

Fuel Savings 

($)

Social 

Costs

Social 

Benefits

Social 

Net 

Benefits

0 Month Payback 14.0% 14.5% 15.1% 5.6% 15.1% 18.2% 

12 Month Payback -4.8% -4.7% -4.5% -2.5% -4.7% -5.4% 

18 Month Payback -29.2% -28.1% -26.5% -14.1% -26.8% -31.1% 

24 Month Payback -42.9% -42.4% -41.9% -23.2% -42.1% -48.4% 

AEO-Low 3.3% 3.5% -27.9% -10.8% -22.2% -26.1% 

AEO-High -7.0% -7.2% 23.3% 1.4% 19.5% 25.6% 

AEO-Low, 0 Month 

Payback 18.6% 19.3% -16.5% -3.4% -10.1% -12.3% 

AEO-High, 24 

Month Payback -63.8% -64.6% -54.4% -49.9% -55.7% -57.7% 

50pct 

Owner/operator 

Benefit 

0.0% 0.0% -50.0% 0.0% -34.6% -46.2% 

75pct 

Owner/operator 

Benefit 

0.0% 0.0% -25.0% 0.0% -17.3% -23.1% 

Low SCC 0.0% 0.0% 0.0% 0.0% -10.6% -14.1% 

Low SCC, 0 Month 

Payback 14.0% 14.5% 15.1% 5.6% 2.9% 2.0% 

High SCC 0.0% 0.0% 0.0% 0.0% 7.8% 10.4% 

High SCC, 0 Month 

Payback 14.0% 14.5% 15.1% 5.6% 24.0% 30.1% 

Very High SCC 0.0% 0.0% 0.0% 0.0% 28.7% 38.4% 

Very High SCC, 0 

Month Payback 14.0% 14.5% 15.1% 5.6% 48.0% 62.2% 

05 Pct Rebound 

Effect 4.6% 4.6% 4.6% -12.9% 0.4% 4.8% 

15 Pct Rebound 

Effect -4.6% -4.6% -4.6% 12.9% -0.4% -4.8% 

20 Pct Rebound 

Effect -9.1% -9.2% -9.2% 25.7% -0.8% -9.7% 

RPE-Based Markup -3.2% -1.5% 0.3% 31.4% -0.1% -10.6% 

Mass Fatality Coeff 

05pct 0.0% 0.0% 0.0% -23.6% 0.0% 7.9% 

Mass Fatality Coeff 

95pct 0.0% 0.0% 0.0% 23.9% 0.0% -8.0% 

NoSHEVs, -6.9% -6.2% -5.3% 19.2% -5.4% -13.7% 
10-49 

NoSHEVs, 0 Month 

Payback 7.7% 9.1% 10.7% 29.0% 10.5% 4.3% 

NoSHEVs, GM 

Turbo Vans -6.7% -5.8% -5.0% 2.3% -5.1% -7.6% 

NoSHEVs, GM 

Turbo Vans, 0 

Month Payback 

8.2% 9.8% 11.5% -1.2% 11.3% 15.4% 

Lower Effectiveness -7.8% -7.8% -8.1% 39.5% -8.0% -23.9% 

Higher Effectiveness -10.6% -10.3% -10.0% -23.3% -10.2% -5.8% 

Lower Direct Costs 0.9% 2.7% 4.8% 18.4% 4.3% -0.4% 

Higher Direct Costs -4.1% -3.8% -3.5% 75.3% -3.8% -30.3% 

Wider Diesel 

Downsizing -1.5% -1.0% -0.6% -10.3% -0.8% 2.4% 

07 Pct Discount Rate 0.0% 0.0% -100.0% -41.7% -100.0% -119.5% 

07 Pct DR, 0 Month 

Payback 14.0% 14.5% -37.9% -30.7% -30.7% -30.7% 

Allow Gas To Diesel 15.5% 5.3% -100.0% 16.8% -100.0% -139.1% 

Allow Gas To 

Diesel, 0 Month 

Payback 

32.1% 22.6% 14.5% 46.8% 17.0% 7.0% 

flat mix after 2016 1.1% 0.9% 0.7% 2.6% 0.8% 0.2% 

Note: 

a For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1. 

For some of the cases for which results are presented above, the sensitivity of results to 

changes in inputs is simple, direct, and easily observed. For example, changes to valuation of 

avoided GHG emissions impact only this portion of the estimated economic benefits; 

manufacturers’ responses and corresponding costs are not impacted. Similarly, a higher discount 

rate does not affect physical quantities saved (gallons of fuel and metric tons of CO2 in the 

table), but reduces the value of the costs and benefits attributable to the proposed standards in an 

intuitive way. Some other cases warrant closer consideration: 

First, cases involving alternatives to the reference six-month payback period involve 

different degrees of fuel consumption improvement, and these differences are greatest in the noaction alternative defining the baseline. Because all estimated impacts of the proposed standards 

are shown as incremental values relative to this baseline, longer payback periods correspond to 

smaller estimates of incremental impacts, as fuel economy increasingly improves in the absence 

of the rule and manufacturers are compelled to add less technology in order to comply with the 

standards. 

Second, cases involving different fuel prices similarly involve different degrees of fuel 

economy improvement in the absence of the standard, as more, or less, improvement occurs as a 

result of more, or fewer, technologies appearing cost effective to owner/operators. Lower fuel 

prices correspond to increases in fuel savings on a volumetric basis, as the standard is 

responsible for a greater amount of the fuel economy improvement, but the value of fuel savings 

decreases because each gallon saved is worth less when fuel prices are low. Higher fuel prices 

correspond to reductions in the volumetric fuel savings attributable to the proposed standards, 
10-50 

but lead to increases in the value of fuel saved because each gallon saved is worth more when 

fuel prices are high. 

Third, because the payback period and fuel price inputs work in opposing directions, the 

relative magnitude of each is important to consider for the combined sensitivity cases. While the 

low price and 0-month payback case leads to significant volumetric savings compared to the 

main analysis, the low fuel price is still sufficient to produce a negative change in net benefits. 

Similarly, the high price and 24-month payback case results in large reductions to volumetric 

savings that can be attributed to the proposed standards, but the presence of high fuel prices is 

not sufficient to lead to increases in either the dollar value of fuel savings or net social benefits. 

Fourth, the cases involving different inputs defining the availability of some technologies 

do not impact equally the estimated impacts across all manufacturers. Section VI.C.8 of the 

Preamble provides a discussion of a sensitivity analysis that excludes strong hybrids and includes 

the use of downsized turbocharged engines in vans currently equipped with large V-8 engines. 

The modeling results for this analysis are provided in Section IV.C.8 and in the table above. The 

no strong hybrid analysis shows that GM could comply with the proposed preferred Alternative 3 

without strong hybrids based on the use of turbo downsizing on all of their HD gasoline vans. 

Alternatively, when the analysis is modified to allow for wider application of diesel engines, 

strong HEV application for GM drops slightly (from 19 percent to 17 percent) in MY2030, 

average per-vehicle costs drop slightly (by about $50), but MY2030 additional penetration rates 

of diesel engines increase by about 10 percent. Manufacturer-specific model results 

accompanying today’s rule show the extent to which individual manufacturers’ potential 

responses to the standards vary with these alternative assumptions regarding the availability and 

applicability of fuel-saving technologies. However, across all of these sensitivity cases, the 

model projects social costs increase (as a result of increases in technology costs) when 

manufacturers choose to comply with the proposed regulations without the use of strong hybrids. 

Fifth, the cases that vary the effectiveness and direct cost of available technologies 

produce nuanced results in the context of even the 0-month payback case. In the case of 

effectiveness changes, both sensitivity cases result in reductions to the volumetric fuel savings 

attributable to the proposal; lower effectiveness because the technologies applied in response to 

the standards save less fuel, and higher effectiveness because more of the increase in fuel 

economy occurs in the baseline. However, for both cases, social costs (a strong proxy for 

technology costs) move in the intuitive direction. 

The cases that vary direct costs show volumetric fuel savings increasing under lower 

direct technology costs despite additional fuel economy improvements in the baseline, as more 

aggressive technology becomes cost effective. Higher direct costs lead to decreases in volumetric 

fuel savings, as more of the fuel economy improvement can be attributed to the rule. In both 

cases, social costs (as a result of technology costs) move in the intuitive direction. 

If instead, the main analysis had used the same assumptions as the sensitivity cases 

described above, the impacts of the proposed standards for HD Pickups and Vans would be as 

described in Table 10-26. 
10-51 

Table 10-26: Costs and Benefits of Proposed Standards for HD Pickups and Vans Under Alternative 

Assumptions 

Sensitivity Case 

Fuel 

Savings 

(billion 

gallons) 

CO2 

Reduction 

(MMT) 

Fuel 

Savings 

($billion)

Social 

Costs 

($billion)

Social 

Benefits 

($billion) 

Net 

Social 

Benefits 

($billion)

6 Month Payback (main) 7.8 94.1 15.9 5.5 23.5 18.0 

0 Month Payback 8.9 107.7 18.3 5.8 27.0 21.3 

12 Month Payback 7.4 87.2 15.2 5.6 21.9 16.3 

18 Month Payback 5.5 65.8 11.7 4.9 16.8 11.9 

24 Month Payback 4.5 52.7 9.2 4.4 13.3 8.9 

AEO-Low 8.1 94.7 11.5 5.1 17.8 12.7 

AEO-High 7.3 84.9 19.6 5.8 27.4 21.6 

AEO-Low, 0 Month Payback 9.3 109.1 13.3 5.6 20.6 15.1 

AEO-High, 24 Month Payback 2.8 32.4 7.2 2.9 10.2 7.3 

50pct Owner/operator Benefit 7.8 91.5 8.0 5.8 15.0 9.2 

75pct Owner/operator Benefit 7.8 91.5 11.9 5.8 19.0 13.2 

Low SCC 7.8 91.5 15.9 5.8 20.5 14.8 

Low SCC, 0 Month Payback 8.9 104.7 18.3 6.1 23.6 17.5 

High SCC 7.8 91.5 15.9 5.8 24.7 19.0 

High SCC, 0 Month Payback 8.9 104.7 18.3 6.1 28.5 22.4 

Very High SCC 7.8 91.5 15.9 5.8 29.5 23.8 

Very High SCC, 0 Month Payback 8.9 104.7 18.3 6.1 34.0 27.9 

05 Pct Rebound Effect 8.2 95.7 16.6 5.0 23.0 18.0 

15 Pct Rebound Effect 7.5 87.2 15.2 6.5 22.9 16.4 

20 Pct Rebound Effect 7.1 83.0 14.4 7.2 22.8 15.5 

RPE-Based Markup 7.6 90.1 16.0 7.6 22.9 15.4 

Mass Fatality Coeff 05pct 7.8 91.5 15.9 4.4 23.0 18.5 

Mass Fatality Coeff 95pct 7.8 91.5 15.9 7.1 23.0 15.8 

NoSHEVs 7.2 84.3 14.6 8.0 21.1 13.1 

NoSHEVs, 0 Month Payback 7.0 82.0 14.3 4.4 20.6 16.2 

Lower Effectiveness 7.9 94.0 16.7 6.8 23.9 17.1 

Higher Effectiveness 7.5 88.0 15.3 10.1 22.1 12.0 

Lower Direct Costs 7.7 90.5 15.8 5.2 22.8 17.6 

Higher Direct Costs 7.8 91.5 8.5 3.8 13.8 10.0 

Wider Diesel Downsizing 8.9 104.7 9.9 4.0 15.9 11.9 

07 Pct Discount Rate 9.0 96.3 15.3 7.2 22.7 15.5 

07 Pct DR, 0 Month Payback 10.3 112.2 18.2 8.5 26.9 18.4 

Allow Gas To Diesel 7.9 92.3 16.0 5.9 23.1 17.2 

Allow Gas To Diesel, 0 Month 

Payback 7.3 85.8 15.1 6.9 21.7 14.8 

Flat mix after 2016 8.4 99.8 17.6 7.4 25.4 17.9 
10-52 

10.1.1.13 Probabilistic Uncertainty Analysis 

OMB Circular A-4 directs agencies to conduct formal probabilistic uncertainty analysis 

of complex rules where there are large, multiple uncertainties whose analysis raises technical 

challenges or where effects cascade and where the impacts of the rule exceed $1 billion. The 

proposed increase in MD-HD vehicle fuel economy/GHG standards meets all of these criteria. 

As for previous rules, NHTSA has conducted an uncertainty analysis to determine the extent to 

which uncertainty about input assumptions could impact the costs and benefits attributable to the 

proposed rule. Throughout the course of the main analysis, input values were selected from a 

variety of often conflicting sources. Best estimates were selected based on the preponderance of 

data and analyses available, but there is inevitably a level of uncertainty in these selections, 

particularly given the time frame of the rulemaking. Some of these inputs contributed less to the 

overall variations of the outcomes, and, thus, are less significant. Some inputs depend on others 

or are closely related (e.g., oil import externalities), and thus can be combined. With the vast 

number of uncertainties embedded in this regulatory analysis, this uncertainty analysis identifies 

only the major independent uncertainty factors having appreciable variability and impact on the 

end results and quantifies them by probability distributions. The values of these uncertainties are 

then randomly selected and fed back into the CAFE model to determine the net benefits using the 

Monte Carlo statistical simulation technique. 

Using point estimates for the large number of variables in this analysis provides only a 

limited view of the potential results, and provides, likewise, a limited measure of confidence in 

the estimated outcome, beyond the assertion that it is the “most likely.” Correctly estimating the 

exact total costs and benefits of a program as complex as the proposal, especially over such a 

long time frame, is, of course, not possible. This is why the direction in A-4 suggests analysis of 

the sources and consequences of uncertainty in the results. Using Monte Carlo simulations to 

explicitly consider the uncertainty around the important inputs to the analysis, enables decisionmakers to see the probabilities associated with a large range of outcomes and develop confidence 

in achieving acceptable levels of net benefit from the existing program specification, even 

without perfect information about future conditions. Having confidence that a rule will perform 

as expected under a range of potential future states of the world is a valuable outcome. 

Unlike the preceding sensitivity analysis, which is useful for understanding how 

alternative values of a single input assumption may influence the estimated impacts of the 

proposed standards, the uncertainty analysis considers multiple states of the world, characterized 

by specific values of all relevant inputs, based on their relative probability of occurrence. A 

sensitivity analysis varies a single parameter of interest, holding all others constant at whatever 

nominal values are used to generate the single point estimate in the main analysis, and measures 

the resulting deviation. However, the uncertainty analysis allows all of those parameters to vary 

simultaneously – relaxing the assumption that “all else is equal.” 

Each trial, of which there are 14,000 in this analysis, represents a different state of the 

world in which the standards are implemented. To gauge the robustness of the estimates of 

impacts in the proposal, NHTSA varied technology costs and effectiveness, fuel prices, market 

demand for fuel economy improvements in the absence of the rule, the amount of additional 

driving associated with fuel economy improvements (the rebound effect), and the on-road gaps 

between realized fuel economy and laboratory test values for gasoline and diesel vehicles. The 
10-53 

shapes and types of the probability distributions used in the analysis vary by uncertainty, though 

the costs and effectiveness values for technologies are sampled as groups to minimize issues 

associated with interdependence. 

Similar technology costs are sampled in such a way that they are simultaneously at 

similar points in their respective cost distributions, even if the distributions themselves are 

different. For example, different levels of low rolling resistance tires might have different 

underlying distributions describing the degree of certainty associated with the point-estimate 

value of cost. The distributions of cost might be different to represent different degrees of 

technology readiness (for example). For tires, however, one would expect advances in 

technology to be shared across models, so the cost an advanced low rolling resistance tire would 

not be expected to diverge from the value in the main analysis. The sample design of technology 

costs (and effectiveness) for the proposal’s Monte Carlo analysis attempts to account for such 

similarities. 

The most important input to the uncertainty analysis, fuel prices (which drive the 

majority of benefits from the proposed standards), are drawn from a range of fuel prices 

characterized by permutations of the Low, Reference, and High fuel price cases in the Annual 

Energy Outlook 2014. 

10.1.1.13.1 Summary of Uncertainties Varied in Analysis 

NHTSA reviewed the inputs and relationships that drive the CAFE model to identify the 

factors that are both uncertain and important to the estimation of net benefits. Several factors 

were identified as potentially contributing to uncertainty to the estimated impacts of higher 

CAFE standards, although not all were ultimately selected to be run in the simulation. In 

particular, the social cost of damages caused by criteria pollutant and greenhouse gas emissions 

have been omitted from the analysis, the latter based on guidance from the interagency working 

group that developed the cost estimates used in the central analysis. The list of included 

uncertainties is: 

(1) Technology costs; 

(2) Technology effectiveness; 

(3) Fuel prices; 

(4) Manufacturers’ decision to produce vehicles with fuel economies higher than the 

levels mandated by CAFE standards; 

(5) The rebound effect; 

(6) The on-road gap between achieved real-world fuel economy and the test cycle for 

gasoline and diesel vehicles. 

10.1.1.13.2 Technology Costs and Effectiveness 
10-54 

The costs incurred by manufacturers to modify their vehicles to meet new CAFE levels 

are assumed to be passed on to consumers in the form of higher new vehicle prices. These 

technology costs are the primary determinant of the overall cost of improving fuel economy. 

For each of the technologies considered applicable to this vehicle segment for the 

purpose of improving fuel economy, the agency used what it considered to be the most likely 

value in the main analysis. Unlike previous analyses of light-duty technology costs and 

effectiveness, that relied on a broader knowledge base, there are fewer studies of technology cost 

and effectiveness for medium-duty vehicles. As such, the distributions used to characterize the 

uncertainty are more agnostic than the set used in the last light-duty rule, for example. The cost 

distributions in this analysis are generally very flat Beta distributions, which behave like uniform 

distributions with fuzzy boundaries in practice – appropriate since even the range of potential 

values is unknown. 

The effectiveness uncertainty, for the purpose of gauging compliance, is generally 

characterized by a normal distribution for each technology. The standard deviation of the normal 

distribution is based on the complexity assigned to the technology, where low, medium, and high 

complexity technologies are assigned normal distributions with standard deviations of 0.145, 

0.29, and 0.435, respectively. Each draw results in a scalar that is used to modify the value in the 

main analysis through simple multiplication, values less than one represent lower 

cost/effectiveness, values greater than one represent higher cost/effectiveness. It is worth noting 

that cost and effectiveness are treated as independent – so a technology may simultaneously be 

less expensive and more effective (or vice versa) than anticipated in the main analysis for a given 

simulation in this exercise. 

10.1.1.13.3 Fuel Prices 

For this analysis, fuel prices are sampled as a scaling factor that determines a complete 

time series of prices for all years covered by the analysis. The scaling factor scales the interannual differences of fuel prices in the High Oil Price case for the 2014 Annual Energy Outlook. 

Values within the sampled range produce series that have shapes similar to the high, reference, 

and low oil price cases, and are generally bounded above and below by the high and low price 

case, respectively. As EIA makes no claims about the relative likelihood of the fuel price cases, 

we make none here – the range of values is sampled uniformly, suggesting that any single time 

series of prices within that range is as likely as another. 

10.1.1.13.4 Market-Driven Fuel Economy Improvements in the Absence of the 

Proposed Standards 

The CAFE model includes the capability to apply technology under varying fuel price 

cases by including a variable that represents manufacturers’ assumption about consumer 

willingness-to-pay for fuel economy technology. In this case, “willingness-to-pay” is 

characterized as the payback period for fuel economy technology investments, meaning the 

number of years’ worth of fuel savings necessary to balance the cost of the new technology. In 

the main analysis, the model alters that variable to be zero once a manufacturer reaches 

compliance (for baseline 1a) or six months (for baseline 1b). In the case of baseline 1a, no 

additional technology is added beyond the standards in the baseline, or in any of the other 
10-55 

regulatory scenarios. In the case of baseline 1b, only those technologies whose fuel savings in 

the first six-months of ownership exceed the incremental cost of the technology would be added. 

For the main analysis, which uses a single fuel price projection and economic and technological 

parameter values consistent with it, the zero-year and six-month payback assumptions are 

generally consistent with the collection of other assumptions. However, under more extreme fuel 

price scenarios, short payback periods become increasingly unrealistic. 

To address this limitation, NHTSA has included the length of the payback period (that 

manufacturers assume consumers desire) in the uncertainty analysis. Assuming some non-zero 

payback period ensures that when fuel prices are very high, manufacturers will continue to add 

cost-effective fuel economy technologies even when the standards are not sufficient to force 

these additions. As one might expect, higher fuel prices and longer payback periods result in 

more fuel economy technology being added beyond the level mandated by the standards, while 

lower fuel prices and shorter payback periods result in less. 

 The cases that most challenge internal consistency are naturally found at the extremes, 

low-price-long-payback, for example. In the low-price-long-payback case, the fuel savings 

would still be very low, technology would still not be very attractive, and only a small amount of 

additional fuel economy technology (if any at all) would be added to vehicles already in 

compliance. So one could credibly argue that there is uncertainty about the degree to which 

manufacturers understand consumers’ willingness-to-pay for fuel economy and add slightly more 

than would be demanded. 

Similarly, under the high-price-short-payback draws, fuel prices are high enough to make 

some technology additions occur in the baseline, but maybe not the ideal amount under those 

conditions because manufacturers assumed a shorter payback period than consumers have when 

faced with very high fuel prices. Since there is uncertainty about manufacturers’ ability to 

perfectly respond to consumer preferences, these draws produce results that are still plausible (if 

less probable than others). 

The payback period used in the analysis is drawn from a Beta distribution with shape 

parameters equal to 2 and 5. This places about 75 percent of the probability distribution’s mass 

below 2 years, with steeply decreasing probabilities afterward. 

10.1.1.13.5 The Rebound Effect 

By reducing the amount of gasoline used and, thus, the cost of operating a vehicle, more 

stringent fuel economy and GHG standards are anticipated to result in a slight increase in annual 

miles driven per vehicle. This rebound effect impacts net societal benefits because the increase 

in miles driven offsets a portion of the fuel savings that results from more fuel-efficient vehicles. 

Although operators derive value from this extra driving, it also leads to increases in crashes, 

congestion, noise, and pollution costs associated with driving. 

On the basis of previous studies devoted to the impact of fuel economy changes on the 

vehicle miles traveled of comparable light-duty trucks, NHTSA employed a rebound effect of 10 

percent in the main analysis. A more complete discussion of the rebound effect is included in 

Chapter VIII. For the uncertainty analysis, a range of 5 to 30 percent was used and employed in 
10-56 

a slightly skewed Beta distribution which produced a mean of approximately 14.2 percent. The 

difference reflects the more cost-conscious behavior of commercial vehicle operators, 

particularly for HD pickups and vans which are often registered at residences and may be used to 

substitute VMT for other household vehicles. 

10.1.1.13.6 On-Road Gaps 

As is the case of fuel economy ratings for light-duty vehicles, medium-duty work trucks 

can achieve different levels of fuel economy in real-world applications. Medium-duty vehicles 

may achieve lower fuel economy than their ratings because of driver habits (like faster 

acceleration) or terrain/conditions. However, unlike light-duty fuel economy, where vehicles are 

typically used for moving people, even when driving style and conditions vary from the test 

procedure, medium-duty vehicles are typically used for multiple purposes. Vehicle usage – in 

particular, loading – is an additional source of bias. 

In order to estimate the impact of both driving profiles and vehicle loading on fuel 

consumption, we used simulation results from Southwest Research Institute, who conducted a 

simulation study under a NHTSA contract to estimate the fuel consumption improvement 

associated with applying specific technologies to medium and heavy-duty trucks. 15 We then 

computed the difference between each driving cycle and the corresponding test cycle, then 

created a distribution from those differences for each fuel type (gasoline and diesel). The 

resulting distributions were multi-modal for both fuel types, largely as a result of the different 

sources of discrepancy from the test cycle: city/highway splits, driving profile, vehicle loading, 

and a relatively sparse set of results. To smooth the distributions, we split the computed gaps 

into two bins – one of gaps (strictly) less than 0.2 and the other containing larger gaps. Then we 

bootstrapped the computed gaps by repeatedly sampling (with replacement) from each bin, 

randomly choosing a weighting parameter from a uniform distribution spanning [0.3, 0.7], then 

taking a weighted average of the low and high gap bins to create a distribution. 

The resulting empirical distributions for both gasoline and diesel were then fit using 

gamma distributions, and the gamma distributions were sampled in the uncertainty analysis. 

10.1.1.13.7 Results 

Figure 10-6 displays the distribution of net benefits estimated by the ensemble of 

simulation runs. As Figure 10-6 indicates, the analysis produces a wide distribution of possible 

outcomes that are much broader than the range of estimates characterized by only the difference 

between the more and less dynamic baselines. While the expected value, the probabilityweighted average outcome, is only about 70 percent of the net benefits estimated in the main 

analysis, almost all of the trials produce positive net benefits. In fact, the distribution suggests 

there is only a one percent chance of the proposal producing negative net benefits for HD 

pickups and vans. So, while the estimated net benefits in the main analysis may be higher than 

 15 Reinhart, T.E. (2015, June). Commercial Medium- and Heavy-Duty Truck Fuel Efficiency Technology Study – 

Report #1. (Report No. DOT HS 812 146). Washington, DC: National Highway Traffic Safety Administration. 
10-57 

the expected value when uncertainty is considered, net benefits at least as high as those estimated 

in the main analysis are still 20 times as likely as an outcome that results in net costs. 

Figure 10-6 Distribution of Net Benefits from Proposed Standards for HD Pickups and Vans 

Figure 10-7 shows the distribution of payback periods (in years) for Model Year 2029 

trucks across 14,000 simulation runs. The “payback period” typically refers to the number of 

years of vehicle use that occur before the savings on fuel expenditures offset the additional 

technology cost associated with improved fuel economy. As Figure 10-7 illustrates, the 

incremental technology cost of both Phase 1 and Phase 2 is eclipsed by the value of fuel savings 

by year three of ownership in most cases. 
10-58 

Figure 10-7 Average Payback Period for MY 2029 HD Pickup or Van based on Phase 1 and Phase 

2 (combined) Technology Costs

This is an important metric for consumer acceptability and, though Figure 10-7 illustrates 

the long right tail of the payback distribution (where payback periods are likely to be 

unacceptably long), fewer than ten percent of the trials result in payback periods longer than four 

years. This suggests that, even in the face of uncertainty about future fuel prices and fuel 

economy in real-world driving conditions, buyers of the vehicles that are modified to comply 

with the requirements of the proposal will still see fuel savings greater than their additional 

vehicle cost in a relatively short period of time. 
11-1 

Chapter 11: Results of the Preferred and Alternative 

Standards 

The heavy-duty truck segment is very complex. The sector consists of a diverse group of 

impacted parties, including engine manufacturers, chassis manufacturers, truck manufacturers, 

trailer manufacturers, truck fleet owners and the public. The proposed standards are largely 

shaped to optimize the environmental and fuel savings benefits of the program, while balancing 

the relevant statutory factors and respecting the unique and varied nature of the sector. In 

developing this proposed rulemaking, we considered a number of alternatives that could result in 

fewer or potentially greater GHG and fuel consumption reductions than the preferred alternative. 

This section summarizes the alternatives we considered and presents assessments of technology 

costs, CO2 reductions, and fuel savings associated with each alternative. See the preamble for a 

discussion of how the agencies balanced the relevant statutory factors to select the preferred 

alternative. 

For this rule, the agencies conducted coordinated and complementary analyses by 

employing both DOT’s CAFE model and EPA’s MOVES model. These models were used to 

project fuel consumption and GHG emissions impacts resulting from the proposed standards. 

The agencies used EPA’s MOVES model to estimate fuel consumption and emissions impacts 

for tractor-trailers (including the engines which power the vehicle), and vocational vehicles 

(including the engine which powers the vehicle). For heavy-duty pickups and vans, the agencies 

performed complementary analyses using the CAFE model (“Method A”) and the MOVES 

model (“Method B”) to estimate fuel consumption and emissions from these vehicles. For both 

methods, the agencies analyzed the impact of the proposed rules, relative to two different 

reference cases – less dynamic and more dynamic. The less dynamic baseline projects very little 

improvement in new vehicles in the absence of new Phase 2 standards. In contrast, the more 

dynamic baseline projects more improvements in vehicle fuel efficiency. See Chapter 5 for a 

discussion of the EPA’s MOVES model (which was used for both methods) and Chapter 10 for 

discussion of the DOT’s CAFE model (which was used for Method A). 

11.1 What Are the Alternatives that the Agencies Considered? 

The five alternatives below represent a broad range of potential stringency levels, and 

thus a broad range of associated technologies, costs and benefits for a HD vehicle fuel efficiency 

and GHG emissions program. 

In developing alternatives, NHTSA must consider EISA's requirement for the MD/HD 

fuel efficiency program noted above. 49 U.S.C. 32902(k)(2) and (3) contain the following three 

requirements specific to the MD/HD vehicle fuel efficiency improvement program: (1) The 

program must be “designed to achieve the maximum feasible improvement”; (2) the various 

required aspects of the program must be appropriate, cost-effective, and technologically feasible 

for MD/HD vehicles; and (3) the standards adopted under the program must provide not less than 

four model years of lead time and three model years of regulatory stability. In considering these 

various requirements, NHTSA will also account for relevant environmental and safety 

considerations. 
11-2 

Each of the alternatives presented by NHTSA and EPA represents, in part, a different 

way the agencies could establish a HD program pursuant to EISA and the CAA. The agencies 

are proposing Alternative 3. The alternatives below represent a broad range of approaches under 

consideration for finalizing the HD vehicle fuel efficiency and GHG emissions standards. 

Sections 11.1.1 through 11.2 summarize the alternatives that were analyzed and how they 

were modeled. See Section 11.3 for details about the technology mix projected for each 

alternative and each regulatory category. 

11.1.1 Alternative 1: No Action (the Baseline for Phase 2) 

OMB guidance regarding regulatory analysis indicates that proper evaluation of the 

benefits and costs of regulations and their alternatives requires agencies to identify a baseline: 

“You need to measure the benefits and costs of a rule against a baseline. This 

baseline should be the best assessment of the way the world would look absent the 

proposed action. The choice of an appropriate baseline may require 

consideration of a wide range of potential factors, including: 

• evolution of the market, 

• changes in external factors affecting expected benefits and costs, 

• changes in regulations promulgated by the agency or other government entities, 

and 

• the degree of compliance by regulated entities with other regulations. 

It may be reasonable to forecast that the world absent the regulation will 

resemble the present. If this is the case, however, your baseline should reflect the 

future effect of current government programs and policies. For review of an 

existing regulation, a baseline assuming no change in the regulatory program 

generally provides an appropriate basis for evaluating regulatory alternatives. 

When more than one baseline is reasonable and the choice of baseline will 

significantly affect estimated benefits and costs, you should consider measuring 

benefits and costs against alternative baselines. In doing so you can analyze the 

effects on benefits and costs of making different assumptions about other 

agencies’ regulations, or the degree of compliance with your own existing rules. 

In all cases, you must evaluate benefits and costs against the same baseline. You 

should also discuss the reasonableness of the baselines used in the sensitivity 

analyses. For each baseline you use, you should identify the key uncertainties in 

your forecast.”1

A no-action alternative is also required as a baseline against which to measure 

environmental impacts of the proposed standards and alternatives. NHTSA, as required by the 

National Environmental Policy Act, is documenting these estimated impacts in the draft EIS 

published with this NPRM.2 
11-3 

The No Action Alternative for today’s analysis, alternatively referred to as the “baseline” 

or “reference case,” assumes that the agencies would not issue new rules regarding MD/HD fuel 

efficiency and GHG emissions. That is, this alternative assumes that the Phase 1 MD/HD fuel 

efficiency and GHG emissions program’s model year 2018 standards would be extended 

indefinitely and without change. 

The agencies recognize that there are a number of factors that create uncertainty in 

projecting a baseline against which to compare the future effects of the proposed action and the 

remaining alternatives. The composition of the future fleet—such as the relative position of 

individual manufacturers and the mix of products they each offer—cannot be predicted with 

certainty at this time. As reflected, in part, by the market forecast underlying the agencies’ 

analysis, we anticipate that the baseline market for medium- and heavy-duty vehicles will 

continue to evolve within a competitive market that responds to a range of factors. Additionally, 

the heavy-duty vehicle market is diverse, as is the range of vehicle purchasers. 

Heavy-duty vehicle manufacturers have reported that their customers’ purchasing 

decisions are influenced by their customers’ own determinations of minimum total cost of 

ownership, which can be unique to a particular customer’s circumstances. For example, some 

customers (e.g., less-than-truckload or package delivery operators) operate their vehicles within a 

limited geographic region and typically own their own vehicle maintenance and repair centers 

within that region. These operators tend to own their vehicles for long time periods, and 

sometimes for the entire service life of the vehicle. Their total cost of ownership is influenced by 

their ability to better control their own maintenance costs, and thus they can afford to consider 

fuel efficiency technologies that have longer payback periods, outside of the vehicle 

manufacturer’s warranty period. Other customers (e.g. truckload or long-haul operators) tend to 

operate cross-country, and thus must depend upon truck dealer service centers for repair and 

maintenance. Some of these customers tend to own their vehicles for about four to seven years, 

so that they typically do not have to pay for repair and maintenance costs outside of either the 

manufacturer’s warranty period or some other extended warranty period. Many of these 

customers tend to require seeing evidence of fuel efficiency technology payback periods on the 

order of 18 to 24 months before seriously considering evaluating a new technology for potential 

adoption within their fleet (NAS 2010, Roeth et al. 2013, Klemick et al. 2014). Purchasing 

decisions, however, are not based exclusively on payback period, but also include the 

considerations discussed in this section. For the baseline analysis, the agencies use payback 

period as a proxy for all of these considerations, and therefore the payback period for the 

baseline analysis is shorter than the payback period industry uses as a threshold for the further 

consideration of a technology. 

