Proposed Rulemaking for Greenhouse
Gas Emissions and Fuel Efficiency
Standards for Medium- and Heavy-Duty
Engines and Vehicles-Phase 2
Draft Regulatory Impact Analysis
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Proposed Rulemaking for Greenhouse Gas
Emissions and Fuel Efficiency
Standards for Medium- and Heavy-Duty
Engines and Vehicles-Phase 2
Draft Regulatory Impact Analysis
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
EPA-420-D-15-900
June 2015
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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
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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 COi 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
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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
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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
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Table of Contents, Acronym List, and Executive Summary
List of Acronyms
jig Microgram
|im 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
BAG 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
CCheq 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
Energy Information Administration (part of the U.S. Department of
EIA 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-3
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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 ton- Gallons of fuel used to move one ton of payload (2,000 pounds) over
mile 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
Greenhouse Gases, Regulated Emissions, and Energy Use in
GREET 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
1C Indirect Costs
ICCT International Council on Clean Transport
ICD International Classification of Diseases
ICF ICF International
ES-4
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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
Ib 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
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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
NAF A 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-C AN National Research Council of Canada
NREL National Renewable Energy Laboratory
NTP National Toxicology Program
NVH Noise Vibration and Harshness
O&M Operating and maintenance
Os Ozone
OAQPS Office of Air Quality Planning and Standards
OC Organic Carbon
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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
PMio Coarse Particulate Matter (diameter of 10 jim or less)
PM2.5 Fine Particulate Matter (diameter of 2.5 jim 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
S AE 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
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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 Inj ection
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
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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
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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 (CCh) 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 CCh 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 Ibs. 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.
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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 Value3
(Billions of 2012 Dollars)
Lifetime Present Value - 3% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits
Net Benefits
-$25.2
-$1.1
$170.1
$93.8
$238
Annualized Value - 3% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits
Net Benefits
-$1.0
-$0.04
$6.7
$3.7
$9.4
Lifetime Present Value - 7% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits
Net Benefits
-$17
-$0.6
$91.7
$66.1
$140
Annualized Value - 7% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits
Net Benefits
-$1.2
-$0.04
$6.7
$4.8
$10.2
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A.I
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 Value3
(Billions of 2012 Dollars)
Lifetime Present Value0 - 3% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits'3
-$25
-$1.1
$171
$97
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Net Benefits'1
$242
Annualized Value6 - 3% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits'3
Net Benefits'1
-$1.3
-$0.1
$8.7
$4.9
$12.3
Lifetime Present Value0 - 7% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits b
Net Benefits'1
-$17
-$0.6
$90
$65
$138
Annualized Value6 - 7% Discount Rate
Vehicle Program
Maintenance
Fuel Savings
Benefits'3
Net Benefits'1
-$1.3
$0.0
$7.3
$4.2
$10.1
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A.I
b EPA estimated the benefits associated with four different values of a one ton CCh 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 CCh 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.
0 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%.
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TABLES SUMMARY OF
PROPOSED 2027
STANDARDS INCLUDING
AVERAGE PER VEHICLE
COSTS AND PROJECTED
IMPROVEMENTREGULATORY
SUBCATEGORY
C02 GRAMS
PER TON-MILE
(FOR ENGINES,
CO 2 GRAMS
PER BRAKE
HORSEPOWER-
HOUR; FOR HD
PUV, GRAMS
PER MILE)
FUEL
CONSUMPTION
GALLON PER
1,000 TON-MILE
(FOR ENGINES,
GALLONS PER
100 BRAKE
HORSEPOWER-
HOUR; 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
ANDC02
IMPROVEMENT
IN MY 2027
RELATIVE TO
MY 2017
Tractors
Class 7 Low Roof Day Cab
Class 7 Mid Roof Day Cab
Class 7 High Roof Day Cab
Class 8 Low Roof Day Cab
Class 8 Mid Roof Day Cab
Class 8 High Roof Day Cab
Class 8 Low Roof Sleeper Cab
Class 8 Mid Roof Sleeper Cab
Class 8 High Roof Sleeper Cab
87
96
96
70
76
76
62
69
67
8.5462
9.4303
9.4303
6.8762
7.4656
7.4656
6.0904
6.7780
6.5815
$10,140
$10,140
$10,099
$10,204
$10,204
$10,209
$12,744
$12,744
$12,842
19%
19%
21%
19%
18%
20%
22%
21%
24%
Trailers
Long Dry Box Trailer
Short Dry Box Trailer
Long Refrigerated Box Trailer
Short Refrigerated Box Trailer
77
140
80
144
7.5639
13.7525
7.8585
14.1454
$1,409
$1,280
$1,253
$1,253
8%
7%
5%
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
C02 GRAMS PER
TON-MILE (FOR
ENGINES, C02
GRAMS PER
BRAKE
HORSEPOWER-
HOUR; FOR HD
PUV, GRAMS PER
MILE)
FUEL
CONSUMPTION
GALLON PER 1,000
TON-MILE (FOR
ENGINES,
GALLONS PER 100
BRAKE
HORSEPOWER-
HOUR; 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
ANDCO2
IMPROVEMENT IN
MY 2027 RELATIVE
TO MY 2017
Vocational Diesel
LHD Urban
LHD Multi-Purpose
LHD Regional
MHD Urban
MHD Multi-Purpose
MHD Regional
HHD Urban
HHD Multi-Purpose
HHD Regional
272
280
292
172
174
170
182
183
174
26.7191
27.5049
28.6837
16.8959
17.0923
16.6994
17.8782
17.9764
17.0923
$3,489
$3,490
$1,407
$4,696
$4,696
$1,395
$7,422
$7,422
$4,682
16%
16%
16%
16%
16%
16%
16%
16%
16%
Vocational Gasoline
LHD Urban
LHD Multi-Purpose
LHD Regional
MHD Urban
MHD Multi-Purpose
MHD Regional
HHD Urban
HHD Multi-Purpose
HHD Regional
299
308
321
189
191
187
196
198
188
33.6446
34.6574
36.1202
21.2670
21.4921
21.0420
22.0547
22.2797
21.1545
$3,086
$3,087
$1,004
$4,327
$4,327
$1,026
$7,053
$7,053
$4,313
12%
12%
12%
13%
13%
13%
12%
12%
12%
Diesel Engines3
LHD Vocational
MHD Vocational
HHD Vocational
MHD Tractor
HHD Tractor
553
553
533
466
441
5.4322
5.4322
5.2358
4.5776
4.3320
$471
$437
$437
$1,698
$1,698
4%
4%
4%
4%
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.
ES-14
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Diesel Standards
O
u 500
4500 5000 5500
Work Factor
Figure 1 EPA Proposed CCh Target Standards and NHTSA Proposed Fuel Consumption Target Standards
for Diesel HD Pickups and Vans
Gasoline Standards
J soo
£
E 550
7.44 -i
'£
8
6.94 ^
O
5.44
-------
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 (Ib) - Curb Weight (Ib)
xwd = 500 Ib if the vehicle is equipped with 4wd, otherwise equals 0 Ib.
Towing Capacity = GCWR (Ib) - GVWR (Ib)
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
201 8-2020 a
2021
2022
2023
2024
2025
2026
2027 and later
a
0.0416
0.0406
0.0395
0.0386
0.0376
0.0367
0.0357
0.0348
b
320
312
304
297
289
282
275
268
c
0.0004086
0.0003988
0.0003880
0.0003792
0.0003694
0.0003605
0.0003507
0.0003418
d
3.143
3.065
2.986
2.917
2.839
2.770
2.701
2.633
Gasoline Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025
2026
2027 and later
a
0.044
0.0429
0.0418
0.0408
0.0398
0.0388
0.0378
0.0369
b
339
331
322
314
306
299
291
284
c
0.0004951
0.0004827
0.0004703
0.0004591
0.0004478
0.0004366
0.0004253
0.0004152
d
3.815
3.725
3.623
3.533
3.443
3.364
3.274
3.196
Note:
a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only.
ES-16
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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 CCh 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 (CCh), methane (CH4), nitrous oxide
ES-17
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(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 (PIVh.s) 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-18
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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.
ES-19
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Chapter 1: Industry Characterization
1.1 Introduction
The proposed fuel consumption and CCh emissions standards described in the
preamble of this NPRM would be applicable to three currently-regulated 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. 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 kingpin) on the front of the trailer and a horseshoe-shaped coupling
device called & 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
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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.
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Adapted from http://www.wbmcgyire.com/links/Guides/TruckTraiterGuide.pdf
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
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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.
ACT Research 2013 Factory Shipments
Dry Van
Refrigerated Van
Platform
HeavyLowbed
MediumLowbed
Dump
Tank Liquid
.TankBulk ^^^ ^^ 56%
Grain
Other-Trailer
Chassis
15%
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.
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Trailer Body Builders 2013 North American Truck Trailer Output Report
(244,864 Total Trailers)
Wabash National Corporation
Great Dane Limited Partnership
Utility Trailer Manufacturing
Hyundai Translead
Vanguard National Trailer Corp/CIMC
eStoughton Trailers
MANAC
Fontaine Trailer Company
Wilson Trailer Company (est)
Timpte Inc*
« MAC Trailer Manufacturing
Strick Corporation'
Heil Trailer International. Co.
Reitnouer Inc'
Con-Way Manufacturing
Next 14 Companies (None >1.0%)"
Small business according to SBA definition of cSQO employees
' S of 14 are smail businesses
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 (SB A) 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
> 1000M
$500M - $999M
$400M - $499M
$300M - $399M
$200M - $299M
$100M - $199M
$50M - $99M
$40M - $49M
$15M-$19M
$10M-$14M
$5M - $9M
<$5M
Total Companies
Total Revenue ($M)
Average Revenue ($M)
Business Size
All Sizes
1
0
1
0
3
3
13
14
9
3
26
41
114
4965
44
Large
1
0
1
0
3
3
7
2
1
0
0
1
19
3799
200
Small3
0
0
0
0
0
0
6
12
8
3
26
40
95
1166
12
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Note:
Box Trailer Mfrs
Non-Box Trailer Mfrs
14
109
9
17
5
92
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.
ACT Research Annual Factory Shipments
400000
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 long-
haul 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
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non-box trailers are most frequently used for short-haul. Figure 1-5 summarizes these
findings.
U.S. Census 2002 Vehicle Inventory and Use Survey
Tractor-Trailer Primary Trip Length
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
53>DiyVan 53'+Reefer <53*DryVan < S3'Reefer
Flatbed. Tank (Dfy Bulk) Tank (Liquid.
Platform. Gases)
Curtamside. etc
Short-Haul (<500 mi) Long-Haul
Figure 1-5 2002 Vehicle Inventory and Use Survey Considering Primary Trip Length for Tractor-
Trailers
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 temperature-
controlled. 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.
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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-on-
flatcar (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.
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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/420rll901.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-policv/transportation-policv/us-transportation-policv. Accessed: August 18,
2014.
5 "Wabash Shows What a 33-Foot Pup Would Look Like". Berg, Tom. Heavy-Duty Trucking
Truckinglnfo.com. March 31, 2014. Available at: www.truckinginfo.com/blog/trailer-
talk/storv/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.
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Chapter 2: Technology and Cost
2.1 Overview of Technologies
In discussing the potential for CCh 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 CCh
(about 22 pounds (10 kg) of CCh 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
CCh 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 CCh 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
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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 CCh 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 Heavy-
Duty 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 CCh 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.
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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.
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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
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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-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.
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
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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 CCh
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
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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 CCh emissions
reductions indicate that the potential for reducing CCh 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 CCh
emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other publicly-
available sources suggest a fuel consumption and CCh 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
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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
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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
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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, Federal-
Mogul 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 Aftertreatm ent 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
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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
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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 Ibs
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.
5-15years
3-5years
Applted R/D
Prototype
Production
Prototype component available
Testing compEete engine/vehicle
Production start-up phase
Release long-fead
Complete Market launch
Initial results en operating
stability
Verified with production
parts on customer fleets and
oad testing
Design Released into production
All product components availablf
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.,
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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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620
'.60
Baseline-No-Anernotneter
Tractw-Mountad-Anernomater
Slight drag increase along entire vel icle
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 Smart Way
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.
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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 Man ufacturer Commercial In itiatives
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:
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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 Super Truck
initiative, to improve commercially-available products.
2.4.2.1.2 Supplier Research: SABIC Roof Fairing Tech n ology an d
Man ufacturing
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).
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Figure 1: baseline roof fairing
Surface X-Fore» (dimless)
Figure 2: SABIC concept roof fairing
-0.3CO
-0.150
o.ooo
0.150
0,300
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 3: SABIC concept roof Figure
fairing showing directed airflow fairing :
4: SABIC concept
.howing airflow detail
roof
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.
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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 l/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 l/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
1
DELTA WACD
@55MPH
0.003
% DELTA CD VS.
CD
OPEN GRILLE
0.60%
Table 2-2 Reduced Scale Wind Tunnel Results for Open versus Close Grille Configurations
TRACTOR
MODEL
A
B
C
DELTA WACD
@55MPH
0.010
0.012
0.009
% DELTA CD VS.
CD
OPEN GRILLE
1.69%
1.89%
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.
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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
OEM Side Mirrors
OEM Fender Mirrors
Wheel Covers (Tractor and Trailer)
Tractor Drive Axle Wrap-Around Splash Guards
Roof Fairing Rear-Edge Filler
DELTA
WACD
-0.0156
-0.0098
0.0020
0.0049
0.0137
Based on this table, there is the potential to improve tractor aerodynamics by 206 counts
(0.0206 WACo) 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.
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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 Super Truck 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
Super Truck 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 Ibs. 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.
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2.4.2.2.2 Government Sponsored Advanced Aerodynamic Research:
Lawrence Liver more 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.
Tractor-trailer integration is the next step in
achieving a radical improvement in fuel econor
> 90% aerodynamic drag
reduction compared to
heavy vehicles on the
road today
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 tractor-
trailer 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
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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 Ibs 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.
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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 CCh emissions in operation, while tires with lower
rolling resistance lose less energy, and use less fuel and produce less CCh 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 CCh 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 Ibs. 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 Ibs. on a single axle at 500 Ibs/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 CCh 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 CCh 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 CCh 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
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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 CCh 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 cost-
effective 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 CCh emissions. The effectiveness of fuel consumption and CCh 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.
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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.
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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 heavy-
duty 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 high-
performance 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 SOW 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 6x2
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 Ibs, 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 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 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 Ibs
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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 continually-
improving CAE tools to optimize vehicle designs. For example, the Future Steel Vehicle (FSV)
project sponsored by World Auto Steel 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 considerableestimated 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 Smart Way 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 on-
highway 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 (BAG) 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
CCh 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 CCh 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 widely-
used refrigerant - R134a, has a much higher global warming potential (GWP) than CO2,
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therefore a small leakage of this refrigerant has a much greater global warming impact than a
similar amount of emissions of CCh 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. CCh- 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 CCh emissions due to air conditioning systems in heavy-duty trucks are
minimal (compared to their overall fuel consumption and emissions of CCh). 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 CCh/ton- mile. Therefore, a
projected 24 percent improvement of the air conditioning system (the level projected in the light-
duty GHG rulemaking), would only reduce CCh emissions by less than 0.3 g CCh/ton-mile, or
approximately 0.3 percent of the baseline Class 8 sleeper cab CCh 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. CARS'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 CCh 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 CCh 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 CCh 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
VI0 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 OW-20, to improve cold-
flow 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 (DWL)
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 CCh
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 CCh emissions
reductions indicate that the potential for reducing CCh 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 CCh
emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other publicly-
available sources suggest a fuel consumption and CCh 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 electrically-
powered 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 engine-
driven. 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 light-
duty 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 superfmishing and improved transmission
lubricants.
2.5.3.3 Electric Power Steering (EPS)
Electric power steering (EPS) provides a potential reduction in CCh 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 CCh 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 CCh emissions. The effectiveness of fuel consumption and CCh 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 CCh 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
CCh 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 CCh 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 shutters6, rear visors, larger under body panels or low-profile roof racks resulting
in a 10 percent aerodynamic drag reduction. The agencies estimated the CCh 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
World Auto Steel 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 Ib 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".uo Results contain a cost
curve for various mass reduction percentages with the main solution being evaluated for a 21.4
percent (511 kg/1124 Ib) 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 GEM-
certified. 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 Ibs GVWR
as vocational vehicles under the GEM certification procedures including separate engine GHG
certification, if the engine is also engine-certified for criteria pollutants.0 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
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 Ib GVWR) complete or incomplete vehicles to be chassis certified and thereby
included within the Class 2b/3 fleet average.0 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 Ibs 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 non-
integrated 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 Cl-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 VI0 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.htnrflargeng.
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Under this special Phase 1 provision, these loose engines need not be certified to engine-
based 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).0 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-as-
vehicle 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 CCh 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
Coupled Cam Phasing
Engine friction reduction
Gasoline direct injection
ADOPTION
RATE
100%
100%
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)(l 1) 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 above-
described 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
Level 1 Low friction lubricants and Engine
friction reduction
Level 2 Low friction lubricants and Engine
friction reduction0
Cylinder deactivation (overhead valve)
Variable valve timing
Gasoline direct injection
REFERENCE CASE3
2018
100%
35 to 40%
8 to 9%
0%
0%
PROPOSAL (2.5% PER YEAR)b
With strong
hybrids
100%
100%
56%
56%
0%
Without strong
hybrids
100%
100%
56%
56%
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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0 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.1 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?
1 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 CCh emission standards for SI engines
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for use in vocational vehicles: 7.06 gallon/100 bhp-hr and 627 g CCh/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 CCh performance over the FTP as well as SET for the baseline engines were
developed through manufacturer reporting of CCh 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 CCh correction factor to bring them to a 0.20 g/bhp-hr
NOx emissions rate. The CCh correction factor is derived based on available experimental data
obtained from manufacturers and public literature. The agencies then sales-weighted the CCh
performance to derive a baseline CCh 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 CCh 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 CCh performance for each diesel engine category is included in
Table 2-6.
Table 2-6 Baseline CCh Performance (g/bhp-hr)
1LHDD - FTP
576
MHDD - FTP
576
HHDD - FTP
555
HHDD - SET
487
HHDD - SET
460
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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
Idle
A, 100
B, 50
B, 75
A, 50
A, 75
A, 25
B, 100
B, 25
C, 100
C, 25
C, 75
C, 50
Total
A:
B:
C:
WEIGHTING FACTOR IN
PHASE 1 (%)
15
8
10
10
5
5
5
9
10
8
5
5
5
100
23
39
23
PROPOSED WEIGHTING
FACTOR IN PHASE 2 (%)
12
9
10
10
12
12
12
9
9
2
1
1
1
100
45
38
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-
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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
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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
Turbo compound with clutch
WHR (Rankine cycle)
Parasitic/Friction (Cyl Kits,
pumps, FIE), lubrication
Aftertreatment (lower dP)
EGMntake & exhaust
manifolds/Turbo /WT/Ports
Combustion/FI/Control
Downsizing
Weighted reduction (%)
SET
WEIGHTED
REDUCTION
(%) 2020-2027
1.8%
3.6%
1.4%
0.6%
1.1%
1.1%
0.3%
MARKET
PENETRATION
(2021)
5%
1%
45%
45%
45%
45%
10%
1.5%
MARKET
PENETRATION
(2024)
10%
5%
95%
95%
95%
95%
20%
3.7%
MARKET
PENETRATION
(2027)
10%
15%
100%
100%
100%
100%
30%
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
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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
Model based control
Parasitic /Friction
EGR/Air/VVT /Tuibo
Improved AT
Combustion Optimization
Weighted reduction (%)-
L/M/HHD
GHG
EMISSIONS
REDUCTION
2020-2027
2.0%
1.5%
1.0%
0.5%
1.0%
MARKET
PENETRATION
2021
25%
60%
50%
50%
50%
2.0%
MARKET
PENETRATION
2024
30%
90%
90%
90%
90%
3.5%
MARKET
PENETRATION
2027
40%
100%
100%
100%
100%
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
CO2 Emissions (g COa/bhp-hr)
Fuel Consumption (gal/ 100 bhp-hr)
MHDD- SET
479
4.71
HHDD-
SET
453
4.45
The cost estimates for the complete HHD diesel engine packages can be developed
accordingly as shown in Table 2-11.
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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
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved thermal
management)
Turbocharger (improved efficiency)
Turbo Compounding
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved pressure
regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Waste Heat Recovery
"Right sized" engine
Total
MEDIUM
HD
$7
$82
$3
$9
$50
$2
$43
$2
$2
$5
$5
$1
$39
$105
-$40
$314
HEAVY
HD
$7
$82
$3
$9
$50
$2
$43
$2
$2
$5
$5
$1
$39
$105
-$40
$314
Note:
a Costs presented here include application rates.
2.7.6 2021 Model Year LHD/MHD/HHD Diesel Engine Package for
Vocational Vehicles
From Table 2-9, the proposed weighted reduction for 2021 model years of all
LHD/MHD/HHD vocational diesel engines is 2.0 percent. Table 2-12 lists the numerical
stringency values in 2021 model year.
Table 2-12 2021 Model Year Proposed Standards - Vocational
CO2 Emissions (g CC-2/bhp-hr)
Fuel Consumption (gal/100 bhp-hr)
LHDD-
FTP
565
5.55
MHDD-
FTP
565
5.55
HHDD-
FTP
544
5.34
The cost estimates for the MY2021 vocational diesel engines are shown in Table 2-13.
We present technology cost estimates along with adoption rates in Chapter 2.12 of this draft
RIA. We present package cost estimates in greater detail in Chapter 2.13 of this draft RIA.
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Table 2-13 Technology Costs as Applied in Expected Packages for MY2021 Vocational Diesel Engines under
the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved
thermal management)
Turbocharger (improved efficiency)
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved
pressure regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher
working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Model Based Controls
Total
LIGHT
HD
$8
$91
$6
$10
$2
$57
$3
$3
$7
$8
$1
$69
$28
$293
MEDIUM
HD
$8
$91
$3
$10
$2
$57
$3
$3
$6
$6
$1
$52
$28
$270
HEAVY
HD
$8
$91
$3
$10
$2
$57
$3
$3
$6
$6
$1
$52
$28
$270
Note:
a Costs presented here include application rates.
2.7.7 2024 Model Year HHDD Engine Package for Tractor
The agencies assessed the impact of technologies over each of the SET modes to project
an overall improvement in the 2024 model year. The agencies considered additional
improvements in the technologies included in the 2021 model year package. Compared to 2021
technology package, the technology package in 2024 considers higher market adoption as shown
in Table 2-8, thus deriving the stringency at 3.7 percent. Table 2-14 below shows the proposed
2024 model year tractor engine standards.
Table 2-14 2024 Model Year Proposed Standards - Tractors
CO2 Emissions (g COa/bhp-hr)
Fuel Consumption (gal/ 100 bhp-hr)
MHDD- SET
469
4.61
HHDD - SET
443
4.35
The costs for the MY2024 tractor diesel engines are shown in Table 2-15. We present
technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We
present package cost estimates in greater detail in Chapter 2.13 of this draft RIA.
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Table 2-15 Technology Costs as Applied in Expected Packages for MY2024 Tractor Diesel Engines under the
Preferred Alternative relative to the Less Dynamic Baseline (2012$)a
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved thermal
management)
Turbocharger (improved efficiency)
Turbo Compounding
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved pressure
regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Waste Heat Recovery
"Right sized" engine
Total
MEDIUM
HD
$14
$166
$6
$17
$92
$3
$84
$4
$4
$9
$10
$3
$75
$502
-$85
$904
HEAVY
HD
$14
$166
$6
$17
$92
$3
$84
$4
$4
$9
$10
$3
$75
$502
-$85
$904
Note:
a Costs presented here include application rates.
278 2024 Model Year LHD/MHD/HHD Diesel Engine Package for
Vocational Vehicles
The agencies developed the 2024 model year LHD/MHD/HHD diesel engine package
based on additional improvements in the technologies included in the 2021 model year package
as shown in Table 2-9. The projected impact of these technologies provides an overall reduction
of 3.5 percent over the 2017 model year baseline. Table 2-16 below shows the proposed 2024
model year standards in numerical values.
Table 2-16 2024 Model Year Proposed Standards - Vocational
CO2 Emissions (g CO2/bhp-hr)
Fuel Consumption (gal/100 bhp-hr)
LHDD-
FTP
556
5.46
MHDD-
FTP
556
5.46
HHDD-
FTP
536
5.26
Costs for MY 2024 vocational diesel engines are shown in Table 2-17. We present
technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We
present package cost estimates in greater detail in Chapter 2.13 of this draft RIA.
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Table 2-17 Technology Costs as Applied in Expected Packages for MY2024 Vocational Diesel Engines under
the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved
thermal management)
Turbocharger (improved efficiency)
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved
pressure regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher
working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Model Based Controls
Total
LIGHT
HD
$13
$157
$10
$16
$3
$79
$4
$4
$10
$13
$2
$95
$31
$437
MEDIUM
HD
$13
$157
$6
$16
$3
$79
$4
$4
$9
$10
$2
$71
$31
$405
HEAVY
HD
$13
$157
$6
$16
$3
$79
$4
$4
$9
$10
$2
$71
$31
$405
Note:
a Costs presented here include application rates.
2.7.9 2027 Model Year HHDD Engine Package for Tractor
The agencies assessed the impact of technologies over each of the SET modes to project
an overall improvement in the 2027 model year. The agencies considered additional
improvements in the technologies included in the 2021 model year package. Compared to 2021
technology package, the technology package in 2027 considers higher market adoption as shown
in Table 2-8, thus deriving the stringency at 4.2 percent. Table 2-18 below shows the proposed
2027 model year tractor engine standards.
Table 2-18 2027 Model Year Proposed Standards - Tractors
CO2 Emissions (g COa/bhp-hr)
Fuel Consumption (gal/ 100 bhp-hr)
MHDD- SET
466
4.58
HHDD - SET
441
4.33
The costs for the MY2027 tractor diesel engines are shown in Table 2-19. We present
technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We
present package cost estimates in greater detail in Chapter 2.13 of this draft RIA.
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Table 2-19 Technology Costs as Applied in Expected Packages for MY2027 Tractor Diesel Engines under the
Preferred Alternative relative to the Less Dynamic Baseline (2012$)a
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved thermal
management)
Turbocharger (improved efficiency)
Turbo Compounding
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved pressure
regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Waste Heat Recovery
"Right sized" engine
Total
MEDIUM
HD
$14
$169
$6
$17
$87
$3
$84
$4
$4
$9
$10
$3
$75
$1,340
-$127
$1,698
HEAVY
HD
$14
$169
$6
$17
$87
$3
$84
$4
$4
$9
$10
$3
$75
$1,340
-$127
$1,698
Note:
a Costs presented here include application rates.
2.7.10 2027 Model Year LHD/MHD/HHD Diesel Engine Package for
Vocational Vehicles
The agencies developed the 2027 model year LHD/MHD/HHD diesel engine package
based on additional improvements in the technologies included in the 2021 model year package
as shown in Table 2-9. The projected impact of these technologies provides an overall reduction
of 4.0 percent over the 2017 model year baseline. Table 2-20 below shows the proposed 2027
model year standards in numerical values.
Table 2-20 2027 Model Year Proposed Standards - Vocational
CO2 Emissions (g CCh/bhp-hr)
Fuel Consumption (gal/ 100 bhp-hr)
LHDD - FTP
553
5.43
MHDD- FTP
553
5.43
HHDD - FTP
533
5.23
Costs for MY 2027 vocational diesel engines are shown in Table 2-21. We present
technology cost estimates along with adoption rates in Chapter 2.12 of this draft RIA. We
present package cost estimates in greater detail in Chapter 2.13 of this draft RIA.
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Table 2-21 Technology Costs as Applied in Expected Packages for MY2027 Vocational Diesel Engines under
the Preferred Alternative relative to the Less Dynamic Baseline (2012$)a
Aftertreatment system (improved effectiveness SCR, dosing, DPF)
Valve Actuation
Cylinder Head (flow optimized, increased firing pressure, improved
thermal management)
Turbocharger (improved efficiency)
EGR Cooler (improved efficiency)
Water Pump (optimized, variable vane, variable speed)
Oil Pump (optimized)
Fuel Pump (higher working pressure, increased efficiency, improved
pressure regulation)
Fuel Rail (higher working pressure)
Fuel Injector (optimized, improved multiple event control, higher
working pressure)
Piston (reduced friction skirt, ring and pin)
Valve Train (reduced friction, roller tappet)
Model Based Controls
Total
LIGHT
HD
$14
$169
$10
$17
$3
$84
$4
$4
$11
$13
$3
$100
$39
$471
MEDIUM
HD
$14
$169
$6
$17
$3
$84
$4
$4
$9
$10
$3
$75
$39
$437
HEAVY
HD
$14
$169
$6
$17
$3
$84
$4
$4
$9
$10
$3
$75
$39
$437
Note:
a Costs presented here include application rates.
2.7.11 HD Diesel Engine Packages under the More Stringent Alternative 4
The more stringent alternative 4 would impose new standards in MYs 2021 and 2024,
with the MY2024 standards essentially equivalent to the MY2027 standards under the preferred
alternative. The resultant HDD engine costs for both tractors and vocational engines in MYs
2021 and 2024 are shown in Table 2-22. Note that, while the technology application rates in
MY2024 under alternative 4 are essentially identical to those for MY2027 under alternative 3,
the costs are higher under alternative 4 due to learning effects and markup changes that are
estimated to have occurred by MY2027 under alternative 3.
Table 2-22 Technology Costs as Applied in Expected Packages for HD Diesel Engines under the More
Stringent Alternative 4 relative to the Less Dynamic Baseline (2012$)a
MODEL
YEAR
2021
2024
MHDD
TRACTOR
$656
$1,885
HHDD
TRACTOR
$656
$1,885
LHDD
VOCATIONAL
$372
$493
MHDD
VOCATIONAL
$345
$457
HHDD
VOCATIONAL
$345
$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 CCh emissions of combination tractors vary depending on the
configuration of the tractor. Many aspects of the tractor impact its performance, including the
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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 CCh 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 FID 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 CCh 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
Day Cab
Low
Roof
Mid
Roof
High Roof
CLASS 8
Day Cab
Low Roof
Mid
Roof
High Roof
Engine
20 17 MY
11L
Engine
350 HP
2017 MY
11L
Engine
350 HP
2017 MY
11L
Engine
350 HP
2017 MY
15L
Engine
455 HP
20 17 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
Sleeper Cab
Low Roof
Mid
Roof
High
Roof
2017 MY
15L
Engine
455 HP
2017 MY
15L
Engine
455 HP
20 17 MY
15L
Engine
455 HP
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
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Table 2-24 Class 7 and 8 Tractor Baseline CCh Emissions and Fuel Consumption
CO2 (grams
CWton-mile)
Fuel
Consumption
(gal/1,000 ton-
mile)
CLASS 7
Day Cab
Low
Roof
107
10.5
Mid
Roof
118
11.6
High
Roof
121
11.9
CLASS 8
Day Cab
Low
Roof
86
8.4
Mid
Roof
93
9.1
High
Roof
95
9.3
Sleeper Cab
Low
Roof
79
7.8
Mid
Roof
87
8.5
High
Roof
88
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.
455 HP /15 L : 2018 Baseline BSFC (g/kW-hr)
300 800
1000 1200 1400 1600
Engine Speed (RPM)
Figure 2-11 2018MY 15L Engine Fuel Map
1800 2000
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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 CCh 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.1U 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.
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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 CCh 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 CCh 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.
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Table 2-25 Proposed Phase 2 Technology Inputs
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Engine
2021M
Y 11L
Engine
350 HP
2021M
Y11L
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
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
5.3
4.8
4.3
4.0
6.7
6.2
5.7
5.4
7.6
7.1
6.5
5.8
5.3
4.9
4.5
5.3
4.8
4.3
4.0
6.7
6.2
5.7
5.4
7.6
7.1
6.5
5.8
5.3
4.9
4.5
5.3
4.8
4.3
4.0
6.7
6.2
5.7
5.4
7.4
6.9
6.3
5.6
5.1
4.7
4.3
Steer Tires (CRR in kg/metric ton)
Base
Level 1
Level 2
Level 3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
7.8
6.6
5.7
4.3
Drive Tires (CRR in kg/metric ton)
Base
Level 1
Level 2
Level 3
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
8.2
7.0
6.0
4.5
Idle Reduction (% reduction)
APU
Other
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
5%
7%
5%
7%
5%
7%
Transmission Type (% reduction)
Manual
AMT
Auto
Dual Clutch
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
0%
2%
2%
2%
Driveline (% reduction)
Axle
Lubricant
6x2 or 4x2
Axle
Downspeed
Accessory Im
A/C
Electric or
Mech.
Access.
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
0.5%
2.5%
1.8%
provements (% reduction)
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
0.5%
1%
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Off-Cycle Technologies (% reduction)
Predictive
Cruise
Control
Automated
Tire
Inflation
System
2%
1%
2%
1%
2%
1%
2%
1%
2%
1%
2%
1%
2%
1%
2%
1%
2%
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 CCh 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 CCh 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
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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
Super Truck 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.
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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 CCh emissions.
2.8.3.4 Idle Reduction Technology Adoption Rate
Idle reduction technologies provide significant reductions in fuel consumption and CCh
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
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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/
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
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
202 1 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%
1 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
on performance of VSLs (i.e. VSL is an on-cycle technology).
> not based
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Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
75%
25%
0%
75%
25%
0%
0%
40%
35%
20%
5%
0%
75%
25%
0%
75%
25%
0%
0%
40%
35%
20%
5%
0%
75%
25%
0%
75%
25%
0%
0%
40%
35%
20%
5%
0%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
5%
60%
25%
10%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
80%
80%
80%
Transmission Type
Manual
AMT
Auto
Dual Clutch
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
Driveline
Axle
Lubricant
6x2 or 4x2
Axle
Downspeed
20%
20%
20%
20%
20%
20%
20%
10%
20%
20%
10%
20%
20%
20%
20%
20%
10%
20%
20%
10%
20%
20%
20%
20%
Accessory Improvements
A/C
Electric
Access.
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
Off-Cycle Technologies
Predictive
Cruise
Control
Automated
Tire
Inflation
System
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
2-90
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Table 2-27 Technology Adoption Rates for Class 7 and 8 Tractors for Determining the Proposed 2024 MY
Standards
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
2024MY Engine Technology Package
100%
100%
100%
100%
100%
100%
100%
100%
100%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
60%
38%
2%
0%
60%
38%
2%
0%
0%
30%
30%
25%
13%
2%
0%
60%
38%
2%
0%
60%
38%
2%
0%
0%
30%
30%
25%
13%
2%
0%
60%
38%
2%
0%
60%
38%
2%
0%
0%
30%
30%
25%
13%
2%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
5%
50%
30%
15%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
90%
90%
90%
Transmission Type
Manual
AMT
Auto
Dual Clutch
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
Driveline
Axle
Lubricant
6x2 or 4x2
Axle
Downspeed
Direct Drive
40%
40%
50%
40%
40%
50%
40%
40%
50%
40%
20%
40%
50%
40%
20%
40%
50%
40%
60%
40%
50%
40%
20%
40%
50%
40%
20%
40%
50%
40%
60%
40%
50%
Accessory Improvements
A/C
Electric
Access.
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Other Technologies
Predictive
Cruise
40%
40%
40%
40%
40%
40%
40%
40%
40%
2-91
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Control
Automated
Tire
Inflation
System
40%
40%
40%
40%
40%
40%
40%
40%
40%
2-92
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Table 2-28 Technology Adoption Rates for Class 7 and 8 Tractors for Determining the Proposed 2027 MY
Standards
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
2027 MY Engine Technology Package
100%
100%
100%
100%
100%
100%
100%
100%
100%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
90%
90%
90%
Transmission Type
Manual
AMT
Auto
Dual Clutch
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
10%
50%
30%
10%
Driveline
Axle
Lubricant
6x2 Axle
Downspeed
Direct Drive
40%
60%
50%
40%
60%
50%
40%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
Accessory Improvements
A/C
Electric
Access.
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
Other Technologies
Predictive
Cruise
Control
Automated
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
2-93
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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 CCh 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 FID 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
See draft RIA Chapter 2.7 explaining the derivation of the proposed engine standards.
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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
Class 8 Combination
Class 8 Combination
Class 8 Combination
Class 8 Combination
Class 8 Combination
Class 8 Combination
Class 7 Combination
Class 7 Combination
Class 7 Combination
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
ENGINE FUEL MAP
15L - 455 HP
15L - 455 HP
15L-455HP
15L - 455 HP
15L-455HP
15L-455HP
11L-350HP
11L-350HP
11L-350HP
48B HP MB L: 2021 Mwidvd
800 1000 1200 1400 1600 1800 2000
Englm Ipnd (RPNQ
Figure 2-12 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2021MY Standards
2-95
-------
4U HP MB L: 2084 ttuidvd
800 1000 1200 1400 1600 1800 2000
Englni IpMd [RFM]
Figure 2-13 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2024MY Standards
2-96
-------
4U HP MB L: 2027 ttuidvd
800 1000 1200 1400 1600 1800 2000
Engine Ipnd (RPBD
Figure 2-14 455 HP Engine fuel map used in HD Phase 2 version of GEM to Set 2027MY Standard
2-97
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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
2-98
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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
2-99
-------
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.
2-100
-------
Table 2-33 Proposed 2021,2024, and 2027 Model Year Tractor Standards
2021 MODEL YEAR CO2 GRAMS PER TON-MILE
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
97
107
109
Class 8
78
84
86
Sleeper Cab
Class 8
70
78
77
2021 Model Year Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
9.5285
10.5108
10.7073
Class 8
7.5639
8.2515
8.4479
Sleeper Cab
Class 8
6.8762
7.6621
7.5639
2024 Model Year CO2 Grams per Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
90
100
101
Class 8
72
78
79
Sleeper Cab
Class 8
64
71
70
2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
8.8409
9.8232
9.9214
Class 8
7.0727
7.5639
7.7603
Sleeper Cab
Class 8
6.2868
6.9745
6.8762
2027 Model Year CO2 Grams per Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
87
96
96
Class 8
70
76
76
Sleeper Cab
Class 8
62
69
67
2027 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
8.5462
9.4303
9.4303
Class 8
6.8762
7.4656
7.4656
Sleeper Cab
Class 8
6.0904
6.7780
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
2-101
-------
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 sub category.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.
2-102
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Table 2-34 GEM Inputs for Proposed 2021,2024 and 2027 MY Heavy-Haul Tractor Standards
HEAVY-HAUL TRACTOR
Baseline
Engine = 20 17 MY 15L
Engine with 600 HP
2021MY
Engine = 2021 MY 15L
Engine with 600 HP
2024MY
Engine = 2024 MY 15L
Engine with 600 HP
2027MY
Engine = 2027 MY 15L
Engine with 600 HP
Aerodynamics (CdA in m2) = 5.00
Steer Tires (CRR in
kg/metric ton) = 7.0
Drive Tires (CRR in
kg/metric ton) = 7.4
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
Drive axle Ratio = 3.55
Steer Tires (CRR in
kg/metric ton) = 6.2
Drive Tires (CRR in
kg/metric ton) = 6.6
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
6x2 Axle Disconnect
Weighted Effectiveness = 0%
Low Friction Axle
Lubrication = 0.1%
AMT 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
Steer Tires (CRR in
kg/metric ton) = 6.0
Drive Tires (CRR in
kg/metric ton) = 6.4
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
6x2 Axle Disconnect
Weighted Effectiveness =
0%
Low Friction Axle
Lubrication = 0.2%
AMT 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
Steer Tires (CRR in
kg/metric ton) = 5.8
Drive Tires (CRR in
kg/metric ton) = 6.2
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
6x2 Axle Disconnect
Weighted Effectiveness = 0%
Low Friction Axle
Lubrication = 0.2%
AMT 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
The baseline 2017 MY heavy-haul tractor would emit 57 grams of CCh 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
Grams of CCh per
Ton-Mile Standard
Gallons of Fuel per
1,000 Ton-Mile
2021 MY
54
5.3045
2024 MY
52
5.1081
2027 MY
51
5.010
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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)
Engine0
Aerodynamics
Tires
Tire inflation
system
Transmission
Axle & axle
lubes
Idle reduction
with APU
Air
conditioning
Other vehicle
technologies
Total
CLASS 7
Day Cab
Low/Mid
Roof
$314
$687
$49
$180
$3,969
$50
$0
$45
$174
$5,468
High
Roof
$314
$511
$9
$180
$3,969
$50
$0
$45
$174
$5,252
CLASS 8
Day Cab
Low/ Mid
Roof
$314
$687
$81
$180
$3,969
$70
$0
$45
$174
$5,520
High
Roof
$314
$511
$15
$180
$3,969
$90
$0
$45
$174
$5,298
Sleeper Cab
Low
Roof
$314
$656
$59
$180
$3,969
$70
$2,449
$45
$174
$7,916
Mid
Roof
$314
$656
$59
$180
$3,969
$70
$2,449
$45
$174
$7,916
High
Roof
$314
$535
$15
$180
$3,969
$90
$2,449
$45
$174
$7,771
Notes:
a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1
standards. These costs include indirect costs via markups along with learning impacts. For a description of the
markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer
to Chapter 2 of the draft RIA (see draft RIA 2.12).
b Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost
expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption
rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular).
0 Engine costs are for a heavy HD diesel engine meant for a combination tractor.
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Table 2-37 Class 7 and 8 Tractor Technology Incremental Costs in the 2024 Model Yeara'b
Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle)
Engine0
Aerodynamics
Tires
Tire inflation
system
Transmission
Axle & axle
lubes
Idle reduction
with APU
Air
conditioning
Other vehicle
technologies
Total
CLASS 7
Day Cab
Low/Mid
Roof
$904
$744
$47
$330
$5,883
$92
$0
$82
$318
$8,400
High
Roof
$904
$684
$11
$330
$5,883
$92
$0
$82
$318
$8,304
CLASS 8
Day Cab
Low/ Mid
Roof
$904
$744
$78
$330
$5,883
$128
$0
$82
$318
$8,467
High
Roof
$904
$684
$18
$330
$5,883
$200
$0
$82
$318
$8,419
Sleeper Cab
Low
Roof
$904
$712
$58
$330
$5,883
$128
$2,687
$82
$318
$11,102
Mid
Roof
$904
$712
$58
$330
$5,883
$128
$2,687
$82
$318
$11,102
High
Roof
$904
$723
$18
$330
$5,883
$200
$2,687
$82
$318
$11,145
Notes:
a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1
standards. These costs include indirect costs via markups along with learning impacts. For a description of the
markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer
to Chapter 2 of the draft RIA (see draft RIA 2.12).
b Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost
expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption
rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12).
0 Engine costs are for a heavy HD diesel engine meant for a combination tractor.
2-105
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Table 2-38 Class 7 and 8 Tractor Technology Incremental Costs in the 2027 Model Yeara'b
Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle)
Engine0
Aerodynamics
Tires
Tire inflation
system
Transmission
Axle & axle
lubes
Idle reduction
with APU
Air
conditioning
Other vehicle
technologies
Total
CLASS 7
Day Cab
Low/Mid
Roof
$1,698
$771
$45
$314
$6,797
$97
$0
$117
$302
$10,140
High
Roof
$1,698
$765
$10
$314
$6,797
$97
$0
$117
$302
$10,099
CLASS 8
Day Cab
Low/ Mid
Roof
$1,698
$771
$75
$314
$6,797
$131
$0
$117
$302
$10,204
High
Roof
$1,698
$765
$17
$314
$6,797
$200
$0
$117
$302
$10,209
Sleeper Cab
Low
Roof
$1,698
$733
$56
$314
$6,797
$131
$2,596
$117
$302
$12,744
Mid
Roof
$1,698
$733
$56
$314
$6,797
$131
$2,596
$117
$302
$12,744
High
Roof
$1,698
$802
$17
$314
$6,797
$200
$2,596
$117
$302
$12,842
Notes:
a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1
standards. These costs include indirect costs via markups along with learning impacts. For a description of the
markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer
to Chapter 2 of the draft RIA (see draft RIA 2.12).
b Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost
expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption
rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular).
0 Engine costs are for a heavy HD diesel engine meant for a combination tractor.
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Table 2-39 Heavy-Haul Tractor Technology Incremental Costs in the 2021,2024, and 2027 Model Yeara'b
Preferred Alternative vs. the Less Dynamic Baseline (2012$ per vehicle)
Engine0
Tires
Tire inflation system
Transmission
Axle & axle lubes
Air conditioning
Other vehicle technologies
Total
2021 MY
$314
$81
$180
$3,969
$70
$45
$174
$4,833
2024 MY
$904
$78
$330
$5,883
$128
$82
$318
$7,723
2027 MY
$1,698
$75
$314
$6,797
$200
$117
$302
$9,503
Notes:
a Costs shown are for the specified model year and are incremental to the costs of a
tractor meeting the phase 1 standards. These costs include indirect costs via
markups along with learning impacts. For a description of the markups and
learning impacts considered in this analysis and how it impacts technology costs
for other years, refer to Chapter 2 of the draft RIA (see draft RIA 2.12).
b Note that values in this table include adoption rates. Therefore, the technology
costs shown reflect the average cost expected for each of the indicated tractor
classes. To see the actual estimated technology costs exclusive of adoption rates,
refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular).
0 Engine costs are for a heavy HD diesel engine meant for a combination tractor.
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Table 2-40 Class 7 and 8 Tractor Technology Incremental Costs in the 2021 Model Yeara'b
Alternative 4 vs. the Less Dynamic Baseline (2012$ per vehicle)
Engine0
Aerodynamics
Tires
Tire inflation
system
Transmission
Axle & axle
lubes
Idle reduction
with APU
Air
conditioning
Other vehicle
technologies
Total
CLASS 7
Day Cab
Low/Mid
Roof
$656
$769
$50
$271
$6,794
$56
$0
$90
$261
$8,946
High
Roof
$656
$632
$11
$271
$6,794
$56
$0
$90
$261
$8,769
CLASS 8
Day Cab
Low/ Mid
Roof
$656
$769
$83
$271
$6,794
$75
$0
$90
$261
$8,999
High
Roof
$656
$632
$18
$271
$6,794
$95
$0
$90
$261
$8,816
Sleeper Cab
Low
Roof
$656
$740
$61
$271
$6,794
$75
$2,449
$90
$261
$11,397
Mid
Roof
$656
$740
$61
$271
$6,794
$75
$2,449
$90
$261
$11,397
High
Roof
$656
$665
$18
$271
$6,794
$115
$2,449
$90
$261
$11,318
Notes:
a Costs shown are for the 2021 model year and are incremental to the costs of a tractor meeting the phase 1
standards. These costs include indirect costs via markups along with learning impacts. For a description of the
markups and learning impacts considered in this analysis and how it impacts technology costs for other years, refer
to Chapter 2 of the draft RIA (see draft RIA 2.12).
b Note that values in this table include adoption rates. Therefore, the technology costs shown reflect the average cost
expected for each of the indicated tractor classes. To see the actual estimated technology costs exclusive of adoption
rates, refer to Chapter 2 of the draft RIA (see draft RIA 2.12 in particular).
0 Engine costs are for a heavy HD diesel engine meant for a combination tractor.
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Table 2-41 Class 7 and 8 Tractor Technology Incremental Costs in the 2024 Model Yeara'b
Alternative 4 vs. the Less Dynamic Baseline (2012$ per vehicle)
Engine0
Aerodynamics
Tires
Tire inflation
system
Transmission
Axle & axle
lubes
Idle reduction
with APU
Air
conditioning
Other vehicle
technologies
Total
CLASS 7
Day Cab
Low/Mid
Roof
$1,885
$805
$50
$330
$7,143
$102
$0
$123
$318
$10,757
High
Roof
$1,885
$935
$14
$330
$7,143
$102
$0
$123
$318
$10,851
CLASS 8
Day Cab
Low/ Mid
Roof
$1,885
$805
$83
$330
$7,143
$138
$0
$123
$318
$10,826
High
Roof
$1,885
$935
$23
$330
$7,143
$210
$0
$123
$318
$10,968
Sleeper Cab
Low
Roof
$1,885
$773
$63
$330
$7,143
$138
$2,687
$123
$318
$13,461
Mid
Roof
$1,885
$773
$63
$330
$7,143
$138
$2,687
$123
$318
$13,461
High
Roof
$1,885
$997
$23
$330
$7,143
$210
$2,687
$123
$318
$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).
0 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
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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 MUD 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 HUD 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
Heavy Heavy -Duty (Class 8)
Heavy Heavy -Duty (Class 8)
Medium Heavy-Duty (Class 6-7)
Light Heavy -Duty (Class 2b-5)
Regional Duty Cycle
Multi-Purpose and Urban Duty Cycles
Regional, Multi-Purpose, and Urban
Duty Cycles
Regional, Multi-Purpose, and Urban
Duty Cycles
ENGINE FUEL MAP
15L - 455 HP
11L-345HP
7L - 270 HP
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.
455 HP /15 L : 2018 Baseline BSFC (g/kW-hr)
2200
2000
1800
1600
^ 1400
E
'Z, 1200
i-1000
>_
° 800
600
400
200
0
600 800 1000 1200 1400 1600 1800 2000
Engine Speed (RPM)
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 Cl-powered GEM-
certified vocational vehicles. Using the HD pickup and van stringency curves and coefficients
for the MY 2018 targets from Phase 1, the ratio of CCh 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, MUD 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
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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)
Regional Duty
Cycle
HHD (CLASS 8)
Multi-Purpose
Duty Cycle
HHD (CLASS 8)
Urban Duty Cycle
Transmission
Number of Forward Gears
Gear Ratio for Each Gear
Architecture Type
10
12.8, 9.25, 6.76,
4.9,3.58,2.61,
1.89, 1.38, 1,0.73
Manual
5
4.6957,2.213,
1.5291, 1,0.7643
Automatic
5
4.6957,2.213,
1.5291, 1,0.7643
Automatic
Axle
Axle Ratio
Advanced Axle Lubrication
6x2 Axle
3.76
No
No
4.33
No
No
5.29
No
No
Idle Reduction
Neutral Idle
Stop-Start
No
No
No
No
No
No
Tires
Steer Tire CRR
Drive Tire CRR
Tire Loaded Radius
Weight Reduction (Ibs)
7.7
7.7
0.483
No
7.7
7.7
0.483
No
7.7
7.7
0.483
No
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Table 2-44 Medium Heavy-Duty User-Enterable Modeling Parameters for Vocational Baseline
GEM INPUT
Transmission
Number of Forward Gears
Gear Ratio for Each Gear
Architecture Type
Axle
Axle Ratio
Advanced Axle Lubrication
6x2 Axle
Idle Reduction
Neutral Idle
Stop-Start
Tires
Steer Tire CRR
Drive Tire CRR
Tire Loaded Radius
Weight Reduction (Ibs)
MHD (CLASS 6-
7)
Regional Duty
Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.33
No
No
No
No
7.7
7.7
0.462
No
MHD (CLASS 6-
7)
Multi-Purpose
Duty Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.88
No
No
No
No
7.7
7.7
0.462
No
MHD (CLASS 6-7)
Urban Duty Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.88
No
No
No
No
7.7
7.7
0.426
No
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Table 2-45 Light Heavy-Duty User-Enterable Modeling Parameters for Vocational Baseline
GEM INPUT
Transmission
Number of Forward Gears
Gear Ratio for Each Gear
Architecture Type
Axle
Axle Ratio
Advanced Axle Lubrication
6x2 Axle
Idle Reduction
Neutral Idle
Stop-Start
Tires
Steer Tire CRR
Drive Tire CRR
Tire Loaded Radius
Weight Reduction (Ibs)
LHD (CLASS 2B-
5)
Regional Duty
Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.1
No
No
No
No
7.7
7.7
0.378
No
LHD (CLASS 2B-
5)
Multi-Purpose
Duty Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.56
No
No
No
No
7.7
7.7
0.378
No
LHD (CLASS 2B-
5)
Urban Duty Cycle
5
3.102, 1.8107,
1.4063, 1,0.7117
Automatic
4.56
No
No
No
No
7.7
7.7
0.378
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 FfflD
weight class, this was the Regional vehicle. For the MHD and LFtD 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.
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Table 2-46 Vocational Baseline Normalizing
CLASS 2B-5
Urban
Multi-
purpose
Rural,
Regional
CLASS 6-7
Urban
Multi-
purpose
Rural,
Regional
CLASS 8
Urban
Multi-
purpose
Rural,
Regional
Straight GEM Baseline Performance
318
328
331
200
204
Normalization
0.97
1.00
| 1.04
1.00
1.01
199
Factor
0.99
223
1.01
216
1.02
187
| 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 CCh 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 CCh 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
Two More Gears
(over 5-speed)
DCT or AMT
(over automatic)
HHD DCT or
AMT (over
manual)b
Strong Hybrid
Deep Driveline
Integration
PROJECTED
IMPROVEMENT
OVER TEST
CYCLEA
ARB Transient
55 mph Cruise
65 mph Cruise
ARB Transient
55 mph Cruise
65 mph Cruise
ARB Transient
55 mph Cruise
65 mph Cruise
ARB Transient
55 mph Cruise
65 mph Cruise
ARB Transient
55 mph Cruise
65 mph Cruise
2%
0%
0%
4%
0%
0%
4%
1.5%
1.5%
27%
0%
0%
7%
2%
2%
REGIONAL
COMPOSITE
CYCLE
1.1
2.1
2.3 (2.95)
14.7
4.7
MULTI-PURPOSE
COMPOSITE
CYCLE
1.7
3.4
N/A
22.9
6.2
URBAN
COMPOSITE
CYCLE
1.9
3.8
N/A
25.6
6.7
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 Arch itectural 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
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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 six-
speed wet dual clutch transmission was determined to be roughly $100 less than the cost of a six-
speed 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 Transmissions"
TECHNOLOGY
Manual to AMT
Manual to AT
Manual to DCT
2021 COST
$4,472
$3,764
$4,472
2027 COST
$3,795
$3,470
$3,795
Note:
a Costs include markups (2012$)
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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
CCh 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
Hino
Allison
BAE
XL
Crosspoint Kinetics
Lightning Hybrids
Parker Hannifin
Freightliner Custom Chassis
Morgan Olson
Autocar-Parker
Eatona
Odyne
PRODUCT
Class 5 cab-over-engine battery-
electric hybrid
HHD parallel hybrid
HHD series or parallel hybrid
Class 3-4 mild electric hybrid
Class 3-7 mild electric hybrid
Class 2-5 hydraulic hybrid
MHD hydraulic hybrid
MHD hydraulic hybrid
MHD hydraulic hybrid
Runwise hydraulic hybrid
HHD parallel electric hybrid
Plug-in electric hybrid, E-PTO
EXAMPLE APPLICATION
Delivery Trucks
Transit Bus
Transit Bus
Shuttle Bus
Delivery trucks, shuttle buses
Delivery trucks
Delivery trucks
Delivery trucks
Delivery trucks
Refuse Trucks
Trucks and Buses
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 CCh 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 FfHD 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 CFR535.4 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 baseline-
level 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 CCh 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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Daily Zero Speed Cycle Percentage Distribution for Delivery Trucks
Zero Speed Time {% of Cycle)
# of Vehicles Reporting: 9 Generated: Thu Mar 07, 2013 # of Days Included: 128
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.
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Table 2-50 Daily Zero Speed Percentage Distribution by Vehicle Type
Service Vans
Delivery Trucks
Delivery Vans
Bucket Trucks
School Buses
Average
Average
Without Buses
LESS THAN 10
59%
76%
38%
18%
41%
47%
48%
10 TO 14
18%
16%
35%
45%
3%
23%
28%
15 TO 20
23%
9%
2%
37%
7%
16%
18%
OVER 20
0%
0%
25%
0%
48%
15%
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 hybrid-
PTO 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 Ibs. 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, MUD 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
Weight Reduction
Static Test Weight (kg)
Dynamic Test Weight (kg)
Payload (ton)
Transient CCh Emission (g CCh ptm)
55mph CO2 Emission (g CCh ptm)
65mph CO2 Emission (g CCh ptm)
Effectiveness over Transient
Effectiveness over 55 mph
Effectiveness over 65 mph
Urban Cycle Effectiveness (94%
Transient, 6% 55 mph)
Multi -Purpose Cycle Effectiveness
(82% Transient, 15% 55 mph, 3% 65
mph)
Regional Cycle Effectiveness (50%
Transient, 28% 55 mph, 22% 65
mph)
HHD
200
19,006
19,573
7.55
240
181
216
-0.8%
-0.7%
-0.7%
-0.8%
-0.8%
-0.8%
0
19,051
19,618
7.5
242
182
217
MHD
200
11,363
11,703
5.65
224
182
230
-1.1%
-1.0%
-1.0%
-1.1%
-1.1%
-1.0%
0
11,408
11,748
5.6
227
184
232
LHD
200
7,212
7,552
2.9
351
332
394
-2.0%
-1.8%
-1.8%
-2.0%
-1.9%
-1.9%
0
7,257
7,597
2.85
358
338
401
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
Axle Hubs - Non-Drive
Axle Hubs - Non-Drive
Axle - Non-Drive
Axle - Non-Drive
Brake Drams - Non-Drive
Brake Drams - Non-Drive
Axle Hubs - Drive
Axle Hubs - Drive
Brake Drams - Drive
Brake Drums - Drive
Clutch Housing
Clutch Housing
Suspension Brackets,
Hangers
Suspension Brackets,
Hangers
Transmission Case
Transmission Case
Crossmember - Cab
Crossmember - Cab
Crossmember - Non-
Suspension
Crossmember - Non-
Suspension
Crossmember -Suspension
Crossmember -Suspension
Driveshaft
Driveshaft
Frame Rails
Frame Rails
Wheels- Dual
Wheels- Dual
Wheels- Dual
Wheels - Wide Base Single
MATERIAL
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Aluminum
High Strength Steel
Lightweight
Aluminum
Aluminum
VOCATIONAL VEHICLE CLASS
Class 2b-5
Class 6-7
40
5
60
15
60
8
40
10
70
5.5
34
9
67
20
45
11
10
2
15
5
15
4
12
5
120
24
126
48
180
278
14
4
18
6
20
5
40
10
300
40
126
48
180
278
Class 8
40
5
60
15
60
8
80
20
140
11
40
10
100
30
50
12
15
5
21
7
25
6
50
12
440
87
210
80
300
556
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COMPONENT
Wheels - Wide Base Single
Wheels - Wide Base Single
Permanent 6x2 Axle
Configuration
MATERIAL
High Strength Steel
Lightweight
Aluminum
Multi
VOCATIONAL VEHICLE CLASS
Class 2b-5
168
294
N/A
Class 6-7
168
294
N/A
Class 8
336
588
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 al.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 high-
quality, 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 cost-
effective and technologically feasible. The cooperative industry and government Improved
Mobile Air Conditioning (EVIAC) 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 EVIAC 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
Class 6 Box
Truck
Class 4 Box
Truck
SKIRTS
X
FRONT
FAIRING
(NOSE
CONE)
X
REAR
FAIRING
(TAIL)
X
WHEEL COVERS
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 often 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
CCh 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
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 Resistan ce 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 CCh 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 CCh 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
Drive
Steer
Drive
Steer
Drive
Steer
Drive
Steer
LEVEL OF ROLLING
RESISTANCE
Baseline CRR (7.7)
Baseline CRR (7.7)
5% Lower CRR (7.3)
10% Lower CRR (6.9)
10% Lower CRR (6.9)
15% Lower CRR (6.5)
15% Lower CRR (6.5)
20% Lower CRR (6.2)
MY 2021
ADOPTION RATE
50
20
50
80
0
0
0
0
MY 2024
ADOPTION RATE
20
10
50
30
30
60
0
0
MY 2027
ADOPTION
RATE
10
0
25
20
50
30
15
50
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Drive
Steer
Average Improvement
inCRR
Average Improvement
inCRR
3%
8%
6%
12%
9%
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
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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
Urban
Multi-
purpose
Regional
CLASS 6-7
Urban
Multi-
purpose
Regional
CLASS 8
Urban
Multi-
purpose
Regional
Cl Engine3
2021 MY 7L, 200 hp Engine
2021 MY 7L, 270 hp Engine
2021 MY11L, 345
hp Engine
2021 MY
15L455hp
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%
Neutral
70%
70%
70%
70%
5%
5%
0%
0% 0%
Idle (adoption rate)
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 (Ib)
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
Urban
Multi-
purpose
Regional
CLASS 6-7
Urban
Multi-
purpose
Regional
CLASS 8
Urban
Multi-
purpose
Regional
Cl Engine3
2024 MY 7L, 200 hp Engine
2024 MY 7L, 270 hp Engine
2024 MY 11L, 345
hp Engine
2024 MY
15L455hp
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%
Neutral
85%
| 85%
| 85%
85%
15%
15%
15%
15%
| 1 5%
Idle (adoption rate)
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 (Ib)
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14
12
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
Urban
Multi-
purpose
Regional
CLASS 6-7
Urban
Multi-
purpose
Regional
CLASS 8
Urban
Multi-
purpose
Regional
Cl Engine3
2027 MY 7L, 200 hp Engine
2027 MY 7L, 270 hp Engine
2027 MY 11L, 345
hp Engine
2027 MY
15L455hp
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%
Neutral
25%
30%
30%
25%
70%
70%
70%
70% 70%
Idle (adoption rate)
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 (Ib)
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 CCh 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 CCh 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.
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Table 2-58 Proposed EPA CCh Standards for MY2021 Class 2b-8 Vocational Vehicles
EPA Standard for Vehicle with CI Engine Effective MY2021 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
296
305
318
Medium Heavy -Duty
Class 6-7
188
190
186
Heavy Heavy -Duty
Class 8
198
200
189
EPA Standard for Vehicle with SI Engine Effective MY2021 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
320
329
343
Medium Heavy -Duty
Class 6-7
203
205
201
Heavy Heavy -Duty
Class 8
214
216
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
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
29.0766
29.9607
31.2377
Medium Heavy -Duty
Class 6-7
18.4676
18.6640
18.2711
Heavy Heavy -Duty
Class 8
19.4499
19.6464
18.5658
NHTSA Standard for Vehicle with SI Engine Effective MY 2021 (Fuel Consumption
gallon per 1,000 ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
36.0077
37.0204
38.5957
Medium Heavy -Duty
Class 6-7
22.8424
23.0674
22.6173
Heavy Heavy -Duty
Class 8
24.0801
24.3052
22.9549
EPA's proposed vocational vehicle CCh 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.
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Table 2-60 Proposed EPA CCh Standards for MY2024 Class 2b-8 Vocational Vehicles
EPA Standard for Vehicle with CI Engine Effective MY2024 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
284
292
304
Medium Heavy -Duty
Class 6-7
179
181
178
Heavy Heavy -Duty
Class 8
190
192
182
EPA Standard for Vehicle with SI Engine Effective MY2024 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
312
321
334
Medium Heavy -Duty
Class 6-7
197
199
196
Heavy Heavy -Duty
Class 8
208
210
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
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
27.8978
28.6837
29.8625
Medium Heavy -Duty
Class 6-7
17.5835
17.7800
17.4853
Heavy Heavy -Duty
Class 8
18.6640
18.8605
17.8782
NHTSA Standard for Vehicle with SI Engine Effective MY 2024 (Fuel Consumption
gallon per 1,000 ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
35.1075
36.1202
37.5830
Medium Heavy -Duty
Class 6-7
22.1672
22.3923
22.0547
Heavy Heavy -Duty
Class 8
23.4050
23.6300
22.3923
EPA's proposed vocational vehicle CCh 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 CCh Standards for MY2027 Class 2b-8 Vocational Vehicles
EPA Standard for Vehicle With CI Engine Effective MY2027 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
272
280
292
Medium Heavy -Duty
Class 6-7
172
174
170
Heavy Heavy -Duty
Class 8
182
183
174
EPA Standard for Vehicle with SI Engine Effective MY2027 (gram CCh/ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
299
308
321
Medium Heavy -Duty
Class 6-7
189
191
187
Heavy Heavy -Duty
Class 8
196
198
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
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
26.7191
27.5049
28.6837
Medium Heavy -Duty
Class 6-7
16.8959
17.0923
16.6994
Heavy Heavy -Duty
Class 8
17.8782
17.9764
17.0923
NHTSA Standard for Vehicle with SI Engine Effective MY 2027 (Fuel Consumption
gallon per 1,000 ton-mile)
Duty Cycle
Urban
Multi-Purpose
Regional
Light Heavy -Duty
Class 2b-5
33.6446
34.6574
36.1202
Medium Heavy -Duty
Class 6-7
21.2670
21.4921
21.0420
Heavy Heavy -Duty
Class 8
22.0547
22.2797
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 lower-
cost 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 $40,000 per vehicle for vocational vehicles in MY 2021. The engine costs listed
represent the cost of an average package of diesel engine technologies. Individual technology
adoption rates for engine packages are described above in Chapter 2.7. The details behind these
costs are presented in draft RIA Chapter 2.12, including the markups and learning effects applied
and how the costs shown here are weighted to generate an overall cost for the vocational sector.
Table 2-64 Technology Package Incremental Costs for Vocational Vehicles for MY2021a'b (2012$)
Engine0
Tires
Transmission
Axle related
Weight
Reduction
Idle reduction
Electrification
& hybridization
Air
Conditioning
Total
LIGHT HD
Urban
$293
$7
$81
$99
$27
$49
$547
$22
$1,125
Multi-
purpose
$293
$7
$81
$99
$27
$49
$547
$22
$1,125
Regional
$293
$7
$81
$99
$48
$49
$0
$22
$598
MEDIUM HD
Urban
$270
$7
$81
$99
$27
$51
$861
$22
$1,418
Multi-
purpose
$270
$7
$81
$99
$27
$51
$861
$22
$1,418
Regional
$270
$7
$81
$99
$41
$51
$0
$22
$571
HEAVY HD
Urban
$270
$7
$81
$148
$27
$6
$1,437
$22
$1,998
Multi-
purpose
$270
$7
$81
$148
$27
$6
$1,437
$22
$1,998
Regional
$270
$7
$2,852
$219
$34
$0
$0
$22
$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.
0 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 $33,000 per vehicle for vocational vehicles.
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Table 2-65 Technology Package Incremental Costs for Vocational Vehicles for MY2024a'b (2012$)
Engine0
Tires
Transmission
Axle related
Weight Reduction
Idle reduction
Electrification &
hybridization
Air Conditioning
Total
LIGHT HD
Urban
$437
$17
$123
$90
$24
$119
$906
$20
$1,737
Multi-
purpose
$437
$17
$123
$90
$24
$119
$906
$20
$1,737
Regional
$437
$17
$123
$90
$43
$119
$0
$20
$849
MEDIUM HD
Urban
$405
$17
$123
$90
$24
$125
$1,423
$20
$2,228
Multi-
purpose
$405
$17
$123
$90
$24
$125
$1,423
$20
$2,228
Regional
$405
$17
$123
$90
$37
$125
$0
$20
$817
HEAVY HD
Urban
$405
$23
$123
$136
$24
$224
$2,377
$20
$3,332
Multi-
purpose
$405
$23
$123
$136
$24
$224
$2,377
$20
$3,332
Regional
$405
$23
$3,915
$224
$30
$217
$0
$20
$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.
0 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 $68 added to the average SI vocational vehicle package cost in MY
2027, which represents about 56 percent of those vehicles upgrading beyond Level 1 engine
friction reduction. Further details on how these SI vocational vehicle costs were estimated are
provided above in Chapter 2.9.1.
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Table 2-66 Technology Package Incremental Costs for Vocational Vehicles for MY2027a'b (2012$)
Engine0
Tires
Transmission
Axle related
Weight Reduction
Idle reduction
Electrification &
hybridization
Air Conditioning
Total
LIGHT HD
Urban
$471
$20
$244
$86
$29
$498
$2,122
$19
$3,489
Multi-
purpose
$471
$20
$244
$86
$29
$499
$2,122
$19
$3,490
Regional
$471
$20
$267
$86
$46
$499
$0
$19
$1,407
MEDIUM HD
Urban
$437
$20
$244
$86
$29
$526
$3,336
$19
$4,696
Multi-
purpose
$437
$20
$244
$86
$29
$526
$3,336
$19
$4,696
Regional
$437
$20
$267
$86
$40
$526
$0
$19
$1,395
HEAVY HD
Urban
$437
$29
$244
$129
$29
$964
$5,571
$19
$7,422
Multi-
purpose
$437
$29
$244
$129
$29
$964
$5,571
$19
$7,422
Regional
$437
$29
$2,986
$215
$35
$962
$0
$19
$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.
0 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 technology-
specific 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 stop-
start in the six MHD and LHD subcategories for MY 2021 and zero for the FED 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 A Item ative 4 Stan dards
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
$40,000 per vehicle in MY 2021, as shown in Chapter 2.12.7 of the draft RIA. The engine costs
listed represent the cost of an average package of diesel engine technologies with Alternative 4
adoption rates described in the preamble at Section II.D.2(e).
Table 2-67 Vocational Vehicle Technology Incremental Costs for Alternative 4Standards in the 2021 Model
Yeara'b (2012$)
Engine0
Tires
Transmission
Axle related
Weight
Reduction
Idle reduction
Electrification
&
hybridization
Air
Conditioning
Total
LIGHT HD
Urban
$O T>
372
$7
$148
$99
$27
$110
$1,384
$22
$2,169
Multi-
purpose
$O T>
372
$7
$148
$99
$27
$110
$1,384
$22
$2,169
Regional
$372
$7
$148
$99
$48
$110
$0
$22
$805
MEDIUM HD
Urban
$345
$7
$148
$99
$27
$116
$2,175
$22
$2,938
Multi-
purpose
$345
$7
$148
$99
$27
$116
$2,175
$22
$2,938
Regional
$345
$7
$148
$99
$41
$116
$0
$22
$777
HEAVY HD
Urban
$345
$7
$148
$148
$27
$8
$3,633
$22
$4,337
Multi-
purpose
$345
$7
$148
$148
$27
$8
$3,633
$22
$4,337
Regional
$345
$7
$2,042
$243
$34
$0
$0
$22
$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.
0 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 $74 added to the average SI
vocational vehicle package cost in MY 2024, which represents about 56 percent of those vehicles
upgrading beyond Level 1 engine friction reduction. Further details on how these SI vocational
vehicle costs were estimated are provided above in Chapter 2.9.1. The details behind all these
costs are presented in draft RIA Chapter 2.12, including the markups and learning effects applied
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and how the costs shown here are weighted to generate an overall cost for the vocational vehicle
segment.