Purchasers of HD pickups and vans that want better fuel efficiency will demand that fuel 

consumption improvements pay back within approximately one to three years, but not all 

purchasers fall into this category. Some HD pickup and van owners accrue relatively few 

vehicle miles traveled per year, such that they may be less likely to adopt new fuel efficiency 

technologies, while other owners who use their vehicle(s) with greater intensity may be even 

more willing to pay for fuel efficiency improvements. Regardless of the type of customer, their 

determination of minimum total cost of ownership involves the customer balancing their own 

unique circumstances with a heavy-duty vehicle’s initial purchase price, availability of credit and 

lease options, expectations of vehicle reliability, resale value and fuel efficiency technology 
11-4 

payback periods. The degree of the incentive to adopt additional fuel efficiency technologies also 

depends on customer expectations of future fuel prices, which directly impacts customer 

expectations of the payback period. 

Another factor the agencies considered is that other federal and state-level policies and 

programs are specifically aimed at stimulating fuel efficiency technology development and 

deployment. Particularly relevant to this sector are DOE’s 21st Century Truck Partnership, 

EPA’s voluntary SmartWay Transport program, and California’s AB32 fleet requirements.3,4,5 

The future availability of more cost-effective technologies to reduce fuel consumption could 

provide manufacturers an incentive to produce more fuel-efficient medium- and heavy-duty 

vehicles, which in turn could provide customers an incentive to purchase these vehicles. The 

availability of more cost-effective technologies to reduce fuel consumption could also lead to a 

substitution of less cost-effective technologies, where overall fuel efficiency could remain fairly 

flat if buyers are less interested in fuel consumption improvements than in reduced vehicle 

purchase prices and/or improved vehicle performance and/or utility. 

Although we have estimated the cost and efficacy of fuel-saving technologies assuming 

performance and utility will be held constant, some uncertainty remains regarding whether these 

conditions will actually be observed. In particular, we have assumed payload will be preserved 

(and possibly improved via reduced vehicle curb weight); however, some fuel-saving 

technologies, such as natural gas fueled vehicles and hybrid electric vehicles, could reduce 

payload via increased curb weight due to the fuel tanks or added electrical machine, batteries and 

controls. It is also possible that under extended high power demand resulting from a vehicle 

towing up a road grade, certain types of hybrid powertrains could experience a temporary loss of 

towing capacity if the capacity of the hybrid’s energy storage device (e.g., batteries, hydraulic 

accumulator) is insufficient for the extended power demand. We have also assumed that fuelsaving technologies will be no more or less reliable than technologies already in production. 

However, if manufacturers pursue risky technologies or if the agencies provide insufficient leadtime to fully develop new technologies, they could prove to be less reliable, perhaps leading to 

increased repair costs and out-of-service time. This was observed as an unintended consequence 

of certain manufacturers’ initial introduction of certain emissions control technologies to meet 

EPA’s most stringent heavy-duty engine standards. If the fuel-saving technologies considered 

here ultimately involve similar reliability problems, overall costs will be greater than we have 

estimated. We have assumed drivers will be as accepting of new fuel-saving technologies as 

they are of technologies already in service. However, drivers could be less accepting of newer 

technologies -- particularly any which must be deployed manually. Except for increased costs to 

replace more efficient tires, we have assumed that routine maintenance costs will not increase or 

decrease. However, maintenance of new technologies could involve unique tools and parts. 

Therefore, maintenance costs could increase, and maintenance could involve increased vehicle 

out-of-service time. On the other hand new technologies can sometimes prove to be more 

reliable and require less maintenance than the technologies they replace. One example of this is 

the auxiliary power unit (APU) frequently installed on heavy-duty sleeper cab tractors. In the 

past these have been typically powered by small nonroad diesel engines that can require more 

frequent maintenance than the main engine of the tractor itself. However, more recently, as 

electric battery technology has advanced, some tractor manufacturers have introduced battery 

APUs instead of engine-driven APUs. A comparison of recent sales of small engine driven 

APUs versus battery APUs suggests that customers may prefer battery APUs6

, and some 
11-5 

operators and tractor dealerships have also told the agencies that the decrease in routine 

maintenance was an important factor in purchase decisions in favor of battery APUs. Again, 

insofar as these unaccounted-for costs or savings actually occur, overall costs could be larger or 

smaller than we have estimated. We have also applied the EIA’s AEO estimates of future fuel 

prices; however, heavy-duty vehicle customers could have different expectations about future 

fuel prices, and could therefore be more inclined or less inclined to apply new technology to 

reduce fuel consumption than might be expected based on EIA’s forecast. We expect that 

vehicle customers will be uncertain about future fuel prices, and that this uncertainty will be 

reflected in the degree of enthusiasm to apply new technology to reduce fuel consumption. 

Considering all of these factors, the agencies have approached the definition of the No 

Action Alternative separately for each vehicle and engine category covered by today’s proposal. 

For trailers, the agencies considered two No Action alternatives to cover a nominal range 

of uncertainty. The trailer category is unique in the context of this rulemaking because it is the 

only heavy-duty category not regulated under Phase 1. In both No Action cases, the agencies 

projected that the combination of EPA’s voluntary SmartWay program, DOE’s 21st Century 

Truck Partnership, California’s AB32 trailer requirements for fleets, and the potential for 

significantly reduced operating costs should result in continuing improvement to new trailers. 

Taking this into account, the agencies project that in 2018, 50 percent of new 53’ dry van and 

reefer trailers would have technologies qualifying for the SmartWay label (5 percent 

aerodynamic improvements and lower rolling resistance tires) and 50 percent would have 

automatic tire inflation systems to maintain optimal tire pressure. We also project that adoption 

of those same technologies would increase 1 percent per year until each technology is being used 

on 60 percent of new trailers. In the first case, Alternative 1a, this means that the agencies 

project that in the absence of new standards, the new trailer fleet technology would stabilize in 

2027 to a level of 60 percent adoption in 2027 for the No Action alternative. In the second case, 

Alternative 1b, the agencies projected that the fraction of the in-use fleet qualifying for 

SmartWay would continue to increase beyond 2027 as older trailers are replaced by newer 

trailers. We projected that these improvements would continue until 2040 when 75 percent of 

new trailers would be assumed to include skirts. 

For vocational vehicles, the agencies considered one No Action alternative. For the 

vocational vehicle category the agencies recognized that these vehicles tend to operate over 

fewer vehicle miles travelled per year. Therefore, the projected payback periods for fuel 

efficiency technologies available for vocational vehicles are generally longer than the payback 

periods the agencies consider likely to lead to their adoption based solely on market forces. This 

is especially true for vehicles used in applications in which the vehicle operation is secondary to 

the primary business of the company using the vehicle. For example, since the fuel consumption 

of vehicles used by utility companies to repair power lines would generally be a smaller cost 

relative to the other costs of repairing lines, fuel saving technologies would generally not be as 

strongly demanded for such vehicles. Thus, the agencies project that fuel-saving technologies 

would either not be applied or only be applied as a substitute for more expensive fuel efficiency 

technologies, except as necessitated by the Phase 1 fuel consumption and GHG standards. 

For tractors, the agencies considered two No Action alternatives to cover a nominal range 

of uncertainty. For Alternative 1a the agencies project that fuel-saving technologies would either 
11-6 

not be applied or only be applied as a substitute for more expensive fuel efficiency technologies 

to tractors (thereby enabling manufacturers to offer tractors that are less expensive to purchase), 

except as necessitated by the Phase 1 fuel consumption and GHG standards. In Alternative 1b 

the agencies estimated that some available technologies would save enough fuel to pay back 

fairly quickly – within the first six months of ownership. The agencies considered a range of 

information to formulate these two baselines for tractors. 

Both public7 and confidential historical information shows that tractor trailer fuel 

efficiency improved steadily through improvements in engine efficiency and vehicle 

aerodynamics over the past 40 years, except for engine efficiency which decreased or was flat 

between 2000 and approximately 2007 as a consequence of incorporating technologies to meet 

engine emission regulations. Today vehicle manufacturers, the Federal Government, academia 

and others continue to invest in research to develop fuel efficiency improving technologies for 

the future. 

There is also evidence that manufacturers have, in the past, applied technologies to 

improve fuel efficiency absent a regulatory requirement to do so. Some manufacturers have 

even taken regulatory risk in order to increase fuel efficiency; in the 1990s, when fuel was 

comparatively inexpensive, some tractor manufacturers designed tractor engine controls to 

determine when the vehicle was not being emissions tested and, under such conditions, shift to 

more fuel-efficient operation even though doing so caused the vehicles to violate federal 

standards for NOX emissions. Also, some manufacturers have recently expressed concern that 

the Phase 1 tractor standards do not credit them for fuel-saving technologies they had already 

implemented before the Phase 1 standards were adopted. 

In public meetings and in meetings with the agencies, the trucking industry stated that 

fuel cost for tractors is the number one or number two expense for many operators, and therefore 

is a very important factor for their business. However, the pre-Phase 1 market suggests that, 

tractor manufacturers and operators could be slow to adopt some new technologies, even where 

the agencies have estimated that the technology would have paid for itself within a few months 

of operation. Tractor operators have told the agencies they generally require technologies to be 

demonstrated in their fleet before widespread adoption so they can assess the actual fuel savings 

for their fleet and any increase in cost associated with effects on vehicle operation, maintenance, 

reliability, mechanic training, maintenance and repair equipment, stocking unique parts and 

driver acceptance, as well as effects on vehicle resale value. Tractor operators have publicly 

stated they would consider conducting an assessment of technologies when provided with data 

that show the technologies may payback costs through fuel savings within 18 to 24 months, 

based on their assumptions about future fuel costs. In these cases, an operator may first conduct 

a detailed paper study of anticipated costs and benefits. If that study shows likely payback in 18 

to 24 months for their business, the fleet may acquire one or several tractors with the technology 

to directly measure fuel savings, costs and driver acceptance for their fleet. Small fleets may not 

have resources to conduct assessments to this degree and may rely on information from larger 

fleets or observations of widespread acceptance of the technology within the industry before 

adopting a technology. This uncertainty over the actual fuel savings and costs and the lengthy 

process to assess technologies significantly slows the pace at which fuel efficiency technologies 

are adopted. 
11-7 

The agencies believe that using the two baselines addresses the uncertainties we have 

identified for tractors. The six-month payback period of Alternative 1b reflects the agencies’ 

consideration of factors, discussed above, that could limit—yet not eliminate—manufacturers’ 

tendencies to voluntarily improve fuel consumption. In contrast, Alternative 1a reflects a 

baseline for vehicles other than trailers wherein manufacturers either do not apply fuel efficiency 

technologies or only apply them as a substitute for more expensive fuel efficiency technologies, 

except as necessitated by the Phase 1 fuel consumption and GHG standards. 

For HD pickups and vans, the agencies considered two No Action alternatives to cover a 

nominal range of uncertainty: a less-dynamic baseline (designated Alternative 1a) where no 

improvements are modeled beyond those needed to meet Phase 1 standards and a dynamic 

baseline (designated Alternative 1b) where certain cost-effective technologies (i.e., those that 

payback within a 6 month period) are assumed to be applied by manufacturers to improve fuel 

efficiency beyond the Phase 1 requirements in the absence of new Phase 2 standards. In 

Alternative 1b the agencies considered additional technology application, which involved the 

explicit estimation of the potential to add specific fuel-saving technologies to each specific 

vehicle model included in the agencies’ HD pickup and van fleet analysis, as discussed in 

Section VI of the Preamble. Estimated technology application and corresponding impacts 

depend on the modeled inputs. Also, under this approach a manufacturer that has improved fuel 

consumption and GHG emissions enough to achieve compliance with the standards is assumed 

to apply further improvements, provided those improvements reduce fuel outlays by enough 

(within a specified amount of time, the payback period) to offset the additional costs to purchase 

the new vehicle. These calculations explicitly account for and respond to fuel prices, vehicle 

survival and mileage accumulation, and the cost and efficacy of available fuel-saving 

technologies. Therefore, all else being equal, more technology is applied when fuel prices are 

higher and/or technology is more cost-effective. Manufacturers of HD pickups and vans have 

reported to the agencies that buyers of these vehicles consider the total cost of vehicle ownership, 

not just new vehicle price, and that manufacturers plan as if buyers will expect fuel consumption 

improvements to “pay back” within periods ranging from approximately one to three years. For 

example, some manufacturers made decisions to introduce more efficient HD vans and HD 

pickup transmissions before such vehicles were subject to fuel consumption and/or GHG 

standards. However, considering factors discussed above that could limit manufacturers’ 

tendency to voluntarily improve HD pickup and van fuel consumption, Alternative 1b applies a 

6-month payback period. In contrast for Alternative 1a the agencies project that fuel-saving 

technologies would either not be applied or only be applied as a substitute for more expensive 

fuel efficiency technologies, except as necessitated by the Phase 1 fuel consumption and GHG 

standards. The Method A sensitivity analysis presented Chapter 10 of this draft RIA also 

examines other payback periods. In terms of impacts under reference case fuel prices, the 

payback period input plays a more significant role under the No-Action Alternatives (defined by 

a continuation of model year 2018 standards) than under the more stringent regulatory 

alternatives described next. 

11.1.1.1 Alternative 1a 

For an explanation of analytical Methods A and B identified in some of the following 

tables, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and 

more dynamic baseline, 1b, please see Preamble Section X.A.1. The estimated reductions in 
11-8 

CO2 emission rates8

 used in MOVES for Alternative 1a are presented inChapter 10 discusses the 

agencies’ use of the CAFE model in greater detail. 

Table 11-1 The projected use of auxiliary power units (APU) during extended idling for 

Alternative 1a is presented in Table 11-2. The reductions in aerodynamic and tire rolling 

resistance coefficients, and the absolute changes in average vehicle weight are presented in Table 

11-3. Chapter 10 discusses the agencies’ use of the CAFE model in greater detail. 

Table 11-1 Estimated Reductions in CO2 Emission Rates for Method B Alternative 1a Modeling in MOVES 

VEHICLE TYPE FUEL MODEL 

YEARS 

FUEL/CO2

REDUCTION 

Long-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018-2024 0.5% 

2025+ 0.6% 

Short-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018+ 0.2% 

Single-Frame 

Vocational9

 

Diesel and CNG 2021-2023 0% 

2024+ 0% 

Gasoline 2021-2023 0% 

2024+ 0% 

HD Pickup Trucks 

and Vansa

Diesel and 

Gasoline 

2021 0% 

2022 0% 

2023 0% 

2024 0% 

2025+ 0% 

Note: 

a Chapter 10 presents CAFE model inputs for the Method A analysis. 

Table 11-2 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for 

Alternative 1a Modeling in MOVES 

VEHICLE TYPE MODEL YEARS APU PENETRATION 

Combination LongHaul 

Tractors 

2010+ 30% 
11-9 

Table 11-3 Estimated Reductions in Road Load Factors for Alternative 1a Modeling in MOVES 

VEHICLE TYPE MODEL 

YEARS 

REDUCTION IN 

TIRE ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG 

COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)a

Combination Long-haul 

Tractor-Trailers 

2018 2.1% 3.0% -129 

2019-2020 2.2% 3.1% -129 

2021 2.2% 3.2% -129 

2022-2023 2.3% 3.3% -129 

2024 2.4% 3.4% -129 

2025 2.4% 3.5% -129 

2026 2.5% 3.5% -129 

2027+ 2.5% 3.6% -129 

Combination Shorthaul Tractor-Trailers10

2018 0.8% 1.1% -49 

2019-2020 0.8% 1.2% -49 

2021-2022 0.9% 1.2% -49 

2023-2026 0.9% 1.3% -49 

2027+ 1.0% 1.4% -49 

Intercity Buses 2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Transit and School 

Buses 

2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Refuse Trucks 2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Single Unit Short-haul 

Trucks 

2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Single Unit Long-haul 

Trucks 

2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Motor Homes 2021-2023 0% 0% 0 

2024+ 0% 0% 0 

Note: 

a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine 

improvements. 

11.1.1.2 Alternative 1b 

The estimated reductions in CO2 emission rates used in MOVES and the projected use of 

auxiliary power units (APU) during extended idling for Alternative 1b are presented in Table 

11-4 and Table 11-5, respectively. The reductions in aerodynamic and tire rolling resistance 

coefficients, and the absolute changes in average vehicle weight are presented in Table 11-6. 
11-10 

Table 11-4 Estimated Reductions in CO2 Emission Rates for Method B Alternative 1b Modeling in MOVES 

VEHICLE TYPE FUEL MODEL 

YEARS 

FUEL/CO2

REDUCTION 

Long- and ShortHaul TractorTrailer and HHD 

Vocational 

Diesel 2018+ 0% 

Single-Frame 

Vocational11 

Diesel and CNG 2021-2023 0% 

2024+ 0% 

Gasoline 2021-2023 0% 

2024+ 0% 

HD pickup trucks 

and vansa

Diesel and 

Gasoline 

2021-2022 2.18% 

2023 2.71% 

2024+ 2.86% 

Note: 

a Chapter 10 presents CAFE model inputs for the Method A analysis. 

Table 11-5 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for 

Alternative 1b Modeling in MOVES 

VEHICLE TYPE MODEL 

YEARS 

APU 

PENETRATION 

Combination Long-Haul 

Tractors 

2010+ 30% 
11-11 

Table 11-6 Estimated Reductions in Road Load Factors for Alternative 1b Modeling in MOVES 

TRUCK TYPE MODEL 

YEARS 

REDUCTION IN TIRE 

ROLLING RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)a

Combination 

Long-haul 

Tractor-Trailers 

2019 0.2% 0.5% 0 

2020 0.2% 0.7% 0 

2021 0.4% 1.2% 0 

2022 0.6% 1.7% 0 

2023 0.8% 2.5% 0 

2024 1.1% 3.4% 0 

2025 1.4% 4.4% 0 

2026 1.7% 5.3% 0 

2027 2.0% 6.1% 0 

2028 2.1% 6.6% 0 

Combination 

Short-haul 

Tractor-Trailers12

2019 0.3% 0.4% 0 

2020 0.4% 0.7% 0 

2021 0.7% 1.0% 0 

2022 1.0% 1.6% 0 

2023 1.5% 2.2% 0 

2024 2.0% 3.1% 0 

2025 2.6% 3.9% 0 

2026 3.1% 4.8% 0 

2027 3.6% 5.4% 0 

2028 3.9% 6.0% 0 

Intercity Buses 2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Transit and 

School Buses 

2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Refuse Trucks 2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Single Unit 

Short-haul 

Trucks 

2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Single Unit 

Long-haul Trucks 

2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Motor Homes 2021-

2023 

0% 0% 0 

2024+ 0% 0% 0 

Note: 

a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine 

improvements. 
11-12 

11.1.2 Alternative 2: Less Stringent than the Preferred Alternative 

For vocational vehicles and combination tractor-trailers, Alternative 2 represents a 

stringency level which is approximately half as stringent overall as the preferred alternative. The 

agencies developed Alternative 2 to reflect a continuation of the Phase 1 approach of– applying 

off-the-shelf technologies rather than requiring the development of new technologies or 

fundamental improvements to existing technologies. For tractors and vocational vehicles, this 

also involved less integrated optimization of the vehicles and engines. Alternative 2 would not 

set standards for MY 2027. 

The agencies’ decisions regarding which technologies could be applied to comply with 

Alternative 2 considered not only assuming the use of off-the shelf technologies, but also 

considered other factors, as well, such as how broadly certain technologies fit in-use applications 

and regulatory structure. The resulting Alternative 2 could be met with most of the same 

technologies the agencies project could be used to meet the proposed standards, although at 

lower application rates. Alternative 2 is estimated to be achievable without the application of 

some technologies, at any level. These and other differences are described below by category. 

The agencies project that Alternative 2 combination tractor standards could be met by 

applying lower adoption rates of the projected technologies for Alternative 3. This includes a 

projection of slightly lower per-technology effectiveness for Alternative 2 versus 3. Alternative 

2 also assumes that there would be little optimization of combination tractor powertrains. 

The agencies project that the Alternative 2 vocational vehicle standard could be met 

without any use of strong hybrids. Rather, it could be met with lower adoption rates of the other 

technologies that could be used to meet Alternative 3, our proposed standards. This includes a 

projection of slightly lower per-technology effectiveness for Alternative 2 versus 3 and little 

optimization of vocational vehicle powertrains. 

The Alternative 2 trailer standards would apply to only 53-foot dry and refrigerated box 

trailers and could be met through the use of less effective aerodynamic technologies and higher 

rolling resistance tires versus what the agencies projected could be used to meet Alternative 3. 

As discussed above in Chapter 5, the HD pickup truck and van alternatives are 

characterized by an annual required percentage change (decrease) in the functions defining 

attribute-based targets for per-mile fuel consumption and GHG emissions. Under the HD pickup 

and van standards in Alternative 2 and each other alternative, a manufacturer’s fleet would, 

setting aside any changes in production mix, be required to achieve average fuel 

consumption/GHG levels that increase in stringency every year relative to the standard defined 

for MY2018 (and held constant through 2020) that establishes fuel consumption/GHG targets for 

individual vehicles. A manufacturer’s specific fuel consumption/GHG requirement is the salesweighted average of the targets defined by the work-factor curve in each year. Therefore, 

although the alternatives involve steady increases in the functions defining the targets, stringency 

increases faced by any individual manufacturer may not be steady if changes in the 

manufacturer’s product mix cause fluctuations in the average fuel consumption and GHG levels 

required of the manufacturer. See Section VI.D. of the Preamble for additional discussion of this 

topic. Alternative 2 represents a 2.0 percent annual improvement in the target curve through 
11-13 

2025 in fuel consumption/GHG emissions relative to the work-factor curve in 2020.This would 

be 0.5 percent less stringent per year compared to the proposed standards of Alternative 3 and 

would not increase in stringency for MYs 2026 or 2027. For HD pickups and vans the agencies 

project that most manufacturers could comply with the standards defining Alternative 2 by 

applying technologies similar to those that could be applied in order to comply with the proposed 

standards, but at lower application rates than could be necessitated by the proposed standards. 

The biggest technology difference the agencies project between Alternative 2 and the proposed 

standards of Alternative 3 would be that we project that most manufacturers could meet the 

Alternative 2 standards without any use of stop-start or other mild or strong hybrid technologies. 

The analytical inputs for Alternative 2 are shown in the following tables. The estimated reductions in CO2

emission rates used in MOVES and the projected use of auxiliary power units (APU) during extended idling 

are presented in Table 11-7 and Table 11-8, respectively. The reductions in aerodynamic and tire rolling 

resistance coefficients, and the absolute changes in average vehicle weight are presented in 

Table 11-9. 

Table 11-7 Estimated Reductions in CO2 Emission Rates for Method B Alternative 2 Modeling in MOVES 

VEHICLE TYPE FUEL MODEL 

YEARS 

FUEL/CO2

REDUCTION 

Long-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018-2020 0.9% 

2021-2023 3.9% 

2024+ 6.9% 

Short-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018-2020 0.3% 

2021-2023 3.2% 

2024+ 6.4% 

Single-Frame 

Vocational13 

Diesel and CNG 2021-2023 2.1% 

2024+ 5.1% 

Gasoline 2021-2023 1.1% 

2024+ 2.1% 

HD pickup trucks 

and vansa

Diesel and 

Gasoline 

2021 2.00% 

2022 3.96% 

2023 5.88% 

2024 7.76% 

2025+ 9.61% 

Note: 

a Chapter 10 presents CAFE model inputs for the Method A analysis. 
11-14 

Table 11-8 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for 

Alternative 2 Modeling in MOVES 

VEHICLE TYPE MODEL 

YEARS 

APU 

PENETRATION 

Combination Long-Haul 

Tractors 

2010-2020 30% 

2021-2023 60% 

2024+ 80% 

Table 11-9 Estimated Reductions in Road Load Factors for Alternative 2 Modeling in MOVES 

VEHICLE 

TYPE 

MODEL 

YEARS 

REDUCTION IN TIRE 

ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)a

Combination 

Long-haul 

Tractor-Trailers 

2018-

2020 

4.0% 5.1% -131 

2021-

2023 

5.8% 11.0% -135 

2024+ 9.0% 12.4% -140 

Combination 

Short-haul 

Tractor-Trailers14

2018-

2020 

1.2% 1.6% -41 

2021-

2023 

5.3% 6.4% -42 

2024+ 6.6% 7.4% -43 

Intercity Buses 2021-

2023 

6.5% 0% 0 

2024+ 7.6% 0% 0 

Transit and 

School Buses 

2021-

2023 

0% 0% 0 

2024+ 2.7% 0% 0 

Refuse Trucks 2021-

2023 

0% 0% 0 

2024+ 2.7% 0% 0 

Single Unit 

Short-haul 

Trucks 

2021-

2023 

4.8% 0% 0 

2024+ 5.6% 0% 0 

Single Unit 

Long-haul Trucks 

2021-

2023 

6.5% 0% 0 

2024+ 7.6% 0% 0 

Motor Homes 2021-

2023 

3.0% 0% 0 

2024+ 5.9% 0% 0 

Note: 

a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine 

improvements. 
11-15 

11.1.3 Alternative 3: Preferred Alternative and Proposed Standards 

Alternative 3 represents the agencies’ preferred approach. This alternative consists of the 

preferred fuel efficiency and GHG standards for HD engines, HD pickup trucks and vans, Class 

2b through Class 8 vocational vehicles, and Class 7 and 8 combination tractors. 

Details regarding modeling of this preferred alternative are included in Chapter 5 of this 

draft RIA as the control case (Section 5.3.2.3.1). Note that the impacts of this alternative are 

summarized in RIA Chapter 0, along with the impacts of the other alternatives. 

11.1.4 Alternative 4: Achieving Proposed Standards with Less Lead-Time 

Alternative 4 represents standards that are effective on a more accelerated timeline in 

comparison to the timeline of the proposed standards in Alternative 3. Alternatives 3 and 4 were 

both designed to achieve similar fuel efficiency and GHG emission levels in the long term but 

with Alternative 4 being accelerated in its implementation timeline. Specifically, Alternative 4 

reflects the same or similar standard stringency levels as Alternative 3, but 3 years sooner (2 

years for heavy-duty pickups and vans), so that the final phase of the standards would occur in 

MY 2024, or (for heavy duty pickups and vans) 2025. 

The estimated reductions in CO2 emission rates used in MOVES and the projected use of 

auxiliary power units (APU) during extended idling for Alternative 4 are presented in Table 

11-10 and Table 11-11, respectively. The reductions in aerodynamic and tire rolling resistance 

coefficients, and the absolute changes in average vehicle weight are presented in Table 11-12. 
11-16 

Table 11-10 Estimated Reductions in CO2 Emission Rates for Method B Alternative 4 Modeling in MOVES 

VEHICLE TYPE FUEL MODEL 

YEARS 

FUEL/CO2

REDUCTION 

Long-haul Tractor-Trailer 

and HHD Vocational 

Diesel 2018-2020 1.3% 

2021-2023 6.6% 

2024+ 10.4% 

Short-haul Tractor-Trailer 

and HHD Vocational 

Diesel 2018-2020 0.9% 

2021-2023 6.9% 

2024+ 10.4% 

Single-Frame Vocational15 Diesel and CNG 2021-2023 7.7% 

2024+ 13.3% 

Gasoline 2021-2023 5.2% 

2024+ 10.3% 

HD pickup trucks and vansa Diesel and 

Gasoline 

2021 3.50% 

2022 6.88% 

2023 10.14% 

2024 13.28% 

2025+ 16.32% 

Note: 

a Chapter 10 presents CAFE model inputs for the Method A analysis. 

Table 11-11 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for 

Alternative 4 Modeling in MOVES 

VEHICLE TYPE MODEL 

YEARS 

APU PENETRATION 

Combination 

Long-Haul 

Tractors 

2010-2020 30% 

2021-2023 80% 

2024+ 90% 
11-17 

Table 11-12 Estimated Reductions in Road Load Factors for Alternative 4 Modeling in MOVES 

VEHICLE 

TYPE 

MODEL 

YEARS 

REDUCTION IN TIRE 

ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)a

Combination 

Long-haul 

Tractor-Trailers 

2018-

2020 

5.5% 5.1% -131 

2021-

2023 

12.6% 19.3% -246 

2024+ 17.9% 26.9% -304 

Combination 

Short-haul 

Tractor-Trailers16

2018-

2020 

4.0% 1.6% -41 

2021-

2023 

13.0% 11.6% -100 

2024+ 17.6% 15.9% -127 

Intercity Buses 2021-

2023 

6.5% 0% 0 

2024+ 16.5% 0% 0 

Transit Buses 2021-

2023 

0% 0% 0 

2024+ 3.0% 0% 0 

School Buses 2021-

2023 

0% 0% 0 

2024+ 4.0% 0% 0 

Refuse Trucks 2021-

2023 

0% 0% 20 

2024+ 3.0% 0% 25 

Single Unit 

Short-haul 

Trucks 

2021-

2023 

4.8% 0% 5.8 

2024+ 13.0% 0% 7 

Single Unit 

Long-haul 

Trucks 

2021-

2023 

6.5% 0% 20 

2024+ 16.5% 0% 25 

Motor Homes 2021-

2023 

3.0% 0% 0 

2024+ 7.4% 0% 0 

Note: 

a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and 

engine improvements. 

11.1.5 Alternative 5: More Stringent Standards 

Alternative 5 represents even more stringent standards compared to Alternatives 3 and 4, 

as well as the same implementation timeline as Alternative 4. As discussed in the feasibility 

discussions in the preamble, we are not proposing Alternative 5 because we cannot project that 

manufacturers can develop and introduce in sufficient quantities the technologies that could be 

used to meet Alternative 5 standards. We believe that for some or all of the categories, the 

Alternative 5 standards are technically infeasible within the lead time allowed. We have not 
11-18 

fully estimated costs for this alternative for tractors and vocational vehicles because we believe 

that there would be such substantial additional costs related to pulling ahead the development of 

so many additional technologies that we cannot accurately predict these costs. We also believe 

this alternative could result in a decrease in the in-use reliability and durability of new heavyduty vehicles and that we do not have the ability to accurately quantify the costs that would be 

associated with such problems. Instead we merely note that costs would be significantly greater 

than the estimated costs for Alternatives 3 and 4. 

The tractor and vocational vehicle standards would be based on higher adoption rates of 

the projected technologies and higher effectiveness. In addition, it assumes some adoption of allelectric vocational vehicles. 

The trailer standards in Alternative 5 are more stringent than Alternatives 3 and 4, but 

rely on the same technologies. The greater reductions would be projected to be achieved through 

a combination of slightly higher effectiveness and higher adoption rates. 

The Alternative 5 HD pickup truck and van standards would be based on more extensive 

use of mild and strong hybrid technology and its use by more manufacturers. The result would 

be that over half of the HD gasoline pickup fleet would need to incorporate some form of strong 

hybrid technology. If achievable, Alternative 5 would require the average pickup truck or van 

fuel consumption and GHG emissions to decrease by approximately 4.0 percent per year relative 

to Phase 1 for model years 2021, 2022, 2023, 2024 and 2025. This is more aggressive than 

Alternative 3 by 1.50 percent per year over the same model years. The estimated reductions in 

CO2 emission rates used in MOVES and the projected use of auxiliary power units (APU) during 

extended idling for Alternative 5 are presented in Table 11-13 and Table 11-14, respectively. 

The reductions in aerodynamic and tire rolling resistance coefficients, and the absolute changes 

in average vehicle weight are presented in Table 11-15. 
11-19 

Table 11-13 Estimated Reductions in CO2 Emission Rates for Method B Alternative 5 Modeling in MOVES 

VEHICLE TYPE FUEL MODEL 

YEARS 

FUEL/CO2

REDUCTION 

Long-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018-2020 1.3% 

2021-2023 10.3% 

2024+ 13.5% 

Short-haul 

Tractor-Trailer 

and HHD 

Vocational 

Diesel 2018-2020 0.9% 

2021-2023 11.3% 

2024+ 14.9% 

Single-Frame 

Vocational17 

Diesel and CNG 2021-2023 14.0% 

2024+ 18.5% 

Gasoline 2021-2023 11.0% 

2024+ 15.0% 

HD pickup trucks 

and vansa

Diesel and 

Gasoline 

2021 4.00% 

2022 7.84% 

2023 11.53% 

2024 15.07% 

2025+ 18.46% 

Note: 

a Chapter 10 presents CAFE model inputs for the Method A analysis. 

Table 11-14 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for 

Alternative 5 Modeling in MOVES 

VEHICLE TYPE MODEL YEARS APU PENETRATION 

Combination LongHaul Tractors 2010-2020 30% 

2021+ 100% 
11-20 

Table 11-15 Estimated Reductions in Road Load Factors for Alternative 5 Modeling in MOVES 

VEHICLE TYPE MODEL 

YEARS 

REDUCTION IN 

TIRE ROLLING 

RESISTANCE 

COEFFICIENT 

REDUCTION IN 

AERODYNAMIC 

DRAG 

COEFFICIENT 

WEIGHT 

REDUCTION 

(LB)a 

Combination Long-haul 

Tractor-Trailers 

2018-2020 5.4% 7.3% -170 

2021-2023 14.3% 24.6% 936 

2024+ 19.2% 31.4% 850 

Combination Short-haul 

Tractor-Trailers18

2018-2020 3.9% 2.3% -53 

2021-2023 14.5% 15.0% 997 

2024+ 18.8% 19.0% 943 

Intercity Buses 2021-2023 6.5% 0% 0 

2024+ 16.5% 0% 0 

Transit Buses 2021-2023 0% 0% 0 

2024+ 8.0% 0% 0 

School Buses 2021-2023 0% 0% 0 

2024+ 9.1% 0% 0 

Refuse Trucks 2021-2023 0% 0% 255 

2024+ 8.0% 0% 255 

Single Unit Short-haul 

Trucks 

2021-2023 4.8% 0% 58 

2024+ 15.2% 0% 95 

Single Unit Long-haul 

Trucks 

2021-2023 6.5% 0% 185 

2024+ 16.5% 0% 185 

Motor Homes 2021-2023 3.0% 0% 0 

2024+ 12.4% 0% 0 

Note: 

a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine 

improvements.