Table 2-68 Vocational Vehicle Technology Incremental Costs for Alternative 4 Standards in the 2024 Model
Yeara'b (2012$)
Engine0
Tires
Transmission
Axle related
Weight Reduction
Idle reduction
Electrification &
hybridization
Air Conditioning
Total
LIGHT HD
Urban
$493
$26
$256
$90
$30
$561
$2,264
$20
$3,741
Multi-
purpose
$493
$26
$256
$90
$30
$524
$2,264
$20
$3,704
Regional
$493
$26
$280
$90
$49
$524
$0
$20
$1,482
MEDIUM HD
Urban
$457
$26
$256
$90
$30
$592
$3,559
$20
$5,030
Multi-
purpose
$457
$26
$256
$90
$30
$553
$3,559
$20
$4,992
Regional
$457
$26
$280
$90
$43
$553
$0
$20
$1,469
HEAVY HD
Urban
$457
$40
$256
$136
$30
$1,014
$5,943
$20
$7,895
Multi-
purpose
$457
$40
$256
$136
$30
$1,014
$5,943
$20
$7,895
Regional
$457
$40
$3,123
$224
$37
$1,011
$0
$20
$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.
0 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 CCh 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.0 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 CCh 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 CCh 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
Front
Rear
Underside
EXAMPLE TECHNOLOGIES
Front fairings and gap-reducing
fairings
Rear fairings, boat tails and flow
diffusers
Side fairings and skirts, and
underbody devices
INTENDED IMPACT ON AERODYNAMICS
Reduce cross-flow through gap and smoothly
transition airflow from tractor to the trailer
Reduce pressure drag induced by the trailer wake
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
0 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).
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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. To-
date, 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 CCh 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 CCh 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 tractor-
trailers 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.
CM
o
o
CO2 Reductions for Simulated Long-Haul Dry Van
(Compare GEM Cycles)
16%
14%
12%
x 10%
65-MPH Cruise
Aerodynamic Drag Area, CdA (m2)
-Long-Haul Weighted -55-MPH Cruise Transient
Figure 2-17 Comparison of Weighted and 65-mph Cruise Results using EPA's GEM for a Long Dry Van
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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 m2in 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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Wind Tunnel Tests: Average CdA
(Average of Four Tractors)
I Baseline Tail Skirt 1
I Skirt 2 Skirt 2 +Tail
Trailer 1 Trailer 2 Trailers
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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Effect on Absolute CdA
(Multiple Tractors, Multiple Test Methods, Single Trailer)
Baseline
Tail BSkirtl Skirt2 Skirt2+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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Effect of Aerodynamic Trailer Devices
(Multiple Tractors, Multiple Test Methods, Single Trailer)
Wind Tunnel, Tractor I
Coastdown. Tractor 1'
I Wind Tunnel. Tractor 2
Coastdown. Tractor 2
Wind Tunnel. Tractor 3
Coastdown, Tractor 3'
Wind Tunnel, Tractor 4
Baseline Gap Tail Skin I Skirt 2 Skirt 2+Tail
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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Effect of Aerodynamic Trailer Devices
(Solo 28-Foot Trailer)
u
1.2
1.1
10
0,9
0,8
0,7
0,6
0,5
Bin vi
EinV
Bin IV
Baseline Skirt Skiit+Gap
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
Bin I
Bin II
Bin
III
Bin
IV
BinV
Bin
VI
Bin
VII
Bin
VIII
DELTA CDA
<0.09
0.10-0.19
0.20-0.39
0.40-0.59
0.60-0.79
0.80-1.19
1.20-1.59
>1.60
AVERAGE
DELTA CDA
0.0
0.1
0.3
0.5
0.7
1.0
1.4
1.8
EXAMPLE TECHNOLOGIES
53-FOOT DRY VAN
No Aero Devices
Gap Reducer
Basic Skirt or Basic Tail
Advanced Skirt or Tail
Basic Combinations
Advanced Combinations
(including SmartWay Elite)
Optimized Combinations
Changes to Trailer Construction
28-FOOT DRY VAN
No Aero Devices
Skirt
Skirt + Gap Reducer
Adv. Skirt + Gap Reducer
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
Long Dry Van
Short Dry Van
Long Ref. Van
Short Ref. Van
Special Purpose Box
Non-Box Highway
DRY VAN
6.2
6.1
6.1
6.0
6.1
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 wind-
average 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 wind-
averaged 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.
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Comparison of Zero Yaw and Wind-Averaged Drag Results
(Wind Tunnel Results, Trailers Results are Average of Four Tractors)
1.8
1.6
1.4
o 1.0
Q 0.6
i Trailer!,Zero Yaw
Trailer 1, Wind-Averaged
Trailer 2, Zero Yaw
Trailer 2, Wind-Averaged
Trailer 3, Zero Yaw
Trailer 3, Wind-Averaged
Tail
Skirtt
Skirt2
Skirt2+Tail Skirt2+Tail+Gap
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 Resistan ce 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 wide-
based 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 Per for man ce 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 CCh 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
Baseline
Level 1
Level 2
CRR (KG/TON)
6.0
5.1
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
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attending to under-inflated tires, the trailer may have much higher rolling resistance and much
higher CCh emissions and fuel consumption.
2.10.2.3.1 Types of Tire Pressure Mon itoring 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 CCh 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 CCh 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 CCh 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 lower-
weight 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 CCh 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
Hub and Dram (per axle)
Floor
Floor
Floor Crossmembers
Landing Gear
Rear Door
Rear Door Surround
Roof Bows
Side Posts
Slider Box
Structure for Suspension Assembly
Upper Coupler Assembly
MATERIAL
SUBSTITUTION
Cast Iron to Aluminum
Hardwood to Aluminum
Hardwood to Composite
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
Steel to Aluminum
WEIGHT
REDUCTION (LB)
80
375
245
203
50
187
150
100
300
150
280
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 CD A, M2)
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
0.0
0.1
0.3
0.5
0.7
1.0
1.4
1.8
Tire Rolling Resistance (CRR, kg/ton)
Tire Baseline
Tire Level 1
Tire Level 2
6.0
5.1
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 CCh emissions and fuel consumption reductions, making them relatively effective
technologies.
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Table 2-75 Effectiveness (Percent Change in CCh Emissions and Fuel Consumption) of Technologies for the
Proposed Trailer Program
AERODYNAMICS
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Tire Rolling Resistance
Baseline
Level 1
Level 2
Weight Reduction
Baseline
Option 1
Option 2
Option 3
Option 4
DELTA CDA (M2)
0.0
0.1
0.3
0.5
0.7
1.0
1.4
1.8
CRR (kg/ton)
6.0
5.1
4.7
Weight (Ib)
0.0
168
280
430
556
DRY VAN
Long
0%
-1%
-2%
-3%
-5%
-7%
-10%
-13%
Short
0%
-1%
-2%
-4%
-5%
-7%
-10%
-13%
Dry Van
Long
0%
-2%
-3%
Short
0%
-1%
-2%
Dry Van
Long
0.0%
-0.2%
-0.3%
-0.5%
-1%
Short
0.0%
-0.3%
-1%
-1%
-1%
REFRIGERATED VAN
Long
0%
-1%
-2%
-3%
-5%
-7%
-9%
-12%
Short
0%
-1%
-2%
-3%
-5%
-7%
-10%
-12%
Refrigerated Van
Long
0%
-2%
-3%
Short
0%
-1%
-2%
Refrigerated Van
Long
0.0%
-0.2%
-0.3%
-0.5%
-1%
Short
0.0%
-0.3%
-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
Trailer Length
Tractor Class
Tractor Cab Type
Tractor Roof Height
Engine
Frontal Area (m2)
Baseline Drag Area, CdA (m2)
Steer Tire RR (kg/ton)
Drive Tire RR (kg/ton)
Trailer Tire RR (kg/ton)
Total Weight (kg)
Payload (tons)
ATI System Use
Weight Reduction (Ib)
Drive Cycle Weightings
65-MPH Cruise
55-MPH Cruise
Transient Driving
DRY VAN
Long
Class 8
Sleeper
High
2018 MY
15L,
455 HP
10.4
6.2
6.54
6.92
6.00
31978
19
0
0
86%
9%
5%
Short
Class 8
Day
High
2018 MY
15L,
455 HP
10.4
6.1
6.54
6.92
6.00
21028
10
0
0
64%
17%
19%
REFRIGERATED
VAN
Long
Class 8
Sleeper
High
2018 MY
15L,
455 HP
10.4
6.1
6.54
6.92
6.00
33778
19
0
0
86%
9%
5%
Short
Class 8
Day
High
2018 MY
15L,
455 HP
10.4
6.0
6.54
6.92
6.00
22828
10
0
0
64%
17%
19%
AERO-
EXCLUDED
BOX
All Lengths
Class 8
Day
High
2018 MY
15L,
455 HP
10.4
6.1
6.54
6.92
6.00
21028
10
0
0
64%
17%
19%
NON-BOX
HIGHWAY
All Lengths
Class 8
Day
Low
2018 MY
15L,
455 HP
6.9
4.9
6.54
6.92
6.00
29710
19
0
0
64%
17%
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 Smart Way 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
Model Year
LONG BOX
DRY & REFRIGERATED
VANS
2018
2021
2024
2027
SHORT BOX,
NON-AERO BOX,
& NON-BOX TRAILERS
2018-2027
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) "
45%
-
20%
30%
5%
-
-
-
0.2
41%
-
20%
34%
5%
-
-
-
0.3
38%
-
20%
37%
5%
-
-
-
0.3
35%
-
20%
40%
5%
-
-
0.3
100%
-
-
-
-
-
-
-
0.0
Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) a
50%
50%
-
5.55
47%
53%
-
5.52
43%
57%
-
5.49
40%
60%
-
5. 46
100%
-
-
6.0
Tire Inflation
ATI
Average % Reduction a
50%
0.8%
53%
0.8%
57%
0.9%
60%
0.9%
0%
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 CCh 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 CCh 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.
2-170
-------
Table 2-78 Adoption Rates and Average Performance Parameters for the Alternative Reference Case
TECHNOLOGY
Model Year
LONG BOX
DRY & REFRIGERATED VANS
2018
2021
2024
2027
2040
SHORT BOX,
NON-AERO BOX,
& NON-BOX TRAILERS
2018-2027
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) "
45%
-
20%
30%
5%
-
-
-
0.2
41%
-
20%
34%
5%
-
-
-
0.3
38%
-
20%
37%
5%
-
-
-
0.3
35%
-
20%
40%
5%
-
-
0.3
20%
-
20%
55%
5%
-
-
-
0.4
100%
-
-
-
-
-
-
-
0.0
Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) a
50%
50%
-
5.6
47%
53%
-
5.5
43%
57%
-
5.5
40%
60%
-
5.5
25%
75%
-
5.3
100%
-
-
6.0
Tire Inflation
ATI
Average % Reduction a
50%
0.8%
53%
0.8%
57%
0.9%
60%
0.9%
75%
1.1%
0%
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 CCh
emissions and fuel consumption performance of the reference tractor-trailers. The results of
these simulations are shown in Table 2-79. We used these CCh 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 CCh Emissions and Fuel Consumption Results for the Reference Case Tractor-Trailers
Length
CO2 Emissions
(kg/ton-mile)
Fuel Consumption
(gal/100 ton-miles)
DRY VAN
Long
85
8.3497
Short
147
14.4401
REFRIGERATED
VAN
Long
87
8.5462
Short
151
14.8330
2-171
-------
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 work-
performing 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.
2-172
-------
Table 2-80 Adoption Rates and Average Performance Parameters for the Long Box Trailers
TECHNOLOGY
Model Year
LONG BOX
DRY VANS
2018
2021
2024
2027
LONG BOX
REFRIGERATED VANS
2018
2021
2024
2027
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) "
5%
-
30%
60%
5%
-
-
-
0.4
-
-
5%
55%
10%
30%
-
-
0.7
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
10%
50%
40%
-
1.1
5%
-
30%
60%
5%
-
-
-
0.4
-
-
5%
55%
10%
30%
-
-
0.7
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
20%
60%
20%
-
1.0
Trailer Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) a
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
Tire Inflation System
ATI
Average ATI Reduction (%) a
85%
1.3%
95%
1.4%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
95%
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
2-173
-------
Table 2-81 Adoption Rates and Average Performance Parameters for the Short Box Trailers
TECHNOLOGY
Model Year
SHORT BOX
DRY VANS
2018
2021
2024
2027
SHORT BOX
REFRIGERATED VANS
2018
2021
2024
2027
Aerodynamic Technologies "
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) b
100%
-
-
-
-
-
-
-
0.4
5%
95%
-
-
-
-
-
-
0.7
-
70%
30%
-
-
-
-
-
0.8
-
30%
60%
10%
-
-
-
-
1.1
100%
-
-
-
-
-
-
-
0.4
5%
95%
-
-
-
-
-
-
0.7
-
70%
30%
-
-
-
-
-
0.8
-
55%
40%
5%
-
-
-
-
1.0
Trailer Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) b
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
Tire Inflation System
ATI
Average ATI Reduction (%) c
85%
1.3%
95%
1.4%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
95%
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
0 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.
2-174
-------
Table 2-82 Adoption Rates and Average Performance Parameters for the Non-Aero Box and Non-Box
Trailers
TECHNOLOGY
Model Year
NON-AERO BOX
& NON-BOX TRAILERS
2018
2021
2024+
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) "
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
Trailer Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) a
-
100%
-
5.1
-
100%
-
5.1
-
-
100%
4.7
Tire Inflation System
ATI
Average ATI Reduction (%) "
100%
1.5%
100%
1.5%
100%
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 CCh 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 CCh 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.
2-175
-------
Table 2-83 CCh Emissions and Fuel Consumption Based on Projected Technology Adoption Rates for
Proposed Standards
MODEL
YEAR
2018 - 2020
2021-2023
2024 - 2026
2027 +
SUBCATEGORY
Length
EPA Standard
(COa Grams per Ton-Mile)
Voluntary NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(COa Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
DRY VAN
Long
83
8.1532
81
7.9568
79
7.7603
77
7.5639
Short
144
14.1454
142
13.9489
141
13.8507
140
13.7525
REFRIGERATED
VAN
Long
84
8.2515
82
8.0550
81
7.9568
80
7.8585
Short
147
14.4401
146
14.3418
144
14.1454
144
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 $)
Aerodynamics
Tires
Tire inflation system
Total
53 -FOOT
DRY VAN
$285
$65
$239
$588
53-FOOT
REF. VAN
$285
$65
$239
$588
28-FOOT
DRY VAN
$0
$78
$435
$514
NON-BOX
HIGHWAY
$0
$185
$683
$868
2-176
-------
Table 2-85 Trailer Technology Incremental Costs in the 2021 Model Year for the Proposed Alternative
(2012 $)
Aerodynamics
Tires
Tire inflation system
Total
53 -FOOT
DRY VAN
$602
$65
$234
$901
53-FOOT
REF. VAN
$602
$65
$234
$901
28-FOOT
DRY VAN
$468
$79
$426
$974
NON-BOX
HIGHWAY
$0
$175
$632
$807
Table 2-86 Trailer Technology Incremental Costs in the 2024 Model Year for the Proposed Alternative
(2012 $)
Aerodynamics
Tires
Tire inflation system
Total
53 -FOOT
DRY VAN
$836
$61
$220
$1,116
53-FOOT
REF. VAN
$836
$61
$220
$1,116
28-FOOT
DRY VAN
$608
$76
$412
$1,097
NON-BOX
HIGHWAY
$0
$160
$578
$739
Table 2-87 Trailer Technology Incremental Costs in the 2024 Model Year for the Proposed Alternative
(2012 $)
Aerodynamics
Tires
Tire inflation system
Total
53 -FOOT
DRY VAN
$1,163
$54
$192
$1,409
53-FOOT
REF. VAN
$1,034
$54
$192
$1,280
28-FOOT
DRY VAN
$788
$74
$391
$1,253
NON-BOX
HIGHWAY
$0
$155
$549
$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.
2-177
-------
Table 2-88 Adoption Rates and Average Performance Parameters for the Long Box Trailers in the More
Stringent Alternative
TECHNOLOGY
Model Year
LONG BOX
DRY VANS
2018
2021
2024
LONG BOX
REFRIGERATED VANS
2018
2021
2024
Aerodynamic Technologies "
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) "
5%
-
30%
60%
5%
-
-
-
0.4
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
10%
50%
40%
-
1.1
5%
-
30%
60%
5%
-
-
-
0.4
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
20%
60%
20%
-
1.0
Trailer Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) a
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
Tire Inflation System
ATI
Average ATI Reduction (%) "
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
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
2-178
-------
Table 2-89 Adoption Rates and Average Performance Parameters for the Short Box Trailers in the More
Stringent Alternative
TECHNOLOGY
Model Year
SHORT BOX
DRY VANS
2018
2021
2024
SHORT BOX
REFRIGERATED VANS
2018
2021
2024
Aerodynamic Technologies "
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Average Delta CdA (m2) b
100%
-
-
-
-
-
-
-
0.4
-
70%
30%
-
-
-
-
-
0.8
-
30%
60%
10%
-
-
-
-
1.1
100%
-
-
-
-
-
-
-
0.4
-
70%
30%
-
-
-
-
-
0.8
-
55%
40%
5%
-
-
-
-
1.0
Trailer Tire Rolling Resistance
Baseline tires
Level 1 tires
Level 2 tires
Average CRR (kg/ton) b
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
Tire Inflation System
ATI
Average ATI Reduction (%) b
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
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
0 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 sub category.
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
CCh 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.
2-179
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Table 2-90 Trailer CCh and Fuel Consumption Standards for Box Trailers in the More Stringent Alternative
MODEL
YEAR
2018 - 2020
2021-2023
2024 +
SUBCATEGORY
Length
EPA Standard
(CO2 Grams per Ton-Mile)
Voluntary NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(COa Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
DRY VAN
Long
83
8.1532
80
7.8585
77
7.5639
Short
144
14.1454
142
13.9489
140
13.7525
REFRIGERATED
VAN
Long
84
8.2515
81
7.9568
80
7.8585
Short
147
14.4401
145
14.2436
144
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$)
Aerodynamics
Tires
Tire inflation system
Total
>50-FOOT
DRY VAN
$285
$65
$239
$588
>50-FOOT
REF. VAN
$285
$65
$239
$588
<35-FOOT
DRY VAN
$0
$78
$435
$514
NON-BOX
HIGHWAY
$0
$185
$683
$868
2-180
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Table 2-92 Trailer Technology Incremental Costs in the 2021 Model Year for the More Stringent Alternative
(2012$)
Aerodynamics
Tires
Tire inflation system
Total
>50-FOOT
DRY VAN
$908
$65
$234
$1,207
>50-FOOT
REF. VAN
$908
$65
$234
$1,207
<35-FOOT
DRY VAN
$641
$79
$426
$1,146
NON-BOX
HIGHWAY
$0
$175
$632
$807
Table 2-93 Trailer Technology Incremental Costs in the 2024 Model Year for the More Stringent Alternative
(2012$)
Aerodynamics
Tires
Tire inflation system
Total
>50-FOOT
DRY VAN
$1,223
$61
$220
$1,504
>50-FOOT
REF. VAN
$1,090
$61
$220
$1,371
<3 5-FOOT
DRY VAN
$816
$76
$412
$1,304
NON-BOX
HIGHWAY
$0
$160
$578
$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 CCh 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 CoA), 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 CoA, 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
CCh emissions; use a conversion of 10,180 grams CCh per gallon of diesel fuel to calculate the
corresponding fuel consumption values. Figure 2-23 through Figure 2-26 show GEM's CCh
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 CCh 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 CCh 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 CCh result.
2-181
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Impact of ACoA Parameter on a Long Dry Van
O 82 j
O y =-1.7x + 87.6
80 j
78
76 J
-2-10123
Delta CRR (kg/ton)
(b)
Impact of WR Parameter on a Long Dry Van
(ACoA = 0 m2, TRRL = 6.0 kg/ton)
90 -i
f 88 1
f T ~+ A y = -O.OOIx + 87.6
I 86 j ~~~^-V-_
ro 84 j *""---*
O 82
o
80 -j
78 -
76 J
0 500 1000 1500 2000 2500 3000 3500
Weight Reduction (Ib)
(c)
Figure 2-23 Impact of (a) Delta CnA, (b) Delta CRR, and (c) Weight Reduction on CCh Results of a GEM-
Simulated Long Dry Van
2-182
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Impact of AC oA Parameter on a Long Reefer Van
(TRRL= 6.0 kg/ton, WR = 0 Ib)
= 86
3 84 -
S82
o
80
78
76
y = -6 .Ox + 89.1
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Delta CdA(m2)
1.6
(a)
92
f 90
I 88
2 86
S 84
O
82
80
78
Impact of ATRRL Parameter on a Long Reefer Van
{ACoA = 0 m2, WR = 0 Ib)
-2
y = -1.8x + 89,1
-1
0 1
Delta CRR (kg/ton)
(b)
o
S
O
90
Impact of WR Parameter on a Long Reefer Van
(ACoA = 0 m2, TRRL = 6.0 kg/ton)
86
84
82 -
80
78
76
0
y = -O.OOIx + 89.1
500
1000
1500 2000 2500
Weight Reduction (Ib)
3000 3500
(c)
Figure 2-24 Impact of (a) Delta CnA, (b) Delta CRR, and (c) Weight Reduction on CCh Results of a GEM-
Simulated Long Refrigerated Van
2-183
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155 -i
'I 150 \
I t
I145]
8 14°
135 J
130 '
0.0
Impact of ACoA Parameter on a Short Dry Van
(TRRL= 6.0 kg/ton, WR = 0 \b)
y = -10.5x -»- 147.2
0.2 0.4 0.6 0.8 1.0
Delta CdA (m2)
1.2
1.4
1.6
(a)
155
Impact of ATRRL Parameter on a Short Dry Van
(ACoA = 0 m2, WR = 0 Ib)
'I 150 H
c
o
145
O 140
135 ]
130 '
-2
^---»
r = -2.4x +147.2
0 1 2
Delta CRR (kg/ton)
155
f 150
(b)
Impact of WR Parameter on a Short Dry Van
(ACoA = 0 m2, TRRL = 6.0 kg/ton)
y = -0.003x + 147.0
o
B 145
w
O 140
135
130
0 500 1000 1500 2000 2500 3000 3500
Weight Reduction (Ib)
(c)
Figure 2-25 Impact of (a) Delta CnA, (b) Delta CRR, and (c) Weight Reduction on CCh Results of a GEM-
Simulated Short Dry Van
2-184
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155 i
Impact of AC oA Parameter on a Short Reefer Van
(TRRL= 6.0 kg/ton, WR = 0 Ib)
E 150 f
c
o
I 145 j
O 140
135 \
130 J
0.0
y = -10.3x + 150.7
0.2 0.4 0.6 0.8 1.0
Delta CdA
-------
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 OEM's CCh results were
additive. Similar trends were seen with the simulations for the other trailer subcategories, though
the results are not shown here.
90
f 88
Combined Impact of ACoA and TRRL
(WR = 0 Ib)
*TRRL=6.0. WR=0 «TRRL=5.1, WR=0 ' TRRL=4.7, WR=0
0.2
0.4
0.6 0.8 1.0
Delta CdA (m2)
1.2
1.4
1.6
Figure 2-27 Combined Impact of Drag Area and Tire Rolling Resistance Level on CCh Results of a GEM-
Simulated Long Dry Van with No Weight Reduction
Combined Impact of ACoA and WR
{TRRL = 5.1 kg/ton)
»TRRL=5.1. WR=0
TRRL=5.1. WR=500 TRRL=5.1. WR=1000
y = -6.1x + 86.0
y = -6.1x + 85.5
y = -6.1x + 84.9
0.2
0.4
0.6 0.8 1.0
Delta CdA (m2)
1.2
1.4
1.6
Figure 2-28 Combined Impact of Drag Area and Weight Reduction on CCh 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 CCh emissions. Equation 1 is the result of
combining the curve fit equations for long box dry vans.
2-186
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y = 87.6 - 6.1(ACD4) - 1.7(A77?/?L) - O.OOl(VKfl)
(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.
y = 77.4 - 6.1(ACD^) + l.7(TRRL~) - O.OOl(VKfl)
(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.
ec02 =
(3)
Table 2-94 Constants for GEM-Based CCh Equation for Trailer Subcategories (See Equation 3)
TRAILER SUBCATEGORY
Long Dry Van
Long Refrigerated Van
Short Diy Van
Short Refrigerated Van
Ci
77.4
78.3
134.0
136.3
C2
-6.1
-6.0
-10.5
-10.3
C3
1.7
1.8
2.2
2.4
C4
-0.001
-0.001
-0.003
-0.003
Over 100 GEM vehicle simulations were performed for a range of delta CoA, 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.
2-187
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Compare GEM and Calculated CO2 Results
Long Dry Van
CO, = 77.4 - 6.1 ACdA + 1 ,7{TRRL - 0.001 WR
78 80 82 84 86
GEM Simulation CO2 Result (g/lon-mi)
Figure 2-29 Comparison of GEM and Calculated CCh Results for a Long Dry Van
90
~ 88
o 86
I84
cc
3 82
T5 80
3
_o
5
0 78
76
Compare GEM and Calculated CO2 Results
Long Refrigerated Van
C02 = 78.3 - 6.0(ACdA) + 1.8{TRRL) - 0.001 (WR)
76 78 80 82 84 86 88
GEM Simulation CO2 Result {g/ton-mi)
90
Figure 2-30 Comparison of GEM and Calculated CCh Results for a Long Refrigerated Van
2-188
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150
=-145
tc 135
8
1130
.2
1
3125
120
Compare GEM and Calculated CO2 Results
Short Dry Van
y= LOOOOx
R2 = 0.9986
CO2 = 134.0 -10.5(ACdA) + 2.2(TRRL) - 0.003(WR)
120 125 130 135 140 145
GEM Simulation CO2 Result (g/ton-mi)
150
Figure 2-31 Comparison of GEM and Calculated CCh Results for a Short Dry Van
155
,3130
125
Compare GEM and Calculated CO2 Results
Short Refrigerated Van
C02 = 136.3 -10.3(ACdA} + 2.4(TRRL) - 0.003(WR)
125 130 135 140 145 150
GEM Simulation CO2 Result (g/ton-mi)
155
Figure 2-32 Comparison of GEM and Calculated CCh Results for a Short Refrigerated Van
2-189
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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 CCh 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, Cs, to address the use of ATI. Constant Cs 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 CCh per gallon of diesel fuel to calculate the
corresponding fuel consumption values.
ec02 = [d + C2 (TRRL) + C3
(4)
Table 2-95 Constants for GEM-Based CCh Equation for Trailer Subcategories (See Equation 4)
TRAILER SUBCATEGORY
Long Dry Van
Long Refrigerated Van
Short Diy Van
Short Refrigerated Van
Ci
77.4
78.3
134.0
136.3
C2
1.7
1.8
2.2
2.4
C3
-6.1
-6.0
-10.5
-10.3
C4
-0.001
-0.001
-0.003
-0.003
2.11 Natural Gas
2.11.1 Sealed Crankcase
EPA regulations allow venting to the atmosphere crankcase emissions from compression-
ignition 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.
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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 (1C) 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 la and 3 where la 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.l.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
2-191
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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 light-
duty 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 systemthose 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
Heavy engine manufacturers
Heavy truck manufacturers
Light-duty vehicle manufacturers
RPE
1.28
1.36
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 peer-
reviewed 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.
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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 heavy-
duty 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 factorsthose resulting from the averaging of the values
from their original report with the modified-Delphi valuesto 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
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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 light-
duty 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 Analysis"
CLASS
HD Pickup Trucks and Vans
Loose diesel engines
Loose gasoline engines
Vocational Vehicles,
Combination Tractors and
Trailers
COMPLEXITY
Low
Medium
Highl
High2
Low
Medium
Highl
High2
Low
Medium
Highl
High2
Low
Medium
Highl
NEAR
TERM
1.24
.39
.56
.77
.15
.24
.28
.44
.24
.39
.56
.77
.18
.30
.43
LONG
TERM
1.19
1.29
1.35
1.50
1.13
1.18
1.19
1.29
1.19
1.29
1.35
1.50
1.14
1.23
1.27
2-195
-------
I High2 | 1.57 | 1.37 ||
l*~ I V I I II
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.
2-196
-------
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
CLASS
HD Pickup and
Vans
Loose diesel
engines
Loose gasoline
engines
Vocational
Vehicles,
Combination
Tractors and
Trailers
COMPLEXITY
Low
Medium
Highl
High2
Low
Medium
Highl
High2
Low
Medium
Highl
High2
Low
Medium
Highl
High2
SHORT-TERM
WARRANTY
0.012
0.045
0.065
0.074
0.006
0.022
0.032
0.037
0.012
0.045
0.065
0.074
0.013
0.051
0.073
0.084
NON-
WARRANTY
0.230
0.343
0.499
0.696
0.149
0.213
0.249
0.398
0.230
0.343
0.499
0.696
0.165
0.252
0.352
0.486
LONG-TERM
WARRANTY
0.005
0.031
0.032
0.049
0.003
0.016
0.016
0.025
0.005
0.031
0.032
0.049
0.006
0.035
0.037
0.056
NON-
WARRANTY
0.187
0.259
0.314
0.448
0.122
0.165
0.176
0.265
0.187
0.259
0.314
0.448
0.134
0.190
0.233
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
Cylinder head improvements 1
Cylinder head improvements 2
Turbo efficiency improvements 1
Turbo efficiency improvements 2
EGR cooler efficiency improvements 1
APPLIED TO
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH, HD Pickup &
Van Engines
LH/MH/HH Engines
LH/MH/HH Engines
ICM
COMPLEXITY
Low
Low
Low
Low
Low
NEAR TERM
THRU
2022
2027
2022
2027
2022
2-197
-------
EGR cooler efficiency improvements 2
Water pump improvements 1
Water pump improvements 2
Oil pump improvements 1
Oil pump improvements 2
Fuel pump improvements 1
Fuel pump improvements 2
Fuel rail improvements 1
Fuel rail improvements 2
Fuel injector improvements 1
Fuel injector improvements 2
Piston improvements 1
Piston improvements 2
Valve train friction reductions 1
Valve train friction reductions 2
Turbo compounding 1
Turbo compounding 2
Aftertreatment improvements 1
Aftertreatment improvements 2
Model based control
Waste heat recovery
Engine friction reduction 1
Engine friction reduction 2
Engine changes to accommodate low friction
lubes
Variable valve timing - coupled
Variable valve timing - dual
Stoichiometric gasoline direct injection
Cylinder deactivation
Cooled EGR
Turbocharging & downsizing
"Right sized" diesel engine
6 speed transmission
8 speed transmission
Automated manual transmission (AMT)
Auto transmission, power-shift
Conversion from manual to auto trans
Dual clutch transmission
Improved transmission
Lower RR tires 1
Lower RR tires 2
Low drag brakes
Electric power steering
High efficiency transmission
Driveline friction reduction
Improved accessories (electrification)
Improved accessories (electrification)
Improved fan
Lower RR tires 1
Lower RR tires 2
Lower RR tires 3
Lower RR tires 4
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
HH Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van Engines
HD Pickup & Van vehicles,
Tractors
HD Pickup & Van vehicles
HD Pickup & Van vehicles,
Vocational
Vocational, Tractors
Tractors
Vocational
Vocational, Tractors
Vocational
HD Pickup & Van vehicles
HD Pickup & Van vehicles
HD Pickup & Van vehicles
HD Pickup & Van vehicles
HD Pickup & Van vehicles
HD Pickup & Van vehicles
HD Pickup & Van vehicles
Tractors
Tractors
Vocational , Tractors,
Trailers
Vocational , Tractors,
Trailers
Vocational , Tractors,
Trailers s
Vocational , Tractors,
Trailers
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
Low
Medium
Medium
Medium
Medium
Medium
Medium
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
2027
2022
2027
2022
2027
2022
2027
2022
2027
2022
2027
2022
2027
2022
2027
2022
2027
2022
2024
2022
2025
2018
2024
2018
2018
2018
2018
2018
2024
2018
2022
2018
2018
2022
2022
2018
2022
2022
2018
2024
2018
2018
2024
2022
2018
2022
2022
2022
2022
2025
2028
2-198
-------
Automated Tire Inflation System (ATIS)
Aero 1
Aero 2
Aero Bins 1 thru 4
Aero Bin 5 thru 7
Aero Bins 1 thru 8
Weight reduction (via single wide tires and/or
aluminum wheels)
Weight reduction via material changes
Weight reduction via material changes - 200
Ibs, 400 Ibs
Weight reduction via material changes - 1000
Ibs
Weight reduction via material changes
Auxiliary power unit
Air conditioning leakage
Air conditioning efficiency
Neutral idle
Stop-start (no regeneration)
Stop-start (no regeneration)
Mild hybrid
Mild hybrid
Strong hybrid
Strong hybrid
Full electric
Tractors, Trailers
HD Pickup & Van vehicles
HD Pickup & Van vehicles
Tractors
Tractors
Trailers
Tractors
HD Pickup & Van vehicles
Vocational
Vocational
Tractors
Tractors
Vocational, Tractors
Tractors
Vocational
HD Pickup & Van vehicles
Vocational
HD Pickup & Van vehicles
Tractors
HD Pickup & Van vehicles
Vocational
Vocational, Tractors
Low
Low
Medium
Low
Medium
Low
Low
Low
Low
Medium
Low
Low
Low
Low
Low
Medium
Medium
Highl
Highl
Highl
Highl
Highl
2022
2018
2024
2022
2025
2018
2022
2018
2022
2022
2022
2022
2022
2022
2022
2018
2022
2024
2025
2024
2022
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
2-199
-------
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 assumedas both
agencies have done in past regulatory analysesto 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 steep-
portion 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
2-200
-------
learning at 3 percent per year becomes effective for 5 years. For technologies having the flat-
portion 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
Cylinder head improvements 1
Cylinder head improvements 2
Turbo efficiency improvements 1
Turbo efficiency improvements 2
EGR cooler efficiency improvements 1
EGR cooler efficiency improvements 2
Water pump improvements 1
Water pump improvements 2
Oil pump improvements 1
Oil pump improvements 2
Fuel pump improvements 1
Fuel pump improvements 2
Fuel rail improvements 1
Fuel rail improvements 2
Fuel injector improvements 1
Fuel injector improvements 2
Piston improvements 1
Piston improvements 2
Valve train friction reductions 1
Valve train friction reductions 2
Turbo compounding 1
Turbo compounding 2
Aftertreatment improvements 1 & 2
Model based control
Waste heat recovery
Engine friction reduction 1 & 2
Engine changes to accommodate low
friction lubes
Variable valve timing
APPLIED TO
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH, HD Pickup
& Van Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
LH/MH/HH Engines
HH Engines
HD Pickup & Van
Engines
HD Pickup & Van
Engines
HD Pickup & Van
LEARNING
ALGORITHM
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
None
None
Flat
LEARNING
FACTOR
"CURVE" A
2
13
2
13
2
13
2
13
2
13
2
13
2
13
2
13
2
13
2
13
2
13
2
13
12
1
1
8
2-201
-------
Stoichiometric gasoline direct injection
Cylinder deactivation
Cooled EGR
Turbocharging & downsizing
"Right sized" diesel engine
6 speed transmission
8 speed transmission
Automated manual transmission (AMT)
Auto transmission, power-shift
Conversion from manual to auto trans
Dual clutch transmission
Improved transmission
Lower RR tires 1
Lower RR tires 2
Low drag brakes
Electric power steering
High efficiency transmission
Driveline friction reduction
Improved accessories (electrification)
Improved accessories
Improved fan
Lower RR tires 1
Lower RR tires 2
Lower RR tires 3
Lower RR tires 4
Automated Tire Inflation System (AXIS)
Aero 1 & 2
Aero Bins 1 & 2
Aero Bin 3
Aero Bins 4 thru 7
Aero Bins 1 thru 8
Weight reduction (via single wide tires
and/or aluminum wheels)
Engines
HD Pickup & Van
Engines
HD Pickup & Van
Engines
HD Pickup & Van
Engines
HD Pickup & Van
Engines
HD Pickup & Van
vehicles, Tractors
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles, Vocational
Vocational, Tractors
Tractors
Vocational
Vocational, Tractors
Vocational
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
HD Pickup & Van
vehicles
Tractors
Tractors
Vocational , Tractors,
Trailers
Vocational , Tractors,
Trailers
Vocational , Tractors,
Trailers
Vocational , Tractors,
Trailers
Tractors, Trailers
HD Pickup & Van
vehicles
Tractors
Tractors
Tractors
Trailers
Tractors
Flat
Flat
Flat
Flat
None
Flat
Flat
Flat
Flat
Flat
Flat
Flat
None
Steep
None
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
None
Flat
Steep
Flat
Flat
7
8
7
7
1
7
7
12
12
7
12
13
1
11
1
8
6
3
8
12
12
2
2
12
13
12
8
1
2
4
2
2
2-202
-------
Weight reduction via material changes
Weight reduction via material changes
Auxiliary power unit
Air conditioning leakage
Air conditioning efficiency
Neutral idle
Stop-start (no regeneration)
Stop-start (no regeneration)
Mild hybrid
Mild hybrid
Strong hybrid
Full electric
HD Pickup & Van
vehicles
Vocational, Tractors
Tractors
Vocational, Tractors
Tractors
Vocational
HD Pickup & Van
vehicles
Vocational
HD Pickup & Van
vehicles
Tractors
HD Pickup & Van
vehicles, Vocational
Vocational, Tractors
Flat
Flat
Flat
Flat
Flat
None
Steep
Flat
Flat
Flat
Steep
Steep
6
13
2
2
12
1
9
13
6
12
11
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
1
2
o
5
4
6
7
8
9
11
12
13
2014
.000
.000
.031
.000
.096
0.941
.031
.250
.563
.130
.238
2015
1.000
0.970
1.000
1.000
1.063
0.913
1.000
1.000
1.563
1.096
1.201
2016
1.000
0.941
0.970
0.800
1.031
0.885
0.970
1.000
1.563
1.063
1.165
2017
1.000
0.913
0.941
0.800
1.000
0.868
0.951
0.970
1.563
1.031
1.130
2018
1.000
0.885
0.913
0.640
0.970
0.850
0.932
0.941
1.563
1.000
1.096
2019
1.000
0.868
0.894
0.621
0.941
0.833
0.913
0.913
1.250
0.970
1.063
2020
1.000
0.850
0.877
0.602
0.913
0.817
0.895
0.885
1.250
0.941
1.031
2021
1.000
0.833
0.859
0.584
0.885
0.800
0.877
0.859
1.000
0.913
1.000
2022
1.000
0.817
0.842
0.567
0.859
0.784
0.859
0.833
0.970
0.894
0.970
2023
1.000
0.800
0.825
0.550
0.842
0.769
0.842
0.808
0.941
0.877
0.941
2024
1.000
0.784
0.808
0.533
0.825
0.753
0.825
0.784
0.913
0.859
0.913
2025
1.000
0.769
0.792
0.517
0.808
0.738
0.809
0.760
0.885
0.842
0.894
Note:
a Curves 5 and 10 were generated but subsequently not used so are not included in the table.
2-203
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1.60
1.40
1.20
1.00
0.80 -
0.60 -
0.40
--1
-m-2
-A-3
-*-4
9
--11
-A-12
-X-13
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
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
2-204
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case; however, for some technologiesnotably aero and tire technologiesthe 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 FID 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
Price index for GDP
Factor applied for 2012$
2005
91.991
1.141
2006
94.818
1.107
2007
97.335
1.079
2008
99.236
1.058
2009
100
1.050
2010
101.211
1.037
2011
103.199
1.017
2012
105.002
1.000
2013
106.588
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 (1C) 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 la 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
technologiesnotably tire and aero technologiesthere 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,
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etc.) to the MOVES sourcetypes (i.e., transit bus, refuse truck, combination long haul, etc.) in
order to develop program costs. This final stepmoving 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
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$14
$2
$17
$0
2019
$14
$2
$16
$0
2020
$13
$2
$16
$0
2021
$13
$2
$16
50%
$8
2022
$13
$2
$15
50%
$8
2023
$13
$2
$15
50%
$8
2024
$12
$2
$15
90%
$13
2025
$12
$2
$14
90%
$13
2026
$12
$2
$14
90%
$13
2027
$12
$2
$14
100%
$14
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-206
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Table 2-104 Costs of Aftertreatment Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
Aftertreatment improvements
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$14
$2
$17
$0
2019
$14
$2
$16
$0
2020
$13
$2
$16
$0
2021
$13
$2
$16
45%
$7
2022
$13
$2
$15
45%
$7
2023
$13
$2
$15
45%
$7
2024
$12
$2
$15
95%
$14
2025
$12
$2
$14
95%
$13
2026
$12
$2
$14
95%
$13
2027
$12
$2
$14
100%
$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
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
DMC
1C
TC
Alt
la
Alt 3
TCp
2018
$11
$2
$12
$0
2019
$11
$2
$12
$0
2020
$10
$2
$12
$0
2021
$10
$2
$11
50%
$6
2022
$10
$2
$11
50%
$6
2023
$9
$2
$11
50%
$5
2024
$9
$2
$11
90%
$10
2025
$9
$2
$10
90%
$9
2026
$9
$2
$10
90%
$10
2027
$9
$2
$10
100%
$10
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-207
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Table 2-106 Costs for Cylinder Head Improvements - Level 2
Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$6
$1
$7
$0
2019
$6
$1
$7
$0
2020
$6
$1
$7
$0
2021
$6
$1
$7
50%
$3
2022
$6
$1
$7
50%
$3
2023
$5
$1
$6
50%
$3
2024
$5
$1
$6
90%
$6
2025
$5
$1
$6
90%
$5
2026
$5
$1
$6
90%
$5
2027
$5
$1
$6
100%
$6
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-107 Costs for Cylinder Head Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
Cylinder head improvements
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$6
$1
$7
$0
2019
$6
$1
$7
$0
2020
$6
$1
$7
$0
2021
$6
$1
$7
45%
$3
2022
$6
$1
$7
45%
$3
2023
$5
$1
$6
45%
$3
2024
$5
$1
$6
95%
$6
2025
$5
$1
$6
95%
$6
2026
$5
$1
$6
95%
$6
2027
$5
$1
$6
100%
$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.
2-208
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Table 2-108 Costs for Turbocharger Efficiency Improvements - Level 2
Light/Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$18
$3
$21
$0
2019
$18
$3
$20
$0
2020
$17
$3
$20
$0
2021
$17
$3
$19
50%
$10
2022
$16
$3
$19
50%
$9
2023
$16
$3
$18
50%
$9
2024
$15
$3
$18
90%
$16
2025
$15
$3
$17
90%
$16
2026
$14
$3
$17
90%
$15
2027
$14
$3
$17
100%
$17
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-109 Costs for Turbocharger Efficiency Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
Turbo efficiency
improvements - level 2
DMC
1C
TC
Alt
la
Alt 3
TCp
2018
$18
$3
$21
$0
2019
$18
$3
$20
$0
2020
$17
$3
$20
$0
2021
$17
$3
$19
45%
$9
2022
$16
$3
$19
45%
$8
2023
$16
$3
$18
45%
$8
2024
$15
$3
$18
95%
$17
2025
$15
$3
$17
95%
$16
2026
$14
$3
$17
95%
$16
2027
$14
$3
$17
100%
$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
Turbo efficiency improvements - level 1
Turbo efficiency improvements - level 1
Turbo efficiency improvements - level 1
DMC
1C
TC
2021
$14
$3
$16
2022
$14
$3
$16
2023
$13
$2
$15
2024
$13
$2
$15
2025
$13
$2
$15
2026
$13
$2
$15
2027
$13
$2
$15
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
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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
Turbo compounding -
level 2
Turbo compounding -
level 2
Turbo compounding -
level 2
Turbo compounding -
level 2
Turbo compounding -
level 2
Turbo compounding -
level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$942
$134
$1,076
$0
2019
$914
$133
$1,047
$0
2020
$886
$133
$1,020
$0
2021
$860
$133
$993
5%
$50
2022
$834
$133
$967
5%
$48
2023
$809
$133
$942
5%
$47
2024
$785
$133
$917
10%
$92
2025
$769
$133
$902
10%
$90
2026
$754
$132
$886
10%
$89
2027
$738
$132
$871
10%
$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.
2-210
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Table 2-112 Costs for Valve Actuation
Light/Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Valve actuation
Valve actuation
Valve actuation
Valve actuation
Valve actuation
Valve actuation
DMC
1C
TC
Alt
la
Alt3
All
2018
$146
$60
$207
$0
2019
$143
$45
$188
$0
2020
$141
$45
$186
$0
2021
$138
$45
$183
50%
$91
2022
$135
$45
$180
50%
$90
2023
$132
$45
$177
50%
$89
2024
$130
$45
$174
90%
$157
2025
$127
$45
$172
90%
$154
2026
$126
$45
$170
90%
$153
2027
$125
$44
$169
100%
$169
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-113 Costs for Valve Actuation
HDD Tractor Engines (2012$)
TECHNOLOGY
Valve actuation
Valve actuation
Valve actuation
Valve actuation
Valve actuation
Valve actuation
DMC
1C
TC
Alt
la
Alt 3
All
2018
$146
$60
$207
$0
2019
$143
$45
$188
$0
2020
$141
$45
$186
$0
2021
$138
$45
$183
45%
$82
2022
$135
$45
$180
45%
$81
2023
$132
$45
$177
45%
$80
2024
$130
$45
$174
95%
$166
2025
$127
$45
$172
95%
$163
2026
$126
$45
$170
95%
$162
2027
$125
$44
$169
100%
$169
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
For HD pickups and vans, we have estimated the costs of dual cam phasing based on the
DMC, 1C and TC presented above in Table 2-112.
For discrete variable valve lift (DVVL), we have again used the 2017-2025 light-duty
FRM values updated to 2012$ 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
Discrete variable valve lift
(DVVL)
Discrete variable valve lift
(DVVL)
Discrete variable valve lift
(DVVL)
DMC
1C
TC
2021
$227
$74
$301
2022
$223
$74
$297
2023
$218
$74
$292
2024
$214
$74
$288
2025
$210
$74
$283
2026
$207
$73
$281
2027
$205
$73
$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 $3
2-211
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(DMC, 2008$, 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
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$4
$1
$4
$0
2019
$4
$1
$4
$0
2020
$3
$1
$4
$0
2021
$3
$1
$4
50%
$2
2022
$3
$1
$4
50%
$2
2023
$3
$1
$4
50%
$2
2024
$3
$1
$4
90%
$3
2025
$3
$1
$o
3
90%
$3
2026
$3
$1
$3
90%
$3
2027
$3
$1
$3
100%
$3
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-116 Costs for EGR Cooler Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
EGR cooler - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$4
$1
$4
$0
2019
$4
$1
$4
$0
2020
$3
$1
$4
$0
2021
$3
$1
$4
45%
$2
2022
$3
$1
$4
45%
$2
2023
$3
$1
$4
45%
$2
2024
$3
$1
$4
95%
$3
2025
$3
$1
$3
95%
$3
2026
$3
$1
$3
95%
$3
2027
$3
$1
$3
100%
$3
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
For HD pickups and vans, we have estimated the costs of adding cooled EGR to a
gasoline engine based on the values used in the 2017-2025 light-duty FRM. We have scaled
upward the light-duty value by 25 percent and converted to 2012$ 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
Cooled EGR
Cooled EGR
Cooled EGR
DMC
1C
TC
2021
$253
$120
$373
2022
$248
$120
$368
2023
$243
$119
$363
2024
$239
$119
$358
2025
$234
$89
$o T3
626
2026
$231
$89
$321
2027
$229
$89
$318
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-212
-------
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
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$91
$13
$103
$0
2019
$88
$13
$101
$0
2020
$85
$13
$98
$0
2021
$83
$13
$95
60%
$57
2022
$80
$13
$93
60%
$56
2023
$78
$13
$91
60%
$54
2024
$75
$13
$88
90%
$79
2025
$74
$13
$87
90%
$78
2026
$72
$13
$85
90%
$77
2027
$71
$13
$84
100%
$84
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-119 Costs for Water Pump Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
Water pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$91
$13
$103
$0
2019
$88
$13
$101
$0
2020
$85
$13
$98
$0
2021
$83
$13
$95
45%
$43
2022
$80
$13
$93
45%
$42
2023
$78
$13
$91
45%
$41
2024
$75
$13
$88
95%
$84
2025
$74
$13
$87
95%
$82
2026
$72
$13
$85
95%
$81
2027
$71
$13
$84
100%
$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
2-213
-------
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
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$5
$1
$5
$0
2019
$4
$1
$5
$0
2020
$4
$1
$5
$0
2021
$4
$1
$5
60%
$3
2022
$4
$1
$5
60%
$3
2023
$4
$1
$5
60%
$3
2024
$4
$1
$4
90%
$4
2025
$4
$1
$4
90%
$4
2026
$4
$1
$4
90%
$4
2027
$4
$1
$4
100%
$4
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-121 Costs for Oil Pump Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
Oil pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$5
$1
$5
$0
2019
$4
$1
$5
$0
2020
$4
$1
$5
$0
2021
$4
$1
$5
45%
$2
2022
$4
$1
$5
45%
$2
2023
$4
$1
$5
45%
$2
2024
$4
$1
$4
95%
$4
2025
$4
$1
$4
95%
$4
2026
$4
$1
$4
95%
$4
2027
$4
$1
$4
100%
$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.
2-214
-------
Table 2-122 Costs for Fuel Pump Improvements - Level 2
Light/Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$5
$1
$5
$0
2019
$4
$1
$5
$0
2020
$4
$1
$5
$0
2021
$4
$1
$5
60%
$3
2022
$4
$1
$5
60%
$3
2023
$4
$1
$5
60%
$3
2024
$4
$1
$4
90%
$4
2025
$4
$1
$4
90%
$4
2026
$4
$1
$4
90%
$4
2027
$4
$1
$4
100%
$4
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-123 Costs for Fuel Pump Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
Fuel pump - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$5
$1
$5
$0
2019
$4
$1
$5
$0
2020
$4
$1
$5
$0
2021
$4
$1
$5
45%
$2
2022
$4
$1
$5
45%
$2
2023
$4
$1
$5
45%
$2
2024
$4
$1
$4
95%
$4
2025
$4
$1
$4
95%
$4
2026
$4
$1
$4
95%
$4
2027
$4
$1
$4
100%
$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.
2-215
-------
Table 2-124 Costs for Fuel Rail Improvements - Level 2
Light HDD Vocational Engines (2012$)
TECHNOLOGY
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$12
$2
$13
$0
2019
$11
$2
$13
$0
2020
$11
$2
$13
$0
2021
$11
$2
$12
60%
$7
2022
$10
$2
$12
60%
$7
2023
$10
$2
$12
60%
$7
2024
$10
$2
$11
90%
$10
2025
$10
$2
$11
90%
$10
2026
$9
$2
$11
90%
$10
2027
$9
$2
$11
100%
$11
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-125 Costs for Fuel Rail Improvements - Level 2
Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$10
$1
$11
$0
2019
$10
$1
$11
$0
2020
$9
$1
$11
$0
2021
$9
$1
$11
60%
$6
2022
$9
$1
$10
60%
$6
2023
$9
$1
$10
60%
$6
2024
$8
$1
$10
90%
$9
2025
$8
$1
$10
90%
$9
2026
$8
$1
$9
90%
$8
2027
$8
$1
$9
100%
$9
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-126 Costs for Fuel Rail Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
Fuel rail - level 2
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$10
$1
$11
$0
2019
$10
$1
$11
$0
2020
$9
$1
$11
$0
2021
$9
$1
$11
45%
$5
2022
$9
$1
$10
45%
$5
2023
$9
$1
$10
45%
$4
2024
$8
$1
$10
95%
$9
2025
$8
$1
$10
95%
$9
2026
$8
$1
$9
95%
$9
2027
$8
$1
$9
100%
$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
2-216
-------
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
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$14
$2
$17
$0
2019
$14
$2
$16
$0
2020
$14
$2
$16
$0
2021
$13
$2
$15
50%
$8
2022
$13
$2
$15
50%
$7
2023
$12
$2
$14
50%
$7
2024
$12
$2
$14
90%
$13
2025
$12
$2
$14
90%
$12
2026
$12
$2
$14
90%
$12
2027
$11
$2
$13
100%
$13
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-128 Costs for Fuel Injector Improvements - Level 2
Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$11
$2
$12
$0
2019
$11
$2
$12
$0
2020
$10
$2
$12
$0
2021
$10
$2
$11
50%
$6
2022
$10
$2
$11
50%
$6
2023
$9
$2
$11
50%
$5
2024
$9
$2
$11
90%
$10
2025
$9
$2
$10
90%
$9
2026
$9
$2
$10
90%
$9
2027
$9
$2
$10
100%
$10
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-129 Costs for Fuel Injector Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
Fuel injectors - level 2
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$11
$2
$12
$0
2019
$11
$2
$12
$0
2020
$10
$2
$12
$0
2021
$10
$2
$11
45%
$5
2022
$10
$2
$11
45%
$5
2023
$9
$2
$11
45%
$5
2024
$9
$2
$11
95%
$10
2025
$9
$2
$10
95%
$10
2026
$9
$2
$10
95%
$10
2027
$9
$2
$10
100%
$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
2-217
-------
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
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$3
$0
$3
$0
2019
$3
$0
$3
$0
2020
$3
$0
$3
$0
2021
$2
$0
$3
50%
$1
2022
$2
$0
$3
50%
$1
2023
$2
$0
$3
50%
$1
2024
$2
$0
$3
90%
$2
2025
$2
$0
$3
90%
$2
2026
$2
$0
$3
90%
$2
2027
$2
$0
$3
100%
$3
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-131 Costs for Fuel Pump Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
Piston improvements - level
2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$3
$0
$3
$0
2019
$3
$0
$3
$0
2020
$3
$0
$3
$0
2021
$2
$0
$3
45%
$1
2022
$2
$0
$3
45%
$1
2023
$2
$0
$3
45%
$1
2024
$2
$0
$3
95%
$3
2025
$2
$0
$3
95%
$2
2026
$2
$0
$3
95%
$2
2027
$2
$0
$3
100%
$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
2-218
-------
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
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$109
$15
$124
$0
2019
$105
$15
$121
$0
2020
$102
$15
$118
$0
2021
$99
$15
$115
60%
$69
2022
$96
$15
$112
60%
$67
2023
$93
$15
$109
60%
$65
2024
$91
$15
$106
90%
$95
2025
$89
$15
$104
90%
$94
2026
$87
$15
$102
90%
$92
2027
$85
$15
$100
100%
$100
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-133 Costs for Valvetrain Friction Improvements - Level 2
Medium/Heavy HDD Vocational Engines (2012$)
TECHNOLOGY
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$82
$12
$93
$0
2019
$79
$12
$91
$0
2020
$77
$12
$88
$0
2021
$74
$12
$86
60%
$52
2022
$72
$12
$84
60%
$50
2023
$70
$11
$81
60%
$49
2024
$68
$11
$79
90%
$71
2025
$67
$11
$78
90%
$70
2026
$65
$11
$77
90%
$69
2027
$64
$11
$75
100%
$75
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-219
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Table 2-134 Costs for Valvetrain Friction Improvements - Level 2
HDD Tractor Engines (2012$)
TECHNOLOGY
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
Valvetrain friction reduction
- level 2
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$82
$12
$93
$0
2019
$79
$12
$91
$0
2020
$77
$12
$88
$0
2021
$74
$12
$86
45%
$39
2022
$72
$12
$84
45%
$38
2023
$70
$11
$81
45%
$37
2024
$68
$11
$79
95%
$75
2025
$67
$11
$78
95%
$74
2026
$65
$11
$77
95%
$73
2027
$64
$11
$75
100%
$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.
2-220
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Table 2-135 Costs for "Right-sized" HDD Tractor Engines (2012$)
TECHNOLOGY
Right-sized
diesel engine
Right-sized
diesel engine
Right-sized
diesel engine
Right-sized
diesel engine
Right-sized
diesel engine
Right-sized
diesel engine
DMC
1C
TC
Alt la
Alt3
TCp
2018
-$493
$88
-$405
$0
2019
-$493
$88
-$405
$0
2020
-$493
$88
-$405
$0
2021
-$493
$88
-$405
10%
-$40
2022
-$493
$88
-$405
10%
-$40
2023
-$493
$69
-$424
10%
-$42
2024
-$493
$69
-$424
20%
-$85
2025
-$493
$69
-$424
20%
-$85
2026
-$493
$69
-$424
20%
-$85
2027
-$493
$69
-$424
30%
-$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.
2-221
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Table 2-136 Costs for Waste Heat Recovery
HDD Tractor Engines (2012$)
ITEM
Waste
heat
recovery
Waste
heat
recovery
Waste
heat
recovery
Waste
heat
recovery
Waste
heat
recovery
Waste
heat
recovery
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$8,692
$2,628
$11,320
$0
2019
$8,431
$2,615
$11,046
$0
2020
$8,179
$2,602
$10,780
$0
2021
$7,933
$2,589
$10,523
1%
$105
2022
$7,775
$2,581
$10,356
1%
$104
2023
$7,619
$2,574
$10,193
1%
$102
2024
$7,467
$2,566
$10,032
5%
$502
2025
$7,317
$2,558
$9,876
5%
$494
2026
$7,171
$1,908
$9,079
5%
$454
2027
$7,028
$1,903
$8,931
15%
$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
Model-based control
Model-based control
Model-based control
Model-based control
Model-based control
Model-based control
DMC
1C
TC
Alt la
Alt3
TCp
2018
$108
$15
$123
$0
2019
$105
$15
$120
$0
2020
$102
$15
$117
$0
2021
$99
$15
$114
25%
$28
2022
$96
$15
$111
25%
$28
2023
$93
$12
$105
25%
$26
2024
$90
$12
$102
30%
$31
2025
$88
$12
$100
30%
$30
2026
$86
$12
$99
30%
$30
2027
$85
$12
$97
40%
$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$
2-222
-------
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
Engine friction reduction - level
1
Engine friction reduction - level
1
Engine friction reduction - level
1
DMC
1C
TC
2021
$5
$1
$6
2022
$5
$1
$6
2023
$5
$1
$6
2024
$5
$1
$6
2025
$5
$1
$6
2026
$5
$1
$6
2027
$5
$1
$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 $1 I/cylinder
(DMC, 2006$, 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
Engine friction reduction - level
1
Engine friction reduction - level
1
Engine friction reduction - level
1
DMC
1C
TC
2021
$97
$19
$116
2022
$97
$19
$116
2023
$97
$19
$116
2024
$97
$19
$116
2025
$97
$19
$116
2026
$97
$19
$116
2027
$97
$19
$116
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
For engine friction reduction level 2 (EFR2, which includes costs for accommodating low
friction lubes) we have used the same approach as used in the light-duty 2017-2025 rule in that
we have doubled the DMC associated with LUB and EFR1. As with those technologies, we
consider EFR2 to be beyond learning (curve 1) and have applied low complexity markups but
have applied near term markups through 2024. The resultant costs for gasoline HD pickups and
vans are shown in Table 2-140.
2-223
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Table 2-140 Costs for Engine Friction Reduction - Level 2
Gasoline HD Pickups and Vans (2012$)
ITEM
Engine friction reduction - level
2
Engine friction reduction - level
2
Engine friction reduction - level
2
DMC
1C
TC
2021
$205
$50
$254
2022
$205
$50
$254
2023
$205
$50
$254
2024
$205
$50
$254
2025
$205
$39
$244
2026
$205
$39
$244
2027
$205
$39
$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 FID 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
Engine friction reduction -
diesel
Engine friction reduction -
diesel
Engine friction reduction -
diesel
DMC
1C
TC
2021
$397
$96
$494
2022
$397
$96
$494
2023
$397
$87
$484
2024
$397
$87
$484
2025
$397
$77
$474
2026
$397
$77
$474
2027
$397
$77
$474
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.12.2.18
Cylinder deactivation
For cylinder deactivation on HD pickups and vans, we have based the costs on values
presented in the light-duty 2017-2025 FRM with updates to 2012$ 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
Cylinder
deactivation
Cylinder
deactivation
Cylinder
deactivation
DMC
1C
TC
2021
$148
$48
$196
2022
$145
$48
$193
2023
$142
$48
$190
2024
$139
$48
$187
2025
$137
$48
$185
2026
$135
$48
$183
2027
$134
$48
$182
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-224
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2.12.2.19 Stoichiometric gasoline direct injection (SGDI)
For gasoline direct injection on HD pickups and vans, we have based the costs on values
presented in the light-duty 2017-2025 FRM with updates to 2012$ 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
Gasoline direct
injection
Gasoline direct
injection
Gasoline direct
injection
DMC
1C
TC
2021
$333
$118
$451
2022
$327
$118
$445
2023
$320
$118
$438
2024
$314
$117
$431
2025
$307
$117
$425
2026
$304
$117
$422
2027
$301
$117
$418
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost.
2.12.2.20
Turbocharging & downsizing
For turbocharging and downsizing (TDS) on HD pickups and vans, we have based the
costs on values presented in the light-duty 2017-2025 FRM with updates to 2012$. 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
Adding
twin turbos
Adding
twin turbos
Adding
twin turbos
DMC
1C
TC
2021
$588
$208
$796
2022
$576
$208
$784
2023
$565
$208
$772
2024
$553
$207
$761
2025
$542
$207
$749
2026
$537
$207
$744
2027
$531
$207
$738
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-225
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Table 2-145 Costs for Downsizing from an OHV V8 to an OHC V6
Gasoline HD Pickups and Vans (2012$)
ITEM
Downsizing from OHV
V8 to OHC V6
Downsizing from OHV
V8 to OHC V6
Downsizing from OHV
V8 to OHC V6
DMC
1C
TC
2021
$301
$97
$398
2022
$292
$97
$389
2023
$286
$97
$383
2024
$280
$97
$377
2025
$275
$96
$371
2026
$269
$96
$365
2027
$264
$96
$360
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
Table 2-146 Costs for Downsizing from an OHC V8 to an OHC V6
Gasoline HD Pickups and Vans (2012$)
ITEM
Downsizing from OHC
V8 to OHC V6
Downsizing from OHC
V8 to OHC V6
Downsizing from OHC
V8 to OHC V6
DMC
1C
TC
2021
-$236
$112
-$125
2022
-$232
$112
-$120
2023
-$227
$111
-$116
2024
-$223
$111
-$111
2025
-$218
$83
-$135
2026
-$216
$83
-$133
2027
-$214
$83
-$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 light-
duty 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 light-
duty 2017-2025 final rule.
2-226
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Table 2-147 Costs for Adding 2 Gears to an Automatic Transmission
Vocational Light/Medium/Heavy HD Urban/Multipurpose Vehicles (2012$)
TECHNOLOGY
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
DMC
1C
TC
Alt la
Alt3
TCp
2018
$413
$143
$557
$0
2019
$405
$107
$512
$0
2020
$397
$107
$504
$0
2021
$389
$106
$495
5%
$25
2022
$381
$106
$487
5%
$24
2023
$374
$106
$479
5%
$24
2024
$366
$105
$472
5%
$24
2025
$359
$105
$464
5%
$23
2026
$355
$105
$460
5%
$23
2027
$352
$105
$457
5%
$23
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-148 Costs for Adding 2 Gears to an Automatic Transmission
Vocational Light/Medium HD Regional Vehicles (2012$)
TECHNOLOGY
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
Adding additional gears
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$413
$143
$557
$0
2019
$405
$107
$512
$0
2020
$397
$107
$504
$0
2021
$389
$106
$495
5%
$25
2022
$381
$106
$487
5%
$24
2023
$374
$106
$479
5%
$24
2024
$366
$105
$472
5%
$24
2025
$359
$105
$464
5%
$23
2026
$355
$105
$460
5%
$23
2027
$352
$105
$457
10%
$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.
2-227
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Table 2-149 Costs for an AMT Transmission
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$3,694
$1,117
$4,811
$0
2019
$3,583
$1,111
$4,695
$0
2020
$3,476
$1,106
$4,582
$0
2021
$3,372
$1,101
$4,472
22%
$984
2022
$3,304
$1,097
$4,401
22%
$968
2023
$3,238
$818
$4,056
22%
$892
2024
$3,173
$815
$3,989
33%
$1,316
2025
$3,110
$813
$3,923
33%
$1,295
2026
$3,048
$811
$3,859
33%
$1,273
2027
$2,987
$809
$3,795
25%
$949
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
Table 2-150 Costs for an AMT Transmission
Tractors (2012$)
TECHNOLOGY
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
Manual to AMT
DMC
1C
TC
Alt
la
Alt 3
TCp
2018
$3,694
$1,117
$4,811
$0
2019
$3,583
$1,111
$4,695
$0
2020
$3,476
$1,106
$4,582
$0
2021
$3,372
$1,101
$4,472
40%
$1,789
2022
$3,304
$1,097
$4,401
40%
$1,761
2023
$3,238
$818
$4,056
40%
$1,622
2024
$3,173
$815
$3,989
50%
$1,994
2025
$3,110
$813
$3,923
50%
$1,961
2026
$3,048
$811
$3,859
50%
$1,929
2027
$2,987
$809
$3,795
50%
$1,898
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
2.12.3.3 Automatic Transmission Powershift
TCp=total cost applied to the package;
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.
2-228
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Table 2-151 Costs for a Powershift Automatic Transmission
Tractors (2012$)
TECHNOLOGY
Manual to AT
powershift
Manual to AT
powershift
Manual to AT
powershift
Manual to AT
powershift
Manual to AT
powershift
Manual to AT
powershift
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$11,670
$3,528
$15,199
$0
2019
$11,320
$3,511
$14,831
$0
2020
$10,981
$3,493
$14,474
$0
2021
$10,651
$3,477
$14,128
10%
$1,413
2022
$10,438
$3,466
$13,904
10%
$1,390
2023
$10,229
$2,583
$12,812
10%
$1,281
2024
$10,025
$2,576
$12,601
20%
$2,520
2025
$9,824
$2,569
$12,393
20%
$2,479
2026
$9,628
$2,562
$12,190
20%
$2,438
2027
$9,435
$2,555
$11,990
30%
$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
AMI 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
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$0
$0
$0
$0
2019
$0
$0
$0
$0
2020
$0
$0
$0
$0
2021
$0
$0
$0
5%
$0
2022
$0
$0
$0
5%
$0
2023
$0
$0
$0
5%
$0
2024
$0
$0
$0
15%
$0
2025
$0
$0
$0
15%
$0
2026
$0
$0
$0
15%
$0
2027
$0
$0
$0
5%
$0
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-229
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Table 2-153 Costs for a Dual Clutch Transmission (DCT)
Vocational Light/Medium HD Regional Vehicles (2012$)
TECHNOLOGY
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
Auto trans to DCT
DMC
1C
TC
Alt la
Alt3
TCp
2018
$0
$0
$0
$0
2019
$0
$0
$0
$0
2020
$0
$0
$0
$0
2021
$0
$0
$0
5%
$0
2022
$0
$0
$0
5%
$0
2023
$0
$0
$0
5%
$0
2024
$0
$0
$0
15%
$0
2025
$0
$0
$0
15%
$0
2026
$0
$0
$0
15%
$0
2027
$0
$0
$0
10%
$0
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-154 Costs for a Dual Clutch Transmission (DCT)
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$3,694
$1,117
$4,811
$0
2019
$3,583
$1,111
$4,695
$0
2020
$3,476
$1,106
$4,582
$0
2021
$3,372
$1,101
$4,472
22%
$984
2022
$3,304
$1,097
$4,401
22%
$968
2023
$3,238
$818
$4,056
22%
$892
2024
$3,173
$815
$3,989
33%
$1,316
2025
$3,110
$813
$3,923
33%
$1,295
2026
$3,048
$811
$3,859
33%
$1,273
2027
$2,987
$809
$3,795
25%
$949
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
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
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
Manual to DCT
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$12,676
$o oo ^
J,OJZ
$16,509
$0
2019
$12,296
$3,813
$16,109
$0
2020
$11,927
$3,794
$15,721
$0
2021
$11,569
$3,776
$15,346
5%
$767
2022
$11,338
$3,765
$15,102
5%
$755
2023
$11,111
$2,806
$13,917
5%
$696
2024
$10,889
$2,798
$13,687
10%
$1,369
2025
$10,671
$2,790
$13,461
10%
$1,346
2026
$10,458
$2,783
$13,240
10%
$1,324
2027
$10,248
$2,775
$13,024
10%
$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
2-230
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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
Improved trans
Improved trans
Improved trans
Improved trans
Improved trans
Improved trans
DMC
1C
TC
Alt la
Alt3
TCp
2018
$346
$57
$403
$0
2019
$336
$57
$393
$0
2020
$326
$57
$382
$0
2021
$316
$56
$372
15%
$56
2022
$307
$56
$363
15%
$54
2023
$297
$44
$342
15%
$51
2024
$288
$44
$333
30%
$100
2025
$283
$44
$327
30%
$98
2026
$277
$44
$321
30%
$96
2027
$271
$44
$315
70%
$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.
2-231
-------
Table 2-157 Cost for an Automatic Transmission
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Manual to auto
trans
Manual to auto
trans
Manual to auto
trans
Manual to auto
trans
Manual to auto
trans
Manual to auto
trans
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$3,141
$1,089
$4,230
$0
2019
$3,078
$812
$3,890
$0
2020
$3,017
$810
$3,826
$0
2021
$2,956
$808
$3,764
22%
$828
2022
$2,897
$806
$3,703
22%
$815
2023
$2,839
$804
$3,643
22%
$801
2024
$2,782
$802
$3,584
33%
$1,183
2025
$2,727
$800
$3,526
33%
$1,164
2026
$2,699
$799
$3,498
33%
$1,154
2027
$2,672
$798
$3,470
25%
$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
Move from
6 to 8 gears
Move from
6 to 8 gears
Move from
6 to 8 gears
DMC
1C
TC
2021
$97
$34
$131
2022
$95
$34
$129
2023
$93
$34
$127
2024
$91
$34
$125
2025
$89
$34
$123
2026
$88
$34
$123
2027
$88
$34
$122
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-232
-------
Table 2-159 Costs for High Efficiency Gearbox (HEG)
HD Pickups and Vans (2012$)
ITEM
High
efficiency
gearbox
High
efficiency
gearbox
High
efficiency
gearbox
DMC
1C
TC
2021
$232
$63
$296
2022
$225
$63
$288
2023
$221
$63
$284
2024
$217
$63
$279
2025
$212
$50
$262
2026
$208
$50
$258
2027
$204
$50
$254
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
Table 2-160 Costs for Aggressive Shift Logic Level 1
HD Pickups and Vans (2012$)
ITEM
Aggressive
shift logic 1
Aggressive
shift logic 1
Aggressive
shift logic 1
DMC
1C
TC
2021
$25
$5
$30
2022
$24
$5
$30
2023
$24
$5
$29
2024
$23
$5
$29
2025
$23
$5
$28
2026
$22
$5
$28
2027
$22
$5
$28
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
Table 2-161 Complete Cost of Moving from the Base 6 Speed to 8 Speed Transmission
2 Gears+HEG+ASLl
HD Pickups and Vans (2012$)
ITEM
Move from 6speed to
Sspeed Transmission
TC
2021
$457
2022
$447
2023
$440
2024
$433
2025
$414
Notes: TC=total cost.
2026
$409
2027
$403
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.
2-233
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Table 2-162 Costs for Direct Air Conditioning Controls
All Vocational HD Vehicles (2012$)
TECHNOLOGY
A/C direct
A/C direct
A/C direct
A/C direct
A/C direct
A/C direct
DMC
1C
TC
Alt la
Alt3
TCp
2018
$19
$4
$23
$0
2019
$19
$4
$23
$0
2020
$18
$4
$22
$0
2021
$18
$4
$22
100%
$22
2022
$18
$4
$22
100%
$22
2023
$17
$3
$20
100%
$20
2024
$17
$3
$20
100%
$20
2025
$17
$3
$20
100%
$20
2026
$17
$3
$20
100%
$20
2027
$16
$3
$19
100%
$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
A/C indirect
A/C indirect
A/C indirect
A/C indirect
A/C indirect
A/C indirect
DMC
1C
TC
Alt la
Alt3
TCp
2018
$158
$28
$186
$0
2019
$153
$28
$181
$0
2020
$148
$28
$176
$0
2021
$144
$28
$172
10%
$17
2022
$141
$28
$169
10%
$17
2023
$138
$22
$160
10%
$16
2024
$135
$22
$157
20%
$31
2025
$133
$22
$155
20%
$31
2026
$130
$22
$152
20%
$30
2027
$127
$22
$149
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.
2-234
-------
Table 2-164 Costs for 6x2 Axles
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
DMC
1C
TC
Alt la
Alt3
TCp
2018
$181
$32
$213
$0
2019
$176
$32
$208
$0
2020
$170
$32
$203
$0
2021
$165
$32
$197
23%
$44
2022
$162
$32
$194
23%
$44
2023
$159
$25
$184
23%
$41
2024
$156
$25
$181
30%
$54
2025
$152
$25
$178
30%
$53
2026
$149
$25
$175
30%
$52
2027
$146
$25
$172
30%
$51
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-165 Costs for 6x2 Axles
Class 8 Day Cab Low and Sleeper Cab Low/Mid Roof Tractors (2012$)
TECHNOLOGY
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$181
$32
$213
$0
2019
$176
$32
$208
$0
2020
$170
$32
$203
$0
2021
$165
$32
$197
10%
$20
2022
$162
$32
$194
10%
$19
2023
$159
$25
$184
10%
$18
2024
$156
$25
$181
20%
$36
2025
$152
$25
$178
20%
$36
2026
$149
$25
$175
20%
$35
2027
$146
$25
$172
20%
$34
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-166 Costs for 6x2 Axles
Class 8 Day Cab and Sleeper Cab High Roof Tractors (2012$)
TECHNOLOGY
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
Axle 6x2
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$181
$32
$213
$0
2019
$176
$32
$208
$0
2020
$170
$32
$203
$0
2021
$165
$32
$197
20%
$39
2022
$162
$32
$194
20%
$39
2023
$159
$25
$184
20%
$37
2024
$156
$25
$181
60%
$108
2025
$152
$25
$178
60%
$107
2026
$149
$25
$175
60%
$105
2027
$146
$25
$172
60%
$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.
2-235
-------
Table 2-167 Costs for Axle Disconnect
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Axle disconnect
Axle disconnect
Axle disconnect
Axle disconnect
Axle disconnect
Axle disconnect
DMC
1C
TC
Alt la
Alt3
TCp
2018
$101
$18
$120
$0
2019
$101
$18
$120
$0
2020
$101
$18
$120
$0
2021
$101
$18
$120
46%
$55
2022
$101
$18
$120
46%
$55
2023
$101
$14
$116
46%
$53
2024
$101
$14
$116
61%
$71
2025
$101
$14
$116
30%
$35
2026
$101
$14
$116
30%
$35
2027
$101
$14
$116
30%
$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
Axle downspeed
Axle downspeed
Axle downspeed
Axle downspeed
Axle downspeed
Axle downspeed
DMC
1C
TC
Alt la
Alt3
TCp
2018
$49
$9
$58
$0
2019
$48
$9
$57
$0
2020
$46
$9
$55
$0
2021
$45
$9
$54
20%
$11
2022
$44
$9
$53
20%
$11
2023
$43
$7
$50
20%
$10
2024
$42
$7
$49
40%
$20
2025
$41
$7
$48
40%
$19
2026
$41
$7
$47
40%
$19
2027
$40
$7
$47
60%
$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.
2-236
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Table 2-169 Costs for Low Friction Axle Lubes
Vocational Light/Medium HD Urban/Multipurpose/Regional Vehicles (2012$)
TECHNOLOGY
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
DMC
1C
TC
Alt la
Alt3
TCp
2018
$121
$22
$142
$0
2019
$117
$22
$139
$0
2020
$114
$21
$135
$0
2021
$110
$21
$132
75%
$99
2022
$108
$21
$129
75%
$97
2023
$106
$17
$123
75%
$92
2024
$104
$17
$121
75%
$90
2025
$102
$17
$118
75%
$89
2026
$100
$17
$116
75%
$87
2027
$98
$17
$114
75%
$86
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-170 Costs for Low Friction Axle Lubes
Vocational Heavy HD Urban/Multipurpose/Regional Vehicles (2012$)
TECHNOLOGY
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$181
$32
$213
$0
2019
$176
$32
$208
$0
2020
$170
$32
$203
$0
2021
$165
$32
$197
75%
$148
2022
$162
$32
$194
75%
$146
2023
$159
$25
$184
75%
$138
2024
$156
$25
$181
75%
$136
2025
$152
$25
$178
75%
$133
2026
$149
$25
$175
75%
$131
2027
$146
$25
$172
75%
$129
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-171 Costs for Low Friction Axle Lubes
Tractors (2012$)
TECHNOLOGY
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
Axle low friction lubes
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$181
$32
$213
$0
2019
$176
$32
$208
$0
2020
$170
$32
$203
$0
2021
$165
$32
$197
20%
$39
2022
$162
$32
$194
20%
$39
2023
$159
$25
$184
20%
$37
2024
$156
$25
$181
40%
$72
2025
$152
$25
$178
40%
$71
2026
$149
$25
$175
40%
$70
2027
$146
$25
$172
40%
$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.
2-237
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Table 2-172 Costs for Auxiliary Power Units (APU)
Sleeper Cab Tractors (2012$)
TECHNOLOGY
APU
APU
APU
APU
APU
APU
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$4,296
$859
$5,154
30%
30%
$0
2019
$4,210
$857
$5,067
30%
30%
$0
2020
$4,126
$856
$4,982
30%
30%
$0
2021
$4,043
$855
$4,899
30%
70%
$1,959
2022
$3,962
$854
$4,817
30%
70%
$1,927
2023
$3,883
$674
$4,557
30%
70%
$1,823
2024
$3,805
$673
$4,479
30%
80%
$2,239
2025
$3,729
$673
$4,402
30%
90%
$2,641
2026
$3,692
$673
$4,365
30%
90%
$2,619
2027
$3,655
$672
$4,327
30%
90%
$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
Neutral idle
Neutral idle
Neutral idle
Neutral idle
Neutral idle
Neutral idle
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$7
$1
$9
$0
2019
$7
$1
$9
$0
2020
$7
$1
$9
$0
2021
$7
$1
$9
70%
$6
2022
$7
$1
$9
70%
$6
2023
$7
$1
$8
70%
$6
2024
$7
$1
$8
85%
$7
2025
$7
$1
$8
85%
$7
2026
$7
$1
$8
85%
$7
2027
$7
$1
$8
25%
$2
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-174 Costs for Neutral Idle Technology
Vocational Light/Medium/Heavy HD Multipurpose Vehicles and
Vocational Light/Medium HD Regional Vehicles
And Vocational Heavy HD Urban Vehicles (2012$)
TECHNOLOGY
Neutral idle
Neutral idle
Neutral idle
Neutral idle
Neutral idle
Neutral idle
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$7
$1
$9
$0
2019
$7
$1
$9
$0
2020
$7
$1
$9
$0
2021
$7
$1
$9
70%
$6
2022
$7
$1
$9
70%
$6
2023
$7
$1
$8
70%
$6
2024
$7
$1
$8
85%
$7
2025
$7
$1
$8
85%
$7
2026
$7
$1
$8
85%
$7
2027
$7
$1
$8
30%
$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
2-238
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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
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
DMC
1C
TC
Alt la
Alt3
TCp
2018
$719
$202
$921
$0
2019
$698
$201
$898
$0
2020
$677
$200
$876
$0
2021
$656
$198
$855
5%
$43
2022
$637
$197
$834
5%
$42
2023
$618
$147
$764
5%
$38
2024
$599
$146
$745
15%
$112
2025
$587
$146
$733
15%
$110
2026
$575
$145
$721
15%
$108
2027
$564
$145
$709
70%
$496
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-176 Costs for Stop-start
Vocational Medium HD Urban/Multipurpose/Regional Vehicles (2012$)
TECHNOLOGY
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$759
$213
$972
$0
2019
$736
$212
$948
$0
2020
$714
$211
$925
$0
2021
$693
$209
$902
5%
$45
2022
$672
$208
$880
5%
$44
2023
$652
$155
$807
5%
$40
2024
$632
$154
$786
15%
$118
2025
$620
$154
$773
15%
$116
2026
$607
$153
$761
15%
$114
2027
$595
$153
$748
70%
$524
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
2-239
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Table 2-177 Costs for Stop-start
Vocational Heavy HD Urban/Multipurpose/Regional Vehicles (2012$)
TECHNOLOGY
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
Stop-start
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$1,394
$391
$1,785
$0
2019
$1,352
$389
$1,741
$0
2020
$1,312
$387
$1,698
$0
2021
$1,272
$385
$1,657
$0
2022
$1,234
$383
$1,617
$0
2023
$1,197
$284
$1,482
$0
2024
$1,161
$283
$1,444
15%
$217
2025
$1,138
$282
$1,420
15%
$213
2026
$1,115
$282
$1,397
15%
$210
2027
$1,093
$281
$1,374
70%
$962
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
For HD pickups and vans, we have based our costs for stop-start systems on the values
used in the light-duty 2017-2025 final rule, but have scaled upward those costs by 25 percent to
account for the larger and harder starting HD engines. Using this approach and converting to
2012$ 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
Stop-start
Stop-start
Stop-start
DMC
1C
TC
2021
$404
$134
$539
2022
$392
$134
$526
2023
$380
$134
$514
2024
$369
$133
$502
2025
$358
$133
$491
2026
$351
$133
$483
2027
$344
$132
$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.
2-240
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Table 2-179 Costs of Strong Hybrid
HD Pickups and Vans (2012$)
ITEM
Strong HEV
Strong HEV
Strong HEV
DMC
1C
TC
2021
$4,335
$2,443
$6,779
2022
$4,205
$2,435
$6,640
2023
$4,079
$2,427
$6,506
2024
$3,957
$2,419
$6,376
2025
$3,838
$1,482
$5,320
2026
$3,723
$1,478
$5,201
2027
$3,648
$1,476
$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
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$16,674
$4,975
$21,649
$0
2019
$13,340
$4,731
$18,070
$0
2020
$13,340
$4,731
$18,070
$0
2021
$10,672
$4,536
$15,207
4%
$547
2022
$10,351
$4,512
$14,864
4%
$535
2023
$10,041
$2,849
$12,890
4%
$464
2024
$9,740
$2,838
$12,578
7%
$906
2025
$9,448
$2,827
$12,275
7%
$884
2026
$9,164
$2,817
$11,981
7%
$863
2027
$8,981
$2,810
$11,791
18%
$2,122
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-241
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Table 2-181 Costs for Strong Hybrid
Vocational Medium HD Urban/Multipurpose Vehicles (2012$)
ITEM
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$26,210
$7,820
$34,030
$0
2019
$20,968
$7,436
$28,404
$0
2020
$20,968
$7,436
$28,404
$0
2021
$16,774
$7,129
$23,904
4%
$861
2022
$16,271
$7,093
$23,364
4%
$841
2023
$15,783
$4,478
$20,262
4%
$729
2024
$15,310
$4,461
$19,771
7%
$1,424
2025
$14,850
$4,444
$19,295
7%
$1,389
2026
$14,405
$4,428
$18,833
7%
$1,356
2027
$14,117
$4,418
$18,534
18%
$3,336
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-182 Costs for Strong Hybrid
Vocational Heavy HD Urban/Multipurpose Vehicles (2012$)
ITEM
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
Strong
HEV
DMC
1C
TC
Alt
la
Alt3
TCp
2018
$43,770
$13,059
$56,829
$0
2019
$35,016
$12,418
$47,435
$0
2020
$35,016
$12,418
$47,435
$0
2021
$28,013
$11,906
$39,919
4%
$1,437
2022
$27,173
$11,844
$39,017
4%
$1,405
2023
$26,357
$7,479
$33,836
4%
$1,218
2024
$25,567
$7,450
$33,017
7%
$2,377
2025
$24,800
$7,422
$32,222
7%
$2,320
2026
$24,056
$7,395
$31,451
7%
$2,264
2027
$23,575
$7,377
$30,952
18%
$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.
2-242
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Table 2-183 Costs of Mild Hybrid
HD Pickups and Vans (2012$)
ITEM
MildHEV
Mild HEV
MildHEV
DMC
1C
TC
2021
$1,677
$1,053
$2,730
2022
$1,626
$1,050
$2,677
2023
$1,594
$1,048
$2,642
2024
$1,562
$1,046
$2,608
2025
$1,531
$643
$2,173
2026
$1,500
$642
$2,142
2027
$1,470
$641
$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
Table 2-184 Costs for Mild Hybrid
Tractors (2012$)
ITEM
Mild
HEV
Mild
HEV
Mild
HEV
DMC
1C
TC
2018
$14,487
$6,157
$20,644
2019
$14,052
$6,125
$20,178
2020
$13,631
$6,094
$19,725
2021
$13,222
$6,065
$19,287
2022
$12,958
$6,045
$19,003
2023
$12,698
$6,026
$18,725
2024
$12,444
$6,008
$18,452
2025
$12,196
$5,989
$18,185
2026
$11,952
$3,806
$15,758
2027
$11,713
$3,798
$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
Full
EV
Full
EV
Full
EV
DMC
1C
TC
2018
$36,108
$22,492
$58,600
2019
$35,025
$22,413
$57,438
2020
$33,974
$22,336
$56,310
2021
$32,955
$22,262
$55,216
2022
$31,966
$22,189
$54,155
2023
$31,007
$22,119
$53,126
2024
$30,077
$22,051
$52,128
2025
$29,174
$21,985
$51,159
2026
$28,591
$21,942
$50,533
2027
$28,019
$21,900
$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
2-243
-------
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
Full
EV
Full
EV
Full
EV
DMC
1C
TC
2018
$95,529
$59,507
$155,036
2019
$92,664
$59,297
$151,961
2020
$89,884
$59,094
$148,978
2021
$87,187
$58,897
$146,084
2022
$84,572
$58,705
$143,277
2023
$82,034
$58,520
$140,554
2024
$79,573
$58,340
$137,913
2025
$77,186
$58,165
$135,351
2026
$75,642
$58,052
$133,694
2027
$74,130
$57,941
$132,071
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.12.8 Tires
2.12.8.1 Lower rolling resistance tires ($/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 $22
value valid in 2014, level 3 in 2018, and level 4 in 2021. We consider this technology to be on
the flat portion of the curve with LRR tires level 1 and 2 on curve 4, LRR tires level 3 on curve
12 and LRR tires level 4 on curve 13. We have applied a low complexity markup to LRR tires
levels 1 and 3 with short term markups through 2022. For LRR tires level 3, we have applied a
medium complexity markup with short term markups through 2025 and, for LRR tires level 4,
we have applied a medium complexity markup with short term markups through 2028. As a
result, despite using the same DMC for each level of rolling resistance, our tire costs can vary
considerably year-over-year for each of the 4 levels of rolling resistance considered. The
resultant costs on a per-tire basis are shown in Table 2-187. Table 2-188 through Table 2-202
show the costs per vehicle depending on the number of tires present on the vehicle.
2-244
-------
Table 2-187 Costs for Lower Rolling Resistance Tires
at each LRR level (2012$/tire)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
DMC
DMC
DMC
DMC
1C
1C
1C
1C
TC
TC
TC
TC
2018
$19
$19
$22
$24
$4
$4
$7
$7
$23
$23
$28
$30
2019
$19
$19
$21
$23
$4
$4
$7
$7
$23
$23
$28
$30
2020
$18
$18
$20
$22
$4
$4
$7
$7
$22
$22
$27
$29
2021
$18
$18
$20
$22
$4
$4
$6
$7
$22
$22
$26
$28
2022
$18
$18
$19
$21
$4
$4
$6
$7
$22
$22
$26
$28
2023
$17
$17
$19
$20
$3
$3
$6
$7
$20
$20
$25
$27
2024
$17
$17
$19
$20
$3
$3
$6
$6
$20
$20
$25
$26
2025
$17
$17
$18
$19
$3
$3
$6
$6
$20
$20
$25
$26
2026
$17
$17
$18
$19
$3
$3
$5
$6
$20
$20
$23
$25
2027
$16
$16
$18
$19
$3
$3
$5
$6
$19
$19
$22
$25
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.12.8.2 Lower RR steer tires, All Vocational Vehicles
Table 2-188 Costs for Lower Rolling Resistance Steer Tires
All Vocational Vehicles
(2012$/vehicle @ 2 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
2018
$46
$46
$57
$61
100%
100%
2019
$45
$45
$55
$59
100%
100%
2020
$45
$45
$54
$58
100%
100%
2021
$44
$44
$53
$57
100%
20%
2022
$43
$43
$52
$55
100%
20%
2023
$41
$41
$51
$54
100%
20%
2024
$40
$40
$50
$53
100%
10%
2025
$39
$39
$49
$52
100%
10%
2026
$39
$39
$45
$51
100%
10%
2027
$39
$39
$45
$50
100%
0%
2-245
-------
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
Alt3
Alt3
AltS
TCp
TCp
TCp
TCp
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
80%
-$35
$0
$42
$0
80%
-$35
$0
$41
$0
80%
-$33
$0
$41
$0
30%
60%
-$36
$0
$15
$32
30%
60%
-$35
$0
$15
$31
30%
60%
-$35
$0
$14
$31
20%
80%
-$39
$0
$9
$40
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.3 Lower RR drive tires, All Vocational Light/Medium HD Vehicles
Table 2-189 Costs for Lower Rolling Resistance Drive Tires
Vocational Light/Medium HD Vehicles
(2012$/vehicle @ 4 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
AltS
AltS
AltS
TCp
TCp
TCp
2018
$92
$92
$113
$122
100%
100%
$0
$0
$0
2019
$91
$91
$110
$119
100%
100%
$0
$0
$0
2020
$89
$89
$108
$116
100%
100%
$0
$0
$0
2021
$88
$88
$105
$113
100%
50%
50%
-$44
$44
$0
2022
$86
$86
$104
$110
100%
50%
50%
-$43
$43
$0
2023
$82
$82
$102
$108
100%
50%
50%
-$41
$41
$0
2024
$80
$80
$100
$105
100%
20%
50%
30%
-$64
$40
$30
2025
$79
$79
$99
$104
100%
20%
50%
30%
-$63
$39
$30
2026
$78
$78
$91
$102
100%
20%
50%
30%
-$63
$39
$27
2027
$78
$78
$89
$100
100%
10%
25%
50%
15%
-$70
$19
$45
2-246
-------
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
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
AltS
TCp
TCp
TCp
TCp
2018
$185
$185
$226
$244
100%
100%
$0
$0
$0
$0
2019
$182
$182
$221
$238
100%
100%
$0
$0
$0
$0
2020
$178
$178
$216
$232
100%
100%
$0
$0
$0
$0
2021
$175
$175
$210
$226
100%
50%
50%
-$88
$88
$0
$0
2022
$173
$173
$207
$221
100%
50%
50%
-$86
$86
$0
$0
2023
$163
$163
$204
$216
100%
50%
50%
-$82
$82
$0
$0
2024
$160
$160
$201
$210
100%
20%
50%
30%
-$128
$80
$60
$0
2025
$158
$158
$198
$207
100%
20%
50%
30%
-$126
$79
$59
$0
2026
$156
$156
$182
$204
100%
20%
50%
30%
-$125
$78
$54
$0
2027
$155
$155
$179
$201
100%
10%
25%
50%
15%
-$140
$39
$89
$30
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-247
-------
2.12.8.5 Lower RR steer tires, Day cab low roof tractors
Table 2-191 Costs for Lower Rolling Resistance Steer Tires
Day Cab Low Roof Tractors
(2012$/vehicle @ 2 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$46
$46
$57
$61
50%
10%
50%
10%
$0
$0
$0
$0
2019
$45
$45
$55
$59
50%
10%
50%
10%
$0
$0
$0
$0
2020
$45
$45
$54
$58
50%
10%
50%
10%
$0
$0
$0
$0
2021
$44
$44
$53
$57
50%
10%
60%
25%
10%
$4
$7
$5
$0
2022
$43
$43
$52
$55
50%
10%
60%
25%
10%
$4
$6
$5
$0
2023
$41
$41
$51
$54
50%
10%
60%
25%
10%
$4
$6
$5
$0
2024
$40
$40
$50
$53
50%
10%
50%
30%
15%
$0
$8
$8
$0
2025
$39
$39
$49
$52
50%
10%
50%
30%
15%
$0
$8
$7
$0
2026
$39
$39
$45
$51
50%
10%
50%
30%
15%
$0
$8
$7
$0
2027
$39
$39
$45
$50
50%
10%
20%
50%
25%
-$12
$16
$11
$0
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.6 Lower RR steer tires, Day cab high roof tractors
Table 2-192 Costs for Lower Rolling Resistance Steer Tires
Day Cab High Roof Tractors
(2012$/vehicle @ 2 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
TC
TC
TC
2018
$46
$46
$57
2019
$45
$45
$55
2020
$45
$45
$54
2021
$44
$44
$53
2022
$43
$43
$52
2023
$41
$41
$51
2024
$40
$40
$50
2025
$39
$39
$49
2026
$39
$39
$45
2027
$39
$39
$45
2-248
-------
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
$61
70%
20%
70%
20%
$0
$0
$0
$0
$59
70%
20%
70%
20%
$0
$0
$0
$0
$58
70%
20%
70%
20%
$0
$0
$0
$0
$57
70%
20%
60%
25%
10%
-$4
$2
$5
$0
$55
70%
20%
60%
25%
10%
-$4
$2
$5
$0
$54
70%
20%
60%
25%
10%
-$4
$2
$5
$0
$53
70%
20%
50%
30%
15%
-$8
$4
$8
$0
$52
70%
20%
50%
30%
15%
-$8
$4
$7
$0
$51
70%
20%
50%
30%
15%
-$8
$4
$7
$0
$50
70%
20%
20%
50%
25%
-$19
$12
$11
$0
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.7 Lower RR steer tires, Sleeper cab low/mid roof tractors
Table 2-193 Costs for Lower Rolling Resistance Steer Tires
Sleeper Cab Low/Mid Roof Tractors
(2012$/vehicle @ 2 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
2018
$46
$46
$57
$61
60%
10%
60%
2019
$45
$45
$55
$59
60%
10%
60%
2020
$45
$45
$54
$58
60%
10%
60%
2021
$44
$44
$53
$57
60%
10%
60%
2022
$43
$43
$52
$55
60%
10%
60%
2023
$41
$41
$51
$54
60%
10%
60%
2024
$40
$40
$50
$53
60%
10%
50%
2025
$39
$39
$49
$52
60%
10%
50%
2026
$39
$39
$45
$51
60%
10%
50%
2027
$39
$39
$45
$50
60%
10%
20%
2-249
-------
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
Alt 3
Alt 3
Alt3
TCp
TCp
TCp
TCp
10%
$0
$0
$0
$0
10%
$0
$0
$0
$0
10%
$0
$0
$0
$0
25%
10%
$0
$7
$5
$0
25%
10%
$0
$6
$5
$0
25%
10%
$0
$6
$5
$0
30%
15%
-$4
$8
$8
$0
30%
15%
-$4
$8
$7
$0
30%
15%
-$4
$8
$7
$0
50%
25%
-$16
$16
$11
$0
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.8 Lower RR steer tires, Sleeper cab high roof tractors
Table 2-194 Costs for Lower Rolling Resistance Steer Tires
Sleeper Cab High Roof Tractors
(2012$/vehicle @ 2 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
Alt3
TCp
TCp
TCp
2018
$46
$46
$57
$61
70%
20%
70%
20%
$0
$0
$0
2019
$45
$45
$55
$59
70%
20%
70%
20%
$0
$0
$0
2020
$45
$45
$54
$58
70%
20%
70%
20%
$0
$0
$0
2021
$44
$44
$53
$57
70%
20%
60%
25%
10%
-$4
$2
$5
2022
$43
$43
$52
$55
70%
20%
60%
25%
10%
-$4
$2
$5
2023
$41
$41
$51
$54
70%
20%
60%
25%
10%
-$4
$2
$5
2024
$40
$40
$50
$53
70%
20%
50%
30%
15%
-$8
$4
$8
2025
$39
$39
$49
$52
70%
20%
50%
30%
15%
-$8
$4
$7
2026
$39
$39
$45
$51
70%
20%
50%
30%
15%
-$8
$4
$7
2027
$39
$39
$45
$50
70%
20%
20%
50%
25%
-$19
$12
$11
2-250
-------
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
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt 3
TCp
TCp
TCp
TCp
2018
$92
$92
$113
$122
50%
10%
50%
10%
$0
$0
$0
$0
2019
$91
$91
$110
$119
50%
10%
50%
10%
$0
$0
$0
$0
2020
$89
$89
$108
$116
50%
10%
50%
10%
$0
$0
$0
$0
2021
$88
$88
$105
$113
50%
10%
60%
25%
10%
$9
$13
$11
$0
2022
$86
$86
$104
$110
50%
10%
60%
25%
10%
$9
$13
$10
$0
2023
$82
$82
$102
$108
50%
10%
60%
25%
10%
$8
$12
$10
$0
2024
$80
$80
$100
$105
50%
10%
50%
30%
15%
$0
$16
$15
$0
2025
$79
$79
$99
$104
50%
10%
50%
30%
15%
$0
$16
$15
$0
2026
$78
$78
$91
$102
50%
10%
50%
30%
15%
$0
$16
$14
$0
2027
$78
$78
$89
$100
50%
10%
20%
50%
25%
-$23
$31
$22
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-251
-------
2.12.8.10
Lower RR drive tires, Class 8 Day cab low roof tractors
Table 2-196 Costs for Lower Rolling Resistance Drive Tires
Class 8 Day Cab Low Roof Tractors
(2012$/vehicle @ 8 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$185
$185
$226
$244
50%
10%
50%
10%
$0
$0
$0
$0
2019
$182
$182
$221
$238
50%
10%
50%
10%
$0
$0
$0
$0
2020
$178
$178
$216
$232
50%
10%
50%
10%
$0
$0
$0
$0
2021
$175
$175
$210
$226
50%
10%
60%
25%
10%
$18
$26
$21
$0
2022
$173
$173
$207
$221
50%
10%
60%
25%
10%
$17
$26
$21
$0
2023
$163
$163
$204
$216
50%
10%
60%
25%
10%
$16
$24
$20
$0
2024
$160
$160
$201
$210
50%
10%
50%
30%
15%
$0
$32
$30
$0
2025
$158
$158
$198
$207
50%
10%
50%
30%
15%
$0
$32
$30
$0
2026
$156
$156
$182
$204
50%
10%
50%
30%
15%
$0
$31
$27
$0
2027
$155
$155
$179
$201
50%
10%
20%
50%
25%
-$47
$62
$45
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-252
-------
2.12.8.11
Lower RR drive tires, Class 7 Day cab high roof tractors
Table 2-197 Costs for Lower Rolling Resistance Drive Tires
Class 7 Day Cab High Roof Tractors
(2012$/vehicle @ 4 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$92
$92
$113
$122
70%
20%
70%
20%
$0
$0
$0
$0
2019
$91
$91
$110
$119
70%
20%
70%
20%
$0
$0
$0
$0
2020
$89
$89
$108
$116
70%
20%
70%
20%
$0
$0
$0
$0
2021
$88
$88
$105
$113
70%
20%
60%
25%
10%
-$9
$4
$11
$0
2022
$86
$86
$104
$110
70%
20%
60%
25%
10%
-$9
$4
$10
$0
2023
$82
$82
$102
$108
70%
20%
60%
25%
10%
-$8
$4
$10
$0
2024
$80
$80
$100
$105
70%
20%
50%
30%
15%
-$16
$8
$15
$0
2025
$79
$79
$99
$104
70%
20%
50%
30%
15%
-$16
$8
$15
$0
2026
$78
$78
$91
$102
70%
20%
50%
30%
15%
-$16
$8
$14
$0
2027
$78
$78
$89
$100
70%
20%
20%
50%
25%
-$39
$23
$22
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-253
-------
2.12.8.12
Lower RR drive tires, Class 8 Day cab high roof tractors
Table 2-198 Costs for Lower Rolling Resistance Drive Tires
Class 8 Day Cab High Roof Tractors
(2012$/vehicle @ 8 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$185
$185
$226
$244
70%
20%
70%
20%
$0
$0
$0
$0
2019
$182
$182
$221
$238
70%
20%
70%
20%
$0
$0
$0
$0
2020
$178
$178
$216
$232
70%
20%
70%
20%
$0
$0
$0
$0
2021
$175
$175
$210
$226
70%
20%
60%
25%
10%
-$18
$9
$21
$0
2022
$173
$173
$207
$221
70%
20%
60%
25%
10%
-$17
$9
$21
$0
2023
$163
$163
$204
$216
70%
20%
60%
25%
10%
-$16
$8
$20
$0
2024
$160
$160
$201
$210
70%
20%
50%
30%
15%
-$32
$16
$30
$0
2025
$158
$158
$198
$207
70%
20%
50%
30%
15%
-$32
$16
$30
$0
2026
$156
$156
$182
$204
70%
20%
50%
30%
15%
-$31
$16
$27
$0
2027
$155
$155
$179
$201
70%
20%
20%
50%
25%
-$78
$47
$45
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-254
-------
2.12.8.13
Lower RR drive tires, Class 8 Sleeper cab low/mid roof tractors
Table 2-199 Costs for Lower Rolling Resistance Drive Tires
Class 8 Sleeper Cab Low/Mid Roof Tractors
(2012$/vehicle @ 8 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$185
$185
$226
$244
60%
10%
60%
10%
$0
$0
$0
$0
2019
$182
$182
$221
$238
60%
10%
60%
10%
$0
$0
$0
$0
2020
$178
$178
$216
$232
60%
10%
60%
10%
$0
$0
$0
$0
2021
$175
$175
$210
$226
60%
10%
60%
25%
10%
$0
$26
$21
$0
2022
$173
$173
$207
$221
60%
10%
60%
25%
10%
$0
$26
$21
$0
2023
$163
$163
$204
$216
60%
10%
60%
25%
10%
$0
$24
$20
$0
2024
$160
$160
$201
$210
60%
10%
50%
30%
15%
-$16
$32
$30
$0
2025
$158
$158
$198
$207
60%
10%
50%
30%
15%
-$16
$32
$30
$0
2026
$156
$156
$182
$204
60%
10%
50%
30%
15%
-$16
$31
$27
$0
2027
$155
$155
$179
$201
60%
10%
20%
50%
25%
-$62
$62
$45
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-255
-------
2.12.8.14
Lower RR drive tires, Class 8 Sleeper cab high roof tractors
Table 2-200 Costs for Lower Rolling Resistance Drive Tires
Class 8 Sleeper Cab High Roof Tractors
(2012$/vehicle @ 8 tires/vehicle)
ITEM
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
LRR-
level 1
LRR-
level 2
LRR-
level 3
LRR-
level 4
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt 3
Alt3
TCp
TCp
TCp
TCp
2018
$185
$185
$226
$244
70%
20%
70%
20%
$0
$0
$0
$0
2019
$182
$182
$221
$238
70%
20%
70%
20%
$0
$0
$0
$0
2020
$178
$178
$216
$232
70%
20%
70%
20%
$0
$0
$0
$0
2021
$175
$175
$210
$226
70%
20%
60%
25%
10%
-$18
$9
$21
$0
2022
$173
$173
$207
$221
70%
20%
60%
25%
10%
-$17
$9
$21
$0
2023
$163
$163
$204
$216
70%
20%
60%
25%
10%
-$16
$8
$20
$0
2024
$160
$160
$201
$210
70%
20%
50%
30%
15%
-$32
$16
$30
$0
2025
$158
$158
$198
$207
70%
20%
50%
30%
15%
-$32
$16
$30
$0
2026
$156
$156
$182
$204
70%
20%
50%
30%
15%
-$31
$16
$27
$0
2027
$155
$155
$179
$201
70%
20%
20%
50%
25%
-$78
$47
$45
$0
Notes: TC=total cost;
0% adoption
TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
2-256
-------
2.12.8.15 Lower RR tires, 53-foot dry van & reefer
Table 2-201 Costs for Lower Rolling Resistance Tires
53-foot Dry Van & Reefer Highway Trailers
(2012$/trailer @ 8 tires/trailer)
ITEM
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
TC
TC
Alt la
Alt la
Alt3
Alt3
TCp
TCp
2018
$185
$185
50%
85%
$65
$0
2019
$182
$182
51%
85%
$62
$0
2020
$178
$178
52%
85%
$59
$0
2021
$175
$175
53%
90%
$65
$0
2022
$173
$173
54%
90%
$62
$0
2023
$163
$163
56%
90%
$56
$0
2024
$160
$160
57%
95%
-$91
$152
2025
$158
$158
58%
95%
-$91
$150
2026
$156
$156
59%
95%
-$92
$149
2027
$155
$155
60%
95%
-$93
$147
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.16 Lower RR tires, 28-foot dry van
Table 2-202 Costs for Lower Rolling Resistance Tires
28-foot Dry Van Trailers
(2012$/trailer @ 4 tires/trailer)
ITEM
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
TC
TC
Alt la
Alt la
Alt3
Alt3
TCp
TCp
2018
$92
$92
85%
$78
$0
2019
$91
$91
85%
$77
$0
2020
$89
$89
85%
$76
$0
2021
$88
$88
90%
$79
$0
2022
$86
$86
90%
$78
$0
2023
$82
$82
90%
$73
$0
2024
$80
$80
95%
$0
$76
2025
$79
$79
95%
$0
$75
2026
$78
$78
95%
$0
$74
2027
$78
$78
95%
$0
$74
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2.12.8.17 Lower RR tires, Non-box highway trailers
Table 2-203 Costs for Lower Rolling Resistance Tires
Other Non-Box Highway Trailers
(2012$/trailer @ 4 tires/trailer)
ITEM
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
LRR-level 1
LRR-level 2
TC
TC
Alt la
Alt la
AltS
Alt3
TCp
TCp
2018
$185
$185
100%
$185
$0
2019
$182
$182
100%
$182
$0
2020
$178
$178
100%
$178
$0
2021
$175
$175
100%
$175
$0
2022
$173
$173
100%
$173
$0
2023
$163
$163
100%
$163
$0
2024
$160
$160
100%
$0
$160
2025
$158
$158
100%
$0
$158
2026
$156
$156
100%
$0
$156
2027
$155
$155
100%
$0
$155
Notes: TC=total cost; TCp=total cost applied to the package; alt=alternative; empty cells for adoption rates denote
0% adoption
2-257
-------
2.12.8.18 Lower RR tires, HD pickup & van ($/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 FID pickups and vans,
we have scaled upward both of those costs by 50 percent to account for the heavier and larger
FID 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 FID 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
LRR - level 1
LRR -level 2
LRR - level 1
LRR -level 2
LRR - level 1
LRR -level 2
DMC
DMC
1C
1C
TC
TC
2021
$8
$63
$2
$15
$10
$78
2022
$8
$61
$2
$15
$10
$76
2023
$8
$59
$2
$15
$10
$74
2024
$8
$58
$2
$15
$10
$73
2025
$8
$56
$2
$12
$10
$68
2026
$8
$54
$2
$12
$10
$66
2027
$8
$53
$2
$12
$10
$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
ATIS
ATIS
ATIS
ATIS
ATIS
ATIS
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$828
$148
$975
$0
2019
$803
$147
$950
$0
2020
$779
$147
$926
$0
2021
$755
$147
$902
20%
$180
2022
$740
$147
$887
20%
$177
2023
$725
$115
$841
20%
$168
2024
$711
$115
$826
40%
$330
2025
$697
$115
$812
40%
$325
2026
$683
$115
$798
40%
$319
2027
$669
$115
$784
40%
$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
2-258
-------
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 non-
box highway trailers.
Table 2-206 Costs for Automatic Tire Inflation Systems
53-foot Dry and Reefer Van Trailers (2012$)
TECHNOLOGY
AXIS
AXIS
AXIS
AXIS
AXIS
AXIS
DMC
1C
XC
Alt la
Alt3
XCp
2018
$579
$103
$683
50%
85%
$239
2019
$562
$103
$665
51%
85%
$226
2020
$545
$103
$648
52%
85%
$214
2021
$529
$103
$632
53%
90%
$234
2022
$518
$103
$621
54%
90%
$224
2023
$508
$81
$589
56%
90%
$200
2024
$498
$81
$578
57%
95%
$220
2025
$488
$81
$568
58%
95%
$210
2026
$478
$81
$559
59%
95%
$201
2027
$469
$80
$549
60%
95%
$192
Notes: DMC=direct manufacturing cost; IC=indirect cost;
package; alt=alternative
XC=total cost; XCp=total cost applied to the
Table 2-207 Costs for Automatic Tire Inflation Systems
28-foot Dry Van Trailers (2012$)
XECHNOLOGY
AXIS
AXIS
AXIS
AXIS
AXIS
AXIS
DMC
1C
XC
Alt la
Alt3
XCp
2018
$435
$78
$512
85%
$435
2019
$422
$77
$499
85%
$424
2020
$409
$77
$486
85%
$413
2021
$397
$77
$474
90%
$426
2022
$389
$77
$466
90%
$419
2023
$381
$61
$441
90%
$397
2024
$373
$60
$434
95%
$412
2025
$366
$60
$426
95%
$405
2026
$359
$60
$419
95%
$398
2027
$351
$60
$412
95%
$391
Notes: DMC=direct manufacturing cost; IC=indirect cost; XC=total cost; XCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-208 Costs for Automatic Tire Inflation Systems
Non-box Highway Trailers (2012$)
XECHNOLOGY
AXIS
AXIS
AXIS
AXIS
AXIS
AXIS
DMC
1C
XC
Alt la
Alt3
XCp
2018
$579
$103
$683
100%
$683
2019
$562
$103
$665
100%
$665
2020
$545
$103
$648
100%
$648
2021
$529
$103
$632
100%
$632
2022
$518
$103
$621
100%
$621
2023
$508
$81
$589
100%
$589
2024
$498
$81
$578
100%
$578
2025
$488
$81
$568
100%
$568
2026
$478
$81
$559
100%
$559
2027
$469
$80
$549
100%
$549
Notes: DMC=direct manufacturing cost; IC=indirect cost; XC=total cost; XCp=total cost applied to the
package; alt=alternative; empty cells for adoption rates denote 0% adoption
2-259
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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
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
DMC
DMC
DMC
1C
1C
1C
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt 3
Alt3
Alt3
TCp
TCp
TCp
2018
$1,001
$1,790
$1,650
$179
$358
$448
$1,180
$2,148
$2,098
60%
60%
$0
$0
$0
2019
$1,001
$1,755
$1,601
$179
$357
$447
$1,180
$2,112
$2,048
60%
60%
$0
$0
$0
2020
$1,001
$1,719
$1,553
$179
$357
$447
$1,180
$2,076
$1,999
60%
60%
$0
$0
$0
2021
$1,001
$1,685
$1,506
$179
$356
$446
$1,180
$2,041
$1,952
60%
75%
25%
$177
$510
$0
2022
$1,001
$1,651
$1,461
$179
$356
$445
$1,180
$2,007
$1,906
60%
75%
25%
$177
$502
$0
2023
$1,001
$1,618
$1,417
$140
$281
$354
$1,141
$1,899
$1,771
60%
75%
25%
$171
$475
$0
2024
$1,001
$1,586
$1,375
$140
$281
$354
$1,141
$1,867
$1,728
60%
60%
38%
2%
$0
$709
$35
2025
$1,001
$1,554
$1,333
$140
$280
$353
$1,141
$1,835
$1,687
60%
60%
38%
2%
$0
$697
$34
2026
$1,001
$1,539
$1,307
$140
$280
$353
$1,141
$1,819
$1,660
60%
60%
38%
2%
$0
$691
$33
2027
$1,001
$1,523
$1,281
$140
$280
$353
$1,141
$1,803
$1,634
60%
50%
40%
10%
-$114
$721
$163
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
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
2-260
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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
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
DMC
DMC
DMC
DMC
DMC
1C
1C
1C
1C
1C
TC
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
AltS
AltS
TCp
TCp
TCp
TCp
TCp
2018
$910
$1,311
$1,672
$2,032
$2,393
$182
$356
$742
$902
$1,062
$1,092
$1,667
$2,414
$2,934
$3,455
70%
20%
70%
20%
$0
$0
$0
$0
$0
2019
$892
$1,272
$1,622
$1,971
$2,321
$182
$355
$740
$899
$1,059
$1,073
$1,627
$2,361
$2,870
$3,380
70%
20%
70%
20%
$0
$0
$0
$0
$0
2020
$874
$1,234
$1,573
$1,912
$2,252
$181
$355
$737
$896
$1,055
$1,055
$1,589
$2,310
$2,808
$3,307
70%
20%
70%
20%
$0
$0
$0
$0
$0
2021
$856
$1,197
$1,526
$1,855
$2,184
$181
$354
$735
$893
$1,052
$1,037
$1,551
$2,260
$2,748
$3,236
70%
20%
40%
35%
20%
5%
-$311
$233
$452
$137
$0
2022
$839
$1,161
$1,480
$1,799
$2,118
$181
$354
$732
$890
$1,048
$1,020
$1,515
$2,212
$2,690
$3,167
70%
20%
40%
35%
20%
5%
-$306
$227
$442
$134
$0
2023
$822
$1,126
$1,436
$1,745
$2,055
$143
$281
$730
$888
$1,045
$965
$1,407
$2,166
$2,633
$3,100
70%
20%
40%
35%
20%
5%
-$290
$211
$433
$132
$0
2024
$806
$1,092
$1,393
$1,693
$1,993
$143
$281
$728
$885
$1,042
$949
$1,373
$2,120
$2,578
$3,035
70%
20%
30%
30%
25%
13%
2%
-$379
$137
$530
$335
$61
2025
$790
$1,059
$1,351
$1,642
$1,933
$142
$281
$726
$882
$1,039
$932
$1,340
$2,077
$2,524
$2,972
70%
20%
30%
30%
25%
13%
2%
-$373
$134
$519
$328
$59
2026
$782
$1,038
$1,324
$1,609
$1,895
$142
$281
$544
$661
$779
$924
$1,319
$1,868
$2,271
$2,674
70%
20%
30%
30%
25%
13%
2%
-$370
$132
$467
$295
$53
2027
$774
$1,017
$1,297
$1,577
$1,857
$142
$281
$543
$660
$777
$916
$1,298
$1,840
$2,237
$2,634
70%
20%
20%
20%
35%
20%
5%
-$458
$0
$644
$447
$132
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
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
2-261
-------
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
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
Aero Bin2
Aero Bin3
Aero Bin4
DMC
DMC
DMC
1C
1C
1C
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
TCp
TCp
TCp
2018
$1,222
$2,048
$1,888
$218
$409
$512
$1,440
$2,457
$2,400
70%
70%
$0
$0
$0
2019
$1,222
$2,007
$1,831
$218
$409
$512
$1,440
$2,416
$2,343
70%
70%
$0
$0
$0
2020
$1,222
$1,967
$1,776
$218
$408
$511
$1,440
$2,375
$2,287
70%
70%
$0
$0
$0
2021
$1,222
$1,928
$1,723
$218
$408
$510
$1,440
$2,335
$2,233
70%
75%
25%
$72
$584
$0
2022
$1,222
$1,889
$1,671
$218
$407
$509
$1,440
$2,296
$2,181
70%
75%
25%
$72
$574
$0
2023
$1,222
$1,851
$1,621
$171
$321
$405
$1,393
$2,172
$2,026
70%
75%
25%
$70
$543
$0
2024
$1,222
$1,814
$1,572
$171
$321
$405
$1,393
$2,135
$1,977
70%
60%
38%
2%
-$139
$811
$40
2025
$1,222
$1,778
$1,525
$171
$321
$404
$1,393
$2,099
$1,930
70%
60%
38%
2%
-$139
$797
$39
2026
$1,222
$1,760
$1,495
$171
$321
$404
$1,393
$2,081
$1,899
70%
60%
38%
2%
-$139
$791
$38
2027
$1,222
$1,742
$1,465
$171
$320
$404
$1,393
$2,063
$1,869
70%
50%
40%
10%
-$279
$825
$187
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
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.
2-262
-------
Table 2-212 Costs of Aero Technologies
Sleeper Cab High Roof Tractors (2012$)
TECHNOLOGY
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
DMC
DMC
DMC
DMC
DMC
1C
1C
1C
1C
1C
TC
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
AltS
AltS
TCp
TCp
TCp
TCp
TCp
2018
$1,228
$1,523
$1,942
$2,360
$2,779
$245
$413
$862
$1,048
$1,234
$1,474
$1,936
$2,803
$3,408
$4,013
70%
20%
70%
20%
$0
$0
$0
$0
$0
2019
$1,204
$1,477
$1,883
$2,290
$2,696
$245
$413
$859
$1,044
$1,229
$1,449
$1,890
$2,742
$3,334
$3,925
70%
20%
70%
20%
$0
$0
$0
$0
$0
2020
$1,180
$1,433
$1,827
$2,221
$2,615
$245
$412
$856
$1,041
$1,225
$1,425
$1,845
$2,683
$3,262
$3,840
70%
20%
70%
20%
$0
$0
$0
$0
$0
2021
$1,156
$1,390
$1,772
$2,154
$2,536
$245
$412
$853
$1,037
$1,221
$1,401
$1,801
$2,625
$3,192
$3,758
70%
20%
40%
35%
20%
5%
-$420
$270
$525
$160
$0
2022
$1,133
$1,348
$1,719
$2,090
$2,460
$244
$411
$851
$1,034
$1,217
$1,377
$1,759
$2,569
$3,124
$3,678
70%
20%
40%
35%
20%
5%
-$413
$264
$514
$156
$0
2023
$1,110
$1,308
$1,667
$2,027
$2,387
$193
$327
$848
$1,031
$1,214
$1,303
$1,634
$2,515
$3,058
$3,600
70%
20%
40%
35%
20%
5%
-$391
$245
$503
$153
$0
2024
$1,088
$1,268
$1,617
$1,966
$2,315
$192
$326
$845
$1,028
$1,210
$1,281
$1,595
$2,463
$2,994
$3,525
70%
20%
30%
30%
25%
13%
2%
-$512
$159
$616
$389
$71
2025
$1,066
$1,230
$1,569
$1,907
$2,246
$192
$326
$843
$1,025
$1,207
$1,259
$1,557
$2,412
$2,932
$3,452
70%
20%
30%
30%
25%
13%
2%
-$503
$156
$603
$381
$69
2026
$1,056
$1,206
$1,537
$1,869
$2,201
$192
$326
$632
$768
$904
$1,248
$1,532
$2,169
$2,637
$3,105
70%
20%
30%
30%
25%
13%
2%
-$499
$153
$542
$343
$62
2027
$1,045
$1,182
$1,507
$1,832
$2,157
$192
$326
$631
$767
$903
$1,237
$1,508
$2,137
$2,598
$3,060
70%
20%
20%
20%
35%
20%
5%
-$619
$0
$748
$520
$153
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
2.12.9.5 Aero improvements, trailers
TCp=total cost applied to the package;
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
2-263
-------
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
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
DMC
DMC
DMC
DMC
DMC
DMC
1C
1C
1C
1C
1C
1C
TC
TC
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
AltS
AltS
AltS
TCp
TCp
TCp
TCp
TCp
TCp
2018
$449
$641
$1,026
$1,218
$1,411
$1,860
$90
$128
$205
$244
$282
$372
$539
$769
$1,231
$1,462
$1,693
$2,231
20%
30%
5%
30%
60%
5%
$54
$231
$0
$0
$0
$0
2019
$440
$628
$1,006
$1,194
$1,383
$1,822
$71
$101
$161
$192
$222
$293
$511
$729
$1,167
$1,386
$1,604
$2,115
20%
31%
5%
30%
60%
5%
$51
$212
$0
$0
$0
$0
2020
$431
$616
$985
$1,170
$1,355
$1,786
$71
$101
$161
$191
$222
$292
$502
$717
$1,147
$1,362
$1,577
$2,078
20%
32%
5%
30%
60%
5%
$50
$201
$0
$0
$0
$0
2021
$422
$604
$966
$1,147
$1,328
$1,750
$71
$101
$161
$191
$222
$292
$493
$704
$1,127
$1,338
$1,549
$2,042
20%
33%
5%
5%
55%
10%
30%
-$74
$155
$56
$0
$465
$0
2022
$414
$591
$946
$1,124
$1,301
$1,715
$70
$101
$161
$191
$221
$292
$484
$692
$1,107
$1,315
$1,523
$2,007
20%
34%
5%
5%
55%
10%
30%
-$73
$145
$55
$0
$457
$0
2023
$406
$580
$927
$1,101
$1,275
$1,681
$70
$101
$161
$191
$221
$292
$476
$680
$1,088
$1,292
$1,496
$1,973
20%
36%
5%
5%
55%
10%
30%
-$71
$129
$54
$0
$449
$0
2024
$398
$568
$909
$1,079
$1,250
$1,647
$70
$100
$161
$191
$221
$291
$468
$669
$1,070
$1,270
$1,471
$1,939
20%
37%
5%
25%
10%
65%
-$94
-$80
$53
$0
$956
$0
2025
$390
$557
$891
$1,058
$1,225
$1,614
$70
$100
$161
$191
$221
$291
$460
$657
$1,051
$1,249
$1,446
$1,906
20%
38%
5%
25%
10%
65%
-$92
-$85
$53
$0
$940
$0
2026
$386
$551
$882
$1,047
$1,212
$1,598
$70
$100
$161
$191
$221
$291
$456
$652
$1,042
$1,238
$1,433
$1,889
20%
39%
5%
25%
10%
65%
-$91
-$91
$52
$0
$932
$0
2027
$382
$546
$873
$1,037
$1,200
$1,582
$70
$100
$161
$191
$221
$291
$452
$646
$1,034
$1,227
$1,421
$1,873
20%
40%
5%
10%
50%
40%
-$90
-$258
$52
$0
$711
$749
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
2-264
-------
Table 2-214 Costs of Aero Technologies
Reefer Van Trailers (2012$)
TECHNOLOGY
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin6
Aero Bin?
Aero Bin8
DMC
DMC
DMC
DMC
DMC
DMC
1C
1C
1C
1C
1C
1C
TC
TC
TC
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
AltS
AltS
AltS
TCp
TCp
TCp
TCp
TCp
TCp
2018
$449
$641
$1,026
$1,218
$1,411
$1,860
$90
$128
$205
$244
$282
$372
$539
$769
$1,231
$1,462
$1,693
$2,231
20%
30%
5%
30%
60%
5%
$54
$231
$0
$0
$0
$0
2019
$440
$628
$1,006
$1,194
$1,383
$1,822
$71
$101
$161
$192
$222
$293
$511
$729
$1,167
$1,386
$1,604
$2,115
20%
31%
5%
30%
60%
5%
$51
$212
$0
$0
$0
$0
2020
$431
$616
$985
$1,170
$1,355
$1,786
$71
$101
$161
$191
$222
$292
$502
$717
$1,147
$1,362
$1,577
$2,078
20%
32%
5%
30%
60%
5%
$50
$201
$0
$0
$0
$0
2021
$422
$604
$966
$1,147
$1,328
$1,750
$71
$101
$161
$191
$222
$292
$493
$704
$1,127
$1,338
$1,549
$2,042
20%
33%
5%
5%
55%
10%
30%
-$74
$155
$56
$0
$465
$0
2022
$414
$591
$946
$1,124
$1,301
$1,715
$70
$101
$161
$191
$221
$292
$484
$692
$1,107
$1,315
$1,523
$2,007
20%
34%
5%
5%
55%
10%
30%
-$73
$145
$55
$0
$457
$0
2023
$406
$580
$927
$1,101
$1,275
$1,681
$70
$101
$161
$191
$221
$292
$476
$680
$1,088
$1,292
$1,496
$1,973
20%
36%
5%
5%
55%
10%
30%
-$71
$129
$54
$0
$449
$0
2024
$398
$568
$909
$1,079
$1,250
$1,647
$70
$100
$161
$191
$221
$291
$468
$669
$1,070
$1,270
$1,471
$1,939
20%
37%
5%
25%
10%
65%
-$94
-$80
$53
$0
$956
$0
2025
$390
$557
$891
$1,058
$1,225
$1,614
$70
$100
$161
$191
$221
$291
$460
$657
$1,051
$1,249
$1,446
$1,906
20%
38%
5%
25%
10%
65%
-$92
-$85
$53
$0
$940
$0
2026
$386
$551
$882
$1,047
$1,212
$1,598
$70
$100
$161
$191
$221
$291
$456
$652
$1,042
$1,238
$1,433
$1,889
20%
39%
5%
25%
10%
65%
-$91
-$91
$52
$0
$932
$0
2027
$382
$546
$873
$1,037
$1,200
$1,582
$70
$100
$161
$191
$221
$291
$452
$646
$1,034
$1,227
$1,421
$1,873
20%
40%
5%
20%
60%
20%
-$90
-$258
$155
$0
$853
$375
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost;
alt=alternative; empty cells for adoption rates denote 0% adoption
TCp=total cost applied to the package;
2-265
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Table 2-215 Costs of Aero Technologies
28-foot Dry Van Trailers (2012$)
TECHNOLOGY
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin3
Aero Bin4
Aero Bin5
Aero Bin3
Aero Bin4
Aero Bin5
DMC
DMC
DMC
1C
1C
1C
TC
TC
TC
Alt
la
Alt
la
Alt
la
Alt3
Alt3
AltS
TCp
TCp
TCp
2018
$449
$641
$1,090
$90
$128
$218
$539
$769
$1,308
$0
$0
$0
2019
$440
$628
$1,068
$71
$101
$171
$511
$729
$1,240
$0
$0
$0
2020
$431
$616
$1,047
$71
$101
$171
$502
$717
$1,218
$0
$0
$0
2021
$422
$604
$1,026
$71
$101
$171
$493
$704
$1,197
95%
$468
$0
$0
2022
$414
$591
$1,006
$70
$101
$171
$484
$692
$1,177
95%
$460
$0
$0
2023
$406
$580
$985
$70
$101
$171
$476
$680
$1,156
95%
$452
$0
$0
2024
$398
$568
$966
$70
$100
$171
$468
$669
$1,137
70%
30%
$328
$281
$0
2025
$390
$557
$946
$70
$100
$171
$460
$657
$1,117
70%
30%
$322
$276
$0
2026
$386
$551
$937
$70
$100
$171
$456
$652
$1,108
70%
30%
$319
$274
$0
2027
$382
$546
$928
$70
$100
$171
$452
$646
$1,098
30%
60%
10%
$136
$543
$110
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2.12.9.6 Aero improvements, HD pickups and vans
For HD pickups and vans, we have based our aero improvement costs on values used in
our light-duty 2017-2025 final rule. Using those values updated to 2012$ 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
Aero 1 - passive aero
Aero 1 - passive aero
Aero 1 - passive aero
DMC
1C
TC
2021
$42
$9
$51
2022
$41
$9
$50
2023
$40
$9
$49
2024
$39
$9
$48
2025
$38
$9
$47
2026
$38
$9
$47
2027
$38
$9
$47
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2-266
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Table 2-217 Costs for Active Aero Treatments
Gasoline & Diesel HD Pickups and Vans (2012$)
ITEM
Aero 2 - active aero
Aero 2 - active aero
Aero 2 - active aero
DMC
1C
TC
2021
$125
$54
$179
2022
$122
$54
$177
2023
$120
$54
$174
2024
$118
$54
$172
2025
$115
$40
$156
2026
$114
$40
$154
2027
$113
$40
$153
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
Table 2-218 Costs for Aero 2 (passive plus active aero)
Gasoline & Diesel HD Pickups and Vans (2012$)
ITEM
Aero 2 - active aero
Aero 2 - active aero
Aero 2 - active aero
DMC
1C
TC
2021
$166
$63
$230
2022
$163
$63
$227
2023
$160
$63
$223
2024
$157
$63
$220
2025
$154
$50
$203
2026
$152
$49
$201
2027
$151
$49
$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
Advanced cruise control
Advanced cruise control
Advanced cruise control
Advanced cruise control
Advanced cruise control
Advanced cruise control
DMC
1C
TC
Alt la
Alt3
TCp
2018
$797
$142
$939
$0
2019
$773
$142
$915
$0
2020
$750
$142
$891
$0
2021
$727
$141
$868
20%
$174
2022
$713
$141
$854
20%
$171
2023
$698
$111
$809
20%
$162
2024
$684
$111
$795
40%
$318
2025
$671
$111
$782
40%
$313
2026
$657
$111
$768
40%
$307
2027
$644
$111
$755
40%
$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.
2-267
-------
Table 2-220 Costs for Improved Accessories
Tractors (2012$)
TECHNOLOGY
Improved accessories
Improved accessories
Improved accessories
Improved accessories
Improved accessories
Improved accessories
DMC
1C
TC
Alt la
Alt3
TCp
2018
$254
$45
$299
$0
2019
$246
$45
$291
$0
2020
$239
$45
$284
$0
2021
$231
$45
$276
10%
$28
2022
$227
$45
$272
10%
$27
2023
$222
$35
$258
10%
$26
2024
$218
$35
$253
20%
$51
2025
$213
$35
$249
20%
$50
2026
$209
$35
$244
20%
$49
2027
$205
$35
$240
30%
$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
Improved accessories 1 (IACC1)
Improved accessories 1 (IACC2)
Improved accessories 1 (IACC1)
Improved accessories 1 (IACC2)
Improved accessories 1 (IACC1)
Improved accessories 1 (IACC2)
DMC
DMC
1C
1C
TC
TC
2021
$67
$109
$15
$24
$82
$132
2022
$66
$106
$15
$24
$80
$130
2023
$64
$104
$15
$24
$79
$128
2024
$63
$102
$15
$24
$78
$126
2025
$62
$100
$15
$24
$77
$124
2026
$61
$99
$15
$24
$76
$123
2027
$61
$98
$15
$24
$75
$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.
2-268
-------
Table 2-222 Costs for a 200 Pound Weight Reduction
Vocational Light/Medium/Heavy HD Urban/Multipurpose Vehicles (2012$)
TECHNOLOGY
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
DMC
1C
TC
Alt la
Alt3
TCp
2018
$635
$104
$739
$0
2019
$616
$104
$720
$0
2020
$597
$104
$701
$0
2021
$579
$103
$683
4%
$27
2022
$562
$103
$665
4%
$27
2023
$545
$81
$626
4%
$25
2024
$529
$81
$610
4%
$24
2025
$518
$81
$599
4%
$24
2026
$508
$81
$589
4%
$24
2027
$498
$81
$578
5%
$29
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-223 Costs for a 200 Pound Weight Reduction
Vocational Light HD Regional Vehicles (2012$)
TECHNOLOGY
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$635
$104
$739
$0
2019
$616
$104
$720
$0
2020
$597
$104
$701
$0
2021
$579
$103
$683
7%
$48
2022
$562
$103
$665
7%
$47
2023
$545
$81
$626
7%
$44
2024
$529
$81
$610
7%
$43
2025
$518
$81
$599
7%
$42
2026
$508
$81
$589
7%
$41
2027
$498
$81
$578
8%
$46
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-224 Costs for a 200 Pound Weight Reduction
Vocational Medium HD Regional Vehicles (2012$)
TECHNOLOGY
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
DMC
1C
TC
Alt la
Alt 3
TCp
2018
$635
$104
$739
$0
2019
$616
$104
$720
$0
2020
$597
$104
$701
$0
2021
$579
$103
$683
6%
$41
2022
$562
$103
$665
6%
$40
2023
$545
$81
$626
6%
$38
2024
$529
$81
$610
6%
$37
2025
$518
$81
$599
6%
$36
2026
$508
$81
$589
6%
$35
2027
$498
$81
$578
7%
$40
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
Table 2-225 Costs for a 200 Pound Weight Reduction
Vocational Heavy HD Regional Vehicles (2012$)
TECHNOLOGY
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
Weight reduction, 200 Ibs
DMC
1C
TC
Alt la
Alt3
TCp
2018
$635
$104
$739
$0
2019
$616
$104
$720
$0
2020
$597
$104
$701
$0
2021
$579
$103
$683
5%
$34
2022
$562
$103
$665
5%
$33
2023
$545
$81
$626
5%
$31
2024
$529
$81
$610
5%
$30
2025
$518
$81
$599
5%
$30
2026
$508
$81
$589
5%
$29
2027
$498
$81
$578
6%
$35
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative; empty cells for adoption rates denote 0% adoption
2-269
-------
Table 2-226 Costs for a 400 Pound Weight Reduction
Vocational vehicles (2012$)
TECHNOLOGY
Weight
reduction, 400
Ibs
Weight
reduction, 400
Ibs
Weight
reduction, 400
Ibs
DMC
1C
TC
2018
$1,905
$312
$2,217
2019
$1,848
$312
$2,159
2020
$1,792
$311
$2,103
2021
$1,738
$310
$2,049
2022
$1,686
$310
$1,996
2023
$1,636
$243
$1,879
2024
$1,587
$243
$1,829
2025
$1,555
$242
$1,797
2026
$1,524
$242
$1,766
2027
$1,493
$242
$1,735
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost; TCp=total cost applied to the package;
alt=alternative
Table 2-227 Costs for a 1000 Pound Weight Reduction
Vocational vehicles (2012$)
TECHNOLOGY
Weight
reduction, 1000
Ibs
Weight
reduction, 1000
Ibs
Weight
reduction, 1000
Ibs
DMC
1C
TC
2018
$6,349
$1,780
$8,129
2019
$6,159
$1,770
$7,929
2020
$5,974
$1,761
$7,735
2021
$5,795
$1,752
$7,547
2022
$5,621
$1,743
$7,364
2023
$5,452
$1,296
$6,748
2024
$5,289
$1,290
$6,579
2025
$5,183
$1,286
$6,469
2026
$5,079
$1,283
$6,362
2027
$4,978
$1,279
$6,257
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.12.10.4
Weight reduction in HD pickups and vans
For this proposal, we are estimating weight reduction costs for HD pickups and vans
using the same cost curve used in support of the 2017-2025 light-duty GHG/CAFE FRM. That
curve can be expressed as:
Mass Reduction Direct Manufacturing Cost (DMC) ($/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.
2-270
-------
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
Electric power steering (EPS)
Electric power steering (EPS)
Electric power steering (EPS)
DMC
1C
TC
2021
$124
$27
$151
2022
$121
$27
$148
2023
$119
$27
$146
2024
$117
$27
$144
2025
$114
$27
$141
2026
$113
$27
$140
2027
$112
$27
$139
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.12.10.6
Low drag brakes, HD pickups and vans
We have based the costs for low drag brakes on the costs used in the light-duty 2017-
2025 FRM but have scaled upward that cost by 50 percent to account for the larger HD vehicles.
Using that cost and converting to 2012$ 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
Low drag brakes
Low drag brakes
Low drag brakes
DMC
1C
TC
2021
$91
$18
$109
2022
$91
$18
$109
2023
$91
$18
$109
2024
$91
$18
$109
2025
$91
$18
$109
2026
$91
$18
$109
2027
$91
$18
$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.
2-271
-------
Table 2-230 Costs for Driveline Friction Reduction
Diesel HD Pickups and Vans (2012$)
ITEM
Driveline friction reduction
Driveline friction reduction
Driveline friction reduction
DMC
1C
TC
2021
$108
$30
$139
2022
$106
$30
$136
2023
$104
$24
$128
2024
$102
$24
$126
2025
$100
$24
$124
2026
$99
$24
$123
2027
$98
$24
$122
Notes: DMC=direct manufacturing cost; IC=indirect cost; TC=total cost
2.13Package Costs
Section Opresents detailed technology costs along with adoption rates to illustrate how
each technology is accounted for in the package costs. Here we present package costs by
regulated sector (i.e., vocational heavy HD, urban vehicles) and package costs by MOVES
sourcetype (i.e., diesel refuse trucks). We determine package costs by MOVES sourcetype so
that we can calculate total program costs (i.e., package costs multiplied by vehicle sales) since
sourcetypes are the sales figures that we can glean from MOVES. As a result, the sourcetype
package costs presented here are the costs used in our program cost estimations.
2.13.1 Package Costs by Regulated Sector
2.13.1.1 Vocational vehicles
We have estimated costs for 9 vocational segments and 2 fuels. We present package
costs in Table 2-231 through Table 2-238 for these for alternatives 3 and 4, both relative to
alternatives la and Ib and separately for diesel and gasoline vehicles.
Table 2-231 Package Costs for Regulated Vocational Segment
Alternative 3 Incremental to Alternative la
Diesel (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$1,125
$1,125
$598
$1,418
$1,418
$571
$1,998
$1,998
$3,404
2022
$1,100
$1,100
$585
$1,386
$1,386
$559
$1,954
$1,954
$3,348
2023
$1,008
$1,008
$563
$1,254
$1,254
$537
$1,748
$1,748
$3,160
2024
$1,737
$1,737
$849
$2,228
$2,228
$817
$3,332
$o o o *>
J,JJz
$4,834
2025
$1,701
$1,701
$835
$2,180
$2,180
$803
$3,258
$3,258
$4,755
2026
$1,664
$1,664
$819
$2,132
$2,132
$788
$3,183
$3,183
$4,683
2027
$3,489
$3,490
$1,407
$4,696
$4,696
$1,395
$7,422
$7,422
$4,682
2028
$3,427
$3,427
$1,378
$4,616
$4,616
$1,367
$7,298
$7,298
$4,607
2-272
-------
Table 2-232 Package Costs for Regulated Vocational Segment
Alternative 3 Incremental to Alternative la
Gasoline (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$832
$832
$305
$1,147
$1,147
$300
$1,728
$1,728
$3,134
2022
$814
$814
$299
$1,122
$1,122
$294
$1,690
$1,690
$3,083
2023
$729
$729
$284
$996
$996
$279
$1,491
$1,491
$2,902
2024
$1,299
$1,299
$412
$1,823
$1,823
$412
$2,927
$2,927
$4,429
2025
$1,271
$1,271
$405
$1,782
$1,782
$405
$2,860
$2,860
$4,357
2026
$1,240
$1,240
$395
$1,740
$1,740
$395
$2,791
$2,791
$4,290
2027
$3,086
$3,087
$1,004
$4,327
$4,328
$1,026
$7,053
$7,053
$4,314
2028
$3,037
$3,038
$989
$4,259
$4,259
$1,010
$6,941
$6,941
$4,251
Table 2-233 Package Costs for Regulated Vocational Segment
Alternative 4 Incremental to Alternative la
Diesel (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$2,169
$2,169
$805
$2,938
$2,938
$777
$4,337
$4,337
$2,693
2022
$2,120
$2,120
$788
$2,873
$2,873
$760
$4,240
$4,240
$2,647
2023
$1,911
$1,911
$756
$2,560
$2,560
$729
$3,745
$3,745
$2,504
2024
$3,741
$3,704
$1,482
$5,030
$4,992
$1,469
$7,895
$7,895
$4,912
2025
$3,661
$3,625
$1,457
$4,920
$4,882
$1,444
$7,719
$7,719
$4,831
2026
$3,582
$3,546
$1,430
$4,810
$4,772
$1,417
$7,542
$7,542
$4,752
2027
$3,525
$3,490
$1,407
$4,733
$4,696
$1,395
$7,422
$7,422
$4,682
2028
$3,462
$3,427
$1,378
$4,652
$4,616
$1,367
$7,298
$7,298
$4,607
Table 2-234 Package Costs for Regulated Vocational Segment
Alternative 4 Incremental to Alternative la
Gasoline (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$1,796
$1,796
$433
$2,594
$2,594
$432
$3,992
$3,992
$2,348
2022
$1,757
$1,757
$424
$2,536
$2,536
$423
$3,903
$3,903
$2,310
2023
$1,556
$1,556
$401
$2,232
$2,232
$400
$3,416
$3,416
$2,175
2024
$3,322
$3,285
$1,063
$4,648
$4,609
$1,086
$7,512
$7,512
$4,529
2025
$3,245
$3,209
$1,041
$4,539
$4,501
$1,063
$7,339
$7,339
$4,450
2026
$3,172
$3,136
$1,020
$4,435
$4,398
$1,042
$7,168
$7,168
$4,378
2027
$3,122
$3,087
$1,004
$4,365
$4,328
$1,026
$7,053
$7,053
$4,314
2028
$3,072
$3,038
$989
$4,296
$4,259
$1,010
$6,941
$6,941
$4,251
2-273
-------
Table 2-235 Package Costs for Regulated Vocational Segment
Alternative 3 Incremental to Alternative Ib
Diesel (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$1,125
$1,125
$598
$1,418
$1,418
$571
$1,998
$1,998
$3,404
2022
$1,100
$1,100
$585
$1,386
$1,386
$559
$1,954
$1,954
$3,348
2023
$1,008
$1,008
$563
$1,254
$1,254
$537
$1,748
$1,748
$3,160
2024
$1,737
$1,737
$849
$2,228
$2,228
$817
$o o o ^
J,JJz
$3,332
$4,834
2025
$1,701
$1,701
$835
$2,180
$2,180
$803
$3,258
$3,258
$4,755
2026
$1,664
$1,664
$819
$2,132
$2,132
$788
$3,183
$3,183
$4,683
2027
$3,489
$3,490
$1,407
$4,696
$4,696
$1,395
$7,422
$7,422
$4,682
2028
$3,427
$3,427
$1,378
$4,616
$4,616
$1,367
$7,298
$7,298
$4,607
Table 2-236 Package Costs for Regulated Vocational Segment
Alternative 3 Incremental to Alternative Ib
Gasoline (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$843
$843
$316
$1,159
$1,159
$312
$1,740
$1,740
$3,146
2022
$825
$825
$310
$1,134
$1,134
$306
$1,702
$1,702
$3,095
2023
$740
$740
$295
$1,008
$1,008
$291
$1,502
$1,502
$2,914
2024
$1,311
$1,311
$423
$1,835
$1,835
$424
$2,938
$2,938
$4,441
2025
$1,282
$1,282
$416
$1,793
$1,793
$416
$2,871
$2,871
$4,368
2026
$1,251
$1,251
$406
$1,750
$1,750
$406
$2,802
$2,802
$4,301
2027
$3,097
$3,097
$1,015
$4,338
$4,339
$1,037
$7,064
$7,064
$4,324
2028
$3,048
$3,049
$999
$4,270
$4,270
$1,021
$6,952
$6,952
$4,262
Table 2-237 Package Costs for Regulated Vocational Segment
Alternative 4 Incremental to Alternative Ib
Diesel (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$2,169
$2,169
$805
$2,938
$2,938
$777
$4,337
$4,337
$2,693
2022
$2,120
$2,120
$788
$2,873
$2,873
$760
$4,240
$4,240
$2,647
2023
$1,911
$1,911
$756
$2,560
$2,560
$729
$3,745
$3,745
$2,504
2024
$3,741
$3,704
$1,482
$5,030
$4,992
$1,469
$7,895
$7,895
$4,912
2025
$3,661
$3,625
$1,457
$4,920
$4,882
$1,444
$7,719
$7,719
$4,831
2026
$3,582
$3,546
$1,430
$4,810
$4,772
$1,417
$7,542
$7,542
$4,752
2027
$3,525
$3,490
$1,407
$4,733
$4,696
$1,395
$7,422
$7,422
$4,682
2028
$3,462
$3,427
$1,378
$4,652
$4,616
$1,367
$7,298
$7,298
$4,607
2-274
-------
Table 2-238 Package Costs for Regulated Vocational Segment
Alternative 4 Incremental to Alternative Ib
Gasoline (2012$)
WEIGHT CLASS
Light HD
Light HD
Light HD
Medium HD
Medium HD
Medium HD
Heavy HD
Heavy HD
Heavy HD
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
2021
$1,808
$1,808
$445
$2,605
$2,605
$444
$4,004
$4,004
$2,360
2022
$1,768
$1,768
$436
$2,547
$2,547
$435
$3,915
$3,915
$2,321
2023
$1,567
$1,567
$413
$2,243
$2,243
$412
$3,428
$3,428
$2,187
2024
$3,334
$3,297
$1,075
$4,659
$4,620
$1,097
$7,524
$7,524
$4,540
2025
$3,256
$3,220
$1,052
$4,550
$4,512
$1,074
$7,350
$7,350
$4,461
2026
$3,183
$3,147
$1,031
$4,446
$4,408
$1,053
$7,178
$7,178
$4,389
2027
$3,132
$3,097
$1,015
$4,375
$4,339
$1,037
$7,064
$7,064
$4,324
2028
$3,083
$3,049
$999
$4,306
$4,270
$1,021
$6,952
$6,952
$4,262
2.13.1.2 Tractors
We have estimated costs for 7 tractor segments and 1 fuel. We present package costs in
Table 2-239 through Table 2-242 for these for alternatives 3 and 4, both relative to alternatives
la and Ib.