11.2 How Do These Alternatives Compare in Overall GHG Emissions 

Reductions and Fuel Efficiency and Cost? 

As noted earlier, the agencies analyzed the impact of each alternative on both 

downstream and upstream emissions using two complementary methods. The results of Method 

A are shown in section 11.2.1. The results of Method B are shown in section 11.2.2. 

11.2.1 Comparison of Alternatives Using Method A 

The following tables compare the overall fuel consumption and GHG emissions 

reductions and benefits and costs of each of the regulatory alternatives the agencies considered. 

Note that for tractors, trailers, pickups and vans the agencies compared overall fuel 

consumption and GHG emissions reductions and benefits and costs relative to two different 

baselines, described above in the section on the No Action alternative. Therefore, for tractors, 

trailers, pickups and vans two results are listed; one relative to each baseline, namely Alternative 

1a and Alternative 1b. 
11-21 

Also note that the agencies analyzed pickup and van overall fuel consumption and 

emissions reductions and benefits and costs using the NHTSA CAFE model (Method A). In 

addition, the agencies used EPA’s MOVES model to estimate pickup and van fuel consumption 

and emissions and a cost methodology that applied vehicle costs in different model years 

(Method B). In both cases, the agencies used the CAFE model to estimate average per vehicle 

cost, and this analysis extended through model year 2030.A The agencies concluded that in these 

instances the choice of baseline and the choice of modeling approach (Method A versus Method 

B) did not impact the agencies’ decision to propose Alternative 3 as the preferred alternative and 

hence the proposed standards for HD pickups and vans. 

Table 11-16 compares the fuel savings, technology costs, avoided GHG emissions, costs, 

and benefits (at three percent) for the above regulatory alternatives, as estimated under Method 

A. Table 11-17 provides the same comparisons for the alternative relative to baseline 1b. Table 

11-18 and Table 11-19 show the same summary, discounted at seven percent. Subsequent tables 

(Table 11-20 and Table 11-21) summarize segment-specific impacts on fuel consumption and 

GHG emissions. 

 

 

A Although the agencies have considered regulatory alternatives involving standards increasing in stringency 

through, at the latest, 2027, the agencies extended the CAFE modeling analysis through model year 2030 rather than 

model year 2027 in order to obtain more fully stabilized results given projected product cadence, multiyear 

planning, and application of earned credits. 
11-22 

Table 11-16 Summary of Costs and Benefits through MY 2029 by Alternative, Discounted at 3% (Relative to 

Baseline 1a), Method Aa

VEHICLE SEGMENT ALT 2 ALT 3 ALT 4 ALT 5 

Discounted pre-tax fuel savings ($billion) 

HD pickups and Vans 11.7 18.3 22.3 24.8

Vocational Vehicles 5.6 18.4 24.3 38.5

Tractors/Trailers 88.1 138.4 151.7 196.8

Total 105.4 175.1 198.3 260.2

Discounted Total technology costs ($billion) 

HD pickups and Vans 3.0 5.0 8.2 9.9 

Vocational Vehicles 1.2 7.6 10.8 26.0 

Tractors/Trailers 9.2 12.8 15.3 34.8 

Total 13.4 25.4 34.3 70.6 

Discounted value of emissions reductions ($billon) 

HD pickups and Vans 3.1 5.0 6.1 6.8

Vocational Vehicles 0.6 2.6 3.5 5.7

Tractors/Trailers 21.5 32.7 35.1 45.1

Total 25.2 40.3 44.7 57.7

Total costs($billion) 

HD pickups and Vans 3.5 5.7 9.1 15.2

Vocational Vehicles 3.0 9.5 12.8 28.1

Tractors/Trailers 11.5 15.5 18.1 37.5

Total 18.0 30.8 40.0 80.8

Total benefits($billion) 

HD pickups and Vans 17.2 27.0 33.0 36.7

Vocational Vehicles 12.7 31.2 39.7 60.2

Tractors/Trailers 142.5 217.5 236.7 304.2

Total 172.4 275.8 309.4 401.1

Net benefits($billion) 

HD pickups and Vans 13.7 21.3 23.9 21.5

Vocational Vehicles 9.6 21.7 26.9 32.1

Tractors/Trailers 131.0 202.0 218.7 266.7

Total 154.3 245.0 269.4 320.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1.
11-23 

Table 11-17 Summary of Program Benefits and Costs through MY 2029, Discounted at 3% (Relative to 

Baseline 1b) Method A a

VEHICLE SEGMENT ALT 2 ALT 3 ALT 4 ALT 5 

Discounted pre-tax fuel savings ($billion) 

HD pickups and Vans 9.6 15.9 19.1 22.2 

Vocational Vehicles 5.6 18.4 24.3 38.5 

Tractors/Trailers 80.5 130.8 144.0 189.2 

Total 95.6 165.1 187.4 250.0 

Discounted Total technology costs ($billion) 

HD pickups and Vans 2.5 5.0 7.2 9.7 

Vocational Vehicles 1.2 7.6 10.8 25.9 

Tractors/Trailers 8.9 12.5 15.0 34.4 

Total 12.5 25.0 32.9 70.0 

Discounted value of emissions reductions ($billon) 

HD pickups and Vans 2.8 4.5 5.4 6.3

Vocational Vehicles 1.7 6.1 8.1 13.1

Tractors/Trailers 37.5 59.4 64.6 84.4

Total 41.9 70.1 78.2 103.8

Total costs($billion) 

HD pickups and Vans 2.8 5.5 7.8 10.4

Vocational Vehicles 3.0 9.5 12.8 28.0

Tractors/Trailers 11.2 15.2 17.7 37.2

Total 17.0 30.3 38.4 75.7

Total benefits($billion) 

HD pickups and Vans 14.1 23.5 28.3 32.9

Vocational Vehicles 12.7 31.2 39.7 60.2

Tractors/Trailers 131.1 206.2 225.4 292.8

Total 157.9 260.9 293.3 385.9

Net benefits($billion) 

HD pickups and Vans 11.3 18.0 20.4 22.5

Vocational Vehicles 9.6 21.7 26.9 32.1

Tractors/Trailers 119.9 191.0 207.6 255.6

Total 140.9 230.7 254.9 310.3

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1.

The following two tables summarize results for each of the segments covered by today’s 

proposal, discounted at 7 percent. 
11-24 

Table 11-18 Summary of Program Benefits and Costs through MY 2029, discounted at 7% (Relative to 

Baseline 1a) Method A a

VEHICLE SEGMENT ALT 2 ALT 3 ALT 4 ALT 5 

Discounted pre-tax fuel savings ($billion) 

HD pickups and Vans 6.4 9.9 12.2 13.6 

Vocational Vehicles 2.9 9.7 13.0 20.9 

Tractors/Trailers 47.7 74.6 82.3 107.3 

Total 57.0 94.2 107.5 141.8 

Discounted Total technology costs ($billion) 

HD pickups and Vans 2.1 3.4 5.7 6.9 

Vocational Vehicles 0.8 5.0 7.3 17.8 

Tractors/Trailers 6.3 8.7 10.5 23.9 

Total 9.1 17.1 23.5 48.6 

Discounted value of emissions reductions ($billon) 

HD pickups and Vans 2.7 4.3 5.3 5.9

Vocational Vehicles 1.4 5.0 6.6 10.6

Tractors/Trailers 29.9 46.3 50.4 65.4

Total 34.0 55.6 62.3 81.8

Total costs($billion) 

HD pickups and Vans 2.4 3.8 6.2 10.1 

Vocational Vehicles 1.8 6.1 8.4 19.0 

Tractors/Trailers 7.6 10.3 12.1 25.5 

Total 11.8 20.2 26.7 54.6 

Total benefits($billion) 

HD pickups and Vans 10.4 16.3 20.1 22.3

Vocational Vehicles 7.3 18.3 23.6 36.2

Tractors/Trailers 85.1 130.0 142.2 183.5

Total 102.9 164.6 185.8 242.1

Net benefits($billion) 

HD pickups and Vans 8.1 12.4 13.9 12.2

Vocational Vehicles 5.5 12.2 15.2 17.2

Tractors/Trailers 77.5 119.7 130.1 158.0

Total 91.1 144.4 159.1 187.5

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1.
11-25 

Table 11-19 Summary of Program Benefits and Costs through MY 2029, discounted at 7% (Relative to 

Baseline 1b) Method A a

VEHICLE SEGMENT ALT 2 ALT 3 ALT 4 ALT 5 

Discounted pre-tax fuel savings ($billion) 

HD pickups and Vans 5.2 8.5 10.4 12.2 

Vocational Vehicles 2.9 9.7 13.0 20.9 

Tractors/Trailers 44.0 71.0 78.6 103.7 

Total 52.2 89.2 102.0 136.8 

Discounted Total technology costs ($billion) 

HD pickups and Vans 1.7 3.4 4.9 6.7 

Vocational Vehicles 0.8 5.0 7.3 17.8 

Tractors/Trailers 6.0 8.4 10.3 23.7 

Total 8.5 16.8 22.5 48.2 

Discounted value of emissions reductions ($billon) 

HD pickups and Vans 2.5 4.0 4.8 5.5

Vocational Vehicles 1.4 5.0 6.6 10.6

Tractors/Trailers 27.5 43.9 48.0 63.0

Total 31.4 52.9 59.4 79.1

Total costs($billion) 

HD pickups and Vans 1.9 3.7 5.3 7.1 

Vocational Vehicles 1.8 6.1 8.4 19.0 

Tractors/Trailers 7.3 10.0 11.9 25.3 

Total 11.1 19.8 25.6 51.4 

Total benefits($billion) 

HD pickups and Vans 8.6 14.1 17.1 20.0

Vocational Vehicles 7.3 18.3 23.6 36.2

Tractors/Trailers 78.9 123.7 135.9 177.3

Total 94.8 156.2 176.6 233.5

Net benefits($billion) 

HD pickups and Vans 6.7 10.5 11.9 12.9

Vocational Vehicles 5.5 12.2 15.2 17.2

Tractors/Trailers 71.5 113.7 124.0 152.0

Total 83.7 136.4 151.1 182.2

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1. 

While the agencies’ explicit analysis of manufacturers’ potential responses to today’s 

standards extends through model year 2030, the resulting fuel savings and avoided emissions 

summarized in the following two tables occur as those vehicles. 
11-26 

Table 11-20 Fuel Savings and GHG Emissions Reductions by Vehicle Segment, Relative to Baseline 1a, 

Method Aa

MY 2018 - 2029 TOTAL 

FUEL 

REDUCTIONS 

UPSTREAM & 

DOWNSTREAM 

GHG 

REDUCTIONS 

(billion gallons) (MMT) 

Alternative 2 

HD Pickup Trucks/Vans 5.5 67.5 

Vocational Vehicles 2.5 33.632.2 

Tractors and Trailers 37.8 518.8493.0 

Total 45.8 619.9592.7 

Alt. 3 - Preferred Alternative 

HD Pickup Trucks/Vans 8.8 107.6 

Vocational Vehicles 8.3 110.3107.0 

Tractors and Trailers 59.5 816.4775.7 

Total 76.7 1,034.3990.4 

Alt. 4 

HD Pickup Trucks/Vans 10.7 130.5 

Vocational Vehicles 10.9 143139.8 

Tractors and Trailers 65.0 892.1847.7 

Total 86.7 1,166.4118.0 

Alt. 5 

HD Pickup Trucks/Vans 12.0 145.4 

Vocational Vehicles 17.3 226.9221.0 

Tractors and Trailers 84.2 1,155.1097.6 

Total 113.4 1,527.4464.1 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1. 
11-27 

Table 11-21 Fuel Savings and GHG Emissions Reductions by Vehicle Segment, Relative to Baseline 1b 

Method Aa

MY 2018 - 2029 

TOTAL 

FUEL 

REDUCTIONS 

UPSTREAM & 

DOWNSTREAM 

GHG 

REDUCTIONS 

(billion gallons) (MMT) 

Alternative 2 

HD Pickup Trucks/Vans 4.5 55.5 

Vocational Vehicles 2.5 33.6 

Tractors and Trailers 34.4 471.9 

Total 41.4 561.0 

Alt. 3 - Preferred Alternative 

HD Pickup Trucks/Vans 7.8 94.1 

Vocational Vehicles 8.3 110.3 

Tractors and Trailers 56.1 769.4 

Total 72.2 973.8 

Alt. 4 

HD Pickup Trucks/Vans 9.3 112.8 

Vocational Vehicles 10.9 143.8 

Tractors and Trailers 61.6 845.2 

Total 81.8 1,101.8 

Alt. 5 

HD Pickup Trucks/Vans 10.8 130.5 

Vocational Vehicles 17.3 226.9 

Tractors and Trailers 80.7 1,108.2 

Total 108.8 1,465.6 

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1. 
11-28 

11.2.2 Comparison of Alternatives Using Method B 

Method B analyzed the impact of each alternative on both downstream and upstream 

emissions, as shown in Table 11-22. The table contains the annual GHG and fuel consumption 

impacts and technology costs in 2035 and 2050 for each alternative (relative to the reference 

scenario of Alternative 1a), presenting both the total impacts across all regulatory categories and 

for each individual regulatory category. Note that by 2050 when all the alternative would be 

almost completely phased in, Alternatives 3 and 4 would provide essentially the same annual 

benefits. 

Table 11-22 Annual GHG & Fuel Reductions and Technology Costs vs. the Less Dynamic Baseline and using 

Method B: Calendar Years 2035 and 2050 a

 UPSTREAM 

+DOWNSTREAM 

GHG REDUCTIONS 

(MMT) 

FUEL REDUCTIONS 

(BILLION GALLONS)

TECHNOLOGY 

COST (MILLIONS OF 

2012$) 

2035 2050 2035 2050 2035 2050 

Alternative 1a (relative 

to itself) 

0 0 0 0 $0 $0

Alt. 2 Less StringentTotal 

72 101 5.2 7.3 $2,559 $3,090

Tractors 59 84 4.2 6.0 $1,855 $2,279

HD Pickups & Vans 8 11 0.7 0.9 $471 $541

Vocational Vehicles 5 7 0.3 0.5 $233 $271

Alt. 3 Preferred – Total 127 183 9.3 13.4 $5,856 $6,987

Tractors 97 141 7.0 10.1 $2,888 $3,548

HD Pickups & Vans 14 19 1.1 1.6 $892 $1,024

Vocational Vehicles 16 23 1.2 1.7 $2,076 $2,414

Alt. 4 More Stringent– 

Total 

132 184 9.7 13.5 $6,180 $7,358

Tractors 100 141 7.2 10.1 $2,888 $3,548

HD Pickups & Vans 15 19 1.2 1.6 $1,215 $1,395

Vocational Vehicles 17 23 1.3 1.7 $2,077 $2,415

Alt. 5 More Stringent– 

Total 

168 232 12.4 17.0 N/A N/A

Tractors 126 176 9.0 12.6 N/A N/A

HD Pickups & Vans 17 22 1.4 1.8 N/A N/A

Vocational Vehicles 26 34 1.9 2.5 N/A N/A

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 11-23 presents a summary of all costs and benefits for each program alternative 

relative to the Alternative 1a baseline case. Table 11-24 shows cost per ton of GHG reduced.
11-29 

Table 11-23 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline 

and using Method B 

(Billions of 2012$, Except GHG Reductions) a, b

 Alt.1 

Baseline

Alt.2 Less 

Stringent

Alt.3 

Preferred

Alt.4 More 

Stringent

Alt.5 More 

Stringent 

203

5 

Vehicle Program 

Costsc

$0 -$2.6 -$5.9 -$6.2 N/A

Maintenance costs $0 -$0.06 -$0.13 -$0.14 N/A

Fuel Expenditures 

(pre-tax) 

$0 $20.9 $37.2 $38.7 $49.4

Benefits $0 $12.8 $20.5 $21.1 $26.3

Net Benefits $0 $31.1 $51.7 $53.5 N/A

GHG reductions 

(MMT) 

0 71.9 127.1 132.0 168.3

205

0 

Vehicle Program 

Costsc

$0 -$3.1 -$7.0 -$7.4 N/A

Maintenance costs $0 -$0.06 -$0.13 -$0.14 N/A

Fuel Expenditures 

(pre-tax) 

$0 $31.5 $57.5 $57.6 $72.7

Benefits $0 $19.9 $32.9 $32.9 $40.6

Net Benefits $0 $48.3 $83.2 $83.0 N/A

GHG Reductions 

(MMT) 

0 101.2 183.4 183.8 231.8

NP

V, 

3% 

Vehicle Program 

Costsc

$0 -$39.8 -$86.8 -$98.6 N/A

Maintenance costs $0 -$0.88 -$1.80 -$1.91 N/A

Fuel Expenditures 

(pre-tax) 

$0 $280.0 $495.6 $517.6 $664.3

Benefits $0 $175.2 $279.7 $289.7 $361.5

Net Benefits $0 $414.5 $686.8 $706.8 N/A

NP

V, 

7% 

Vehicle Program 

Costsc

$0 -$19.3 -$41.1 -$48.4 N/A

Maintenance costs $0 -$0.42 -$0.86 -$0.92 N/A

Fuel Expenditures 

(pre-tax) 

$0 $118.1 $206.7 $219.0 $283.0

Benefits $0 $105.5 $173.5 $180.7 $228.0

Net Benefits $0 $203.8 $338.1 $350.5 N/A

Notes: 

a

 Benefits and net benefits calculated using the 3% average Social Cost of CO2 value. The net present value of 

reduced CO2 emissions is calculated differently than other benefits. The same discount rate used to discount the 

value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present value of 

SCC for internal consistency. Refer to the SCC TSD for more detail. 

b

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the 

less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 c Vehicle program costs include compliance costs and R&D. 
11-30 

Table 11-24 Annual Cost per Metric Ton of CO2eq Emissions Reduced in Each Control Case Alternative 

 Vs. the Less Dynamic Baseline and using Method B 

(Dollar Values are 2012$) a

YEAR ALT.2 LESS 

STRINGENT

ALT.3 

PREFERRED

ALT.4 

MORE 

STRINGENT

ALT.5 

MORE 

STRINGENT

2035 $/metric ton 

w/o fuel 

$36 $47 $48 N/A 

$/metric ton 

w/ fuel 

-$250 -$250 -$250 N/A

2050 $/metric ton 

w/o fuel 

$31 $39 $41 N/A

$/metric ton 

w/ fuel 

-$280 -$270 -$270 N/A

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

11.2.2.1 Tractors and Trailers 

Table 11-25 presents a summary of all costs and benefits for each tractor/trailer program 

Alternative relative to the alternative 1a baseline case. Table 11-26 shows cost per ton of GHG 

reduced.
11-31 

Table 11-25 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline 

and using Method B 

Tractor/Trailers 

(Billions of 2012$, Except GHG Reductions) b

 ALT.1 

BASELI

NE

ALT.2 

LESS 

STRING

ENT

ALT.3 

PREFER

RED

ALT.4 

MORE 

STRINGE

NT

ALT.5 

MORE 

STRINGE

NT 

2035 Vehicle Program 

Costsc

$0 -$1.9 -$2.9 -$2.9 N/A

Maintenance costs $0 -$0.03 -$0.08 -$0.08 N/A

Fuel Expenditures 

(pre-tax) 

$0 $17.2 $28.4 $29.2 $36.8

Benefits $0 $10.3 $15.7 $16.0 $19.7

Net Benefits $0 $25.6 $41.0 $42.2 N/A

GHG Reductions 

(MMT) 

0 59.1 97.2 100.0 125.9

2050 Vehicle Program 

Costsc

$0 -$2.3 -$3.6 -$3.6 N/A

Maintenance costs $0 -$0.03 -$0.08 -$0.08 N/A

Fuel Expenditures 

(pre-tax) 

$0 $26.1 $44.0 $44.0 $54.8

Benefits $0 $16.1 $25.2 $25.2 $30.7

Net Benefits $0 $39.9 $65.5 $65.6 N/A

GHG Reductions 

(MMT) 

0 83.8 140.9 141.1 175.7

NPV, 

3% 

Vehicle Program 

Costsc

$0 -$28.8 -$43.7 -$46.2 N/A

Maintenance costs $0 -$0.47 -$1.19 -$1.22 N/A

Fuel Expenditures 

(pre-tax) 

$0 $231.7 $381.5 $394.5 $499.5

Benefits $0 $141.7 $215.7 $221.6 $274.2

Net Benefits $0 $344.1 $552.3 $568.8 N/A

NPV, 

7% 

Vehicle Program 

Costsc

$0 -$13.9 -$20.9 -$22.7 N/A

Maintenance costs $0 -$0.23 -$0.59 -$0.60 N/A

Fuel Expenditures 

(pre-tax) 

$0 $98.1 $160.1 $167.5 $213.4

Benefits $0 $85.8 $133.8 $138.1 $172.4

Net Benefits $0 $169.8 $272.4 $282.3 N/A

Notes: 

a

 Benefits and net benefits calculated using the 3% average Social Cost of CO2 value applied only to CO2

reductions. The net present value of reduced CO2 emissions is calculated differently than other benefits. 

The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 

percent) is used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for 

more detail. 

b

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 c Vehicle program costs include compliance costs and R&D. 
11-32 

Table 11-26 Annual Cost per Metric Ton of CO2eq Emissions Reduced in Each Control Case Alternative 

 Vs. the Less Dynamic Baseline and using Method B 

Tractor/Trailers 

(Dollar Values are 2012$) a

YEAR ALT.2 LESS 

STRINGENT

ALT.3 

PREFERRED

ALT.4 

MORE 

STRINGENT

ALT.5 

MORE 

STRINGENT

2035 $/metric ton 

w/o fuel 

$32 $31 $30 N/A 

$/metric ton 

w/ fuel 

-$260 -$260 -$260 N/A

2050 $/metric ton 

w/o fuel 

$28 $26 $26 N/A

$/metric ton 

w/ fuel 

-$280 -$290 -$290 N/A

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

11.2.2.2 Vocational Vehicles 

Table 11-27 presents a summary of all costs and benefits for each vocational program 

alternative relative to the Alternative 1a baseline case. Table 11-28 shows cost per ton of GHG 

reduced. 
11-33 

Table 11-27 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline 

and using Method B 

Vocational Vehicles 

(Billions of 2012$, Except GHG Reductions) a, b

 ALT.1 

BASELI

NE

ALT.2 

LESS 

STRING

ENT

ALT.3 

PREFER

RED

ALT.4 ALT.5 

MORE 

STRINGE

NT 

2035 Vehicle Program 

Costsc

$0 -$0.2 -$2.1 -$2.1 N/A

Maintenance costs $0 -$0.02 -$0.03 -$0.04 N/A

Fuel Expenditures 

(pretax) 

$0 $1.3 $4.7 $5.1 $7.6

Benefits $0 $1.1 $2.6 $2.8 $3.9

Net Benefits $0 $2.2 $5.2 $5.8 

GHG Reductions 

(MMT) 

0 4.7 16.1 17.4 25.8

2050 Vehicle Program 

Costsc

$0 -$0.3 -$2.4 -$2.4 N/A

Maintenance costs $0 -$0.02 -$0.03 -$0.04 N/A

Fuel Expenditures 

(pretax) 

$0 $2.0 $7.3 $7.3 $10.7

Benefits $0 $1.7 $4.2 $4.2 $5.9

Net Benefits $0 $3.4 $9.0 $9.1 N/A

GHG Reductions 

(MMT) 

0 6.5 23.2 23.3 33.9

NPV, 

3% 

Vehicle Program 

Costsc

$0 -$3.6 -$29.6 -$32.8 N/A

Maintenance costs $0 -$0.22 -$0.42 -$0.52 N/A

Fuel Expenditures 

(pretax) 

$0 $16.9 $60.6 $66.3 $99.9

Benefits $0 $14.8 $34.8 $37.4 $52.7

Net Benefits $0 $27.9 $65.4 $70.3 N/A

NPV, 

7% 

Vehicle Program 

Costsc

$0 -$1.7 -$13.8 -$16.0 N/A

Maintenance costs $0 -$0.10 -$0.19 -$0.24 N/A

Fuel Expenditures 

(pretax) 

$0 $6.9 $24.7 $27.9 $42.5

Benefits $0 $8.3 $21.5 $23.4 $33.8

Net Benefits $0 $13.4 $32.2 $35.0 N/A

Notes: 

a

 Benefits and net impacts calculated using the 3% average Social Cost of CO2 value. The net present value 

of reduced CO2 emissions is calculated differently than other benefits. The same discount rate used to 

discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. b

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 c Vehicle program costs include compliance costs and R&D. 

 
11-34 

Table 11-28 Annual Cost per Metric Ton of CO2eq Emissions Reduced in Each Control Case Alternative 

 Vs. the Less Dynamic Baseline and using Method B 

Vocational Vehicles 

(Dollar Values are 2012$) a

YEAR ALT.2 LESS 

STRINGENT

ALT.3 

PREFERRED

ALT.4 ALT.5 

MORE 

STRINGENT

2035 $/metric ton 

w/o fuel 

$53 $130 $120 N/A 

$/metric ton 

w/ fuel 

-$230 -$160 -$170 N/A

2050 $/metric ton 

w/o fuel 

$44 $110 $110 N/A

$/metric ton 

w/ fuel 

-$260 -$210 -$210 N/A

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for 

an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1

11.2.2.3 HD Pickups and Vans 

Table 11-29 presents a summary of all costs and benefits for each HD pickup and van 

program alternative relative to the Alternative 1a baseline case. Table 11-30 shows cost per ton 

of GHG reduced. 
11-35 

Table 11-29 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline 

and using Method B 

HD Pickups and Vans 

(Billions of 2012$, Except GHG Reductions) a, b

 ALT.1 

BASELI

NE

ALT.2 

LESS 

STRING

ENT

ALT.3 

PREFER

RED

ALT.4 

MORE 

STRINGE

NT

ALT.5 

MORE 

STRINGE

NT 

2035 Vehicle Program 

Costsc

$0 -$0.5 -$0.9 -$1.2 N/A

Maintenance costs $0 -$0.01 -$0.01 -$0.01 N/A

Fuel Expenditures 

(pretax) 

$0 $2.5 $4.2 $4.4 $5.0

Benefits $0 $1.4 $2.2 $2.3 $2.6

Net Benefits $0 $3.4 $5.5 $5.5 N/A

GHG Reductions 

(MMT) 

0 8.1 13.9 14.6 16.6

2050 Vehicle Program 

Costsc

$0 -$0.5 -$1.0 -$1.4 N/A

Maintenance costs $0 -$0.01 -$0.01 -$0.01 N/A

Fuel Expenditures 

(pretax) 

$0 $3.5 $6.3 $6.3 $7.2

Benefits $0 $2.1 $3.5 $3.5 $4.0

Net Benefits $0 $5.1 $8.7 $8.4 N/A

GHG Reductions 

(MMT) 

0 10.8 19.3 19.4 22.1

NPV, 

3% 

Vehicle Program 

Costsc

$0 -$7.5 -$13.5 -$19.6 N/A

Maintenance costs $0 -$0.18 -$0.18 -$0.18 N/A

Fuel Expenditures 

(pretax) 

$0 $31.4 $53.5 $56.8 $64.9

Benefits $0 $18.7 $29.2 $30.7 $34.6

Net Benefits $0 $42.4 $69.1 $67.7 N/A

NPV, 

7% 

Vehicle Program 

Costsc

$0 -$3.7 -$6.5 -$9.7 N/A

Maintenance costs $0 -$0.08 -$0.08 -$0.08 N/A

Fuel Expenditures 

(pretax) 

$0 $13.1 $21.9 $23.7 $27.1

Benefits $0 $11.4 $18.2 $19.3 $21.8

Net Benefits $0 $20.7 $33.5 $33.2 N/A

Notes: 

a

 Benefits and net impacts calculated using the 3% average Social Cost of CO2 value. The net present value 

of reduced CO2 emissions is calculated differently than other benefits. The same discount rate used to 

discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. b

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of 

the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1 c Vehicle program costs include compliance costs and R&D. 
11-36 

Table 11-30 Annual Cost per Metric Ton of CO2eq Emissions Reduced in Each Control Case Alternative 

 Vs. the Less Dynamic Baseline and using Method B 

HD Pickups and Vans 

(Dollar Values are 2012$) a

YEAR ALT.2 LESS 

STRINGENT

ALT.3 

PREFERRED

ALT.4 

MORE 

STRINGENT

ALT.5 

MORE 

STRINGENT

2035 $/metric ton 

w/o fuel 

$59 $65 $84 N/A 

$/metric ton 

w/ fuel 

-$240 -$240 -$220 N/A

2050 $/metric ton 

w/o fuel 

$51 $53 $73 N/A

$/metric ton 

w/ fuel 

-$270 -$270 -$250 N/A

Note: 

a

 For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an 

explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1

11.3 Detailed Technology Projections for Each Category 

The alternatives were developed to reflect different levels of technology (in terms of 

effectiveness, adoption rate, and timing) that would be required to meet increasing levels of 

stringency. For each of these alternatives, the agencies projected a fleet mix of technologies that 

would be capable of meeting the standards. These projections are summarized below for each 

category. Note that for trailers, the alternatives differ in terms of which trailers would be subject 

to the standards, in addition to the level of technology necessary to the meet the standards. Note 

also that the same technology projections applied for both Method A and Method B. Details 

regarding the preferred alternative are included in Chapter 2 of this draft RIA (Sections 2.5-

2.10). 

 
11-37 

11.3.1 Tractor Technology 

Table 11-31 Alternative 2 2021MY Technology Adoption Rates for Tractors 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 2 Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 10% 10% 0% 10% 10% 0% 10% 10% 0% 

Bin II 80% 80% 0% 80% 80% 0% 80% 80% 0% 

Bin III 10% 10% 45% 10% 10% 45% 10% 10% 45% 

Bin IV 0% 0% 35% 0% 0% 35% 0% 0% 35% 

Bin V 20% 20% 20% 

Bin VI 0% 0% 0% 

Bin VII 0% 0% 0% 

Steer Tires 

Base 15% 15% 5% 15% 15% 5% 15% 15% 15% 

Level 1 60% 60% 65% 60% 60% 65% 60% 60% 65% 

Level 2 25% 25% 30% 25% 25% 30% 25% 25% 20% 

Level 3 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Drive Tires 

Base 15% 15% 5% 15% 15% 5% 15% 15% 5% 

Level 1 60% 60% 65% 60% 60% 65% 60% 60% 60% 

Level 2 25% 25% 30% 25% 25% 30% 25% 25% 35% 

Level 3 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 30% 30% 30% 

Other N/A N/A N/A N/A N/A N/A 0% 0% 0% 

Transmission Type 

Manual 45% 45% 45% 45% 45% 45% 45% 45% 45% 

AMT 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Auto 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Dual Clutch 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Driveline 

Axle 

Lubricant 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

6x2 or 4x2 

Axle 

 10% 10% 20% 10% 10% 10% 

Downspeed 20% 20% 0% 0% 0% 0% 0% 0% 0% 

Accessory Improvements 

A/C 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Electric 

Access. 

10% 10% 10% 10% 10% 10% 10% 10% 10% 
11-38 

Other Technologies 

Predictive 

Cruise 

Control 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Automated 

Tire 

Inflation 

System 

10% 10% 10% 10% 10% 10% 10% 10% 10% 
11-39 

Table 11-32 Alternative 2 2024MY Technology Adoption Rates for Tractors 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 2 Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 10% 10% 0% 10% 10% 0% 10% 10% 0% 

Bin II 70% 70% 0% 70% 70% 0% 70% 70% 0% 

Bin III 20% 20% 35% 20% 20% 35% 20% 20% 35% 

Bin IV 0% 0% 45% 0% 0% 45% 0% 0% 45% 

Bin V 20% 20% 20% 

Bin VI 0% 0% 0% 

Bin VII 0% 0% 0% 

Steer Tires 

Base 10% 10% 5% 10% 10% 5% 10% 10% 10% 

Level 1 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Level 2 30% 30% 35% 30% 30% 35% 30% 30% 30% 

Level 3 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Drive Tires 

Base 10% 10% 5% 10% 10% 5% 10% 10% 5% 

Level 1 60% 60% 60% 60% 60% 60% 60% 60% 50% 

Level 2 30% 30% 35% 30% 30% 35% 30% 30% 45% 

Level 3 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 40% 40% 40% 

Other N/A N/A N/A N/A N/A N/A 10% 10% 10% 

Transmission Type 

Manual 20% 20% 20% 20% 20% 20% 20% 20% 20% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle 

Lubricant 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 or 4x2 

Axle 

 20% 20% 20% 20% 20% 20% 

Downspeed 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Accessory Improvements 

A/C 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Electric 

Access. 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Other Technologies 

Predictive 

Cruise 

Control 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Automated 20% 20% 20% 20% 20% 20% 20% 20% 20% 
11-40 

Tire 

Inflation 

System 
11-41 

Table 11-33 Alternative 4Adoption Rates for 2021 MY 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 4 2021MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 65% 65% 0% 65% 65% 0% 65% 65% 0% 

Bin III 30% 30% 35% 30% 30% 35% 30% 30% 35% 

Bin IV 5% 5% 30% 5% 5% 30% 5% 5% 30%

Bin V 25% 25% 25% 

Bin VI 10% 10% 10% 

Bin VII 0% 0% 0% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 35% 35% 35% 35% 35% 35% 35% 35% 35% 

Level 2 45% 45% 45% 45% 45% 45% 45% 45% 45% 

Level 3 15% 15% 15% 15% 15% 15% 15% 15% 15% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 35% 35% 35% 35% 35% 35% 35% 35% 35% 

Level 2 45% 45% 45% 45% 45% 45% 45% 45% 45% 

Level 3 15% 15% 15% 15% 15% 15% 15% 15% 15% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 80% 80% 80% 

Transmission Type 

Manual 25% 25% 25% 25% 25% 25% 25% 25% 25% 

AMT 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Auto 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Dual Clutch 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Driveline 

Axle 

Lubricant 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

6x2 Axle 10% 10% 20% 10% 10% 30% 

Downspeed 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Accessory Improvements 

A/C 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Electric 

Access. 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Other Technologies 

Predictive 

Cruise 

Control 

30% 30% 30% 30% 30% 30% 30% 30% 30% 

Automated 

Tire 

30% 30% 30% 30% 30% 30% 30% 30% 30% 
11-42 

Inflation 

System 
11-43 

Table 11-34 Alternative 4 Adoption Rates for 2024 MY 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 4 2024 MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 50% 50% 0% 50% 50% 0% 50% 50% 0% 

Bin III 40% 40% 20% 40% 40% 20% 40% 40% 20% 

Bin IV 10% 10% 20% 10% 10% 20% 10% 10% 20% 

Bin V 35% 35% 35% 

Bin VI 20% 20% 20% 

Bin VII 5% 5% 5% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 25% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 100% 100% 100% 

Transmission Type 

Manual 0% 0% 0% 0% 0% 0% 0% 0% 0% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle Lubricant 40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 Axle 20% 20% 60% 20% 20% 60% 

Downspeed 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Accessory Improvements 

A/C 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Electric Access. 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Other Technologies 

Predictive 

Cruise Control 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Automated Tire 

Inflation 

System 

40% 40% 40% 40% 40% 40% 40% 40% 40% 
11-44 

Table 11-35 Alternative 5 Adoption Rates for 2021 MY 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 5 2021 MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Additional 

Waste Heat 

Recovery 

 10% 10% 10% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 50% 50% 0% 50% 50% 0% 50% 50% 0% 

Bin III 40% 40% 20% 40% 40% 20% 40% 40% 20% 

Bin IV 10% 10% 20% 10% 10% 20% 10% 10% 20% 

Bin V 35% 35% 35% 

Bin VI 20% 20% 20% 

Bin VII 5% 5% 5% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 30% 30% 30% 30% 30% 30% 30% 30% 20% 

Level 2 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 35% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 30% 30% 30% 30% 30% 30% 30% 30% 20% 

Level 2 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Level 3 25% 25% 25% 25% 25% 25% 25% 25% 35% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 100% 100% 100% 

Transmission Type 

Manual 0% 0% 0% 0% 0% 0% 0% 0% 0% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle 

Lubricant 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 Axle 20% 20% 20% 20% 20% 30% 

Downspeed 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Accessory Improvements 

A/C 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Electric 

Access. 

30% 30% 30% 30% 30% 30% 30% 30% 30% 

Other Technologies 

Predictive 

Cruise 

40% 40% 40% 40% 40% 40% 40% 40% 40% 
11-45 

Control 

Automated 

Tire Inflation 

System 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Hybrid 

Powertrain 

with 

Electrified 

Accessories 

20% 20% 20% 20% 20% 20% 20% 20% 20% 

Weight 

Reduction 

(lbs) 

1,200 1,200 1,200 1,200 1,200 1,200 1,200 1,200 1,200 

Table 11-36 Alternative 5 Adoption Rates for 2024 MY 

 CLASS 7 CLASS 8 

Day Cab Day Cab Sleeper Cab 

 Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Low 

Roof 

Mid 

Roof 

High 

Roof 

Alternative 5 2024 MY Engine Technology Package 

 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Additional 

Waste Heat 

Recovery 

 10% 10% 10% 

Aerodynamics 

Bin I 0% 0% 0% 0% 0% 0% 0% 0% 0% 

Bin II 40% 40% 0% 40% 40% 0% 40% 40% 0% 

Bin III 50% 50% 15% 50% 50% 15% 50% 50% 15% 

Bin IV 10% 10% 15% 10% 10% 15% 10% 10% 15% 

Bin V 35% 35% 35% 

Bin VI 25% 25% 25% 

Bin VII 10% 10% 10% 

Steer Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Level 3 35% 35% 35% 35% 35% 35% 35% 35% 35% 

Drive Tires 

Base 5% 5% 5% 5% 5% 5% 5% 5% 5% 

Level 1 20% 20% 20% 20% 20% 20% 20% 20% 20% 

Level 2 40% 40% 40% 40% 40% 40% 40% 40% 40% 

Level 3 35% 35% 35% 35% 35% 35% 35% 35% 35% 

Extended Idle Reduction 

APU N/A N/A N/A N/A N/A N/A 100% 100% 100% 

Transmission Type 

Manual 0% 0% 0% 0% 0% 0% 0% 0% 0% 

AMT 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Auto 40% 40% 40% 40% 40% 40% 40% 40% 40% 
11-46 

Dual Clutch 10% 10% 10% 10% 10% 10% 10% 10% 10% 

Driveline 

Axle 

Lubricant 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

6x2 Axle 20% 20% 60% 20% 20% 60% 

Downspeed 60% 60% 60% 60% 60% 60% 60% 60% 60% 

Direct Drive 50% 50% 50% 50% 50% 50% 50% 50% 50% 

Accessory Improvements 

A/C 30% 30% 30% 30% 30% 30% 30% 30% 30% 

Electric 

Access. 

30% 30% 30% 30% 30% 30% 30% 30% 30% 

Other Technologies 

Predictive 

Cruise 

Control 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Automated 

Tire 

Inflation 

System 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Hybrid 

Powertrain 

with 

Electrified 

Accessories 

40% 40% 40% 40% 40% 40% 40% 40% 40% 

Weight 

Reduction 

(lbs) 

1,200 1,200 1,200 1,200 1,200 1,200 1,200 1,200 1,200 
11-47 

11.3.2 Trailer Technology 

Table 11-37 Alternative 1 Trailer Adoption Rates 

TECHNOLOGY LONG BOX 

DRY & REFRIGERATED VANS 

SHORT BOX, 

NON-AERO BOX, 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024 2027 2040a 2018+ 

Aerodynamics 

Bin I 45% 41% 38% 35% 20% 100% 

Bin II - - - - - 

Bin III 20% 20% 20% 20% 20% - 

Bin IV 30% 34% 37% 40% 55% - 

Bin V 5% 5% 5% 5% 5% - 

Bin VI - - - - - - 

Bin VII - - - - - 

Bin VIII - - - - - - 

Avg Delta CdA (m2) 0.2 0.3 0.3 0.3 0.4 0.0 

Tire Rolling Resistance 

Level 1 tires 50% 47% 43% 40% 25% 100% 

Level 2 tires 50% 53% 57% 60% 75% - 

Level 3 tires - - - - - - 

Avg CRR (kg/ton) 5.6 5.5 5.5 5.5 5.3 6.0 

Tire Inflation 

ATI 50% 53% 57% 60% 75% 0% 

Avg % Reduction 0.8% 0.8% 0.9% 0.9% 1.1% 0.0% 

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 

Note: 

a

 Considered in Alternative 1b only 

 
11-48 

Table 11-38 Alternative 2 Trailer Adoption Rates 

TECHNOLOGY LONG BOX 

DRY & REFRIGERATED VANS 

SHORT BOX, 

NON-AERO BOX, 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024 2018+ 

Aerodynamics 

Bin I 5% 5% 5% 100% 

Bin II - 

Bin III 30% 20% 10% - 

Bin IV 60% 70% 75% - 

Bin V 5% 5% 10% - 

Bin VI - - - - 

Bin VII - - - - 

Bin VIII - - - - 

Avg Delta CdA (m2) 0.4 0.4 0.5 0.0 

Tire Rolling Resistance 

Level 1 tires 15% 10% 5% 100% 

Level 2 tires 85% 90% - 

Level 3 tires 95% - 

Avg CRR (kg/ton) 5.2 5.2 4.8 6.0 

Tire Inflation 

ATI 85% 90% 95% 0% 

Avg % Reduction 1.3% 1.4% 1.4% 0.0% 

Weight Reduction (pounds) 

Weight 0 0 0 0 

Table 11-39 Alternative 3 Long Box Trailer Adoption Rates 

TECHNOLOGY LONG BOX 

DRY VANS 

LONG BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2027 2018 2021 2024 2027 

Aerodynamic Technologies 

Bin I 5% - - - 5% - - - 

Bin II - - - - - - - - 

Bin III 30% 5% - - 30% 5% - - 

Bin IV 60% 55% 25% - 60% 55% 25% - 

Bin V 5% 10% 10% 10% 5% 10% 10% 20% 

Bin VI - 30% 65% 50% - 30% 65% 60% 

Bin VII - - - 40% - - - 20% 

Bin VIII - - - - - - - - 

Avg Delta CdA (m2) 0.4 0.7 0.8 1.1 0.4 0.7 0.8 1.0 

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 5% 15% 5% 5% 5%

Level 2 tires 85% 95% - - 85% 95% - -

Level 3 tires - - 95% 95% - - 95% 95%

Avg CRR (kg/ton) 5.2 5.1 4.8 4.8 5.2 5.1 4.8 4.8 

Tire Inflation System 

ATI 85% 95% 95% 95% 85% 95% 95% 95% 

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.4% 1.3% 1.4% 1.4% 1.4% 

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 0 0 
11-49 

Table 11-40 Alternative 3 Short Box Trailer Adoption Rates 

TECHNOLOGY SHORT BOX 

DRY VANS 

SHORT BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2027 2018 2021 2024 2027 

Aerodynamic Technologies

Bin I 100% 5% - - 100% 5% - - 

Bin II - 95% 70% 30% - 95% 70% 55% 

Bin III - - 30% 60% - - 30% 40% 

Bin IV - - - 10% - - - 5% 

Bin V - - - - - - - - 

Bin VI - - - - - - - - 

Bin VII - - - - - - - - 

Bin VIII - - - - - - - - 

Avg Delta CdA (m2) 0.4 0.7 0.8 1.1 0.4 0.7 0.8 1.0

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 5% 15% 5% 5% 5%

Level 2 tires 85% 95% - - 85% 95% - -

Level 3 tires - - 95% 95% - - 95% 95%

Avg CRR (kg/ton) 5.2 5.1 4.8 4.8 5.2 5.1 4.8 4.8

Tire Inflation System 

ATI 85% 95% 95% 95% 85% 95% 95% 95% 

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.4% 1.3% 1.4% 1.4% 1.4%

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 0 0 

Table 11-41 Alternative 3 Non-Aero Box and Non-Box Trailer Adoption Rates 

TECHNOLOGY NON-AERO BOX 

& NON-BOX TRAILERS 

Model Year 2018 2021 2024 2027 

Aerodynamic Technologies 

Bin I 100% 100% 100% 100%

Bin II - - - -

Bin III - - - -

Bin IV - - - -

Bin V - - - -

Bin VI - - - -

Bin VII - - - -

Bin VIII - - - -

Avg Delta CdA (m2) 0.0 0.0 0.0 0.0

Trailer Tire Rolling Resistance 

Level 1 tires - - - -

Level 2 tires 100% 100% - -

Level 3 tires - - 100% 100%

Avg CRR (kg/ton) 5.1 5.1 4.7 4.7

Tire Inflation System 

ATI 100% 100% 100% 100%

Avg ATI Reduction (%) 1.5% 1.5% 1.5% 1.5%

Weight Reduction (pounds) 

Weight 0 0 0 0

 
11-50 

Table 11-42 Alternative 4 Long Box Trailer Adoption Rates 

TECHNOLOGY LONG BOX 

DRY VANS 

LONG BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies

Bin I 5% - - 5% - - 

Bin II - - - - - - 

Bin III 30% - - 30% - - 

Bin IV 60% 25% - 60% 25% - 

Bin V 5% 10% 10% 5% 10% 20% 

Bin VI - 65% 50% - 65% 60%

Bin VII - - 40% - - 20% 

Bin VIII - - - - - - 

Avg Delta CdA (m2) 0.4 0.8 1.1 0.4 0.8 1.0

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 15% 5% 5%

Level 2 tires 85% 95% - 85% 95% -

Level 3 tires - - 95% - - 95%

Avg CRR (kg/ton) 5.2 5.1 4.8 5.2 5.1 4.8

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95%

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.3% 1.4% 1.4%

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 

Table 11-43 Alternative 4 Short Box Trailer Adoption Rates 

TECHNOLOGY SHORT BOX 

DRY VANS 

SHORT BOX 

REFRIGERATED VANS 

Model Year 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies

Bin I 100% - - 100% - - 

Bin II - 70% 30% - 70% 55% 

Bin III - 30% 60% - 30% 40% 

Bin IV - - 10% - - 5% 

Bin V - - - - - - 

Bin VI - - - - - -

Bin VII - - - - - - 

Bin VIII - - - - - - 

Avg Delta CdA (m2) 0.4 0.8 1.1 0.4 0.8 1.0

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 15% 5% 5%

Level 2 tires 85% 95% - 85% 95% -

Level 3 tires - - 95% - - 95%

Avg CRR (kg/ton) 5.2 5.1 4.8 5.2 5.1 4.8

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95%

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.3% 1.4% 1.4%

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 
11-51 

Table 11-44 Alternative 4 Non-Aero Box and Non-Box Trailer Adoption Rates 

TECHNOLOGY NON-AERO BOX 

& NON-BOX TRAILERS

Model Year 2018 2021 2024 

Aerodynamic Technologies 

Bin I 100% 100% 100%

Bin II - - -

Bin III - - -

Bin IV - - -

Bin V - - -

Bin VI - - -

Bin VII - - -

Bin VIII - - -

Avg Delta CdA (m2) 0.0 0.0 0.0

Trailer Tire Rolling Resistance 

Level 1 tires - - -

Level 2 tires 100% 100% -

Level 3 tires - - 100%

Avg CRR (kg/ton) 5.1 5.1 4.7

Tire Inflation System 

ATI 100% 100% 100%

Avg ATI Reduction (%) 1.5% 1.5% 1.5%

Weight Reduction (pounds) 

Weight 0 0 0

Table 11-45 Alternative 5 Box Trailer Adoption Rates 

TECHNOLOGY LONG BOX 

DRY VANS 

LONG BOX 

REFRIGERATED VANS 

SHORT BOX DRY & 

REFRIGERATED VANS

Model Year 2018 2021 2024 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies

Bin I 5% - - 5% 0% 0% 100% - -

Bin II - - - - - - - 65% 10%

Bin III 5% - - 5% - - - 25% 90%

Bin IV 55% 15% - 55% 15% - - - -

Bin V 10% - 10% - - - - -

Bin VI 25% 85% 10% 25% 85% 100% - - -

Bin VII - - 90% - - - - - -

Bin VIII - - - - - - - - -

Avg Delta CdA (m2) 0.6 0.9 1.4 0.6 0.9 1.0 0.0 0.1 0.3

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 15% 5% 5% 15% 5% 5%

Level 2 tires 85% 95% - 85% 95% - 85% 95% -

Level 3 tires - - 95% - - 95% - - 95%

Avg CRR (kg/ton) 5.2 5.1 4.8 5.2 5.1 4.8 5.2 5.1 4.8

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95% 85% 95% 95%

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.3% 1.4% 1.4% 1.3% 1.4% 1.4%

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 0 0 0
11-52 

Table 11-46 Alternative 5 Non-Box Trailer Adoption Rates 

TECHNOLOGY CONTAINER 

CHASSIS 

FLATBED TANKS NON-AERO BOX 

& OTHER NONBOX 

Model Year 2018 2021 2024 2018 2021 2024 2018 2021 2024 2018 2021 2024 

Aerodynamic Technologies

Bin I 100% 80% 30% 100% 80% 30% 100% 67% 0% 100% 100% 100%

Bin II - - - - - - - - - - - -

Bin III - 20% 55% - 20% 55% - 33% - - - -

Bin IV - - 15% - - 15% - - 100% - - -

Bin V - - - - - - - - - - - -

Bin VI - - - - - - - - - - - -

Bin VII - - - - - - - - - - - -

Bin VIII - - - - - - - - - - - -

Avg Delta CdA (m2) 0.0 0.1 0.2 0.0 0.1 0.2 0.0 0.1 0.5 0.0 0.0 0.0

Trailer Tire Rolling Resistance 

Level 1 tires 15% 5% 5% 15% 5% 5% 15% 5% 5% - - -

Level 2 tires 85% 95% - 85% 95% - 85% 95% - 100% 100% -

Level 3 tires - - 95% - - 95% - - 95% - - 100%

Avg CRR (kg/ton) 5.2 5.1 4.8 5.2 5.1 4.8 5.2 5.1 4.8 5.1 5.1 4.7

Tire Inflation System 

ATI 85% 95% 95% 85% 95% 95% 85% 95% 95% 100% 100% 100%

Avg ATI Reduction (%) 1.3% 1.4% 1.4% 1.3% 1.4% 1.4% 1.3% 1.4% 1.4% 1.5% 1.5% 1.5%

Weight Reduction (pounds) 

Weight 0 0 0 0 0 0 0 0 0 0 0 0

11.3.3 Vocational Vehicle Technology 

Table 11-47 Alternative 2 Technology Adoption Rates for All Vocational Subcategories 

TECHNOLOGY 2021 2024

HFC Leakage 100% 100% 

Axle - Low Friction Lubes 40% 75% 

Baseline Drive Tire CRR 50% 30% 

Baseline Steer Tire CRR 20% 10% 

5% better Drive Tire CRR 50% 70% 

10% better Steer Tire CRR 80% 60% 

15% better Steer Tire CRR 0% 30% 

Neutral-idle 43% 80% 
11-53 

Table 11-48 Alternative 3 Technology Adoption Rates for Vocational Subcategories: LHD, MHD, HHD 

Multipurpose & Urban 

TECHNOLOGY 2021 2024 2027 

Add Two Trans Gears 5% 5% 5% 

HFC Leakage 100% 100% 100% 

Axle - Low Friction Lubes 75% 75% 75% 

Strong Hybrid 4% 7% 18% 

Baseline Drive Tire CRR 50% 20% 10% 

Baseline Steer Tire CRR 20% 10% 0% 

5% better Drive Tire CRR 50% 50% 25% 

10% better Drive Tire CRR 0% 30% 50% 

10% better Steer Tire CRR 80% 30% 20% 

15% better Drive Tire CRR 0% 0% 15% 

15% better Steer Tire CRR 0% 60% 30% 

20% better Steer Tire CRR 0% 0% 50% 

Neutral-idle 70% 85% 30% 

Stop-start 5% 15% 70% 

Dual Clutch Transmission 5% 15% 5% 

Trans_Improved 15% 30% 70% 

200 Pounds Lightweighting 4% 4% 5% 

Note: 

a

 Idle reduction values in MY 2027 are for multipurpose vehicles. For Urban vehicles, Neutral Idle is 25% and StopStart is 75% in MY 2027. 
11-54 

Table 11-49 Alternative 3 Technology Adoption Rates for Vocational Subcategories: LHD & MHD Regional 

TECHNOLOGY 2021 2024 2027 

Add Two Trans Gears 5% 5% 5% 

HFC Leakage 100% 100% 100% 

Axle - Low Friction Lubes 75% 75% 75% 

Baseline Drive Tire CRR 50% 20% 10% 

Baseline Steer Tire CRR 20% 10% 0% 

5% better Drive Tire CRR 50% 50% 25% 

10% better Drive Tire CRR 0% 30% 50% 

10% better Steer Tire CRR 80% 30% 20% 

15% better Drive Tire CRR 0% 0% 15% 

15% better Steer Tire CRR 0% 60% 30% 

20% better Steer Tire CRR 0% 0% 50% 

Neutral-idle 70% 85% 30% 

Stop-start 5% 15% 70% 

Dual Clutch Transmission 5% 15% 5% 

Trans_Improved 15% 30% 70% 

200 Pounds Lightweighting 7% 7% 8% 

Note: 

a

 Weight reduction values are given for LHD. For MHD, adoption rates are 6% in MYs 2021 & 2024, and 7% in 

MY 2027. 
11-55 

Table 11-50 Alternative 3 Technology Adoption Rates for Vocational Subcategories: HHD Regional 

TECHNOLOGY 2021 2024 2027 

HFC Leakage 100% 100% 100% 

Axle 6x2 45% 60% 60% 

Axle - Low Friction Lubes 75% 75% 75% 

Baseline Drive Tire CRR 50% 20% 10% 

Baseline Steer Tire CRR 20% 10% 0% 

5% better Drive Tire CRR 50% 50% 25% 

10% better Drive Tire CRR 0% 30% 50% 

10% better Steer Tire CRR 80% 30% 20% 

15% better Drive Tire CRR 0% 0% 15% 

15% better Steer Tire CRR 0% 60% 30% 

20% better Steer Tire CRR 0% 0% 50% 

Automatic Transmission 22% 33% 25% 

Stop-start 0% 15% 70% 

Automated Manual Transmission 22% 33% 25% 

Dual Clutch Transmission 22% 33% 25% 

Trans_Improved 15% 30% 70% 

200 Pounds Lightweighting 5% 5% 6% 

Notes: 

a

 Adoption rates of Automatic, AMT, and DCT transmissions include those that would certify using GEM plus those 

that would certify using the powertrain test as improved/integrated. 
11-56 

Table 11-51 Alternative 4 Technology Adoption Rates for Vocational Subcategories: LHD, MHD, HHD 

Multipurpose & Urban 

Technology 2021 2024 

Add Two Trans Gears 11% 10% 

HFC Leakage 100% 100% 

Axle - Low Friction Lubes 75% 75% 

Strong Hybrid 9% 18% 

Baseline Drive Tire CRR 50% 10% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 25% 

10% better Drive Tire CRR 0% 50% 

10% better Steer Tire CRR 80% 20% 

15% better Drive Tire CRR 0% 15% 

15% better Steer Tire CRR 0% 30% 

20% better Steer Tire CRR 0% 50% 

Neutral-idle 88% 30% 

Stop-start 12% 70% 

Dual Clutch Transmission 10% 10% 

Trans_improved 25% 70% 

200 Pounds Lightweighting 4% 5% 

Note: 

1 Idle reduction values in MY 2024 are for multipurpose vehicles. For Urban vehicles, Neutral Idle is 25% and StopStart is 75% in MY 2024. 
11-57 

Table 11-52 Alternative 4 Technology Adoption Rates for Vocational Subcategories: LHD & MHD Regional 

TECHNOLOGY 2021 2024 

Add Two Trans Gears 11% 10% 

HFC Leakage 100% 100% 

Axle - Low Friction Lubes 75% 75% 

Baseline Drive Tire CRR 50% 10% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 25% 

10% better Drive Tire CRR 0% 50% 

10% better Steer Tire CRR 80% 20% 

15% better Drive Tire CRR 0% 15% 

15% better Steer Tire CRR 0% 30% 

20% better Steer Tire CRR 0% 50% 

Neutral-idle 88% 30% 

Stop-start 12% 70% 

Dual Clutch Transmission 10% 10% 

Trans_improved 25% 70% 

200 Pounds Lightweighting 7% 8% 

Note: 

1 Weight reduction values are given for LHD. For MHD, adoption rates are 6% in MY 2021 & 7% in MY 2024. 
11-58 

Table 11-53 Alternative 4 Technology Adoption Rates for Vocational Subcategories: HHD Regional 

TECH 2021 2024 

HFC Leakage 100% 100% 

Axle 6x2 60% 60% 

Axle - Low Friction Lubes 75% 75% 

Baseline Drive Tire CRR 50% 10% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 25% 

10% better Drive Tire CRR 0% 50% 

10% better Steer Tire CRR 80% 20% 

15% better Drive Tire CRR 0% 15% 

15% better Steer Tire CRR 0% 30% 

20% better Steer Tire CRR 0% 50% 

Automatic Transmission 15% 25% 

Stop-start 0% 70% 

Automated Manual Transmission 15% 25% 

Dual Clutch Transmission 15% 25% 

Trans_improved 25% 70% 

200 Pounds Lightweighting 5% 6% 

Notes: 

1 Adoption rates of Automatic, AMT, and DCT transmissions include those that would certify using GEM plus 

those that would certify using the powertrain test as improved/integrated. 
11-59 

Table 11-54 Alternative 5 Technology Adoption Rates for Vocational Subcategories: LHD Regional, LHD & 

HHD Multipurpose, LHD & HHD Urban 

TECH 2021 2024 

Add Two Trans Gears 16% 11% 

HFC Leakage 100% 100% 

Axle - Low Friction Lubes 100% 100% 

Electric Vehicle 3% 3% 

Strong Hybrid 27% 36% 

Baseline Drive Tire CRR 50% 0% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 10% 

10% better Drive Tire CRR 0% 45% 

10% better Steer Tire CRR 80% 0% 

15% better Drive Tire CRR 0% 45% 

15% better Steer Tire CRR 0% 20% 

20% better Steer Tire CRR 0% 80% 

Neutral-idle 60% 40% 

Stop-start 40% 60% 

Dual Clutch Transmission 10% 5% 

Trans_improved 35% 60% 

Notes: 

1 Strong Hybrid is zero for the LHD Regional subcategory 
11-60 

Table 11-55 Alternative 5 Technology Adoption Rates for Vocational Subcategories: MHD Regional, MHD 

Multipurpose, & MHD Urban 

TECHNOLOGY 2021 2024 

Add Two Trans Gears 16% 11% 

HFC Leakage 100% 100% 

Axle - Low Friction Lubes 100% 100% 

Electric Vehicle 3% 3% 

Strong Hybrid 27% 36% 

Baseline Drive Tire CRR 50% 0% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 10% 

10% better Drive Tire CRR 0% 45% 

10% better Steer Tire CRR 80% 0% 

15% better Drive Tire CRR 0% 45% 

15% better Steer Tire CRR 0% 20% 

20% better Steer Tire CRR 0% 80% 

Neutral-idle 60% 40% 

Stop-start 40% 60% 

Dual Clutch Transmission 10% 5% 

Trans_improved 35% 60% 

1,000 Pounds Lightweighting 28% 42% 

Note: 

1 Strong Hybrid is zero for the MHD Regional subcategory 
11-61 

Table 11-56 Alternative 5 Technology Adoption Rates for Vocational Subcategories: HHD Regional 

TECHNOLOGY 2021 2024 

HFC Leakage 100% 100% 

Axle_disconnect 61% 61% 

Axle - Low Friction Lubes 100% 100% 

Baseline Drive Tire CRR 50% 0% 

Baseline Steer Tire CRR 20% 0% 

5% better Drive Tire CRR 50% 10% 

10% better Drive Tire CRR 0% 45% 

10% better Steer Tire CRR 80% 0% 

15% better Drive Tire CRR 0% 45% 

15% better Steer Tire CRR 0% 20% 

20% better Steer Tire CRR 0% 80% 

Automatic Transmission 33% 33% 

Stop-start 40% 60% 

Automated Manual Transmission 33% 33% 

Dual Clutch Transmission 33% 33% 

Trans_improved 29% 34% 

Note: 

1 Adoption rates of Automatic, AMT, and DCT transmissions include those that would certify using GEM plus 

those that would certify using the powertrain test as improved/integrated. 

11.3.4 Pickup and Van Technology 

11.3.4.1 Pickup and Van Technology for Method A 

This section describes the penetration of selected technologies across the whole fleet, as 

well as across the fleet of the manufacturers as a percentage of the respective fleet. The model 

year represented for the Method A technology penetration is 2030. 

Table 11-57 presents the fleet profile for the total industry as well as for each 

manufacturer showing total sales of HD vans and pickups, as well as share of different vehicle 

types within that total. 
11-62 

Table 11-57 Fleet Profile for Model Year 2030 under Method A using the Dynamic Baseline 

Overall

Fleet Daimler Fiat Ford

General

Motors Nissan

Total Vehicles Sales 755,787 24,188 110,622 335,385 272,441 13,152

Van Share 39.3% 100.0% 20.6% 41.4% 35.9% 100.0%

Pickup Share 60.7% 0.0% 79.4% 58.6% 64.1% 0.0%

Gasoline Share 56.4% 0.0% 40.1% 57.8% 64.3% 100.0%

Diesel Share 43.6% 100.0% 59.9% 42.2% 35.7% 0.0%

Gasoline Van Share 32.3% 0.0% 17.2% 34.0% 35.9% 100.0%

Diesel Van Share 6.9% 100.0% 3.3% 7.4% 0.0% 0.0%

Gasoline Pickup Share 24.1% 0.0% 22.8% 23.7% 28.4% 0.0%

Diesel Pickup Share 36.6% 0.0% 56.6% 34.9% 35.7% 0.0%

Table 11-58 presents the total penetration rates for select technologies in 2030 for the entire fleet. Table 11-59 

through 
11-63 

Table 11-63 present the penetration rates for the same technologies for each 

manufacturer. 

Table 11-58 Technology Penetration Rates for the Overall Fleet in Model Year 2030 under Method A using 

the Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 25% 56% 56% 49% 49%

Engine friction

reduction 29% 94% 94% 87% 87%

Cylinder

deactivation 10% 29% 21% 21% 21%

Variable valve

timing 19% 19% 19% 19% 19%

Gasoline direct

injection 14% 17% 25% 31% 32%

Turbo Machinery

Improvements 38% 38% 38% 38% 53%

8 speed

transmission 33% 67% 96% 96% 97%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 23% 78% 78% 75%

Mass reduction

and materials 0% 36% 38% 65% 65%

Electric power

steering 24% 50% 92% 78% 76%

Improved

accessories 22% 23% 63% 58% 73%

Stop/start engine

systems 0% 0% 0% 3% 14%

Mild hybrid 0% 0% 0% 12% 34%

Strong hybrid 0% 0% 8% 13% 16%
11-64 

Table 11-59 Technology Penetration Rates for the Daimler in Model Year 2030 under Method A using the 

Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 0% 0% 0% 0% 0%

Engine friction

reduction 44% 44% 44% 44% 44%

Cylinder

deactivation 0% 0% 0% 0% 0%

Variable valve

timing 0% 0% 0% 0% 0%

Gasoline direct

injection 0% 0% 0% 0% 0%

Turbo Machinery

Improvements 44% 44% 44% 44% 44%

8 speed

transmission 0% 0% 44% 44% 100%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 0% 0% 0% 0%

Mass reduction

and materials 0% 0% 0% 0% 0%

Electric power

steering 0% 0% 0% 0% 0%

Improved

accessories 0% 0% 0% 0% 0%

Stop/start engine

systems 0% 0% 0% 0% 0%

Mild hybrid 0% 0% 0% 0% 0%

Strong hybrid 0% 0% 0% 0% 0%
11-65 

Table 11-60 Technology Penetration Rates for the Fiat in Model Year 2030 under Method A using the 

Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 0% 40% 40% 40% 40%

Engine friction

reduction 18% 97% 97% 97% 97%

Cylinder

deactivation 13% 23% 23% 23% 23%

Variable valve

timing 17% 17% 17% 17% 17%

Gasoline direct

injection 0% 17% 17% 17% 17%

Turbo Machinery

Improvements 57% 57% 57% 57% 57%

8 speed

transmission 48% 65% 88% 88% 88%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 0% 100% 100% 79%

Mass reduction

and materials 0% 0% 100% 100% 100%

Electric power

steering 0% 0% 100% 100% 83%

Improved

accessories 0% 0% 63% 63% 72%

Stop/start engine

systems 0% 0% 0% 0% 0%

Mild hybrid 0% 0% 0% 0% 0%

Strong hybrid 0% 0% 3% 3% 12%
11-66 

Table 11-61 Technology Penetration Rates for the Ford in Model Year 2030 under Method A using the 

Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 0% 58% 58% 41% 41%

Engine friction

reduction 0% 93% 93% 76% 76%

Cylinder

deactivation 0% 18% 0% 0% 0%

Variable valve

timing 34% 34% 34% 34% 34%

Gasoline direct

injection 16% 16% 34% 34% 34%

Turbo Machinery

Improvements 35% 35% 35% 35% 69%

8 speed

transmission 59% 100% 100% 100% 100%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 0% 59% 59% 59%

Mass reduction

and materials 0% 0% 0% 59% 59%

Electric power

steering 0% 30% 90% 59% 59%

Improved

accessories 0% 0% 66% 59% 59%

Stop/start engine

systems 0% 0% 0% 0% 32%

Mild hybrid 0% 0% 0% 0% 8%

Strong hybrid 0% 0% 2% 12% 18%
11-67 

Table 11-62 Technology Penetration Rates for the General Motors in Model Year 2030 under Method A 

using the Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 64% 64% 64% 64% 64%

Engine friction

reduction 64% 100% 100% 100% 100%

Cylinder

deactivation 18% 47% 47% 47% 47%

Variable valve

timing 0% 0% 0% 0% 0%

Gasoline direct

injection 18% 18% 18% 36% 36%

Turbo Machinery

Improvements 36% 36% 36% 36% 36%

8 speed

transmission 0% 36% 100% 100% 100%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 64% 100% 100% 100%

Mass reduction

and materials 0% 100% 64% 64% 64%

Electric power

steering 66% 100% 100% 100% 100%

Improved

accessories 61% 63% 69% 63% 100%

Stop/start engine

systems 0% 0% 0% 9% 0%

Mild hybrid 0% 0% 0% 34% 82%

Strong hybrid 0% 0% 19% 20% 18%
11-68 

Table 11-63 Technology Penetration Rates for the Nissan in Model Year 2030 under Method A using the 

Dynamic Baseline 

Alternative

1a

(0% per

year)

Alternative

2

2% per year

Alternative

3 (2.5% per

year)

Alternative

4  

(3.5% per

year)

Alternative

5

(4% per

year)

Low friction

lubricants 100% 100% 100% 100% 100%

Engine friction

reduction 100% 100% 100% 100% 100%

Cylinder

deactivation 100% 49% 49% 49% 49%

Variable valve

timing 100% 100% 100% 100% 100%

Gasoline direct

injection 0% 51% 51% 51% 100%

Turbo Machinery

Improvements 0% 0% 0% 0% 0%

8 speed

transmission 0% 0% 51% 51% 51%

Low rolling

resistance tires 100% 100% 100% 100% 100%

Aerodynamic drag

reduction 0% 0% 100% 100% 100%

Mass reduction

and materials 0% 0% 0% 100% 100%

Electric power

steering 0% 0% 100% 100% 100%

Improved

accessories 0% 0% 0% 0% 28%

Stop/start engine

systems 0% 0% 0% 0% 0%

Mild hybrid 0% 0% 0% 0% 27%

Strong hybrid 0% 0% 0% 0% 1%

11.3.4.2 Pickup and Van Technology for Method B 

This section describes the penetration of selected technologies as separated by pickups 

and vans, as well as separated by fuel type (gasoline or diesel) using Method B. The model year 

represented for the technology penetration is 2030. 
11-69 

The technology mix that is projected to be sufficient to meet the 2025/27 standards for 

each pickup and van alternative in the Method B analysis is shown in Table 11-65 through Table 

11-67. 