Table 2-239 Package Costs for Regulated Tractor Segment
Alternative 3 Incremental to Alternative la
Diesel (2012$)
CLASS
7
7
8
8
8
8
8
TYPE
Day cab, low roof
Day cab, high roof
Day cab, low roof
Day cab, high roof
Sleeper cab,
low roof
Sleeper cab,
mid roof
Sleeper cab,
high roof
2021
$5,468
$5,252
$5,520
$5,298
$7,916
$7,916
$7,771
2022
$5,381
$5,161
$5,432
$5,206
$7,786
$7,786
$7,637
2023
$5,008
$4,811
$5,057
$4,854
$7,281
$7,281
$7,156
2024
$8,400
$8,304
$8,467
$8,419
$11,102
$11,102
$11,145
2025
$8,259
$8,160
$8,325
$8,274
$10,912
$10,912
$10,952
2026
$8,095
$7,913
$8,159
$8,024
$10,723
$10,723
$10,666
2027
$10,140
$10,099
$10,204
$10,209
$12,744
$12,744
$12,842
2028
$9,968
$9,923
$10,031
$10,030
$12,548
$12,548
$12,640
2-275
-------
Table 2-240 Package Costs for Regulated Tractor Segment
Alternative 4 Incremental to Alternative la
Diesel (2012$)
CLASS
7
7
8
8
8
8
8
TYPE
Day cab, low roof
Day cab, high roof
Day cab, low roof
Day cab, high roof
Sleeper cab,
low roof
Sleeper cab,
mid roof
Sleeper cab,
high roof
2021
$8,946
$8,769
$8,999
$8,816
$11,397
$11,397
$11,318
2022
$8,799
$8,621
$8,852
$8,666
$11,208
$11,208
$11,126
2023
$8,180
$8,035
$8,230
$8,079
$10,456
$10,456
$10,411
2024
$10,757
$10,851
$10,826
$10,968
$13,461
$13,461
$13,717
2025
$10,574
$10,664
$10,642
$10,780
$13,229
$13,229
$13,481
2026
$10,306
$10,270
$10,371
$10,382
$12,934
$12,934
$13,039
2027
$10,140
$10,099
$10,204
$10,209
$12,744
$12,744
$12,842
2028
$9,968
$9,923
$10,031
$10,030
$12,548
$12,548
$12,640
Table 2-241 Package Costs for Regulated Tractor Segment
Alternative 3 Incremental to Alternative Ib
Diesel (2012$)
CLASS
7
7
8
8
8
8
8
TYPE
Day cab, low roof
Day cab, high roof
Day cab, low roof
Day cab, high roof
Sleeper cab,
low roof
Sleeper cab,
mid roof
Sleeper cab,
high roof
2021
$5,244
$5,159
$5,296
$5,205
$7,642
$7,556
$7,653
2022
$5,086
$5,040
$5,137
$5,085
$7,426
$7,332
$7,497
2023
$4,642
$4,681
$4,691
$4,724
$6,863
$6,770
$7,019
2024
$7,966
$8,156
$8,033
$8,271
$10,610
$10,528
$10,996
2025
$7,765
$7,995
$7,831
$8,108
$10,381
$10,283
$10,788
2026
$7,564
$7,726
$7,628
$7,836
$10,140
$10,044
$10,497
2027
$9,504
$9,885
$9,569
$9,995
$12,078
$11,944
$12,640
2028
$9,337
$9,710
$9,400
$9,818
$11,882
$11,758
$12,443
Table 2-242 Package Costs for Regulated Tractor Segment
Alternative 4 Incremental to Alternative Ib
Diesel (2012$)
CLASS
7
7
8
8
8
8
8
TYPE
Day cab, low roof
Day cab, high roof
Day cab, low roof
Day cab, high roof
Sleeper cab,
low roof
Sleeper cab,
mid roof
Sleeper cab,
high roof
2021
$8,721
$8,676
$8,775
$8,722
$11,124
$11,037
$11,200
2022
$8,505
$8,499
$8,557
$8,545
$10,848
$10,753
$10,985
2023
$7,815
$7,905
$7,865
$7,949
$10,038
$9,944
$10,273
2024
$10,323
$10,703
$10,392
$10,820
$12,969
$12,887
$13,567
2025
$10,080
$10,499
$10,148
$10,614
$12,698
$12,599
$13,317
2026
$9,774
$10,083
$9,840
$10,195
$12,351
$12,255
$12,870
2027
$9,504
$9,885
$9,569
$9,995
$12,078
$11,944
$12,640
2028
$9,337
$9,710
$9,400
$9,818
$11,882
$11,758
$12,443
2-276
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2.13.1.3 Trailers
We have estimated costs for 7 trailer types. We present package costs in Table 2-243 and
Table 2-244 for these for alternatives 3 and 4 relative to alternative la. We present package
costs in Table 2-245 and Table 2-246 for alternative 3 and 4 relative to alternative Ib.
Table 2-243 Costs for Trailers
Alternative 3 Incremental to Alternative la (2012$)
TYPE
5 3 -foot dry van
5 3 -foot reefer van
Container chassis
28-foot dry van
Platform
Tanker
Other highway
2018
$588
$588
$868
$514
$868
$868
$868
2019
$550
$550
$847
$501
$847
$847
$847
2020
$524
$524
$827
$489
$827
$827
$827
2021
$901
$901
$807
$974
$807
$807
$807
2022
$870
$870
$793
$957
$793
$793
$793
2023
$817
$817
$752
$923
$752
$752
$752
2024
$1,116
$1,116
$739
$1,097
$739
$739
$739
2025
$1,083
$1,083
$726
$1,078
$726
$726
$726
2026
$1,059
$1,059
$715
$1,065
$715
$715
$715
2027
$1,409
$1,280
$704
$1,253
$704
$704
$704
2028
$1,396
$1,267
$693
$1,239
$693
$693
$693
Table 2-244 Costs for Trailers
Alternative 4 Incremental to Alternative la (2012$)
TYPE
5 3 -foot dry van
5 3 -foot reefer van
Container chassis
28-foot dry van
Platform
Tanker
Other highway
2018
$588
$588
$868
$514
$868
$868
$868
2019
$550
$550
$847
$501
$847
$847
$847
2020
$524
$524
$827
$489
$827
$827
$827
2021
$1,207
$1,207
$807
$1,146
$807
$807
$807
2022
$1,172
$1,172
$793
$1,127
$793
$793
$793
2023
$1,113
$1,113
$752
$1,090
$752
$752
$752
2024
$1,504
$1,371
$739
$1,304
$739
$739
$739
2025
$1,465
$1,333
$726
$1,282
$726
$726
$726
2026
$1,437
$1,306
$715
$1,267
$715
$715
$715
2027
$1,409
$1,280
$704
$1,253
$704
$704
$704
2028
$1,396
$1,267
$693
$1,239
$693
$693
$693
Table 2-245 Costs for Trailers
Alternative 3 Incremental to Alternative Ib (2012$)
TYPE
5 3 -foot dry van
5 3 -foot reefer van
Container chassis
28-foot dry van
Platform
Tanker
Other highway
2018
$588
$588
$868
$514
$868
$868
$868
2019
$550
$550
$847
$501
$847
$847
$847
2020
$524
$524
$827
$489
$827
$827
$827
2021
$901
$901
$807
$974
$807
$807
$807
2022
$856
$856
$793
$957
$793
$793
$793
2023
$817
$817
$752
$923
$752
$752
$752
2024
$1,116
$1,116
$739
$1,097
$739
$739
$739
2025
$1,083
$1,083
$726
$1,078
$726
$726
$726
2026
$1,059
$1,059
$715
$1,065
$715
$715
$715
2027
$1,409
$1,280
$704
$1,253
$704
$704
$704
2028
$1,382
$1,254
$693
$1,239
$693
$693
$693
2-277
-------
Table 2-246 Costs for Trailers
Alternative 4 Incremental to Alternative Ib (2012$)
TYPE
5 3 -foot dry van
5 3 -foot reefer van
Container chassis
28-foot dry van
Platform
Tanker
Other highway
2018
$588
$588
$868
$514
$868
$868
$868
2019
$550
$550
$847
$501
$847
$847
$847
2020
$524
$524
$827
$489
$827
$827
$827
2021
$1,207
$1,207
$807
$1,146
$807
$807
$807
2022
$1,157
$1,157
$793
$1,127
$793
$793
$793
2023
$1,113
$1,113
$752
$1,090
$752
$752
$752
2024
$1,504
$1,371
$739
$1,304
$739
$739
$739
2025
$1,465
$1,333
$726
$1,282
$726
$726
$726
2026
$1,437
$1,306
$715
$1,267
$715
$715
$715
2027
$1,409
$1,280
$704
$1,253
$704
$704
$704
2028
$1,382
$1,254
$693
$1,239
$693
$693
$693
2.13.1.4 HD Pickups and Vans
The costs presented in Table 2-247 are CAFE model outputs used in this analysis. We
describe the CAFE model and how these costs were generated in Chapter 11 of this draft RIA.
The costs presented here spread evenly over MYs 2021 through 2026 the costs estimated by the
CAFE model for MYs 2017-2020.
Table 2-247 Package Costs for HD Pickups and Vans (2012$)
ALTERNATIVE
o
J
4
3
4
BASELINE CASE
la
la
Ib
Ib
2021
$516
$1,050
$493
$909
2022
$508
$1,033
$485
$894
2023
$791
$1,621
$766
$1,415
2024
$948
$1,734
$896
$1,532
2025
$1,161
$1,825
$1,149
$1,627
2026
$1,224
$1,808
$1,248
$1,649
2027
$1,342
$1,841
$1,366
$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.
2-278
-------
Table 2-248 Fleet Mix by MOVES Sourcetype and Regulated Sector - Vocational3
ENGINE
Light
HD
Light
HD
Light
HD
Medium
HD
Medium
HD
Medium
HD
Heavy
HD
Heavy
HD
Heavy
HD
Light
HD
Light
HD
Light
HD
Medium
HD
Medium
HD
Medium
HD
Heavy
HD
Heavy
HD
Heavy
HD
FUEL
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
SPEED
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
Urban
Multipurpose
Regional
INTERCITY
BUS
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
2%
0%
0%
15%
0%
0%
83%
TRANSIT
BUS
7%
20%
0%
2%
7%
0%
16%
48%
0%
0%
0%
0%
0%
0%
0%
25%
75%
0%
SCHOOL
BUS
0%
1%
0%
0%
95%
0%
0%
4%
0%
0%
1%
0%
0%
95%
0%
0%
5%
0%
REFUSE
TRUCKS
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
3%
0%
0%
97%
0%
0%
SINGLE
UNIT
SHORT
HAUL
4%
49%
28%
1%
11%
7%
0%
0%
0%
2%
25%
14%
2%
20%
12%
1%
15%
9%
SINGLE
UNIT
LONG
HAUL
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
25%
0%
0%
37%
0%
0%
37%
MOTOR
HOMES
0%
0%
54%
0%
0%
41%
0%
0%
5%
0%
0%
54%
0%
0%
41%
0%
0%
5%
Note:
a Columns add to 100% or 0% for gasoline rows and 100% for diesel rows.
2-279
-------
Table 2-249 Fleet Mix by MOVES Sourcetype and Regulated Sector- Tractors3
ENGINE
Medium
HD
Heavy
HD
Heavy
HD
MOVES
SOURCETYPE
Combination
Short haul
Combination
Short haul
Combination
Long haul
CLASS
7
DAY
CAB
LOW
ROOF
11%
CLASS
7
DAY
CAB
HIGH
ROOF
11%
CLASS
8
DAY
CAB
LOW
ROOF
39%
CLASS
8
DAY
CAB
HIGH
ROOF
39%
CLASS 8
SLEEPER
CAB
LOW
ROOF
5%
CLASS 8
SLEEPER
CAB
MID
ROOF
15%
CLASS 8
SLEEPER
CAB
HIGH
ROOF
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.
2-280
-------
Table 2-250 Package Costs by MOVES Sourcetype
Alternative 3 Incremental to Alternative la (2012$)
SOURCETYPE
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Comb
ShortHaul
Comb
LongHaul
53' dry van
53'rfrvan
Container ch
28' dry van
Platform
Tanker
Other trailer
Vocational
Vocational
Vocational
Tractor
Trailer
Tractor/Trailer
FUEL
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Gasoline
Weighted
Avg
Weighted
Avg
Weighted
Avg
Weighted
Avg
2018
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$588
$588
$868
$514
$868
$868
$868
$0
$0
$0
$0
$639
$639
2019
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$550
$550
$847
$501
$847
$847
$847
$0
$0
$0
$0
$613
$613
2020
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$524
$524
$827
$489
$827
$827
$827
$0
$0
$0
$0
$592
$592
2021
$2,913
$1,998
$1,442
$1,983
$1,400
$1,637
$736
$0
$1,434
$1,170
$0
$686
$0
$453
$5,398
$7,800
$901
$901
$807
$974
$807
$807
$807
$1,366
$612
$1,152
$6,708
$898
$7,606
2022
$2,864
$1,954
$1,410
$1,939
$1,371
$1,608
$721
$0
$1,402
$1,145
$0
$671
$0
$444
$5,309
$7,667
$870
$870
$793
$957
$793
$793
$793
$1,337
$599
$1,128
$6,605
$877
$7,482
2023
$2,705
$1,748
$1,275
$1,735
$1,261
$1,525
$690
$0
$1,240
$1,016
$0
$606
$0
$421
$4,945
$7,181
$817
$817
$752
$923
$752
$752
$752
$1,230
$547
$1,037
$6,184
$832
$7,016
2024
$4,138
$3,332
$2,275
$3,302
$2,155
$2,327
$1,047
$0
$2,388
$1,868
$0
$1,053
$0
$625
$8,423
$11,137
$1,116
$1,116
$739
$1,097
$739
$739
$739
$2,102
$916
$1,766
$9,935
$1,012
$10,947
2025
$4,070
$3,258
$2,226
$3,230
$2,112
$2,289
$1,030
$0
$2,334
$1,826
$0
$1,031
$0
$615
$8,279
$10,944
$1,083
$1,083
$726
$1,078
$726
$726
$726
$2,060
$898
$1,731
$9,774
$989
$10,763
2026
$4,008
$3,183
$2,177
$3,156
$2,069
$2,252
$1,011
$0
$2,277
$1,782
$0
$1,006
$0
$602
$8,072
$10,677
$1,059
$1,059
$715
$1,065
$715
$715
$715
$2,018
$877
$1,695
$9,542
$971
$10,513
2027
$4,112
$7,422
$4,813
$7,350
$3,975
$2,627
$1,576
$0
$5,736
$4,439
$0
$2,512
$0
$1,189
$10,187
$12,823
$1,409
$1,280
$704
$1,253
$704
$704
$704
$3,892
$2,086
$3,381
$11,684
$1,165
$12,849
2028
$4,045
$7,298
$4,730
$7,226
$3,905
$2,581
$1,545
$0
$5,645
$4,368
$0
$2,472
$0
$1,170
$10,012
$12,622
$1,396
$1,267
$693
$1,239
$693
$693
$693
$3,824
$2,053
$3,323
$11,503
$1,152
$12,655
2-281
-------
Table 2-251 Package Costs by MOVES Sourcetype
Alternative 4 Incremental to Alternative la (2012$)
SOURCETYPE
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Comb
ShortHaul
Comb
LongHaul
53' dry van
53'rfrvan
Container ch
28' dry van
Platform
Tanker
Other trailer
Vocational
Vocational
Vocational
Tractor
Trailer
Tractor/Trailer
FUEL
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Gasoline
Weighted
Avg
Weighted
Avg
Weighted
Avg
Weighted
Avg
2018
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$588
$588
$868
$514
$868
$868
$868
$0
$0
$0
$0
$639
$639
2019
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$550
$550
$847
$501
$847
$847
$847
$0
$0
$0
$0
$613
$613
2020
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$524
$524
$827
$489
$827
$827
$827
$0
$0
$0
$0
$592
$592
2021
$2,361
$4,337
$2,997
$4,300
$2,388
$1,500
$894
$0
$3,273
$2,649
$0
$1,417
$0
$534
$8,896
$11,334
$1,207
$1,207
$807
$1,146
$807
$807
$807
$2,333
$1,134
$1,994
$10,225
$1,084
$11,310
2022
$2,320
$4,240
$2,930
$4,204
$2,336
$1,473
$875
$0
$3,201
$2,590
$0
$1,386
$0
$524
$8,748
$11,142
$1,172
$1,172
$793
$1,127
$793
$793
$793
$2,282
$1,109
$1,950
$10,065
$1,059
$11,124
2023
$2,197
$3,745
$2,610
$3,714
$2,109
$1,400
$838
$0
$2,807
$2,279
$0
$1,235
$0
$495
$8,144
$10,420
$1,113
$1,113
$752
$1,090
$752
$752
$752
$2,061
$997
$1,760
$9,404
$1,012
$10,416
2024
$4,314
$7,895
$5,115
$7,819
$4,216
$2,760
$1,658
$0
$6,112
$4,727
$0
$2,673
$0
$1,256
$10,877
$13,666
$1,504
$1,371
$739
$1,304
$739
$739
$739
$4,126
$2,218
$3,586
$12,431
$1,231
$13,662
2025
$4,243
$7,719
$5,002
$7,645
$4,128
$2,714
$1,630
$0
$5,971
$4,616
$0
$2,612
$0
$1,231
$10,691
$13,430
$1,465
$1,333
$726
$1,282
$726
$726
$726
$4,041
$2,168
$3,511
$12,227
$1,204
$13,431
2026
$4,174
$7,542
$4,890
$7,470
$4,040
$2,668
$1,601
$0
$5,832
$4,510
$0
$2,554
$0
$1,207
$10,357
$13,018
$1,437
$1,306
$715
$1,267
$715
$715
$715
$3,955
$2,121
$3,436
$11,859
$1,184
$13,043
2027
$4,112
$7,422
$4,813
$7,350
$3,976
$2,627
$1,576
$0
$5,739
$4,439
$0
$2,513
$0
$1,189
$10,187
$12,823
$1,409
$1,280
$704
$1,253
$704
$704
$704
$3,892
$2,087
$3,382
$11,684
$1,165
$12,849
2028
$4,045
$7,298
$4,730
$7,227
$3,907
$2,581
$1,545
$0
$5,648
$4,368
$0
$2,474
$0
$1,170
$10,012
$12,622
$1,396
$1,267
$693
$1,239
$693
$693
$693
$3,825
$2,054
$3,324
$11,503
$1,152
$12,655
2-282
-------
Table 2-252 Package Costs by MOVES Sourcetype
Alternative 3 Incremental to Alternative Ib (2012$)
SOURCETYPE
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Comb
ShortHaul
Comb
LongHaul
53 ' dry van
53'rfrvan
Container ch
28' dry van
Platform
Tanker
Other trailer
Vocational
Vocational
Vocational
Tractor
Trailer
Tractor/Trailer
FUEL
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Gasoline
Weighted
Avg
Weighted
Avg
Weighted
Avg
Weighted
Avg
2018
$0
$0
$0
$0
$0
$0
$0
$0
$11
$11
$0
$11
$0
$11
$0
$0
$588
$588
$868
$514
$868
$868
$868
$0
$11
$3
$0
$639
$639
2019
$0
$0
$0
$0
$0
$0
$0
$0
$12
$12
$0
$12
$0
$12
-$59
-$70
$550
$550
$847
$501
$847
$847
$847
$0
$12
$3
-$65
$613
$548
2020
$0
$0
$0
$0
$0
$0
$0
$0
$12
$12
$0
$12
$0
$12
-$114
-$115
$524
$524
$827
$489
$827
$827
$827
$0
$12
$3
-$114
$592
$478
2021
$2,913
$1,998
$1,442
$1,983
$1,400
$1,637
$736
$0
$1,445
$1,182
$0
$697
$0
$465
$5,240
$7,638
$901
$901
$807
$974
$807
$807
$807
$1,366
$624
$1,156
$6,547
$898
$7,445
2022
$2,864
$1,954
$1,410
$1,939
$1,371
$1,608
$721
$0
$1,414
$1,156
$0
$683
$0
$456
$5,101
$7,468
$856
$856
$793
$957
$793
$793
$793
$1,337
$611
$1,132
$6,402
$870
$7,273
2023
$2,705
$1,748
$1,275
$1,735
$1,261
$1,525
$690
$0
$1,252
$1,028
$0
$617
$0
$432
$4,698
$6,974
$817
$817
$752
$923
$752
$752
$752
$1,230
$558
$1,040
$5,958
$832
$6,790
2024
$4,138
$3,332
$2,275
$3,302
$2,155
$2,327
$1,047
$0
$2,399
$1,880
$0
$1,065
$0
$636
$8,132
$10,906
$1,116
$1,116
$739
$1,097
$739
$739
$739
$2,102
$928
$1,770
$9,678
$1,012
$10,690
2025
$4,070
$3,258
$2,226
$3,230
$2,112
$2,289
$1,030
$0
$2,345
$1,837
$0
$1,041
$0
$625
$7,950
$10,692
$1,083
$1,083
$726
$1,078
$726
$726
$726
$2,060
$909
$1,734
$9,488
$989
$10,476
2026
$4,008
$3,183
$2,177
$3,156
$2,069
$2,252
$1,011
$0
$2,288
$1,793
$0
$1,017
$0
$613
$7,713
$10,411
$1,059
$1,059
$715
$1,065
$715
$715
$715
$2,018
$888
$1,698
$9,235
$971
$10,206
2027
$4,112
$7,422
$4,813
$7,350
$3,975
$2,627
$1,576
$0
$5,747
$4,449
$0
$2,522
$0
$1,199
$9,763
$12,508
$1,409
$1,280
$704
$1,253
$704
$704
$704
$3,892
$2,096
$3,384
$11,321
$1,165
$12,487
2028
$4,045
$7,298
$4,730
$7,226
$3,905
$2,581
$1,545
$0
$5,655
$4,379
$0
$2,483
$0
$1,181
$9,590
$12,312
$1,382
$1,254
$693
$1,239
$693
$693
$693
$3,824
$2,063
$3,326
$11,145
$1,146
$12,292
2-283
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Table 2-253 Package Costs by MOVES Sourcetype
Alternative 4 Incremental to Alternative Ib (2012$)
SOURCETYPE
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Intercity Bus
Transit Bus
School Bus
Refuse Truck
SingleUnit
ShortHaul
SingleUnit
LongHaul
MotorHome
Comb ShortHaul
Comb LongHaul
53' dry van
53' rfrvan
Container ch
28' dry van
Platform
Tanker
Other trailer
Vocational
Vocational
Vocational
Tractor
Trailer
Tractor/Trailer
FUEL
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Gasoline
Weighted
Avg
Weighted
Avg
Weighted
Avg
Weighted
Avg
2018
$0
$0
$0
$0
$0
$0
$0
$0
$11
$11
$0
$11
$0
$11
$0
$0
$588
$588
$868
$514
$868
$868
$868
$0
$11
$3
$0
$639
$639
2019
$0
$0
$0
$0
$0
$0
$0
$0
$12
$12
$0
$12
$0
$12
-$59
-$70
$550
$550
$847
$501
$847
$847
$847
$0
$12
$3
-$65
$613
$548
2020
$0
$0
$0
$0
$0
$0
$0
$0
$12
$12
$0
$12
$0
$12
$114
$115
$524
$524
$827
$489
$827
$827
$827
$0
$12
$3
$114
$592
$478
2021
$2,361
$4,337
$2,997
$4,300
$2,388
$1,500
$894
$0
$3,285
$2,661
$0
$1,429
$0
$546
$8,737
$11,172
$1,207
$1,207
$807
$1,146
$807
$807
$807
$2,333
$1,146
$1,997
$10,065
$1,084
$11,149
2022
$2,320
$4,240
$2,930
$4,204
$2,336
$1,473
$875
$0
$3,212
$2,602
$0
$1,397
$0
$535
$8,540
$10,944
$1,157
$1,157
$793
$1,127
$793
$793
$793
$2,282
$1,121
$1,954
$9,862
$1,053
$10,915
2023
$2,197
$3,745
$2,610
$3,714
$2,109
$1,400
$838
$0
$2,819
$2,290
$0
$1,246
$0
$507
$7,897
$10,212
$1,113
$1,113
$752
$1,090
$752
$752
$752
$2,061
$1,009
$1,763
$9,179
$1,012
$10,191
2024
$4,314
$7,895
$5,115
$7,819
$4,216
$2,760
$1,658
$0
$6,124
$4,738
$0
$2,685
$0
$1,268
$10,586
$13,435
$1,504
$1,371
$739
$1,304
$739
$739
$739
$4,126
$2,230
$3,589
$12,174
$1,231
$13,404
2025
$4,243
$7,719
$5,002
$7,645
$4,128
$2,714
$1,630
$0
$5,981
$4,627
$0
$2,623
$0
$1,241
$10,361
$13,178
$1,465
$1,333
$726
$1,282
$726
$726
$726
$4,041
$2,179
$3,514
$11,941
$1,204
$13,144
2026
$4,174
$7,542
$4,890
$7,470
$4,040
$2,668
$1,601
$0
$5,843
$4,521
$0
$2,565
$0
$1,218
$9,998
$12,752
$1,437
$1,306
$715
$1,267
$715
$715
$715
$3,955
$2,132
$3,439
$11,552
$1,184
$12,736
2027
$4,112
$7,422
$4,813
$7,350
$3,976
$2,627
$1,576
$0
$5,750
$4,449
$0
$2,524
$0
$1,199
$9,763
$12,508
$1,409
$1,280
$704
$1,253
$704
$704
$704
$3,892
$2,098
$3,385
$11,321
$1,165
$12,487
2028
$4,045
$7,298
$4,730
$7,227
$3,907
$2,581
$1,545
$0
$5,659
$4,379
$0
$2,484
$0
$1,181
$9,590
$12,312
$1,382
$1,254
$693
$1,239
$693
$693
$693
$3,825
$2,065
$3,327
$11,145
$1,146
$12,292
2-284
-------
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http://www.dft.gov.uk/vosa/newsandevents/pressreleases/2006pressreleases/28-12-06speedlimiterlegislation.htm
83 Transport Canada. Summary Report - Assessment of a Heavy Truck Speed Limiter Requirement in Canada. Last
viewed on January 6, 2010 at http://www.tc.gc.ca/eng/roadsafetv/tp-tpl4808-menu-370.htm
84 See TIAX 2009, Note 2, at page 4-98.
85 Gaines, L. and D. Santini. Argonne National Laboratory, Economic Analysis of Commercial Idling Reduction
Technologies
86ICF International. Investigation of Costs for Strategies to Reduce Greenhouse Gas Emissions for Heavy-Duty On-
Road Vehicles. July 2010. Docket Identification Number EPA-HQ-OAR-2010-0162-0044
87 NACFE, June 2014. Confidence Report: Idle-Reduction Solutions. Available at
http://www.carbonwarroom.com/sites/default/files/reports/Idle-Reduction Confidence Report.pdf (accessed
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/420rl0901.pdf
97 "Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 - 2014", EPA-
420-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.,
Coulson, G., Hilditch, J. Iyer, C.O., Moilanen, P., Papaioannou, G., Reiche, D. Shelby, M., VanDerWege, B.,
Weaver, C. Xu, Z., Davis, G., Hinds, B. Schamel, A. SAE Technical Paper No. 2009-01-1494, 2009, Docket EPA-
HQ-OAR-2009-0472-2860.
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
http://www.sae.org/technical/papers/2007-01-1410 (last accessed Nov. 9, 2008)
100 Yves Boccadoro, Loi'c Kermanac'h, Laurent Siauve, and Jean-Michel Vincent, Renault Powertrain Division,
"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
System Approach for an Attractive Future of Gasoline Engines?" AVL Engine & Environment Conference,
September 2005,
102 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/420rl0901.pdf
103 "All-new-Ford-engineered, Ford-tested, Ford-built diesel maximizes 2011 Super Duty productivity," Ford press
release, August 3, 2010. Available at: http://www.media.ford.com (last accessed June 27, 2011).
104 Stanton, Donald. "Enabling High Efficiency Clean Combustion." 2009 Semi-Mega Merit Review of the
Department of Energy. May 21, 2009. Last accessed on August 25, 2010 at
http://wwwl.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/sustainabilitv (last accessed February 8, 2010).
IDS "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-dutv-
pickups/ (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/420rl2901 .pdf
116 Vehicle Valuation Services, Inc, 2000, available at http://www.wsi.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.tmckinginfo.com/channel/equipment/article/storv/2014/10/2015-medium-dutv-trucks-the-vehicles-and-
trends-to-look-for/page/3 .aspx (downloaded November 2014)
121 See NACFE Confidence Report: Electronically Controlled Transmissions, at
http://www.truckingefficiencv.org/powertrain/automated-manual-transmissions (January 2015). See also
http://www.overdriveonline.com/auto-vs-manual-transmission-autos-finding-solid-ground-bv-sharing-data-with-
engines/ (accessed November 2014)
122 See Eaton Announcement September 2014, available at
http://www.ttnews.com/articles/lmtbase.aspx?storvid=2969&t=Eaton-Unveils-Medium-Dutv-Procision-
Transmission
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/420rl3008.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-technology-
grn/page/l.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/storv/2013/05/rise-of-the-6x2.aspx
129 NACFE, Confidence Findings on the Potential of 6x2 Axles, available at http://nacfe.org/wp-
content/uploads/2014/01/Trucking-Efficiencv-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
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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-start-
stop-bv-2017--fuel-.html. See also Allison-Cummins announcement July 2014,
http://www.oemoffhighwav.com/press release/12000208/allison-stop-
start?utm_source=OOH+Industry+News+eNL&utm_medium=email&utm campaign=RCL 140723006
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 "fire Pressure Systems - Confidence Report". North American Council for Freight Efficiency. 2013. Available
online: http://nacfe.org/wp-content/uploads/2014/01/TPS-Detailed-Confidence-Reportl.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.
155Scarcelli, Jamie. "Fuel Efficiency for Trailers" Presented at ACEEE/ICCT Workshop: Emerging Technologies
for Heavy-Duty Vehicle Fuel Efficiency, Wabash National Corporation. July 22, 2014
ise "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
163ICF 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 forMedium-
and 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.
lev R-pl 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.
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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-fleetsAVCC-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.
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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 well-
defined, 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 CCh and NOx. Therefore, the agencies are proposing to continue using
the same criteria pollutant test procedures for both the CCh 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 CCh 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 CCh, CH4 and N2O refer to 40
CFR 1065.650.
3-1
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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 CCh measurement, just as in the HD Phase 1 rule. The agencies are
proposing that manufacturers use 8,887 grams of CCh per gallon of gasoline and 10,180 g CCh
per gallon of diesel fuel.
3.1.2.2 Regeneration Impact on Fuel Consumption and CCh 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 CCh emissions and fuel consumption due to
regeneration. However, for Phase 2, we propose to include CCh 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 CCh 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.
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3.1.2.4 Urea Derived CCh Correction
The agencies are proposing to allow manufacturers to correct compression ignition
engine and powertrain CCh emission results (for engines utilizing urea SCR for NOx control) to
account for the contribution of urea derived CCh emissions to the total engine CCh emissions.
Urea derived CCh can account for up to 1 percent of the total CCh emissions. Urea is
produced from gaseous NFb and gaseous CCh that is captured from the atmosphere, thus CCh
derived from urea decomposition in diesel SCR emission control systems results in a net
emission of zero CCh to the environment. In our proposed test procedures for Phase 2, we allow
manufacturers to determine CCh emissions either by measuring the CCh 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 CCh emissions, this will result in a positive CCh bias for CCh emissions
determined by measuring the CCh 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 CCh from urea during the duty cycle. This resulting CCh 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 CCh emissions over the duty cycle calculations in 40 CFR
1037.550. Note that this correction is only allowed for CCh measured from the engine and not
CCh derived from fuel flow measurement.
The calculation for determination of the mass rate of CCh from urea requires the user to
input the urea solution urea percent by mass. This calculation uses prescribed molecular weights
for CCh 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 CCh 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. CCh 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 CCh 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
3-3
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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 bore-
center 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 CCh emissions can be
expected. For this reason, this methodology should continue to be followed when considering
CCh 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.
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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 FID 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 (Cowa) 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
3-5
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specifications and protocols for conducting and analyzing the results of the constant speed test
procedure, and update the conditions for performing CFD analysis.
For FID 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 FID 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 fmalization 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
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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
Average wind speed at the test site
(for each run in each direction)
Maximum wind speed (for each run in each
direction)
Average cross wind speed
(for each run in each direction at the site)
RANGE
< 10 mph
<12.3 mph
< 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 $w were averaged.
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Direction 1
Vr,th
Vr, th
Direction 2
Figure 3-1 Diagram of vehicle speed and air speed vectors during coastdowns in opposite directions
3-8
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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.
w + v 2vwcos9w, direction = 1
w + v + 2vwcos9w, direction = 2
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: i;rth = a0 +
Applied to air speed measurements: vr = a0 + a-^vrimeas
r.meas
80
7(1
60
50
40
30
20
III
. #
vr,th = -0.872 + 0.937vr,meas [mph]
-. .:*
10
20
30
40
50
r sjin.il [mph]
60
70
80
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.
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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 A nalysis
This analysis involves analyzing the coastdown over two separate speed ranges. A low-
speed 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.
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Table 3-2 Drag Area Calculation Steps for High-Low Iteration Analysis
STEP 0: FIND THE TIMES
BRACKETING THE LOW-
SPEED AND HIGH-SPEED
RANGES
VWi
-------
Step 8: Subtract low-speed
aerodynamic forces from low
speed forces to estimate
mechanical forces.
"'mech.i
= Fio-
aero.lo.i
Step 9: Subtract mechanical
forces from high speed forces
to estimate aerodynamic
forces.
'aero,hi,i
'' ''
mech.i
Step 10: Adjust aerodynamic
forces by speed to estimate
low-speed aerodynamic
forces.
aero,lo,i+l
_ r-
*
aero,hi,i+l
"r.lo.avg
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:
1-
aero,hi,i+l
< 0.01
1-
and
mech,lo,i+l
''mech.lo.i
< 0.01
Step 12: Calculate drag area.
2F:
aero,hi,i+l
P^r.hi,
avg
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
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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.
Av
F = MeT- = A + Dv?
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 6>r 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.
2D
CdA=-
n
_ _ _
CdA = a0 + ai\6T + a2 9r
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.
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H /W \
sin ( sin#w), direction = 1
_i/iv - \
sin sin0w , direction = 2
\vr J
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 (5/5 9)]. 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.
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2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5
|Yaw anRlel [deg]
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.
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After evaluating the methods described above, the agencies are proposing that the high-
low 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.
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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 Ib-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
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forces, air speed, and air direction were averaged. For tractors equipped with the driveshaft
torque meter, the road load force was calculated as follows:
rRL,shaft ^TD Ti ''grade
Cr/l ' 17
For tractors also equipped with the wheel torque meters, the road load force was
calculated as follows:
,-, Awheel Awheel ,-,
''RL,wheel ~ ' ''grade
Where:
= driveshaft torque
= engine speed
GR = transmission gear ratio
= road load force calculated from the driveshaft torque
= wheel torque, sum of all four wheel torque measurements
3i = wheel speed, average of all four wheel speed measurements
= 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.
''mech = ''RL.lOmph
Faero = FRL - Fmech (50 & 70 mph runs only)
2F
. £'L aero
d ~ PV?
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.
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
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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
Sleeper 1
Sleeper 2
Sleeper 3
ROOF
HEIGHT
High
High
High
TRAILER
CONFIGURATION
Standard
Standard
Standard
CdA
[m2]
5.9
6.2
6.1
STD.
ERROR
0.5 %
2.1%
2.3 %
# OF VALID
RUNS
14
14
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
Sleeper 1
Sleeper 2
Sleeper 3
ROOF
HEIGHT
High
High
High
TRAILER
CONFIGURATION
Standard
Standard
Standard
CdA
[m2]
6.1
5.9
5.9
STD.
ERROR
0.7%
1.8%
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
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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 ten-
second 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 Navier-
Stokes 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.
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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 FID 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 WAC& 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.
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EPA and NHTSA recognize that wind conditions have a greater impact on real world
CCh 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 l/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.
3-22
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^HTruckA Yaw Sweep
TruckAWACd
^-TruckB Yaw Sweep
Truck BWACd
^^TruckC Yaw Sweep
TruckCWACd
-10.000 -8.000 -6.000 -4.000 -2.000 0.000 2.000 4.000 6.000 8.000 10.000
Figure 3-4 Full yaw sweeps and wind-average coefficients of drag (WACdS) for three manufacturer, l/8th
scale, tractor models in the reduced scale wind tunnel.
3-23
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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
Zero Degree
One Degree
Three Degrees
Six Degrees
Nine Degrees
TRACTOR A VERSUS
TRACTOR B
Delta Cd
0.001
-0.003
-0.018
-0.035
-0.042
Delta Cd %
Difference
0.18%
-0.53%
-3.33%
-5.85%
-6.24%
TRACTOR A VERSUS
TRACTOR C
Delta Cd
-0.031
-0.035
-0.052
-0.078
-0.098
Delta Cd %
Difference
-5.84%
-6.65%
-9.58%
-13.05%
-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 WAC&
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 (WACA) for
Tractor A versus Tractor B and Tractor C.
COMPARISON
Tractor A vs. B
Tractor A vs. C
ABSOLUTE
DELTA
-0.021
-0.069
%
DIFFERENCE
-3.5%
-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
3-24
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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 WAC&A 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.
3-25
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\
Yaw Sweep Curve Shift Concept
r^
0.85
Multiplicative Approac
-GrS-
0.801
xs\
\
-0776
Additive Approach
V
1724
s_
0.65
-9r-
0.607
0.531
-10.000 -8.000 -6.000 -4.000 -2.000 0.000 2.000 4.000 6.000 8.000 10.000
Figure 3-5 Proposed development of a coastdown yaw sweep curve based on additive offset from alternate
method yaw sweep curve.
3-26
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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 Fait-aero; the zero degree yaw and WAC&A 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 WAC&A 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 CvA value, as shown below in
Table 3-7. The CvA based full yaw sweeps produce lower values, on the average of 3.3 percent.
Table 3-7: Cn/4 Values
Sleeper Cab 1
Sleeper Cab 2
Sleeper Cab 3
Sleeper Cab 4
CoA (ZERO YAW)
5.425
5.553
5.622
5.563
CD/4 (AVG ± 6 DEGREES)
6.239
6.619
6.442
6.663
CoA (FULL YAW
SWEEP)
6.043
6.405
6.285
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.
3-27
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CFys = [CoA from full yaw sweep * 0.8330] / [CoA at zero yaw]
3.2.5 Aerodynamics for Trailers
For FID 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 tractor-
trailer 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
Sleeper 1
Sleeper 2
Sleeper 3
ROOF
HEIGHT
High
High
High
CONFIGURATION
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
CaA
K]
5.9
5.4
5.0
6.2
5.6
5.1
6.1
5.6
5.4
STANDARD
ERROR
0.5%
1.0%
1.0%
2.1%
1.3%
1.1%
2.3%
1.2%
1.1%
DELTA CdA
(VS. STANDARD)
--
0.5
0.9
--
0.6
1.1
--
0.5
0.7
% DELTA CdA
(VS. STANDARD)
--
8.5%
15%
--
10%
18%
--
8.2%
11%
3-28
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Table 3-9 Summary of Results from Constant Speed Testing
CAB
TYPE
Sleeper 1
Sleeper 2
Sleeper 3
ROOF
HEIGHT
High
High
High
CONFIGURATION
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
Standard
With Trailer Skirt
With Trailer Skirt and Boat Tail
Co4
K]
6.1
5.2
4.9
5.9
5.5
5.1
5.9
5.7
5.2
STANDARD
ERROR
0.7%
2.2%
0.7%
1.8%
0.7%
1.0%
0.3%
0.4%
0.6%
DELTA C*4
(VS. STANDARD)
--
0.7
1.2
--
0.4
0.8
--
0.2
0.7
% DELTA Cd/4
(VS. STANDARD)
--
12%
20%
--
6.7%
14%
--
3.4%
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 tractor-
trailer 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
3-29
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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 (l/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).
3-30
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Figure 3-6 l/8th scale tractor-trailer model in ARC reduced scale wind tunnel.
3-31
-------
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
(IxlO6) 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.
3-32
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Table 3-10 Results of Reduced-Scale Wind Tunnel Testing to Assess Tractor Trailer Performance with
Various Aerodynamic Trailer Devices.
CAB TYPE
Sleeper 1
Sleeper 2
Sleeper 3
Sleeper 4
Day
ROOF
HEIGHT
High
High
High
High
High
CONFIGURATION
Standard
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Gap Reducer
With Trailer Skirt #2 and
Boat Tail
With Trailer Skirt #2,
Boat Tail, and Trailer Gap
Reducer
Standard
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Gap Reducer
With Trailer Skirt #2 and
Boat Tail
With Trailer Skirt #2,
Boat Tail, and Trailer Gap
Reducer
Standard
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Gap Reducer
With Trailer Skirt #2 and
Boat Tail
With Trailer Skirt #2,
Boat Tail, and Trailer Gap
Reducer
Standard
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Gap Reducer
With Trailer Skirt #2 and
Boat Tail
With Trailer Skirt #2,
Boat Tail, and Trailer Gap
Reducer
Standard
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Gap Reducer
CaA
K]
5.425
5.002
4.914
5.085
5.450
4.453
4.460
5.556
5.054
4.962
5.191
5.565
4.464
4.449
5.622
5.145
5.056
5.305
5.632
4.566
4.560
5.563
5.079
5.003
5.229
5.547
4.436
4.445
5.609
5.353
5.298
5.240
5.596
DELTA CaA
(VS. STANDARD)
~
0.423
0.511
0.340
-0.024
0.972
0.965
~
0.586
0.594
0.365
-0.020
1.092
1.106
~
0.477
0.566
0.318
-0.009
1.056
1.062
~
0.481
0.557
0.332
0.015
1.121
1.112
~
0.256
0.311
0.370
0.014
% DELTA CaA
(VS. STANDARD)
~
7.8%
9.4%
6.3%
-0.5%
17.9%
17.8%
~
10.5%
10.7%
6.6%
-0.4%
19.6%
19.9%
~
8.5%
10.1%
5.6%
-0.2%
18.8%
18.9%
~
8.7%
10.1%
6.0%
-0.3%
20.2%
20.1%
~
4.6%
5.6%
6.6%
0.2%
3-33
-------
With Trailer Skirt #2 and
Boat Tail
With Trailer Skirt #2,
Boat Tail, and Trailer Gap
Reducer
4.910
4.902
0.699
0.707
12.5%
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
Standard
Skirt on First Trailer
Only
Skirt on Second
Trailer Only
Skirt on Both
Trailers
Skirt and Gap
Reducers on Both
Trailers
Skirt and Gap
Reducers on Both
Trailers w/ tail on
second trailer
CdA
[m2]
6.022
5.902
5.735
5.586
5.289
5.208
Delta CdA
(vs. Standard)
~
0.120
0.286
0.436
0.733
0.814
% Delta Cd/4
(vs. Standard)
~
1.99%
4.76%
7.24%
12.2%
13.5%
SINGLE PUP TRAILER RESULTS
Standard
Skirt on Trailer
Skirt and Gap
Reducer on Trailer
5.384
5.226
4.971
~
0.159
0.414
~
2.95%
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
3-34
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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.
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Table 3-12 Results of Reduced-Scale Wind Tunnel Testing of Reefer Trailer Using a 2012 Model Year
Tractor
CONFIGURATION
Standard Reefer
Skirt Only
Boat tail only
Skirt +Boat Tail
Vs. OEM standard
trailer w/ Skirt +
Boat Tail + Gap
CoA
(ZERO
YAW) [m2]
5.840
5.141
5.607
4.596
DELTA CtA
FROM
STANDARD
REEFER (%
DELTA)
~
0.699(12.0%)
0.232 (4.0%)
1.244(21.3%)
DELTA CtA VS.
STANDARD DRY
BOX VAN TRAILER
FROM THE SAME
TRAILER OEM
-0.264
-0.196
-0.448
-0.114
-0.146
% DELTA (VS.
STANDARD
TRAILER)
-4.7%
-4.0%
-8.7%
-2.5%
-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.
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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 tractor-
trailer). 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).
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Table 3-13 53' Dry Box Van Trailer Test Article
53' AIR RIDE DRY BOX VAN TRAILER
Length:
Width:
Height:
Capacity:
Assumed trailer load/capacity:
Suspension:
Corners:
Bogie/Rear Axle Position:
Skin:
Scuff band:
Wheels:
Doors:
Undercarriage/Landing Gear:
Underride Guard
Aerodynamic Trailer Device
Type:
Aerodynamic Trailer Device
Specifications Applicable to
Trailer Side Skirts:
53 feet (636 inches; 1615.44 cm) ± 1 inch
102 inches ± 0.5 inches
102 inches (162 inches or 13 feet, 6 inches (+0.0 inch/-l inch) from the
ground)
3,800 cubic feet
45,000 Ibs
Any (see "trailer ride height" below)
Rounded with a radius of 5.5 inches ± 0.5 inches
Tandem axle (std), 146 inches ±4.0 inches from rear axle centerline to
rear of trailer. Set to California position.
Generally smooth with flush rivets
Generally smooth, flush with sides (protruding < 1/8 inch)
22.5 inches. Duals. Std mud flaps.
Swing doors.
Std landing gear, no storage boxes, no tire storage, 105 inches ± 4.0
inches from front of trailer to centerline of landing gear
Equipped in accordance with per 49 CFR 393.86
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
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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:
Width:
Height:
Capacity:
Suspension:
Tank:
Bogie:
Skin:
Structures:
Wheel fairings:
Wheels:
Tanker Operation
42 feet ± 1 foot, overall
40 feet ± 1 foot, tank
96 inches ± 2
140 inches
(overall, from ground)
7,000 gallons
Any (see "trailer ride height" below)
Generally cylindrical with rounded ends.
Tandem axle (std). Set to furthest rear position.
Generally smooth
(1) Centered, manhole (20 inch opening), (1) ladder generally centered
on side, (1) walkway (extends lengthwise)
24.5 inches. Double wide.
Empty
Table 3-15 Flatbed Trailer Specifications for Special Testing
FLATBED TRAILER
Length
Width
Flatbed Deck Heights:
Wheels / Tires
Bogie
53 feet
102 inches
Front: 60 inches ±0.5 inches
Rear: 55 inches ±0.5 inches
22.5 inch diameter tire with steel or aluminum wheels
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.
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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
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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
Dry van 50 feet and shorter
Dry van longer than 50 feet
Refrigerated van 50 feet and shorter
Refrigerated van longer than 50 feet
FEATURES
Class 8 high roof day cab, pulling solo 28' dry van
Cd/4 = 6. 1, Crr = 6.0 kg/ton
Class 8 high roof sleeper cab pulling a solo 53 ' dry van
Cd/4 = 6.2, Crr = 6.0 kg/ton
Class 8 high roof day cab pulling a solo 28' ref van
Cd/4 = 6.0, Crr = 6.0 kg/ton
Class 8 high roof sleeper cab pulling a solo 53' ref van
Cd/4 = 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
Box trailers shorter than 50 feet
CLASS 8 HIGH ROOF SLEEPER
CAB
DUAL-AXLE
BIN III OR BETTER TRACTOR,
CoA < 6.5
CAB SIDE EXTENDERS
FUEL TANK COVER/CHASSIS
SKIRTS
ROOF FAIRING
Class 8 high roof day cab
Dual-axle
Bin III or better tractor, Cd/4 <6.7
Cab Side extenders
Fuel tank covers/Chassis Skirts
Roof Fairing
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3.2.8 Continued Use of the Aerodynamic Alternate Method Factor (Fait-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 Fait-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 Fait-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 aandb).
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
1.00
Coastdown
Constant Speed
RSWT
(Cd*A @ zero yaw)
Sleeper 1
Sleeper 2
Sleeper 3
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 FID GHG Phase 1 for a standard trailer. The FID 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).
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
1.00
Coastdown
Constant Speed
RSWT
(Cd*A @ zero yaw)
Sleeper 1
Sleeper 2
SleeperS
Figure 3-7a Method Comparison for Coastdown, Constant Speed and RSWT (Zero-Yaw Co) 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.)
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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.
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
Coastdown
Constant Speed
RSWT
(Cd*A @ zero yaw)
Sleeper 1
Sleeper 2
SleeperS
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.)
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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 FID
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 FID 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 Fait-
aero 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 (Fait-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
Sleeper 1
Sleeper 2
Sleeper 3
Sleeper 4
Day
Sleeper
Average (2
out of 4
tractors w/
matching
coastdown
data)
CdA
(ZERO)
4.914
4.962
5.056
5.003
5.298
4.938
CdA DELTA
VS.
STANDARD
TRAILER
0.511
0.594
0.566
0.560
0.311
0.552
CdA%
DELTA VS.
STANDARD
TRAILER
9.42%
10.68%
10.07%
10.07%
5.55%
10.1%
WACdA
(@55
MPH)
5.419
5.705
5.598
5.627
5.755
5.562
WACdA
DELTA VS.
STANDARD
TRAILER
0.624
0.700
0.687
0.714
0.612
0.662
WACdA %
DELTA VS.
STANDARD
TRAILER
10.33%
10.94%
10.93%
11.26%
9.61%
10.6%
WACoA-
CdA
DELTA
(WACdA
OFFSET)
0.505
0.742
0.542
0.625
0.457
0.624
WACoA
- CdA %
DELTA
(VS.
CdA)
10.27%
14.96%
10.73%
12.49%
8.63%
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).
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
1.00
Coastdown
Constant Speed
RSWT
(Cd*A @ zero yaw)
Sleeper 1
Sleeper 2
Sleeper 3
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 FID 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.
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
Coastdown
Constant Speed
RSWT (WACD*A @ 7/55)
Sleeper 1
Sleeper 2
SleeperS
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.
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Next, the results from the RSWT were used to develop a virtual WAC&A 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 Fait-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
Coastdown (reference
method)
Drag area from
RSWT (or other alt.
method)
Wind average drag
area RSWT (or other
alt. method)
Alternate Method
Factor
Wind Average Offset
CdAwad Calculations
(for bin determination
and GEM input):
Variable
(CdAWoastdoWn
( L--d A ) zero,wind
tunnel
\^d^*-/wad wind
tunnel
-F alt-aero
WACd-CdAO
Offset
Wind Average
Equivalent
Coastdown
(CdA)wad
Value
5.5
4.9
5.6
-^alt-aero ~~ \{^d-^-)zero, coastdown / (.^d^-) zero, wind tunnel ~~ J.J/^r.y ~ 1.1 -^
v^d-^/wad, wind tunnel \*^d-^*-/zero,wmd tunnel 3.O T-.V U. /
^^dA-Jwad ^^dA-Jzero, coastdown ~"~ ^^-d-^-Jwad, wind tunnel ^*^-d-^-Jzero,windtunnelJ -Talt-
= 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 CCh emissions and fuel consumption; which is discussed below. For now, the
alternate method factor, Fait-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 Fait-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
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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
Coastdown
(reference method)
Alt. method Drag
area
Alt. method wind
average drag area
Calculated Using
Certified
Configuration in
Table 3-19
Actual Wind
Average Offset
Default Wind
Average Offset
CdAWad Calculations
(for bin
determination and
GEM input):
Variable
l.L'd-rvzerOjCoastdown
(L--d-/vZero,alt method
(CdA)wad,alt method
Alternate Method
FaCtOr (Falt-aero)
WACd-CdAO
Offset
Default Offset
With (CdA)wad, alt
method
Without (CdA)Wad,
ait method; Instead,
USe (CdA)zero,
coastdown ^U
applicable) or
(CdA)zero, alt method
with a default
offset value of
0.80
Example:
Previous Certified Configuration
Data
5.5
4.9
5.6
Additional Configuration using
Alternate Method
(Example: Day Cab Data)
Not Available;
alternate method used
5.3
5.8
-T alt-aero ~~ (^dA)Zero,coastdown / (^dA)zero, wind tunnel ~~ J.J/^r.y ~ 1.1 2
(from Table 3-19 for certified case)
0.7
0.8
(CdA)wad = (CdA)Wad,alt method * Falt-
aero
= 5.6* 1.12
= 6.3
^^--d-^vwad (^^--d-tv zero, coastdown ~"~
default offset
= 5.5 + 0.80
= 6.3
(CdA)wad = (CdA)zero,alt method * Falt-
aero + default offset
= 4.9* 1.12 + 0.80
= 6.3
0.5
0.8
(CdA)wad = (CdA)Wad,alt method *
-T alt-aero
= 5.8* 1.12
= 6.5
Not Applicable;
Only alternate method is used
(C-dA)wad ~~ (*^dA)Zero, alt method
Fait-aero + default offset
= 5.3* 1.12 + 0.80
= 6.7
In contrast to Fait-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
3-51
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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).
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1.20
1.00
0.80
0.60
0.40
0.20
0.00
Coastdown
Constant Speed
RSWT
(Cd*A @ zero yaw)
Sleeper 1
Sleeper 2
SleeperS
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.
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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 CCh 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
Sleeper 1
Sleeper 2
Sleeper 3
CONFIG
skirt
skirt
skirt
CSTDWN
5.4
5.6
5.6
CoAO
4.91
4.96
5.06
WAd
5.42
5.70
5.60
Fdt-ero
1.11
1.15
1.11
WAd
OFFSE
T
0.505
0.742
0.542
WACtX
Fdt-ero
6.0
6.5
6.2
CSTDWN
+ WACt
OFFSET
6.0
6.4
6.2
HD PHASE
2
PROPOSED
BIN#
Bin III
Bin III
Bin III
BIN III
AERO TEST
RESULT
6.0-6.5
6.0-6.5
6.0-6.5
BIN III
AERO GEM
INPUT
6.3
6.3
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 on-
road 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,wmdtunnei- CdAzero.wmdtunnei) * Fdt-
aero) 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
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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 CoAs from Various Tractors, Trailers, Trailer Configurations and Aerodynamic Test
Methods with Corresponding Trailer Aerodynamic Bins
CAB TYPE
CONFIGURATION
DELTA Cd/4
(VS. STANDARD)
TRAILER CERT. BIN
TRAILER AERO
GEM INPUT
Coastdown Testing
Sleeper 1
Sleeper 2
Sleeper 3
With Trailer Skirt #2
With Trailer Skirt and Boat Tail
With Trailer Skirt #2
With Trailer Skirt and Boat Tail
With Trailer Skirt #2
With Trailer Skirt and Boat Tail
0.5
0.9
0.6
1.1
0.5
0.7
III
IV
III
IV
III
IV
0.5
0.9
0.5
0.9
0.5
0.9
Constant Speed Testing
Sleeper 1
Sleeper 2
With Trailer Skirt #2
With Trailer Skirt and Boat Tail
With Trailer Skirt #2
0.7
1.1
0.5
IV
IV
III
0.9
0.9
0.5
Reduced Scale Wind Tunnel Testing (RSWT)
Sleeper 1
Sleeper 2
Sleeper 3
Sleeper 4
Day
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Skirt #2 and Boat Tail
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Skirt #2 and Boat Tail
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Skirt #2 and Boat Tail
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Skirt #2 and Boat Tail
With Trailer Skirt #1
With Trailer Skirt #2
With Boat Tail
With Trailer Skirt #2 and Boat Tail
0.423
0.511
0.340
0.972
0.586
0.594
0.365
1.092
0.477
0.566
0.318
1.056
0.481
0.557
0.332
1.121
0.256
0.311
0.370
0.699
III
III
III
IV
III
III
III
IV
III
III
III
IV
III
III
III
IV
II
III
III
III
0.5
0.5
0.5
0.9
0.5
0.5
0.5
0.9
0.5
0.5
0.5
0.9
0.5
0.5
0.5
0.9
0.2
0.5
0.5
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
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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.
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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,
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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 take-
off 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
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to characterize their in-use operation.20 ERG then segregated the driving into freeway and non-
freeway 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-to-
idle "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 FID 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
FIWFEC, 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 VMT-
weighting. 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
AL
AK
AZ
AR
CA
CO
CN
DE
DC
FL
GA
HA
ID
IL
IN
IA
KA
KE
LA
ME
MA
MS
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
RURAL
INTERSTATE
SPEED LIMIT
70
55
75
65
55
75
65
55
55
70
70
60
65
65
65
70
75
65
70
65
65
70
60
70
70
70
65
75
75
65
65
75
65
70
75
URBAN
INTERSTATE
SPEED LIMIT
65
55
65
55
55
65
55
55
55
65
55
60
65
55
55
55
75
65
70
65
65
70
60
60
70
60
65
65
65
65
55
65
55
70
75
RURAL
INTERSTATE
MILES
5,643
803
6,966
4,510
17,681
4,409
715
-
-
9,591
9,433
110
2,101
8,972
7,140
4,628
3,242
6,566
5,489
2,207
3,484
1,257
5,245
4,150
4,103
5,972
2,350
2,590
1,826
1,235
1,609
4,530
6,176
5,957
1,394
URBAN
INTERSTATE
AND OTHER
FREEWAYS
MILES
7,950
662
13,324
4,794
123,482
11,745
13,485
1,694
813
37,185
21,522
2,403
1,250
23,584
10,850
2,538
5,480
6,834
7,708
958
18,792
20,579
20,931
12,071
4,004
16,957
343
1,653
5,286
2,574
25,330
2,667
37,306
19,216
374
U.S.
WEIGHTED
VMT
FRACTION
RURAL
0.6%
0.1%
0.7%
0.5%
1.9%
0.5%
0.1%
0.0%
0.0%
1.0%
1.0%
0.0%
0.2%
1.0%
0.8%
0.5%
0.3%
0.7%
0.6%
0.2%
0.4%
0.1%
0.6%
0.4%
0.4%
0.6%
0.2%
0.3%
0.2%
0.1%
0.2%
0.5%
0.7%
0.6%
0.1%
U.S.
WEIGHTED
VMT
FRACTION
URBAN
0.8%
0.1%
1.4%
0.5%
13.1%
1.2%
1.4%
0.2%
0.1%
3.9%
2.3%
0.3%
0.1%
2.5%
1.2%
0.3%
0.6%
0.7%
0.8%
0.1%
2.0%
2.2%
2.2%
1.3%
0.4%
1.8%
0.0%
0.2%
0.6%
0.3%
2.7%
0.3%
4.0%
2.0%
0.0%
VMT
WEIGHTED
SPEED
LIMIT
0.968
0.086
1.474
0.591
8.242
1.161
0.837
0.099
0.047
3.279
1.958
0.160
0.231
1.996
1.126
0.492
0.694
0.925
0.981
0.218
1.537
1.623
1.667
1.077
0.602
1.524
0.186
0.320
0.510
0.263
1.590
0.545
2.604
1.871
0.141
3-61
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OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
65
75
55
65
65
70
75
70
70
75
65
70
60
70
65
75
65
70
55
55
55
70
75
70
70
65
55
70
60
65
65
75
9,039
5,029
4,109
10,864
404
7,355
1,960
8,686
15,397
3,117
1,216
8,764
4,392
3,195
5,197
2,482
27,830
7,223
5,734
21,756
2,948
6,879
648
13,414
71,820
6,165
443
18,907
15,816
3,175
9,139
474
1.0%
0.5%
0.4%
1.2%
0.0%
0.8%
0.2%
0.9%
1.6%
0.3%
0.1%
0.9%
0.5%
0.3%
0.6%
0.3%
3.0%
0.8%
0.6%
2.3%
0.3%
0.7%
0.1%
1.4%
7.6%
0.7%
0.0%
2.0%
1.7%
0.3%
1.0%
0.1%
2.544
0.937
0.575
2.020
0.200
1.058
0.208
1.642
6.481
0.674
0.110
2.056
1.287
0.456
0.989
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 county-
specific 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. restricted-
access 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
3-62
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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 CARS'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 2b-
8 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 CCh 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 CCh 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 CCh emission calculation in 40 CFR 1037.510(b). We are
also proposing that GEM would calculate reduced CCh 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.
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Table 3-24 Phase 1 Vehicle Duty Cycle Composite Weightings
VEHICLE
CATEGORY
Vocational
Vocational Hybrid
Vehicles
Day Cabs
Sleeper Cabs
PHASE 1 COMPOSITE WEIGHTINGS OF DUTY
CYCLE MODE
Transient
42%
75%
19%
5%
55 mph Cruise
21%
9%
17%
9%
65 mph Cruise
37%
16%
64%
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
Vocational
Regional
Vocational Multi-
purpose
Vocational Urban
DUTY CYCLE MODE
Transient
50%
82%
94%
55 mph Cruise
28%
15%
6%
65 mph Cruise
22%
3%
0%
Idle
10%
15%
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
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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
Regulatory
Sub category
Tractor Tare
Weight (Ibs)
CLASS 8
Sleeper
Cab High
Roof
19,000
CLASS 8
Sleeper
Cab Mid
Roof
18,750
CLASS 8
Sleeper
Cab Low
Roof
18,500
CLASS 8
Day Cab
High
Roof
17,500
CLASS 8
Day Cab
Low
Roof
17,000
CLASS 7
Day Cab
High
Roof
11,500
CLASS 7
Day Cab
Low
Roof
11,000
The agencies developed the empty tare weights of the vocational vehicles based on the
EOF report23 on GHG management for Medium-Duty Fleets. The EOF 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
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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 (Ibs.) per Distance Travelled and Gross Vehicle Weight Group
(VIUS)27
< 50 miles
51 to 100 miles
101 to 200 miles
201 to 500 miles
> 500 mile
Average
CLASS 3
3,706
3,585
4,189
4,273
3,216
3,794
CLASS 4
4,550
4,913
6,628
7,029
8,052
6,234
CLASS 5
8,023
6,436
8,491
6,360
6,545
7,171
CLASS 6
10,310
10,628
12,747
10,301
12,031
11,203
CLASS 7
18,674
23,270
30,180
25,379
34,210
26,343
CLASS 8
29,628
36,247
39,743
40,243
40,089
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
3-66
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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
Regulatory
Subcategory
Tractor Tare
Weight (Ibs)
Trailer
Weight (Ibs)
Payload
(Ibs)
Total
Weight (Ibs)
CLASS 8
Sleeper
Cab High
Roof
19,000
13,500
38,000
70,500
CLASS 8
Sleeper
Cab Mid
Roof
18,750
10,000
38,000
66,750
CLASS 8
Sleeper
Cab Low
Roof
18,500
10,500
38,000
67,000
CLASS 8
Day Cab
High
Roof
17,500
13,500
38,000
69,000
CLASS 8
Day Cab
Mid Roof
17,100
10,000
38,000
65,100
CLASS 8
Day Cab
Low Roof
17,000
10,500
38,000
65,500
CLASS 7
Day Cab
High
Roof
11,500
13,500
25,000
50,000
CLASS 7
Day Cab
Mid Roof
11,100
10,000
25,000
46,100
CLASS 7
Day Cab
Low
Roof
11,000
10,500
25,000
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
Truck Tare
Weight (Ibs)
Payload (Ibs)
Total Weight (Ibs)
LIGHT
HEAVY
10,300
5,700
16,000
MEDIUM
HEAVY
13,950
11,200
25,150
HEAVY
HEAVY
27,000
15,000
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
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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 CCh 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)(l), 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.
3-68
-------
Table 3-30 Proposed Generic Vehicle Definitions for Class 2b-7 Vehicles
Mass (kg)
Ci4
TireC^
(kg/ton)
Tire Radius
(m)
Rotating
Inertia (kg)
Axle Gear
Efficiency (%)
Accessory
Power (W)
Axle ratio at
engine speed
TEST1
7,257
5.4
6.7
0.426
454
95.5
1300
A
TEST 2
11,408
5.4
6.9
0.426
454
95.5
1300
A
TEST 3
7,257
5.4
6.7
0.426
454
95.5
1300
B
TEST 4
11,408
5.4
6.9
0.426
454
95.5
1300
B
TEST 5
7,257
5.4
6.7
0.426
454
95.5
1300
C
TEST 6
11,408
5.4
6.9
0.426
454
95.5
1300
C
TEST 7
7,257
5.4
6.7
0.426
454
95.5
1300
Maximum
engine
speed
TESTS
11,408
5.4
6.9
0.426
454
95.5
1300
Maximum
engine
speed
Table 3-31 Proposed Generic Vehicle Definitions for Tractors and Class 8 Vocational VehiclesGeneral
Purpose
Mass (kg)
C*4
Tire Cn
(kg/ton)
Tire
Radius
(m)
Rotating
Inertia
(kg)
Axle Gear
Efficiency
(%)
Accessory
Power
(W)
Axle ratio
at engine
speed
TEST1
31,978
5.4
6.9
0.5
1,134
95.5
1300
Minimum
NTE
exclusion
speed
TEST 2
22,679
4.7
6.9
0.5
907
95.5
1300
Minimum
NTE
exclusion
speed
TEST 3
19,051
4.0
6.9
0.5
680
95.5
1300
Minimum
NTE
exclusion
speed
TEST 4
31,978
5.4
6.9
0.5
1,134
95.5
1300
B
TEST 5
22,679
4.7
6.9
0.5
907
95.5
1300
B
TEST 6
19,051
4.0
6.9
0.5
680
95.5
1300
B
TEST 7
31,978
5.4
6.9
0.5
1,134
95.5
1300
Maximum
engine
speed
TESTS
22,679
4.7
6.9
0.5
907
95.5
1300
Maximum
engine
speed
TEST 9
19,051
4.0
6.9
0.5
680
95.5
1300
Maximum
engine
speed
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Table 3-32 Proposed Generic Vehicle Definitions for Class 8 Combination Heavy-Haul Vehicle
Mass (kg)
Cd4
TireCrr
(kg/ton)
Tire
Radius
(m)
Rotating
Inertia
(kg)
Axle Gear
Efficiency
(%)
Accessory
Power
(W)
Axle ratio
at engine
speed
TEST1
40,895
6.1
6.9
0.5
1,134
95.5
1300
Minimum
NTE
exclusion
speed
TEST 2
31,978
5.4
6.9
0.5
907
95.5
1300
Minimum
NTE
exclusion
speed
TESTS
22,679
4.7
6.9
0.5
680
95.5
1300
Minimum
NTE
exclusion
speed
TEST 4
40,895
6.1
6.9
0.5
1,134
95.5
1300
B
TEST 5
31,978
5.4
6.9
0.5
907
95.5
1300
B
TEST 6
22,679
4.7
6.9
0.5
680
95.5
1300
B
TEST 7
40,895
6.1
6.9
0.5
1,134
95.5
1300
Maximum
engine
speed
TESTS
31,978
5.4
6.9
0.5
907
95.5
1300
Maximum
engine
speed
TEST 9
22,679
4.7
6.9
0.5
680
95.5
1300
Maximum
engine
speed
The main outputs of this matrix of tests is grams of CCh, 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 CCh
emissions in GEM taking the work per ton-mile from the GEM simulation and multiplying it by
the interpolated work specific CCh 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. sov/otaq/climate/sem. htm.