Table 11-64 Technology Penetration Rates for Gasoline Pickups using Method B 

ALTERNATIVE

1A

(0% PER YEAR)

ALTERNATIVE

2

2% PER YEAR

ALTERNATIVE

3 (2.5% PER

YEAR)

ALTERNATIVE

4  

(3.5% PER

YEAR)

ALTERNATIVE

5

(4% PER YEAR)

Low friction

lubricants

42% 100% 100% 100% 100%

Engine friction

reduction

42% 100% 100% 100% 100%

Cylinder

deactivation

8% 56% 56% 56% 56%

Variable valve

timing

0% 56% 56% 56% 56%

Gasoline direct

injection

0% 0% 0% 0% 0%

8 speed

transmission

44% 86% 100% 100% 100%

Low rolling

resistance tires

0% 100% 100% 100% 100%

Aerodynamic

drag reduction

0% 42% 100% 100% 100%

Mass reduction

and materials

0% 42% 56% 100% 100%

Electric power

steering

42% 86% 100% 100% 100%

Improved

accessories

41% 41% 86% 86% 92%

Stop/start

engine systems

0% 0% 0% 20% 7%

Mild hybrid 0% 0% 0% 18% 20%

Strong hybrid 0% 1% 25% 48% 66%
11-70 

Table 11-65 Technology Penetration Rates for Gasoline Vans using Method B 

ALTERNATIVE

1A

(0% PER YEAR)

ALTERNATIVE

2

2% PER YEAR

ALTERNATIVE

3 (2.5% PER

YEAR)

ALTERNATIVE

4  

(3.5% PER

YEAR)

ALTERNATIVE

5

(4% PER YEAR)

Low friction

lubricants

45% 100% 100% 100% 100%

Engine friction

reduction

45% 100% 100% 100% 100%

Cylinder

deactivation

23% 23% 23% 23% 23%

Variable valve

timing

40% 40% 40% 40% 40%

Gasoline direct

injection

42% 52% 77% 97% 100%

8 speed

transmission

0% 95% 97% 97% 97%

Low rolling

resistance tires

8% 60% 100% 60% 100%

Aerodynamic

drag reduction

0% 40% 53% 53% 53%

Mass reduction

and materials

0% 0% 8% 13% 13%

Electric power

steering

3% 41% 55% 53% 53%

Improved

accessories

0% 20% 7% 20% 42%

Stop/start

engine systems

0% 0% 0% 2% 0%

Mild hybrid 0% 0% 0% 18% 38%

Strong hybrid 0% 0% 7% 0% 4%

 
11-71 

Table 11-66 Technology Penetration Rates for Diesel Pickups using Method B 

ALTERNATIVE

1A

(0% PER YEAR)

ALTERNATIVE

2

2% PER YEAR

ALTERNATIVE

3 (2.5% PER

YEAR)

ALTERNATIVE

4  

(3.5% PER

YEAR)

ALTERNATIVE

5

(4% PER YEAR)

Low friction

lubricants

0% 0% 0% 0% 0%

Engine friction

reduction

0% 0% 0% 0% 0%

Turbo

machinery

improvements

75% 100% 100% 100% 100%

8 speed

transmission

61% 97% 97% 97% 97%

Low rolling

resistance tires

0% 100% 100% 100% 100%

Aerodynamic

drag reduction

0% 35% 100% 100% 100%

Mass reduction

and materials

0%   35% 58% 100% 100%

Electric power

steering

35% 53% 100% 100% 100%

Improved

accessories

33% 35% 100% 100% 100%

Stop/start

engine systems

0% 0% 0% 11% 31%

Mild hybrid 0% 0% 0% 36% 47%

Strong hybrid 0% 0% 0% 0% 0%

 
11-72 

Table 11-67 Technology Penetration Rates for Diesel Vans using Method B 

ALTERNATIVE

1A

(0% PER YEAR)

ALTERNATIVE

2

2% PER YEAR

ALTERNATIVE

3 (2.5% PER

YEAR)

ALTERNATIVE

4  

(3.5% PER

YEAR)

ALTERNATIVE

5

(4% PER YEAR)

Low friction

lubricants

0% 0% 0% 0% 0%

Engine friction

reduction

0% 0% 0% 0% 0%

Turbo

machinery

improvements

0% 20% 20% 20% 20%

8 speed

transmission

0% 47% 67% 67% 93%

Low rolling

resistance tires

7% 80% 54% 54% 100%

Aerodynamic

drag reduction

0% 0% 7% 7% 7%

Mass reduction

and materials

0% 0% 7% 7% 7%

Electric power

steering

0% 11% 21% 12% 12%

Improved

accessories

0% 0% 7% 7% 7%

Stop/start

engine systems

0% 0% 0% 0% 0%

Mild hybrid 0% 0% 0% 0% 0%

Strong hybrid 0% 0% 0% 0% 0%

 
11-73 

11.4 Numerical Standards Corresponding to Alternative Technology 

Scenarios 

This section summarizes alternative EPA GHG and NHTSA fuel consumption standards 

corresponding to Alternatives 2, 4, and 5, including coefficients for the HD Pickup and Van 

alternative target curves. Note that the proposed standards correspond to Alternative 3. 

11.4.1 Alternative 2 Vehicle Standards 

Potential EPA GHG and NHTSA fuel consumption standards are shown for Alternative 2 in 

Table 11-68 to Table 11-71 and the coefficients for the HD Pickup and Van target curves for 

Alternative 2 are shown in Table 11-72. 

Table 11-68 Alternative 2 Phase 2 Diesel (CI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 305 194 205

Multi-Purpose 314 196 207 

Regional 328 192 195 

2021–2023 Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 29.9468 19.0615 20.0969

Multi-Purpose 30.8574 19.2642 20.2999 

Regional 32.1726 18.8587 19.1834 

2024 and Later Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 296 187 198

Multi-Purpose 304 189 200 

Regional 317 185 190 

2024 and Later Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 29.0602 18.3233 19.4746

Multi-Purpose 29.8789 18.5280 19.6796 

Regional 31.1068 18.2209 18.6547 
11-74 

Table 11-69 Alternative 2 Phase 2 Gasoline (SI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 330 210 221

Multi-Purpose 339 212 223 

Regional 353 207 211 

2021–2023 Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 37.0853 23.5770 24.8811

Multi-Purpose 38.1283 23.8092 25.1137 

Regional 39.7508 23.3446 23.7185 

2024–2026 Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 325 205 217

Multi-Purpose 334 207 219 

Regional 348 204 208 

2024–2026 Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 36.5703 23.0998 24.4215

Multi-Purpose 37.6252 23.3344 24.6563 

Regional 39.1490 22.9826 23.3648 
11-75 

Table 11-70 Alternative 2 Tractor Standards 

2021 MODEL YEAR CO2 GRAMS PER TON-MILE 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 102 82 73 

Mid Roof 112 88 81 

High Roof 114 90 80 

2021 Model Year Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 10.0196 8.055 7.1709 

Mid Roof 11.0020 8.6444 7.9568 

High Roof 11.1984 8.8409 7.8585 

2024 Model Year CO2 Grams per Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 97 77 68 

Mid Roof 107 84 76 

High Roof 109 85 74 

2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 9.5285 7.5639 6.6798 

Mid Roof 10.5108 8.2515 7.4656 

High Roof 10.7073 8.3497 7.2692 

Table 11-71 Alternative 2 Trailer Standards 

MODEL 

YEAR 

SUBCATEGORY DRY VAN REFRIGERATED VAN 

LENGTH LONG SHORT LONG SHORT 

2018 – 2020 

EPA Standard 

(CO2 Grams per Ton-Mile) 83 147 84 151 

Voluntary NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.1532 14.4401 8.2515 14.8330 

2021 – 2023 

EPA Standard 

(CO2 Grams per Ton-Mile) 82 147 84 151 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.0550 14.4401 8.2515 14.8330 

2024 – 2026 

EPA Standard 

(CO2 Grams per Ton-Mile) 81 147 83 151 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.9568 14.4401 8.1532 14.8330 
11-76 

Table 11-72 Alternative 2 HD Pickup and Van Standard Target Curve Coefficients 

Diesel Vehicles

Model Year a b c d

2018-2020 a 0.0416 320 0.0004086 3.143

2021 0.0408 313 0.0004008 3.075

2022 0.0400 307 0.0003929 3.016

2023 0.0392 301 0.0003851 2.957

2024 0.0384 295 0.0003772 2.898

2025 and later 0.0376 289 0.0003694 2.839

Gasoline Vehicles 

Model Year a b c d

2018-2020 a 0.0440 339 0.0004951 3.815

2021 0.0431 332 0.0004850 3.736

2022 0.0422 325 0.0004749 3.657

2023 0.0414 319 0.0004658 3.590

2024 0.0406 312 0.0004568 3.511

2025 and later 0.0398 306 0.0004478 3.443

Note: 

a

 Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only. 

 
11-77 

11.4.2 Alternative 4 Vehicle Standards 

Potential EPA GHG and NHTSA fuel consumption standards are shown for Alternative 4 in 

Table 11-73 to Table 11-76 and the coefficients for the HD Pickup and Van target curves for 

Alternative 4 are shown in Table 11-77. 

Table 11-73 Alternative 4 Phase 2 Diesel (CI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per TonMile

LHD 

(Class 2b5)

MHD 

(Class 6-

7)

HHD 

(Class 8)

Urban 288 183 193

MultiPurpose 297 185 196 

Regional 309 181 185 

2021–2023 Model Year Gallons of Fuel per 

1,000 Ton-Mile

LHD 

(Class 2b5)

MHD 

(Class 6-

7)

HHD 

(Class 8)

Urban 28.3890 17.9764 18.8605

MultiPurpose 29.0766 18.0747 18.9587 

Regional 30.1572 17.6817 17.9764 

2024 and Later Model Year CO2 Grams per 

Ton-Mile

LHD 

(Class 2b5)

MHD 

(Class 6-

7)

HHD 

(Class 8)

Urban 272 172 182

MultiPurpose 280 174 183 

Regional 292 170 174 

2024 and Later Model Year Gallons of Fuel per 

1,000 Ton-Mile

LHD 

(Class 2b5)

MHD 

(Class 6-

7)

HHD 

(Class 8)

Urban 26.7191 16.8959 17.8782

MultiPurpose 27.5049 17.0923 17.9764 

Regional 28.6837 16.6994 17.0923 
11-78 

Table 11-74 Alternative 4 Phase 2 Gasoline (SI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per TonMile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 313 199 210

MultiPurpose 323 201 212

Regional 336 197 201

2021–2023 Model Year Gallons of Fuel per 

1,000 Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 35.2200 22.3923 23.6300

MultiPurpose 36.3452 22.6173 23.8551

Regional 37.8080 22.1672 22.6173

2024 and Later Model Year CO2 Grams per 

Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 299 189 196

MultiPurpose 308 191 198

Regional 321 187 188

2024 and Later Model Year Gallons of Fuel per 

1,000 Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 33.6446 21.2670 22.0547

MultiPurpose 34.6574 21.4921 22.2797

Regional 36.1202 21.0420 21.1545
11-79 

Table 11-75 Alternative 4 Tractor Standards 

DAY CAB SLEEPER CAB 

Class 7 Class 8 Class 8 

Low Roof 94 76 68 

Mid Roof 104 82 76 

High Roof 106 84 75 

2021 Model Year Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 9.2338 7.4656 6.6798 

Mid Roof 10.2161 8.055 7.4656 

High Roof 10.4126 8.2515 7.3674 

2024 Model Year CO2 Grams per Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 87 70 62 

Mid Roof 96 76 69 

High Roof 96 76 67 

2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 8.5462 6.8762 6.0904 

Mid Roof 9.4303 7.4656 6.7780 

High Roof 9.4303 7.4656 6.5815 

Table 11-76 Alternative 4 Trailer Standards 

MODEL 

YEAR 

SUBCATEGORY DRY VAN REFRIGERATED VAN 

LENGTH LONG SHORT LONG SHORT 

2018 - 2020 

EPA Standard 

(CO2 Grams per Ton-Mile) 83 144 84 147 

Voluntary NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.1532 14.1454 8.2515 14.4401 

2021 - 2023 

EPA Standard 

(CO2 Grams per Ton-Mile) 80 142 81 145 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.8585 13.9489 7.9568 14.2436 

2024 - 2026 

EPA Standard 

(CO2 Grams per Ton-Mile) 77 140 80 144 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.5639 13.7525 7.8585 14.1454 
11-80 

Table 11-77 Alternative 4 HD Pickup and Van Standard Target Curve Coefficients 

Diesel Vehicles

Model Year a b c d

2018-2020 a 0.0416 320 0.0004086 3.143

2021 0.0402 308 0.0003949 3.026

2022 0.0388 298 0.0003811 2.927

2023 0.0374 287 0.0003674 2.819

2024 0.0361 277 0.0003546 2.721

2025 and later 0.0348 267 0.0003418 2.623

Gasoline Vehicles 

Model Year a b c d

2018-2020 a 0.0440 339 0.0004951 3.815

2021 0.0425 327 0.0004782 3.680

2022 0.0410 315 0.0004613 3.545

2023 0.0395 304 0.0004445 3.421

2024 0.0381 294 0.0004287 3.308

2025 and later 0.0368 283 0.0004141 3.184

Note: 

a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only. 

 
11-81 

11.4.3 Alternative 5 Vehicle Standards 

Potential EPA GHG and NHTSA fuel consumption standards are shown for Alternative 4 in 

Table 11-78 to Table 11-81 and the coefficients for the HD Pickup and Van target curves for 

Alternative 5 are shown in Table 11-82. 

Table 11-78 Alternative 5 Phase 2 Diesel (CI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 292 185 194

Multi-Purpose 300 186 196 

Regional 313 183 185 

2021–2023 Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 32.8863 20.7809 21.8523

Multi-Purpose 33.8112 20.9856 22.0566 

Regional 35.2499 20.5761 20.8312 

2024 and Later Model Year CO2 Grams per Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 278 175 185

Multi-Purpose 286 177 186 

Regional 298 174 177 

2024 and Later Model Year Gallons of Fuel per 1,000 Ton-Mile

LHD (Class 2b-5) MHD (Class 6-7) HHD (Class 8)

Urban 31.2817 19.7042 20.7621

Multi-Purpose 32.1840 19.9043 20.9617 

Regional 33.4874 19.6042 19.8637 
11-82 

Table 11-79 Alternative 5 Phase 2 Gasoline (SI) Vocational Vehicle Standards 

2021–2023 Model Year CO2 Grams per TonMile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 292 185 194

MultiPurpose 300 186 196

Regional 313 183 185

2021–2023 Model Year Gallons of Fuel per 

1,000 Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 32.8863 20.7809 21.8523

MultiPurpose 33.8112 20.9856 22.0566

Regional 35.2499 20.5761 20.8312

2024 and Later Model Year CO2 Grams per 

Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 278 175 185

MultiPurpose 286 177 186

Regional 298 174 177

2024 and Later Model Year Gallons of Fuel per 

1,000 Ton-Mile 

LHD 

(Class 2b5) 

MHD 

(Class 6-

7) 

HHD 

(Class 8) 

Urban 31.2817 19.7042 20.7621

MultiPurpose 32.1840 19.9043 20.9617

Regional 33.4874 19.6042 19.8637

 
11-83 

Table 11-80 Alternative 5 Tractor Standards 

DAY CAB SLEEPER CAB 

Class 7 Class 8 Class 8 

Low Roof 87 70 63 

Mid Roof 96 75 71 

High Roof 98 77 70 

2021 Model Year Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 8.5462 6.8762 6.1886 

Mid Roof 9.4303 7.3674 6.9745 

High Roof 9.6267 7.5639 6.8762 

2024 Model Year CO2 Grams per Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 82 65 58 

Mid Roof 91 71 64 

High Roof 92 72 63 

2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile 

Day Cab Sleeper Cab 

Class 7 Class 8 Class 8 

Low Roof 8.0550 6.3851 5.6974 

Mid Roof 8.9391 6.9745 6.2868 

High Roof 9.0373 7.0727 6.1886 

Table 11-81 Alternative 5 Trailer Standards 

MODEL 

YEAR 

SUBCATEGORY DRY VAN REFRIGERATED VAN 

LENGTH LONG SHORT LONG SHORT 

2018 - 2020 

EPA Standard 

(CO2 Grams per Ton-Mile) 82 144 83 147 

Voluntary NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 8.0550 14.1454 8.1532 14.4401 

2021 - 2023 

EPA Standard 

(CO2 Grams per Ton-Mile) 79 142 81 145 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.7603 13.9489 7.9568 14.2436 

2024 - 2026 

EPA Standard 

(CO2 Grams per Ton-Mile) 76 140 80 143 

NHTSA Standard 

(Gallons per 1,000 Ton-Mile) 7.4656 13.7525 7.8585 14.0472 

 
11-84 

Table 11-82 Alternative 5 HD Pickup and Van Standard Target Curve Coefficients 

Diesel Vehicles

Model Year a b c d

2018-2020 a 0.0416 320 0.0004086 3.143

2021 0.0400 307 0.0003929 3.016

2022 0.0384 295 0.0003772 2.898

2023 0.0368 283 0.0003615 2.780

2024 0.0354 271 0.0003477 2.662

2025 and later 0.0339 261 0.0003330 2.564

Gasoline Vehicles 

Model Year a b c d

2018-2020 a 0.0440 339 0.0004951 3.815

2021 0.0422 325 0.0004749 3.657

2022 0.0405 312 0.0004557 3.511

2023 0.0389 300 0.0004377 3.376

2024 0.0374 288 0.0004208 3.241

2025 and later 0.0359 276 0.0004040 3.106

Note: 

a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only. 

11.4.4 Alternative Engine Standards 

The alternative standards the agencies considered for heavy-duty tractor engines are 

provided in Table 11-83. 

Table 11-83 Alternative Heavy-Duty Tractor Engine Standards over the SET Cycle

MODEL 

YEAR 

STANDARDS ALTERNATIVE 2 ALTERNATIVE 4 ALTERNATIVE 5

 MHD HHD MHD HHD MHD HHD 

2021 – 

2023 

EPA Standard 481 455 477 451 474 448 

(CO2 Grams 

per Ton-Mile) 

NHTSA 

Standard 

4.7250 4.4695 4.6857 4.4303 4.6562 4.4008 

(Gallons per 

100 Ton-Mile) 

2024 and 

Later 

EPA Standard 471 445 466 441 464 438 

(CO2 Grams 

per Ton-Mile) 

NHTSA 

Standard 

4.6267 4.3713 4.5776 4.3320 4.5580 4.3026 

(Gallons per 

100 Ton-Mile) 
11-85 

Table 11-84 presents the alternative CO2 and fuel consumption standards the agencies 

considered for compression-ignition engines to be installed in vocational vehicles. As with the 

proposed standards, the first set of alternative standards would take effect with MY 2021, and the 

second set would take effect with MY 2024. 

Table 11-84 Alternative Vocational Diesel Engine Standards over the Heavy-Duty FTP Cycle 

MODEL 

YEAR

STANDARD LIGHT 

HEAVYDUTY 

DIESEL 

MEDIUM 

HEAVYDUTY 

DIESEL 

HEAVY 

HEAVYDUTY 

DIESEL 

Alternative 2 

2021-2023 CO2 Standard (g/bhp-hr) 571 571 550 

Fuel Consumption Standard 

(gallon/100 bhp-hr) 

5.6090 5.6090 5.4028 

2024 and 

Later

CO2 Standard (g/bhp-hr) 559 559 539 

Fuel Consumption (gallon/100 

bhp-hr) 

5.4912 5.4912 5.2947 

Alternative 4 

2021-2023 CO2 Standard (g/bhp-hr) 562 562 541 

Fuel Consumption Standard 

(gallon/100 bhp-hr) 

5.5206 5.5206 5.3143 

2024 and 

Later

CO2 Standard (g/bhp-hr) 553 553 533 

Fuel Consumption (gallon/100 

bhp-hr) 

5.4322 5.4322 5.2358 

Alternative 5 

2021-2023 CO2 Standard (g/bhp-hr) 559 559 538 

Fuel Consumption Standard 

(gallon/100 bhp-hr) 

5.4912 5.4912 5.2849 

2024 and 

Later

CO2 Standard (g/bhp-hr) 550 550 530 

Fuel Consumption (gallon/100 

bhp-hr) 

5.4028 5.4028 5.2063 

 
11-86 

References 

1

 OMB Circular A-4, September 17, 2003. Available at http://www.whitehouse.gov/omb/circulars_a004_a-4. 2

 NEPA requires agencies to consider a “no action” alternative in their NEPA analyses and to compare the effects of 

not taking action with the effects of the reasonable action alternatives to demonstrate the different environmental 

effects of the action alternatives. See 40 CFR 1502.2(e), 1502.14(d).CEQ has explained that “[T]he regulations 

require the analysis of the no action alternative even if the agency is under a court order or legislative command to 

act. This analysis provides a benchmark, enabling decision makers to compare the magnitude of environmental 

effects of the action alternatives. [See 40 CFR 1502.14(c).] * * * Inclusion of such an analysis in the EIS is 

necessary to inform Congress, the public, and the President as intended by NEPA. [See 40 CFR 1500.1(a).]'' Forty 

Most Asked Questions Concerning CEQ’s National Environmental Policy Act Regulations, 46 FR 18026 (1981) 

(emphasis added). 

3 http://energy.gov/eere/vehicles/vehicle-technologies-office-21st-century-truck

4 http://www.epa.gov/smartway/

5

 State of California Global Warming Solutions Act of 2006 (Assembly Bill 32, or AB32) 

6

 Confidence Report: Idle-Reduction Solutions, North American Council for Freight Efficiency, Lee, Tessa, 2014, p. 

13. 

7

 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles,” (hereafter, “NAS 2010”). Washington, D.C. The National Academies Press. 

Available electronically from the National Academies Press Website at 

http://www.nap.edu/catalog.php?record_id=12845 (last accessed September 10, 2010). 8

 Note that the “CO2 emission rates” for tractors and vocational vehicles reflect changes in CO2 emissions not 

represented by tire rolling resistance, aerodynamic drag, or vehicle weight. 

9

 Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles with GVWR of less than 14,000 lbs were modeled using 

the energy rate reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these 

vehicles choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups 

and vans). 

10 Vocational tractors are included in the short-haul tractor segment. 11 Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles under 14,000 lbs were modeled using the energy rate 

reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these vehicles 

choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups and vans). 12 Vocational tractors are included in the short-haul tractor segment. 13 Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles with GVWR of less than 14,000 lbs were modeled using 

the energy rate reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these 

vehicles choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups 

and vans). 

14 Vocational tractors are included in the short-haul tractor segment. 15 Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles with GVWR less than 14,000 lbs were modeled using the 

energy rate reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these 

vehicles choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups 

and vans). 

16 Vocational tractors are included in the short-haul tractor segment. 
11-87 

17 Vocational vehicles modeled in MOVES include heavy heavy-duty, medium heavy-duty, and light heavy-duty 

vehicles. However, for light heavy-duty vocational vehicles, class 2b and 3 vehicles are not included in the 

inventories for the vocational sector. Instead, all vehicles under 14,000 lbs were modeled using the energy rate 

reductions described below for HD pickup trucks and vans. In practice, many manufacturers of these vehicles 

choose to average the lightest vocational vehicles into chassis-certified families (i.e., heavy-duty pickups and vans). 18 Vocational tractors are included in the short-haul tractor segment. 
12-1 

Chapter 12: Initial Regulatory Flexibility Analysis 

This chapter discusses the agencies’ Initial Regulatory Flexibility Analysis (IRFA) that 

evaluates the potential impacts of the proposed standards on small entities. The Regulatory 

Flexibility Act, as amended by the Small Business Regulatory Enforcement Fairness Act of 1996 

(SBREFA), generally requires an agency to prepare a regulatory flexibility analysis of any rule 

subject to notice and comment rulemaking requirements under the Administrative Procedure Act 

or any other statute unless the agency certifies that the rule will not have a significant economic 

impact on a substantial number of small entities. Pursuant to this requirement, we have prepared 

an IRFA for the proposed rule. Throughout the process of developing the IRFA, EPA conducted 

outreach and held meetings with representatives from the various small entities that could be 

affected by the rulemaking to gain feedback, including recommendations, on how to reduce the 

impact of the rule on these entities. The small business recommendations stated here reflect the 

comments of the small entity representatives (SERs) and members of the Small Business 

Advocacy Review Panel (SBAR Panel, or ‘the Panel’). NHTSA maintains obligations to 

evaluate small business impacts under the Regulatory Flexibility Act, but is not required to 

convene a SBAR Panel. As a joint rulemaking, EPA and NHTSA have coordinated formulation 

of standards, including flexibilities for small businesses. 

12.1 Overview of the Regulatory Flexibility Act 

In accordance with Section 609(b) of the Regulatory Flexibility Act (RFA), EPA 

convened an SBAR Panel before conducting the IRFA. A summary of the Panel’s 

recommendations is presented in the preamble of this proposed rulemaking. Further detailed 

discussion of the Panel’s outreach, advice and recommendations is found in the Final Panel 

Report contained in the docket for this proposed rulemaking.1 

Section 609(b) of the RFA directs the Panel to report on the comments of small entity 

representatives and make findings on issues related to elements of an IRFA under Section 603 of 

the RFA. Those elements of an IRFA are: 

 A description of, and where feasible, an estimate of the number of small entities to which 

the proposed rule will apply; 

 A description of projected reporting, record keeping, and other compliance requirements 

of the proposed rule, including an estimate of the classes of small entities which will be 

subject to the requirement and the type of professional skills necessary for preparation of 

the report or record; 

 An identification, to the extent practicable, of all relevant Federal rules which may 

duplicate, overlap, or conflict with the proposed rule; 

 A description of any significant alternatives to the proposed rule which accomplish the 

stated objectives of applicable statutes and which minimize any significant economic 

impact of the proposed rule on small entities. 
12-2 

The RFA was amended by SBREFA to ensure that concerns regarding small entities are 

adequately considered during the development of new regulations that affect those entities. 

Although EPA is not required by the Clean Air Act to provide special treatment to small 

businesses, the RFA requires EPA and NHTSA to carefully consider the economic impact that 

our rules will have on small entities. The recommendations made by the Panel may serve to help 

lessen these economic impacts on small entities when consistent with the Clean Air Act 

requirements. 

12.2 Need for Rulemaking and Rulemaking Objectives 

Heavy-duty vehicles are classified as those with gross vehicle weight ratings (GVWR) of 

greater than 8,500 lb. Section 202(a) of the Clean Air Act (CAA) requires EPA to promulgate 

emission standards for pollutant emissions from new motor vehicles and engines which 

emissions cause or contribute to air pollution which may reasonably be anticipated to endanger 

public health or welfare. In 2009, EPA found that six greenhouse gases (GHGs) were 

anticipated to endanger public health or welfare, and that new motor vehicles and new motor 

vehicle engines contribute to that pollution which endangers. As explained in preamble section 

I, the D.C. Circuit upheld this endangerment finding, and further held that EPA had a mandatory 

duty to promulgate standards for emissions of the pollutant which contributes to the 

endangerment: GHGs from new motor vehicles and engines. 

The Energy and Security Independence Act of 2007 (EISA) directs NHTSA to develop 

regulations to increase fuel efficiency for commercial medium-duty and heavy-duty on-highway 

vehicles and work trucks. Fundamentally, EISA seeks energy conservation. In 2010, total fuel 

consumption and GHG emissions from medium- and heavy-duty vehicles accounted for 23 

percent of total U.S. transportation-related GHG emissions. 

EPA and NHTSA’s Phase 1 Heavy-Duty Engines and Vehicles Program, which was 

finalized in September 2011 (76 FR 57106), marked the first greenhouse gas emissions and fuel 

efficiency standards for heavy-duty vehicles and engines. The program addressed medium- and 

heavy-duty GHG emissions and fuel efficiency through the adoption of performance-based 

standards that allow manufacturers to determine the optimal mix of technologies to achieve the 

necessary reductions for their vehicle fleets and engines. 

Building on the Phase 1 rule, this proposed Phase 2 rule would reduce GHG emissions 

and fuel consumption associated with the transportation of goods across the United States post2017. The proposed Phase 2 rulemaking considers changes to existing engine, GHG, and fuel 

efficiency standards, as well as regulatory standards and certification requirements for 

previously-unregulated new trailers pulled by semi-tractors. If such a rule is adopted, 

manufacturers of heavy-duty engines, chassis, vehicles and trailers could be required to 

incorporate GHG-reducing and fuel-saving technologies in order to comply with the agencies’ 

performance-based standards. 

12.3 Definition and Description of Small Businesses 

The RFA defines small entities as including “small businesses,” “small governments,” 

and “small organizations” (5 USC 601) and references the Small Business Act for the definition 
12-3 

of “small businesses” using size standards based on the North American Industry Classification 

System (NAICS) (13 CFR 121.201). The standards being considered by EPA for this 

rulemaking are expected to affect a variety of small businesses. A listing of the NAICS codes 

identified as relevant to the potential rulemaking, along with their respective SBA size 

thresholds, is located in Table 12-1, below. 

The agencies expect that the same industries affected by the Phase 1 rulemaking will also 

be affected by the proposed Phase 2 rulemaking. In addition, small businesses and trailer 

manufacturers are also included in the proposed Phase 2 rule. EPA and NHTSA used the criteria 

for small entities developed by the Small Business Administration (SBA) as a guide to 

identifying Small Entity Representatives (SERs) for this proposed rulemaking. Table 12-1 lists 

industries potentially directly affected by the regulation. The NAICS Code and size threshold 

are shown as well. 

Table 12-1 Industry Sectors Potentially Affected by the Agencies’ Planned Action 

INDUSTRY EXPECTED IN 

RULEMAKING

NAICS 

CODE

NAICS 

DESCRIPTION

SBA SIZE 

THRESHOLD

Alternative Fuel 

Engine Converters 

333999 Misc. General Purpose Machinery 500 employees 

811198 All Other Auto Repair & Maintenance $7.0M (annual receipts) 

HD Pick-up Trucks & Vans 336111 Automobile Manufacturing 1,000 employees 

Vocational Chassis, 

Class 7 & 8 Tractors 

336120 Heavy-Duty Truck Manufacturing 1,000 employees 

Trailers 336212 Truck Trailer Manufacturing 500 employees 

333924 Ind. Truck, Trailer & Stacker Machinery 750 employees 

HD Engines 336310 Motor Vehicle Gasoline Engine & Engine 

Parts 

750 employees 

12.4 Summary of Small Entities to which the Rulemaking will Apply 

Using the information from Table 12-1, with the agencies’ certification data and 

employment information from the Hoover’s online business information database, EPA and 

NHTSA determined that only three of these affected industries contained small businesses: 

vocational chassis manufacturers, alternative fuel engine converters, and trailer manufacturers, as 

described below. The agencies believe there are about 115 trailer manufacturers and 100 of 

these manufacturers qualify as small entities with 500 employees or less. EPA and NHTSA 

identified 21 alternative fuel engine converters from previous certification data and 18 of these 

converters are considered small entities. Currently, 20 manufacturers that make chassis for 

vocational vehicles certify with EPA under the Phase 1 program. Three vocational chassis 

manufacturers contacted EPA and NHTSA to request an exemption from Phase 1 based on their 

small entity status. Gliders are a subset of vehicles being considered for regulation under the 

proposed Phase 2 rulemaking (including for regulation of criteria pollution emissions). Glider 

manufacturers traditionally manufacture new vehicle bodies (vocational vehicles or Class 7 and 8 

tractors) for use with older powertrains. The agencies are aware of four glider manufacturers and 

three are small entities. 
12-4 

12.5 Related Federal Rules 

The Phase 1 rulemaking continues to be in effect in the absence of this proposed rule. 

The Panel noted that it was aware that the proposed Phase 2 rule would be a joint action by EPA 

and Department of Transportation (DOT), through NHTSA, as was done in the Phase 1 

rulemaking. The Panel is also aware of several other state and Federal rules related to heavyduty vehicles and to the proposed Phase 2 rule under consideration. NHTSA has safety 

requirements for medium- and heavy-duty vehicles located at 49 CFR 571. California adopted 

its own greenhouse gas initiative, which places aerodynamic requirements on trailers used in 

long-haul applications. None of these existing regulations were found to conflict with the 

proposed rulemaking. 

12.6 Projected Reporting, Recordkeeping, and Other Compliance 

Requirements 

For any emission control program, EPA must have assurances that the regulated products 

will meet the standards. The program that EPA and NHTSA are considering for manufacturers 

subject to this proposal will include testing, reporting, and recordkeeping requirements. Testing 

requirements for these manufacturers could include use of EPA’s Greenhouse gas Emissions 

Model (GEM) vehicle simulation tool to obtain the overall CO2 emissions rate for certification of 

vocational chassis and trailers, aerodynamic testing to obtain aerodynamic inputs to GEM for 

some trailer manufacturers, and engine dynamometer testing for alternative fuel engine 

converters to ensure their conversions meet the proposed CO2, CH4 and N2O engine standards. 

Reporting requirements would likely include emissions test data or model inputs and results, 

technical data related to the vehicles, and end-of-year sales information. Manufacturers would 

have to keep records of this information. 

12.7 Regulatory Flexibilities 

The Panel developed a range of regulatory flexibilities intended to mitigate the impacts of 

the proposed rulemaking on small businesses, and recommended that EPA propose and seek 

comment on the flexibilities. The Panel’s findings and discussions are based on the information 

that was available during the term of the Panel and issues that were raised by the SERs during 

the outreach meetings and in their written comments. It was agreed that EPA should consider 

the issues raised by the SERs (and issues raised in the course of the Panel) and that EPA should 

consider the comments on flexibility alternatives that would help to mitigate any negative 

impacts on small businesses. 

Alternatives discussed throughout the Panel process include those offered in the 

development of the upcoming rule. Though some of the recommended flexibilities may be 

appropriate to apply to all entities affected by the rulemaking, the Panel’s discussions and 

recommendations are focused mainly on the impacts, and ways to mitigate adverse impacts, on 

small businesses. A summary of the Panel’s recommendations, along with those provisions that 

we are actually proposing in this action, are detailed below. A full discussion of the regulatory 

alternatives and hardship provisions discussed and recommended by the Panel, all written 

comments received from SERs, and summaries of the two outreach meetings that were held with 

the SERs can be found in the SBREFA Final Panel Report.2

 In addition, all of the flexibilities 
12-5 

that are being proposed in the rulemaking for small businesses, as well as those for all entities 

that may be affected by the rulemaking, are described in the preamble to the proposed rule. 

12.7.1 Alternative Fuel Engine Converter Flexibilities 

12.7.1.1 SBAR Panel Recommendations 

To reduce the compliance burden of small business engine converters who convert 

engines in previously-certified complete vehicles, the Panel recommended allowing engine 

compliance to be sufficient for certification. This would mean the converted vehicle would not 

need to be recertified as a vehicle. This flexibility would eliminate the need for these small 

manufacturers to gather all of the additional component-level information (e.g., transmission 

data, aerodynamic performance, tire rolling resistance) in addition to the engine CO2

performance necessary to properly certify a vehicle with GEM. In addition, the Panel 

recommended that small engine converters be able to submit an engineering analysis, in lieu of 

measurement, to show that their converted engines do not increase N2O emissions. Many of the 

small engine converters are converting SI-engines, and the catalysts in these engines are not 

expected to substantially impact N2O production. Small engine converters that convert CIengines could likely certify by ensuring that their controls require changes to the SCR dosing 

strategies. 

Based on the comments received from SERs, the Panel recommended not having separate 

standards for small business natural gas engine manufacturers. The Panel believed this would 

discourage entrance into this emerging market by adding unnecessary costs to a technology that 

has the potential to reduce CO2 tailpipe emissions. In addition, the Panel stated that it believes 

additional leakage requirements beyond a sealed crankcase for small business natural gas-fueled 

CI engines and requirements to follow industry standards for leakage could be waived for small 

businesses with minimal impact on overall GHG emissions. 

Finally, the Panel recommended that small engine converters receive a one-year delay in 

implementation for each increase in stringency throughout the proposed rule. This flexibility 

would provide small converters additional lead time to obtain the necessary equipment and 

perform calibration testing if needed. 

12.7.1.2 The Agencies’ Proposed Regulatory Flexibility Options 

The agencies have chosen to propose the Panel’s recommended regulatory flexibility 

provisions for alternative fuel engine converters. EPA and NHTSA are proposing to offer small 

business engine converters a one year delay in implementation for each increase in stringency 

throughout the proposed rule. In addition, small businesses that convert complete vehicles will 

be able to use an engine-only certification of their final vehicles. Finally, the agencies are 

proposing to allow small business engine converters to use an engineering analysis approach to 

show that their converted engines do not impact N2O production. 
12-6 

12.7.2 Vocational Vehicle Chassis Manufacturer Flexibilities 

12.7.2.1 SBAR Panel Recommendations 

The Panel recommended proposing less stringent standards for emergency vehicle chassis 

manufactured by small businesses. The Panel stated that it believes it is feasible for small 

manufacturers to install a Phase 2-compliant engine, but recommended that the rulemaking 

request comment on whether the use of LLR tires will provide enough CO2 benefits to justify 

requiring small business emergency chassis manufacturers to adopt them. In addition, the Panel 

recommended a simplified certification approach for small manufacturers who make chassis for 

emergency vehicles that reduces the number of inputs these manufacturers would need to obtain 

for GEM. 

The Panel recommended proposing a low volume exemption for small business custom 

chassis manufacturers based on the volume of sales. Similar to the recommendation for 

emergency vehicle chassis manufacturers, the Panel stated it believes it is feasible to require 

installation of a Phase 2-compliant engine and recommended that EPA request comment on the 

benefits of LRR tires in this market segment. The Panel also recommended that the rulemaking 

request comment on how to design a small business exemption by means of a volume exemption 

and what sales volume would be an appropriate threshold. 

The Panel stated that it believes that the number of vehicles produced by small business 

glider manufacturers is too small to have a substantial impact on the total heavy-duty inventory. 

The Panel also stated that there should be an allowance to produce some number of glider kits 

for legitimate purposes, such as for newer vehicles badly damaged in crashes. The Panel 

therefore recommended proposing an explicit allowance for existing small businesses to continue 

assembling glider vehicles without having to comply with the GHG requirements. The Panel 

also recommended that any regulations for glider production be flexible enough to allow sales 

levels as high as the peak levels in the 2010-2012 timeframe. 

12.7.2.2 EPA and NHTSA’s Proposed Regulatory Flexibility Options 

EPA and NHTSA are proposing a flexibility for all emergency vehicles that includes 

fewer technology requirements and a simplified certification approach. The agencies are also 

requesting comment on an appropriate low volume threshold for custom chassis manufacturers 

that would allow them to opt into a standard that has fewer technology requirements. The 

exemption that the agencies are proposing for glider manufacturers is expected to encompass 

small glider manufacturers. See Section XIV of the NPRM preamble for additional details. 

12.7.3 Trailer Manufacturer Flexibilities 

12.7.3.1 SBAR Panel Recommendations 

Box Trailers 

Box trailer manufacturers have the benefit of relying on the aerodynamic technology 

development initiated through EPA’s voluntary SmartWay program. The Panel acknowledged 

EPA’s plan to propose a simplified compliance program for all manufacturers, in which 
12-7 

aerodynamic device manufacturers have the opportunity to test and register their devices with 

EPA as technologies that can be used by trailer manufacturers in their trailer certification. This 

pre-approved technology strategy is intended to provide all trailer manufactures a means of 

complying with the standards without testing. Upon the completion of the SBREFA Panel 

process, it was unclear if this strategy would be available indefinitely, or if it would be an interim 

flexibility to allow manufacturers to ease into a testing-only compliance program. The Panel 

recommended that, in the event that this strategy is limited to the early years of the trailer 

program for all manufacturers, small manufacturers should continue to be given the option to use 

pre-approved devices in lieu of testing. 

The Panel stated its belief that, in the event that small trailer manufacturers adopt preapproved aerodynamic technologies and the appropriate tire technologies for compliance, it 

would not be necessary to require the use of a vehicle emissions model, such as GEM, for 

certification. Instead, the Panel stated that it could be possible for manufacturers to simply 

report to EPA that all of their trailers include approved technologies. 

Non-Box Trailers 

The Panel recommended that EPA not base a standard for non-box trailers on 

performance of aerodynamic devices. Some of the non-box trailer manufacturer SERs have seen 

prototype-level demonstrations of aerodynamic devices on non-box trailers. However, most 

non-box trailer SERs identified unique operations in which their trailers are used that preclude 

the use of those technologies. 

Some non-box trailer manufacturers have experience with LRR tires and ATI systems. 

However, the non-box trailer manufacturer SERs indicated that LRR tires are not currently 

available for some of their trailer types. The SERs noted that tire manufacturers are currently 

focused on box trailer applications and that there are only a few LRR tire models that meet the 

needs of their customers. The Panel stated that it believes EPA should ensure appropriate 

availability of these tires in order for it to be deemed a feasible means of achieving these 

standards and recommends a streamlined compliance process based on the availability of 

technologies. The Panel suggested that the best compliance option from a small business 

perspective would be for the agencies to pre-approve tires once they are available in sufficient 

quantities on the market, similar to the approach being proposed for aerodynamic technologies, 

and to maintain a list that could be used to exempt small businesses when no suitable tires are 

available. However, the Panel stated that it recognizes the difficulties of maintaining an up-todate list of certified technologies. The Panel recommended that, if the rulemaking does not 

adopt the list-based approach, the agency consider a simplified letter-based compliance option 

that allows manufacturers to petition the agencies for an exemption if they are unable to identify 

tires that meet the LRR performance requirements on a trailer family basis. 

Trailers with Unique Use Patterns 

The Panel recommended excluding all trailers that spend a significant amount of time in 

off-road applications. These trailers may not spend much time at highway speeds and 

aerodynamic devices may interfere with the vehicle’s intended purpose. Additionally, tires with 

lower rolling resistance may not provide the type of traction needed in off-road applications. 
12-8 

General Flexibilities for All Small Trailer Manufacturers 

The Panel stated that it recognizes that some manufacturers, who have diverse product 

lines and high sales volumes, may benefit from an emissions averaging, banking and trading 

(ABT) strategy. However, due to the custom-order nature of the trailer industry, SERs have 

expressed their concern that ABT may provide an opportunity for historically loyal customers or 

customers with large fractions of a manufacturer’s business to bargain for the portion of a 

manufacturer’s sales that have minimal requirements. Based on the low volume of sales and 

niche market of many small business trailer manufacturers, small businesses in particular may 

have little leverage in this situation and risk losing their customers to larger manufacturers who 

have credits to spare. In addition, the accounting and reporting burdens of ABT may preclude 

small businesses from participating in the flexibility. 

Due to the potential for reducing a small business’s competitiveness compared to the 

larger manufacturers, as well as the ABT recordkeeping burden, the Panel recommended EPA 

consider small business flexibilities to allow small entities to opt out of ABT without placing 

themselves at a competitive disadvantage to larger firms that adopt ABT, such as a low volume 

exemption or requiring only LRR where appropriate. The Panel recommended that EPA should 

also consider flexibilities for small businesses that would ease and incentivize their participation 

in ABT, such as streamlined the tracking requirements for small businesses. In addition, the 

Panel recommended that the rulemaking request comment on the feasibility and consequences of 

ABT for the trailer program and additional flexibilities that would promote small business 

participation. 

In addition, for all trailer types that will be included in the proposal, the Panel 

recommended a 1-year delay in implementation for small trailer manufacturers at the start of the 

proposed rulemaking to allow them additional lead time to make the proper staffing adjustments 

and process changes and possibly add new infrastructure to meet these requirements. In the 

event that the agencies are unable to provide pre-approved technologies for manufacturers to 

choose for compliance, the Panel recommended that the standards provide small business trailer 

manufacturers an additional 1-year delay for each subsequent increase in stringency. This 

additional lead time would allow these small businesses to research and market the technologies 

required by the new standards. 

12.7.3.2 The Agencies’ Proposed Regulatory Flexibility Options 

The agencies are proposing many of the Panel’s recommendations for small business 

trailer manufacturers, including seeking comment on the possibility of a small volume 

exemption. While many of the smallest trailer manufactures sell significantly fewer trailers than 

the largest small manufacturers, many of the smallest trailer manufacturers produce specialty 

trailers that are already candidates for exemption under the proposed off-highway or heavy-haul 

provisions described in Section IV C. (5) of the preamble to this rulemaking. 

Testing requirements for small businesses are largely reduced by provisions outlined in 

the program for both large and small trailer manufacturers. Tire rolling resistance is measured 

by tire manufacturers and information needed for compliance would be presented to trailer 

manufacturers when they purchase their tires. The agencies are also proposing an option for pre-
12-9 

approved aerodynamic device data to be made available to trailer manufacturers for use in 

complying with aerodynamic requirements. These pre-approved devices would eliminate the 

requirement for trailer manufacturers to complete aerodynamic performance testing for 

certification. A majority of the small trailer manufacturers produce non-box trailers, the 

proposed standards for which are not predicated on use of aerodynamic controls, which reduces 

the number of technologies to investigate, market, and implement. EPA and NHTSA expect the 

six small business box trailer manufacturers the agencies have identified will take advantage of 

the pre-approved aerodynamic devices for most of their trailers. 

Additionally, the agencies are proposing a simplified compliance program with options to 

demonstrate trailer performance without requiring the trailer manufacturers to perform vehicle 

modeling using GEM. Instead, the agencies have developed a GEM-based equation for each box 

trailer subcategory that reproduces the CO2 results of the vehicle model. The standards proposed 

for non-box trailer manufacturers would require the use of LRR tires and ATI systems, and these 

manufacturers would not need to evaluate the performance those technologies using GEM. As a 

result, no trailer manufacturers would use GEM for compliance in this proposal. For the small 

business trailer manufacturers that produce trailers that are regulated in this program, EPA is 

offering a one-year implementation delay at the beginning of the program what will allow small 

business trailer manufacturers to demonstrate compliance starting in model year 2019. This 

provision will allow small businesses additional lead time to make the proper staffing 

adjustments and process changes and possibly add new infrastructure to meet their requirements. 

For the proposed standards, small business trailer manufacturers would already be 

required to comply with EPA standards when NHTSA’s fuel efficiency standards would begin. 

Therefore, NHTSA does not believe that an additional year of delay to comply with its fuel 

efficiency standards would provide beneficial flexibility. 

12.8 Projected Economic Effects of the Proposed Rulemaking 

This section summarizes the economic impact on small businesses of the proposed Phase 

2 rulemaking. To gauge the impact of the proposed standards on small businesses, the agencies 

employed a cost-to-sales ratio test to if small businesses would be impacted by less than one 

percent, between one and three percent, and above three percent of their sales. The costs used in 

this analysis for the proposed requirements are based on the cost estimates developed in Chapter 

7 of this Draft RIA. 

Based on our current analysis, EPA and NHTSA believe that small business trailer sales 

range from 1 to 63 million dollars. As presented in Chapter 7 of this Draft RIA, costs for trailer 

manufacturers range between 95 and 340 thousand dollars, which is greater than a one percent 

impact for most of the small trailer manufacturers. However, these projected costs do not 

account for the small business flexibilities, which we believe will reduce costs for a majority of 

the small trailer manufacturers to less than three percent of their sales. Additionally, many of the 

smallest manufacturers who see revenues below two million dollars produce specialty trailers 

that meet the criteria for exemption from the proposed standards. 

We believe that small businesses in the alternative fuel engine converter sector will be 

able to comply with the agencies’ proposed regulations with minimal incremental cost compared 
12-10 

to their current costs for compliance with EPA’s criteria pollutant programs. As such, at this 

time, we believe they will be impacted at less than one percent of their current annual sales. All 

of the vocational vehicle chassis manufacturers that EPA and NHTSA are aware of at this time 

are eligible for exemptions outlined in Section V B. (4), and the agencies believe they would be 

impacted at less than one percent of their sales. 

For a complete discussion of the economic impacts of the proposed rulemaking, see Chapter 8 of 

this Draft Regulatory Impact Analysis. 
12-11 

References

1

 Final Report of the Small Business Advocacy Review Panel on EPA’s Planned Proposed Rule: Greenhouse Gas 

Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles: Phase 2. Signed on 

January 15, 2015. Available in docket at: EPA–HQ–OAR–2014–0827 

2

 Final Report of the Small Business Advocacy Review Panel on EPA’s Planned Proposed Rule: Greenhouse Gas 

Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles: Phase 2. Signed on 

January 15, 2015. Available in docket at: EPA–HQ–OAR–2014–0827 
13-1 

Chapter 13: Natural Gas Vehicles and Engines 

13.1Detailed Life-Cycle Analysis 

We conducted a review to assess the lifecycle impacts of natural gas used by the heavyduty truck sector. We also present the results of an analysis by the Energy Information 

Administration projecting the future use of natural gas by heavy-duty trucks. Finally, we list a 

number of potential technologies which could help to reduce the methane emissions from natural 

gas trucks. 

13.1.1 Upstream Emissions 

Upstream methane emissions, occurring in the natural gas production, natural gas 

processing, transmission, storage and distribution sectors, are estimated and summarized in an 

annual report “Inventory of U.S. Greenhouse Gas Emissions and Sinks” (GHG Inventory) for the 

United Nations Framework Convention on Climate Change (UNFCCC).1

 As a basis for 

estimating the life-cycle impact of natural gas use by heavy-duty trucks, we used the year 2012 

methane emission estimates in the most recent GHG Inventory, published in 2014. The GHG 

Inventory also includes the quantity of carbon dioxide which is co-produced with methane 

throughout the natural gas system and emitted to the atmosphere through venting, flaring, and as 

fugitive emissions. 

The GHG Inventory is updated annually to account for new emission sources (e.g., new 

natural gas wells), updated data, emission factors and/or methodologies, and to account for 

changes in emissions due to changes in policy, regulations and industry practices. The GHG 

Inventory reflects emission reductions due to existing state regulations, National Emission 

Standards for Hazardous Air Pollutants (NESHAP) promulgated by EPA in 1999, the New 

Source Performance Standards (NSPS) promulgated by EPA in 2012, and Natural Gas Star (a 

flexible, voluntary partnership that encourages oil and natural gas companies to adopt proven, 

cost-effective technologies and practices that improve operational efficiency and reduce methane 

emissions) 

Emission estimates in the GHG Inventory are generally bottom-up estimates which are 

per-unit (compressor, pneumatic valve, etc.) emission estimates based on measured or calculated 

emission rates from such emission sources. 

In addition to the national-level data available through the GHG Inventory, facility-level 

petroleum and natural gas systems data is also available through EPA’s Greenhouse Gas 

Reporting Program (GHGRP). These data represent a significant step forward in understanding 

GHG emissions from this sector and EPA expects that this data will be an important tool for the 

Agency and the public to analyze emissions, and understand emission trends. For some sources, 

EPA has already used GHGRP data to update emission estimates in the GHG inventory, and 

EPA plans to continue to leverage GHGRP data to update future GHG Inventories. 

The natural gas which comprises CNG is expected to be off-loaded from the natural gas 

system where the vehicles using CNG are refueled. This is because the natural gas used as CNG 
13-2 

is compressed at the retail stations and fleet facilities which fuel the CNG vehicles. To get the 

natural gas to the CNG retail facilities, the natural gas must be shipped through the distribution 

system downstream of the natural gas transmission system. When the natural gas is transmitted 

through the distribution system, the methane emissions are higher because the methane 

emissions from the distribution system are added to the rest of the upstream methane emissions. 

Because LNG plants are located separate from the retail facilities, they can be located to 

access the lowest cost feedstock. This means the natural gas for LNG can be sourced from the 

larger natural gas transmission pipelines which are upstream of the distribution pipelines. This 

provides two advantages for LNG: 1) by avoiding the natural gas distribution system, the natural 

gas is priced lower, and 2) avoiding the natural gas distribution system avoids the methane 

emissions which occur from the distribution system. Table 13-1 contains the 2012 methane 

emissions estimate for the UNFCCC document. 

Table 13-1 Methane Emissions from the Natural Gas System in 2012 

EMISSION POINT FROM NG 

FACILITIES 

METHANE EMISSIONS 

(GIGAGRAMS) 

Field Production 1858 

NG Processing 892 

Transmission and Storage 2071 

Subtotal without Distribution 4821 

Distribution 1231 

Total with Distribution 6052 

The GHG Inventory also includes the quantity of carbon dioxide which is co-produced 

with methane throughout the natural gas system and emitted to the atmosphere through venting, 

flaring, and as fugitive emissions. This quantity is summarized in Table 13-2. 

Table 13-2 Carbon Dioxide Emissions from the Natural Gas System in 2012 

EMISSION POINT FROM NG 

FACILITIES 

CARBON DIOXIDE EMISSIONS 

(GIGAGRAMS) 

Production 13,659

NG Processing 21,469

Transportation and Storage 63

Distribution 37

Total 35,228

In the GHG Inventory, EPA assessed the amount of uncertainty with its emission 

estimates and provided a lower and upper bound estimate for its emission estimates. The lower 
13-3 

bound emission estimate is 19 percent lower than the best case estimate in Table 13-1 and the 

upper bound estimate is 30 percent higher than the best case estimate. 

In the Climate Action Plan, EPA projects that methane emissions will increase in the 

future due to increases in natural gas production.2

 Table 13-3 summarizes the projected increase 

in US methane emissions from the Climate Action Plan and the projected increase in natural gas 

production referenced from the proposed power plant rulemaking,3

 which is likely the most 

consistent natural gas production estimate with the projections made in the Climate Action Plan. 

Table 13-3 Projected Natural Gas Production Volume and Methane Emissions (g/million BTU) 

YEAR 2012 2020 2025 

Methane Emissions 

Teregram CO2eq. 

145 140 151 

Natural Gas Production (dry) 

trillion cubic ft 

24.1 26.6 29.3 

As Table 13-3 shows, methane emissions from natural gas facilities are expected to 

increase from 145 teregram CO2eq. in 2012 to 151 teregram CO2 eq. in 2025, about a 4 percent 

increase. At the same time, natural gas production of dry natural gas is expected to increase 

from 24.1 trillion cubic feet in 2012 to 29.3 trillion cubic feet in 2025, about a 22 percent 

increase. When estimating the methane emissions on the same natural gas production basis, the 

methane emissions are projected to be 14 percent lower in 2025 than 2012.A 

In the GHG Inventory, emissions associated with powering the units or equipment (i.e., 

compressors, pumps) used in natural gas production, processing, transmission and distribution 

are aggregated with all the other fossil fuel combustion activities. Rather than attempt to 

disaggregate those specific GHG emissions from the rest of the process emissions in the GHG 

Inventory, we instead used the estimated emissions for these sources provided by GREET.4 

Table 13-4 summarizes the process energy consumed to produce and process natural gas. 

 

A The 14% reduction figure is calculated by multiplying the methane emissions estimate in 2025 by the ratio of 

2012 natural gas production over the 2025 natural gas production (151x24.1/29.3) and the resulting value is 115, 

which is 85.5% of 145, or 14% less. 
13-4 

Table 13-4 Process Energy Demand by the Natural Gas System (BTU/million BTU) 

FUEL TYPE PRODUCTION NATURAL 

GAS 

PROCESSING

TRANSMISSION/ 

DISTRIBUTION 

TOTAL 

Conv 

Wells 

Shale 

Wells 

Weighted 

Avg 

Natural Gas 22,016 20,955 21,307 26,123 0 47,430 

Diesel 2816 2680 2725 272 0 2997 

Electricity 256 244 248 816 0 1064 

Gasoline 256 244 248 0 0 248 

Resid 256 244 248 0 0 248 

Totals 25,600 24,367 24,777 27,211 0 51,988 

Table 13-5 contains the factors we used to convert the GREET process energy demands 

used to operate the equipment used to produce, process and distribute natural gas to carbon 

dioxide emissions for those process fuels.5 

Table 13-5 Carbon Dioxide Emission Factors for Process Fuel Consumption 

PROCESS FUEL GCO2/BTU 

Natural Gas 0.0398 

Diesel 0.0555 

Electricity 0.1549 

Gasoline 0.0535 

Resid 0.0563 

Table 13-6 summarizes the total estimated methane and carbon dioxide emissions emitted 

by the upstream natural gas system. Two estimates are provided, one of which includes the 

emissions from the distribution system representing the upstream emissions for CNG. The 

second estimate summarizes the emissions excluding the emissions from the distribution system 

representing the upstream emissions for LNG, since it is expected to access the natural gas from 

the transmission portion of the natural gas system. 
13-5 

Table 13-6 Summary of Year 2025 Emissions from the Natural Gas System (grams/million BTU) 

 METHANE EMISSIONS CARBON DIOXIDE 

CNG Analysis (includes CH4

emissions from the distribution 

system) 

242 3881 

LNG Analysis (does not include 

CH4 emissions from the 

distribution system) 

192 3881 

13.1.2 Downstream Emissions 

The GHG Inventory does not estimate the methane emissions for natural gas once the 

natural gas is diverted for use by the transportation sector, thus, we obtained information from 

other sources. Natural gas can be used by vehicles either as a compressed gas (CNG) or as 

liquefied natural gas (LNG). We discuss the emissions of both. 

13.1.2.1 Compressed Natural Gas (CNG) 

To make CNG available to trucks, the natural gas must be compressed from the pressure 

that it is available from the distribution pipelines to a pressure over 3600 psi to enable filling the 

truck CNG storage tanks. We used the compression energy available from GREET for this step 

which reflects national-average emissions for electricity generation.6 The emissions associated 

with natural gas compression for CNG are summarized in Table 13-7. 

Table 13-7 Estimated Emissions for Electricity Generated to Power CNG Compressors (g/million BTU) 

 METHANE CARBON DIOXIDE NITROUS OXIDE 

Emissions 6.9 3988 0.06 

An important advantage that CNG has over LNG is that only a single facility, the retail 

outlet, is required for distributing CNG, while LNG requires both a liquefaction plant and a retail 

outlet. The simplified logistics of providing CNG also provides fewer opportunities for 

emissions and leakage to the environment. 

We are aware of the following two types of emissions for CNG which are not estimated 

in the lifecycle analysis due to lack of quantifiable data. The first is CNG refueling emissions. 

CNG trucks are refueled at the retail stations providing CNG. When the refueling hose is 

disconnected from the connection fitting on the vehicle, a small amount of natural gas is released 

to the atmosphere. This CNG refueling vented gas has not been estimated and therefore not 

included in the lifecycle analysis. 

The second is fugitive emissions from small leaks in the CNG fuel storage system. While 

CNG has an advantage over LNG because it is contained in a sealed system, the very high 

pressure at which CNG is stored dramatically increases fugitive emissions if a fitting pipe were 

to develop a leak. The level of fugitive emissions for a certain sized hole is directly proportional 

to the pressure. We do not have any data on the fugitive emissions from CNG trucks, therefore, 

in our lifecycle analysis, we assume that CNG fugitive emissions are zero which likely 

underestimates the methane emissions from CNG trucks. 
13-6 

13.1.2.2 Liquefied Natural Gas (LNG) 

The first step in making LNG available to trucks is the liquefaction step. As discussed 

above, the liquefaction plant is likely to be constructed near natural gas transmission pipelines to 

access the natural gas at the lowest price point. The liquefaction step involves the removal of 

heat from the natural gas until it undergoes a phase change from a gas to a liquid at a low 

pressure. Once the natural gas is liquefied, it is stored in an insulated storage tank to keep the 

LNG liquefied. 

LNG plants are configured depending on their ultimate capacity. World class LNG 

plants produce 5 million metric tons, or more, per year of LNG and the economy of scale of 

these large plants support the significant addition of capital to reduce their operating costs. An 

LNG plant solely producing LNG for truck fuel is expected to be significantly smaller than the 

world class LNG export plants and so the capital invested is expected to be much lower, thus, 

their operating costs would be expected to be much higher, and their energy efficiency much 

lower on a percentage basis. The California Air Resources Board estimates the liquefaction 

plants used for producing truck LNG fuel are 80 percent efficient, compared to 90 percent 

efficient for world class LNG plants.7

 In our lifecycle analysis of LNG as a truck fuel, we also 

assumed that LNG plants are 80 percent efficient. For our GHG analysis, we estimate the carbon 

dioxide emitted when 20 percent of the natural gas is combusted to provide the energy required 

to liquefy the natural gas to LNG. The upstream emissions associated with the natural gas used 

in the liquefaction process must be accounted for and added onto the LNG produced by the plant. 

These emissions are included as indirect emissions. Table 13-8 summarizes the GHG emissions 

attributed to the liquefaction plant. 

Table 13-8 LNG Liquefaction Plant Emissions (g/million BTU) 

 METHANE CARBON DIOXIDE 

Direct Emissions 0 15,175 

Indirect Emissions 48 970 

Total Emissions 48 16,145 

To transport the LNG to the retail station, the LNG is loaded into an insulated horizontal 

trailer designed specifically for transporting LNG. If the LNG in the trailer were to warm 

sufficiently to cause the LNG to reach the pressure relief valve venting pressure, there would be 

boil-off emissions from the trailer. However, since the LNG is super cooled, boil off events are 

likely to be rare. We used a CARB estimate of boil-off emissions for LNG transportation 

between the LNG plant and retail outlets.8

 Table 13-9 contains the estimate of boil off emissions 

and the emissions from the vehicle transporting the LNG to retail. 
13-7 

Table 13-9 Boil-Off Emissions Estimate for LNG Transportation to Retail (g/million BTU) 

 METHANE CARBON DIOXIDE NITROUS OXIDE 

Fuel Use (Diesel Fuel) 0.45 378 0.009 

Methane Boil Off 

Emissions 

0.43 0 0 

Total 0.88 378 0.009 

LNG is stored in the insulated storage tank at the retail facility. Heat gain in the storage 

tank could eventually lead to boil-off emissions. Service stations with little LNG demand are at 

a higher risk of boil-off emissions compared to service stations which have a significant 

throughput volume. LNG stations could be configured to avoid boil-off events to the 

atmosphere, such as venting to a co-located CNG facility, or venting to a nearby natural gas 

pipeline. We used a CARB emission estimate to provide an estimate of the boil-off emissions 

from LNG retail facilities.9

 Table 13-10 summarizes the estimated boil off emissions for LNG 

retail facilities. 

Table 13-10 Boil-Off Emissions Estimate for LNG Retail Facilities (g/million BTU) 

 METHANE 

EMISSIONS 

LNG Retail Boil-Off 

Emissions 

11.1 

The total well to tank emissions for CNG and LNG are summarized in Table 13-12. 

These emissions represent the total of upstream and downstream emissions which includes 

delivering the fuel to the truck fuel storage tank. 

Table 13-11 Total Well to Tank Emissions Estimate for CNG and LNG (g/million BTU) 

 METHANE CARBON DIOXIDE NITROUS OXIDE 

CNG 249 7869 0.07 

LNG 251 20,405 0.009 

13.1.3 Vehicle Emissions 

13.1.3.1 Vehicle Configurations 

There are several different ways that diesel heavy duty engines can be configured to use 

natural gas as a fuel. The first is a spark ignition natural gas (SING), Otto cycle SING heavy 

duty engine burns the fuel stoichiometrically and uses a three-way catalyst and some also add an 

oxidation catalyst to provide the greatest emissions reduction. Stoichiometric combustion is 

used in most light-duty SING engines. Problems with thermal stress and low power density have 

favored the use of the lean-burn combustion system in heavy duty engines. The use of cooled 

EGR provides further potential to increase the engine output and, at the same time, decreases 

NOX emissions. In this case the engine compression ratio is reduced similar to that of a gasoline 
13-8 

engine, about 12 to 1 or more, and thus its thermal efficiency is lower than a diesel-like engine 

by about 10 - 15 percent, depending on the driver. 

The second is a direct injection natural gas (DING), diesel cycle. The DING engine uses 

a small quantity of diesel fuel (pilot injection) or a glow plug as ignition sources. As the 

injection system for the diesel fuel does not have the capability of greater injection quantities, 

this option has no dual-fuel properties. On the other hand, an optimization of the pilot injection 

can be made to achieve lower emissions. An advanced high pressure direct injection (HPDI) fuel 

system combining the injection of both diesel fuel and natural gas can be used for lean burn 

combustion. This enables the engine to maintain the efficiency advantage of a compression ignition 

engine while running mainly CNG/LNG. 

The third is a mixed-fuel natural gas (MFNG), diesel cycle. In a mixed-fuel engine, 

natural gas is mixed with intake air before induction to the cylinder and diesel fuel is used as 

ignition source. Mixed-fuel vehicle/engine means any vehicle/engine engineered and designed 

to be operated on the original fuel(s), or a mixture of two or more fuels that are combusted 

together. Mixed-fuel system means that a diesel engine works with two types of fuels together. 

In fact the engine is a diesel thermodynamic cycle and the energy is given by the diesel and the 

natural gas fuel. In mixed-fuel conversion the original engine is not modified in any way, a 

conversion system is installed in order to permit the engine to run on both fuels. The conversion 

of the engine is totally reversible, in fact it is possible to choose the mode how to run the engine 

(diesel / mixed-fuel). When the engine runs in diesel mode, the engine runs in the same way as 

per the original configuration. Engine results showed that the efficiency of the engine could 

decrease by about 2-5 percent in mixed-fuel mode compared to diesel mode and that the diesel 

replacement was approximately 40-60 percent efficient. 

Each of these natural gas engine types has its merits. The SING engine is less costly, but 

is less fuel efficient and because of the lower compression ratio it has less torque than the two 

diesel cycle engines. The DING engine is likely the most expensive because of the special 

natural gas/diesel fuel injection system and large required amount of natural gas (LNG or CNG) 

storage since the truck must run on natural gas. However, because the truck can run almost 

completely on natural gas, the DING engine has the potential to more quickly pay down the 

higher investment cost of the natural gas truck. The MFNG engine provides the truck owner the 

flexibility to operate on natural gas or diesel fuel, but at the expense of a slower natural gas 

investment pay down rate because it can operate at most 50 percent of the time on natural gas. 