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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 CCh 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 CCh 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.
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140
130 -
1 _U
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Engine-only and GEM CO2 [g/tc
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- Lineal (Engine-Only)
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40 50 60 70 80 90 100 110 120 130 140
Powertrain CO2 [g/ton-mile]
Figure 3-10 Engine only and GEM CCh results vs. powertrain.
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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 CCh 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 (NIV) as
defined by the tire radius and rear-axle ratio.
Table 3-33 GEM Default Parameters for Vehicle Certification Using Powertrain Testing.
REGULATORY CLASS
Class 8
Combination
Class 7
Heavy-Haul
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
ENGINE
2017MY15L
Engine with 600
HP
2017MY15L
Engine with 455
HP
2017MY13L
TRANSMISSION
13 speed
Automated Manual
Transmission
10 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
12.8, 9.25, 6.76,
4.9,3.58,2.61,
1.89, 1.38, 1,
0.73
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Combination
HHD
Vocational
MHD
Vocational
LHD
Vocational
Day Cab - Mid Roof
Day Cab - Low Roof
Regional Duty Cycle
Multi-Purpose Duty Cycle
Uiban Duty Cycle
Regional Duty Cycle
Multi-Purpose Duty Cycle
Urban Duty Cycle
Regional Duty Cycle
Multi-Purpose Duty Cycle
Urban Duty Cycle
Engine with 3 50
HP
2017MY15L
Engine with 455
HP
2017MY11L
Engine with 345
HP
2017 MY 7L
Engine with 270
HP
2017 MY 7L
Engine with 200
HP
5 speed HHD
Automatic
Transmission
5 speed MLHD
Automatic
Transmission
4.6957,2.213,
1.5291, 1,
0.7643
3.102, 1.8107,
1.4063, 1,
0.7117
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.
"'powertrain corrected "'test *acc
'trans.out or wheel hub(+)
Ltest
w,
engine(+)
GEM will use the calculated cycle work and N/Vof the powertrain for the to-be certified
vehicle to interpolate the powertrain input table. For vehicle configurations that have cycle work
or NIV 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
CCh 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 CCh.
efuel
-kWh\
interpolated
milesGEM payload mfuel
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.
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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.
Hybrid
Cont-d
oooo
Tranuneeton f.'itoi Internal
'G*r"«fatSf rmnhi etirtri
Rear Wheel Drive |
I
Enflne
Figure 3-11 Engine Hybrid Test Configuration
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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
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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
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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 (FIFET) consists of
preconditioning highway driving sequence and a measured highway driving sequence. The
FIFET 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 FID 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
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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 CCh 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. Ill 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.
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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.
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I
454 HP i is i: nir SMdua asrc «nvnti
800 1000 1200 UOO 1600 1SOO 2000
ingmt Sputa ftPU>
EPA default engine fuel map
Output CO2 g/ton-
mile from GEM
Certification Data
Figure 3-12 Phase 1 Rule for Certification Using GEM
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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(l) 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.
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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.
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3.10.2.1
Vehicle Certification
Run GEM with a range of vehicle
parameters (Cd, Crr) over three
EPA certification cycles
Output engine torques
and speeds to engine
dyno
Run parent engine tests
OOOC
Engine dyno tests
I
CO2 (g/hp-hr) map as
function work and N/V
aw two 1200 two two two sow
[noun Sown IHPU!
EPA default engine
fuel map
Figure 3-13 Alternative Phase 2 Certification Option II
Run GEM with a specific to-be-
certified vehicle configuration
Work(hp-hr),
N/V, and mile
Interpolate CO2 from
C02 map with [ CO2 (g/hp-hr) ji
function of work and ^
N/V I ,>'
(Work (hp-hr)/ton-mile)
xC02 (g/hp-hr)
= CO2g/ton-mile)
EPA default engine fuel map
Figure 3-14 Phase 2 Certification Process with Option II
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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 CW. 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 CCh consumption in g/hp-hr as a function of Work in hp-
hr 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 CCh 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 CCh 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 CCh 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 CCh 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 CCh 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:
3-85
-------
Would the fuel consumption or CCh 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: F016E3IOC-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.
3-86
-------
55mph
200-
190
180
170
160
150
25 20
20 45
Work
N/V (rpm/(km/hr))
Figure 3-15 Contour plot of CCh as function of cycle work and N/V for 55 mph cycle
65mph
-3
200.
190.
180.
170.
150.
140.
30
25
15 50
N/V (rpm/(km/hr))
Work (hp-hr)
Figure 3-16 Contour plot of CCh as function of cycle work and N/V for 65 mph cycle
3-87
-------
ARB
210
205
200
e- 195
1
I 19°
o
^ 185
&
180
175-
170-
90
\
o
|bsfc
450hp rating
80
70
60 -I4.5 14
13.5
13
12.5
12
11.5
Work
N/V (rpm/(km/hr))
Figure 3-17 Contour plot of CCh 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.
-------
All Cycles
280
260-1
240
"CT"
i. 220-
si
~3
o 200
I
180-
160-
140
bsfc
O 450hp rating
20
40
10
20
30
40
50
Work (hp-hr)
60 w
N/V (rpm/(km/hr))
Figure 3-18 Surface plot of CCh 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.
3-89
-------
55m ph
195-
190-
185 -J
180
£ 175
=5 170-
% 165
_Q
160
155.
150
145 J
30
\bsic
O 400hp rating
O 450hp rating
40 35 30
Work (hp-hr)
20
20 45
N/V (rpm/(km/hr))
Figure 3-19 Surface plot of CCh with child rating engine for 55mph cycle
65mph
200.
190
180
170
160
150
140
30
\bsfc
O 400hp rating
O 450hp rating
25
15 50
Work
N/V (rpm/(km/hr))
Figure 3-20 Surface plot of CCh with child rating engine N/V for 65 mph cycle
3-90
-------
ARB
210,
205,
200,
195,
190,
185,
180,
175,
170J
90
|bsfc
O 400hp rating
3 450hp rating
70
60 14.5 14
13.5 13
Work (hp-hr)
N/V (rpm/(km/hr))
Figure 3-21 Surface plot of CCh with child rating engine for ABB cycle
Figure 3-19 to Figure 3-21 demonstrate the well behaving nature of CCh 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 CCh 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.
3-91
-------
55mph
s
a
v>
200 x
190,,
180,,
170,,
160,,
150,,
140.,
30
Ibsfc
O All Points
O 9 Points
25 ^\.
20
45
N/V (rpm/(km/hr))
35 30
Work (hp-hr)
25
20
Figure 3-22 Contour plot of CCh with only 9 points for 55mph cycle
65mph
200
190
,_. 180
i.
I 17°
£
£ 160-
150.
140.
30
Ibsfc
O All Points
O 9 Points
25
N/V (rpm/(km/hr))
25
15 50
Figure 3-23 Contour plot of CCh with only 9 points N/V for 65 mph cycle
3-92
-------
ARB
210,
205,
200-
-c- 195-
i.
=§, 190,
| 185,
180,
175,
170J
90
| bsfc
O All Points
O 9 Points
80
70
60 14.5 14
13.5 13
Work (hp-hr)
12.5
12
11.5
N/V (rpm/(km/hr))
Figure 3-24 Contour plot of CCh 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 (C&A\ coefficients of rolling resistance (CW) and
vehicle masses. For Class 8 vehicles the highest mass and highest C&A 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
3-93
-------
vehicles mass and CW 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
1
2
3
4
5
6
7
8
9
10
TRANSMISSION TYPE AND GEAR RATIOS
10 speed AMT
12.8
9.25
6.76
4.9
3.58
2.61
1.89
1.38
1
0.73
6 speed HH AT
4.6957
2.213
1.5291
1
0.7643
0.6716
6 speed MH AT
3.102
1.8107
1.4063
1
0.7117
0.61
N/A
3.10.6 Certification Point Determination from Alternative
Table 3-30 to Table 3-32 define the numbers of vehicle configurations so that a well-
defined 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
3-94
-------
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.
gCO2/kW-hr
3U.U -
29.0 -
28.0 -
27.0 -
26.0 -
25.0 -
24.0 -
23.0 -
22.0 -
21.0 -
20.0 -
19.0 -
18.0 -
17.0 -
10
o o o o
o
A O
0
-------
33.0 -
32.0 -
31.0 -
30.0 -
29.0 -
28.0 -
-c 27.0 -
1 26.0 -
25.0 -
24.0 -
23.0 -
22.0 -
21.0 -
20.0 -
19 0 -
10
gCO2/kW-hr
0 ooo o
o o
0 0
ooooo o °o
. 0 Qo j, o oo o°
0
- o Oo Oo
0
o o o
o ooo o«
-
" o O
1 1 1 1 1 1
.0 12.0 14.0 16.0 18.0 20.0 22.0 24
SN/SV
Map
Points
o Iran 1
o Vehl
o Veh2
o Iran 2
o Iran 3
0
Figure 3-26 Alternative certification map for 65 mph certification cycle
11.5 \
11.0 ^
10.5 :
1 n n -
9.5 ^
^ 9.0 i
.C
1 8'5 ;
s n -
7 5 -
7.0 ^
6.5 \
6.0 :
55-
5n -
37.5
o
O OO
O OO
o]
000
o
o
o
<
38.5
)°8
0§
39.5
gCO2/kW-hr
o
o
0
0 §0
o
o
0
o
o
o
o
o |
<
c
o
o
(
D
)
0
3
0
C
o
o
,
40.5 41.5
SN/SV
0
Map
Points
o Irani
o Vehl
o Veh2
o Iran 2
° Iran 3
42.5
Figure 3-27 Alternative certification map for ARB certification cycle
3-96
-------
In order to prove the concept and numerical accuracy in determining appropriate CCh 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 CCh 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 CCh g/hp-hr between the test points and interpolated or extrapolated values. For
the sake of simplicity, both values of CCh 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.
3-97
-------
Table 3-35 Proof of Concept and Numerical Scheme Accuracy for 55MPH Cycle
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
INTERP/EXTRAP POINTS
SN/ZVi
12.61
12.31
13.30
12.47
12.31
13.30
12.42
12.31
13.30
11.85
12.80
11.85
12.80
12.14
12.04
13.01
14.19
19.26
11.99
12.04
13.01
14.19
19.26
11.98
12.04
13.01
14.19
19.26
14.44
14.90
12.97
17.08
13.82
18.55
12.71
14.18
18.06
kW-hn Error
(%)
27.37
27.73
27.78
23.81
24.09
24.13
20.46
20.72
20.76
25.84
25.89
22.36
22.40
20.97
21.19
21.24
21.30
21.61
20.14
20.33
20.38
20.44
20.73
19.36
19.54
19.59
19.65
19.93
21.32
21.34
21.24
21.47
21.28
21.56
27.75
27.82
28.03
0.5%
-0.4%
-0.2%
1.6%
1.0%
0.8%
1.8%
1.7%
1.2%
0.2%
0.3%
1.3%
1.6%
0.2%
-0.3%
-0.6%
-2.1%
-2.0%
0.3%
-0.2%
-0.9%
-1.6%
-1.4%
0.4%
0.0%
-0.3%
-1.2%
-0.9%
-2.3%
-2.9%
-0.6%
-1.4%
-1.8%
-1.7%
-0.2%
-0.5%
-1.3%
i
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
INTERP/EXTRAP POINTS
SN/ZVi
12.71
14.18
18.06
13.31
12.71
14.18
18.06
13.31
11.0727
11.3574
11.9048
12.8518
11.1814
11.3602
11.9048
12.8518
12.882
15.1703
17.4585
19.7468
22.0351
23.1592
18.3492
20.7542
15.9442
13.5392
10.8136
10.4952
10.4952
14.4455
14.4455
14.4455
19.5963
19.5963
19.5963
17.0015
17.0015
kW-hn
24.11
24.17
24.36
24.13
20.74
20.80
20.98
20.76
20.474
20.298
20.342
20.428
22.769
22.55
22.609
22.7
26.145
21.851
22.132
22.449
22.745
22.9
22.253
22.579
21.943
26.215
25.072
19.698
16.748
26.005
19.938
16.927
26.267
20.22
17.21
26.129
20.072
Error
(%)
1.0%
0.4%
2.3%
0.8%
1.2%
0.9%
3.0%
1.2%
0.5%
1.5%
1.9%
1.7%
0.3%
1.0%
1.5%
1.6%
0.5%
-0.7%
-0.8%
-1.8%
-3.3%
-4.0%
-1.1%
-2.8%
-0.2%
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.5%
1.8%
3-98
-------
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
i
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
INTERP/EXTRAP POINTS
SN/ZVi
11.40
12.31
13.30
11.40
12.31
13.30
11.40
12.31
13.30
11.85
12.80
11.85
12.80
11.15
12.04
13.01
14.19
19.26
11.15
12.04
13.01
14.19
19.26
11.15
12.04
13.01
14.19
19.26
14.44
14.90
12.97
17.08
13.82
18.55
12.71
kW-
hr,
32.68
32.85
32.94
27.86
27.93
27.99
23.33
23.38
23.42
30.33
30.41
25.57
25.61
27.02
27.08
27.13
27.21
27.57
25.75
25.80
25.86
25.94
26.30
24.56
24.61
24.67
24.75
25.11
27.23
27.26
27.13
27.41
27.18
27.52
32.89
Error
(%)
0.6%
-0.9%
-1.2%
0.9%
1.8%
1.5%
1.5%
2.1%
2.1%
1.1%
0.6%
1.9%
2.4%
1.1%
1.2%
1.7%
0.4%
1.2%
1.2%
2.0%
0.7%
0.2%
1.0%
0.3%
1.5%
0.4%
-0.3%
0.4%
0.0%
-0.7%
1.8%
1.5%
0.8%
2.0%
-1.0%
i
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
INTERP/EXTRAP POINTS
ZN/ZVi
12.71
14.18
18.06
13.31
12.71
14.18
18.06
13.31
10.287
11.1382
11.9048
12.8518
10.4864
11.1382
11.9048
12.8518
12.882
15.1703
17.4585
19.7468
22.0351
23.1592
18.3492
20.7542
15.9442
13.5392
10.4952
10.4953
10.4952
14.4455
14.4456
14.4455
19.5963
19.5964
19.5963
kw-hn
27.96
28.04
28.26
27.99
23.39
23.47
23.67
23.42
23.201
23.307
23.383
23.471
26.581
26.689
26.797
26.895
31.126
27.695
28.007
28.315
25.395
23.919
28.124
27.401
27.802
31.201
29.49
23.234
19.715
29.917
23.441
19.921
30.142
23.766
20.247
Error
(%)
1.8%
0.8%
2.1%
1.5%
2.2%
1.9%
4.2%
2.1%
0.1%
1.3%
2.0%
2.3%
0.1%
0.9%
1.2%
2.3%
0.1%
0.5%
1.6%
0.1%
2.3%
3.4%
2.0%
-0.3%
1.0%
-0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
3-99
-------
36
37
38
14.18
18.06
13.31
33.00
33.20
32.94
-1.8%
-1.6%
-1.2%
74
75
76
17.0015
17.0016
17.0015
30.063
23.594
20.075
1.0%
0.6%
0.3%
Table 3-37 Proof Of Concept and Numerical Scheme Accuracy for ARB Cycle
i
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
INTERP/EXTRAP POINTS
SN/ZVi
41.00
40.16
39.48
40.98
40.13
39.43
40.95
40.03
39.39
41.40
39.47
41.13
39.43
40.17
39.40
38.82
38.89
40.73
40.03
39.40
38.75
38.88
40.73
40.02
39.31
38.75
38.88
40.73
39.85
40.32
38.40
40.88
38.21
kW-
hn
9.66
9.65
9.64
9.30
9.30
9.28
8.93
8.93
8.91
9.48
9.45
9.10
9.09
6.25
6.25
6.26
6.26
6.32
6.17
6.17
6.19
6.18
6.25
6.11
6.11
6.12
6.12
6.18
6.27
6.29
6.23
6.73
6.34
Error
(%)
-1.2%
-0.9%
-1.0%
-1.0%
-2.9%
-2.3%
-2.1%
-2.0%
-1.9%
-0.7%
-1.3%
-1.1%
-2.3%
-4.1%
-4.2%
-4.3%
-4.4%
-3.9%
-4.5%
-4.5%
-4.6%
-4.6%
-4.1%
-4.8%
-4.7%
-4.8%
-4.8%
-4.4%
-4.2%
-3.9%
-4.5%
-3.3%
-4.5%
i
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
INTERP/EXTRAP POINTS
SN/ZVi
39.26
40.34
40.56
39.49
39.23
40.31
40.53
39.00
39.1403
40.0123
39.2724
38.9924
39.2179
40.0236
39.3053
38.9921
39.1243
38.1574
40.5059
42.0372
43.4797
43.1689
44.334
44.0081
41.7231
41.8206
39.4411
38.2432
37.8895
41.2466
40.3708
40.0346
42.1071
kw-hn
9.29
9.30
9.33
9.28
8.92
8.93
8.97
8.91
9.2227
9.2143
9.1879
9.1411
9.3688
9.3563
9.3359
9.2828
10.353
7.0408
7.0397
7.087
7.1226
7.1632
7.1471
7.1626
7.0508
10.136
11.832
8.6276
7.3752
11.14
8.1927
7.1138
10.8
Error
(%)
-2.7%
-1.3%
-1.9%
-2.5%
-2.3%
-2.6%
-2.8%
-2.0%
1.0%
0.4%
0.7%
1.0%
0.4%
0.2%
0.4%
0.9%
1.2%
0.2%
0.7%
1.2%
1.6%
0.9%
0.8%
0.0%
-2.9%
1.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
3-100
-------
34
35
36
37
38
41.58
39.31
40.36
40.69
39.57
6.69
9.65
9.66
9.69
9.64
-2.9%
-1.3%
-1.3%
-1.8%
-1.2%
72
73
74
75
76
41.6921
41.4929
42.1548
41.6017
41.4366
8.0995
7.0552
10.83
8.0873
7.0472
0.0%
0.0%
0.2%
0.4%
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 C& 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: F016E3 IOC-LAS
3-101
-------
190
180
170
S
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~ 16°
k.
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120
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JO 140 150 160 170 180 190
Engine BSFC (g/hp-hr) - measured from Engine tests
Figure 3-28 Comparisons between Alternative and GEM Based Approaches
3-102
-------
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.
3-103
-------
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/fy 14osti/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.eom/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: NHTSA-
2008-0121-0019).
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.
24ICF 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 Pay load Equivalent Factor. Table 11. Last
viewed on March 9, 2010 at
http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_ll_12_tables.htm
3-104
-------
27 Excerpted from The U.S. Federal Highway Administration. Development of Truck Pay load Equivalent Factor.
Table 11. Last viewed on March 9, 2010 at
http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_ll_12_tables.htm
28 The U.S. Federal Highway Administration. Development of Truck Pay load Equivalent Factor. Table 11. Last
viewed on March 9, 2010 at
http://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf2_reports/reports9/s510_ll_12_tables.htm
3-105
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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 CCh
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 Medium-
and Heavy-Duty Vehicles." 2010. Recommendation 8-4.
4-1
-------
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 heavy-
duty (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.
4-2
-------
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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, under-
powered 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.
4-4
-------
GEM CVM ConventionalPowertrain Model
[v'steri _buT
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.
4-5
-------
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 CCh 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 testingS.
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 cycleS. 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.
4-6
-------
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 CCh 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
4-7
-------
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
E Study Results (Box Truck)
d Efficiency v. Look-up Table
Fuel
lomy (MPG)
Drive Cycle
Look-up Table
MPG
Fixed 95.5%
Efficiency MPG
Difference
55 mph
11.19
11.15
0.36%
65 mph
CARB HHDDT
9.13
8.43
9.06
8.29
0.77%
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
4-8
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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 (CARS) 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.
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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 CCh 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/Stateflow-
based 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.
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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
Engine /Rated
Power (hp)
Transmission
2013 Kenworth
T700
Cummins ISX
455
Eaton
F016E3 IOC-LAS
20 12 Kenworth
T270
Cummins ISB
240
Allison
2100
20 11 Ford
F-650 Tow truck
Cummins
ISB 270
Allison
2200 RDS
20 12 New Flyer
Refuse
Cummins
ISL 345
Eaton
FO16E3 IOC-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
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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 CCh 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 paniculate filter. The following parameters were
measured or recorded during all tests:
4-12
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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, CCh, 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.
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13
Q.
2
2
UJ
(D
7 _
x .X"
' S^ -4
S j£ s' * CC IV/IPI-I
x/V * 65MPH
x^/^^* A CARB
^^^ x utility
j$?' WHVC
jt. JX>^ 1 - 1
jifxx ---1:1-5%
/J^' - 1:1+5%
iS?
2 4 6 8 10
Chassis Dynamometer [MPG]
Figure 4-3 GEM Validation against Class 8 Kenworth T700 Truck chassis tests
10 12
PowertrainTest[MPG]
Figure 4-4 GEM validation against Class 6 Ford F-650 tow truck powertrain tests
4-14
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g
s.
LJJ
13 -
m -
x ,'
x ' x X x*^
x2?_
x -t^a * 55MPH
.x 'xX--X ' 65MPH
^'^k ^ A CARB
.^^f,.-^
A^A? -x x Parcel
x ,-'. '
x- x^ x Utility
.'/^.' WHVC
x-^;x ::;:L.
,,',' - 1:1+5%
5 7 9 11 13
Chassis Dynamometer [MPG]
Figure 4-5 GEM validation against Class 6 Kenworth T270 box truck chassis tests
X
X x'
Xx"
X x'
x'
x .x
55MPH
65MPH
CARB
x'X .'
c Utility
WHVC
1:1
1:1-5%
1:1+5%
345
Chassis Dynamometer [MPG]
Figure 4-6 GEM validation against New Flyer refuse truck chassis tests
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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.
6 8 10 12 14
Experimental Tests (MPG)
16
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.
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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/ton-
mile. 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 OEM'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
65 mph
65 mph
65 mph
65 mph
65 mph
65 mph
65 mph
Vehicle
Attribute
Variables
Baseline
+907 kg
+15% Crr
-15% Crr
+15% Cd
-15% Cd
Optimized
Package
Chassis
Test Fuel
Economy
Result
(MPG)
6.84
6.86
6.57
7.27
6.31
7.63
8.08
GEM Fuel
Economy
Result
(MPG)
6.61
6.55
6.28
6.96
6.05
7.25
7.65
Impact of
Variable
on Chassis
Test Result
0.0%
-0.3%
3.9%
-6.3%
7.7%
-11.5%
-18.1%
Impact of
Variable on
GEM
Simulation
Result
0.0%
0.9%
4.9%
-5.3%
8.4%
-9.8%
-15.8%
Delta
0.0%
-1.2%
-1.0%
-1.0%
-0.7%
-1.8%
-2.3%
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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 CARS 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
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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 CCh 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 CCh 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.
100
200
300 400
Time (s)
500
600
700
Figure 4-9 Engine speed comparisons over the WHVC for a Class 8 T700 truck
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100 200
300 400
Time (s)
500 600
700
Figure 4-10 Engine fuel rate comparisons over the WHVC for a Class 8 T700 truck
100 200
300 400
Time (s)
500 600
700
Figure 4-11 Transmission gear comparisons over the WHVC for a Class 8 T700 truck
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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.
2500
2000
Figure 4-12 Engine speed comparisons over the WHVC for an F-650 tow truck
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2000
Test
GEM
2000
-1000
Time (s)
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 CCh 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.
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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
CCh 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
algorithms. 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.
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Table 4-4 Impact of the Agencies' Default Shifting Strategy Compared to Transmission Manufacturers'
Strategies
Truck
T270 Box Truck
T270 Box Truck
T270 Box Truck
F-650 Tow Truck
F-650 Tow Truck
F-650 Tow Truck
T700 Class 8 Truck
T700 Class 8 Truck
T700 Class 8 Truck
Cycle
GEM 55 mph
GEM 65 mph
CARBHHDDT
GEM 55 mph
GEM 65 mph
CARBHHDDT
GEM 55 mph
GEM 65 mph
CARBHHDDT
Manufacturer
Shift Strategy
Fuel Economy
(MPG)
11.36
9.24
8.39
14.49
12.03
10.32
7.96
6.59
3.9
EPA Defined
Shift Strategy
Fuel Economy
(MPG)
11.36
9.24
8.44
14.57
12.14
10.72
7.96
6.59
3.94
Difference (%)
0
0
0.60
0.55
0.91
3.88
0
0
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.
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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 Ibs, while for Class 7
tractors weight ranges from 46,500 to 50,000 Ibs. 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 A.I A Inertia
All of the inertias for rotational parts, including engine, transmission and axle, are pre-
defined 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
4A.I.I Idle Cycle and Its Modeling
As described in Chapter 3.4.2 of this draft RIA, we are proposing the addition of an idle-
only 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
4-25
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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 CCh emission calculation in 40 CFR 1037.510(b). We are
also proposing that GEM would calculate reduced CCh 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, MUD
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 CCh 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
4-26
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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
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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
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Number of Axles
Default Axle Configuration
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental Air Temperature (°C)
Payload (tons)
Weight Reduction (Ibs)
TireCrr
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Class 8 Combination
Sleeper Cab - High
Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other
gears
95.5%
31978
5
6x4
300
1000
25
19
Add 1/3* weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.05
0.09
0.86
Class 8 Combination
Sleeper Cab - Mid
Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other gears
95.5%
30277
5
6x4
300
1000
25
19
Add 1/3* weight
reduction to Payload
tons
=0.425*Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.05
0.09
0.86
Class 8 Combination
Sleeper Cab - Low Roof
98% for the gear with a
1 : 1 gear ratio, and 96%
for all other gears
95.5%
30390
5
6x4
300
1000
25
19
Add 1/3* weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.05
0.09
0.86
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Table 4-6 Class 8 Combination Tractor Day Cab Predefined Modeling Parameters
Regulatory Subcategory
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Number of Axles
Default Axle Configuration
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental air temperature (°C)
Payload (tons)
Weight Reduction (Ibs)
TireCrr
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Class 8 Combination
Day Cab -High Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other gears
95.5%
31297
5
6x4
300
1000
25
19
Add l/3*weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
0.64
Class 8 Combination
Day Cab - Mid Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other gears
95.5%
29529
5
6x4
300
1000
25
19
Add 1/3* weight
reduction to Payload
tons
=0.425*Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
0.64
Class 8 Combination
Day Cab - Low Roof
98% for the gear with a
1 : 1 gear ratio, and 96%
for all other gears
95.5%
29710
5
6x4
300
1000
25
19
Add 1/3* weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
0.64
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Table 4-7 Class 7 Combination Tractor Predefined Modeling Parameters
Regulatory Subcategory
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Axle Base
Default Axle Configuration
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental air temperature (°C)
Payload (tons)
Weight Reduction (Ibs)
Tire Crr
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Class 7 Combination
Day Cab -High Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other gears
95.5%
22679
4
4x2
300
1000
25
12.5
Add l/3*weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
0.64
Class 7 Combination
Day Cab - Mid Roof
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other gears
95.5%
20910
4
4x2
300
1000
25
12.5
Add 1/3* weight
reduction to Payload
tons
=0.425*Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
0.64
Class 7 Combination
Day Cab - Low Roof
98% for the gear with a
1 : 1 gear ratio, and 96%
for all other gears
95.5%
21091
4
4x2
300
1000
25
12.5
Add 1/3* weight
reduction to Payload
tons
=0.425 *Trailer
Crr+0.425*Drive
Crr+0.15*SteerCrr
0.19
0.17
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 F£HD 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
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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
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Number of Axles
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental Air Temperature (°C)
CdA (m2)
TireCrr
Payload (tons)
Weight Reduction (Ibs)
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Idle cycle
HHD
Regional Duty Cycle
98% for the gear with
a 1 : 1 gear ratio, and
96% for all other
gears
95.5%
19051
3
300
1000
25
6.86
=0.7*DriveCrr +
0.3*SteerCrr
7.50
Add 0.5* weight
reduction to Payload
tons
0.50
0.28
0.22
0.10
HHD
Multi-Purpose Duty
Cycle
98% for all gears
95.5%
19051
3
300
1000
25
6.86
0.7*DriveCrr +
0.3*SteerCrr
7.50
Add 0.5* weight
reduction to Payload
tons
0.82
0.15
0.03
0.15
HHD
Urban Duty Cycle
98% for all gears
95.5%
19051
3
300
1000
25
6.86
=0.7*DriveCrr +
0.3*SteerCrr
7.50
Add 0.5* weight
reduction to Payload
tons
0.94
0.06
0.00
0.20
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Table 4-9 Vocational MHD Vehicle Predefined Modeling Parameters
Regulatory Subcategory
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Number of Axles
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental Air Temperature (°C)
CdA (m2)
TireCrr
Payload (tons)
Weight Reduction (Ibs)
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Idle cycle
MHD
Regional Duty Cycle
98% for all gears
95.5%
11408
2
300
1000
25
5.40
=0.7*DriveCrr +
0.3*SteerCrr
5.60
Add 0.5* weight
reduction to Payload
tons
0.50
0.28
0.22
0.10
MHD
Multi-Purpose Duty
Cycle
98% for all gears
95.5%
11408
2
300
1000
25
5.40
0.7*DriveCrr +
0.3*SteerCrr
5.60
Add 0.5* weight
reduction to Payload
tons
0.82
0.15
0.03
0.15
MHD
Urban Duty Cycle
98% for all gears
95.5%
11408
2
300
1000
25
5.40
=0.7*DriveCrr +
0.3*SteerCrr
5.60
Add 0.5* weight
reduction to Payload
tons
0.94
0.06
0.00
0.20
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Table 4-10 Vocational LHD Vehicle Predefined Modeling Parameters
Regulatory Subcategory
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Number of Axles
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Environmental Air Temperature (°C)
CdA (m2)
TireCrr
Payload (tons)
Weight Reduction (Ibs)
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Idle cycle
LHD
Regional Duty Cycle
98% for all gears
95.5%
7257
2
300
1000
25
5.40
=0.7*DriveCrr +
0.3*SteerCrr
2.85
Add 0.5* weight
reduction to Payload
tons
0.50
0.28
0.22
0.10
LHD
Multi-Purpose Duty
Cycle
98% for all gears
95.5%
7257
2
300
1000
25
5.40
0.7*DriveCrr +
0.3*SteerCrr
2.85
Add 0.5* weight
reduction to Payload
tons
0.82
0.15
0.03
0.15
LHD
Urban Duty Cycle
98% for all gears
95.5%
7257
2
300
1000
25
5.40
=0.7*DriveCrr +
0.3*SteerCrr
2.85
Add 0.5* weight
reduction to Payload
tons
0.94
0.06
0.00
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
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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 (AXIS) 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
Tractor Type
Engine Fuel Map
Gear ratio
Gearbox Efficiency
Axle Mechanical Efficiency
Total weight (kg)
Baseline CdA Values (m2)
Number of Axles
Payload (tons)
Default Axle Configuration
Electrical Accessory Power (W)
Mechanical Accessory Power (W)
Steer Tire RR
Drive Tire RR
Tire Radius (m)
Axle Drive Ratio
Tire Crr
Weight Reduction (Ibs)
Drive Cycles & Weightings:
CARB HHDDT
GEM 55 mph
GEM 65 mph
Payload (tons)
Long Box Long Box
Dry Van Refrigerated Van
C8 Sleeper Cab - High Roof
Short Box
Dry Van
C8 Day Cab
Short Box
Refrigerated Van
- High Roof
15L-455HP
12.8, 9.25, 6.76, 4.9, 3.58, 2.61, 1.89, 1.38, 1, 0
.73
98% for 1 : 1 gear ratio, and 96 for all other gears
95.5%
31978 33778
6.2 6.1
5
19
15191
6.1
16991
6.0
4
10
6x4
300
1000
6.54
6.92
0.5
3.7
=0.425*Trailer Crr+0.425*Drive Crr+0.15*Steer Crr
Add 1/3* weight reduction to Payload tons
0.05
0.09
0.86
19
0.19
0.17
0.64
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.
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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 CCh 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.
4-35
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Table 4-12 Tractor Technology Improvement Values
Technology Improvement
Single Drive Axle (6x2 or 4x2)
Configuration
Part-time Single Drive Axle 6x2
Configuration3
Low Friction Axle Lubricant
Automated Manual, Automatic,
and Dual Clutch Transmissions
Predictive Cruise control
High Efficiency Air
Conditioning Compressor
Electric Accessories
Extended Idle Reduction
Automatic Tire Inflation System
(ATIS)
Class 8 Sleeper
Cabs
2.5%
2.5%
0.5%
2%
2%
0.5%
1%
5%
1%
Class 8 Day
Cabs
2.5%
2.5%
0.5%
2%
2%
0.5%
1%
N/A
1%
Class 7 Day
Cabs
N/A
N/A
0.5%
2%
2%
0.5%
1%
N/A
1%
Class 8 Heavy Haul
Tractors
2.5%
2.5%
0.5%
2%
2%
0.5%
1%
N/A
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
HHD Single Drive Axle (6x2 or
4x2) Configuration
HHD Part-time Single Drive Axle
6x2 Configuration3
Low Friction Axle Lubricant
HHD Automated Manual or Dual
Clutch Transmissions
PTO Delta Fuel (g/ton-mile)
Neutral Idle
Stop-Start Idle Reduction
Regional Duty Cycle
2.5%
1.0%
0.5%
2.3%
OtoSO
Multi-Purpose Duty
Cycle
2.5%
0.3%
0.5%
N/A
OtoSO
Urban Duty Cycle
2.5%
0.1%
0.5%
N/A
OtoSO
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, respectively13
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
Automatic Tire Inflation Systems
Effectiveness
1.5%
4-36
-------
If a manufacturer believes that the CCh 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.
4-37
-------
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^20-R-15-009
3 K. Newman, J. Kargul, and D. Barba, "Development and Testing of an Automatic Transmission Shift Schedule
Algorithm for Vehicle Simulation, "SAEInt. J. Engines 8(3):2015, doi:10.4271/2015-01-1142
4Reinhart, 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/regs-
heaw-dutv.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/smartwavlogistics/newsroom/documents/e-update-iuly-10.pdf
4-38
-------
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 (CCh), 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
CCh, 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, CCh 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 (CCh), 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.s), 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.
5-1
-------
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 Ib using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
27.2
9.3
0.09
36.6
2035 (MMT CO2EQ)
86.9
29.7
0.25
116.9
2050 (MMT CO2EQ)
123.0
42.0
0.3
165.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-2 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025,2035 and 2050 -
Preferred Alternative vs. Alt la using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
28.1
9.6
0.09
37.8
2035 (MMT CO2EQ)
94.6
32.3
0.25
127.2
2050 (MMT CO2EQ)
134.9
46.1
0.3
181.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-3 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025,2035 and 2050
Preferred Alternative vs. Alt la using Analysis Method Ba
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
27.4
9.3
0.1
36.8
2035 (MMT CO2EQ)
94.7
32.2
0.25
127.2
2050 (MMT CO2EQ)
136.5
46.5
0.3
183.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-2
-------
Table 5-4 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Preferred Alternative vs. Alt Ib
using Analysis Method Aa
Diesel
Gasoline
Total
CY2025
Billion
Gallons
2.5
0.2
2.7
% Reduction
5.1%
2.4%
4.7%
CY2035
Billion
Gallons
7.6
0.9
8.5
% Reduction
14.3%
11.0%
13.8%
CY2050
Billion
Gallons
10.8
1.2
12.0
% Reduction
17.0%
13.2%
16.5%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-5 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Preferred Alternative vs. Alt la
using Analysis Method Aa
Diesel
Gasoline
Total
CY2025
Billion
Gallons
2.5
0.2
2.7
% Reduction
5.2%
2.6%
4.8%
CY2035
Billion
Gallons
8.3
1.0
9.3
% Reduction
15.4%
11.5%
14.8%
CY2050
Billion
Gallons
11.9
1.3
13.2
% Reduction
18.4%
13.7%
17.8%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-6 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Preferred Alternative vs. Alt la
using Analysis Method Ba
Diesel
Gasoline
Total
CY2025
Billion
Gallons
2.5
0.2
2.7
% Reduction
5.1%
2.1%
4.7%
CY2035
Billion
Gallons
8.5
0.8
9.3
% Reduction
15.6%
10.4%
14.9%
CY2050
Billion
Gallons
12.3
1.1
13.4
% Reduction
18.7%
12.8%
18.0%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-7 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025,2035 and 2050 -
Alternative 4 vs. Alt Ib using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
33.5
11.5
0.09
45.1
2035 (MMT CO2EQ)
90.9
31.0
0.25
122.2
2050 (MMT CO2EQ)
124.0
42.3
0.3
166.6
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-3
-------
Table 5-8 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025,2035 and 2050
Alternative 4 vs. Alt la using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
34.6
11.8
0.09
46.5
2035 (MMT CO2EQ)
98.7
33.7
0.25
132.7
2050 (MMT CO2EQ)
136.0
46.5
0.3
182.8
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-9 Annual Total Reductions of Heavy-Duty GHG Emissions in Calendar Years 2025,2035 and 2050
Alternative 4 vs. Alt la using Analysis Method Ba
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
33.7
11.5
0.1
45.3
2035 (MMT CO2EQ)
98.3
33.4
0.25
132.0
2050 (MMT CO2EQ)
136.9
46.6
0.3
183.8
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-10 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Alternative 4 vs. Alt Ib using
Analysis Method Aa
Diesel
Gasoline
Total
CY2025
Billion
Gallons
3.0
0.3
3.3
% Reduction
6.1%
3.9%
5.8%
CY2035
Billion
Gallons
7.9
1.0
8.9
% Reduction
14.8%
12.1%
14.4%
CY2050
Billion
Gallons
10.8
1.3
12.1
% Reduction
17.0%
13.8%
16.6%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-11 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Alternative 4 vs. Alt la using
Analysis Method Aa
Diesel
Gasoline
Total
CY2025
Billion
Gallons
3.1
0.3
3.4
% Reduction
6.3%
4.3%
6.0%
CY2035
Billion
Gallons
8.6
1.1
9.7
% Reduction
16.0%
12.5%
15.5%
CY2050
Billion
Gallons
12.0
1.3
13.3
% Reduction
18.5%
14.3%
18.0%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-4
-------
Table 5-12 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 - Alternative 4 vs. Alt la using
Analvsis Method Ba
Diesel
Gasoline
Total
CY2025
Billion
Gallons
3.1
0.3
3.4
% Reduction
6.2%
3.4%
5.9%
CY2035
Billion
Gallons
8.8
0.9
9.7
% Reduction
16.1%
11.1%
15.5%
CY2050
Billion
Gallons
12.3
1.1
13.4
% Reduction
18.7%
12.9%
18.0%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 PIVfo.s 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 Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOx
PM25
SOX
VOC
CY2025
US Short
Tons
9
672
97
145
30,282
2,119
101,916
376
6,213
16,227
% Reduction
3%
10%
10%
5%
3%
11%
7%
1%
5%
6%
CY2035
US Short
Tons
25
1,893
273
421
87,286
5,969
291,282
1,535
19,905
49,080
% Reduction
13%
30%
31%
18%
8%
32%
26%
3%
14%
18%
CY2050
US Short
Tons
34
2,682
387
595
123,876
8,460
413,501
2,199
28,101
69,525
% Reduction
16%
36%
37%
22%
10%
37%
31%
4%
17%
22%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-5
-------
Table 5-14 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions -
Preferred Alternative vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM2.5
SOx
VOC
CY2025
US Short
Tons
9
672
97
145
30,487
2,119
102,983
419
6,421
16,403
% Reduction
3%
10%
10%
5%
3%
11%
7%
1%
5%
6%
CY2035
US Short
Tons
25
1,891
273
425
88,724
5,969
299,911
1,910
21,672
50,812
% Reduction
13%
30%
31%
18%
8%
32%
26%
4%
15%
19%
CY2050
US Short
Tons
35
2,680
386
603
126,081
8,461
427,332
2,791
30,850
72,253
% Reduction
16%
36%
37%
22%
10%
37%
32%
5%
18%
23%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-15 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions -
Preferred Alternative vs. Alt la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
SOx
PM25
VOC
CY2025
US Short
Tons
9
674
97
149
29,622
2,121
102,502
386
6,070
16,724
% Reduction
2.7%
10.1%
9.8%
5.4%
1.9%
11.4%
7.2%
0.6%
4.9%
5.6%
CY2035
US Short
Tons
25
1,902
274
445
85,961
5,978
298,907
1,883
20,777
52,872
% Reduction
15.1%
30.5%
31.3%
18.8%
6.6%
31.7%
26.6%
4.2%
15.3%
18.8%
CY2050
US Short
Tons
36
2,697
388
633
122,659
8,475
426,610
2,815
30,000
75,521
% Reduction
19.4%
36.0%
36.9%
22.9%
8.4%
37.0%
32.1%
5.4%
18.4%
22.7%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-6
-------
Table 5-16 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions -
Alternative 4 vs. Alt Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
9
673
97
152
31,383
2,123
105,693
639
7,682
18,006
% Reduction
3%
10%
10%
6%
3%
11%
7%
1%
6%
6%
CY2035
US Short
Tons
25
1,893
273
426
88,047
5,970
293,918
1,703
20,849
50,189
% Reduction
13%
30%
31%
18%
8%
32%
26%
4%
15%
19%
CY2050
US Short
Tons
34
2,682
387
595
124,137
8,460
413,967
2,237
28,385
69,796
% Reduction
16%
36%
37%
22%
10%
37%
31%
4%
17%
22%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-17 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions -
Alternative 4 vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
9
672
97
153
31,637
2,123
106,822
689
7,941
18,222
% Reduction
3%
10%
10%
6%
3%
11%
7%
1%
6%
6%
CY2035
US Short
Tons
25
1,891
273
430
89,514
5,969
302,575
2,082
22,646
51,924
% Reduction
13%
30%
31%
18%
8%
32%
26%
5%
16%
19%
CY2050
US Short
Tons
35
2,679
386
603
126,360
8,460
427,805
2,833
31,151
72,509
% Reduction
16%
36%
37%
22%
10%
37%
32%
5%
18%
23%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-7
-------
Table 5-18 Annual Total Reductions (Upstream and Downstream) of Heavy-Duty Non-GHG Emissions -
Alternative 4 vs. Alt la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
sox
PM25
VOC
CY2025
US Short
Tons
9
676
97
157
30,580
2,125
106,180
646
7,450
18,652
% Reduction
2.8%
10.1%
9.8%
5.7%
1.9%
11.4%
7.4%
1.1%
6.1%
6.2%
CY2035
US Short
Tons
26
1,903
274
450
86,526
5,980
301,339
2,036
21,550
53,966
% Reduction
15.2%
30.6%
31.3%
18.9%
6.6%
31.7%
26.8%
4.6%
15.9%
19.2%
CY2050
US Short
Tons
36
2,697
388
634
122,703
8,476
426,796
2,827
30,364
75,621
% Reduction
19.4%
36.0%
36.9%
22.9%
8.4%
37.0%
32.1%
5.4%
18.4%
22.7%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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.s.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 CCh.
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.
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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 CCheq terms, each gas is
weighted by its heat trapping ability relative to that of CCh. 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
CO2
CH4
N2O
HFC134a
GLOBAL WARMING POTENTIAL
(C02EQ)
1
25
298
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
5-9
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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 heavy-
duty 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
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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 Ib) and the less dynamic baseline (Alternative la). 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 I,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
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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 la 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 Smart Way 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.6
5.3.2.2 Model inputs and Assumptions for the More Dynamic Reference Case
The more dynamic reference case (identified as Alternative Ib 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 CCh 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.
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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:
Equation 5-1
J scale
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],
fscaie = 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
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expected from the technologies which could be used to meet the proposed standards were
modified in the "sourceusetypephysics" table.0
For vocational vehicles and tractor-trailers, the agencies also used the percent reduction
in CCh 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 CCh 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 HID 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 CCh 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 tractor-
trailers, 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 FID
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.
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Table 5-20 Aggregation of Trailer Types into MOVES Combination Tractor-Trailer Categories
TRAILER TYPE
Long Dry Van
Short Dry Van
Long Refrigerated Van
Short Refrigerated Van
Special Purpose Van
Container Chassis
Flatbed
Tank
Other On-Highway Trailers
Off-Road Trailers
Combination Long-Haul
Tractor-Trailers
55.5%
12.3%
18.2%
5.2%
0.0%
0.2%
6.9%
0.4%
1.2%
0.0%
Combination Short-Haul Tractor-
Trailers
20.3%
20.2%
2.6%
3.8%
6.4%
1.8%
10.9%
1.5%
2.7%
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
Long-haul Tractor-
Trailers
Short-haul Tractor-
Trailers0
FUEL
Diesel
Diesel
MODEL YEARS
2018-2020
2021-2023
2024-2026
2027+
2018-2020
2021-2023
2024-2026
2027+
REDUCTION FROM
REFERENCE CASE
1.3%
5.2%
9.7%
10.4%
0.9%
5.0%
9.5%
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.
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Table 5-22 Estimated Reductions in Road Load Factors for the Proposed Standards for Tractor-Trailers
VEHICLE TYPE
Combination Long-
haul Tractor-
Trailers
Combination Short-
haul Tractor-
TrailersE
MODEL
YEARS
2018-2020
2021-2023
2024-2026
2027+
2018-2020
2021-2023
2024-2026
2027+
REDUCTION IN
TIRE ROLLING
RESISTANCE
COEFFICIENT
5.5%
9.8%
15.7%
17.9%
4.0%
10.5%
13.9%
17.6%
REDUCTION IN
AERODYNAMIC
DRAG COEFFICIENT
5.1%
15.3%
20.5%
26.9%
1.6%
9.3%
12.3%
15.9%
WEIGHT
REDUCTION
(LB)A
-131
-199
-246
-304
-41
-79
-100
-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 tractor-
trailers by modifying the "hotellingactivitydistribution" table in MOVES.
Table 5-23 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers
VEHICLE TYPE
Combination
Long-Haul
Trucks3
MODEL
YEARS
2010-2020
2021-2023
2024+
APU
PENETRATION
30%
80%
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, single-
unit short-haul truck, single-unit long-haul truck, and motor home.
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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
Single-Frame
Vocational0
FUEL
Diesel & CNG
Gasoline
MODEL
YEARS
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
REDUCTION FROM
REFERENCE CASE
5.3%
8.9%
13.3%
3.3%
5.4%
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
Short Haul Straight Truck
Long Haul Straight Truck, Motor
Home, Intercity Bus
School Bus
Transit Bus
Refuse
All Class 4-5
All Class 6-7
All Class 8
RURAL
35%
100%
0%
0%
0%
23%
15%
7%
MULTI-PURPOSE
60%
0%
100%
75%
0%
21%
19%
8%
URBAN
5%
0%
0%
25%
100%
2%
1%
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 Ibs 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).
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"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
Intercity
Buses
Transit Buses
School Buses
Refuse Trucks
Single Unit
Short-haul
Trucks
Single Unit
Long-haul
Trucks
Motor Homes
MODEL
YEARS
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
2021-2023
2024-2026
2027+
REDUCTION IN TIRE
ROLLING
RESISTANCE
COEFFICIENT
6.5%
9.2%
16.5%
0%
2.9%
3.0%
0%
2.9%
4.0%
0%
2.9%
3.0%
4.8%
8.3%
13.0%
6.5%
9.2%
16.5%
3.0%
6.2%
7.4%
REDUCTION IN
AERODYNAMIC
DRAG COEFFICIENT
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)
0
0
0
0
0
0
0
0
0
20
20
25
5.8
5.8
7
20
20
25
0
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 CAPE 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
CFLt and N2O emissions (as well, as discussed below, of non-GHG pollutants), and applies per-
gallon upstream emission factors from GREET in order to calculate upstream GHG (and non-
GHG) emissions.
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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 CCh 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 CCh 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 CCh 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 CCh emission rates for the two No Action Alternatives
(Alternative la and Ib) 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 CCh 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 CCh emission rates could correspondingly be reduced to
about the same levels. If, as represented by Alternative Ib, 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 la, 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 CCh 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).
5-19
-------
Table 5-27 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved Fuel
Consumption Rates for Method A, Relative to Alternative Iba
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028*
2029*
2030*
STRINGENCY
(VS. 2018)
MYs2014-
2020 Subject to
Phase 1
Standards
2.5%
4.9%
7.3%
9.6%
11.9%
14.1%
16.2%
16.2%
16.2%
16.2%
AVE. REQUIRED FUEL CONS.
(GAL./100 MI.)
No Action
6.41
6.41
6.27
6.11
5.80
5.78
5.78
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
Proposed
6.41
6.41
6.27
6.11
5.80
5.78
5.78
5.64
5.50
5.38
5.25
5.12
4.98
4.86
4.86
4.86
4.86
Reduction
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.2%
4.7%
6.8%
9.0%
11.4%
13.7%
15.8%
15.8%
15.8%
15.8%
AVE. ACHIEVED FUEL CONS.
(GAL./100 MI.)
No Action
6.21
6.12
6.15
5.89
5.75
5.72
5.69
5.63
5.63
5.63
5.63
5.63
5.63
5.62
5.62
5.62
5.62
Proposed
6.21
6.12
6.15
5.88
5.70
5.68
5.64
5.42
5.42
5.28
5.23
4.99
4.93
4.86
4.86
4.85
4.85
Reduction
0.0%
0.0%
0.0%
0.2%
0.8%
0.7%
0.8%
3.8%
3.8%
6.3%
7.1%
11.5%
12.5%
13.7%
13.7%
13.7%
13.7%
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
* Absent further action, standards assumed to continue unchanged after model year 2027.
5-20
-------
Table 5-28 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved CCh
Emission Rates for Method A, Relative to Alternative Iba
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028*
2029*
2030*
STRINGENCY
(VS. 2018)
MYs2014-
2020 Subject to
Phase 1
Standards
2.5%
4.9%
7.3%
9.6%
11.9%
14.1%
16.2%
16.2%
16.2%
16.2%
AVE. REQUIRED CO2 RATE
(G./MI.)
No
602
608
593
578
548
545
545
544
544
544
544
544
544
544
544
544
544
Proposed
602
608
593
578
548
545
545
532
519
507
495
482
470
458
458
458
458
Reduction
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.2%
4.7%
6.8%
9.1%
11.3%
13.6%
15.8%
15.8%
15.8%
15.8%
AVE. ACHIEVED CO2 RATE
(G./MI.)
No
581
578
580
556
543
539
536
530
530
530
530
530
530
529
529
529
529
Proposed
581
578
580
554
538
535
532
510
510
496
492
470
465
458
458
458
458
Reduction
0.0%
0.0%
0.0%
0.2%
0.8%
0.7%
0.8%
3.8%
3.8%
6.4%
7.2%
11.3%
12.3%
13.4%
13.4%
13.5%
13.5%
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
* Absent further action, standards assumed to continue unchanged after model year 2027.
5-21
-------
Table 5-29 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved Fuel
Consumption Rates for Method A, Relative to Alternative laa
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028*
2029*
2030*
STRINGENCY
(VS. 2018)
MYs2014-
2020 Subject to
Phase 1
Standards
2.5%
4.9%
7.3%
9.6%
11.9%
14.1%
16.2%
16.2%
16.2%
16.2%
AVE. REQUIRED FUEL CONS.
(GAL./100 MI.)
No Action
6.41
6.41
6.27
6.11
5.80
5.78
5.78
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
5.77
Proposed
6.41
6.41
6.27
6.11
5.80
5.78
5.78
5.64
5.50
5.38
5.25
5.12
4.98
4.86
4.86
4.86
4.86
Reduction
0.0%
0.0%
0.0%
0.0%
-0.1%**
0.0%
0.0%
2.3%
4.7%
6.8%
9.1%
11.4%
13.7%
15.8%
15.8%
15.8%
15.8%
AVE. ACHIEVED FUEL CONS.
(GAL./100 MI.)
No Action
6.21
6.12
6.15
5.89
5.75
5.73
5.73
5.72
5.72
5.72
5.72
5.72
5.72
5.72
5.72
5.72
5.72
Proposed
6.21
6.12
6.15
5.87
5.70
5.68
5.68
5.44
5.44
5.29
5.23
4.98
4.94
4.87
4.87
4.86
4.86
Reduction
0.0%
0.0%
0.0%
0.3%
0.9%
0.8%
0.8%
4.8%
4.8%
7.6%
8.5%
12.9%
13.6%
14.9%
14.9%
15.0%
15.0%
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
* 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.
5-22
-------
Table 5-30 Stringency of HD Pickup and Van Standards, Estimated Average Required and Achieved CCh
Emission Rates for Method A, Relative to Alternative laa
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028*
2029*
2030*
STRINGENCY
(VS. 2018)
MYs2014-
2020 Subject to
Phase 1
Standards
2.5%
4.9%
7.3%
9.6%
11.9%
14.1%
16.2%
16.2%
16.2%
16.2%
AVE. REQUIRED CO2 RATE
(G./MI.)
No
602
608
593
578
548
545
545
544
544
544
544
544
544
544
544
544
544
Proposed
602
608
593
578
548
546
545
532
519
507
495
482
470
458
458
458
458
Reduction
0.0%
0.0%
0.0%
0.0%
-0.1%**
-0.1%**
-0.1%**
2.2%
4.7%
6.8%
9.1%
11.4%
13.6%
15.8%
15.8%
15.8%
15.8%
AVE. ACHIEVED CO2 RATE
(G./MI.)
No
581
578
580
556
543
539
539
538
538
538
538
538
538
538
538
538
538
Proposed
581
578
580
554
538
535
535
512
512
497
492
470
466
459
459
459
459
Reduction
0.0%
0.0%
0.0%
0.3%
0.9%
0.8%
0.8%
4.9%
4.9%
7.7%
8.6%
12.7%
13.4%
14.7%
14.7%
14.8%
14.8%
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
* 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 COa emission rate.
While the above tables show the agencies' estimates of average fuel consumption and
CCh 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 Bfor 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.
5-23
-------
Table 5-31 Estimated Total Vehicle CCh Reductions for the Proposed Standards and In-Use Emissions for
HD Pickup Trucks and Vans in Method Ba
VEHICLE TYPE
HD Pickup Trucks
and Vans
FUEL
Gasoline
and Diesel
MODEL YEAR
2021
2022
2023
2024
2025
2026
2027+
CO2 REDUCTION
FROM REFERENCE
CASE
2.50%
4.94%
7.31%
9.63%
11.89%
14.09%
16.24%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble
Section X.A.I
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 CAPE 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
5-24
-------
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.
5-25
-------
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 Emissions11
EPA is proposing new air conditioning (A/C) leakage standards for vocational vehicles to
reduce FIFC 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 emissionsYearx = A/C Systemsvearx 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.1 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.
1 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.
5-26
-------
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/
Table 5-32 Annual In-use Vehicle HFC134a Emission Rate from Vintaging Model
KIND OF LOSS
Leakage
Maintenance /Servicing
End of Life
LOSS FRACTION
8%
10%
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.
1 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 CARD 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.
5-27
-------
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
CCheq emissions in 2035; and 299,590 metric tons CCheq 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 heavy-
duty 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 No-
Action and Preferred alternatives, respectively. The first shows results assuming manufacturers
would voluntarily make improvements that pay back within six months (i.e., Alternative Ib).
The second shows results assuming manufacturers would only make improvements as needed to
L Using a Global Wanning Potential of 1,430 for HFC-134a.
5-28
-------
achieve compliance with standards (i.e., Alternative la). 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 Ib a
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
FUEL CONSUMPTION (B. GAL.)
OVER FLEET'S USEFUL LIFE
No Action
9.41
9.53
9.72
9.49
9.26
9.20
9.19
9.10
9.13
9.11
9.32
9.49
9.67
9.78
9.90
10.02
10.03
Proposed
9.41
9.53
9.72
9.47
9.19
9.14
9.12
8.79
8.82
8.59
8.72
8.49
8.56
8.55
8.66
8.75
8.76
Reduction
0.0%
0.0%
0.0%
0.2%
0.7%
0.7%
0.7%
3.4%
3.4%
5.7%
6.4%
10.5%
11.5%
12.6%
12.6%
12.6%
12.6%
GHG EMISSIONS (MMT CO2EQ)
OVER FLEET'S USEFUL LIFE
No Action
115
117
119
116
113
113
112
111
112
111
114
116
118
120
121
122
123
Proposed
115
117
119
116
113
112
112
107
108
105
107
104
105
105
106
107
107
Reduction
0.0%
0.0%
0.0%
0.2%
0.7%
0.7%
0.7%
3.4%
3.4%
5.7%
6.4%
10.4%
11.3%
12.3%
12.3%
12.4%
12.4%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-29
-------
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 laa
MODEL
YEAR
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
FUEL CONSUMPTION (B. GAL.)
OVER FLEET'S USEFUL LIFE
No Action
9.41
9.53
9.72
9.49
9.27
9.20
9.25
9.23
9.26
9.23
9.45
9.62
9.81
9.93
10.05
10.17
10.18
Proposed
9.41
9.53
9.72
9.46
9.19
9.14
9.18
8.82
8.85
8.60
8.72
8.48
8.58
8.57
8.68
8.77
8.78
Reduction
0.0%
0.0%
0.0%
0.3%
0.8%
0.7%
0.7%
4.4%
4.4%
6.9%
7.7%
11.8%
12.5%
13.7%
13.7%
13.7%
13.7%
GHG EMISSIONS (MMT CO2EQ)
OVER FLEET'S USEFUL LIFE
No Action
115
117
119
116
114
113
113
113
113
113
116
118
120
121
123
124
124
Proposed
115
117
119
116
113
112
112
108
108
105
107
104
105
105
106
108
108
Reduction
0.0%
0.0%
0.0%
0.3%
0.8%
0.7%
0.8%
4.4%
4.4%
6.9%
7.7%
11.7%
12.3%
13.5%
13.5%
13.5%
13.5%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
To more clearly communicate these trends visually, the following two charts present the
above results graphically for Method A, relative to Alternative Ib. As shown, fuel consumption
and GHG emissions follow parallel though not precisely identical paths. Though not presented,
charts for analysis relative to Alternative la would appear sufficiently similar that differences
between Alternative la and Alternative Ib remain best communicated by comparing values in
the above tables.
5-30
-------
c
o
E.
E
8
01
11 n
10
01
5 9
S3
in
=) 8
01
.2 7
0)
o
"ro
oo
4
*d-
-------
130
01
!S no
8 100
£ 90
a
a
H 80
o 70
cr
-------
Table 5-35 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
C02
(MMT)
-2.4
-2.4
-22.1
-26.9
-9.4
-11.9
-64.7
-86.0
-11.8
-17.2
-92.6
-121.6
CH4
(MMT
CO2EQ)
0
0
-0.4
-0.4
0
0
-1
-1
0
0
-1.4
-1.4
N2O
(MMT
CO2EQ)A
0
0
0
0
0
0
0
0
0
0
0
0
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-2.4
-2.4
-22.4
-27.2
-9.4
-11.9
-65.6
-86.9
-11.8
-17.2
-94.0
-123.0
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-36 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-2.9
-2.4
-22.5
-27.7
-10.5
-11.9
-71.1
-93.6
-13.1
-17.2
-103.2
-133.5
CH4
(MMT
CO2EQ)
0
0
-0.4
-0.4
0
0
-1
-1
0
0
-1.4
-1.4
N2O
(MMT
CO2EQ)A
0
0
0
0
0
0
0
0
0
0
0
0
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-2.9
-2.4
-22.8
-28.1
-10.5
-11.9
-72.1
-94.6
-13.1
-17.2
-104.6
-134.9
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-33
-------
Table 5-37 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Alternative 4 vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
C02
(MMT)
-3.3
-4.0
-25.9
-33.2
-10.3
-12.9
-66.7
-89.9
-12.6
-17.3
-92.8
-122.6
CH4
(MMT
CO2EQ)
0
0
-0.4
-0.4
0
0
-1
-1
0
0
-1.4
-1.4
N2O
(MMT
CO2EQ)A
0
0
0
0
0
0
0
0
0
0
0
0
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-3.3
-4.0
-26.2
-33.5
-10.3
-12.9
-67.7
-90.9
-12.6
-17.3
-94.2
-124.0
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-38 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Alternative 4 vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-4.0
-4.0
-26.2
-34.3
-11.6
-12.9
-73.2
-97.7
-14.0
-17.3
-103.3
-134.6
CH4
(MMT
CO2EQ)
0
0
-0.4
-0.4
0
0
-1
-1
0
0
-1.4
-1.4
N2O
(MMT
CO2EQ)A
0
0
0
0
0
0
0
0
0
0
0
0
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-4.0
-4.0
-26.6
-34.6
-11.6
-12.9
-74.2
-98.7
-14.0
-17.3
-104.7
-136.0
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-34
-------
Table 5-39 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Preferred Alternative vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.1
0.2
2.2
2.5
0.3
1.0
6.3
7.6
0.3
1.4
9.1
10.8
GASOLINE SAVINGS
(BILLION GALLONS)
0.1
0
0
0.2
0.7
0.2
0
0.9
0.9
0.3
0
1.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-40 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Preferred Alternative vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.1
0.2
2.2
2.5
0.3
1.0
7.0
8.3
0.4
1.4
10.1
11.9
GASOLINE SAVINGS
(BILLION GALLONS)
0.2
0
0
0.2
0.8
0.2
0
1.0
1.0
0.3
0
1.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-35
-------
Table 5-41 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Alternative 4 vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.1
0.3
2.5
3.0
0.3
1.0
6.5
7.9
0.4
1.4
9.1
10.8
GASOLINE SAVINGS
(BILLION GALLONS)
0.2
0.1
0
0.3
0.8
0.2
0
1.0
1.0
0.3
0
1.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-42 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Alternative 4 vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.2
0.3
2.6
3.1
0.4
1.0
7.2
8.6
0.5
1.4
10.1
12.0
GASOLINE SAVINGS
(BILLION GALLONS)
0.3
0.1
0
0.3
0.8
0.2
0
1.1
1.0
0.3
0
1.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5.4.1.1.1
Upstream Impacts
5-36
-------
Table 5-43 Annual Upstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-0.6
-0.7
-7.0
-8.4
-2.5
-3.6
-20.6
-26.6
-3.1
-5.1
-29.5
-37.7
CH4
(MMT CO2EQ)
-0.1
-0.1
-0.7
-0.9
-0.4
-0.4
-2.1
-2.8
-0.4
-0.6
-3.0
-4.0
N2O
(MMT CO2EQ)A
0
0
0
-0.1
-0.1
0
-0.1
-0.2
-0.1
0
-0.1
-0.3
TOTAL
UPSTREAM
(MMT CO2EQ)
-0.7
-0.8
-7.8
-9.3
-2.9
-4.0
-22.8
-29.7
-3.7
-5.7
-32.6
-42.0
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-44 Annual Upstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
C02
(MMT)
-0.7
-0.7
-7.1
-8.6
-2.8
-3.6
-22.6
-29.0
-3.5
-5.1
-32.8
-41.4
CH4
(MMT CO2EQ)
-0.1
-0.1
-0.7
-0.9
-0.4
-0.4
-2.3
-3.1
-0.5
-0.6
-3.3
-4.4
N2O
(MMT CO2EQ)A
0
0
0
-0.1
-0.1
0
-0.1
-0.2
-0.1
0
-0.2
-0.3
TOTAL
UPSTREAM
(MMT CO2EQ)
-0.9
-0.8
-7.9
-9.6
-3.3
-4.0
-25.0
-32.3
-4.1
-5.7
-36.3
-46.1
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-37
-------
Table 5-45 Annual Upstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Alternative 4 vs. Alt Ib using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-0.9
-1.2
-8.2
-10.3
-2.7
-3.8
-21.2
-27.8
-3.3
-5.1
-29.5
-38.0
CH4
(MMT CO2EQ)
-0.1
-0.1
-0.8
-1.1
-0.4
-0.4
-2.2
-3.0
-0.5
-0.6
-3.0
-4.0
N2O
(MMT CO2EQ)A
0
0
0
-0.1
-0.1
0
-0.1
-0.2
-0.1
0
-0.1
-0.3
TOTAL
UPSTREAM
(MMT CO2EQ)
-1.0
-1.3
-9.1
-11.5
-3.2
-4.3
-23.5
-31.0
-4.0
-5.7
-32.7
-42.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-46 Annual Upstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Alternative 4 vs. Alt la using Analysis Method Aa
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
C02
(MMT)
-1.0
-1.2
-8.4
-10.6
-3.1
-3.8
-23.3
-30.2
-3.7
-5.1
-32.9
-41.7
CH4
(MMT CO2EQ)
-0.2
-0.1
-0.8
-1.1
-0.4
-0.4
-2.4
-3.2
-0.5
-0.6
-3.3
-4.4
N2O
(MMT CO2EQ)A
0
0
0
-0.1
-0.1
0
-0.1
-0.2
-0.1
0
-0.2
-0.3
TOTAL
UPSTREAM
(MMT CO2EQ)
-1.2
-1.3
-9.2
-11.8
-3.6
-4.3
-25.8
-33.7
-4.4
-5.7
-36.4
-46.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-38
-------
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 CCheq in 2025, 253,118 metric tons of CCheq in 2035, and
299,590 metric tons CCheq 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 Ib using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-27.2
-9.3
-0.09
-36.6
2035 (MMT CO2EQ)
-86.9
-29.7
-0.25
-116.9
2050 (MMT CO2EQ)
-123.0
-42.0
-0.3
-165.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-48 Annual Total GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 - Preferred
Alternative vs. Alt la using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-28.1
-9.6
-0.09
-37.8
2035 (MMT CO2EQ)
-94.6
-32.3
-0.25
-127.2
2050 (MMT CO2EQ)
-134.9
-46.1
-0.3
-181.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-49 Annual Total GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 - Alternative 4 vs.
Alt Ib using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-33.5
-11.5
-0.09
-45.1
2035 (MMT CO2EQ)
-90.9
-31.0
-0.25
-122.2
2050 (MMT CO2EQ)
-124.0
-42.3
-0.3
-166.6
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-39
-------
Table 5-50 Annual Total GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 - Alternative 4 vs.
Alt la using Analysis Method Aa
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-34.6
-11.8
-0.09
-46.5
2035 (MMT CO2EQ)
-98.7
-33.7
-0.25
-132.7
2050 (MMT CO2EQ)
-136.0
-46.5
-0.3
-182.8
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 Aa
NO-ACTION ALTERNATIVE
(BASELINE)
Fuel Savings (Billion Gallons)
HD Pickups and Vans
Vocational
Tractor/Trailers
Total GHG Reductions (MMT CO2eq)
HD Pickups and Vans
Vocational
Tractor/Trailers
ALTERNATIVE 3
(PROPOSED)
Ib (More
Dynamic)
72.2
7.8
8.3
56.1
986.5
94.8
109.7
782.0
la (Less
Dynamic)
76.7
8.9
8.3
59.5
1,047.4
108.5
109.7
829.2
ALTERNATIVE 4
Ib (More
Dynamic)
81.9
9.4
10.9
61.6
1,114.8
113.7
143.0
858.1
la (Less
Dynamic)
86.7
10.8
10.9
65.0
1,181.1
132.8
143.0
905.3
Note:
a For an explanation of analytical Methods A and B, please see Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
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 la) and control case (Alternative 3) inventories were totaled for all heavy-
duty 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
5-40
-------
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 la, 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 la. The reductions
in CCh 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.0 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 (EO) and 76,330 BTU/gallon ethanol
(E100) were weighted 90% and 10%, respectively, forElO and 85% and 15%, respectively, forE15 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-P-
05-003, March 2005. http://www.epa.gov/otaq/models/ngm/420p05003.pdf
0 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.
5-41
-------
Table 5-52 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt la using Analysis Method B a
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-2.1
-2.4
-22.5
-27.0
-10.6
-11.9
-71.2
-93.7
-14.8
-17.2
-103.1
-135.1
CH4
(MMT CO2EQ)
0.0003
0.0009
-0.4
-0.4
0.0007
0.002
-1.0
-1.0
0.0009
0.003
-1.4
-1.4
N2O
(MMT CO2EQ)
0.0005
0.0007
0.0006
0.002
0.001
0.002
0.001
0.004
0.001
0.002
0.002
0.005
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-2.1
-2.4
-22.9
-27.4
-10.6
-11.9
-72.2
-94.7
-14.8
-17.2
-104.5
-136.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-53 Annual Downstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category -Alternative 4 vs. Alt la using Analysis Method B a
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-3.1
-4.0
-26.2
-33.3
-11.2
-12.9
-73.2
-97.3
-14.9
-17.3
-103.3
-135.5
CH4
(MMT CO2EQ)
0.0003
0.0009
-0.4
-0.4
0.0007
0.0024
-1.0
-1.0
0.0009
0.003
-1.4
-1.4
N2O
(MMT CO2EQ)
0.0005
0.0007
0.0006
0.002
0.001
0.002
0.001
0.004
0.001
0.002
0.002
0.005
TOTAL
DOWNSTREAM
(MMT CO2EQ)
-3.1
-4.0
-26.6
-33.7
-11.2
-12.9
-74.2
-98.3
-14.9
-17.3
-104.7
-136.9
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-42
-------
Table 5-54 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Preferred Alternative vs. Alt la using Analysis Method B a
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.1
0.2
2.2
2.5
0.5
1.0
7.0
8.5
0.8
1.4
10.1
12.3
GASOLINE SAVINGS
(BILLION GALLONS)
0.1
0.05
0
0.2
0.6
0.2
0
0.8
0.8
0.3
0
1.1
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-55 Annual Fuel Savings in Calendar Years 2025,2035 and 2050 by Heavy-Duty Vehicle Category -
Alternative 4 vs. Alt la using Analysis Method Ba
CY
2025
2035
2050
VEHICLE CATEGORY
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
HD Pickups and Vans
Vocational
Tractor-Trailers
Total
DIESEL SAVINGS
(BILLION GALLONS)
0.2
0.3
2.6
3.1
0.6
1.0
7.2
8.8
0.8
1.4
10.1
12.3
GASOLINE SAVINGS
(BILLION GALLONS)
0.2
0.1
0
0.3
0.6
0.3
0
0.9
0.8
0.3
0
1.1
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 la, 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
5-43
-------
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 Heavy-
Duty Vehicle Category - Preferred Alternative vs. Alt la using Analysis Method Ba
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-0.6
-0.7
-7.1
-8.4
-2.9
-3.6
-22.6
-29.1
-4.0
-5.1
-32.8
-41.9
CH4
(MMT CO2EQ)
-0.1
-0.1
-0.7
-0.9
-0.3
-0.4
-2.3
-3.0
-0.5
-0.6
-3.3
-4.4
N2O
(MMT CO2EQ)
-0.003
-0.004
-0.03
-0.04
-0.02
-0.02
-0.1
-0.1
-0.02
-0.03
-0.2
-0.2
TOTAL
UPSTREAM
(MMT CO2EQ)
-0.7
-0.8
-7.8
-9.3
-3.2
-4.0
-25.0
-32.2
-4.5
-5.7
-36.3
-46.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
5-44
-------
Table 5-57 Annual Upstream GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 by Heavy-
Duty Vehicle Category - Alternative 4 vs. Alt la using Analysis Method Ba
CY
2025
2035
2050
VEHICLE
CATEGORY
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
HD Pickups and
Vans
Vocational
Tractor-Trailers
Total
CO2
(MMT)
-0.8
-1.2
-8.4
-10.4
-3.0
-3.8
-23.3
-30.1
-4.0
-5.1
-32.9
-42.0
CH4
(MMT CO2EQ)
-0.1
-0.1
-0.8
-1.0
-0.4
-0.4
-2.4
-3.2
-0.5
-0.6
-3.3
-4.4
N2O
(MMT CO2EQ)
-0.005
-0.01
-0.04
-0.1
-0.02
-0.02
-0.1
-0.1
-0.02
-0.03
-0.2
-0.2
TOTAL
UPSTREAM
(MMT CO2EQ)
-1.0
-1.3
-9.2
-11.5
-3.4
-4.2
-25.8
-33.4
-4.5
-5.7
-36.4
-46.6
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 CCheq in 2025, 253,118 metric
tons of CCheq in 2035, and 299,590 metric tons CCheq 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 la, 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.