An important advantage of LNG is the increased energy density compared to CNG. At 

present, CNG stored at its maximum storage pressure is only 25 percent of the energy density of 

diesel fuel, while LNG contains about 60 percent of the energy density of diesel fuel. Because of 

its higher energy density, LNG is favored over CNG for long-haul trucking. An adsorbent for 

natural gas (ANG) material technologyB called metal organic framework (MOF) for storing CNG 

has been invented and is being tested for large scale use. The technology involves filling the 

 

B Menon, V.C., Komarneni, S. 1998 “Porous Adsorbents for Vehicular Natural Gas Storage: A Review”, Journal of 

Porous Materials 5, 43-58 (1998); Burchell, T “Carbon Fiber Composite Adsorbent Media for Low Pressure Natural 

Gas Storage” Oak Ridge National Laboratory 
13-9 

CNG tank with a specially designed substance which looks similar to a pelletized catalyst. The 

substance establishes a matrix which causes the methane molecules in natural gas to become 

better organized and store the same quantity of natural gas in a smaller volume at the same 

pressure, or store the same density of natural gas at a lower pressure. This MOF could improve 

the energy density of CNG which would make it a better candidate for natural gas storage for 

long range combination trucks, while avoiding the boil-off events that are a risk with using LNG. 

13.1.3.2 Tailpipe Emissions 

When assessing the methane emissions from both CNG and LNG trucks, it is important 

to separate those trucks built or converted before 2014 to those built or converted in 2014 and 

later. The trucks built before 2014 are only required to meet a nonmethane hydrocarbon 

(NMHC) standard, which means that the methane emissions from these trucks are unregulated. 

Our certification data shows that the methane tailpipe emissions from these trucks/buses ranges 

from 2 – 5 g/bhp-hr for both spark ignition (gasoline type) and compression ignition (diesel type) 

engines. 

For 2014 and later OEM compression ignition natural gas trucks or natural gas 

conversions of 2014 and later diesel trucks, the trucks must meet a 0.1 g/bhp-hr methane 

emission standard in the case of a larger truck engine tested with an engine dynamometer, and a 

0.05 g/mile methane emission standard in the case of smaller trucks tested on a chassis 

dynamometer. For spark ignition (gasoline style) engines, the standards take effect in 2016.10 

The natural gas truck manufacturers are allowed to offset methane emissions over the standard 

by converting the methane emission exceedances into CO2 equivalent emissions and using CO2

credits. For the initial natural gas engine certifications that EPA received for 2014, the truck 

manufactures chose to continue to emit high levels of methane (around 2 g/bhp-hr) and use 

carbon dioxide credits to offset those emissions. We don’t know if this practice of using CO2

credits to offset high methane emissions will continue in the future, however, for evaluating the 

lifecycle impacts of natural gas heavy-duty vehicles, the 2014 and later natural gas heavy-duty 

trucks may in fact have an emissions profile more like the pre-2014 trucks and not like the 2014 

and later trucks as depicted below in the figures. Furthermore, our emissions analysis assumes 

that these trucks are emitting GHG emissions as designed. In cases when these trucks experience 

an increase in emissions due to deterioration or malfunction of the engines, fuel supplies or 

associated emission control devices on these trucks, the methane emissions could be higher than 

estimated. Table 13-12 summarizes the emission standards and the estimated methane emissions 

from heavy-duty trucks assumed in the analysis. 

Table 13-12 Methane Emission Standards and Estimated Emissions from Heavy-Duty Trucks 

PRE-2014 2014 AND LATER 

Methane Standard None g/bhp‐hr

Estimated Emissions g/bhp-hr 2 – 5 0.05 

g/million BTU 214 – 534 5.3 

13.1.3.3 Boil-off, Venting and other Fugitive Emissions 

Truck drivers requiring LNG fuel drive up to an LNG retail outlet or fleet refueling 

facility and fill up with LNG fuel. When the refueling nozzle is disconnected from the LNG tank 
13-10 

nozzle, a small amount of methane is released to the environment. In addition, prior to refueling 

it may be necessary or advantageous, due to high pressure in the truck’s LNG tank, to reduce the 

pressure in the truck’s LNG tank to speed up the refueling process. In some cases the retail 

station is equipped with another hose and associated piping to vent the excess gas to the retail 

stations’ storage tank, or perhaps to a natural gas pipeline. However, for those retail outlets 

without such vent lines to the storage tank, the truck driver may simply vent the truck’s storage 

tank to atmosphere. As part of a sensitivity analysis for our lifecycle analysis, we estimate the 

emissions for venting an LNG tank prior to refueling. 

A major GHG issue for LNG trucks is boil-off emissions from the trucks themselves. 

When the liquefied natural gas is pumped into the truck LNG tanks, it is “supercooled,” meaning 

that the temperature of the LNG is well below the boil-off pressure and temperature. If the truck 

is driven extensively the drawdown of liquid level will cause some of the fuel to boil off and thus 

cool the rest of the liquid in the LNG storage tank. It is possible that the fuel would maintain its 

supercooled temperature or possibly even cool further below its supercooled temperature all the 

time until the LNG is completely consumed. 

If the truck is not driven or is driven very little, the very low temperature LNG warms 

through ambient temperature gradient through the tank wall causing the temperature and pressure 

of the LNG to rise. When the pressure reaches a maximum of 230 psi there is a safety release 

valve on the LNG storage tank which releases the methane gas directly to the atmosphere until 

the pressure drops to 170 psi, the pressure at which the safety release resets. There are two 

industry standards used to design tanks to reduce the temperature increase, one for a 3 day hold 

timeC and one for a 5 day hold time.D Hold time is the minimum time elapsed between when the 

truck’s LNG tank is refueled and when it begins to vent. 

If there is a boil-off event, a large amount of methane would be released. If aware of the 

impending boil-off such as when the truck is being maintained, the truck driver could hook up 

the LNG tank to a hose which would vent the natural gas emissions to a CNG system which 

would reuse the boil-off natural gas as CNG, or vent the natural gas emission to a natural gas 

pipeline. Otherwise the boil-off emission would simply vent to the atmosphere. 

When an LNG fuel tank venting (refueling venting or boil-off) incident occurs, there are 

two separate processes which occur that contribute to methane emissions during the venting. 

The most obvious process is the pressure drop, from 230 to 170 psi, in the gaseous space above 

the liquid. The volume of gas vented is proportional to the reduction in absolute pressure in the 

tank. Since the drop in absolute pressure is 244 to 184 psi (14.7 psi is added to the 230 and 170 

psi gauge pressure), about 25 percent of the gas in the tank is vented (184 psi is 25 percent of the 

way from 244 psi to zero pressure). The second process is the vaporization of liquid during the 

pressure reduction in the LNG tank. The boiling point of any liquid decreases as the pressure 

decreases. Thus, when the LNG undergoes the pressure reduction during a venting/boil-off, the 

 

C National Fire Protection Association 52, Compressed Natural Gas (CNG) Vehicular Fuel System Code, 2002 

Edition 

D SAE International (2008) SAE J2343: Recommended Practice for LNG Medium and Heavy-Duty Powered 

Vehicles. Warrendale, Pennsylvania. 
13-11 

boiling point of the methane decreases and to balance the system, some of the liquid methane 

must boil off to cause the cooling of the liquid. The quantity liquid methane which must boil off 

from the liquid is calculated from methane’s heat of vaporization over the boiling point 

temperature change, which drops from -178 F to -189 F as the pressure drops from 230 to 170 

psi. 

The amount of natural gas which boils off during a venting event varies based on the 

quantity of liquid in the LNG storage tank. The greatest amount of natural gas which is lost 

during a venting/boil off event occurs when the tank is closest to being full. For a 200 gallon 

tank system, each boil off event has the potential to release on the order of 3-9 gallons or 5,300 – 

15,800 grams of CH4 which translates to 132 – 400K grams of CO2-equivalent emissions, 

assuming methane has global warming potential (GWP) of 25 over a 100 year lifetime. If the 

vehicle continues to sit after boil-off events begin to occur with boil-off events each day and up 

to several boil-offs per day, as much as million grams of CO2-equivalent emissions may be 

emitted over the twenty or so days at which point the vehicle LNG tank would be completely 

empty. 

Table 13-13 summarizes the starting and ending conditions and the loss from the tank for 

venting incidents (200 gallon LNG tank decreases in pressure from 230 to 170 psi) when the 

LNG tank is 90 percent, 50 percent and 10 percent full. A refueling venting event is more likely 

to occur when the tank is mostly empty, so the 50 and 10 percent cases are the most likely cases 

to consider. 

Table 13-13 Estimated Quantity of Boil-Off from a 200 Gallon LNG Fuel Tank for a Single Boil-Off Event 

 PERCENT FULL 

(INITIAL) 

PERCENT FULL 

(FINAL) 

LIQUID LOSS 

(GALS) 

TOTAL MASS 

LOSS (LBS) 

Boil-off Scenarios 

90 83.2 13.6 38.7 

50 46.2 7.6 24.8 

10 9.3 1.5 11.0 

Table 13-13 shows that if a truck had 200 gallon of LNG storage capacity, the estimated 

quantity of liquid boil-off volume would range from 2 to 14 liquid gallons of LNG depending on 

the fill level of the LNG tank. When the quantity of LNG gas loss is included, the total loss 

ranges from 11 to 39 lbs. 

The quantity of LNG tank boil-off or venting per distance driven by the truck depends on 

the frequency of boil-off or venting incidents. As described above, a truck’s driving profile 

plays a key role in determining the boil-off risk from LNG trucks. Fleets which purchase LNG 

trucks do so with the intent of driving the LNG truck extensively to pay off the much higher 

purchase price of the LNG truck. For this reason, there is likely to be few boil-off incidents, 

except for cases when the truck is forced out of its routine. Examples of when the truck might be 

sidelined include times when the truck is being maintained, the immediate period after the truck 

is involved in an accident or perhaps when the owning company experiences a loss of workload 

or files for bankruptcy. We have no data which would allow us to estimate the frequency when 

these sorts of incidents would occur, and even if we did, we still could not estimate the frequency 

of boil-offs that occur in these cases. 
13-12 

As the truck ages, it likely would be sold by the company which originally purchased the 

truck to avoid having to deal with the increased maintenance that occurs with older trucks. 

Figure 13-1 shows the estimated vehicle miles traveled by class 8 trucks as they age (the data is 

from the MOVES Model. 

Figure 13-1 Vehicle Miles Traveled by Combination Trucks in 2014 

Figure 13-1 shows that the mileage driven by combination trucks decreases as they age. 

By the time that a combination truck is about 17 years old, it is driven about half the number of 

miles per year as a new truck. It would seem that the risk of boil-off incidents increases with 

these older trucks. 

Venting incidents during refueling can occur at any time, and there is an incentive to do 

so when it is time to refuel. The decision to vent an LNG tank in most cases is solely up to the 

truck driver who is often under pressure to complete his work in less time to maximize profits. 

There is a lot of uncertainty in estimating the quantity of boil-off and venting from an 

LNG truck. To reflect this uncertainty, we assume two different boil-off/venting emission 

estimates. The low estimate assumes that 35 grams per million BTU of fuel consumed is 

emitted, which is from GREET. The high estimate assumes a boil-off event and a venting event 

each time the truck is refueled and before that tank full of LNG is used up, and this quantity is 

estimated to be 734 g per million BTU of fuel consumed. 

The crankcase of these engines receives leakage from across the piston rings, which can 

contain methane. The crankcase of the spark ignition engines is normally vented into the intake 

of the engines, thus, any methane emissions from the crankcase which is not combusted in the 

engine would be accounted for in both the engine-out and tailpipe emissions. For compression 

ignition engines, however, the crankcase emissions are typically vented into the exhaust pipe 

downstream of the aftertreatment devices although they are accounted for in addition to the 

engine-out emissions during certification. Engine-out emissions are subjected to deterioration 

factors based on well-established procedure, which may make them more robust than 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

2015 2010 2005 2000 1995

Miles Driven per Year

Truck Year
13-13 

deterioration factors for vented crankcase emissions. Moreover, deterioration of crankcase 

emissions may be more variable. Thus, sealed crankcases would achieve more robust control of 

methane emissions. 

Another potential source of methane emissions from CNG and LNG trucks is leaks from 

the fuel piping which to the engine which is another source of fugitive emissions. Thus, either 

while parked or operated, the vehicle fuel and engine systems could leak methane to the 

environment. We do not have nor did we attempt to estimate this type of methane fugitive 

emissions from CNG or LNG trucks. 

Table 13-14 summarize the estimated tailpipe emissions for CNG trucks, and Table 

13-15 summarizes the estimated tailpipe and boil-off and venting emissions for LNG trucks. 

Table 13-14 Estimated Tailpipe Emissions for CNG Trucks (g/mmbtu) 

 METHANE CARBON 

DIOXIDE 

NITROUS 

OXIDE 

2014 and Later Direct 5.3 60,702 2 

Indirect 0.1 3 0 

Total 5.4 60,705 2 

Pre-2014 Direct 374 60,702 2 

Indirect 6 189 0 

Total 380 60,891 2 

Table 13-15 Estimated Tailpipe and Boil-Off Emissions for LNG Trucks (g/mmbtu) 

 METHANE CARBON 

DIOXIDE 

NITROUS 

OXIDE 

2014 and Later

assuming low 

Venting and BoilOff Emissions

Direct 5.3 60,702 2 

Indirect 0.07 5.5 0 

Total 40.4 60.707 2 

2014 and Later 

Assuming High 

Venting and 

Boil-Off 

Emissions 

Direct 5.3 60,702 2 

Indirect 0.07 5.5 0 

Total 739 60,707 2 

Pre-2014 

Assuming Low 

Venting and 

Boil-Off 

Emissions 

Direct 374 60,702 2 

Indirect 4.8 386 0 

Total 413 61,088 2 
13-14 

13.1.3.4 Thermal Efficiency 

While not an emission source per se, the thermal efficiency of the natural gas engine also 

plays a role in the lifecycle emissions of the truck. Thermal efficiency is defined by the amount 

of energy that is obtained to propel the truck compared to the energy consumed by the engine. If 

a fuel-engine is less thermally efficient, then it consumes more fuel, or more BTUs, to travel the 

same distance, thus, emitting more carbon dioxide per distance traveled, or work performed. 

We estimate that SING engines can be as much as 15 percent less efficient than 

compressed ignition engines which operate on diesel fuel. Conversely, DING and MFNG 

engines which operate at a higher compression ratio, are estimated to be 5 percent less energy 

efficient compared to a diesel engine. In our lifecycle analysis, we provide two different 

sensitivities for natural gas vehicles assuming that they may be 5 percent and 15 percent less 

efficient. 

13.1.4 Results of Life Cycle Analysis 

To estimate the lifecycle impact of natural gas used by heavy-duty trucks, we totaled the 

carbon dioxide, methane and the nitrous oxide emissions for the upstream and downstream 

portions of the natural gas system. The methane and nitrous oxide emissions are converted to 

carbon dioxide-equivalent emissions using global warming potentials ((GWPs); these are a 

measure of the relative contribution of global warming of emissions of a given gas in comparison 

to that of carbon dioxide over a given time period). The GWPs EPA is currently using is from 

the AR4 (2007) IPCC report for 100 year timeframe, which is 25 and 298 for methane and N2O, 

respectively. 

To establish the impacts of natural gas use in the heavy-duty fleet, it was necessary to 

compare the lifecycle impacts of natural gas against its replacement, which is a diesel fueled 

heavy-duty truck. The lifecycle impact diesel fuel was estimated by the National Energy 

Technology Laboratory (NETL) for the production and use of diesel fuel in 2005. EPA used this 

lifecycle assessment for the Renewable Fuel Standard Rulemaking. We used this NETL diesel 

fuel lifecycle estimate for the baseline for comparison with the natural gas lifecycle assessment. 

The vehicle nitrous oxide and methane emissions are from the MOVES vehicle model developed 

by EPA. Table 13-16 summarizes the lifecycle emissions for diesel fuel estimated by NETL. 

Table 13-16 Estimated Diesel Fuel Lifecycle Greenhouse Gas Emissions (g/million BTU) 

 CARBON 

DIOXIDE 

METHANE NITROUS 

OXIDE 

TOTALS 

CO2EQ* 

Well to Tank 15,838 98 0.3 18,377 

Tank to Wheels 78,308 1 2 78,929 

Well to Wheels 94,146 99 2.3 97,306 

Note: 

*The totals are calculated using 25 and 298 for the GWPs for methane and nitrous oxide, respectively. 

NETL is in the process of updating its lifecycle analysis from the 2005 analysis year to 

2009 as the analysis year. While the revised lifecycle analysis is not yet available, one of the 
13-15 

authors of the analysis explained that the 2009 analysis appears to be quite similar to the 2005 

analysis.11 

To illustrate the relative full lifecycle impact of natural gas-fueled heavy-duty vehicles 

versus diesel fueled heavy-duty vehicles, we assessed a couple different scenarios. The first 

scenario is a conversion of a diesel engine to use CNG. Of the tens of thousands of heavy-duty 

natural gas trucks currently in use, over 90 percent are of this type. These are conversions of 

older trucks so they are not regulated by the 2014 methane standard. For future year heavy-duty 

trucks, we also estimated the lifecycle emissions if the trucks were meeting a 0.1 g/bhp-hr or a 

0.05 g/mile methane tailpipe standard. We provide two estimates for the lower thermal 

efficiencies of CNG trucks, one assumes that the truck is 5 percent less thermally efficient and 

the second assumes that the truck is 15 percent less thermally efficient (10 percent less efficient 

than the 5 percent less thermally efficient case). The estimated lifecycle emissions of CNG 

trucks, assuming projected upstream emissions in 2025, is summarized in Table 13-17. 

Table 13-17 Full Lifecycle Analysis of a CNG Truck (g/million BTU) 

TRUC

K 

TYPE 

EMISSION 

CATEGOR

Y 

CARBO

N 

DIOXID

E 

METHAN

E 

NITROU

S OXIDE 

TOTA

L 

CO2

EQ.* 

THERMAL 

EFFICIENC

Y 5% AND 

15% 

CO2EQ.* 

TOTALS 

INCLUDIN

G 

THERMAL 

EFFICIENC

Y IMPACT 

CO2EQ. * 

Pre2014 

CNG 

Truck 

Well to 

Tank 

7869 249 0.07 14,107 705 

2116 

14,812 

16,223 

Tank to 

Wheels 

60,891 380 2 70,980 3548 

10647 

74,528 

81,627 

Well to 

Wheels 

68,760 628 2 85087 4254 

12,763 

89,379 

97,850 

2014 or 

Later 

Truck 

Well to 

Tank 

7869 249 0.07 14,107 705 

2116 

14,812 

16,223 

Tank to 

Wheels 

60,704 5 2 61,436 3072 

9215 

64,508 

70,651 

Well to 

Wheels 

68574 254 2 75,544 3777 

11,331 

79,321 

86,875 

Note: 

*The CO2eq. totals are calculated using 25 and 298 for the GWPs for methane and nitrous oxide, respectively. 

The CNG lifecycle assessment relative to a diesel truck lifecycle analysis is shown in 

Figure 13-2. 
13-16 

Figure 13-2 Full Lifecycle Analysis of a CNG Truck 

(Projected Upstream Methane Emissions in 2025, Methane GWP of 25) 

In the first two bars of Figure, it shows that based solely on tailpipe emissions (with and 

without thermal efficiency adjustments and assuming no increased methane emissions at the 

truck), natural gas trucks are estimated to emit about 20 percent less GHG emissions than diesel 

engines. But this advantage decreases if the natural gas engine is less thermally efficient. The 

three full lifecycle analyses represented by the right three bars in the figure shows that pre-2014 

CNG trucks are estimated to emit about the same GHG emissions as diesel trucks, although if 

their thermal efficiency is much lower or if a higher GWP for methane were used, they would 

likely be somewhat higher emitting in GHG emissions. When such trucks are complying with 

the 2014 and later methane emission standards, their methane emissions are much lower and 

these trucks are expected to be lower emitting than diesels, even considering if they are less 

thermally efficient. 

The second scenario is a combination truck which is assumed to be in compliance with 

the 2014 methane standard. Because it is high mileage truck, the truck most realistically must 

use LNG as a fuel to provide the necessary range for the dedicated natural gas engine. We make 

two different assumptions with respect to refueling and boil off emissions. In the natural gas 

average case, we assume a modest quantity of refueling and boil-off methane emissions which is 

equal to the combined boil-off emissions from the liquefaction, transportation and retail station 

as estimated by GREET. The second boil-off emission estimate is based on venting the LNG 

storage tank to the atmosphere each time the driver refills his tank, and one LNG boil-off event 

between each time the driver must refuel his tank. As we discussed in the discussion about 

refueling and truck boil-off emissions, we don’t expect this to be as common practices for newer 

trucks that are operated regularly. However, as the use of these trucks decreases as they age and 

are sold into the secondary market, the risk for refueling and boil-off emission events increases – 

this estimate provides a simple sensitivity emission estimate. The estimated lifecycle emissions 

of LNG trucks, assuming projected upstream emissions in 2025, is summarized in Table 13-18. 

0

20,000

40,000

60,000

80,000

100,000

120,000

Diesel

Tailpipe

CNG

Tailpipe

Diesel CNG pre‐

2014

CNG 2014

and later

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2
13-17 

Table 13-18 Full Lifecycle Analysis of an LNG Truck (g/million BTU) 

TRUCK 

TYPE 

EMISSION 

CATEGOR

Y 

CARBO

N 

DIOXID

E 

METHAN

E 

NITROU

S 

OXIDE 

TOTALS

* 

CO2EQ. 

THERMAL 

EFFICIENC

Y 5% AND 

15% 

CO2EQ.* 

TOTALS 

INCLUDIN

G 

THERMAL 

EFFICIENC

Y IMPACT 

CO2EQ.* 

Pre2014 

LNG 

Trucks 

assumin

g low 

Venting 

and 

Boil-Off 

Emissio

ns 

Well to 

Tank 

20,405 251 0.01 26,693 1334 

4004 

28,027 

30,697 

Tank to 

Wheels 

61,088 413 2 72,896 3645 

10,934 

76,542 

83,831 

Well to 

Wheels 

81,494 665 2 98,732 4935 

14,807 

103,668 

113,540 

2014 

and 

Later 

LNG 

Trucks 

assumin

g low 

Venting 

and 

Boil-Off 

Emissio

ns 

Well to 

Tank 

20,405 251 0.01 26,693 1334 

4004 

28,027 

30,697 

Tank to 

Wheels 

60,707 40 2 62314 3116 

9347 

65,429 

71,661 

Well to 

Wheels 

81,113 291 2 89,024 4450 

13,350 

93.475 

102,375 

2014 

and 

Later 

LNG 

Trucks 

with 

High 

Venting 

and 

Boil-Off 

Emissio

ns 

Well to 

Tank 

20,405 251 0.01 26,693 1334 

4004 

28,027 

30,697 

Tank to 

Wheels 

60,707 990 2 86,037 3035 

9106 

89,072 

95,143 

Well to 

Wheels 

81,113 990 2 112,720 4370 

13,109 

117,090 

125,829 

Note: 

*The totals are calculated using 25 and 298 for the GWPs for methane and nitrous oxide, respectively. 

The LNG truck lifecycle analysis relative to a diesel truck lifecycle analysis is shown in 

Figure 13-3. 
13-18 

Figure 13-3 Full Lifecycle Analysis of an LNG Truck 

(Projected Upstream Methane Emissions in 2025, Low and High Refueling and Boil-Off Emission, 

Methane GWP of 25) 

Figure 13-3 shows that LNG trucks have about the same greenhouse gas footprint as 

diesel trucks when we assume a low quantity of refueling and boil-off emissions. In comparing 

CNG to LNG, the LNG trucks appear higher emitting than CNG trucks because of the low 

thermal efficiency of the small liquefaction facilities. If these LNG trucks emit high levels of 

methane when refueling and by experiencing boil-off events, their GHG emissions can 

potentially be much greater than that from diesel trucks. 

It is important to point out the uncertainties associated with the lifecycle estimates 

provided Figures 13-2 and 13-3. As discussed above, there is uncertainty in both the upstream 

and downstream methane emission estimates for natural gas facilities and equipment, and the 

trucks that consume natural gas. In the GHG Inventory, EPA estimates a range of natural gas 

emissions from the upstream natural gas production sector. The range varies from -19 percent to 

+30 percent relative to the principal estimate. To illustrate the impact the range has on the 

relative life cycle impacts of natural gas versus diesel trucks, Figure 13-4 shows the impact on 

the relative life cycle emissions for CNG trucks when the low and high methane emissions are 

compared to the best estimate case we used in the above analyses for a CNG truck complying 

with the methane emissions standards. 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2
13-19 

Figure 13-4 Full Lifecycle Analysis of a CNG Truck - Low, Avg and High Upstream Methane Emissions 

(Projected Upstream Methane Emissions in 2025, Methane GWP of 25) 

Figure 13-4 shows that higher and lower upstream emissions, based on the uncertainty 

factors provided in the GHG Inventory, does impact the relative GHG lifecycle impact of CNG 

trucks, but the effect is quite modest relative to the other emissions effects depicted in the figures 

presented earlier. 

As new methane emission information becomes available, we will update our methane 

emission estimates which would reduce the uncertainty. In addition to the new methane 

emissions information from the GHG Reporting Program, there will likely be other studies as 

well. For example, a number of studies are being conducted to quantify the methane emissions 

and life cycle impacts of natural gas by Environmental Defense Fund (EDF). The final reports 

for these studies have not yet been released but we will review them once they are available. 

The GWPs used to assess the relative climate impacts of methane and nitrous oxide can 

also effect the relative life cycle impacts natural gas trucks compared to diesel trucks. The 

GWPs of methane and nitrous oxide vary based on the timescale assumed. To illustrate this 

point, we added two more sets of figures as sensitivities for comparing the life cycle impacts of 

CNG and LNG natural gas trucks to diesel trucks if the greenhouse gas emissions are evaluated 

over a different lifetime. The GWPs that we use are the two alterative GWPs reported by IPCC 

in its 4th Assessment Report evaluated at 20 year and 500 year GHG lifetimes. Table 13-19 

summarizes the GWPs at the different lifetimes along with the GWPs used in the primary 

analysis summarized above. 

0

20,000

40,000

60,000

80,000

100,000

120,000

Diesel

Tailpipe

Nat Gas

Tailpipe

Diesel Nat Gas

Low

Nat Gas

Avg

Nat Gas

High

Thermal High

Thermal Low

CH4:&N2O

CO2
13-20 

Table 13-19 Summary of GWPs 

 PRIMARY 

ANALYSIS 

SENSITIVITY ANALYSES 

100 Year 20 Year 500 Year 

Methane (CH4) 25 72 7.6 

Nitrous Oxide 

(N2O) 

298 289 153 

It is important to point out that while there are fairly significant differences in methane 

emissions between the various natural gas cases being studies and compared to diesel trucks, the 

nitrous oxide emissions vary very little across all the cases. Therefore, when comparing the 

relative lifecycle impacts using different GWPs, the impact on relative lifecycle emissions is 

almost exclusively due to changes in the methane GWP. Figures 13-5 through 13-8 show the 

relative lifecycle effects of natural gas trucks compared to diesel trucks when the GWPs used are 

based on 20 year and 500 year lifetimes. 

Figure 13-5 Full Lifecycle Analysis of a CNG Truck 

(Projected Upstream Methane Emissions in 2025, Methane GWP of 72) 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Diesel

Tailpipe

CNG

Tailpipe

Diesel CNG pre‐

2014

CNG 2014

and later

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2
13-21 

Figure 13-6 Full Lifecycle Analysis of an LNG truck

(Projected Upstream Methane Emissions in 2025, 

Low and High Refueling and Boil-Off Emission, methane GWP of 72) 

Figure 13-7 Full Lifecycle Analysis of a CNG Truck 

(Projected Upstream Methane Emissions in 2025, Methane GWP of 7.6) 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2

0

20,000

40,000

60,000

80,000

100,000

120,000

Diesel

Tailpipe

CNG

Tailpipe

Diesel CNG pre‐

2014

CNG 2014

and later

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2
13-22 

Figure 13-8 Full Lifecycle Analysis of an LNG truck

(Projected Upstream Methane Emissions in 2025, 

Low and High Refueling and Boil-Off Emission, Methane GWP of 7.6) 

Figures 13-5 through 13-8 show that when evaluated over a shorter timescale, the higher 

GWP for methane increases the relative lifecycle impact of natural gas trucks compared to diesel 

trucks. Conversely, when evaluated over a longer timescale, the lower GWP for methane 

decreases the relative lifecycle impact of natural gas trucks compared to diesel trucks. 

We compared our lifecycle emission estimates for natural gas, relative to diesel fuel, with 

the estimates provided by the California Air Resources Board (CARB) for its Low Carbon Fuel 

Standard (LCFS). For our emissions estimate used in the comparison we used the carbon 

dioxide-equivalent (CO2eq) emissions estimated for 2014 and later engines, which must comply 

with a methane tailpipe emissions standard, and assumed that the engine was 5 percent less 

thermally efficient than a comparable diesel engine. Both analyses used GWPs based on 100 

year timescale (i.e., a GWP of 25 for methane and 298 for nitrous oxide). For the CARB 

emissions estimates, we used the estimates made for what it terms purposes” using the 2013 

version of the CARB GREET model as published in August, 2014.12 CARB estimates that CNG 

engines emit 76 percent of the CO2eq emissions as a diesel truck, while our analysis estimates 

that CNG engines emit 81 percent of the CO2eq emissions as a diesel truck. The most likely 

explanation for CARB’s lower estimated CO2eq emissions for CNG engines is that a much larger 

portion of the electricity used to compress natural gas is renewable in California than the rest of 

the country. CARB estimates LNG engines emit 94.5 percent of the CO2eq emissions, as a 

diesel truck while our analysis estimates LNG trucks emit 96 percent of the CO2eq emissions as 

a diesel truck. CARB assumes no boil-off or venting emissions from LNG trucks and for this 

comparison, we used our more modest boil-off and venting assumption, as described above, 

0

20,000

40,000

60,000

80,000

100,000

120,000

Emissions (gCO2eq/mmBtu)

Thermal High

Thermal Low

CH4:&N2O

CO2
13-23 

which is close to CARB’s. Overall, our estimates are very similar to those estimated by CARB 

and when there are differences, the differences are as expected.E 

A UC Davis report recently released estimated that CNG and LNG trucks using spark 

ignition engines (SING) emit about the same amount of CO2-equivalent emissions, and these 

emissions are slightly higher than that of diesel engines.13 The HPDI engines (DING) fueled by 

LNG are estimated to be the lowest emitting of the several scenarios analyzed by the study. 

Because the study did not discuss vehicle boil-off emissions, it is likely that the study either 

assumed that these emissions are zero or assumed the default vehicle boil-off emission estimates 

made by GREET. It is likely that the study assumed that the liquefaction plants are 90 percent 

efficient as this is the default assumption in GREET, which leads to lower GHG emissions by 

LNG trucks. 

13.2Projecting Natural Gas use in HD Trucks 

EPA reviewed several information sources and projections to estimate how much natural 

gas is currently being used and is projected to be used by heavy-duty trucks. An obvious set of 

projections to review was the set of projections provided in the National Academy of Sciences 

(NAS) report.14 The NAS report attached a figure, sourced from Citi Research, which provided 

projections by ACT, PACCAR, Frost and Sullivan and the National Petroleum Council.15 This 

figure is reproduced below as Figure 13-9. 

 

E Per Anthy Alexiades of CARB: CARB is planning to propose a new draft lifecycle analysis for CNG and LNG 

trucks at an April 2015 public meeting. While the CNG lifecycle is expected to be about the same, the LNG 

lifecycle analysis is expected to have lower emissions based on using a 90% efficiency for liquefaction plants 

instead of the 80% efficiency it was using previously. Lifecycle emissions for both CNG and LNG trucks will be 

adjusted to be 10% higher if using a spark ignition engine to account for their lower thermal efficiency. These 

estimates are solely for hypothetical analyses. LCFS credits are awarded based on GHG emissions for each specific 

application. 
13-24 

Figure 13-9 Near-Term Class 8 Natural Gas Penetration Forecasts 

The first observation we can make about all these reports is that they start out assuming 

that natural gas use is 2 percent of the Class 8 heavy duty truck fleet in 2012. However, that 

level of natural gas vehicle penetration of the heavy-duty fleet is not supported by other data 

sources. In the Energy Information Administration’s Annual Energy Outlook 2014, EIA shows 

natural gas use comprising about only 0.2 percent of total heavy duty fuel consumption in 2012, 

and natural gas use by Class 8 trucks is under 0.1 percent.16 In 2014, AEO 2014 shows natural 

gas comprising about 0.4 percent of total heavy-duty fuel demand and about the same for Class 8 

heavy-duty truck demand. 

One estimate of the number of natural gas trucks supports this level of fuel demand made 

by EIA. In a meeting with the Natural Gas Vehicle for America (NGVA), NGVA presented 

their estimate that 62,000 heavy-duty trucks are fueled by natural gas. The MOVES data base 

estimates that there are 12.4 million heavy-duty trucks in 2014. Combined, the NGVA and 

MOVES numbers estimate that natural gas heavy-duty trucks comprise 0.5 percent of the heavyduty truck population. 

We also evaluated the growth rates for natural gas trucks, including reviewing two of the 

studies referenced in the NAS report. The ACT Research study shows the most aggressive 
13-25 

growth rate for natural gas heavy-duty trucks. The ACT Research projection did not seem to 

consider the economics of natural gas versus diesel fuel. Instead, the ACT projection seemed to 

be based on a consumer acceptance profile of a new technology, presumably assuming that the 

technology is already economically competitive. In a recent ACT press release for a more recent 

report, it was acknowledged that the growth rates ACT projected earlier were too aggressive and 

a more modest growth rate is more likely.17 The NPC projection shows a similar growth rate as 

that estimated by ACT Research, but NPC’s projection for increased uptake of the natural gas 

technology begins in 2015 instead of 2012. In its study, NPC assumed that the increased capital 

cost for a natural gas truck compared to a diesel truck study decreases from $60,000 to $20,000 

by 2040.18 This cost decrease seems excessive, and it is likely an important reason why the NPC 

study shows such a large increase in natural gas use by heavy-duty trucks. We did not have 

access to core assumptions used in the PACCAR and Frost and Sullivan projections to assess 

their viability. 

We searched for the Citigroup report on the Web and in addition to the figure provided in 

the NAS report, we found Citi Group’s projection shown in the context of the other projections 

referenced by NAS from the Citi bank report in Figure 13-10.19 Citi Group’s projection is less 

optimistic than the ACT projection, but is more optimistic than the NPC reference case 

projection. 
13-26 

Figure 13-10 Long-Term Class 8 Natural Gas Penetration Forecasts 

In its Annual Energy Outlook, EIA projects the use of different fuels by the 

transportation sector. 20 This projection was not referenced in the NAS report, but our review 

found it to be especially credible. 

First, as described above, EIA estimates that natural gas fueled 0.4 percent of the energy 

use of heavy-duty trucks in 2014 and this estimate is consistent with the fraction of heavy-duty 

fleet which are fueled by natural gas. 

Second, the EIA projection is based on an economic analysis which considers the 

increased cost of manufacturing a natural gas truck over a diesel truck, the fuel savings for using 

natural gas instead of diesel fuel, and whether the payback time of the fuel savings against the 

increased truck cost would trigger purchases of natural gas trucks. As part of this analysis, EIA 

assumes that lighter heavy-duty trucks would use CNG which is a lower cost technology suited 

for the shorter driving distances for these trucks. The long haul trucks, however, require larger 

stores of fuel to extend the driving range which is satisfied by storing the natural gas as a liquid. 

LNG has about 60 percent of the energy density of diesel fuel, compared to CNG which has only 

25 percent of the energy density of diesel fuel. To satisfy the long driving range of the long haul 
13-27 

trucks, EIA assumed that they would use LNG as a fuel. The assumptions used by EIA for 

conducting its economic analysis all seem reasonable. 

Third, EIA is one of the several well-respected organizations in the world for collecting 

and analyzing today’s fuel prices and projecting future fuel prices. According to the Alternative 

Fuels Data Center, one of the most important assumptions in projecting the future use of natural 

gas in the transportation sector is the relative price of natural gas to the price of diesel fuel. 

Figure 13-11 summarizes the total retail prices and the cost components that make up the final 

average year 2014 retail prices of diesel fuel, CNG and LNG, whose prices are expressed on a 

diesel gallon-equivalent basis. 

Figure 13-11 Relative Retail Cost of CNG and LNG to Diesel Fuel ($/gal dge) 

In 2014, the natural gas price purchased by industrial users was about $6 per million 

BTU, which corresponds to $0.60 per diesel gallon equivalent. The price of crude oil has been 

volatile during 2014 as the Brent crude oil price started at about $110 per barrel ($2.38/gallon), 

but decreased to under $50 per barrel towards the end of 2014. From EIA’s website, the average 

retail diesel fuel price in the first part of 2014 was about $3.80 cents per gallon. When 

comparing the natural gas spot market price on a diesel equivalent basis to the diesel fuel price, it 

appears that natural gas is priced about one quarter of the diesel fuel price. However, if used as 

compressed natural gas, the natural gas must be distributed through smaller distribution pipeline 

system that exists in cities, which dramatically increases the price of the natural gas to $1.15 per 

DGE. Then the natural gas must be compressed and stored at a retail outlet which adds another 

$1.30 per DGE. The estimated retail price of CNG is $2.35 on a diesel gallon equivalent basis 

(DGE), or about $1.45 DGE less than diesel fuel. 

Similarly, if natural gas is converted to LNG, the resulting retail LNG price is much 

higher than the raw natural gas price. LNG liquefaction plants are assumed to be located close to 

large transmission pipelines away from cities, thus, they would likely pay the same low price as 

industrial users. However, for producing LNG, the natural gas must be liquefied which adds 

about $0.75 DGE. When the LNG is transported to retail outlets and marked up, the LNG is 
13-28 

priced $60 per DGE higher. The tax applied to LNG is on a per gallon basis, thus, is much more 

than on a DGE basis because of LNG’s lower energy density. All these steps add substantially to 

the price of the LNG and the estimated retail price of LNG is $2.65 DGE, or $1.15 DGE less 

than diesel fuel. 

In its projections, EIA estimates that crude oil prices in the upcoming years will decline 

modestly until after 2020 when they start increasing until they reach $140/bbl in 2040. Natural 

gas prices are expected to only slightly increase over this period. 

The fifth reason why the EIA projections seem reasonable is because the payback hurdle 

assumptions assumed for truck fleet owners seem reasonable. EIA projects that natural gas 

trucks begin to be purchased when the payback times are 4 years or less based on a survey 

conducted by the American Trucking Associations. The ATA survey found that 24 percent of 

respondents would choose natural gas trucks over diesel trucks if the payoff is 4 years, another 

57 percent would choose natural gas if the payoff is 3 years, the next 15 percent would choose 

natural gas if the payoff is 2 years and the last 5 percent would choose natural gas if the payoff is 

1 year or less.21 This is consistent with some conversations we have had with some fleet owners. 

The NAS cites the pay back for the extra cost of natural gas trucks as 2 years, but other sources 

report a longer return closer to 4 years.F 

The results of EIA’s economic analysis and projected natural gas use in heavy duty trucks 

presented in the 2014 Annual Energy Outlook is presented in Figure 13-12.22

Figure 13-12 EIA Projection of Fuel Prices and Natural Gas Use by HD Engines 

Figure 13-12 shows, as we discussed above, that natural gas currently supplies only about 

0.5 percent of total heavy-duty truck fuel demand and it expected to continue to do so until about 

2023. Starting in 2023, EIA estimates that the rising price of diesel fuel relative to that of natural 

 

F Early LNG Adopters Experience Mixed Results; Truck News, October 1, 2013 

0

1

2

3

4

5

6

7

8

9

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

2010 2015 2020 2025 2030 2035 2040

Cost ($/gal)

Natural Gas Percent of total HD

Demand

Year

NG percent of

HD Demand

NG Price DGE

Diesel Price
13-29 

gas, which begins to change about 2019, creates the economic incentive to purchase natural gas 

trucks. As expected, the EIA projection that the price differential between natural gas and diesel 

fuel continues to increase results in the effect that the uptake of natural gas use in the heavy-duty 

truck fleet accelerates as the price differential increases. 

A very interesting conclusion of the EIA projection is the natural gas penetration 

differences between the different heavy-duty truck classes. Figure 13-13 summarizes the 

projected use of natural gas by the AEO for different truck classes. 

Figure 13-13 EIA Projection of NG use by Truck Weight Class 

Figure 13-13 shows that the only heavy-duty sector which is projected to see a large 

penetration of natural gas is the heavy, heavy-duty sector which increases to 10 percent by 2040. 

The light and medium classes of the heavy-duty truck fleet only show modest increases in 

natural gas use. The likely reason EIA’s analysis shows little CNG or LNG use by light and 

medium heavy-duty trucks is because they are driven far less and their use does not justify the 

higher purchase price. According to the Vehicle Inventory and Use Survey, light and medium 

heavy duty trucks average less than 1/3rd the annual mileage of the heaviest trucks.23 EIA is 

using a distribution of VMT for new class 7 and 8 trucks as shown in Figure 13-14. 

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

2005 2010 2015 2020 2025 2030 2035 2040 2045

Percentage

Year

Heavy HD

Medium HD

Light HD
13-30 

Figure 13-14 Percent of Class 7 and 8 Truck Fleet by Annual Miles Traveled 

Figure 13-14 shows that although about half of class 7 and 8 trucks are driven less than 

60 thousand miles per year, the other half is driven from 60 thousand to over 200 thousand miles 

per year. It is these high mileage long haul trucks which are prime candidates for using LNG 

because of their ability to pay down the high marginal natural gas truck cost. 

Since EIA does not report the payback times as an output of its projections, we conducted 

our own analysis of sample payback times solely for illustrative purposes. We assessed the time 

required for the lower fuel cost of LNG to payback the incremental truck cost of using LNG 

assuming that a truck averages 120,000 miles per year. There were several important aspects of 

the payoff analysis that we conducted. First, based on the EIA analysis which found that the 

heavy, heavy-duty trucks sector is the only one which will see natural gas use increase 

dramatically, we solely studied the payback of natural gas in this truck sector. Second, as 

concluded by EIA, we also assume that the higher energy density of LNG will make it the most 

likely natural gas fuel type used by the heavy, heavy-duty trucks. Third, the higher natural gas 

truck cost was approximated from the analysis EIA conducted for its Annual Energy Outlook. 

Fourth, the analysis presents a simple payback as well as a discounted payback using a 7 percent 

discount factor. Fifth, we evaluated the payback in 2014, and we also assessed what the payback 

might be in 2020 and 2030 and assume some changes in the future years as discussed in some 

example evaluation cases below. 

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

0 50 100 150 200

Percentage of Truck Fleet

Annual Miles Traveled (thosand miles)
13-31 

Table 13-20 Payback Analysis 

CASE 1 

2014 DUEL 

FUELED 

CASE 2 

2014 HPDI 

CASE 3 

2020 HPDI 

CASE 4 

2030 HPDI 

Miles per Year 120,000 120,000 120,000 120,000 

Miles per Gallon 6.7 6.7 7.1 7.2 

Incremental NG Truck Cost 55,000 70,000 65,000 60,000 

Incremental NG Maintenance 

Cost per year 

970 1613 1935 1935 

Diesel Fuel Price ($/gal) 3.80 3.80 4.16 5.65 

Natural Gas Price ($/gal DGE) 2.64 2.64 2.66 2.83 

Diesel Fuel Cost per Year 68,263 56,886 70,608 94,167 

Natural Gas Fuel Cost Per Year 60,062 43,978 50,614 53,810 

Lower NG Efficiency (%) 5% 5% 5% 5% 

Vehicle NG Use (%) 50% 95% 95% 95% 

Simple Payback (years) 6.7 4.5 3.3 1.5 

Discounted Payback (years) 9.1 5.5 3.7 1.6 

We evaluated two different cases for 2014. Case 1 assumes an LNG fueled, heavy-duty 

truck which exceeds 26,000 gross vehicle weight rating and averages 120,000 miles per year. It 

is a mixed-fuel (MFNG) natural gas truck and is assumed to operate 50 percent on natural gas 

and 50 percent on diesel fuel. But because this truck can operate on diesel only, the truck can 

manage with a more modest storage quantity of LNG, thus reducing the cost of LNG storage. 

When this truck is fueled by LNG, it is estimated to be 5 percent less thermally efficient. The 

fuel costs are consistent with the prices during the first part of 2014. Accounting for the fuel cost 

savings based on the average fuel economy and also accounting for the increased maintenance 

cost for operating on LNG,G this truck only achieves a discounted payback time of 9.1 years for 

paying down the $55,000 increased cost for this truck. 

The second case we evaluated for 2014 is a direct injection natural gas (DING) truck. 

Because this truck must fuel on LNG or be parked (the diesel fuel is simply used to enhance the 

combustion process), there must be more LNG storage capacity and the truck purchase price is 

estimated to be $70,000 more than a diesel truck. This case also assumes 120,000 miles 

accumulated per year. The discounted payback time is 5.5 years, which is less than the first case 

because the truck runs more of the time on natural gas. 

If we used the lower diesel fuel prices that we experienced later on in 2014 and early 

2015 ($2.90/gallon), the payback time would be much longer. Even on a simple payback basis, 

the payback time is over 20 years for both Case 1 and Case 2. 

For Case 3, we assessed a 2020 case using EIA fuel price projections. Like the second 

case, the truck is a DING truck, but because it is six years later, we assumed a modest cost 

 

G FPInnovations estimates that natural gas truck maintenance costs are $0.01/per kilometer more than diesel trucks. 
13-32 

reduction due to a learning curve. Due to the large price spread between diesel and natural gas, 

this truck’s discounted payback time is 3.7 years. 

For Case 4, we assessed a 2030 case using EIA fuel price projections. Like the previous 

two cases, the truck is a DING truck, and we assumed a further modest cost reduction due to a 

learning curve. This truck is also assumed to accumulate 120,000 annual miles fueled on LNG. 

Due to the even larger price spread between diesel and natural gas, this truck’s discounted 

payback time is 1.6 years. 

The payback time for both 2014 high mileage heavy-duty truck cases we evaluated are 

over 4 years. Since fleets become interested in purchasing natural gas trucks purchased when the 

payback times are 4 years or less, it explains the low penetration of natural gas in the heavy-duty 

sector. 

Given the apparently poor payback times for natural gas vehicles in 2014, it suggests that 

existing subsidies for natural gas likely play an important role in encouraging its use. According 

to EIA, half the natural gas consumption by cars and trucks is in California and may be partially 

due to subsidies and other incentives California offers. California subsidies the purchase price of 

natural gas vehicles, and also offsets the cost of natural gas dispensing stations. The Low 

Carbon Fuel Standard (LCFS) in place in California also incentivizes natural gas use because 

natural gas is considered to cause less of an impact on the climate than petroleum-based gasoline 

and diesel fuel. The majority of the other half of the NG fleet is also in states which subsidize 

the natural gas truck or service station costs. 

Based on the EIA projections for crude oil and natural gas prices, the payback time of 

LNG trucks is expected remain long (more than 4 years) until sometime around 2020 when crude 

oil prices are projected to begin increasing. Thus, natural gas use by heavy-duty trucks is not 

projected to increase above 1 percent of the heavy-duty fuel demand until after 2025. Even if the 

economics improve for using CNG and LNG in the heavy-duty fleet, another hurdle is fuel 

availability since these fuels are not already widely available. Figure 13-15 shows the number of 

CNG and LNG public and private service stations relative to the number of gasoline and diesel 

fuel service stations and truck stops, respectively. 
13-33 

Figure 13-15 CNG and LNG Availability at Service Stations 

As Figure 13-15 shows, CNG and LNG fuel availability at service stations is 1 percent or 

less of the availability of gasoline and diesel fuel. Even if a business owner finds the purchase of 

one or more new natural gas trucks an attractive investment, if the fuel is not available in the 

area, the business owner may have to forgo purchasing the natural gas trucks. A fleet owner 

might be in the position to also install a natural gas service station or establish a contract with a 

third party fuel provider to provide the fuel, but that may require making a large purchase of 

trucks to justify the installation of the service station or the establishment of the contract. If the 

fleet owner would need to build a CNG or LNG refueling station to enable purchasing the natural 

gas trucks, then the combined cost of the service station installation and the natural gas trucks 

purchase could make the prospect uneconomic even if the natural gas truck purchase by itself is 

justified. LNG availability is particularly challenging because in addition to an LNG service 

station, a LNG liquefaction plant would be needed as well. If the economics turn favorable for 

using natural gas in the truck fleet, the conversion to natural gas is likely to be slow due to the 

need to build out the fuel availability. 

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Number of

Service Stations

Selling CNG

Total Number of

Service Stations

Number of

Trucks Stops

Selling LNG

Total Number of

Truck Stops

Number of Service Stations and Truck Stops

Planned

About 1% of

Service Stations

~150,000 total

Service Stations

less than 1% ofTruck Stops
13-34 

13.2.1 Dimethyl Ether 

Although NAS focused its recommendations on natural gas, it also discussed dimethyl 

ether (DME), which is a potential heavy-duty truck fuel sourced from natural gas. Dimethyl 

ether has a high cetane number (more than 55), although its energy density is about 60 percent of 

that of diesel fuel. Dimethyl ether is a volatile fuel, like liquid petroleum gas, that can be stored 

as a liquid at normal ambient temperatures under moderate pressure. Typical DME fuel tanks 

would be designed to prevent any significant evaporative emissions. 

A DME fueled truck is only modestly more expensive than a diesel fuel truck. The fuel 

tank is more expensive than a diesel fuel tank, but much less expensive than an LNG tank since 

it does not need to be heavily insulated. The engine modifications to enable using DME are also 

modest. Because DME does not have carbon-carbon bonds that form particulate particles during 

combustion, the particulate filter, which is normally installed on recent year diesel trucks, can be 

eliminated. This offsets some of the increased DME engine and fuel tank costs. 

Although DME is sourced from cheap natural gas, the conversion of natural gas to DME 

and moving the fuel to retail outlets greatly increases the cost of the fuel. As Figure 13-16 

shows, DME is more expensive than LNG, but still lower in cost than diesel fuel. Similar to 

Figure 13-11, the diesel fuel price used in Figure 13-16 is based on crude oil prices in early 2014. 

Figure 13-16 Relative Retail Cost of DME to CNG, LNG and Diesel Fuel ($/gal dge) 

DME is estimated to cost $3.50/ DGE, or $0.30 DGE less than diesel fuel. 

Because there is very little DME use in the US (there is only a very small fleet of DME 

trucks being contemplated in California), we did not conduct a lifecycle assessment of DME. 

We will, however, discuss a few aspects of a lifecycle analysis for DME. First, since DME is 

sourced from natural gas, the upstream methane emissions from the natural gas industry would 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

CNG LNG Diesel DME

Cost ($/gal)

Raw Fuel Cost/Crude Oil Liquifaction/Refining Distribution and Marketing Taxes
13-35 

still be allocated to DME. Second, there are not venting issues associated with DME as with 

LNG or CNG refueling. Third, but very significantly, DME’s global warming potential is 

estimated to be 0.3 when assessed over a 100 year lifetime, which is about 1 percent of 

methane’s GWP.24 

13.3 Natural Gas Emission Control Measures 

13.3.1 Proposed Control Measures 

EPA is proposing some control measures to reduce potential methane emissions from 

natural gas vehicles. The cost discussion for each is below. 

13.3.1.1 Crankcase Emissions

 The proposal would require that all natural gas engines have closed crankcases, rather 

than continuing the provision that allows compression-ignition engines to separately measure 

and account for crankcase emissions that are vented to the atmosphere. This allowance has 

historically been in place to account for the technical limitations related to recirculating 

crankcase gases with high PM emissions back into the engine’s air intake. Natural gas engines 

have inherently low PM emissions, so there is no technological limitation that would prevent 

manufacturers from closing the crankcase and recirculating all crankcase gases into the engine’s 

air intake. The methane standard that was introduced in Phase 1 of this rule accounts for 

crankcase emissions by requiring methane in crankcase emissions be measured and included. 

However, there can be significant deterioration with respect to volatile crankcase emission such 

as methane, and it is difficult to ensure that all deterioration is fully reflected in the 

manufacturer’s deterioration factor. When the system is sealed and emissions are routed to the 

engine intake, crankcase emissions are zero and deterioration ceases to be a concern. See the 

Preamble Section II. D. for a description of the proposed requirement. 

Most (if not all) NG engines are derived from either a gasoline engine or a diesel engine. 

Since it is already required for gasoline engines to seal the crankcase, it is not necessary to 

propose any crankcase changes for gasoline-derived engines. Diesel engines are not required to 

seal the crankcase, but are required to include the crankcase emissions in the emissions test. 

Many OEMs already close the crankcase for their engines, and EPA projects the average costs to 

comply with this proposed requirement would be negligible. 

13.3.1.2 Require 5 Day Hold Time

Boil-off emissions from LNG vehicles were not addressed nor accounted for in Phase 1 

of this rule. As more testing has been done in this area since that time for this emerging issue, a 

minimal requirement EPA is proposing as described in the Preamble Section XII is to require 

manufacturers to follow current industry recommended practice, SAE J2343 for five day hold 

time to limit boil-off emissions from LNG vehicles. The ANSI standard for NG vehicles was 

adopted in 1994 when we required evaporative standards for NG vehicles (59 FR 48472 

September 21, 1994). This was updated to the more recent ANSI NGV1-2006 in the Tier 3 Rule 
13-36 

(79 FR 23414, April 28, 2014) to the proposal would adopt the analogous requirements for LNG, 

found in SAE J2343 in Section 4.2. 

The specifications of this safety related standard will only affect new LNG vehicles to 

prevent boil-off initially and does not address aging vehicles as their insulating properties 

diminish such as loosing vacuum over time and may eventually result in much shorter hold 

times. The SAE J2343 test is done at 72o

F, therefore it is reasonable to assume that hold times 

will be shorter for very hot summer days and high solar loading. 

Since the majority or all of the NG vehicles are already compliant with the NFPA 52 and 

SAE J2343 recommended practices for 3 and 5 day hold times, there would appear to be zero to 

minimal costs for the requirement for a 5 day hold time. 

13.3.2 Potential Controls 

EPA is also investigating additional controls to further reduce potential methane 

emissions from natural gas vehicles. 

There are not well defined cost estimates for many of these options since they are ideas 

that will require research and development. 

13.3.2.1 Update to CO2 Credits Program 

The Phase 1 Heavy-duty vehicle rulemaking establishing greenhouse gas emission 

standards included a compliance alternative allowing heavy-duty manufacturers and conversion 

companies to comply with the respective methane or nitrous oxide standards by means of overcomplying with CO2 standards (40 CFR 85.525 (ii)). The heavy-duty rules allow averaging 

only between vehicles or engines of the same designated type (referred to as an “averaging set” 

in the rules). Specifically, the Phase 1 Heavy-duty rulemaking added a CO2 credits program 

which allowed heavy-duty manufacturers to average and bank pollutant emissions to comply 

with the methane and nitrous oxide requirements after adjusting the CO2 emission credits 

(generated from the same averaging set) based on the relative GHG equivalents. To establish 

the GHG equivalents used by the CO2 credits program, the Phase 1 Heavy-duty vehicle 

rulemaking incorporated the IPCC Fourth Assessment Report GWP values of 25 for CH4 and 

298 for N2O, which are assessed over a 100 year lifetime. 

Since the Phase 1 rule was finalized, a new IPCC report has been released (the Fifth 

Assessment Report), with new GWP estimates. This is prompting us to look again at the relative 

CO2 equivalency of methane and to seek comment on whether the methane GWP used to 

establish the GHG equivalency value for the CO2 Credit program should be updated to those 

established by IPCC in its Fifth Assessment Report. The Fifth Assessment Report provides four 

100 year GWPs for methane ranging from 28 to 36. Therefore, we not only request comment on 

whether to update the GWP for methane to that of the Fifth Assessment Report, but also on 

which value to use from this report. 

The costs for changing the GWPs used for the CO2 Credits Program can be estimated by 

the cost to the manufacturer for reducing CO2 emissions. If for example the GWP of methane is 
13-37 

increased from 25 to 34, then the number of CO2 credits that will be necessary to offset methane 

emissions above the standard can be calculated by this change. The cost involved would be the 

cost per ton of CO2 for the difference in CO2 credits. See chapter 7 for a discussion of our 

estimated cost per ton of CO2 credit. 

13.3.2.2 Deterioration Factors for NG Tailpipe Emissions 

The current deterioration factors are based on diesel technology. 

13.3.2.3 Vehicle Boil-Off Warning 

A simple means to help limit boil-off emissions would be to require that natural gas 

truck drivers be alerted to expected near-future boil-off events. Such an alert could be in the 

form of a warning light and associated alarm that would indicate that the LNG storage tank is 

approaching a pressure which would require that the tank vent. Knowing this, the truck driver 

could take evasive action to prevent such a release. 

A second alarm could be required when the LNG tank is venting. This would alert the 

truck driver to take action to avoid the potential for an explosive environment forming from the 

vented natural gas emissions. 

To alert for a boil-off event a pressure sensor integrated with the vehicle’s horn would 

cost on the order of $5. 

13.3.2.4 Extend 5 Day Hold Time 

The specifications of the proposed 5 Day Hold Time SAE 2343 safety related standard 

will only affect new LNG vehicles to prevent boil-off initially and does not address aging 

vehicles as their insulating properties diminish such as loosing vacuum over time and may 

eventually result in much shorter hold times. LNG tank manufacturers are further developing 

their technologies for improvement of hold times and reducing boil-off from LNG storage tanks 

on trucks. These improvements can be incorporated by requiring longer hold times. It may be 

possible using these improvements in technology to extend the hold times to ten days or longer. 

One example of an existing technology to address boil-off emissions has shown a 10 day 

or more hold time depending on ambient temperatures and solar loading experienced by the 

vehicle. Westport Innovations, Inc. Ice Pack technologyH is an integrated vehicle design which 

requires low pressure, lower temperature fuel, referred to as “blue” fuel, for the longer hold 

times. The system does accept the typical LNG fuel which is higher pressure and temperature, 

referred to as “green” fuel, at the expense of shorter hold times. Fleet owners of these innovative 

vehicles have installed refueling stations which service their own fleets. The Ice Pack 

technology used by Westport has been on the market since 2007 and has gone through several 

revisions to work out issues. The technology involves a pump at the bottom of the tank which 

 

H Reiskin, Jonathon S. “Expensive Fuel Tanks, Systems Drive High Price of Nat-Gas Trucks”, Transport Topics 

Special Report “Alternative Fuels” December 2013 
13-38 

pumps the liquid fuel to the engine. Through a series of pressure changes and pumps, vapor is 

fed to the engine. At the same time a purge system takes the high pressure warming vapor in the 

fuel tank through a series of valves and feed it to the engine using the pressure. This purge 

system reduces the pressure of the fuel tank which further extends the hold time. 

The range of enhancements necessary to achieve extended hold time of at least 10 days 

will range in cost from $5000 to $20,000 per vehicle.I

 Ice Pack technology used by Westport is 

on the higher end of that range due to the additional vehicle pump and complicated engineering 

system which depressurizes the LNG for use in the engine while purging the gas during engine 

operation and keeping the LNG in the tank cold. Other approaches used by OEMs include 

increasing the vacuum size between the interior and exterior tank walls to maximize the 

insulation, keeping heat transfer to a minimum. The colder the LNG stays, the longer the hold 

time. 

13.3.2.5 Capturing and/or Converting Methane Refueling or Boil-Off Emissions 

A methane canister using adsorbents such as ANG (adsorbed natural gas) could be added 

to capture the methane which otherwise would be released to the environment during a refueling 

or boil-off event. Once captured, steps could be taken to route the methane to the engine intake 

once the vehicle is operating again, or to take steps to converting the methane to less GHGpotent CO2. ANG has been a patented technology since the 1950’s.J

 The Department of Energy 

(DOE) Advanced Research Projects Agency (ARPA-E) has awarded over $10 million in 2012 to 

four different projects to develop new sorbent materials for on-board natural gas storage. Gas 

Technology Institute (GTI), a research and development organization which serves energy and 

environmental markets has been utilizing the grant to develop lightweight, affordable, natural gas 

tanks for natural gas vehicles. Methane will adsorb more efficiently with high pressure such as a 

boil-off event onto this material. 

As shown in Figure 13-17, for a 115 gallon LNG tank which would boil off completely in 

approximately 35 daysK the boil-off methane emissions would be on the order of 18 lbs over two 

days, and close to 50 lbs in 5 days. This would require a canister of approximately 15-50 gallons 

of the adsorbent material for a 2-5 day hold time,L as show in Figure 13-18. 

A methane canister using adsorbents such as ANG will need to be on the order of 15-50 

gallons for 2-5 day hold time. It is still early to say what the costs for this application will be 

because there is so much engineering development and testing yet to do, but it is estimated that 

the casing and adsorbents for this size of canister will range from $1,500-$8,000. 

 

I

 As per confidential discussions with OEM’s, June 27, 2014, and November 20, 2014. 

J

 Menon, V.C., Komarneni, S. “Porous Adsorbents from Vehicular natural Gas Storage: A Review”, Journal of 

Porous Materials 5, 43-58 (1998) 

KPowars, Charles A. “Best Practices to Avoid LNG Fueling Station Venting Losses”, St. Croix Research for 

Brookhaven National Laboratory, 2010 

L LNG-ANG Venting Calculations Spreadsheet, “LNG-ANG Venting Calcs.xlsx” 
13-39 

Figure 13-17 Boil Off Rate for a 90% Filled 115 gallon LNG Tank with 18 Watts of Heat Input 

Figure 13-18 ANG Control of 115 gallon LNG Tank with 18 watts of Heat Input 

If being discharged to the environment, the methane potentially could be burned to CO2

using a burner. Another potential option would be to convert the methane captured in a canister 

to CO2 over a catalyst. If a catalyst were to be developed, it would have to be designed to 

transform any escaping methane into carbon dioxide to reduce the global warming potential of 

methane down to that of carbon dioxide. 

A methane catalyst for a boil-off event on a vehicle has not yet been designed. We 

acknowledge that this would be a challenge and expect a good deal of engineering will be 

required for this application which will include an external heat source to be triggered upon a 

boil-off event initiation, possibly a pressure sensor which notices when the pressure builds to 

close to 230 psi when boil-off occurs. 
13-40 

13.3.2.6 Reducing Refueling Emissions 

In addition to the boil-off issue is the recurrence of manual venting at refueling by LNG 

truck operators. Under high pressure circumstances, such as when the vehicle has been sitting for 

some time period in warmer temperatures, it is necessary to decrease the pressure in the fuel tank 

before new fuel can enter the tank. The recommended practice is to transfer the extra vaporized 

fuel to the gas station or natural gas pipeline, but this can take extra time. In some areas it has 

turned into common practice to just vent to the atmosphere to keep the down time at the 

refueling station to a minimum. In other areas there is an incentive to reroute the gas into the 

station storage tank or natural gas pipeline with credit towards the fuel purchase. One option 

would be to require a system for rerouting back to fuel storage tank, whether it is to a CNG tank, 

a CNG pipeline or re-liquefying system for an LNG storage tank. There are opinions within the 

industry that the technology at the refueling station and the vehicles are advancing so that the 

newer pumps and vehicles will automatically vent any pressurized gas back through the refueling 

nozzle which will quickly cool with LNG in the fuel line and go back into the vehicle as a liquid 

which ultimately lowers the pressure with the drop in temperature. Most LNG refueling stations 

are already equipped with a vent line to take excess vapors back to the station storage tank before 

refueling with the cold liquid fuel. For the older LNG stations which are not equipped with a 

vent line, there would be an overhead cost of approximately $10,000-$15,000 to install a vent 

line system at each pump. Advancing this feature to tie into a NG pipeline for monetary credit 

would add another overhead of similar magnitude. The technology exists to install a very small 

methane liquefaction facility at a retail station which would allow the retail outlet to re-liquefy 

the vented gas and put it back into their storage tank. 

Another option would be to control refueling emissions for LNG trucks with an on-board 

vapor recovery refueling system similar to light duty gasoline vehicles. This would likely 

involve a canister with carbon designed to adsorb methane specifically. See discussion in 

13.3.2.5.4. 

Onboard refueling will require research and development for an onboard canister with 

methane adsorbents with a canister sized to take the vapors from a refueling event. The resulting 

canister and therefore costs will likely be similar to the discussion in Section B4.2.4. 

The recently promulgated Tier 3 rule requires use of the ANSI-NGV1-206 standard 

practice to meet the evaporative emissions refueling requirement. Small puffs of up to 200 cc/hr 

(which equates to 72 grams of methane per hour) of leakage are allowed with these tests. For 

CNG the current recommended practice for refueling involves a vent line taking a small amount 

of NG away from the operator for safety reasons, but this small amount is generally released to 

the atmosphere. Multiplied out for all of the refueling events of the CNG fleet this small amount 

can add up quickly especially with projected fleet expansion. When it is again multiplied by the 

methane GWP it would be worth investigating options for a system that would recompress the 

gas and reroute back into the CNG storage tank or pipeline. 

Rerouting the vent line from a CNG refueling event will be a slightly more complex 

endeavor due to the mixing of air involved. In order to reroute the vent line to a NG pipeline for 

credit or to compress for usage will both require some engineering and development. This will 

be an overhead cost for the station. 
13-41 

13.3.2.7 OBD Requirements for Fuel System Methane Leak Detection 

Onboard diagnostics (OBD) are required to detect and provide a warning for when 

methane leaks occur due to wear of connections and components of the CNG or LNG fuel 

system. Methane leaks occur due to wear of connections and components of the fuel system just 

as in gasoline or diesel systems. This can result in cracks, holes or structural breaks in the 

components. The HD OBD Rule requires comprehensive component monitoring with rationality 

and functionality checks for all HD vehicles including NG, by 2013. By 2019 all HD vehicles, 

including NG, must verify that their emissions control systems are functioning (74 FR 8310 

February 24, 2009. The implementation schedule is found in 86.010-18(o)). 

The requirements are already in place for methane leak detection. Therefore there would 

be minimal additional costs to include a pressure sensor and warning light for impending boiloff. The OBD code should already be programmed to record all associated methane leak events 

and keep a running record. Therefore adding accounting for boil-off venting should be minimal. 

There would be additional hardware and programming involved to detect whether the operator is 

venting high pressure gas to the refueling storage tank or to the atmosphere. Depending on the 

amount of engineering development required for a system of pressure and temperature sensors 

this approach could be relatively cost effective. The incremental costs for a vehicle with an 

OBD system already in place would be minimal, on the order of $5-10. 

 
13-42 

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(EPA 430-R-14-003). 

2

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United Nations Framework Convention on Climate Change. Table 5.2. 

3

 Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission 

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4

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