5-45
-------
Table 5-58 Annual Total GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 - Preferred
Alternative vs. Alt la using Analysis Method Ba
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-27.4
-9.3
-0.1
-36.8
2035 (MMT CO2EQ)
-94.7
-32.2
-0.25
-127.2
2050 (MMT CO2EQ)
-136.5
-46.5
-0.3
-183.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 5-59 Annual Total GHG Emissions Impacts in Calendar Years 2025,2035 and 2050 - Alternative 4 vs.
Alt la using Analysis Method B'
CY
Downstream
Upstream
HFC
Total
2025 (MMT CO2EQ)
-33.7
-11.5
-0.1
-45.3
2035 (MMT CO2EQ)
-98.3
-33.4
-0.25
-132.0
2050 (MMT CO2EQ)
-136.9
-46.6
-0.3
-183.8
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
5-46
-------
1200
1100
1000
sr
8'
900
800
700
600
Phase 1
Reduction
Phase 2
Proposal
Reduction
Calendar Year
Pre-Phase 1 Phase 2-Less Dynamic Baseline - Phase 2 - More Dynamic Baseline Phase 2 - Preferred Alternative
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 lifetime1" of heavy-duty vehicles, relative to
Alternative la. 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.
5-47
-------
Table 5-60 Lifetime GHG Reductions and Fuel Savings by Heavy-Duty Vehicle Category - Summary for
Model Years 2018-2029 using Analysis Method Ba
NO-ACTION ALTERNATIVE (BASELINE)
Fuel Savings (Billion Gallons)
HD Pickups and Vans
Vocational
Tractor/Trailers
Total GHG Reductions (MMT CO2eq)
HD Pickups and Vans
Vocational
Tractor/Trailers
ALTERNATIVE 3
(PROPOSED)
la (Less Dynamic)
75.8
8.0
8.3
59.5
1,036.4
97.5
109.7
829.2
ALTERNATIVE 4
la (Less Dynamic)
85.4
9.5
10.9
65.0
1,163.1
114.8
143.0
905.3
Note:
a For an explanation of analytical Methods A and B, please see Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
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 Ba
Phase 1
MY 20 14-20 18
MY 20 19-2029
Phase 2 - Proposed
MY 20 18-2029
Combined Total
TOTAL GHG REDUCTIONS
(MMT CO2EQ)
313
1,020
1,036
2,369
FUEL SAVINGS
(BILLION GALLONS)
23
75
76
174
Note:
a For an explanation of analytical Methods A and B, please see Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
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
5-48
-------
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 Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM2.5
SOx
VOC
CY2025
US Short
Tons
-8
-669
-97
-123
-26,485
-2,100
-92,444
643
-229
-13,161
% Reduction
-3%
-10%
-10%
-6%
-3%
-12%
-7%
2%
-4%
-6%
CY2035
US Short
Tons
-21
-1,882
-272
-347
-75,199
-5,910
-260,949
1,722
-715
-38,051
% Reduction
-12%
-31%
-31%
-19%
-8%
-32%
-28%
8%
-13%
-21%
CY2050
US Short
Tons
-30
-2,667
-385
-490
-106,756
-8,376
-370,663
2,410
-1,026
-54,139
% Reduction
-16%
-36%
-37%
-24%
-9%
-37%
-34%
10%
-15%
-26%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-63 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035
and 2050 - Preferred Alternative vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-8
-669
-97
-123
-26,576
-2,100
-93,197
632
-232
-13,210
% Reduction
-3%
-10%
-10%
-6%
-3%
-12%
-8%
2%
-4%
-6%
CY2035
US Short
Tons
-21
-1,880
-271
-346
-75,571
-5,904
-266,890
1,635
-776
-38,964
% Reduction
-12%
-31%
-31%
-19%
-8%
-32%
-29%
8%
-14%
-22%
CY2050
US Short
Tons
-30
-2,664
-384
-490
-107,287
-8,369
-380,303
2,267
-1,125
-55,628
% Reduction
-16%
-36%
-37%
-24%
-9%
-37%
-35%
9%
-16%
-26%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-49
-------
Table 5-64 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035
and 2050 - Alternative 4 vs. Alt Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-8
-669
-97
-124
-26,705
-2,100
-93,984
619
-280
-13,925
% Reduction
-2%
-10%
-10%
-6%
-3%
-12%
-8%
2%
-5%
-7%
CY2035
US Short
Tons
-21
-1,882
-271
-347
-75,407
-5,908
-262,150
1,705
-742
-38,472
% Reduction
-12%
-31%
-31%
-19%
-8%
-32%
-28%
8%
-13%
-22%
CY2050
US Short
Tons
-30
-2,667
-385
-490
-106,874
-8,375
-370,704
2,412
-1,029
-54,150
% Reduction
-16%
-36%
-37%
-24%
-9%
-37%
-34%
10%
-15%
-26%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-65 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035
and 2050 - Alternative 4 vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-8
-668
-97
-124
-26,821
-2,099
-94,724
609
-282
-13,971
% Reduction
-2%
-10%
-10%
-6%
-3%
-12%
-8%
2%
-5%
-7%
CY2035
US Short
Tons
-21
-1,880
-271
-346
-75,795
-5,902
-268,075
1,618
-803
-39,383
% Reduction
-12%
-31%
-31%
-19%
-8%
-32%
-29%
8%
-14%
-22%
CY2050
US Short
Tons
-29
-2,664
-384
-489
-107,414
-8,367
-380,328
2,269
-1,127
-55,638
% Reduction
-16%
-36%
-37%
-24%
-9%
-37%
-35%
9%
-16%
-26%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5.5.1.1.2
Upstream Impacts
5-50
-------
Table 5-66 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035 and
2050 - Preferred Alternative vs. Alt Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-1
-3
0
-21
-3,798
-19
-9,472
-1,019
-5,983
-3,066
% Reduction
-5%
-3%
-4%
-4%
-5%
-5%
-5%
-5%
-5%
-4%
CY2035
US Short
Tons
-3
-10
-1
-74
-12,087
-59
-30,333
-3,257
-19,190
-11,029
% Reduction
-14%
-11%
-12%
-13%
-14%
-14%
-14%
-14%
-14%
-13%
CY2050
US Short
Tons
-5
-15
-2
-104
-17,120
-84
-42,839
-4,609
-27,074
-15,386
% Reduction
-17%
-13%
-15%
-15%
-17%
-17%
-17%
-17%
-17%
-15%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-67 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035 and
2050 - Preferred Alternative vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-1
-3
0
-22
-3,911
-19
-9,787
-1,051
-6,189
-3,193
% Reduction
-5%
-3%
-4%
-4%
-5%
-5%
-5%
-5%
-5%
-4%
CY2035
US Short
Tons
-4
-11
-1
-80
-13,153
-65
-33,021
-3,545
-20,896
-11,848
% Reduction
-15%
-12%
-13%
-14%
-15%
-15%
-15%
-15%
-15%
-13%
CY2050
US Short
Tons
-5
-16
-2
-113
-18,794
-92
-47,028
-5,058
-29,726
-16,625
% Reduction
-18%
-14%
-15%
-16%
-18%
-18%
-18%
-18%
-18%
-16%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-51
-------
Table 5-68 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035 and
2050 - Alternative 4 vs. Alt Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-1
-4
-1
-28
-4,679
-23
-11,708
-1,259
-7,402
-4,081
% Reduction
-6%
-5%
-5%
-5%
-6%
-6%
-6%
-6%
-6%
-5%
CY2035
US Short
Tons
-3
-11
-1
-78
-12,640
-62
-31,769
-3,408
-20,107
-11,717
% Reduction
-15%
-12%
-13%
-13%
-15%
-15%
-15%
-15%
-15%
-13%
CY2050
US Short
Tons
-5
-15
-2
-105
-17,263
-85
-43,263
-4,649
-27,356
-15,645
% Reduction
-17%
-14%
-15%
-16%
-17%
-17%
-17%
-17%
-17%
-15%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 5-69 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035 and
2050 - Alternative 4 vs. Alt la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-1
-4
-1
-29
-4,816
-24
-12,098
-1,298
-7,658
-4,251
% Reduction
-6%
-5%
-5%
-5%
-6%
-6%
-6%
-6%
-6%
-5%
CY2035
US Short
Tons
-4
-12
-1
-84
-13,720
-67
-34,501
-3,700
-21,843
-12,541
% Reduction
-16%
-12%
-13%
-14%
-16%
-16%
-16%
-16%
-16%
-14%
CY2050
US Short
Tons
-5
-16
-2
-114
-18,945
-93
-47,477
-5,101
-30,024
-16,870
% Reduction
-18%
-14%
-16%
-17%
-18%
-18%
-18%
-18%
-18%
-16%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5.5.1.1.3
Total Impacts
5-52
-------
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 Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-9
-672
-97
-145
-30,282
-2,119
-101,916
-376
-6,213
-16,227
% Reduction
-3%
-10%
-10%
-5%
-3%
-11%
-7%
-1%
-5%
-6%
CY2035
US Short
Tons
-25
-1,893
-273
-421
-87,286
-5,969
-291,282
-1,535
-19,905
-49,080
% Reduction
-13%
-30%
-31%
-18%
-8%
-32%
-26%
-3%
-14%
-18%
CY2050
US Short
Tons
-34
-2,682
-387
-595
-123,876
-8,460
-413,501
-2,199
-28,101
-69,525
% Reduction
-16%
-36%
-37%
-22%
-10%
-37%
-31%
-4%
-17%
-22%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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 la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-9
-672
-97
-145
-30,487
-2,119
-102,983
-419
-6,421
-16,403
% Reduction
-3%
-10%
-10%
-5%
-3%
-11%
-7%
-1%
-5%
-6%
CY2035
US Short
Tons
-25
-1,891
-273
-425
-88,724
-5,969
-299,911
-1,910
-21,672
-50,812
% Reduction
-13%
-30%
-31%
-18%
-8%
-32%
-26%
-4%
-15%
-19%
CY2050
US Short
Tons
-35
-2,680
-386
-603
-126,081
-8,461
-427,332
-2,791
-30,850
-72,253
% Reduction
-16%
-36%
-37%
-22%
-10%
-37%
-32%
-5%
-18%
-23%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-53
-------
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 Ib using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-9
-673
-97
-152
-31,383
-2,123
-105,693
-639
-7,682
-18,006
% Reduction
-3%
-10%
-10%
-6%
-3%
-11%
-7%
-1%
-6%
-6%
CY2035
US Short
Tons
-25
-1,893
-273
-426
-88,047
-5,970
-293,918
-1,703
-20,849
-50,189
% Reduction
-13%
-30%
-31%
-18%
-8%
-32%
-26%
-4%
-15%
-19%
CY2050
US Short
Tons
-34
-2,682
-387
-595
-124,137
-8,460
-413,967
-2,237
-28,385
-69,796
% Reduction
-16%
-36%
-37%
-22%
-10%
-37%
-31%
-4%
-17%
-22%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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 la using Analysis Method Aa
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
sox
VOC
CY2025
US Short
Tons
-9
-672
-97
-153
-31,637
-2,123
-106,822
-689
-7,941
-18,222
% Reduction
-3%
-10%
-10%
-6%
-3%
-11%
-7%
-1%
-6%
-6%
CY2035
US Short
Tons
-25
-1,891
-273
-430
-89,514
-5,969
-302,575
-2,082
-22,646
-51,924
% Reduction
-13%
-30%
-31%
-18%
-8%
-32%
-26%
-5%
-16%
-19%
CY2050
US Short
Tons
-35
-2,679
-386
-603
-126,360
-8,460
-427,805
-2,833
-31,151
-72,509
% Reduction
-16%
-36%
-37%
-22%
-10%
-37%
-32%
-5%
-18%
-23%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-54
-------
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
NO-ACTION ALTERNATIVE
(BASELINE)
NOX
HD Pickups and Vans
Vocational
Tractor/Trailers
PM2.5
HD Pickups and Vans
Vocational
Tractor/Trailers
SOX
HD Pickups and Vans
Vocational
Tractor/Trailers
ALTERNATIVE 3
(PROPOSED)
Ib (More
Dynamic)
2,359,548
24,663
15,810
2,319,075
13,496
2,502
2,509
8,485
167,415
19,221
17,295
130,899
la (Less
Dynamic)
2,409,738
27,772
15,810
2,366,156
15,706
2,842
2,509
10,355
177,948
21,813
17,295
138,840
ALTERNATIVE 4
Ib (More
Dynamic)
2,420,931
30,213
24,265
2,366,453
17,524
3,038
3,421
11,065
189,670
23,395
22,416
143,859
la (Less
Dynamic)
2,472,021
34,222
24,265
2,413,534
19,839
3,484
3,421
12,934
200,992
26,776
22,416
151,800
Note:
a For an explanation of analytical Methods A and B, please see Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
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 la) 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 la. 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 non-
GHG 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 paniculate 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.
5-55
-------
volatile organic compounds (VOC), carbon monoxide (CO), fine particulate matter (PM2.s), 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 la using Analysis Method B a
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOx
PM2.5
SOx
VOC
CY2025
US Short
Tons
-8
-670
-97
-125
-25,824
-2,102
-93,220
634
-254
-13,440
% Reduction
-2.6%
-10.3%
-9.9%
-5.9%
-1.7%
-11.5%
-7.5%
1.6%
-4.8%
-6.4%
CY2035
US Short
Tons
-22
-1,884
-272
-353
-72,960
-5,911
-267,125
1,631
-876
-40,148
% Reduction
-15.1%
-31.0%
-31.6%
-21.0%
-6.0%
-32.1%
-29.1%
7.6%
-15.0%
-21.7%
CY2050
US Short
Tons
-31
-2,671
-385
-501
-103,887
-8,379
-380,721
2,257
-1,264
-57,308
% Reduction
-19.6%
-36.5%
-37.3%
-25.7%
-7.6%
-37.5%
-35.2%
9.1%
-18.1%
-26.1%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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.
5-56
-------
Table 5-76 Annual Downstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035
and 2050 - Alternative 4 vs. Alt la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-8
-670
-97
-126
-25,919
-2,101
-94,787
610
-313
-14,310
% Reduction
-2.6%
-10.3%
-9.9%
-5.9%
-1.7%
-11.5%
-7.6%
1.5%
-5.9%
-6.8%
CY2035
US Short
Tons
-22
-1,884
-272
-354
-73,041
-5,910
-268,373
1,611
-909
-40,640
% Reduction
-15.1%
-31.0%
-31.6%
-21.0%
-6.0%
-32.1%
-29.2%
7.5%
-15.6%
-22.0%
CY2050
US Short
Tons
-31
-2,671
-385
-501
-103,891
-8,378
-380,810
2,256
-1,267
-57,348
% Reduction
-19.6%
-36.5%
-37.3%
-25.7%
-7.6%
-37.5%
-35.2%
9.1%
-18.1%
-26.1%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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 parti culate 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 PMi.s Emissions of Further PMi.s Control on APUs using Analysis Method
Ba
CY
2035
2050
PROPOSED
PROGRAM
INVENTORY
WITHOUT
FURTHER PM2 5
CONTROL ON
APUS
23,083
26,932
PROPOSED
PROGRAM
INVENTORY
WITH FURTHER
PM2 5 CONTROL
ON APUS
19,999
22,588
NET IMPACT OF
FURTHER PM2 5
CONTROL ON
APUS
-3,084
-4,344
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble
Section X.A.I
5-57
-------
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 CCh 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).
- Phase 1 Control . Phase 2 Reference -B-Phase 2 Control
120 -
133
s »
Q.
0.
40
20
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 MOVES2014.8 The
results are shown in Table 5-78. The differences in downstream reduction estimates between
5 The emissions modeling for Phase 1 was performed using MOVES2010a.
5-58
-------
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 Ba
ICY
2050
NOx
403,915
voc
69,415
sox
2,111
PM2.5fe
-1,890
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X. A. 1
* Negative reduction reflects an increase in emissions.
5.5.2.7.2
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 la. 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 la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOx
PM25
SOX
VOC
CY2025
US Short
Tons
-1
-4
-0.5
-24
-3,798
-19
-9,282
-1,020
-5,817
-3,283
% Reduction
-5.0%
-3.0%
-3.4%
-3.8%
-4.9%
-4.7%
-4.9%
-4.9%
-4.9%
-3.7%
CY2035
US Short
Tons
-4
-18
-2
-92
-13,001
-67
-31,782
-3,514
-19,902
-12,724
% Reduction
-15.3%
-11.9%
-12.7%
-13.4%
-15.3%
-14.9%
-15.3%
-15.2%
-15.3%
-13.2%
CY2050
US Short
Tons
-5
-26
-3
-132
-18,772
-98
-45,888
-5,072
-28,736
-18,214
% Reduction
-18.4%
-14.6%
-15.5%
-16.3%
-18.4%
-18.0%
-18.4%
-18.2%
-18.4%
-16.1%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
5-59
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Table 5-80 Annual Upstream Impacts of Heavy-Duty Non-GHG Emissions in Calendar Years 2025,2035 and
2050 - Alternative 4 vs. Alt la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM2.5
SOx
VOC
CY2025
US Short
Tons
-1
-6
-1
-32
-4,661
-24
-11,393
-1,256
-7,137
-4,342
% Reduction
-6.1%
-4.3%
-4.7%
-5.0%
-6.1%
-5.9%
-6.1%
-6.0%
-6.1%
-4.9%
CY2035
US Short
Tons
-4
-20
-2
-97
-13,485
-70
-32,965
-3,647
-20,641
-13,326
% Reduction
-15.9%
-12.6%
-13.3%
-14.0%
-15.9%
-15.5%
-15.9%
-15.7%
-15.9%
-13.8%
CY2050
US Short
Tons
-5
-26
-3
-133
-18,812
-97
-45,986
-5,083
-28,797
-18,273
% Reduction
-18.4%
-14.7%
-15.5%
-16.3%
-18.4%
-18.0%
-18.4%
-18.3%
-18.4%
-16.1%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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.1 The results are shown both in changes in absolute
tons and in percent reductions from the less dynamic reference to the alternatives for the heavy-
duty 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.
5-60
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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 la using Analysis Method Ba
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM25
SOX
VOC
CY2025
US Short
Tons
-9
-674
-97
-149
-29,622
-2,121
-102,502
-386
-6,070
-16,724
% Reduction
-2.7%
-10.1%
-9.8%
-5.4%
-1.9%
-11.4%
-7.2%
-0.6%
-4.9%
-5.6%
CY2035
US Short
Tons
-25
-1,902
-274
-445
-85,961
-5,978
-298,907
-1,883
-20,777
-52,872
% Reduction
-15.1%
-30.5%
-31.3%
-18.8%
-6.6%
-31.7%
-26.6%
-4.2%
-15.3%
-18.8%
CY2050
US Short
Tons
-36
-2,697
-388
-633
-122,659
-8,475
-426,610
-2,815
-30,000
-75,521
% Reduction
-19.4%
-36.0%
-36.9%
-22.9%
-8.4%
-37.0%
-32.1%
-5.4%
-18.4%
-22.7%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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 la using Analysis Method B a
POLLUTANT
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
CO
Formaldehyde
NOX
PM2.5
SOx
VOC
CY2025
US Short
Tons
-9
-676
-97
-157
-30,580
-2,125
-106,180
-646
-7,450
-18,652
% Reduction
-2.8%
-10.1%
-9.8%
-5.7%
-1.9%
-11.4%
-7.4%
-1.1%
-6.1%
-6.2%
CY2035
US Short
Tons
-26
-1,903
-274
-450
-86,526
-5,980
-301,339
-2,036
-21,550
-53,966
% Reduction
-15.2%
-30.6%
-31.3%
-18.9%
-6.6%
-31.7%
-26.8%
-4.6%
-15.9%
-19.2%
CY2050
US Short
Tons
-36
-2,697
-388
-634
-122,703
-8,476
-426,796
-2,827
-30,064
-75,621
% Reduction
-19.4%
-36.0%
-36.9%
-22.9%
-8.4%
-37.0%
-32.1%
-5.4%
-18.4%
-22.7%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
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
5-61
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shows the fleet-wide reductions of NOx, PM2.5 and SOx from the preferred alternative and
Alternative 4, relative to Alternative la, through the lifetime11 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
NO-ACTION ALTERNATIVE
(BASELINE)
NOX
HD Pickups and Vans
Vocational
Tractor/Trailers
PM2.5
HD Pickups and Vans
Vocational
Tractor/Trailers
SOx
HD Pickups and Vans
Vocational
Tractor/Trailers
ALTERNATIVE 3
(PROPOSED)
la (Less Dynamic)
2,399,990
18,024
15,810
2,366,156
15,206
2,342
2,509
10,355
169,436
13,301
17,295
138,840
ALTERNATIVE 4
la (Less Dynamic)
2,459,497
21,698
24,265
2,413,534
19,151
2,796
3,421
12,934
189,904
15,688
22,416
151,800
Note:
a For an explanation of analytical Methods A and B, please see Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
' A lifetime of 30 years is assumed in MOVES.
5-62
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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-Inventorv-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. EPA-
HQ-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. EPA-
HQ-OAR-2009-0472-0216
22 The Minnesota refrigerant leakage data: http://www.pca.sMe.nm.us/climatechange/mobileair.htmMeakdata
23 Eastern Research Group. "A Study of R134a Leaks in Heavy Duty Vehicles." CARD Contract 06-342. Presented
during CARD Seminar on January 6, 2011.
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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
(|im, or 10"6 meter) in diameter (for reference, a typical strand of human hair is 70 jim in
diameter and a grain of salt is about 100 jim). 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 ultrafme particles (UFP, aerodynamic diameter <0.1 jim),
"fine" particles (PIVh.s; particles with a nominal mean aerodynamic diameter less than or equal to
2.5 |im), and "thoracic" particles (PMio; particles with a nominal mean aerodynamic diameter
less than or equal to 10 jim). Particles that fall within the size range between PIVb.s and PMio,
are referred to as "thoracic coarse particles" (PMio-2.5, particles with a nominal mean
aerodynamic diameter less than or equal to 10 jim and greater than 2.5 jim). EPA currently has
standards that regulate PM2.5 and PMio.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 PIVfo.s 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 PMio standard
provides protection against effects associated with short-term exposure to thoracic coarse particles (i.e., PMi0-2.5).
6-1
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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 Paniculate 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, PMio-2.5, and ultrafme 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.6
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).0
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 short-
term 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).
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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 PIVfo.s 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 post-
neonatal period (i.e., 1 month to 12 months of age). With regard to cancer effects, "[mjultiple
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 PMio-2.5, the 2009 PM ISA concluded that available evidence was suggestive of a
causal relationship between short-term exposures to PMio-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 PMio-2.5 and various health effects. 17'18>19
For ultrafme 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
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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 short-
term 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.
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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.0 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.
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6.1.1.3 Nitrogen Oxides
6.1.1.3.1 Background on Nitrogen Oxides
Nitrogen dioxide (NCh) is a member of the nitrogen oxide (NOx) family of gases. Most
NCh is formed in the air through the oxidation of nitric oxide (NO) emitted when fuel is burned
at a high temperature. NCh 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 maj or 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
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Oxides of Nitrogen concluded that evidence was "inadequate to infer the presence or absence of
a causal relationship" between short-term NCh exposure and cardiovascular effects as well as
between long-term NCh 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 NCh
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 (SCh), 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. SCh and its 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 SCh. Additional
information on the health effects of SCh 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
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that although copollutant adjustment has varying degrees of influence on the SCh effect
estimates, the effect of SCh 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 SCh on respiratory endpoints occur independent of the effects of other ambient air
pollutants.
Consistent associations between short-term exposure to SCh 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 SCh 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 short-
term exposure to SCh and mortality. Significant associations between short-term exposure to
SCh 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 SCh 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.
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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.
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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 jim), of which
a significant fraction is ultrafme particles (< 0.1 jim). 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 jig/m3 for diesel exhaust measured as diesel particulate matter. This RfC
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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 paniculate 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 |ig/m3. In
2012, EPA revised the annual PM2.5 NAAQS to 12 |ig/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."
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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.11'41 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 jig/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
|ig/m3 for 1-14 days exposure.52'1
6.1.1.7.2 Health Effects ofl,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
ug/m3 benzene in air.
1 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.
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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 x 10'5 per jig/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 |ig/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
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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 x 10'6 per |ig/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
|ig/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 ofAcrolein
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
carcinogen!city.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 |ig/m3 and an RfD of 0.5 jig/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 hyper-
responsiveness. Based on animal data (more pronounced respiratory irritancy in mice with
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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 jig/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 |ig/m3 and 0.7 |ig/m3, respectively.90
6.1.1.7.6 Health Effects of Poly cyclic Organic Matter (POM)
The term poly cyclic organic matter (POM) defines a broad class of compounds that
includes the poly cyclic 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[l,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 |ig/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 A ir 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/'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
1 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 lexicological studies to whether or not the studies provided "general mechanistic support" for
the inferences of causality made on the basis of epidemiology.
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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.
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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.
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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 traffic-
related 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.
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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 |j,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 (COPT))
The HEI panel reviewed available studies examining COPD in the context of traffic-
associated 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 Fun ction
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 FEVi. FEVi and PEF reflect the function of the large airways. FVC
and FEVi, along with their ratio (FVC/FEVi) 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
(FEFys) 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.
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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
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given to protecting visibility in these areas. For more information on visibility see the final 2009
PMISA.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.
Light from
scattered into
tight path
Image-form Ing
light scattered
out of sight path
Sunlight
scattered
Light reflected
from ground
scattered Into
sight path
image-forming
light absorbed
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 SCh 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.
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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.
Produced by NPS Air Resources Division
1 Rainbow Lake, Wl and Bradwell Bay^ FL are Class 1 Areas
where visibility is not an important air quality related value
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
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scene measurements at some of the sites. Aerosol measurements are taken for PMio 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 USD A PLANTS database) have been studied with respect to ozone
sensitivity.
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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 ecosystems0, 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.
0 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.
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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.
Ambtern Air
CofKCntration
Oxidation
SOi > HiSO4
NO, *HNOj
Wet Deposition
», NH4*. NOj .
Dry deposition
NO,, NH,. SO.
Foliar and
nutriorrt effects
Acidification of water * Eutrophication
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 acid-
sensitive lakes and streams. Other factors contribute to the sensitivity of soils and surface waters
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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 factor^ 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. SCh 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 SCh exposures over the growing season.
Besides foliar injury, chronic exposure to low SCh 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. SCh 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 (Hg°), 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 SO42" 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 SO42" 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 ofPM
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 near-
road 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 poly cyclic 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 Participate 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 PIVh.s: an annual standard (12.0 ug/m3) and a 24-hour standard (35 ug/m3)
with a 98th percentile form, and two secondary NAAQS for PIVh.s: an annual standard (15.0
ug/m3) and a 24-hour standard (35 ug/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 ug/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
ug/m3to 12.0 ug/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.*2
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.8
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.1 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 NCh in January 2010.
There are two primary NAAQS for NCh: 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 NCh NAAQS based on data from the existing air quality
monitoring network. EPA and state agencies are working to establish an expanded network of
NCh 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 NCh
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 SCh NAAQS in June 2010. The
current primary NAAQS for SCh 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 SCh 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 ug/m3. Half of the DPM and diesel
exhaust organic gases can be attributed to onroad diesels.
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2005 NATA Estimated Tract Level Total Diesel PM Concentration
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
5th Percentile
25th Percentile
50th Percentile
75th Percentile
95th Percentile
Onroad Contribution to Median Census Tract Concentrations
AMBIENT CONCENTRATION
(ug/m3)
0.03
0.17
0.53
1.22
2.91
50%
Note:
a This table is generated from data contained in the diesel paniculate 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).
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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.
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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 PIVfo.s, NCh, 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 PIVfo.s, 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.
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12US2 domain A, V ^ ~
x,y origin: -2412000rti,ll62W»tJOtn
col: 396 row:246 1 ^ X. V
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 three-
dimensional 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-
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5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-
longitude). The predictions were used to provide one-way dynamic boundary conditions at one-
hour 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 CCh 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)/
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/downloadsl2/US-GHG-Inventory-2012-Main-Text.pdf
z For a complete list of core references from IPCC, USGCPJVCCSP, NRC and others relied upon for development
of the TSD for EPA's Endangerment and Cause or Contribute Findings see Section l(b), specifically, Table 1.1 of
the TSD. (Docket EPA-HQ-OAR-2010-0799)
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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 food-
and 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
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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 "[Continued 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 CCh 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 CCh 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
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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 CCh 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 CCh 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 CCh, N2O, CH4, NOx and SCh
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
^ 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 (CCh), methane
(CH4), nitrous oxide (N2O), reactive gases (CO, NOX, VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and
sulfur dioxide (SCh). MAGICC emulates the global-mean temperature responses of more sophisticated coupled
Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy.
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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 CCh 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-CCh 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 CCh 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 CCh 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 CCh, CFU, N2O, HFCs, and tropospheric ozone. It also includes the effects of
temperature changes on stratospheric ozone and the effects of CFLt emissions on stratospheric
water vapor. Changes in CFLt, NOx, VOC, and CO emissions affect both Os concentrations and
CFLt 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.
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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 SCh) 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 CCh 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 CCh 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 CCh
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 CCh concentrations because CCh
concentrations are only weakly coupled to climate sensitivity through the dependence on
temperature of the rate of ocean absorption of CCh, whereas the magnitude of temperature
change response to CCh changes (and therefore sea level rise) is more tightly coupled to climate
sensitivity in the MAGICC model.
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Change in CO2 Concentration
(Primary Alternative - Baseline)
Q.
Q.
-1.4
2000
2020
2040
2060
2080
2100
Figure 6-7 Estimated Projected Reductions in Atmospheric CCh 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)
Change in Global Mean Temperature
(Primary Alternative - Baseline)
u
V)
01
s
00
01
-0.007
2000
2020
2040
2060
2080
2100
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)
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Change in Global Mean Sea Level Rise
(Primary Alternative - Baseline)
0.00
E
u
-0.06
2000
2020
2040
2060
2080
2100
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 °C) 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, direct!onally 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 CCh emitted into the atmosphere is not removed by
natural processes for millennia, and therefore each unit of CCh 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 (CCh) resulting from the
emissions reductions associated with the proposed alternative. EPA used the program developed
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for CCh System Calculations CO2SYS,221 version 1.05, a program which performs calculations
relating parameters of the carbon dioxide (CCh) system in seawater. The program was
developed by Ernie Lewis at Brookhaven National Laboratory and Doug Wallace at the Institut
fur 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 CCh system
[total alkalinity (TA), total inorganic CCh (TC), pH, and either fugacity (fCCh) or partial
pressure of CCh (pCCh)] 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 CCh concentrations was
calculated to be 784.87 in 2100, the proposed alternative relative to the baseline with a CCh
concentration of 784.11, and a calculation for 1990 with a CCh 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 fCCh or pCCh: pCCh
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]x 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 CCh 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 CCh 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 CCh concentrations which were derived from the MAGICC modeling.
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Table 6-2 Impact of the Proposal's GHG Emissions Reductions on Ocean pH
CLIMATE
SENSITIVITY
3.0
DIFFERENCE
INCO2A
-1.2 ppmv
YEAR
2100
PROJECTED
CHANGE
0.0006
Note:
a Represents the change in atmospheric CCh 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 (CCh concentration, global mean temperature, sea level rise, and ocean pH), in
comparison to the total projected changes, they yield results that are repeatable and direct!onally
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
impactsincluding 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
Atmospheric CCh
Concentration
Global Mean Surface
Temperature
Sea Level Rise
Ocean pH
UNITS
ppmv
°C
cm
pH units
YEAR
2100
2100
2100
2100
PROJECTED CHANGE
-1.1 to -1.2
-0.0026 to -0.0065
-0.023 to -0.057
+0.00063
Note:
a The value for projected change in ocean pH is based on a climate sensitivity of 3.0.
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Protection Agency, Washington, DC, EPA/600/R-08/139F, 2009.
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Protection Agency, Washington, DC, EPA/600/R-08/139F.
3 78 FR 3086 (January 15, 2013) pages 3103-3104.
4 77 FR 38890 (June 29, 2012) pages 38906-38911.
5 78 FR 3086 (January 15, 2013) pages 3103-3104.
6 U.S. EPA. (2009). Integrated Science Assessment for Paniculate Matter (Final Report). U.S. Environmental
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10 78 FR 3104 (January 15, 2013).
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14 78 FR 3086 (January 15, 2013) page 3104.
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18 78 FR 3167-8 (January 15, 2013).
19 77 FR 38947-51 (June 29, 2012).
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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 Ib 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
7-1
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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 light-
duty 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 la, 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 Ib 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
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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 Ib. 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.
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Table 7-1 Estimated Technology Costs per Vehicle for the Preferred Alternative versus the Dynamic Baseline
and using Method A (2012$) a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2050
HD PICKUPS
&VANS
$98
$92
$95
$493
$485
$766
$896
$1,149
$1,248
$1,366
$1,356
$1,357
$1,348
$1,348
VOCATIONAL
$0
$0
$0
$1,152
$1,128
$1,037
$1,766
$1,731
$1,695
$3,381
$3,323
$3,271
$3,222
$3,232
TRACTOR/
TRAILERS
$639
$548
$478
$7,445
$7,273
$6,790
$10,690
$10,476
$10,206
$12,487
$12,292
$12,178
$12,070
$11,981
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section
I.D; for an explanation of the less dynamic baseline, la, and more dynamic
baseline, Ib, please see Preamble Section X. A. 1
As noted in the text, MYs 2018-2020 include costs for trailers only, and in MYs
2021 and later the costs include both tractor and trailer costs. Detailed
technology and package costs for all segments can be found in Chapter 2 of this
draft RIA (notably, see Sections 2.12 and 2.13).
Table 7-2 presents the model year lifetime costs for new technology discounted at 3
percent using Method A. And Table 7-3 presents the model year lifetime costs for new
technology discounted at 7 percent using Method A.
7-4
-------
Table 7-2 Discounted MY Lifetime New Technology Costs of the Preferred Alternative
Vs. the Dynamic Baseline and using Method A
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$61
$55
$55
$280
$269
$412
$479
$608
$654
$704
$688
$677
$4,942
VOCATIONAL
$0
$0
$0
$488
$468
$421
$719
$705
$689
$1,363
$1,321
$1,279
$7,452
TRACTOR/
TRAILERS
$104
$99
$95
$1,013
$976
$898
$1,412
$1,396
$1,369
$1,676
$1,642
$1,618
$12,299
SUM
$165
$154
$150
$1,781
$1,713
$1,731
$2,610
$2,709
$2,712
$3,743
$3,651
$3,574
$24,693
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble
Section X.A.I
Table 7-3 Discounted MY Lifetime New Technology Costs of the Preferred Alternative
Vs. the Dynamic Baseline and using Method A
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$54
$47
$46
$223
$206
$304
$340
$415
$430
$446
$419
$397
$3,327
VOCATIONAL
$0
$0
$0
$381
$352
$305
$501
$473
$445
$847
$790
$736
$4,829
TRACTOR/
TRAILERS
$91
$84
$77
$791
$734
$650
$984
$936
$884
$1,041
$982
$932
$8,186
SUM
$145
$131
$123
$1,395
$1,292
$1,259
$1,825
$1,824
$1,759
$2,334
$2,191
$2,065
$16,342
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-5
-------
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 industry-
wide, 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 industry-
wide (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.6
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.0
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 $5.5 million.
7-6
-------
Table 7-4 and Table 7-5 present the MY lifetime costs for new compliance program
elements at 3 percent and 7 percent, respectively.
Table 7-4 Discounted MY Lifetime Compliance Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
VOCATIONAL
$0.0
$0.0
$0.0
$13.7
$3.4
$3.3
$3.2
$3.1
$3.0
$2.9
$2.8
$2.8
$38.3
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$1.2
$1.1
$1.1
$1.1
$1.0
$1.0
$1.0
$1.0
$0.9
$9.4
SUM
$0.0
$0.0
$0.0
$14.9
$4.5
$4.4
$4.3
$4.1
$4.0
$3.9
$3.8
$3.7
$47.7
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
for an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib,
please see Preamble Section X. A. 1
7-7
-------
Table 7-5 Discounted MY Lifetime Compliance Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
VOCATIONAL
$0.0
$0.0
$0.0
$10.7
$2.5
$2.4
$2.2
$2.1
$1.9
$1.8
$1.7
$1.6
$27.0
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$0.9
$0.9
$0.8
$0.7
$0.7
$0.7
$0.6
$0.6
$0.5
$6.4
SUM
$0.0
$0.0
$0.0
$11.6
$3.4
$3.2
$3.0
$2.8
$2.6
$2.4
$2.3
$2.1
$33.4
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
for an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib,
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&Dan additional $6.5 million per year or $26 million total spent by
vocational vehicle and tractor manufacturers. Importantly, EPA estimates that these costs would
occur during normal R&D and vehicle design cycles for both alternatives 3 and 4. EPA has not
included any additional R&D would be spent by trailer manufacturers since our cost estimates
7-8
-------
include R&D conducted by trailer parts suppliers which are subsequently included in the prices
charged by those suppliers to the trailer manufacturer. Additionally, the markups we have
applied to cover indirect costs (see Chapter 2.12 of this draft RIA) include costs associated with
R&D incurred by the trailer manufacturer.
Table 7-6 through Table 7-7 present the model year lifetime R&D costs discounted at 3
percent and 7 percent, respectively.
Table 7-6 Discounted MY Lifetime R&D Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
VOCATIONAL
$0.0
$0.0
$0.0
$93.4
$90.6
$88.0
$85.4
$0.0
$0.0
$0.0
$0.0
$0.0
$357.5
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$104.9
$101.9
$98.9
$96.0
$0.0
$0.0
$0.0
$0.0
$0.0
$401.7
SUM
$0.0
$0.0
$0.0
$198.3
$192.5
$186.9
$181.5
$0.0
$0.0
$0.0
$0.0
$0.0
$759.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
for an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib,
please see Preamble Section X. A. 1
7-9
-------
Table 7-7 Discounted MY Lifetime R&D Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
VOCATIONAL
$0.0
$0.0
$0.0
$72.9
$68.1
$63.7
$59.5
$0.0
$0.0
$0.0
$0.0
$0.0
$264.3
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$81.9
$76.6
$71.6
$66.9
$0.0
$0.0
$0.0
$0.0
$0.0
$297.0
SUM
$0.0
$0.0
$0.0
$154.9
$144.7
$135.3
$126.4
$0.0
$0.0
$0.0
$0.0
$0.0
$561.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
for an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib,
please see Preamble Section X. A. 1
7.1.1.4 Summary of Vehicle-Related Costs of the Proposed Program using Method A
Table 7-8 presents the model year lifetime costs associated with the preferred alternative
discounted at 3 percent relative to the dynamic baseline and using Method A. Table 7-9 presents
the model year lifetime costs associated with the preferred alternative discounted at 7 percent
relative to the dynamic baseline and using Method A.
7-10
-------
Table 7-8 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$61
$55
$55
$280
$269
$412
$479
$608
$654
$704
$688
$677
$4,942
VOCATIONAL
$0
$0
$0
$595
$563
$512
$807
$708
$692
$1,366
$1,324
$1,282
$7,848
TRACTOR/
TRAILERS
$104
$99
$95
$1,119
$1,079
$998
$1,509
$1,397
$1,370
$1,677
$1,643
$1,619
$12,710
SUM
$165
$154
$150
$1,994
$1,911
$1,922
$2,795
$2,713
$2,716
$3,747
$3,655
$3,578
$25,500
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-9 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$54
$47
$46
$223
$206
$304
$340
$415
$430
$446
$419
$397
$3,327
VOCATIONAL
$0
$0
$0
$465
$423
$371
$562
$475
$446
$849
$792
$738
$5,121
TRACTOR/
TRAILERS
$91
$84
$77
$874
$811
$722
$1,051
$937
$885
$1,042
$983
$932
$8,489
SUM
$145
$131
$123
$1,562
$1,440
$1,397
$1,953
$1,827
$1,761
$2,337
$2,194
$2,067
$16,937
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-11
-------
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
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
GASOLINE REDUCTIONS b
HD
PICKUPS
&VANS
44
41
42
172
173
265
306
678
782
884
894
910
voc
0
0
0
54
54
54
127
130
133
297
300
303
TRACTOR/
TRAILERS
0
0
0
0
0
0
0
0
0
0
0
0
SUM
44
41
42
226
227
319
433
808
915
1181
1194
1213
DIESEL REDUCTIONS
HD
PICKUPS
&VANS
25
20
20
136
136
246
283
309
315
323
326
330
VOC
0
0
0
340
340
340
705
722
738
1,193
1,210
1,222
TRACTOR/
TRAILERS
934
870
840
4,091
4,057
3,958
6,289
6,330
6,359
7,350
7,417
7,563
SUM
959
890
860
4567
4533
4544
7277
7361
7412
8866
8953
9115
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the
less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
7-12
-------
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 ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
REDUCED FUEL EXPENDITURES -
RETAIL
HD
PICKUPS
&VANS
$181
$157
$160
$794
$782
$1,281
$1,446
$2,294
$2,494
$2,686
$2,664
$2,654
$17,592
voc
$0
$0
$0
$1,082
$1,064
$1,044
$2,156
$2,167
$2,172
$3,617
$3,591
$3,555
$20,446
TRACTO
R/
TRAILER
S
$2,744
$2,515
$2,386
$11,422
$11,139
$10,675
$16,663
$16,460
$16,217
$18,381
$18,164
$18,144
$144,911
SUM
$2,925
$2,672
$2,547
$13,297
$12,984
$13,000
$20,264
$20,922
$20,883
$24,684
$24,419
$24,352
$182,949
REDUCED FUEL EXPENDITURES -
UNTAXED
HD
PICKUPS
&VANS
$161
$140
$143
$711
$701
$1,151
$1,303
$2,066
$2,247
$2,423
$2,407
$2,402
$15,854
VOC
$0
$0
$0
$966
$952
$937
$1,937
$1,951
$1,959
$3,267
$3,250
$3,222
$18,442
TRACTO
R/
TRAILER
S
$2,435
$2,238
$2,130
$10,222
$9,991
$9,596
$15,008
$14,855
$14,663
$16,650
$16,482
$16,491
$130,760
SUM
$2,596
$2,378
$2,273
$11,898
$11,645
$11,684
$18,248
$18,872
$18,870
$22,340
$22,138
$22,115
$165,056
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-13
-------
Table 7-12 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(7% Discount Rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
REDUCED FUEL EXPENDITURES -
RETAIL
HD
PICKUPS
&VANS
$130
$109
$107
$510
$483
$762
$828
$1,265
$1,324
$1,373
$1,311
$1,257
$9,459
voc
$0
$0
$0
$689
$653
$617
$1,227
$1,188
$1,147
$1,840
$1,758
$1,676
$10,796
TRACTO
R/
TRAILER
S
$1,954
$1,721
$1,566
$7,197
$6,766
$6,249
$9,401
$8,945
$8,487
$9,264
$8,810
$8,472
$78,832
SUM
$2,084
$1,830
$1,673
$8,395
$7,902
$7,628
$11,457
$11,399
$10,957
$12,476
$11,879
$11,406
$99,087
REDUCED FUEL EXPENDITURES -
UNTAXED
HD
PICKUPS
&VANS
$116
$97
$95
$455
$433
$684
$745
$1,138
$1,191
$1,236
$1,183
$1,136
$8,508
VOC
$0
$0
$0
$614
$583
$553
$1,101
$1,068
$1,033
$1,660
$1,589
$1,517
$9,717
TRACTO
R/
TRAILER
S
$1,729
$1,528
$1,395
$6,426
$6,056
$5,605
$8,451
$8,058
$7,661
$8,378
$7,982
$7,690
$70,958
SUM
$1,845
$1,625
$1,490
$7,495
$7,072
$6,842
$10,297
$10,264
$9,884
$11,274
$10,753
$10,343
$89,184
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
7-14
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Chapter 8 of this draft RIA for a discussion of rebound VMT). In evaluating maintenance costs
associated with the proposal relative to Alternative Ib, 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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029 & later
HD
PICKUPS &
VANS
$0.00
$0.00
$0.00
$1.72
$3.45
$5.17
$6.90
$8.62
$8.62
$8.62
$8.62
$8.62
VOCATIONAL
$0.00
$0.00
$0.00
$6.99
$6.91
$8.12
$17.66
$17.47
$13.50
$21.34
$20.26
$15.99
TRACTOR
$0.00
$0.00
$0.00
$33.16
$32.53
$31.66
$33.50
$32.92
$30.01
$32.27
$31.52
$31.22
TRAILER
$79.92
$77.77
$75.69
$78.81
$75.98
$71.17
$73.97
$72.02
$70.72
$69.44
$68.18
$66.27
TRACTOR/
TRAILER
$67.16
$65.36
$63.60
$99.38
$96.39
$91.46
$95.66
$93.43
$89.44
$90.62
$88.81
$86.91
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The maintenance category includes only the incremental expenditure required to assure that low rolling resistance,
rather than conventional, tires are used throughout the useful life of the vehicle. The agencies request comment and
information on other relevant maintenance costs
Table 7-14 presents the model year lifetime in-use maintenance costsversus the
dynamic baseline and using Method A discounted at 3 percent. Table 7-15 presents the model
year lifetime in-use maintenance costsversus the dynamic baseline and using Method A
discounted at 7 percent.
7-15
-------
Table 7-14 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$3.3
$6.5
$9.4
$12.3
$15.1
$15.0
$14.7
$14.4
$14.0
$104.6
VOCATIONAL
$0.0
$0.0
$0.0
$14.1
$13.5
$15.4
$33.3
$32.7
$25.0
$38.9
$36.2
$27.9
$237.0
TRACTOR/
TRAILERS
$51.2
$49.2
$47.5
$72.6
$69.0
$64.0
$66.6
$65.1
$62.1
$62.3
$60.2
$57.9
$727.6
SUM
$51.2
$49.2
$47.5
$90.0
$88.9
$88.8
$112.2
$112.9
$102.1
$115.9
$110.7
$99.8
$1,069.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-15 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative
Vs. The Dynamic Baseline and using Method A
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$2.1
$3.9
$5.5
$6.9
$8.3
$7.9
$7.4
$7.0
$6.6
$55.7
VOCATIONAL
$0.0
$0.0
$0.0
$8.9
$8.3
$9.1
$18.9
$17.8
$13.1
$19.7
$17.7
$13.1
$126.6
TRACTOR/
TRAILERS
$35.8
$33.2
$30.9
$45.5
$41.7
$37.3
$37.4
$35.2
$32.4
$31.3
$29.2
$27.1
$417.2
SUM
$35.8
$33.2
$30.9
$56.6
$53.9
$51.9
$63.2
$61.3
$53.4
$58.5
$53.9
$46.8
$599.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-16
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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
FID 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.
7-17
-------
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
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance b
$1,855
$0
$0
$0
$0
$0
$0
$0
Maintenance
expenditures
$0
$0
$0
$0
$0
$0
$0
$0
Fuel
expenditures
C
-$522
-$513
-$467
-$429
-$408
-$369
-$325
-$282
Cumulative
expenditures
$1,333
$820
$353
-$76
-$484
-$852
-$1,177
-$1,459
7% Discount Rate
Technology
cost,
taxes,
insurance b
$1,779
$0
$0
$0
$0
$0
$0
$0
Maintenance
expenditures
$0
$0
$0
$0
$0
$0
$0
$0
Fuel
expenditures
C
-$501
-$471
-$411
-$363
-$330
-$286
-$242
-$201
Cumulative
expenditures
$1,278
$806
$395
$32
-$298
-$584
-$826
-$1,027
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
7.2 Vehicle 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 la)
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 periodsthe 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.
7-18
-------
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 light-
duty 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 la, 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 Ib 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.0 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
7-19
-------
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.
7-20
-------
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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2050
HD PICKUPS
&VANS
$0
$0
$0
$558
$551
$834
$991
$1,204
$1,267
$1,342
$1,332
$1,334
$1,324
$1,324
VOCATIONAL
$0
$0
$0
$1,152
$1,128
$1,037
$1,766
$1,731
$1,695
$3,381
$3,323
$3,271
$3,222
$3,232
TRACTOR/
TRAILERS
$639
$613
$592
$7,606
$7,482
$7,016
$10,947
$10,763
$10,513
$12,849
$12,655
$12,548
$12,439
$12,415
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section
ID; for an explanation of the less dynamic baseline, la, and more dynamic baseline,
Ib, please see Preamble Section X. A. 1
As noted in the text, MYs 2018-2020 include costs for trailers only, and in MYs
2021 and later the costs include both tractor and trailer costs, inclusive of
engine-related costs. Detailed technology and package costs for all segments can
be found in Chapter 2 of this draft RIA (notably, see Sections 2.12 and 2.13).
Also, for HD pickups and vans, EPA has taken early costs and spread them over
the years 2021 through 2026 so that those costs can be fully realized while
showing them occurring during the expected years of implementation.
Table 7-18 presents the annual costsversus the less dynamic baseline and using Method
Bfor new engine- and vehicle-related technology along with net present values at 3 percent
and 7 percent. Table 7-19 presents the model year lifetime costsversus the less dynamic
baseline and using Method Bfor new technology discounted at 3 percent. Table 7-20 presents
the model year lifetime costsversus the less dynamic baseline and using Method Bfor new
technology discounted at 7 percent.
7-21
-------
Table 7-18 Annual Technology Costs and Net Present Values Associated with the Preferred Alternative vs.
the Less Dynamic Baseline and using Method B
($Millionsof2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUPS
&VANS
$0
$0
$0
$328
$324
$491
$590
$731
$788
$844
$847
$854
$855
$892
$935
$1,024
$13,475
$6,461
VOCATIONAL
$0
$0
$0
$591
$585
$541
$951
$961
$967
$1,972
$1,969
$1,963
$1,956
$2,076
$2,204
$2,414
$29,183
$13,502
TRACTOR/
TRAILERS
$116
$113
$112
$1,254
$1,252
$1,192
$1,913
$1,955
$1,980
$2,493
$2,519
$2,559
$2,589
$2,888
$3,177
$3,548
$43,268
$20,553
SUM
$116
$113
$112
$2,173
$2,161
$2,224
$3,455
$3,647
$3,736
$5,309
$5,334
$5,376
$5,399
$5,856
$6,316
$6,987
$85,926
$40,516
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-22
-------
Table 7-19 Discounted MY Lifetime New Technology Costs of the Preferred Alternative
Vs. the Less Dynamic Baseline and using Method B
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0
$0
$0
$271
$260
$382
$446
$536
$561
$583
$568
$556
$4,164
VOCATIONAL
$0
$0
$0
$488
$468
$421
$719
$705
$689
$1,363
$1,321
$1,279
$7,452
TRACTOR/
TRAILERS
$104
$99
$95
$1,035
$1,003
$927
$1,445
$1,434
$1,410
$1,723
$1,690
$1,667
$12,632
SUM
$104
$99
$95
$1,794
$1,731
$1,730
$2,610
$2,674
$2,660
$3,670
$3,580
$3,502
$24,248
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-20 Discounted MY Lifetime New Technology Costs of the Preferred Alternative
Vs. the Less Dynamic Baseline and using Method B
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0
$0
$0
$212
$195
$276
$311
$360
$362
$363
$340
$320
$2,738
VOCATIONAL
$0
$0
$0
$381
$352
$305
$501
$473
$445
$847
$790
$736
$4,829
TRACTOR/
TRAILERS
$91
$84
$77
$808
$754
$671
$1,007
$961
$910
$1,071
$1,011
$960
$8,405
SUM
$91
$84
$77
$1,401
$1,302
$1,252
$1,818
$1,793
$1,717
$2,280
$2,141
$2,017
$15,973
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-23
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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 industry-
wide, 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 industry-
wide (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 $5.5 million.
7-24
-------
Table 7-21 through Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-23 present the annual costs for new compliance program elements along with net
present values at 3 percent and 7 percent, and the model year lifetime compliance costs
discounted at 3 percent and 7 percent, respectively.
Table 7-21 Annual Compliance Costs and Net Present Values Associated with the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
($Millionsof2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$16.6
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$4.2
$80.8
$44.2
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$1.4
$23.7
$12.1
SUM
$0.0
$0.0
$0.0
$18.1
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$5.7
$104.4
$56.4
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-25
-------
Table 7-22 Discounted MY Lifetime Compliance Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$13.7
$3.4
$3.3
$3.2
$3.1
$3.0
$2.9
$2.8
$2.8
$000
JO.J
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$1.2
$1.1
$1.1
$1.1
$1.0
$1.0
$1.0
$1.0
$0.9
$9.4
SUM
$0.0
$0.0
$0.0
$14.9
$4.5
$4.4
$4.3
$4.1
$4.0
$3.9
$3.8
$3.7
$47.7
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-23 Discounted MY Lifetime Compliance Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$10.7
$2.5
$2.4
$2.2
$2.1
$1.9
$1.8
$1.7
$1.6
$27.0
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$0.9
$0.9
$0.8
$0.7
$0.7
$0.7
$0.6
$0.6
$0.5
$6.4
SUM
$0.0
$0.0
$0.0
$11.6
$3.4
$3.2
$3.0
$2.8
$2.6
$2.4
$2.3
$2.1
$33.4
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-26
-------
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&Dan additional $6.5 million per year or $26 million total spent by
vocational vehicle and tractor manufacturers. Importantly, EPA estimates that these costs would
occur during normal R&D and vehicle design cycles for both alternatives 3 and 4. EPA has not
included any additional R&D would be spent by trailer manufacturers since our cost estimates
include R&D conducted by trailer parts suppliers which are subsequently included in the prices
charged by those suppliers to the trailer manufacturer. Additionally, the markups we have
applied to cover indirect costs (see Chapter 2.12 of this draft RIA) include costs associated with
R&D incurred by the trailer manufacturer.
Table 7-24 through Table 7-26 present the annual costs for R&D spending along with net
present values at 3 percent and 7 percent, and the model year lifetime R&D costs discounted at 3
percent and 7 percent, respectively.
7-27
-------
Table 7-24 Annual R&D Costs and Net Present Values Associated with the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
($Millionsof2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$113.1
$113.1
$113.1
$113.1
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$357.5
$264.3
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$127.1
$127.1
$127.1
$127.1
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$401.7
$297.0
SUM
$0.0
$0.0
$0.0
$240.3
$240.3
$240.3
$240.3
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$759.2
$561.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-28
-------
Table 7-25 Discounted MY Lifetime R&D Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$93.4
$90.6
$88.0
$85.4
$0.0
$0.0
$0.0
$0.0
$0.0
$357.5
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$104.9
$101.9
$98.9
$96.0
$0.0
$0.0
$0.0
$0.0
$0.0
$401.7
SUM
$0.0
$0.0
$0.0
$198.3
$192.5
$186.9
$181.5
$0.0
$0.0
$0.0
$0.0
$0.0
$759.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-26 Discounted MY Lifetime R&D Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
VOCATIONAL
$0.0
$0.0
$0.0
$72.9
$68.1
$63.7
$59.5
$0.0
$0.0
$0.0
$0.0
$0.0
$264.3
TRACTOR/
TRAILERS
$0.0
$0.0
$0.0
$81.9
$76.6
$71.6
$66.9
$0.0
$0.0
$0.0
$0.0
$0.0
$297.0
SUM
$0.0
$0.0
$0.0
$154.9
$144.7
$135.3
$126.4
$0.0
$0.0
$0.0
$0.0
$0.0
$561.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-29
-------
7.2.1.4 Summary of Vehicle-Related Costs of the Program using Method B
Table 7-27 presents the annual new vehicle costs (including engine-related costs)
associated with the preferred alternative for HD pickups and vans, vocational vehicles, and
tractor and trailer programs along with net present values at 3 percent and 7 percent. This table
presents costs relative to the less dynamic baseline and using the MOVES analysis of all vehicle
categories (Method B). Table 7-28 presents the model year lifetime costs associated with the
preferred alternative discounted at 3 percent relative to the less dynamic baseline and using
Method B. Table 7-29 presents the model year lifetime costs associated with the preferred
alternative discounted at 7 percent relative to the less dynamic baseline and using Method B.
Table 7-27 Annual Vehicle-Related Costs and Net Present Values Associated with the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
($Millionsof2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUPS
&VANS
$0
$0
$0
$328
$324
$491
$590
$731
$788
$844
$847
$854
$855
$892
$935
$1,024
$13,475
$6,461
VOCATIONAL
$0
$0
$0
$721
$702
$659
$1,069
$965
$972
$1,976
$1,973
$1,967
$1,960
$2,080
$2,209
$2,418
$29,621
$13,810
TRACTOR/
TRAILERS
$116
$113
$112
$1,382
$1,381
$1,320
$2,042
$1,956
$1,982
$2,495
$2,520
$2,560
$2,591
$2,890
$3,179
$3,550
$43,693
$20,862
SUM
$116
$113
$112
$2,432
$2,407
$2,470
$3,701
$3,653
$3,742
$5,315
$5,340
$5,381
$5,405
$5,862
$6,322
$6,992
$86,789
$41,133
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-30
-------
Table 7-28 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0
$0
$0
$271
$260
$382
$446
$536
$561
$583
$568
$556
$4,164
VOCATIONAL
$0
$0
$0
$595
$563
$512
$807
$708
$692
$1,366
$1,324
$1,282
$7,848
TRACTOR/
TRAILERS
$104
$99
$95
$1,141
$1,106
$1,027
$1,542
$1,435
$1,411
$1,724
$1,691
$1,668
$13,044
SUM
$104
$99
$95
$2,007
$1,928
$1,921
$2,795
$2,678
$2,664
$3,673
$3,583
$3,506
$25,055
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-29 Discounted MY Lifetime Vehicle-Related Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0
$0
$0
$212
$195
$276
$311
$360
$362
$363
$340
$320
$2,738
VOCATIONAL
$0
$0
$0
$465
$423
$371
$562
$475
$446
$849
$792
$738
$5,121
TRACTOR/
TRAILERS
$91
$84
$77
$891
$832
$743
$1,074
$962
$911
$1,072
$1,012
$960
$8,709
SUM
$91
$84
$77
$1,567
$1,450
$1,390
$1,947
$1,796
$1,719
$2,283
$2,143
$2,019
$16,568
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-31
-------
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
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
GASOLINE REDUCTIONS
HD
PICKUPS
&VANS
0
0
0
5
19
42
73
113
161
217
270
321
370
561
674
783
voc
0
0
0
5
10
15
26
37
48
74
99
123
147
240
294
343
TRACTOR/
TRAILERS
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SUM
0
0
0
10
29
57
99
151
210
291
369
445
516
801
968
1,127
DIESEL REDUCTIONS
HD
PICKUPS
&VANS
0
0
0
5
20
43
76
117
167
224
280
333
383
579
690
803
VOC
0
0
0
29
58
86
146
205
263
357
449
538
622
962
1,169
1,390
TRACTOR/
TRAILERS
74
150
227
489
817
1,147
1,674
2,202
2,722
3,308
3,871
4,407
4,918
6,975
8,349
10,117
SUM
74
150
227
523
894
1,276
1,895
2,523
3,152
3,890
4,600
5,278
5,924
8,517
10,209
12,310
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-32
-------
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
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
GASOLINE REDUCTIONS
HD
PICKUPS
&VANS
0
0
0
58
162
263
366
472
582
685
692
696
VOC
0
0
0
54
54
54
127
130
133
297
300
303
TRACTOR/
TRAILERS
0
0
0
0
0
0
0
0
0
0
0
0
SUM
0
0
0
113
216
317
493
602
714
982
992
999
DIESEL REDUCTIONS
HD
PICKUPS
&VANS
0
0
0
59
164
267
373
481
593
699
707
712
VOC
0
0
0
340
340
340
705
722
738
1,193
1,210
1,222
TRACTOR/
TRAILERS
754
745
738
4,025
4,064
4,096
6,551
6,763
6,958
8,092
8,265
8,427
SUM
754
745
738
4,424
4,568
4,703
7,628
7,967
8,289
9,984
10,181
10,360
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 $5.2 billion by 2050 as shown in Table 7-32.
Table 7-33 presents the model year lifetime fuel savingsversus the less dynamic baseline and
using Method Bdiscounted at 3 percent. Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-33
-------
Table 7-34 presents the model year lifetime costs fuel savingsversus the less dynamic baseline
and using Method Bdiscounted at 7 percent. Note that in Chapters 8 and 11 of this draft RIA,
the overall benefits and costs of the rulemaking are presented and only the pre-tax fuel
expenditure impacts are presented there.
Table 7-32 Annual Reductions in Fuel Expenditures and Net Present Values due to the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(Millions of 2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
REDUCED FUEL EXPENDITURES -
RETAIL
HD
PICKUPS
&VANS
$0
$0
$0
$36
$137
$303
$536
$838
$1,206
$1,643
$2,062
$2,478
$2,877
$4,633
$5,896
$6,859
$59,038
$24,187
voc
$0
$0
$0
$124
$251
$381
$656
$937
$1,218
$1,708
$2,182
$2,658
$3,120
$5,175
$6,681
$7,917
$66,542
$27,169
TRACTOR/
TRAILERS
$261
$540
$834
$1,830
$3,117
$4,435
$6,558
$8,756
$10,953
$13,508
$15,937
$18,349
$20,677
$31,160
$39,501
$47,862
$418,711
$176,228
SUM
$261
$540
$834
$1,989
$3,505
$5,119
$7,750
$10,531
$13,378
$16,859
$20,181
$23,485
$26,675
$40,968
$52,078
$62,638
$544,290
$227,584
REDUCED FUEL EXPENDITURES -
UNTAXED
HD
PICKUPS
&VANS
$0
$0
$0
$31
$120
$267
$474
$742
$1,071
$1,462
$1,837
$2,213
$2,574
$4,190
$5,385
$6,264
$53,537
$21,881
VOC
$0
$0
$0
$109
$222
$337
$581
$833
$1,085
$1,525
$1,952
$2,383
$2,803
$4,698
$6,122
$7,255
$60,566
$24,670
TRACTOR/
TRAILERS
$227
$472
$731
$1,610
$2,753
$3,928
$5,824
$7,797
$9,777
$12,089
$14,289
$16,487
$18,616
$28,354
$36,276
$43,955
$381,492
$160,096
SUM
$227
$472
$731
$1,750
$3,095
$4,532
$6,879
$9,372
$11,934
$15,076
$18,079
$21,083
$23,993
$37,242
$47,783
$57,474
$495,595
$206,646
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-34
-------
Table 7-33 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(3% Discount Rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
REDUCED FUEL EXPENDITURES -
RETAIL
HD
PICKUPS
&VANS
$0
$0
$0
$296
$813
$1,296
$1,774
$2,246
$2,712
$3,135
$3,104
$3,064
$18,440
voc
$0
$0
$0
$1,066
$1,048
$1,029
$2,124
$2,136
$2,140
$3,564
$3,539
$3,503
$20,149
TRACTO
R/
TRAILER
S
$2,183
$2,123
$2,066
$11,074
$10,995
$10,888
$17,103
$17,330
$17,490
$19,944
$19,949
$19,925
$151,070
SUM
$2,183
$2,123
$2,066
$12,436
$12,856
$13,212
$21,001
$21,712
$22,342
$26,643
$26,592
$26,493
$189,659
REDUCED FUEL EXPENDITURES -
UNTAXED
HD
PICKUPS
&VANS
$0
$0
$0
$263
$724
$1,157
$1,587
$2,013
$2,436
$2,821
$2,798
$2,767
$16,567
VOC
$0
$0
$0
$952
$938
$923
$1,909
$1,923
$1,931
$3,220
$3,202
$3,175
$18,173
TRACTO
R/
TRAILER
S
$1,937
$1,890
$1,844
$9,911
$9,862
$9,787
$15,405
$15,640
$15,814
$18,065
$18,101
$18,109
$136,364
SUM
$1,937
$1,890
$1,844
$11,126
$11,525
$11,867
$18,901
$19,577
$20,180
$24,106
$24,101
$24,052
$171,105
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-34 Discounted MY Lifetime Reductions in Fuel Expenditures of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(7% Discount Rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
REDUCED FUEL EXPENDITURES -
RETAIL
HD
PICKUPS
&VANS
$0
$0
$0
$186
$492
$755
$995
$1,213
$1,410
$1,570
$1,496
$1,423
$9,540
VOC
$0
$0
$0
$666
$631
$597
$1,187
$1,149
$1,109
$1,780
$1,701
$1,621
$10,442
TRACTO
R/
TRAILER
S
$1,529
$1,428
$1,331
$6,848
$6,555
$6,255
$9,472
$9,244
$8,986
$9,868
$9,500
$9,135
$80,151
SUM
$1,529
$1,428
$1,331
$7,701
$7,678
$7,607
$11,654
$11,607
$11,505
$13,218
$12,697
$12,179
$100,134
REDUCED FUEL EXPENDITURES -
UNTAXED
HD
PICKUPS
&VANS
$0
$0
$0
$165
$437
$673
$889
$1,086
$1,265
$1,411
$1,347
$1,283
$8,555
VOC
$0
$0
$0
$594
$564
$535
$1,065
$1,033
$999
$1,605
$1,537
$1,468
$9,399
TRACTO
R/
TRAILER
S
$1,352
$1,267
$1,185
$6,115
$5,867
$5,611
$8,515
$8,328
$8,111
$8,924
$8,607
$8,291
$72,174
SUM
$1,352
$1,267
$1,185
$6,874
$6,869
$6,819
$10,469
$10,447
$10,374
$11,940
$11,490
$11,041
$90,128
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-35
-------
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.
7-36
-------
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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029 & later
HD
PICKUPS &
VANS
$0.00
$0.00
$0.00
$1.72
$3.45
$5.17
$6.90
$8.62
$8.62
$8.62
$8.62
$8.62
VOCATIONAL
$0.00
$0.00
$0.00
$6.99
$6.91
$8.12
$17.66
$17.47
$13.50
$21.34
$20.26
$15.99
TRACTOR
$0.00
$0.00
$0.00
$33.16
$32.53
$31.66
$33.50
$32.92
$30.01
$32.27
$31.52
$31.22
TRAILER
$79.92
$77.77
$75.69
$78.81
$76.74
$71.17
$73.97
$72.02
$70.72
$69.44
$68.85
$68.27
TRACTOR/
TRAILER
$67.16
$65.36
$63.60
$99.38
$97.02
$91.46
$95.66
$93.43
$89.44
$90.62
$89.37
$88.59
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-36 Lifetime Maintenance Intervals for the Indicated Model Years
Vs. The Less Dynamic Baseline and using Method Ba'b
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029 & later
HD PICKUPS
&VANS
7.19
7.18
7.16
7.24
7.23
7.23
7.23
7.23
7.25
7.28
7.32
7.37
VOCATIONAL
7.89
7.85
7.82
7.93
7.90
7.88
7.87
7.86
7.87
7.88
7.90
7.93
TRACTOR/
TRAILER
7.74
7.74
7.73
7.74
7.74
7.74
7.75
7.77
7.80
7.85
7.90
7.97
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section
ID; for an explanation of the less dynamic baseline, la, and more dynamic
baseline, Ib, please see Preamble Section X. A. 1
b Includes rebound vehicle miles traveled (VMT).
7-37
-------
Table 7-37 presents the annual in-use maintenance costs associated with the preferred
alternative along with net present values at 3 percent and 7 percent. This table presents costs
relative to the less dynamic baseline and using the MOVES analysis for all vehicle categories
(Method B). Table 7-38 presents the model year lifetime in-use maintenance costsversus the
less dynamic baseline and using Method B discounted at 3 percent. Table 7-39 presents the
model year lifetime in-use maintenance costsversus the less dynamic baseline and using
Method Bdiscounted at 7 percent.
Table 7-37 Annual Increased Maintenance Costs and Net Present Values Associated with the Preferred
Alternative Vs. The Less Dynamic Baseline and using Method B
($Millionsof2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$0.4
$1.3
$2.5
$4.2
$6.3
$8.4
$10.4
$12.3
$14.
$14.
$14.
$14.
$14.
$183.5
$83.7
VOCATIONAL
$0.0
$0.0
$0.0
$1.8
$3.5
$5.6
$10.2
$14.7
$18.1
$23.6
$28.5
$32.0
$32.0
$32.0
$32.0
$32.0
$417.8
$191.1
TRACTOR/
TRAILERS
$5.5
$11.0
$16.5
$25.4
$33.8
$41.5
$49.3
$56.8
$63.4
$69.8
$75.5
$80.7
$80.7
$80.7
$80.7
$80.7
$1,194.7
$585.4
SUM
$5.5
$11.0
$16.5
$27.6
$38.6
$49.6
$63.8
$77.8
$90.0
$103.7
$116.3
$126.8
$126.8
$126.8
$126.8
$126.8
$1,796.0
$860.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-38
-------
Table 7-38 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(3% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$3.3
$6.5
$9.4
$12.3
$15.1
$15.0
$14.7
$14.4
$14.0
$104.6
VOCATIONAL
$0.0
$0.0
$0.0
$14.1
$13.5
$15.4
$33.3
$32.7
$25.0
$38.9
$36.2
$27.9
$237.0
TRACTOR/
TRAILERS
$51.2
$49.2
$47.5
$72.6
$69.4
$64.0
$66.6
$65.1
$62.1
$62.3
$60.6
$59.1
$729.5
SUM
$51.2
$49.2
$47.5
$90.0
$89.4
$88.8
$112.2
$112.9
$102.1
$115.9
$111.1
$101.0
$1,071.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 7-39 Discounted MY Lifetime Maintenance Costs of the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
(7% Discount Rate, SMillions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUPS
&VANS
$0.0
$0.0
$0.0
$2.1
$3.9
$5.5
$6.9
$8.3
$7.9
$7.4
$7.0
$6.6
$55.7
VOCATIONAL
$0.0
$0.0
$0.0
$8.9
$8.3
$9.1
$18.9
$17.8
$13.1
$19.7
$17.7
$13.1
$126.6
TRACTOR/
TRAILERS
$35.8
$33.2
$30.9
$45.5
$42.0
$37.3
$37.4
$35.2
$32.4
$31.3
$29.4
$27.6
$418.2
SUM
$35.8
$33.2
$30.9
$56.6
$54.2
$51.9
$63.2
$61.3
$53.4
$58.5
$54.0
$47.3
$600.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-39
-------
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.
7-40
-------
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
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance b
$1,587
$25
$23
$22
$20
$19
$18
$16
Maintenance
expenditures
$4
$3
$3
$3
$3
$3
$2
$2
Fuel
expenditures
C
-$759
-$734
-$714
-$693
-$651
-$611
-$571
-$536
Cumulative
expenditures
$832
$126
-$561
-$1,229
-$1,857
-$2,446
-$2,997
-$3,514
7% Discount Rate
Technology
cost,
taxes,
insurance b
$1,558
$23
$21
$19
$17
$15
$14
$12
Maintenance
expenditures
$3
$3
$3
$3
$2
$2
$2
$2
Fuel
expenditures
C
-$745
-$694
-$649
-$606
-$549
-$496
-$446
-$403
Cumulative
expenditures
$817
$150
-$476
-$1,060
-$1,590
-$2,067
-$2,497
-$2,886
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
6% sales tax; insurance estimates are described in text.
c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
Table 7-41 and Table 7-42 show the same information for a MY2027 vocational vehicle
and a tractor/trailer, respectively. As shown, payback for vocational vehicles occurs in the 5th
year of ownership (using 3 percent discounting and in the 6th year using 7 percent discounting)
while payback for tractor/trailers occurs early in the 2nd year of ownership.
Table 7-41 Discounted Owner Expenditures & Payback Period for MY2027Vocational Vehicles under the
Preferred Alternative Vs. The Less Dynamic Baseline and using Method B
3% and 7% Discount Rates (2012$)a
Age
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance b
$3,998
$63
$59
$55
$51
$48
$45
$42
Maintenance
expenditures
$10
$9
$9
$9
$8
$7
$7
$6
Fuel
expenditures
c
-$965
-$937
-$914
-$891
-$829
-$771
-$716
-$667
Cumulative
expenditures
$3,043
$2,178
$1,331
$504
-$265
-$981
-$1,645
-$2,264
7% Discount Rate
Technology
cost,
taxes,
insurance b
$3,924
$59
$53
$48
$43
$39
$35
$31
Maintenance
expenditures
$10
$9
$8
$8
$7
$6
$5
$5
Fuel
expenditures
c
-$947
-$885
-$832
-$780
-$699
-$625
-$559
-$501
Cumulative
expenditures
$2,987
$2,169
$1,399
$675
$27
-$554
-$1,073
-$1,538
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b 6% sales tax and 12% excise tax; insurance estimates are described in text.
c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
7-41
-------
Table 7-42 Discounted Owner Expenditures & Payback Period for MY2027Tractor/Trailers under the
Preferred Alternative Vs. The Less Dynamic Baseline and using Method B
3% and 7% Discount Rates (2012$)a
Age
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance13
$15,194
$238
$223
$209
$195
$182
$170
$158
Maintenance
expenditures
$48
$46
$44
$42
$39
$35
$32
$29
Fuel
expenditures
C
-$14,649
-$14,204
-$13,809
-$13,416
-$12,391
-$11,411
-$10,511
-$9,704
Cumulative
expenditures
$593
-$13,327
-$26,869
-$40,034
-$52,191
-$63,385
-$73,694
-$83,211
7% Discount Rate
Technology
cost,
taxes,
insurance b
$14,914
$225
$203
$183
$164
$148
$133
$119
Maintenance
expenditures
$47
$43
$40
$37
$33
$29
$25
$22
Fuel
expenditures
C
-$14,379
-$13,421
-$12,561
-$11,746
-$10,443
-$9,258
-$8,209
-$7,295
Cumulative
expenditures
$582
-$12,571
-$24,889
-$36,415
-$46,661
-$55,743
-$63,794
-$70,949
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
6% sales tax and 12% excise tax; insurance estimates are described in text.
c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
The fuel expenditure column uses retail fuel prices specific to gasoline and diesel fuel as
projected in AEO2014 Early Release. This payback analysis does not include other private
impacts, such as reduced refueling events, or other societal impacts, such as the potential
rebound miles driven or the value of driving those rebound miles, or noise, congestion and
accidents. It also does not include societal impacts such as co-pollutant environmental benefits
or benefits associated with reduced GHG emissions. We use retail fuel prices and exclude these
other private and social impacts because the focus is meant to be on those factors that buyers
think about most while considering a new vehicle purchase and those factors that result in more
or fewer dollars in their pockets.
In an effort to provide further information on payback, we have also looked at the
payback periods for more specific vehicle subcategories. For example, while the tractor/trailer
payback shown in Table 7-42 occurs early in the 2nd year, the payback for a Class 8 sleeper cab
would occur within the first year of ownership as shown in Table 7-43.
7-42
-------
Table 7-43 Discounted Owner Expenditures & Payback Period for MY2027 Sleeper Cab with Trailer under
the Preferred Alternative Vs. The Less Dynamic Baseline and using Method B
3% and 7% Discount Rates (2012$)a
Age
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost, taxes,
insurance b
$16,541
$259
$243
$227
$212
$198
$185
$172
Maintenance
expenditures
$60
$58
$56
$53
$49
$45
$41
$37
Fuel
expenditures
C
-$20,087
-$19,477
-$18,936
-$18,396
-$17,053
-$15,768
-$14,591
-$13,538
Cumulative
expenditures
-$3,486
-$22,646
-$41,285
-$59,401
-$76,193
-$91,718
-$106,083
-$119,412
7% Discount Rate
Technology
cost, taxes,
insurance b
$16,236
$244
$221
$199
$179
$161
$144
$129
Maintenance
expenditures
$59
$55
$50
$47
$41
$36
$32
$28
Fuel
expenditures
C
-$19,717
-$18,403
-$17,224
-$16,107
-$14,373
-$12,793
-$11,395
-$10,178
Cumulative
expenditures
-$3,422
-$21,526
-$38,479
-$54,341
-$68,494
-$81,090
-$92,309
-$102,330
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost, taxes,
insurance b
$4,863
$76
$71
$67
$62
$58
$54
$51
Maintenance
expenditures
$57
$55
$53
$52
$50
$49
$47
$46
Fuel
expenditures °
-$8,388
-$8,203
-$8,046
-$7,881
-$7,734
-$7,593
-$7,459
-$7,357
Cumulative
expenditures
-$3,469
-$11,541
-$19,462
-$27,225
-$34,846
-$42,332
-$49,689
-$56,950
7% Discount Rate
Technology
cost, taxes,
insurance b
$4,773
$72
$65
$58
$53
$47
$42
$38
Maintenance
expenditures
$56
$52
$48
$45
$42
$39
$37
$34
Fuel
expenditures °
-$8,234
-$7,751
-$7,318
-$6,901
-$6,518
-$6,160
-$5,826
-$5,531
Cumulative
expenditures
-$3,405
-$11,032
-$18,237
-$25,034
-$31,457
-$37,530
-$43,277
-$48,735
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
6% sales tax and 12% excise tax; insurance estimates are described in text.
c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
7-43
-------
Table 7-45 Discounted Owner Expenditures & Payback Period for MY2027Transit Bus under the Preferred
Alternative Vs. The Less Dynamic Baseline and using Method B
3% and 7% Discount Rates (2012$)a
Age
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance b
$8,718
$136
$128
$120
$112
$104
$97
$91
Maintenance
expenditures
$45
$42
$40
$37
$35
$33
$31
$29
Fuel
expenditures
C
-$4,328
-$4,098
-$3,892
-$3,689
-$3,503
-$3,330
-$3,170
-$3,026
Cumulative
expenditures
$4,435
$516
-$3,209
-$6,741
-$10,097
-$13,290
-$16,332
-$19,238
7% Discount Rate
Technology
cost,
taxes,
insurance b
$8,558
$129
$116
$105
$94
$85
$76
$68
Maintenance
expenditures
$44
$40
$36
$33
$29
$27
$24
$22
Fuel
expenditures
C
-$4,248
-$3,872
-$3,540
-$3,230
-$2,953
-$2,702
-$2,476
-$2,275
Cumulative
expenditures
$4,353
$650
-$2,738
-$5,830
-$8,659
-$11,249
-$13,625
-$15,810
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
6% sales tax and 12% excise tax; insurance estimates are described in text.
c Fuel expenditures calculated using retail fuel prices according to AEO2014 early release, reference case estimates.
Table 7-46 Discounted Owner Expenditures & Payback Period for MY2027School Bus under the Preferred
Alternative Vs. The Less Dynamic Baseline and using Method B
3% and 7% Discount Rates (2012$)a
Age
1
2
3
4
5
6
7
8
3% Discount Rate
Technology
cost,
taxes,
insurance b
$5,687
$89
$83
$78
$73
$68
$63
$59
Maintenance
expenditures
$7
$7
$6
$6
$6
$6
$6
$6
Fuel
expenditures
c
-$675
-$660
-$647
-$634
-$622
-$611
-$600
-$592
Cumulative
expenditures
$5,019
$4,455
$3,897
$3,348
$2,805
$2,268
$1,737
$1,210
7% Discount Rate
Technology
cost,
taxes,
insurance b
$5,582
$84
$76
$68
$62
$55
$50
$44
Maintenance
expenditures
$7
$6
$6
$6
$5
$5
$4
$4
Fuel
expenditures
c
-$662
-$623
-$589
-$555
-$524
-$495
-$469
-$445
Cumulative
expenditures
$4,926
$4,393
$3,886
$3,405
$2,948
$2,512
$2,098
$1,701
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
7-44
-------
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
Sub category
HD Pickups & Vans (MY2027)
Vocational (MY2027 for each)
Intercity bus
Transit bus
School bus
Refuse truck
Single unit short haul
Single unit long haul
Motor home
All
Tractor/Trailer (MY2027 for each)
Combination short haul
Combination long haul
All
3% Discount Rate
Gasoline
3
N/A
3
13
N/A
4
N/A
>23
5
N/A
N/A
N/A
Diesel
2
1
3
11
4
5
O
>23
5
2
1
2
All
3
1
O
11
4
5
O
>23
5
2
1
2
7% Discount Rate
Gasoline
3
N/A
O
18
N/A
4
N/A
>23
5
N/A
N/A
N/A
Diesel
2
1
O
13
4
5
O
>23
6
2
1
2
All
3
1
O
14
4
5
O
>23
6
2
1
2
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
N/A denotes no such vehicles in this segment.
7.2.5 Cost per Ton of CCh Equivalent Reduced vs. the Less Dynamic Baseline
and using Method B
The agencies have calculated the cost per ton of GHG (CCh-equivalent, or CCheq)
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 ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X. A. 1 GHG reductions include CCh and CCh equivalents of CH4, N2O
and MFCs.
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 long-
term 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
7-45
-------
the fuel savings, the cost per ton-reduction is less than $0 since the estimated value of fuel
savings outweighs the program costs.
Table 7-48 Annual Cost per Metric Ton of CCheq Emissions Reduced in the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
HD Pickups and Vans only (dollar values are 2012$)a
Calendar
Year
2021
2024
2027
2030
2035
2040
2050
Vehicle &
Maintenance
Costs
(SBillions)
$0.3
$0.6
$0.9
$0.9
$0.9
$0.9
$1.0
Fuel
Savings
($Billions)
$0.0
$0.5
$1.5
$2.6
$4.2
$5.4
$6.3
GHG
Reduced
(MMT)
0
2
5
9
14
17
19
$/metric
ton w/o
fuel
$2,600
$330
$160
$95
$65
$57
$54
$/metric
ton w/ fuel
$2,400
$67
-$110
-$190
-$240
-$270
-$270
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X. A. 1 GHG reductions include CCh and CCh equivalents of CH4, N2O
and MFCs.
Table 7-49 Annual Cost per Metric Ton of CCheq Emissions Reduced in the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
Vocational Vehicles only (dollar values are 2012$)a
Calendar
Year
2021
2024
2027
2030
2035
2040
2050
Vehicle &
Maintenance
Costs
($Billions)
$0.7
$1.1
$2.0
$2.0
$2.1
$2.2
$2.5
Fuel
Savings
($Billions)
$0.1
$0.6
$1.5
$2.8
$4.7
$6.1
$7.3
GHG
Reduced
(MMT)
0
2
6
10
16
20
23
$/metric
ton w/o
fuel
$1,500
$460
$340
$190
$130
$110
$110
$/metric
ton w/ fuel
$1,300
$210
$81
-$78
-$160
-$200
-$210
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
7-46
-------
Preamble Section X. A. 1 GHG reductions include CCh and CCh equivalents of CH4, N2O
and MFCs.
Table 7-50 Annual Cost per Metric Ton of CCheq Emissions Reduced in the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
Tractor/Trailers only (dollar values are 2012$)a
Calendar
Year
2021
2024
2027
2030
2035
2040
2050
Vehicle &
Maintenance
Costs
(SBillions)
$1.4
$2.1
$2.6
$2.7
$3.0
$3.3
$3.6
Fuel
Savings
(SBillions)
$1.6
$5.8
$12.1
$18.6
$28.4
$36.3
$44.0
GHG
Reduced
(MMT)
7
23
46
69
97
116
141
$/metric
ton w/o
fuel
$210
$90
$56
$39
$31
$28
$26
$/metric
ton w/ fuel
-$30
-$160
-$210
-$230
-$260
-$280
-$290
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X. A. 1 GHG reductions include CCh and CCh equivalents of CH4, N2O
andHFCs.
Table 7-51 Annual Cost per Metric Ton of CCheq Emissions Reduced in the Preferred Alternative
Vs. The Less Dynamic Baseline and using Method B
All Vehicle Segments (dollar values are 2012$)a
Calendar
Year
2021
2024
2027
2030
2035
2040
2050
Vehicle &
Maintenance
Costs
($Billions)
$2.5
$3.8
$5.4
$5.5
$6.0
$6.4
$7.1
Fuel
Savings
($Billions)
$1.7
$6.9
$15.1
$24.0
$37.2
$47.8
$57.5
GHG
Reduced
(MMT)
7
28
57
88
127
152
183
$/metric
ton w/o
fuel
$330
$140
$94
$63
$47
$42
$39
$/metric
ton w/ fuel
$96
-$110
-$170
-$210
-$250
-$270
-$270
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X. A. 1 GHG reductions include CCh and CCh equivalents of CH4, N2O
andHFCs.
For comparison, Table 7-52 through Table 7-55 show the same information as it was
presented in Chapter 7 of the final RIA for the Phase 1 HD rule.7
7-47
-------
Table 7-52 Annual Cost per Metric Ton of CCheq Emissions Reduced in the HD Phase 1 Final Rule
HD Pickups and Vans only (dollar values are 2009$)
Calendar
Year
2020
2030
2040
2050
Vehicle &
Maintenance
Costs
(SBillions)
$0,8
$0.9
$1.0
$1.2
Fuel
Savings
(SBillions)
$0.9
$3.0
$4.3
$5.5
GHG
Reduced
(MMT)
3
10
14
16
$/metric
ton w/o
fuel
$240
$90
$70
$80
$/metric
ton w/ fuel
-$30
-$200
-$240
-$270
Table 7-53 Annual Cost per Metric Ton of CCheq Emissions Reduced in the HD Phase 1 Final Rule
Vocational Vehicles only (dollar values are 2009$)
Calendar
Year
2020
2030
2040
2050
Vehicle &
Maintenance
Costs
($Billions)
$0.2
$0.2
$0.3
$0.4
Fuel
Savings
($Billions)
$1.1
$2.4
$3.5
$4.7
GHG
Reduced
(MMT)
4
9
12
14
$/metric
ton w/o
fuel
$50
$20
$30
$30
$/metric
ton w/ fuel
-$210
-$250
-$270
-$310
Table 7-54 Annual Cost per Metric Ton of CCheq Emissions Reduced in the HD Phase 1 Final Rule
Tractor/Trailers only (dollar values are 2009$)
Calendar
Year
2020
2030
2040
2050
Vehicle &
Maintenance
Costs
($Billions)
$1.0
$1.1
$1.4
$1.8
Fuel
Savings
($Billions)
$7.7
$15.3
$20.2
$26.4
GHG
Reduced
(MMT)
32
57
68
78
$/metric
ton w/o
fuel
$30
$20
$20
$20
$/metric
ton w/ fuel
-$210
-$250
-$280
-$320
Table 7-55 Annual Cost per Metric Ton of CCheq Emissions Reduced in the HD Phase 1 Final Rule
All Vehicle Segments (dollar values are 2009$)
Calendar
Year
2020
2030
2040
2050
Vehicle &
Maintenance
Costs
($Billions)
$2.0
$2.2
$2.7
$3.3
Fuel
Savings
($Billions)
$9.6
$20.6
$28.0
$36.5
GHG
Reduced
(MMT)
39
76
94
108
$/metric
ton w/o
fuel
$50
$30
$30
$30
$/metric
ton w/ fuel
-$190
-$240
-$270
-$310
7-48
-------
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.
7-49
-------
Table 7-56 Estimated Calendar Year Sales by Vehicle Type using Method B a'b
Calendar Year
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
HD Pickup &
Vans
601,428
592,824
588,718
588,166
588,277
588,468
595,880
607,607
622,297
629,008
635,564
640,235
645,344
650,635
656,130
660,463
665,671
673,795
680,860
688,683
697,131
702,434
706,025
711,730
718,283
724,911
731,657
738,470
745,352
752,303
759,313
766,385
773,523
Vocational
Vehicles
508,986
508,189
511,308
513,187
518,192
522,188
538,609
555,144
570,707
583,170
592,507
600,238
606,945
613,681
620,670
625,768
633,244
643,148
651,588
660,629
670,643
676,957
682,097
682,945
689,604
696,394
703,351
710,389
717,507
724,711
731,984
739,323
746,732
Tractors
152,323
155,452
159,221
161,198
163,719
166,094
171,770
178,532
185,102
190,777
195,631
200,434
204,610
208,775
213,612
217,695
222,677
228,335
233,285
238,296
243,855
247,759
251,554
247,952
251,409
254,917
258,472
262,079
265,734
269,440
273,199
277,009
280,872
Semi-trailers
181,264
184,988
189,473
191,826
194,826
197,652
204,406
212,453
220,271
227,025
232,801
238,516
243,486
248,442
254,198
259,057
264,986
271,719
277,609
283,572
290,187
294,833
299,349
295,063
299,177
303,351
307,582
311,874
316,223
320,634
325,107
329,641
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 ID; for an explanation of the
less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-50
-------
Table 7-57 AEO 2014 Early Release Reference Case Fuel Prices (2012$/gallon)
Calendar
Year
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Pre-Tax
Gasoline
$2.61
$2.62
$2.67
$2.71
$2.77
$2.82
$2.86
$2.89
$2.92
$2.96
$2.98
$3.01
$3.04
$3.08
$3.12
$3.16
$3.24
$3.27
$3.32
$3.36
$3.40
$3.47
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
$3.54
Diesel
$3.07
$3.15
$3.22
$3.29
$3.37
$3.43
$3.48
$3.54
$3.59
$3.65
$3.69
$3.74
$3.79
$3.84
$3.89
$3.95
$4.02
$4.06
$4.11
$4.15
$4.19
$4.26
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
$4.34
Post-Tax
Gasoline
$3.02
$3.03
$3.08
$3.12
$3.17
$3.22
$3.26
$3.29
$3.32
$3.36
$3.37
$3.40
$3.43
$3.46
$3.50
$3.54
$3.61
$3.65
$3.69
$3.73
$3.77
$3.83
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
$3.90
Diesel
$3.53
$3.61
$3.67
$3.74
$3.82
$3.87
$3.92
$3.98
$4.02
$4.08
$4.12
$4.16
$4.20
$4.25
$4.30
$4.36
$4.43
$4.47
$4.51
$4.54
$4.58
$4.65
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
$4.73
7-51
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Table 7-58 AEO 2014 Final Reference Case Fuel Prices Used in Method A Analysis for HD Pickups and
Vans (2012$/gallon)
Calendar
Year
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Pre-Tax
Gasoline
$2.63
$2.64
$2.69
$2.74
$2.79
$2.84
$2.88
$2.92
$2.95
$2.99
$3.00
$3.03
$3.07
$3.10
$3.14
$3.18
$3.27
$3.30
$3.34
$3.38
$3.43
$3.49
$3.56
$3.57
$3.58
$3.59
$3.59
$3.60
$3.61
$3.62
$3.63
$3.63
$3.64
Diesel
$3.10
$3.19
$3.25
$3.32
$3.41
$3.46
$3.51
$3.58
$3.62
$3.68
$3.73
$3.77
$3.81
$3.87
$3.92
$3.98
$4.06
$4.10
$4.14
$4.18
$4.22
$4.29
$4.38
$4.41
$4.44
$4.48
$4.51
$4.54
$4.58
$4.61
$4.65
$4.68
$4.72
Post-Tax
Gasoline
$3.02
$3.03
$3.08
$3.12
$3.17
$3.22
$3.26
$3.29
$3.32
$3.36
$3.37
$3.40
$3.43
$3.46
$3.50
$3.54
$3.62
$3.65
$3.69
$3.73
$3.77
$3.83
$3.90
$3.91
$3.92
$3.93
$3.93
$3.94
$3.95
$3.96
$3.97
$3.97
$3.98
Diesel
$3.53
$3.61
$3.67
$3.74
$3.82
$3.87
$3.92
$3.98
$4.02
$4.08
$4.12
$4.16
$4.20
$4.25
$4.30
$4.36
$4.43
$4.47
$4.51
$4.54
$4.58
$4.65
$4.73
$4.76
$4.80
$4.83
$4.86
$4.90
$4.93
$4.97
$5.00
$5.04
$5.07
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Table 7-59 Depreciation Schedule used in Payback Analysis for Method Ba
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Depreciation
0%
3%
7%
10%
13%
17%
20%
23%
27%
30%
33%
37%
40%
43%
47%
50%
53%
57%
60%
63%
67%
70%
73%
77%
80%
83%
83%
83%
83%
83%
83%
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Reference case
115,829,944,373
113,678,239,954
112,489,612,844
111,945,040,889
111,677,755,878
111,450,161,386
112,709,425,730
114,748,238,188
117,275,813,888
118,386,726,732
119,534,232,399
120,302,585,242
Policy Case
115,829,944,373
113,678,239,954
112,489,612,844
113,266,026,805
112,995,512,359
112,765,269,187
114,039,351,705
116,102,258,642
118,659,694,470
119,783,672,794
120,944,733,432
121,722,263,013
Rebound VMT
0
0
0
,320,985,916
,317,756,481
,315,107,801
,329,925,975
,354,020,454
,383,880,582
,396,946,063
,410,501,033
,419,677,771
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Reference case
118,450,357,579
116,749,449,396
116,127,003,082
115,248,392,233
115,272,965,194
115,175,262,339
117,884,442,538
120,740,410,310
123,305,465,962
125,295,415,305
126,961,637,088
128,161,021,270
Policy Case
118,450,357,579
116,749,449,396
116,127,003,082
117,357,427,520
117,382,447,329
117,282,914,481
120,041,702,114
122,949,989,775
125,561,901,338
127,588,325,885
129,285,065,704
130,506,385,592
Rebound VMT
0
0
0
2,109,035,287
2,109,482,135
2,107,652,142
2,157,259,576
2,209,579,465
2,256,435,376
2,292,910,580
2,323,428,616
2,345,364,323
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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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/Trailera
Model Year
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Reference case
205,633,186,090
208,901,077,400
212,879,741,190
214,192,002,054
216,140,641,720
217,755,803,295
223,503,856,041
230,652,945,572
237,271,789,952
242,810,503,298
247,759,768,627
252,434,280,992
Policy Case
206,868,892,110
210,286,134,790
214,425,143,290
215,883,987,009
217,848,264,966
219,476,052,250
225,269,560,066
232,475,299,910
239,146,338,666
244,728,847,129
249,716,945,459
254,428,339,577
Rebound VMT
1,235,706,020
1,385,057,390
1,545,402,100
1,691,984,954
1,707,623,246
1,720,248,954
1,765,704,025
1,822,354,338
1,874,548,714
1,918,343,832
1,957,176,832
1,994,058,585
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
7-55
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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 Medium-
and 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 Medium-
and 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 Medium-
and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis, EPA-420-R-11-901, August 2011.
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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 CCh 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,6 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
8-1
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the vehicle buyers.0 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.
8-2
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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 climate-
related 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 well-
established 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
8-3
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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 heavy-
duty 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 HDV-
operating 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
8-4
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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.
8-5
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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 trailer-
specific 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
8-6
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boat-tails equipped on trailers to improve vehicle aerodynamics.0 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 after-
market 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.
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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.
-------
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 Smart Way 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
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Phase 2 standards. This first case is referred to as the less dynamic baseline, or Alternative la.
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 Ib.
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.
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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 massincluding 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.
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Reference Case Total Truck Operation Cost Per Miles
Source: ATRI,2013
Dirver benefits
($/miles»
$0.116
7%
Toils
($/miles)
$0.019
Driver
Wages
($/miles)
$0.417
26%
Fuel C
(S/miles)
$0.641
39%
Truck Ins
Premiums ($,
$0.063
4%
Tries ($/miles)
$0.044
3%
Permits a
Licenses ($/miles)
$0.022 B
1%
TruckArailer Lease or
Purchase Payments ($/tniles)
$0.174,11%
Repair & maintenance ($/miles)
$0.138
8%
Fuel Cost ($/miles)
Truck/Trailer Lease or Purchase
Payments ($/miles)
Repair & maintenance ($/miles)
Truck Insurance Premiums
($/miles)
Permits and Licenses ($/miles)
iTries{$/mi!es)
Toils ($/miles)
Driver Wages ($/miles)
Dirver benefits ($/miles)
*Based on $3.97/gallon
diesel in 2012
Total Cost Per Mile: $ 1.633
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
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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
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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 elasticity0 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.
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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.11
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.
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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
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national aggregate model, and data for the 50 individual states from 1994 - 2008 for its state-
level model.1'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
Single Unit
Combination
National Data
Short Run
13-22%
N/A
Long Run
28-45%
12-14%
State Data
Short Run
3-8%
N/A
Long Run
12-21%
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 newly-
available 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 FtDV rebound effect for regulatory assessment purposes. Excerpts
1 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 Ibs." (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 Ibs." (FHWA, 2013).
1 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.
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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
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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 ton-
miles.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).
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Table 8-2 An Illustration of the Impact of Various Factors on the Elasticity of Demand for HDV Freight Services With Respect to the Price of Those
Services
FACTOR OF
VARIABILITY
(1)
Commodity
shipped
Distance
shipped
Competing
mode
Demand
Measure
Region
Model Form
SOURCE(2)
Abdelwahab(1998)
Friedlaender & Spady (1980)
Oumetal(1990)
Winston (1981)
Friedlaender & Spady (1980)
Friedlaender and Spady
(1981)
Campisi and Gastaldi (1996)
Lietal. (2011)
Bonilla (2008)
Beutheetal. (2001)
Winston (1981)
Christidis and Leduc (2009)
Rich etal (2011)
Beutheetal. (2001)
Lietal. (2011)
Abdelwahab(1998)
Friedlaender & Spady (1980)
Lietal. (2011)
Oum etal. (1992)
LEAST
ELASTIC
(3)
-0.75
-1.00
-0.41
-0.14
-0.15
-0.59
-0.27
-1.09
-0.43
-1.06
-0.34
-0.21
-0.08
-0.58
-1.02
-0.80
-1.66
-0.86
-0.69
FACTORS OF
VARIABILITY FOR
LEAST ELASTIC
VALUE (4)
Construction
Food Products
Metallic products
Lumber, wood and
Furniture
Wood/wood products
Petroleum products
Petroleum products
Other
Oil and Coal
<300km
<900 miles (average)
< 800 km
All O-D pairs
Tonnes
Tonnes
USA
Mountain-Pacific USA
Canada
Translog
MOST
ELASTIC
(5)
-1.40
-3.55
-1.07
-2.96
-5.06
-1.72
-1.37
-1.30
-1.75
-1.31
-1.56
-1.15
-0.11
-1.06
-1.30
-2.18
-5.06
-1.96
1.34
FACTORS OF
VARIABILITY FOR
MOST ELASTIC
VALUE (6)
Textile products
Electrical machinery
Fuel oil
Transport Equipment
Electrical machinery
Wood
Minerals
Nature resource
Building materials
>300km
>900 miles (average)
> 1500 km
O-D Pairs w/alternatives
tonne-km
tonne-km
Southwestern USA
Southern USA
Australia
Log-linear
REGION
(7)
USA
USA
Canada
USA
Southern
USA
USA
Italy
USA, Italy
& India
Denmark
Belgium
USA
EU
Scandinavia
Belgium
USA, Italy
& India
Various U.S.
Various U.S.
Various
Various
DEMAND
MEASURE (8)
Mode choice
Ton-miles
Ton-miles
Tons
Ton-miles
tonne-km
tonnes
tonne-km
tonne-km
tonne-km
tons
Tons
tonne-km
-
Mode Choice
Ton-miles
tonne-km
All
COMMODITY
(9)
Varies
Varies
Varies
Varies
Varies
Various
Varies
Varies
Varies
Aggregate
Varies
All
Agricultural
products
Aggregate
Natural
Resources
Metal products
Electrical
Machinery
Natural
Resources
Aggregate
Source: J.J. Winebrake et al./Energy Policy 48 (2012) 252-259
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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 FID Vs. 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.
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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 FID 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 post-
regulation, 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 ton-
miles 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.
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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.
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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.
' The Gately study was also available, however, the agencies were not aware of the work at the time.
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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
Fuel Cost
New Vehicle Cost
Maintenance & Repair Cost
All Other (labor, insurance, etc.)
Total Motor Carrier Costs
ATRI
$0.641
$0.174
$0.138
$0.680
$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
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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
Combination Tractors
HD Pickup &Vans
Vocational Vehicles
TECHNOLOGY COST
$8,372
$985
$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
Fuel Cost
New Vehicle Cost
Maintenance & Repair Cost
All Other (labor, insurance, etc.)
Total Motor Carrier Costs
REFERENCE CASE
$0.620
$0.082
$0.138
$0.680
$1.520
ALTERNATIVES
$0.517
$0.087
$0.138
$0.680
$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
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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
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economy-wide rebound effects, if any, would be significant for this proposed rule.0 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.
0 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).
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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
Fuel Reductions (Billion Gallons)
GHG Reductions (MMT CO2 eq)
Total Costs ($ billion)
Total Benefits ($ billion)
Net Benefits ($ billion)
REBOUND EFFECT
Main
Analysis
10%
7.8
94.1
5.5
23.5
18.0
Sensitivity Cases Using
Alternative Rebound
Assumptions
5%
8.2
95.7
5.0
23.0
18.0
15%
7.5
87.2
6.5
22.9
16.4
20%
7.1
83.0
7.2
22.8
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.
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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 light-
duty 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 heavy-
duty 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 federally-
mandated 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
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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.*2
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.
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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 low-
buys, 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
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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 incentivessee 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 CCh Impacts - Social Cost of Carbon
We estimate the global social benefits of CCh emission reductions expected from the
Proposed HD Phase 2 program using the social cost of carbon (SC-CCh) 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-CCh estimates." The SC-CCh is a metric
that estimates the monetary value of impacts associated with marginal changes in CCh 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 CCh
emissions, or the disbenefit from increasing emissions.
The SC-CCh 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-CCh 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-CCh 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 (lAMs) to develop SC-CO2
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estimates and selected four global values for use in regulatory analyses. The SC-CCh estimates
were first released in February 2010 and updated in 2013 using new versions of each IAM.
The SC-CCh 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-CCh 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; climate-
exacerbated 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-CCh estimates was to enable a consistent
exploration of three lAMs (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-CCh 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.
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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-CCh analysis, including the
incomplete way in which the lAMs 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 lAMs 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-CCh estimates, though taken
together they suggest that the SC-CCh estimates are likely conservative. In particular, the IPCC
Fourth Assessment Report (2007) concluded that "It is very likely that [SC-CCh 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 CCh 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-CCh 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-CCh 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 lAMs, 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 CCh benefits.
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CCh estimates for several discount rates are included because the literature shows that the SC-
CCh 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-CCh 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-CCh distribution (representing less likely,
but potentially catastrophic, outcomes). The SC-CCh 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 CCh, 2012 - 2050a (in 2012$ per Metric Ton)
CALENDAR
YEAR
2012
2015
2020
2025
2030
2035
2040
2045
2050
DISCOUNT RATE AND STATISTIC
5% Average
$12
$12
$13
$15
$17
$20
$23
$26
$28
3% Average
$37
$40
$46
$51
$56
$60
$66
$71
$77
2.5% Average
$58
$61
$69
$74
$81
$86
$93
$99
$100
3%
95th percentile
$100
$120
$140
$150
$170
$190
$210
$220
$240
Note:
a The SC-CCh values are dollar-year and emissions-year specific and have been rounded to two
significant digits. Unrounded numbers from the 2013 SCC TSD were adjusted to 2012$ and used
to calculate the CCh benefits.
Applying the global SC-CCh estimates, shown in Table 8-7, to the estimated reductions
in domestic CCh 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-CCh estimate for each emissions year would be applied to changes in CCh
emissions for that year, and then discounted back to the analysis year using the same discount
rate used to estimate the SC-CCh. For internal consistency, the annual benefits are discounted
back to net present value terms using the same discount rate as each SC-CCh 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.1 The
SC-CCh estimates are presented in and the associated CCh 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 CC>2 emissions are discounted at the 3% and 7% rates
specified in OMB guidance for regulatory analysis.
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Table 8-8 Annual Upstream and Downstream CCh Benefits and Net Present Values for the Given SC-CCh
Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B,a'b (Millions
of 2012$)
CALEND
AR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-CO2
$12 IN 20 12)
$13
$26
$40
$92
$170
$250
$400
$540
$720
$890
$1,100
$1,300
$1,500
$2,500
$3,300
$5,000
$22,000
3%
(AVERAGE SC-CO2
$37 IN 20 12)
$43
$91
$140
$330
$590
$860
$1,300
$1,800
$2,300
$2,900
$3,500
$4,200
$4,800
$7,400
$9,700
$14,000
$100,000
2.5%
(AVERAGE SC-CO2
$58 IN 20 12)
$65
$130
$210
$500
$880
$1,300
$1,900
$2,600
$3,400
$4,200
$5,100
$5,900
$6,900
$11,000
$14,000
$19,000
$160,000
3%
(95 PERCENTILE =
$105 IN 20 12)
$130
$270
$420
$1,000
$1,800
$2,600
$4,000
$5,500
$7,000
$8,900
$11,000
$13,000
$15,000
$23,000
$30,000
$42,000
$320,000
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The SC-CCh values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG
emissions are calculated differently than other benefits. The same discount rate used to discount the value of
damages from future emissions (SC-CCh at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CC>2
for internal consistency. Refer to SCC TSD for more detail.
We also conducted a separate analysis of the CCh benefits over the model year lifetimes
of vehicles sold in the regulatory timeframe. In contrast to the calendar year analysis, the model
year lifetime analysis shows the impacts of the program on each of these MY fleets over the
course of their lifetimes. Full details of the inputs to this analysis can be found in RIA chapter 5.
The CCh benefits in the context of this MY lifetime analysis are shown in Table 8-9 for each of
the four different social cost of carbon values. The CCh benefits shown for each model year
represent the net present value of the benefits in each year in the model year life discounted back
to the first year of the model year. The same discount rate used to discount the value of damages
from future emissions (SC-CCh at 5, 3, and 2.5 percent) is used to calculate the net present value
of SCC for internal consistency.
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Table 8-9 Discounted Model Year Lifetime Upstream & Downstream CCh Benefits for the Given SC-CCh
Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of
2012$)a'b
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
5%
(AVERAGE SC-CO2 =
$12 IN 20 12)
$93
$90
$87
$520
$540
$550
$870
$900
$920
$1,100
$1,100
$1,100
$7,800
3%
(AVERAGE SC-CO2 =
$37 IN 20 12)
$380
$370
$360
$2,200
$2,300
$2,300
$3,700
$3,900
$4,000
$4,800
$4,800
$4,900
$34,000
2.5%
(AVERAGE SC-CO2 =
$58 IN 20 12)
$580
$570
$560
$3,400
$3,500
$3,600
$5,800
$6,100
$6,300
$7,600
$7,600
$7,700
$53,000
3%
(95 PERCENTILE =
$105 IN 2012)
$1,100
$1,100
$1,100
$6,600
$6,900
$7,200
$11,000
$12,000
$12,000
$15,000
$15,000
$15,000
$100,000
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The SC-COa 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-CCh at 5, 3, and 2.5 percent) is used to calculate discounted values
for internal consistency. Refer to SCC TSD for more detail.
less
8.5.2 Sensitivity Analysis - Monetized Non-CCh 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-CCh GHG emissions using an approach analogous to the one used to estimate
the SC-CCh. However, EPA recognizes that non-CCh 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-CCh 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.
8-39
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This approach, henceforth referred to as the "GWP approach," has been used in sensitivity
analyses to estimate the non-CCh 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-CCh GHG
emissions, and considered the GWP approach as an interim method of analysis until social cost
estimates for non-CCh GHGs, consistent with the SC-CCh estimates, were developed.
The GWP is a simple, transparent, and well-established metric for assessing the relative
impacts of non-CCh emissions compared to CCh 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-CCh GHG benefits has several well-
documented 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-CCh GHG emissions relative to CCh 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-CCh GHG relative to a perturbation of CCh 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-CCh 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-CCh 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 CCh concentrations included in the
SC-CCh would be incorrectly allocated to CH4 emissions with the GWP-based valuation
approach.
tmiisiofw
^
_^
Atmospheric
Concentration
^
Radiative
Forcing
^
w
Climate
ImpKK
^i
_^
Environmental
and Socio-
Economic
impacts
^
~w
Monetized
Damages
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-CCh estimates in general, and the SC-CCh 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-CCh 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 CCh, the temporal independence of the GWP could lead the
8-40
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GWP approach to underestimate the SC-CH4 with a larger downward bias under higher discount
rates (Marten andNewbold2012)u
Similar to the approach used in the RIA of the Final Rulemaking for 2017-2025 Light-
Duty 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 CFU, N2O and FIFCs in each calendar year. Under the GWP Approach, EPA
converted CFLt, N2O, and HFC-134a to CCh equivalents using the AR4 100-year GWP for each
gas: CH4 (25), N2O (298), and HFC-134a (1,430).56 These CCh-equivalent emission reductions
are multiplied by the SC-CCh estimate corresponding to each year of emission reductions. As
with the calculation of annual benefits of CCh emission reductions, the annual benefits of non-
CO2 emission reductions based on the GWP approach are discounted back to net present value
terms using the same discount rate as each SC-CCh estimate. The estimated non-CCh 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-CCh
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-CCh is based on a sufficiently low or steeply declining discount
rate.
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Table 8-10 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SC-
CCh 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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-CO2
$12 IN 20 12)
$0.3
$0.6
$1.0
$3.1
$6.0
$8.8
$14
$19
$25
$30
$36
$43
$49
$82
$110
$160
$730
3%
(AVERAGE SC-CO2
$37 IN 2012)
$1.1
$2.2
$3.5
$11
$20
$30
$46
$62
$79
$99
$120
$140
$160
$240
$320
$440
$3,400
2.5%
(AVERAGE SC-CO2
$58 IN 20 12)
$1.6
$3.3
$5.2
$17
$30
$45
$68
$91
$120
$140
$170
$200
$230
$350
$440
$600
$5,400
3%
(95 PERCENTILE
$105 IN 20 12)
$3.2
$6.6
$10
$33
$62
$93
$140
$190
$240
$300
$360
$420
$480
$760
$990
$1,400
$11,000
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The SC-COa values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG
emissions are calculated differently than other benefits. The same discount rate used to discount the value of
damages from future emissions (SC-CCh at 5, 3, and 2.5 percent) is used to calculate discounted values
for internal consistency. Refer to SCC TSD for more detail.
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Table 8-11 Annual Upstream and Downstream NiO GHG Benefits and Net Present Values for the Given SC-
CCh Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using
the GWP Approach (Millions of 2012$) a'b
CALENDA
R
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-CO2
$12 IN 20 12)
$0.0
$0.0
$0.0
$0.1
$0.2
$0.3
$0.4
$0.6
$0.8
$1.0
$1.2
$1.5
$1.6
$2.8
$3.8
$5.6
$25
3%
(AVERAGE SC-CO2
$37 IN 2012)
$0.0
$0.1
$0.2
$0.4
$0.6
$0.9
$1.4
$2.0
$2.6
$3.2
$3.9
$4.6
$5.3
$8.3
$11
$15
$120
2.5%
(AVERAGE SC-CO2
$58 IN 20 12)
$0.1
$0.2
$0.2
$0.5
$1.0
$1.4
$2.1
$2.9
$3.7
$4.7
$5.7
$6.6
$7.7
$12
$15
$21
$180
3%
(95 PERCENTILE
$105 IN 20 12)
$0.2
$0.3
$0.5
$1.1
$1.9
$2.8
$4.4
$6.0
$7.8
$10
$12
$14
$16
$26
$34
$47
$360
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The SC-COa values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG
emissions are calculated differently than other benefits. The same discount rate used to discount the value of
damages from future emissions (SC-CCh at 5, 3, and 2.5 percent) is used to calculate discounted values
for internal consistency. Refer to SCC TSD for more detail.
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Table 8-12 Annual Upstream and Downstream HFC-134a GHG Benefits and Net Present Values for the
Given SC-CCh Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method
B, using the GWP Approach (Millions of 2012$) a'b
CALENDA
R
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-CO2
$12 IN 20 12)
$0.0
$0.0
$0.0
$0.2
$0.5
$0.8
$1.1
$1.4
$1.8
$2.2
$2.5
$3.0
$3.4
$5.2
$6.1
$8.4
$44
3%
(AVERAGE SC-CO2
$37 IN 2012)
$0.0
$0.0
$0.0
$0.8
$1.7
$2.7
$3.7
$4.7
$5.9
$7.1
$8.3
$10
$11
$15
$18
$23
$200
2.5%
(AVERAGE SC-CO2
$58 IN 20 12)
$0.0
$0.0
$0.0
$1.3
$2.6
$4.0
$5.4
$6.9
$8.6
$10
$12
$14
$16
$22
$25
$31
$320
3%
(95 PERCENTILE
$105 IN 20 12)
$0.0
$0.0
$0.0
$2.6
$5.3
$8.1
$11
$14
$18
$22
$25
$29
$34
$48
$56
$71
$630
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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-CCh GHG Benefits Based on Directly Modeled Estimates
Several researchers have directly estimated the social cost of non-CCh emissions using
integrated assessment models (lAMs), though the number of such estimates is small compared to
the large number of SC-CCh 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-CCh GHG are
consistent with the SC-CCh 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-CCh.57
Specifically, the estimation approach of Marten et al. (2014) used the same set of three lAMs,
five socioeconomic-emissions scenarios, equilibrium climate sensitivity distribution, and three
8-44
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constant discount rates used to develop the SC-CCh estimates. Marten et al. also used the same
aggregation method as the SC-CCh 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 N20,2012 - 2050a [2012$ per metric ton]
(Source: Marten et al. (2014))
YEAR
2012
2015
2020
2025
2030
2035
2040
2045
2050
SC-CH4
5%
Average
$440
500
590
710
840
990
1200
1300
1500
3%
Average
$1000
1200
1300
1500
1700
2000
2300
2500
2700
2.5%
Average
$1400
1500
1700
19000
2300
2500
2800
3100
3300
3%
95th percentile
$2800
3100
3500
4100
4600
5400
6000
6800
7400
SC-NiO
5%
Average
$4000
4400
5200
6000
7000
8100
9300
11000
12000
3%
Average
$14000
15000
16000
18000
20000
23000
25000
27000
29000
2.5%
Average
$20000
22000
24000
27000
29000
32000
35000
38000
41000
3%
95th
percentile
$37000
39000
44000
50000
55000
61000
67000
73000
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-CCh 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
CFLt and N2O emissions in a given year resulting from the regulatory action are multiplied by the
SC-CFLt and SC-N2O estimate for that year, respectively. To obtain a present value estimate, the
monetized stream of future non-CCh benefits are discounted back to the analysis year using the
same discount rate used to estimate the social cost of the non-CCh GHG emission changes.
The CFLt 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.
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Table 8-14 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SC-
CH4 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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-CH4
$440 IN 20 12)
$0.6
$1.1
$1.8
$5.8
$11
$17
$26
$35
$46
$57
$69
$82
$95
$160
$230
$350
$1,500
3%
(AVERAGE SC-CH4
$1000 IN 20 12)
$1.3
$2.6
$3.9
$13
$24
$35
$56
$74
$99
$120
$140
$170
$190
$330
$430
$620
$4,600
2.5%
(AVERAGE SC-CH4
$1400 IN 20 12)
$1.6
$3.4
$5.2
$17
$31
$49
$72
$95
$130
$150
$190
$220
$260
$400
$540
$770
$6,400
3%
(95 PERCENTILE
$2800 IN 20 12)
$3.3
$6.8
$10
$35
$65
$97
$150
$200
$260
$320
$390
$460
$520
$870
$1,200
$1,700
$12,000
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The SC-CH4 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG
emissions are calculated differently than other benefits. The same discount rate used to discount the value of
damages from future emissions (SC-CH4 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CH4
for internal consistency.
8-46
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Table 8-15 Annual Upstream and Downstream NiO GHG Benefits and Net Present Values for the Given SC-
NiO Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using
the Directly Modeled Approach, Calendar Year Analysis (Millions of 2012$) a'b
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPVb
5%
(AVERAGE SC-N2O
$4000 IN 20 12)
$0.0
$0.0
$0.1
$0.1
$0.3
$0.4
$0.6
$0.8
$1.0
$1.3
$1.6
$1.9
$2.2
$3.7
$5.2
$7.9
$34
3%
(AVERAGE SC-N2O =
$14000 IN 2012)
$0.1
$0.1
$0.2
$0.4
$0.8
$1.1
$1.8
$2.4
$3.0
$4.0
$4.8
$5.8
$6.5
$10
$14
$20
$150
2.5%
(AVERAGE SC-N2O =
$20000 IN 20 12)
$0.1
$0.2
$0.3
$0.6
$1.1
$1.7
$2.5
$3.5
$4.5
$5.8
$6.9
$8.2
$9.3
$15
$19
$27
$230
3%
(95 PERCENTILE =
$37000 IN 20 12)
$0.2
$0.3
$0.5
$1.2
$2.1
$3.1
$4.7
$6.5
$8.4
$11
$13
$15
$18
$28
$37
$53
$400
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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-CCh GHGs
and CCh. 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.
8-47
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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. CCh 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/airqualirv/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_l_3l_13.pdf
(accessed September 9, 2014).
8-48
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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.s-related health impacts described below. These estimates, which are expressed per ton of
PM2.s-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 (SCh 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.
8-49
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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
YEAR0
ON-ROAD MOBILE SOURCES
Direct PM2 5
S02
NOx
UPSTREAM SOURCES0
Direct PM2 5
S02
NOx
Estimated Using a 3 Percent Discount Rateb
2016
2020
2025
2030
$380-$850
$400-$910
$440-$ 1,000
$480-$!, 100
$20-$45
$22-$49
$24-$55
$27-$61
$7.7-$18
$8.1-$18
$8.8-$20
$9.6-$22
$330-$750
$350-$790
$390-$870
$420-$950
$69-$160
$75-$170
$83-$190
$91 -$200
$6.8-$16
$7.4-$17
$8.1-$18
$8.7-$20
Estimated Using a 7 Percent Discount Rateb
2016
2020
2025
2030
$340-$770
$370-$820
$400-$910
$430-$980
$18-$41
$20-$44
$22-$49
$24-$55
$6.9-$16
$7.4-$17
$8.0-$18
$8.6-$20
$290-$670
$320-$720
$350-$790
$380-$850
$63-$140
$67-$150
$75-$170
$81-$180
$6.2-$14
$6.6-$15
$7.3-$17
$7.9-$18
Notes:
aThe 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.
0 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 foryears 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.s
POLLUTANT
QUANTIFIED AND MONETIZED
IN PRIMARY ESTIMATES
UNQUANTIFIED EFFECTS
CHANGES IN:
PM2
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
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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-per-
ton 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 per-
ton values calculated therefore apply only to tons reduced from those specific pollutant/source
combinations (e.g., NCh 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 8-1 Vindicates, 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.2 These
projected increases reflect rising income levels, which increase affected individuals' willingness
to pay for reduced exposure to health threats from air pollution.^ 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^enmap/models/Source_Apportionment_BPT_TSD_l_3 l_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.
^ 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 P articulate 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/ttnecasl/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)
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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 non-
GHG 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/airqualitv/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_l_3l_13.pdf
(accessed September 9, 2014).
8-52
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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
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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-GHG-
related endpoints we are currently unable to quantify and/or monetize.
Table 8-18 Estimated Quantified and Unqualified 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)
Infant mortality (age <1)
Non-fatal heart attacks (age > 18)
Hospital admissions respiratory (all
ages)
Hospital admissions cardiovascular
(age >20)
Emergency department visits for
asthma (all ages)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-
14)
v'
/
v'
/
v'
-------
BENEFITS
CATEGORY
Reduced
incidence of
premature
mortality and
morbidity from
exposure to
ozone
Reduced
incidence of
morbidity from
exposure to air
toxics
SPECIFIC EFFECT
Upper respiratory symptoms
(asthmatics age 9-1 1)
Asthma exacerbation (asthmatics age
6-18)
Lost work days (age 18-65)
Minor restricted-activity days (age
18-65)
Chronic Bronchitis (age >26)
Emergency department visits for
cardiovascular effects (all ages)
Strokes and cerebrovascular disease
(age 50-79)
Other cardiovascular effects (e.g.,
other ages)
Other respiratory effects (e.g.,
pulmonary function, non-asthma ER
visits, non-bronchitis chronic
diseases, other ages and populations)
Reproductive and developmental
effects (e.g., low birth weight, pre-
term births, etc.)
Cancer, mutagenicity, and
genotoxicity effects
Premature mortality based on short-
term study estimates (all ages)
Premature mortality based on long-
term study estimates (age 30-99)
Hospital admissions respiratory
causes (age > 65)
Hospital admissions respiratory
causes (age <2)
Emergency department visits for
asthma (all ages)
Minor restricted-activity days (age
18-65)
School absence days (age 5-17)
Decreased outdoor worker
productivity (age 18-65)
Other respiratory effects (e.g.,
premature aging of lungs)
Cardiovascular and nervous system
effects
Reproductive and developmental
effects
Cancer (benzene, 1,3 -butadiene,
formaldehyde, acetaldehyde)
Anemia (benzene)
Disruption of production of blood
components (benzene)
EFFECTHAS
BEEN
QUANTIFIED
v'
V
^
V
v<
v'
V
s
s
s
s
EFFECTHAS
BEEN
MONETIZED
v'
v'
v'
v'
^
v'
^
v'
v'
/
/
MORE
INFORMATION
PM NAAQS RIA,
Section 5. 6
PM NAAQS RIA,
Section 5. 6
PM NAAQS RIA,
Section 5. 6
PM NAAQS RIA,
Section 5. 6
PM NAAQS RIA,
Section 5. 6°
PM NAAQS RIA,
Section 5. 6°
PM NAAQS RIA,
Section 5. 6°
PM ISA3
PM ISA3
PM ISA3-b
PM ISA3-b
Ozone ISA
Ozone ISAC
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA3
Ozone ISAb
Ozone ISAb
IRIS3-b
8-55
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BENEFITS
CATEGORY
SPECIFIC EFFECT
Reduction in the number of blood
platelets (benzene)
Excessive bone marrow formation
(benzene)
Depression of lymphocyte counts
(benzene)
Reproductive and developmental
effects (1,3 -butadiene)
Irritation of eyes and mucus
membranes (formaldehyde)
Respiratory irritation (formaldehyde)
Asthma attacks in asthmatics
(formaldehyde)
Asthma-like symptoms in non-
asthmatics (formaldehyde)
Irritation of the eyes, skin, and
respiratory tract (acetaldehyde)
Upper respiratory tract irritation and
congestion (acrolein)
EFFECTHAS
BEEN
QUANTIFIED
EFFECTHAS
BEEN
MONETIZED
MORE
INFORMATION
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.
0 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 PMi.s and Ozone Reductions
ENDPOINT
POLLUTANT
STUDY
STUDY POPULATION
Premature Mortality
Premature mortality -
daily time series
Premature mortality
cohort study, all-cause
Premature mortality,
total exposures
Premature mortality
all-cause
03
PM2.5
PM25
PM25
Multi-city
Bell et al (2004) (NMMAPS study)74 - Non-
accidental
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
Krewski et al. (2009)80
Lepeuleetal. (2012)81
Expert Elicitation (lEc, 2006)82
Woodruff etal. (1997)83
All ages
>29 years
>25 years
>24 years
Infant (<1 year)
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ENDPOINT POLLUTANT STUDY STUDY POPULATION
Chronic Illness
Nonfatal heart attacks
PM25
Peters etal. (200 1)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
Cardiovascular
Asthma-related ER
visits
Asthma-related ER
visits (cont'd)
03
PM25
PM25
PM25
PM25
PM25
03
PM25
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)
Burnett etal. (200 1)93
Pooled estimate:
Zanobetti et al. (2009) ICD 460-519 (All
respiratory)
Kloog et al. (2012) ICD 460-519 (All
Respiratory)94
Moolgavkar (2000) ICD 490-496 (Chronic
lung disease)95
Pooled estimate:
Babin et al. (2007) ICD 493 (asthma)96
Sheppard (2003) ICD 493 (asthma)97
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-, cerebro-
and peripheral vascular disease)98
Peng et al. (2008) ICD 426-427; 428; 430-
438; 410-414; 429; 440-449 (Cardio-, cerebro-
and peripheral vascular disease)99
Bell et al. (2008) ICD 426-427; 428; 430-
438; 410-414; 429; 440-449 (Cardio-, cerebro-
and peripheral vascular disease)100
Moolgavkar (2000) ICD 390-429 (all
cardiovascular)
Pooled estimate:
Peeletal(2005)101
Wilson etal(2005)102
Pooled estimate:
Mar etal. (2010)103
Slaughter etal. (2005)104
Glad etal. (2012)105
>64 years
<2 years
>64 years
18-64 years
<18 years
>64 years
20-64 years
All ages
All ages
All ages
Other Health Endpoints
Acute bronchitis
PM25
Dockeryetal. (1996)106
8-12 years
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ENDPOINT
Upper respiratory
symptoms
Lower respiratory
symptoms
Asthma exacerbations
Work loss days
School absence days
Minor Restricted
Activity Days
(MRADs)
POLLUTANT
PM25
PMz.5
PM25
PM25
03
03
PM25
STUDY
Popeetal. (1991)107
Schwartz and Neas (2000)108
Pooled estimate:
Ostro et al. (200 1)109 (cough, wheeze and
shortness of breath)
Mar et al. (2004) (cough, shortness of breath)
Ostro (1987)110
Pooled estimate:
Gilliland et al. (200 1)111
Chenetal. (2000)112
Ostro and Rothschild (1989)113
Ostro and Rothschild (1989)
STUDY POPULATION
Asthmatics, 9-11
years
7-14 years
6-18 years3
18-65 years
5-17 yearsb
18-65 years
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.
8-58
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Table 8-20 Valuation Metrics Used in BenMAP to Estimate Monetary Co-Benefits
ENDPOINT
Premature mortality
Myocardial Infarctions, Nonfatal
Hospital Admissions
Respiratory, Age 65+
Respiratory, Ages 0-2
Chronic Obstructive Pulmonary
Disease (COPD)
Asthma
Cardiovascular
ER Visits, Asthma
Other Health Endpoints
Acute Bronchitis
Upper Respiratory Symptoms
Lower Respiratory Symptoms
Asthma Exacerbation
Work Loss Days
Minor Restricted Activity Days
School Absence Days
VALUATION METHOD
Assumed Mean VSL
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
COI: Medical Costs + Wage Lost
COI: Medical Costs
COI: Medical Costs + Wage Lost
COI: Medical Costs + Wage Lost
COI: Medical Costs + Wage Lost (18-64)
COI: Medical Costs + Wage Lost (65-99)
COI: Average of Smith et al. (1997)116
and Standford et al. (1999)117
WTP: 6 Day Illness, CV Studies
WTP: 1 Day, CV Studies
WTP: 1 Day, CV Studies
WTP: Bad Asthma Day, Rowe and Chestnut
(1986) 118
Median Daily Wage, County-Specific (median
= $150)
WTP: 1 Day, CV Studies
Median Daily Wage, Women 25+
VALUATION (2011$)
$8,300,000
$37,000
$13,000
$22,000
$17,000
$43,000
$42,000
$440
$470
$32
$21
$57
$66
$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
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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.
8-60
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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
Pickup Truck, Van
Vocational Vehicle
Combination Tractor
High
$0.002
$0.024
$0.054
Middle
$0.001
$0.009
$0.021
Low
$0.000
$0.003
$0.006
Accidents
Pickup Truck, Van
Vocational Vehicle
Combination Tractor
High
$0.086
$0.050
$0.073
Middle
$0.028
$0.017
$0.023
Low
$0.015
$0.008
$0.011
Congestion
Pickup Truck, Van
Vocational Vehicle
Combination Tractor
High
$0.151
$0.344
$0.331
Middle
$0.051
$0.117
$0.113
Low
$0.014
$0.031
$0.030
The agencies are using FHWA's "Middle" estimates for marginal congestion, accident,
and noise costs caused by increased travel from trucks.123 This approach is consistent with the
methodology used in the HD GHG Phase 1 rule and both LD GHG rules. These costs are
multiplied by the annual increases in vehicle miles travelled from the rebound effect to yield the
8-61
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estimated increases in congestion, accident, and noise externality costs during each future year.
The results are shown in Table 8-22 through Table 8-24.
Table 8-22 Annual Costs & Net Present Values Associated with Increased Noise, Accidents and Congestion
for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUP AND
VANS
$0
$0
$0
$9
$18
$27
$35
$44
$52
$59
$66
$72
$78
$100
$111
$126
$1,335
$593
VOCATIONAL
$0
$0
$0
$26
$51
$76
$102
$127
$151
$173
$194
$213
$230
$294
$330
$375
$3,934
$1,743
TRACTOR/TRAILER
$0
$0
$0
$82
$103
$123
$141
$159
$177
$192
$207
$221
$234
$282
$317
$370
$4,066
$1,867
SUM
$0
$0
$0
$117
$172
$226
$279
$330
$379
$425
$467
$506
$542
$676
$758
$871
$9,334
$4,202
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
8-62
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Table 8-23 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and
Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (3%
discount rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP AND
VANS
$0
$0
$0
$71
$69
$66
$65
$64
$64
$63
$61
$60
$583
VOCATIONAL
$0
$0
$0
$203
$197
$191
$190
$189
$188
$185
$182
$178
$1,704
TRACTOR/TRAILER
$132
$146
$162
$176
$173
$169
$168
$169
$168
$167
$166
$164
$1,959
SUM
$132
$146
$162
$450
$438
$427
$424
$422
$420
$415
$409
$402
$4,247
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 8-24 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and
Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (7%
discount rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP AND
VANS
$0
$0
$0
$45
$42
$39
$37
$35
$33
$32
$30
$28
$320
VOCATIONAL
$0
$0
$0
$129
$120
$112
$108
$103
$98
$93
$88
$83
$935
TRACTOR/TRAILER
$85
$94
$103
$110
$104
$98
$94
$91
$88
$84
$80
$76
$1,106
SUM
$85
$94
$103
$284
$266
$250
$239
$229
$219
$209
$198
$187
$2,362
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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
8-63
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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:
(Galreference - GalpoLicy\ / Galper refill . \ /$\
Refueling Benefit = - x h time per refill x
\ Gal per re fill I \Fuelaispenserate ) \nr
\ r J / f \ /
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 ID; for an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X. A. 1
Table 8-27and Table 8-28.
8-64
-------
Table 8-25 Inputs to Calculate Refueling Time Savings
Fuel Dispensing Rate
(gallon/minute)124
Refueling fixed time
(minute s/ref ill) 1 25
Tank volume (gallons)3
Refill amount
(%volume/refill) a
Resultant time/refill
(minutes/refill)
Wage rate
(2012$/hr)126'b
HD PICKUP AND VAN
10
3.5
30
60%
5.3
$27.22
VOCATIONAL
VEHICLE
10
3.5
40
75%
6.5
31.01
TRACTOR
20
3.5
200
75%
11.0
28.56
Notes:
a HD pickup and van values based on a NHTSA survey, other are estimated.
b A wage growth rate of 1.2% has been assumed for future years.
Table 8-26 Annual Refueling Benefits and Net Present Values for the Preferred Alternative Relative to the
Less Dynamic Baseline and using Method B
(Dollar Values in Millions of 2012$)a
CALENDAR
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUP AND VANS
Hours saved
(thousands)
0
0
0
51
192
419
732
1,130
1,611
2,164
2,699
3,211
3,694
5,595
6,695
7,788
Benefits
$0
$0
$0
$2
$6
$13
$23
$36
$52
$70
$89
$107
$125
$200
$254
$334
$2,627
$1,067
VOCATIONAL
Hours saved
(thousands)
0
0
0
122
244
365
620
874
1,124
1,558
1,979
2,387
2,778
4,343
5,286
6,260
Benefits
$0
$0
$0
$4
$9
$13
$22
$32
$41
$58
$74
$91
$107
$177
$229
$305
$2,347
$950
TRACTOR/TRAILER
Hours saved
(thousands)
90
183
278
598
999
1,401
2,046
2,691
3,327
4,043
4,731
5,387
6,011
8,526
10,205
12,365
Benefits
$3
$6
$9
$19
$32
$46
$67
$90
$112
$138
$164
$188
$213
$320
$407
$556
$4,436
$1,851
SUM OF
BENEFITS
$3
$6
$9
$25
$47
$72
$113
$157
$205
$266
$327
$386
$444
$698
$890
$1,195
$9,410
$3,868
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
8-65
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Table 8-27 Discounted Model Year Lifetime Refueling Benefits at 3% for the Preferred Alternative Relative
to the Less Dynamic Baseline and using Method B (Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP
AND VANS
$0
$0
$0
$13
$35
$56
$77
$97
$118
$136
$135
$134
$801
VOCATIONAL
$0
$0
$0
$36
$36
$35
$72
$73
$73
$123
$122
$121
$691
TRACTOR/
TRAILER
$23
$22
$21
$114
$113
$112
$176
$179
$181
$207
$207
$208
$1,563
SUM
$23
$22
$21
$163
$184
$203
$325
$349
$372
$466
$465
$463
$3,055
Note:
a For an explanation of analytical Methods A and B, please see Preamble
Section ID; for an explanation of the less dynamic baseline, la, and more
dynamic baseline, Ib, please see Preamble Section X.A.I
Table 8-28 Discounted Model Year Lifetime Refueling Benefits at 7% for the Preferred Alternative Relative
to the Less Dynamic Baseline and using Method B (Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP
AND VANS
$0
$0
$0
$8
$21
$33
$43
$52
$61
$68
$65
$62
$413
VOCATIONAL
$0
$0
$0
$23
$21
$20
$40
$39
$38
$61
$59
$56
$357
TRACTOR/
TRAILER
$16
$15
$14
$71
$67
$64
$97
$95
$93
$102
$98
$95
$827
SUM
$16
$15
$14
$101
$110
$117
$181
$187
$191
$231
$222
$213
$1,597
Note:
a For an explanation of analytical Methods A and B, please see
Section ID; for an explanation of the less dynamic baseline, la
dynamic baseline, Ib, please see Preamble Section X.A.I
Preamble
and more
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
8-66
-------
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:
Travel Benefit = (VMTrebound)\
policy
g) (VMT,
rebouni
reference
mile
'.icy
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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD PICKUP AND
VANS
$0
$0
$0
$32
$64
$96
$127
$157
$186
$213
$237
$261
$282
$372
$437
$489
$5,021
$2,209
VOCATIONAL
$0
$0
$0
$68
$138
$208
$279
$349
$416
$481
$538
$593
$644
$855
$1,008
$1,141
$11,518
$5,045
TRACTOR/TRAILER
$0
$0
$0
$345
$434
$517
$595
$672
$744
$813
$872
$929
$983
$1,217
$1,428
$1,656
$17,700
$8,062
SUM
$0
$0
$0
$445
$636
$821
$1,001
$1,179
$1,346
$1,506
$1,647
$1,783
$1,909
$2,445
$2,873
$3,286
$34,240
$15,316
Note:
a For an explanation of analytical Methods A and
dynamic baseline, la, and more dynamic baseline.
B, please see Preamble Section ID; for an explanation of the less
Ib, please see Preamble Section X.A.I
8-67
-------
Table 8-30 Discounted Model Year Lifetime Value of Increased Travel for the Preferred Alternative Relative
to the Less Dynamic Baseline and using Method B (3% discount rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP AND
VANS
$0
$0
$0
$252
$244
$236
$232
$229
$227
$223
$220
$218
$2,080
VOCATIONAL
$0
$0
$0
$548
$539
$529
$522
$524
$525
$511
$507
$502
$4,706
TRACTOR/TRAILER
$554
$618
$686
$711
$706
$698
$680
$689
$695
$688
$688
$686
$8,098
SUM
$554
$618
$686
$1,510
$1,488
$1,463
$1,434
$1,442
$1,447
$1,421
$1,415
$1,406
$14,884
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
Table 8-31 Discounted Model Year Lifetime Value of Increased Travel for the Preferred Alternative Relative
to the Less Dynamic Baseline and using Method B (7% discount rate, Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP AND
VANS
$0
$0
$0
$158
$148
$138
$130
$124
$118
$111
$106
$101
$1,134
VOCATIONAL
$0
$0
$0
$343
$325
$307
$292
$282
$272
$255
$244
$232
$2,553
TRACTOR/TRAILER
$353
$390
$429
$441
$422
$402
$377
$368
$358
$341
$328
$315
$4,524
SUM
$353
$390
$429
$942
$894
$847
$799
$774
$748
$708
$678
$649
$8,211
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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 CCh emissions standards more difficult or may reduce the projected
benefits of the program. The primary way that safety regulations can impact fuel efficiency and
8-68
-------
CCh emissions is through increased vehicle weight, which reduces the fuel efficiency (and thus
increases the CCh 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 CCh 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.
8-69
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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 two-
axle 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
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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
119
121
208
Motor coaches only
MD/HD Vehicle Electronic Stability
Control Systems
Total
Motor coaches
Total
All other MD/HD vehicles
ADDED WEIGHT IN
POUNDS
MD/HD VEHICLE
0
Oa
161-269
10
171-279
10
ADDED WEIGHT IN
KILOGRAMS
MD/HD VEHICLE
0
Oa
73-122
4.5
77.5-126.5
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.
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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 light-
duty 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
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(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.00 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.11 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.
11 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 post-
recession 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/~ihamilto/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).
n 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 'TEA Response System for Oil Supply Emergencies."
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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.
U.S. Expenditures on Crude Oil
800
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
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
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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.xls!" 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 NHTS A 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. NHTS A 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.
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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
2020
2025
2030
2040
2050
U.S. OIL
IMPORTS
4.94
5.07
5.38
6.0
X
REDUCTIONS FROM
PROPOSED HD RULE
0.01
0.16
0.37
0.65
0.78
Notes:
a For an explanation of analytical Methods A and B, please see
Preamble Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see Preamble
Section X.A.I
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).
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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)
2020
2025
2030
2040
MONOPSONY
(RANGE)
$4.91
(1.63-9.15)
$5.46
(1.81-10.47)
$6.04
(2.00-11.67)
$7.17
(2.32-14.03)
AVOIDED MACROECONOMIC
DISRUPTION/ADJUSTMENT
COSTS
(RANGE)
$6.35
(3.07-10.15)
$7.29
(3.57-11.67)
$8.39
(4.12-13.41)
$10.74
(5.36-17.22)
TOTAL MID-POINT
(RANGE)
$11.25
(6.67-16.53)
$12.75
(7.58-18.65)
$14.43
(8.54-21.13)
$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.
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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 $4.9 I/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.
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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 policy-
relevant 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
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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
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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
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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.
^ 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.)
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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 price-
elasticity 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
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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 oil-
importing 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.
Projected and Historical U.S. Expenditures,
and Expenditure Share, on Crude Oil
900
800
7%
as
D Domestic
D Imported
DUS Oil Expenditures as Share of GDP
0
0%
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025
Year
2030
155
Figure 8-4 Projected and Historical U.S. Expenditures, and Expenditure Share, on Crude Oil
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
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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.°° 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.
00 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.
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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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2035
2040
2050
NPV, 3%
NPV, 7%
HD
PICKUP
&
VANS
$0
$0
$0
$3
$10
$23
$41
$65
$95
$131
$167
$204
$241
$413
$556
$647
$5,356
$2,163
VOCATIONAL
$0
$0
$0
$6
$12
$19
$33
$47
$63
$91
$120
$150
$180
$322
$443
$522
$4,209
$1,689
TRACTOR/
TRAILER
$10
$20
$31
$69
$118
$170
$255
$344
$438
$548
$660
$772
$884
$1,421
$1,922
$2,328
$19,383
$8,005
SUM
$10
$20
$31
$77
$140
$211
$328
$456
$596
$770
$947
$1,126
$1,306
$2,156
$2,920
$3,498
$28,947
$11,857
Note:
a For an explanation of analytical Methods A and B, please see Preamble
Section ID; for an explanation of the less dynamic baseline, la, and more
dynamic baseline, Ib, please see Preamble Section X. A. 1
Table 8-36 Discounted Model Year Lifetime Energy Security Benefits at a 3% Discount Rate for the
Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$)a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP
AND VANS
$0
$0
$0
$24
$66
$107
$148
$190
$233
$272
$273
$272
$1,587
VOCATIONAL
$0
$0
$0
$55
$54
$54
$116
$118
$120
$216
$217
$217
$1,168
TRACTOR/
TRAILER
$86
$85
$84
$455
$458
$460
$732
$751
$768
$887
$898
$907
$6,571
SUM
$86
$85
$84
$534
$579
$621
$996
$1,060
$1,121
$1,375
$1,388
$1,397
$9,325
Note:
a For an explanation of analytical Methods A and B, please see
Section ID; for an explanation of the less dynamic baseline, la
dynamic baseline, Ib, please see Preamble Section X.A.I
Preamble
and more
8-86
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Table 8-37 Discounted Model Year Lifetime Energy Security Benefits due to the Preferred Alternative at a
7% Discount Rates for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B
using the MOVES Analysis of HD Pickups and Vans and Relative to the Less Dynamic Baseline (Millions of
2012$) a
MODEL
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Sum
HD PICKUP
AND VANS
$0
$0
$0
$15
$40
$62
$82
$102
$120
$135
$130
$125
$810
VOCATIONAL
$0
$0
$0
$34
$32
$31
$64
$63
$62
$107
$104
$100
$597
TRACTOR/
TRAILER
$60
$56
$53
$277
$269
$260
$400
$395
$390
$434
$423
$412
$3,430
SUM
$60
$56
$53
$326
$341
$353
$546
$560
$571
$676
$657
$637
$4,837
Note:
a For an explanation of analytical Methods A and B, please see Preamble
Section ID; for an explanation of the less dynamic baseline, la, and more
dynamic baseline, Ib, please see Preamble Section X.A.I
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.
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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 ID for the preamble. And as discussed in preamble Section X.A.I, 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 la) uses a baseline that projects very little improvement in new vehicles in
the absence of new Phase 2 standards, and the second (Alternative Ib) 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
Vehicle Program: Technology and
Indirect Costs, Normal Profit on
Additional Investments
Additional Routine Maintenance
Congestion, Accidents, and Noise
from Increased Vehicle Use
Total Costs
Fuel Savings (valued at pre-tax prices)
Savings from Less Frequent Refueling
Economic Benefits from Additional
Vehicle Use
Reduced Climate Damages from GHG
Emissions a
Reduced Health Damages from Non-
GHG Emissions
Increased U.S. Energy Security
Total Benefits
Net Benefits
BASELINE 1A
3%
25.4
1.1
4.7
31.1
175.1
3.1
15.1
34.9
38.8
8.9
276
245
7%
17.1
0.6
2.8
20.5
94.2
1.6
8.4
34.9
20.7
4.7
165
144
BASELINE IB
3%
25.0
1.0
4.5
30.5
165.1
2.9
14.7
32.9
37.2
8.1
261
231
7%
16.8
0.6
2.6
20.0
89.2
1.5
8.2
32.9
20.0
4.3
156
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
-------
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
Vehicle
program
Maintenance
Pre-tax Fuel
Energy
security
Accidents/
Congestion/
Noise
Refueling
Travel value
Non-GHG
sec
SC-CO2; 5%
avg
SC-CO2; 3%
avg
SC-CO2;
2.5% avg
SC-CO2; 3%
95th
Net benefits d
SC-CO2; 5%
avg
SC-CO2; 3%
avg
SC-C02;
2.5% avg
SC-CO2; 3%
95th
2018
-$0.1
$0.0
$0.2
$0.0
$0.0
$0.0
$0.0
$0.0 to
$0.1
$0.0
$0.0
$0.1
$0.1
$0.2
$0.2
$0.2
$0.3
2021
-$2.4
$0.0
$1.7
$0.1
-$0.1
$0.0
$0.4
$0.4 to
$0.9
$0.1
$0.3
$0.5
$1.0
$0.4
$0.7
$0.8
$1.3
2024
-$3.7
-$0.1
$6.9
$0.3
-$0.3
$0.1
$1.0
$1.0 to
$2.4
$0.4
$1.3
$1.9
$4.0
$6.4
$7.3
$7.9
$10.0
2030
-$5.4
-$0.1
$24.0
$1.3
-$0.5
$0.4
$1.9
$3.3 to
$8.3
$1.5
$4.8
$6.9
$14.6
$28.8
$32.1
$34.2
$41.9
2035
-$5.9
-$0.1
$37.2
$2.2
-$0.7
$0.7
$2.4
$4.8 to
$12.1
$2.5
$7.4
$10.6
$23.2
$46.8
$51.7
$54.9
$67.5
2040
-$6.3
-$0.1
$47.8
$2.9
-$0.8
$0.9
$2.9
$5.7 to
$14.3
$3.3
$9.7
$13.7
$30.3
$60.6
$66.9
$70.9
$87.6
2050
-$7.0
-$0.1
$57.5
$3.5
-$0.9
$1.2
$3.3
$7.0 to
$17.5
$5.0
$13.6
$18.5
$42.0
$74.6
$83.2
$88.2
$111.7
NPV,
3%
-$86.8
-$1.8
$495.6
$28.9
-$9.3
$9.4
$34.2
$69.7 to
$157.0
$22.1
$103.1
$164.1
$320.5
$605.8
$686.8
$747.8
$904.1
NPV,
7%
-$41.1
-$0.9
$206.7
$11.9
-$4.2
$3.9
$15.3
$26.6 to
$60.4
$22.1
$103.1
$164.1
$320.5
$257.1
$338.1
$399.1
$555.5
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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* 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.
0 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 CCh emissionsand consequently the annual
quantified benefits (i.e., total benefits)for each of four SC-CCh values estimated by the
interagency working group. As discussed in Section 8.5, there are some limitations to the SC-
CCh 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-CCh
GHG emissions (CH4, N2O, HFC) expected under this program. Although EPA has not
monetized the benefits of reductions in non-CCh 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-CCh
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.
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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
Vehicle
Program
Maintenance
Pre-tax Fuel
Energy
Security
Accidents,
Noise,
Congestion
Refueling
Travel value
Non-GHG
sec
SC-C02; 5%
avg
SC-CO2; 3%
avg
SC-C02;
2.5% avg
SC-CO2; 3%
95th
Net benefits d
SC-CO2; 5%
avg
SC-C02; 3%
avg
SC-CO2;
2.5% avg
SC-C02; 3%
95th
2018
-$0.1
-$0.1
$1.9
$0.1
-$0.1
$0.0
$0.6
$0.2
to
$0.5
$0.1
$0.4
$0.6
$1.1
$2.8
$3.0
$3.2
$3.8
2019
-$0.1
$0.0
$1.9
$0.1
-$0.1
$0.0
$0.6
$0.2
to
$0.4
$0.1
$0.4
$0.6
$1.1
$2.7
$3.0
$3.2
$3.8
2020
-$0.1
$0.0
$1.8
$0.1
-$0.2
$0.0
$0.7
$0.2
to
$0.4
$0.1
$0.4
$0.6
$1.1
$2.7
$3.0
$3.2
$3.7
2021
-$2.0
-$0.1
$11.1
$0.5
-$0.4
$0.2
$1.5
$2.0
to
$4.5
$0.5
$2.2
$3.4
$6.6
$14.6
$16.2
$17.4
$20.7
2022
-$1.9
-$0.1
$11.5
$0.6
-$0.4
$0.2
$1.5
$2.0
to
$4.5
$0.5
$2.3
$3.5
$6.9
$15.1
$16.8
$18.1
$21.5
2023
-$1.9
-$0.1
$11.9
$0.6
-$0.4
$0.2
$1.5
$2.0
to
$4.5
$0.5
$2.3
$3.6
$7.2
$15.5
$17.3
$18.6
$22.1
2024
-$2.8
-$0.1
$18.9
$1.0
-$0.4
$0.3
$1.4
$2.9
to
$6.6
$0.9
$3.7
$5.8
$11.5
$23.9
$26.8
$28.9
$34.5
2025
-$2.7
-$0.1
$19.6
$1.1
-$0.4
$0.3
$1.4
$3.0
to
$6.8
$0.9
$3.9
$6.1
$12.0
$25.0
$28.0
$30.2
$36.0
2026
-$2.7
-$0.1
$20.2
$1.1
-$0.4
$0.4
$1.4
$2.6
to
$5.9
$0.9
$4.0
$6.3
$12.4
$25.1
$28.2
$30.5
$36.6
2027
-$3.7
-$0.1
$24.1
$1.4
-$0.4
$0.5
$1.4
$3.1
to
$6.9
$1.1
$4.8
$7.6
$14.9
$29.2
$33.0
$35.7
$43.1
2028
-$3.6
-$0.1
$24.1
$1.4
-$0.4
$0.5
$1.4
$3.1
to
$6.9
$1.1
$4.8
$7.6
$15.0
$29.4
$33.1
$35.9
$43.3
2029
-$3.5
-$0.1
$24.1
$1.4
-$0.4
$0.5
$1.4
$3.1
to
$7.0
$1.1
$4.9
$7.7
$15.1
$29.4
$33.2
$36.0
$43.5
SUM
-$25.1
-$1.1
$171.1
$9.3
-$4.2
$3.1
$14.9
$24.4
to
$55.0
$7.8
$34.0
$53.4
$105.0
$215.5
$241.7
$261.1
$312.7
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
b The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions
expected under this program (see draft RIA Chapter 8.5). Although EPA has not monetized changes in non-CO2
GHGs, the value of any increases or reductions should not be interpreted as zero.
0 Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same
discount rate used to discount the value of damages from future emissions (SC-CO2 at 5, 3, 2.5 percent) is used to
calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail.
d Net impacts are the summation of results within columns of the table with the exception that the net impacts at
each SC-CO2 value include only the SC-CO2 impacts at that value.
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Table 8-41 Discounted Model Year Lifetime Impacts for the Preferred Alternative Relative to the Less
Dynamic Baseline and using Method B (Billions of 2012$; 7% Discount Rate) a'b'c
Vehicle
Program
Maintenance
Pre-tax Fuel
Energy
Security
Accidents,
Noise,
Congestion
Refueling
Travel value
Non-GHG
sec
SC-C02; 5%
avg
SC-CO2; 3%
avg
SC-C02;
2.5% avg
SC-CO2; 3%
95th
Net benefits d
SC-CO2; 5%
avg
SC-C02; 3%
avg
SC-CO2;
2.5% avg
SC-C02; 3%
95th
2018
-$0.1
$0.0
$1.4
$0.1
-$0.1
$0.0
$0.4
$0.1
to
$0.3
$0.1
$0.4
$0.6
$1.1
$1.9
$2.2
$2.4
$2.9
2019
-$0.1
$0.0
$1.3
$0.1
-$0.1
$0.0
$0.4
$0.1
to
$0.3
$0.1
$0.4
$0.6
$1.1
$1.8
$2.1
$2.3
$2.8
2020
-$0.1
$0.0
$1.2
$0.1
-$0.1
$0.0
$0.4
$0.1
to
$0.3
$0.1
$0.4
$0.6
$1.1
$1.7
$2.0
$2.2
$2.8
2021
-$1.6
-$0.1
$6.9
$0.3
-$0.3
$0.1
$0.9
$1.1
to
$2.5
$0.5
$2.2
$3.4
$6.6
$8.7
$10.3
$11.5
$14.8
2022
-$1.4
-$0.1
$6.9
$0.3
-$0.3
$0.1
$0.9
$1.1
to
$2.4
$0.5
$2.3
$3.5
$6.9
$8.7
$10.4
$11.7
$15.1
2023
-$1.4
-$0.1
$6.8
$0.4
-$0.2
$0.1
$0.8
$1.0
to
$2.3
$0.5
$2.3
$3.6
$7.2
$8.7
$10.5
$11.8
$15.3
2024
-$1.9
-$0.1
$10.5
$0.5
-$0.2
$0.2
$0.8
$1.4
to
$3.3
$0.9
$3.7
$5.8
$11.5
$13.0
$15.8
$17.9
$23.6
2025
-$1.8
-$0.1
$10.4
$0.6
-$0.2
$0.2
$0.8
$1.4
to
$3.2
$0.9
$3.9
$6.1
$12.0
$13.1
$16.1
$18.3
$24.2
2026
-$1.7
-$0.1
$10.4
$0.6
-$0.2
$0.2
$0.7
$1.2
to
$2.7
$0.9
$4.0
$6.3
$12.4
$12.7
$15.8
$18.1
$24.2
2027
-$2.3
-$0.1
$11.9
$0.7
-$0.2
$0.2
$0.7
$1.3
to
$3.0
$1.1
$4.8
$7.6
$14.9
$14.3
$18.0
$20.7
$28.1
2028
-$2.1
-$0.1
$11.5
$0.7
-$0.2
$0.2
$0.7
$1.3
to
$2.9
$1.1
$4.8
$7.6
$15.0
$13.8
$17.6
$20.4
$27.8
2029
-$2.0
$0.0
$11.0
$0.6
-$0.2
$0.2
$0.6
$1.3
to
$2.8
$1.1
$4.9
$7.7
$15.1
$13.4
$17.2
$20.0
$27.4
SUM
-$16.6
-$0.6
$90.1
$4.8
-$2.4
$1.6
$8.2
$11.5
to
$26.0
$7.8
$34.0
$53.4
$105.0
$111.8
$138.0
$157.4
$209.0
Notes:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the less
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
0 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.
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8.11 Employment 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 Chapters.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 Herman and Bui (2001, pp. 274-75) model two components that drive
8-93
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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.
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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.
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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://federalregi ster. 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
8-96
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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,
1111 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).
8-97
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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.88
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.
8-98
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estimates of employees/Si 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 labor-
intensive 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 Sector"
SOURCE
BLS ERM
BLS ERM
BLS ERM
ASM
ASM
ASM
ASM
ASM
SECTOR
Mo tor vehicle mfg (3361)
Motor vehicle body & trailer mfg (3362)
Motor vehicle parts mfg (3363)
Motor vehicle mfg (3361)
Light truck & utility vehicle mfg
(336112)
Heavy duty truck mfg (33612)
Motor vehicle body & trailer mfg (3362)
Motor Vehicle Parts Mfg (3363)
RATIO OF
WORKERS PER
$1 MILLION
EXPENDITURE
S
0.460
1.450
1.950
0.538
0.443
0.832
2.797
1.635
RATIO OF WORKERS
PER $1 MILLION
EXPENDITURES,
ADJUSTED FOR
DOMESTIC VS.
FOREIGN
PRODUCTION
0.355
1.153
1.590
0.414
0.341
0.641
2.156
1.260
Note:
"BLS 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.
8-99
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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/Si 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 $1 million in costs, to estimate employment impacts in the regulated
and parts manufacturing sectors. Increased costs of vehicles and parts would, by itself, and
holding labor intensity constant, be expected to increase employment between 2018 and 2027
from none to a few thousand jobs each year.
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While we estimate employment impacts, measured in job-years, beginning with program
implementation, some of these employment gains may occur earlier as motor vehicle
manufacturers and parts suppliers hire staff in anticipation of compliance with the standards. A
job-year is a way to calculate the amount of work needed to complete a specific task. For
example, a job-year is one year of work for one person.
Table 8-43 Employment Effects due to Increased Costs of Vehicles and Parts (Substitution Effect), in Job-
years
YEAR
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
COSTS
(MILLIONS OF
2012$)
$ 116
$ 113
$ 112
$ 2,173
$ 2,161
$ 2,224
$ 3,455
$ 3,647
$ 3,736
$ 5,309
MINIMUM EMPLOYMENT
DUE TO SUBSTITUTION
EFFECT (ERM ESTIMATES,
EXPENDITURES IN THE
PARTS SECTOR3)
0
0
0
300
300
200
300
200
200
200
MAXIMUM EMPLOYMENT DUE
TO SUBSTITUTION EFFECT (ERM
ESTIMATES, EXPENDITURES IN
THE BODY AND TRAILER MFC
SECTOR)
100
100
100
2,300
2,200
2,100
3,200
3,200
3,100
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
8-101
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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
IJU 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.
8-102
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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.
8-103
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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
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
RETAIL
Diesel
Low
$2.94
$2.95
$2.96
$3.00
$3.01
$3.01
$3.02
$3.03
$3.04
$3.03
$3.03
$3.04
$3.06
$3.06
$3.06
$3.07
$3.08
$3.08
$3.09
$3.09
$3.10
$3.11
$3.11
Primary
$3.53
$3.61
$3.67
$3.74
$3.82
$3.87
$3.92
$3.98
$4.02
$4.08
$4.12
$4.16
$4.20
$4.25
$4.30
$4.36
$4.43
$4.47
$4.51
$4.54
$4.58
$4.65
$4.73
High
$4.89
$4.94
$4.99
$5.03
$5.08
$5.10
$5.14
$5.17
$5.24
$5.30
$5.39
$5.47
$5.51
$5.58
$5.66
$5.72
$5.77
$5.83
$5.89
$5.96
$6.03
$6.12
$6.23
Gasoline
Low
$2.54
$2.54
$2.55
$2.59
$2.59
$2.57
$2.56
$2.55
$2.55
$2.54
$2.54
$2.54
$2.55
$2.55
$2.56
$2.57
$2.58
$2.58
$2.59
$2.59
$2.60
$2.61
$2.61
Primary
$3.02
$3.03
$3.08
$3.12
$3.17
$3.22
$3.26
$3.29
$3.32
$3.36
$3.37
$3.40
$3.43
$3.46
$3.50
$3.54
$3.61
$3.65
$3.69
$3.73
$3.77
$3.83
$3.90
High
$4.10
$4.13
$4.16
$4.17
$4.18
$4.19
$4.21
$4.23
$4.26
$4.31
$4.38
$4.43
$4.45
$4.51
$4.57
$4.62
$4.66
$4.71
$4.77
$4.81
$4.87
$4.95
$5.04
UNTAXED
Diesel
Low
$2.48
$2.49
$2.51
$2.55
$2.56
$2.57
$2.58
$2.59
$2.60
$2.60
$2.61
$2.61
$2.64
$2.64
$2.64
$2.66
$2.67
$2.67
$2.68
$2.69
$2.70
$2.71
$2.72
Primary
$3.07
$3.15
$3.22
$3.29
$3.37
$3.43
$3.48
$3.54
$3.59
$3.65
$3.69
$3.74
$3.79
$3.84
$3.89
$3.95
$4.02
$4.06
$4.11
$4.15
$4.19
$4.26
$4.34
High
$4.43
$4.49
$4.54
$4.59
$4.64
$4.67
$4.70
$4.74
$4.81
$4.88
$4.97
$5.06
$5.09
$5.17
$5.25
$5.32
$5.37
$5.44
$5.50
$5.57
$5.65
$5.74
$5.85
Gasoline
Low
$2.13
$2.13
$2.15
$2.19
$2.19
$2.18
$2.16
$2.16
$2.16
$2.15
$2.16
$2.16
$2.17
$2.17
$2.18
$2.20
$2.21
$2.22
$2.23
$2.23
$2.24
$2.25
$2.26
Primary
$2.61
$2.62
$2.67
$2.71
$2.77
$2.82
$2.86
$2.89
$2.92
$2.96
$2.98
$3.01
$3.04
$3.08
$3.12
$3.16
$3.24
$3.27
$3.32
$3.36
$3.40
$3.47
$3.54
High
$3.68
$3.71
$3.74
$3.76
$3.77
$3.78
$3.80
$3.82
$3.86
$3.91
$3.98
$4.03
$4.06
$4.12
$4.18
$4.24
$4.28
$4.33
$4.39
$4.44
$4.49
$4.57
$4.67
Note:
8-104
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Our Primary case values are the AEO reference case values and are taken from AEO2014 Early Release; other
values from AEO2014.
The impacts of using the low and high oil price cases on our estimated fuel savings and
net benefits are shown in Table 8-45.
Table 8-45 MY2018-2029 Lifetime Sensitivity on Net Benefits using AEO2014 Low and High Oil Price Cases
for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Billions of 2012$)a
Vehicle program
Maintenance
Fuel
Benefits
Net benefits
LOW OIL
PRICE
CASE
-$25
-$1.1
$117
$93
$184
PRIMARY
CASE
-$25
-$1.1
$171
$97
$242
HIGH OIL
PRICE
CASE
-$25
-$1.1
$230
$101
$305
Note:
a For an explanation of analytical Methods A and B, please see
Preamble Section ID; for an explanation of the less dynamic
baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A.I
8-105
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2 State of Massachusetts v. EPA, 549 U.S. at 533.
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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/policv/report-acg-
operatingcost2005-2005-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.
8-106
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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/policvinformation/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. EPA-
420-R-l 1-901. (http://www.epa.gov/otaq/climate/documents/420rll901.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,
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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
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http://www.its.ucdavis.edu/events/2009book/Chapterl3.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
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31 See GIFT Solutions, LLC, "Potential for Mode Shift due to Heavy Duty Vehicle Fuel Efficiency Improvements".
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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:
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34 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International
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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,
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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 IIReport, National Cooperative Highway Research Program Project 8-30, June 1995.
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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).
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42 U.S. EPA. Motor Vehicle Emission Simulator (MOVES2014). Last accessed on September 11, 2014 at
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44 EPA/NHTSA, August 2011. Page 7-3, Final Rulemaking to Establish Greenhouse gas Emissions Standards &
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420-R-l 1-901. (http://www.epa.gov/otaq/climate/documents/420rll901.pdf)
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47 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research
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50 See NAS 2010 Report, page 151.
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.
122 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study, see
http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed July 21, 2010).
123 See Federal Highway Administration, 1997 Federal Highway Cost Allocation Study,
http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V-22, V-23, and V-24 (last accessed July 8, 2012).
124 Passenger vehicle fuel dispensing rate per EPA regulations, last viewed on August 4, 2010 at
http://www. epa.gov/oms/regs/ld-hwy/evap/spitback. txt
125 Survey data results on refueling times for light-duty trucks derived from "Evaluation of the Effectiveness of
TPMS in Proper Tire Pressure Maintenance", November 2012. Report No. DOT HS 811 681. http://www-
nrd.nhtsa.dot.gov/Pubs/811681.pdf.
126 U.S. Department of Transportation, Valuation of Travel Guidance, July 9, 2014, at page 14.
127 "Preliminary Regulatory Impact Analysis, FMVSS No. 119, New Pneumatic Tires for Motor Vehicles with a
GVWR of More Than 4,536 kg (10,000 pounds), June 2010.
128 Cost and Weight Analysis of Two Motorcoach Seating Systems: One With and One Without Three-Point
Lap/Shoulder Belt Restraints, Ludtke and Associates, July 2010.
129 "Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012 - MY 2016 Passenger Cars
and Light Trucks", NHTSA, March 2010, (Docket No. NHTSA-2009-0059-0344.1).
130Based on data from the CIA, combining various recent years, https://www.cia.gov/librarv/publications/the-world-
factbook/rankorder/2242rank.html
131 See EIA Annual Energy Review, various editions. For data 2011-2013, and projected data: EIA Annual Energy
Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_ll.xls"
132 For historical data: EIA Annual Energy Review, various editions. For data 2011-2013, and projected data: EIA
Annual Energy Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_ll.xls"
133 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits
and Costs, ORNL-6851, Oak Ridge National Laboratory, November, 1997.
134 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports, ICF, Inc.,
September 2007.
135 Leiby, P., Factors Influencing Estimate of Energy Security Premium for Heavy-Duty Phase II Proposed Rule,
11/18/2014, Oak Ridge National Laboratory.
136 Brown, Stephen P.A. and Hillard G. Huntington. 2013. Assessing the U.S. Oil Security Premium,
Energy Economics, vol. 38, pp 118-127.
137 Reassessing the Oil Security Premium. RFF Discussion Paper Series, (RFF DP 10-05). doi: RFF DP 10-05
138 Greene, D. L. 2010. Measuring energy security: Can the United States achieve oil independence? Energy Policy,
38(4), 1614-1621. doi:10.1016/j.enpol.2009.01.041
139 Reassessing the Oil Security Premium. RFF Discussion Paper Series, (RFF DP 10-05). doi:RFF DP 10-05
140 Ledyard, John O. "Market Failure." The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N.
Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008
141 Gately, Dermot 2004. "OPEC's Incentives for Faster Output Growth", The Energy Journal,25(2):75-96; Gately,
Dermot 2007. "What Oil Export Levels Should We Expect From OPEC?", The Energy Journal, 28(2): 151-173.
142 Bohi, Douglas R. And W. David Montgomery 1982. "Social Cost of Imported and U.S. Import Policy", Annual
Review of Energy, 7:37-60. Energy Modeling Forum, 1981. World Oil, EMF Report 6 (Stanford University Press:
Stanford 39 CA. https//emf.stanford.edu/publications/emf-6-world-oil.
143 Plummer, James L. (Ed.) 1982. Energy Vulnerability, "Basic Concepts, Assumptions and Numercial Results",
pp. 13-36, (Cambridge MA: Ballinger Publishing Co.)
144 Bohi, Douglas R. And W. David Montgomery 1982 "Social Cost of Imported and U.S. Import Policy", Annual
Review of Energy, 7:37-60.
145 Hogan, William W., 1981. "Import Management and Oil Emergencies", Chapter 9 in Deese, 5 David and Joseph
Nye, eds. Energy and Security. Cambridge, MA: Ballinger Publishing Co.
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146 Broadman, H. G. 1986. "The Social Cost of Imported Oil," Energy Policy 14(3):242-252. Broadman H.G. and
W.W. Hogan, 1988. "Is an Oil Import Tariff Justified? An American Debate: The Numbers Say 'Yes' ", The Energy
Journal 9: 7-29.
147 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits
and Costs, ORNL-6851, Oak Ridge National Laboratory, November 1, 1997.
148 Parry, Ian W.H. and Joel Darmstadter 2004. "The Costs of U.S. Oil Dependency," Resources for the Future,
November 17, 2004 (also published as NCEP Technical Appendix Chapter 1: Enhancing Oil Security, the National
Commission on Energy Policy 2004 Ending the Energy Stalemate - A Bipartisan Strategy to Meet America's
Energy Challenges.)
149 National Research Council, 2009. Hidden Costs of Energy: Unpriced Consequences of Energy Production and
Use. National Academy of Science, Washington, DC.
150 See, William Nordhaus, "Who's Afraid of a Big Bad Oil Shock?," available at
http://aida.econ.vale.edu/~nordhaus/homepage/Big Bad Oil Shock Meeting.pdf, and Olivier Blanchard and Jordi
Gali, "The macroeconomic Effects of Oil price Shocks: Why are the 2000s so different from the 1970s?", pp. 373-
421, in The International Dimensions of Monetary Policy. Jordi Gali and Mark Gertler, editors, University of
Chicago Press, February 2010, available at http://www.nber.org/chapters/c0517.pdf
151 Blanchard and Gali, p. 414.
152 See, Oil price Drops on Oversupply, http://www.oil-price.net/en/articles/oil-price-drops-on-oversupplv.php.
10/6/2014.
153 Hamilton, J. D. (2012). Oil Prices , Exhaustible Resources , and Economic Growth. In Handbook of Energy and
Climate Change. Retrieved from http://econweb.ucsd.edu/~jhamilto/handbook_climate.pdf
154 Ramey, V. A., & Vine, D. J. (2010). "Oil, Automobiles, and the U.S. Economy: How Much have Things Really
Changed?", National Bureau of Economic Research Working Papers, WP 16067(June). Retrieved from
http://www.nber.org/papers/wl6067.pdf
155 Historical data are from EIA Annual Energy Review, various editions. For data since 2011 and projected data:
source is EIA Annual Energy Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_ll.xlsx" and Table
20 (Macroeconomic Indicators," (file "aeotab_20.xlsx").
156 http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo 12866/eo 13 563_01182011 .pdf.
157 U.S. Department of Labor, Bureau of Labor Statistics. "Automotive Industry; Employment, Earnings, and
Hours", http://www.bls.gov/iag/tgs/iagauto.htm, accessed 8/18/14.
158 Layard, P.R.G., and A. A. Walters (1978), Microeconomic Theory (McGraw-Hill, Inc.), Chapter 9 (Docket EPA-
HQ-OAR-2011-0135).
159Berman, 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).
160 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy
(Addison Wesley Longman, Inc.),, p. 108.
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.
162 Arrow et al. (1996). "Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of
Principles." American Enterprise Institute, The Annapolis Center, and Resources for the Future. See discussion on
bottom of p. 6. In practice, distributional impacts on individual workers can be important, as discussed later in this
section.
163 Schmalensee, Richard, and Robert N. Stavins. "A Guide to Economic and Policy Analysis of EPA's Transport
Rule." White paper commissioned by Excelon Corporation, March 2011.
164 Klaiber, H. Allen, and V. Kerry Smith (2012). "Developing General Equilibrium Benefit Analyses for Social
Programs: An Introduction and Example." Journal of Benefit-Cost Analysis 3(2).
165 Graff Zivin, J., and M. Neidell (2012). "The Impact of Pollution on Worker Productivity." American Economic
Review 102: 3652-3673).
166 Hamermesh (1993), Labor Demand (Princeton, NJ: Princeton University Press), Chapter 2 (Docket EPA-HQ-
OAR-2011-013 5).
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).
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169Morgenstern, 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-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-OAR-
2011-0135).
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 Pub lie 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_201 l_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
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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").6 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-to-
reducing-the-fuel-consumption-of-medium-and-heaw-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-fuel-
consumption-and-greenhouse-gas-emissions-of-medium~and-heavy-duty-vehicles-phase-two (last accessed June 4,
2015).
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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 XL
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.
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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
safely 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 poundsi.e., the majority of the HD pickup and van fleetmass 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 gas-
CNG 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.
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technologies recently adopted by the MD/HDV fleets. These include Intelligent Transportation
Systems (ITS) and telematics, speed limiters, idle reduction devices, tire technologies (single-
wide tires, and tire pressure monitoring systems-TPMS and Automated Tire Inflation Systems-
ATIS), 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 well-
known 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
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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:
Telematicsintegrating on-board sensors, video, and audio alerts for MD/HDV
driversoffer 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, LOWS, 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).
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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. Safely 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 fail-
safes 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 state-
of-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
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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/FtDVs 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/FtDVs. 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/FtDV fleet.
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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.0 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 (WFtR) 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 WFtR 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.
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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.
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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.
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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 Ibs.
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 CCh g/mi)." These values were
converted to mpg using the factors 8,887 gCCh/gal for gas engines and 10.180 gCCVgal 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."
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In addition to information about each vehicle, the agencies need additional information
about the fuel economy-improving/CCh-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, 250/3 SOseries, 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.
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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.
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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 next-
generation Nissan Titan could debut in model year 2016, and the agencies anticipate that the NV
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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 sales-
weighted 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).
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2b
Total
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.
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Table 10-1 IHS Automotive Market Share Forecast for 2b/3 Vehicles
Manufacturer
Daimler
Fiat
Ford
General Motors
Nissan
Daimler
Fiat
Ford
General Motors
Nissan
Style
Van
Van
Van
Van
Van
Pickup
Pickup
Pickup
Pickup
Pickup
MODEL YEAR MARKET SHARE
2015
3%
2%
16%
12%
2%
0%
14%
28%
23%
0%
2016
3%
2%
17%
12%
2%
0%
14%
27%
23%
0%
2017
3%
2%
17%
11%
2%
0%
14%
30%
21%
0%
2018
3%
2%
17%
12%
2%
0%
14%
30%
21%
0%
2019
3%
2%
18%
13%
2%
0%
11%
30%
21%
0%
2020
3%
2%
18%
13%
2%
0%
12%
27%
22%
0%
2021
3%
3%
18%
13%
2%
0%
12%
26%
23%
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 pre-
model-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.
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Table 10-2 Estimated MY2014 Production by Manufacturer
MANUFACTURER
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
25,327
138,902
330,919
129,435
13,526
638,109
PERCENT
4.0%
21.8%
51.9%
20.3%
2.1%
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 Ibs.
Table 10-3 Estimated MY2014 Production by Class
GVW CLASS
2b (8,501-10,000 Ibs.)
3 (10,001-14,000 Ibs.)
Total
PRODUCTION
506,989
131,120
638,109
PERCENT
79.5%
20.5%
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
Chassis Cab
Cutaway
Pickup
Van
Total
PRODUCTION
19,724
20,539
333,100
264,746
638,109
PERCENT
3.1%
3.2%
52.2%
41.5%
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.
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Table 10-5 Estimated MY2014 Production by Engine Type
ENGINE TYPE
Diesel
Gasoline
FFV
Total
PRODUCTION
252,744
105,604
279,761
638,109
PERCENT
39.6%
16.5%
43.8%
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
4WD
FWD
RWD
Total
PRODUCTION
286,122
23,309
328,678
638,109
PERCENT
44.8%
3.7%
51.5%
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
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
19,556
98,722
262,687
112,498
13,526
506,989
PERCENT
3.9%
19.5%
51.8%
22.2%
2.7%
100.0%
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Table 10-8 Estimated MY2014 Production Class 3 by Manufacturer
MANUFACTURER
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
5,771
40,180
68,232
16,937
-
131,120
PERCENT
4.4%
30.6%
52.0%
12.9%
0.0%
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
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
-
115,593
142,580
74,927
-
333,100
PERCENT
0.0%
34.7%
42.8%
22.5%
0.0%
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
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
21,900
23,309
151,503
54,508
13,526
264,746
PERCENT
8.3%
8.8%
57.2%
20.6%
5.1%
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 10-11 shows the distribution of diesel trucks by manufacturer. Ford is the
leader at 40 percent followed by Fiat at 34 percent.
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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
Daimler
Fiat
Ford
General Motors
Nissan
Total
PRODUCTION
25,327
86,124
100,208
41,085
-
252,744
PERCENT
10.0%
34.1%
39.6%
16.3%
0.0%
100.0%
Table 10-12 Estimated MY2014 Diesel Penetration by Manufacturer
MANUFACTURER
Daimler
Fiat
Ford
General Motors
Nissan
Total
DIESEL
PRODUCTION
25,327
86,124
100,208
41,085
-
252,744
TOTAL
PRODUCTION
25,327
138,902
330,919
129,435
13,526
638,109
PERCENT
DIESEL
100.0%
62.0%
30.3%
31.7%
0.0%
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, CCh-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.
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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
1 : No Action
2: 2.0%/y
3:2.5%/y
4: 3.5%/y
5: 4.0%/y
ANNUAL STRINGENCY INCREASE
2019-2020
None
None
None
None
None
2021-2025
None
2.0%
2.5%
3.5%
4.0%
2026-2027
None
None
2.5%
None
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, andexcept insofar as doing so would compromise confidential business
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information (CBI) manufacturers have provided to NHTSAall 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 fleetthat
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,
1 Analyses can be found at http://www.nhtsa.gov/fuel-economy
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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 CCh 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.
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Expansion of the Monte Carlo simulation procedures used to perform probabilistic
uncertainty analysis.
These changes are reflected in updated model documentation available atNHTSA'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 vehiclesin 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.
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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 high-
end, 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 enginesperhaps six or eight
engines for a large manufacturerand 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.
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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 medium-
duty 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
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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 phase-
in 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
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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.
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21
20
19
<> Required
Achieved
- - Cost
credits from MYs 2020-2021 used
to cover MYs 2025-2026 shorfall
$3,500
credits from MYs 2014-2015 used
to cover MY 2016 shorfa
15
Model Year
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
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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 $3 83-$643
o avoided 0.15-0.50 gal./lOO 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./lOO 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./lOO 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./lOO mi.
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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 FID 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 Ib. : TW rounded to nearest 125 Ib.
4000 Ib. < ALVW < 5,500 Ib. : TW rounded to nearest 250 Ib.
ALVW > 5,500 Ib.: TW rounded to nearest 500 Ib.
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:
ATI/I/ ^ ^unrounded_TW
= A7W X
Where:
ACW = % change in curb weight (from model input),
AFCumounded TW = % change in fuel consumption (from model input), without TW
rounding,
ATW = % change in test weight (calculated), and
AFCrounded TW = % change in fuel consumption (calculated), with TW rounding.
11 Or, equivalently, CW + !/2 payload, where payload = GVWR - CW.
10-23
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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 Ib. 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):
Group
unibody
gasoline pickups > 13k GVWR
other gasoline pickups
diesel SRW pickups
All other
MAXIMUM RATIOS ASSUMED ENABLED BY MASS
REDUCTION
GVWR/CW
1.75
2.00
1.75
1.75
1.75
GCWR/GVWR
1.50
1.50
2.25
2.50
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
10-24
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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, CCh-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.
10-25
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Table 10-13 MY2021 Baseline Costs for Manufacturers in 2b/3 Market Segment in the Dynamic Baseline, or
Alternative Ib
MANUFACTURER
Chrysler/Fiat
Daimler
Ford
General Motors
Nissan
Industry
AVERAGE
TECHNOLOGY
275
18
258
782
282
442
TOTAL COST
INCREASE ($M)
27
0
78
191
3
300
As Table 10-13 shows, the industry as a whole is expected to add about $440 of new
technology to each new vehicle model by 2021 under the no-action alternative defined by the
Phase 1 standards. Reflecting differences in projected product offerings in the analysis fleet,
some manufacturers (notably Daimler) are significantly less constrained by the Phase 1 standards
than others and face lower cost increases as a result. General Motors (GM) shows the largest
increase in average vehicle cost, but results for GM's closest competitors (Ford and
Chrysler/Fiat) do not include the costs of their recent van redesigns, which are already present in
the analysis fleet (discussed in greater detail below).
The above results reflect the assumption that manufacturers having achieved compliance
with standards might act as if buyers are willing to pay for further fuel consumption
improvements that "pay back" within 6 months. It is also possible that manufacturers will
choose not to migrate cost-effective technologies to the 2b/3 market segment from similar
vehicles in the light-duty market. To examine this possibility, all regulatory alternatives were
also using the DOT CAFE model (Method A) with a 0-month payback period in lieu of the 6-
month payback period discussed above. (A sensitivity analysis using Method A, discussed
below, also explores longer payback periods, as well as the combined effect of payback period
and fuel price on vehicle design decisions.) Resultant technology costs in model year 2021
results for the no-action alternative, summarized below, are quite similar to those shown above
for the 6-month payback period:
Table 10-14 MY2021 Baseline Costs for HD Pickups and Vans in the Flat Baseline, or Alternative la
MANUFACTURER
Chrysler/Fiat
Daimler
Ford
General Motors
Nissan
Industry
AVERAGE
TECHNOLOGY COST
($)
268
0
248
767
257
431
TOTAL COST
INCREASE ($M)
27
0
75
188
3
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,
10-26
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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 Ib) 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.
10-27
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Table 10-15 Summary of HD Pickup and Van Alternatives' Impact on Industry versus the Dynamic
Baseline, Alternative Ib
ANNUAL STRINGENCY INCREASE
Stringency Increase Through MY
Total Stringency Increase
2.0%/Y
2025
9.6%
2.5%/Y
2027
16.2%
3.5%/Y
2025
16.3%
4.0%/Y
2025
18.5%
Average Fuel Economy (miles per gallon)
Required
Achieved
19.04
19.14
20.57
20.61
20.57
20.83
21.14
21.27
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
5.25
5.22
4.86
4.85
4.86
4.80
4.73
4.70
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
495
491
458
458
458
453
446
444
Technology Penetration (%)
WT and/or WL
Cylinder Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop Start
Hybridization3
Aero. Improvements
46
29
17
55
67
54
0
0
36
46
21
25
63
96
80
0
8
78
46
21
31
63
96
79
10
35
78
46
21
32
63
97
79
13
51
78
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
239
3.7
243
3.7
325
5.0
313
4.8
Technology Cost (vs. No-Action)
Average ($) b
Total ($m) c
Payback period (m) °
578
437
25
1,348
1,019
31
1,655
1,251
34
2,080
1,572
38
Notes:
"Includes mild hybrids (ISO) and strong HEVs.
b Values used in Methods A & B
0 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 CCh 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
10-28
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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.
10-29
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Table 10-16 Summary of Impacts on General Motors by 2030 in the HD Pickup and Van Market versus the
Dynamic Baseline, Alternative Ib
ANNUAL
STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Average Fuel Economy (miles per gallon)
Required
Achieved
18.38
18.43
19.96
19.95
20
20.24
20.53
20.51
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
5.44
5.42
5.01
5.01
5
4.94
4.87
4.87
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
507
505
467
468
467
461
455
455
Technology Penetration (%)
WT and/or WL
Cylinder Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop Start
Hybridization0
Aero. Improvements
64
47
18
53
36
100
0
0
100
64
47
18
53
100
100
0
19
100
64
47
36
53
100
100
2
79
100
64
47
36
53
100
100
0
100
100
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
325
5.3
161
2.6
158
2.6
164
2.7
Technology Cost (vs. No-Action)
Average ($) a
Total ($m,
undiscounted) b
785
214
1,706
465
2,244
611
2,736
746
Notes:
a Values used in Methods A & B
b Values used in Method A, calculated at a 3% discount rate
c Includes mild hybrids (ISO) and strong HEVs.
10-30
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Table 10-17 Summary of Impacts on Ford by 2030 in the HD Pickup and Van Market versus the Dynamic
Baseline, Alternative Ib
ANNUAL
STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Average Fuel Economy (miles per gallon)
Required
Achieved
19.42
19.5
20.96
21.04
20.92
21.28
21.51
21.8
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
5.15
5.13
4.77
4.75
4.78
4.70
4.65
4.59
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
485
482
449
447
450
443
438
433
Technology Penetration (%)
WT and/or WL
Cylinder Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop Start
Hybridization0
Aero. Improvements
34
18
16
51
100
41
0
0
0
34
0
34
69
100
62
0
2
59
34
0
34
69
100
59
20
14
59
34
0
34
69
100
59
29
30
59
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
210
3.2
202
3
379
5.7
356
5.3
Technology Cost (vs. No-Action)
Average ($) a
Total ($m,
undiscounted) b
506
170
1,110
372
1,353
454
1,801
604
Notes:
a Values used in Methods A & B
b Values used in Method A, calculated at a 3% discount rate
c Includes mild hybrids (ISO) and strong HEVs.
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Table 10-18 Summary of Impacts on Fiat/Chrysler by 2030 in the HD Pickup and Van Market versus the
Dynamic Baseline, Alternative Ib
ANNUAL
STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Average Fuel Economy (miles per gallon)
Required
Achieved
18.73
18.83
20.08
20.06
20.12
20.10
20.70
20.70
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
5.34
5.31
4.98
4.99
4.97
4.97
4.83
4.83
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
515
512
480
481
479
480
466
467
Technology Penetration (%)
WT and/or WL
Cy Under Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop-Start
Hybridization0
Aero. Improvements
40
23
17
74
65
0
0
0
0
40
23
17
74
88
100
0
3
100
40
23
17
74
88
100
0
3
100
40
23
17
74
88
100
0
10
100
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
196
2.8
649
9.1
648
9.1
617
8.7
Technology Cost (vs. No-Action)
Average ($) a
Total ($m,
undiscounted) b
434
48
1,469
163
1,486
164
1,700
188
Notes:
a Values used in Methods A & B
b Values used in Method A, calculated at a 3% discount rate
0 Includes mild hybrids (ISO) 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
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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
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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 gasoline-
powered 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.
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21
20
18
ro ._
O 17
£15
14
12
11
+-Hh
+-H- +
2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500
Work Factor
Bosdy Style Manufacturer
O Pickup Fiat
+ Van Ford
General Motors
Figure 10-3 Comparison of Fuel Economy and Work Factor for Gasoline Vehicles in the Analysis Fleet
10-35
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28
27
26
25
24
23
22
§21
o
I20
§19
L1J
£18
17
16
15
O
1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10000 10500
Work Factor
Bosdy Style Manufacturer
O Pickup Fiat
+ Van Ford
General Motors
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.
10-36
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4000
6000 8000
Work Factor
10000
to
2 §
E
D
O
14
16 18 20
Fuel Economy
22
24
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.
10-37
-------
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.
10-38
-------
Table 10-19 Summary of Impacts on Daimler by 2030 in the HD Pickup and Van Market versus the
Dynamic Baseline, Alternative Ib
ANNUAL
STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Average Fuel Economy (miles per gallon)
Required
Achieved
23.36
25.23
25.19
25.79
25.25
25.79
25.91
26.53
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
4.28
3.96
3.97
3.88
3.96
3.88
3.86
3.77
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
436
404
404
395
404
395
393
384
Technology Penetration (%)
WT and/or WL
Cylinder Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop-Start
Hybridization0
Aero. Improvements
0
0
0
44
0
0
0
0
0
0
0
0
44
44
0
0
0
0
0
0
0
44
44
0
0
0
0
0
0
0
44
100
0
0
0
0
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
0
0
0
0
0
0
0
0
Technology Cost (vs. No-Action)
Average ($) a
Total ($m,
undiscounted) b
0
0
165
4
165
4
374
9
Notes:
a Values used in Methods A & B
b Values used in Method A, calculated at a 3% discount rate
0 Includes mild hybrids (ISO) and strong HEVs.
10-39
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Table 10-20 Summary of Impacts on Nissan by 2030 in the HD Pickup and Van Market versus the Dynamic
Baseline, Alternative Ib
ANNUAL
STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Average Fuel Economy (miles per gallon)
Required
Achieved
19.64
19.84
21.19
21.17
20.92
21.19
21.46
21.51
Average Fuel Consumption (gallons 7100 mi.)
Required
Achieved
5.09
5.04
44.72
4.72
4.78
4.72
4.66
4.65
Average Greenhouse Gas Emissions (g/mi)
Required
Achieved
452
448
419
419
425
419
414
413
Technology Penetration (%)
WT and/or WL
Cy Under Deac.
Direct Injection
Turbocharging
8-Speed AT
EPS, Accessories
Stop-Start
Hybridization0
Aero. Improvements
100
49
51
51
0
0
0
0
0
100
49
51
51
51
100
0
0
100
100
49
51
51
51
100
0
0
100
100
49
100
50
51
100
0
28
100
Mass Reduction (vs. No-Action)
CW (Ib.)
CW (%)
0
0
0
0
307
5
303
4.9
Technology Cost (vs. No-Action)
Average ($) a
Total ($m,
undiscounted) b
378
5
1,150
15.1
1,347
17.7
1,935
25.4
Notes:
a Values used in Methods A & B
b Values used in Method A, calculated at a 3% discount rate
0 Includes mild hybrids (ISO) and strong HEVs.
10-40
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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.
10-41
-------
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 Ib a
ANNUAL
STRINGENCY
INCREASE
INCREASES
Stringency Increase
Through MY
2.0%/Y
2025
2.5%/Y
2027
3.5%/Y
2025
4.0%/Y
2025
Value of Lifetime Fuel Savings (discounted 2012 dollars)
Pretax
Tax
Total
2,068
210
2,278
3,924
409
4,334
4,180
438
4,618
4,676
491
5,168
Economic Benefits (discounted 2012 dollars)
Mobility Benefit
Avoided Refueling
Time
244
86
437
164
472
172
525
193
New Vehicle Purchase (vs. No-Action Alternative)
Avg. Cost Increase
($)
Avg. Payback (years)
Additional costs ($)
578
2.5
120
1,348
o
5
280
1,655
3.4
344
2,080
3.9
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, la, and more dynamic baseline, Ib, 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.
10-42
-------
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 Ib a
ALTERNATIVE
Annual Stringency
Increase
Stringency Increase
Through MY
2
2.0%
2025
3
2.5%
2027
4
3.5%
2025
5
4.0%
2025
Fuel Purchases ($billion)
Pretax Savings
9.6
15.9
19.1
22.2
Fuel Externalities ($billion)
Energy Security
CO2 emissions'3
0.5
1.9
0.9
3.2
1.1
3.8
1.3
4.4
VMT-Related Externalities (Sbillion)
Driving Surplus
Refueling Surplus
Congestion
Accidents
Noise
Fatalities
Criteria Emissions
1.1
0.4
-0.2
-0.1
0
0.1
0.6
1.8
0.7
-0.4
-0.2
0
-0.2
1.1
2.1
0.8
-0.4
-0.2
0
-0.2
1.3
2.4
0.9
-0.5
-0.3
0
-0.5
1.6
Technology Costs vs. No-Action ($billion)
Incremental Cost
Additional Costs
2.5
0.5
5.0
1.0
7.2
1.5
9.7
2.0
Benefit Cost Summary ($billion)
Total Social Cost
Total Social Benefit
Net Social Benefit
3.3
13.9
10.6
6.8
22.7
15.9
9.5
27.4
17.9
13.0
31.7
18.7
All dollar values are discounted at a rate of 3 percent per year from the time of
purchase.
Notes:
10-43
-------
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A. 1
b Using the 3% average social cost of CCh 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 categorysuch 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 CCh and criteria pollutants, see NHTSA's accompanying Draft Environmental
Impact Statement.
10-44
-------
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 Ib a
ANNUAL STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%
2025
2.5%
2027
3.5%
2025
4.0%
2025
Greenhouse Gas Emissions vs. No-Action Alternative
CO2 (MMT)
CH4 and N2O (tons)
54
65,600
91
111,400
110
133,700
127
155,300
Other Emissions vs. No-Action Alternative (tons)
CO
VOCandNOx
PM
S02
Air Toxics
Diesel PM10
10,400
23,800
1,470
11,400
44
2,470
20,700
43,600
2,550
19,900
47
4,350
25,800
53,500
3,090
24,100
49
5,300
30,400
62,200
3,590
28,000
55
6,160
Other Emissions vs. No-Action Alternative (% reduction)
CO
VOCandNOx
PM
SO2
Air Toxics
Diesel PM10
0.1
1.1
1.7
2.9
0.1
2.7
0.3
2.1
3.0
5.1
0.1
4.8
0.4
2.6
3.6
6.2
0.1
5.9
0.4
3.0
4.2
7.2
0.2
6.8
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
10-45
-------
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 laa
ANNUAL STRINGENCY
INCREASE
Stringency Increase
Through MY
2.0%
2025
2.5%
2027
3.5%
2025
4.0%
2025
Greenhouse Gas Emissions vs. No-Action Alternative
CO2 (MMT)
CH4 and N2O (tons)
66
79,700
105
127,400
127
154,800
142
172,800
Other Emissions vs. No-Action Alternative (tons)
CO
VOCandNOx
PM
SO2
Air Toxics
Diesel PM10
11,630
28,280
1,780
13,780
60
2,980
22,160
48,770
2,900
22,580
65
4,930
28,030
60,180
3,550
27,660
72
6,060
32,370
68,050
3,980
31,020
73
6,810
Other Emissions vs. No-Action Alternative (% reduction)
CO
VOCandNOx
PM
SO2
Air Toxics
Diesel PM10
0.2
1.4
2.1
3.5
0.2
3.3
0.3
2.3
3.4
5.7
0.2
5.4
0.4
2.9
4.2
7.0
0.2
6.7
0.4
3.3
4.7
7.9
0.2
7.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation
of the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
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.
2.
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.
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.)
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/.
10-46
-------
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 SOpctOwner/operatorBenefit.)
5. Value of Avoided GHG Emissions: Evaluated side cases involving lower and higher
valuation of avoided CCh 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 OSPctReboundEffect, ISPctReboundEffect
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 MassFatalityCoeffOSpct 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,
10-47
-------
Table Metric =
MJt sen case
-A
'Alt main run
100
MJt main run
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 Ib (Cells are
percent change from base case)a
Sensitivity Case
0 Month Payback
12 Month Payback
18 Month Payback
24 Month Payback
AEO-Low
AEO-High
AEO-Low, 0 Month
Payback
AEO-High, 24
Month Payback
50pct
Owner/operator
Benefit
75pct
Owner/operator
Benefit
Low SCC
Low SCC, 0 Month
Payback
High SCC
High SCC, 0 Month
Payback
Veiy High SCC
Veiy High SCC, 0
Month Payback
05 Pet Rebound
Effect
15 Pet Rebound
Effect
20 Pet Rebound
Effect
RPE-Based Markup
Mass Fatality Coeff
05pct
Mass Fatality Coeff
95pct
NoSHEVs,
Fuel
Savings
(gallons)
14.0%
-4.8%
-29.2%
-42.9%
3.3%
-7.0%
18.6%
-63.8%
0.0%
0.0%
0.0%
14.0%
0.0%
14.0%
0.0%
14.0%
4.6%
-4.6%
-9.1%
-3.2%
0.0%
0.0%
-6.9%
CO2
savings
(MMT)
14.5%
-4.7%
-28.1%
-42.4%
3.5%
-7.2%
19.3%
-64.6%
0.0%
0.0%
0.0%
14.5%
0.0%
14.5%
0.0%
14.5%
4.6%
-4.6%
-9.2%
-1.5%
0.0%
0.0%
-6.2%
Fuel Savings
($)
15.1%
-4.5%
-26.5%
-41.9%
-27.9%
23.3%
-16.5%
-54.4%
-50.0%
-25.0%
0.0%
15.1%
0.0%
15.1%
0.0%
15.1%
4.6%
-4.6%
-9.2%
0.3%
0.0%
0.0%
-5.3%
Social
Costs
5.6%
-2.5%
-14.1%
-23.2%
-10.8%
1.4%
-3.4%
-49.9%
0.0%
0.0%
0.0%
5.6%
0.0%
5.6%
0.0%
5.6%
-12.9%
12.9%
25.7%
31.4%
-23.6%
23.9%
19.2%
Social
Benefits
15.1%
-4.7%
-26.8%
-42.1%
-22.2%
19.5%
-10.1%
-55.7%
-34.6%
-17.3%
-10.6%
2.9%
7.8%
24.0%
28.7%
48.0%
0.4%
-0.4%
-0.8%
-0.1%
0.0%
0.0%
-5.4%
Social
Net
Benefits
18.2%
-5.4%
-31.1%
-48.4%
-26.1%
25.6%
-12.3%
-57.7%
-46.2%
-23.1%
-14.1%
2.0%
10.4%
30.1%
38.4%
62.2%
4.8%
-4.8%
-9.7%
-10.6%
7.9%
-8.0%
-13.7%
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NoSHEVs, 0 Month
Payback
NoSHEVs, GM
Turbo Vans
NoSHEVs, GM
Turbo Vans, 0
Month Payback
Lower Effectiveness
Higher Effectiveness
Lower Direct Costs
Higher Direct Costs
Wider Diesel
Downsizing
07 Pet Discount Rate
07 Pet DR, 0 Month
Payback
Allow Gas To Diesel
Allow Gas To
Diesel, 0 Month
Payback
flat mix after 20 16
7.7%
-6.7%
8.2%
-7.8%
-10.6%
0.9%
-4.1%
-1.5%
0.0%
14.0%
15.5%
32.1%
1.1%
9.1%
-5.8%
9.8%
-7.8%
-10.3%
2.7%
-3.8%
-1.0%
0.0%
14.5%
5.3%
22.6%
0.9%
10.7%
-5.0%
11.5%
-8.1%
-10.0%
4.8%
-3.5%
-0.6%
-100.0%
-37.9%
-100.0%
14.5%
0.7%
29.0%
2.3%
-1.2%
39.5%
-23.3%
18.4%
75.3%
-10.3%
-41.7%
-30.7%
16.8%
46.8%
2.6%
10.5%
-5.1%
11.3%
-8.0%
-10.2%
4.3%
-3.8%
-0.8%
-100.0%
-30.7%
-100.0%
17.0%
0.8%
4.3%
-7.6%
15.4%
-23.9%
-5.8%
-0.4%
-30.3%
2.4%
-119.5%
-30.7%
-139.1%
7.0%
0.2%
Note:
aFor an explanation of analytical Methods A and B, please see Section I.D;
baseline, la, and more dynamic baseline, Ib, please see Section X.A.I.
for an explanation of the less dynamic
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 no-
action 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,
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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.
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Table 10-26: Costs and Benefits of Proposed Standards for HD Pickups and Vans Under Alternative
Assumptions
Sensitivity Case
6 Month Payback (main)
0 Month Payback
12 Month Payback
18 Month Payback
24 Month Payback
AEO-Low
AEO-High
AEO-Low, 0 Month Payback
AEO-High, 24 Month Payback
50pct Owner/operator Benefit
75pct Owner/operator Benefit
Low SCC
Low SCC, 0 Month Payback
High SCC
High SCC, 0 Month Payback
Very High SCC
Very High SCC, 0 Month Payback
05 Pet Rebound Effect
15 Pet Rebound Effect
20 Pet Rebound Effect
RPE-Based Markup
Mass Fatality Coeff 05pct
Mass Fatality Coeff 95pct
NoSHEVs
NoSHEVs, 0 Month Payback
Lower Effectiveness
Higher Effectiveness
Lower Direct Costs
Higher Direct Costs
Wider Diesel Downsizing
07 Pet Discount Rate
07 Pet DR, 0 Month Payback
Allow Gas To Diesel
Allow Gas To Diesel, 0 Month
Payback
Flat mix after 20 16
Fuel
Savings
(billion
gallons)
7.8
8.9
7.4
5.5
4.5
8.1
7.3
9.3
2.8
7.8
7.8
7.8
8.9
7.8
8.9
7.8
8.9
8.2
7.5
7.1
7.6
7.8
7.8
7.2
7.0
7.9
7.5
7.7
7.8
8.9
9.0
10.3
7.9
7.3
8.4
C02
Reduction
(MMT)
94.1
107.7
87.2
65.8
52.7
94.7
84.9
109.1
32.4
91.5
91.5
91.5
104.7
91.5
104.7
91.5
104.7
95.7
87.2
83.0
90.1
91.5
91.5
84.3
82.0
94.0
88.0
90.5
91.5
104.7
96.3
112.2
92.3
85.8
99.8
Fuel
Savings
(Sbillion)
15.9
18.3
15.2
11.7
9.2
11.5
19.6
13.3
7.2
8.0
11.9
15.9
18.3
15.9
18.3
15.9
18.3
16.6
15.2
14.4
16.0
15.9
15.9
14.6
14.3
16.7
15.3
15.8
8.5
9.9
15.3
18.2
16.0
15.1
17.6
Social
Costs
(Sbillion)
5.5
5.8
5.6
4.9
4.4
5.1
5.8
5.6
2.9
5.8
5.8
5.8
6.1
5.8
6.1
5.8
6.1
5.0
6.5
7.2
7.6
4.4
7.1
8.0
4.4
6.8
10.1
5.2
3.8
4.0
7.2
8.5
5.9
6.9
7.4
Social
Benefits
(Sbillion)
23.5
27.0
21.9
16.8
13.3
17.8
27.4
20.6
10.2
15.0
19.0
20.5
23.6
24.7
28.5
29.5
34.0
23.0
22.9
22.8
22.9
23.0
23.0
21.1
20.6
23.9
22.1
22.8
13.8
15.9
22.7
26.9
23.1
21.7
25.4
Net
Social
Benefits
(Sbillion)
18.0
21.3
16.3
11.9
8.9
12.7
21.6
15.1
7.3
9.2
13.2
14.8
17.5
19.0
22.4
23.8
27.9
18.0
16.4
15.5
15.4
18.5
15.8
13.1
16.2
17.1
12.0
17.6
10.0
11.9
15.5
18.4
17.2
14.8
17.9
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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 decision-
makers 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
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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
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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 inter-
annual 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 la) or six months (for baseline Ib). In the case of baseline la, no
additional technology is added beyond the standards in the baseline, or in any of the other
10-54
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regulatory scenarios. In the case of baseline Ib, 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
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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-RoadGaps
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 probability-
weighted 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.
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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.
o
o
o
o
o
o
CO
I
O
O
CD
Main analysis
Expected Value
10 20 30
Net Benefits (Sbillion)
40
50
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.
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c
o
n
cr
QJ
o
o
o
o
o
o
o
O
o
o
o
: :
O
CO
o
o
o
-J
o
o
o
1
0
1
Main analysis (Phase 2 only)
Expected Value
1
i i i i
4 6 8 10 12 14
Average Payback Period (Years)
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.
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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.
11-1
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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. "]
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
11-2
-------
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 fleetsuch as the relative position of
individual manufacturers and the mix of products they each offercannot 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
11-3
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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 fuel-
saving 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 lead-
time 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
11-4
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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 la, 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 Ib, 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 la the agencies project that fuel-saving technologies would either
11-5
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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 Ib
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.
11-6
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The agencies believe that using the two baselines addresses the uncertainties we have
identified for tractors. The six-month payback period of Alternative Ib reflects the agencies'
consideration of factors, discussed above, that could limityet not eliminatemanufacturers'
tendencies to voluntarily improve fuel consumption. In contrast, Alternative la 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 la) where no
improvements are modeled beyond those needed to meet Phase 1 standards and a dynamic
baseline (designated Alternative Ib) 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 Ib 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 Ib applies a
6-month payback period. In contrast for Alternative la 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 la
For an explanation of analytical Methods A and B identified in some of the following
tables, please see Preamble Section ID; for an explanation of the less dynamic baseline, la, and
more dynamic baseline, Ib, please see Preamble Section X.A.I. The estimated reductions in
11-7
-------
CCh emission rates8 used in MOVES for Alternative la 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 la 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 CCh Emission Rates for Method B Alternative la Modeling in MOVES
VEHICLE TYPE
Long-haul
Tractor-Trailer
andHHD
Vocational
Short-haul
Tractor-Trailer
andHHD
Vocational
Single-Frame
Vocational9
HD Pickup Trucks
and Vansa
FUEL
Diesel
Diesel
Diesel and CNG
Gasoline
Diesel and
Gasoline
MODEL
YEARS
2018-2024
2025+
2018+
2021-2023
2024+
2021-2023
2024+
2021
2022
2023
2024
2025+
FUEL/CO2
REDUCTION
0.5%
0.6%
0.2%
0%
0%
0%
0%
0%
0%
0%
0%
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 la Modeling in MOVES
VEHICLE TYPE
Combination Long-
Haul
Tractors
MODEL YEARS
2010+
APU PENETRATION
30%
11-8
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Table 11-3 Estimated Reductions in Road Load Factors for Alternative la Modeling in MOVES
VEHICLE TYPE
Combination Long-haul
Tractor-Trailers
Combination Short-
haul Tractor-Trailers10
Intercity Buses
Transit and School
Buses
Refuse Trucks
Single Unit Short-haul
Trucks
Single Unit Long-haul
Trucks
Motor Homes
MODEL
YEARS
2018
2019-2020
2021
2022-2023
2024
2025
2026
2027+
2018
2019-2020
2021-2022
2023-2026
2027+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
REDUCTION IN
TIRE ROLLING
RESISTANCE
COEFFICIENT
2.1%
2.2%
2.2%
2.3%
2.4%
2.4%
2.5%
2.5%
0.8%
0.8%
0.9%
0.9%
1.0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
REDUCTION IN
AERODYNAMIC
DRAG
COEFFICIENT
3.0%
3.1%
3.2%
3.3%
3.4%
3.5%
3.5%
3.6%
.1%
.2%
.2%
.3%
.4%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)a
-129
-129
-129
-129
-129
-129
-129
-129
-49
-49
-49
-49
-49
0
0
0
0
0
0
0
0
0
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 Ib
The estimated reductions in CCh emission rates used in MOVES and the projected use of
auxiliary power units (APU) during extended idling for Alternative Ib 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.
11-9
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Table 11-4 Estimated Reductions in CCh Emission Rates for Method B Alternative Ib Modeling in MOVES
VEHICLE TYPE
Long- and Short-
Haul Tractor-
Trailer and HHD
Vocational
Single-Frame
Vocational11
HD pickup trucks
and vansa
FUEL
Diesel
Diesel and CNG
Gasoline
Diesel and
Gasoline
MODEL
YEARS
2018+
2021-2023
2024+
2021-2023
2024+
2021-2022
2023
2024+
FUEL/CO2
REDUCTION
0%
0%
0%
0%
0%
2.18%
2.71%
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 Ib Modeling in MOVES
VEHICLE TYPE
Combination Long-Haul
Tractors
MODEL
YEARS
2010+
APU
PENETRATION
30%
11-10
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Table 11-6 Estimated Reductions in Road Load Factors for Alternative Ib Modeling in MOVES
TRUCK TYPE
Combination
Long-haul
Tractor-Trailers
Combination
Short-haul
Tractor-Trailers12
Intercity Buses
Transit and
School Buses
Refuse Trucks
Single Unit
Short-haul
Trucks
Single Unit
Long-haul Trucks
Motor Homes
MODEL
YEARS
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
REDUCTION IN TIRE
ROLLING RESISTANCE
COEFFICIENT
0.2%
0.2%
0.4%
0.6%
0.8%
1.1%
1.4%
1.7%
2.0%
2.1%
0.3%
0.4%
0.7%
1.0%
1.5%
2.0%
2.6%
3.1%
3.6%
3.9%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
REDUCTION IN
AERODYNAMIC
DRAG COEFFICIENT
0.5%
0.7%
1.2%
1.7%
2.5%
3.4%
4.4%
5.3%
6.1%
6.6%
0.4%
0.7%
1.0%
1.6%
2.2%
3.1%
3.9%
4.8%
5.4%
6.0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Note:
a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine
improvements.
11-11
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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 FID 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 FID 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 sales-
weighted 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
11-12
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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 CCh
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 CCh Emission Rates for Method B Alternative 2 Modeling in MOVES
VEHICLE TYPE
Long-haul
Tractor-Trailer
andHHD
Vocational
Short-haul
Tractor-Trailer
andHHD
Vocational
Single-Frame
Vocational13
HD pickup trucks
and vansa
FUEL
Diesel
Diesel
Diesel and CNG
Gasoline
Diesel and
Gasoline
MODEL
YEARS
2018-2020
2021-2023
2024+
2018-2020
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021
2022
2023
2024
2025+
FUEL/CO2
REDUCTION
0.9%
3.9%
6.9%
0.3%
3.2%
6.4%
2.1%
5.1%
1.1%
2.1%
2.00%
3.96%
5.88%
7.76%
9.61%
Note:
a Chapter 10 presents CAFE model inputs for the Method A analysis.
11-13
-------
Table 11-8 Assumed APU Use during Extended Idling for Combination Long-haul Tractor-Trailers for
Alternative 2 Modeling in MOVES
VEHICLE TYPE
Combination Long-Haul
Tractors
MODEL
YEARS
2010-2020
2021-2023
2024+
APU
PENETRATION
30%
60%
80%
Table 11-9 Estimated Reductions in Road Load Factors for Alternative 2 Modeling in MOVES
VEHICLE
TYPE
Combination
Long-haul
Tractor-Trailers
Combination
Short-haul
Tractor-Traile rs : 4
Intercity Buses
Transit and
School Buses
Refuse Trucks
Single Unit
Short-haul
Trucks
Single Unit
Long-haul Trucks
Motor Homes
MODEL
YEARS
2018-
2020
2021-
2023
2024+
2018-
2020
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
REDUCTION IN TIRE
ROLLING
RESISTANCE
COEFFICIENT
4.0%
5.8%
9.0%
1.2%
5.3%
6.6%
6.5%
7.6%
0%
2.7%
0%
2.7%
4.8%
5.6%
6.5%
7.6%
3.0%
5.9%
REDUCTION IN
AERODYNAMIC
DRAG COEFFICIENT
5.1%
11.0%
12.4%
1.6%
6.4%
7.4%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)a
-131
-135
-140
-41
-42
-43
0
0
0
0
0
0
0
0
0
0
0
0
Note:
a Negative weight reductions reflect an expected weight increase as a byproduct of the other vehicle and engine
improvements.
11-14
-------
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 CCh 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.
11-15
-------
Table 11-10 Estimated Reductions in CCh Emission Rates for Method B Alternative 4 Modeling in MOVES
VEHICLE TYPE
Long-haul Tractor-Trailer
and HHD Vocational
Short-haul Tractor-Trailer
and HHD Vocational
Single -Frame Vocational15
HD pickup trucks and vansa
FUEL
Diesel
Diesel
Diesel and CNG
Gasoline
Diesel and
Gasoline
MODEL
YEARS
2018-2020
2021-2023
2024+
2018-2020
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021
2022
2023
2024
2025+
FUEL/CO2
REDUCTION
1.3%
6.6%
10.4%
0.9%
6.9%
10.4%
7.7%
13.3%
5.2%
10.3%
3.50%
6.88%
10.14%
13.28%
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
Combination
Long-Haul
Tractors
MODEL
YEARS
2010-2020
2021-2023
2024+
APU PENETRATION
30%
80%
90%
11-16
-------
Table 11-12 Estimated Reductions in Road Load Factors for Alternative 4 Modeling in MOVES
VEHICLE
TYPE
Combination
Long-haul
Tractor-Trailers
Combination
Short-haul
Tractor-Trailers : 6
Intercity Buses
Transit Buses
School Buses
Refuse Trucks
Single Unit
Short-haul
Trucks
Single Unit
Long-haul
Trucks
Motor Homes
MODEL
YEARS
2018-
2020
2021-
2023
2024+
2018-
2020
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
2021-
2023
2024+
REDUCTION IN TIRE
ROLLING
RESISTANCE
COEFFICIENT
5.5%
12.6%
17.9%
4.0%
13.0%
17.6%
6.5%
16.5%
0%
3.0%
0%
4.0%
0%
3.0%
4.8%
13.0%
6.5%
16.5%
3.0%
7.4%
REDUCTION IN
AERODYNAMIC
DRAG COEFFICIENT
5.1%
19.3%
26.9%
1.6%
11.6%
15.9%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)a
-131
-246
-304
-41
-100
-127
0
0
0
0
0
0
20
25
5.8
7
20
25
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
11-17
-------
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 heavy-
duty 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 all-
electric 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
CCh 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.
11-18
-------
Table 11-13 Estimated Reductions in CCh Emission Rates for Method B Alternative 5 Modeling in MOVES
VEHICLE TYPE
Long-haul
Tractor-Trailer
andHHD
Vocational
Short-haul
Tractor-Trailer
andHHD
Vocational
Single-Frame
Vocational17
HD pickup trucks
and vansa
FUEL
Diesel
Diesel
Diesel and CNG
Gasoline
Diesel and
Gasoline
MODEL
YEARS
2018-2020
2021-2023
2024+
2018-2020
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021
2022
2023
2024
2025+
FUEL/CO2
REDUCTION
1.3%
10.3%
13.5%
0.9%
11.3%
14.9%
14.0%
18.5%
11.0%
15.0%
4.00%
7.84%
11.53%
15.07%
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
Combination Long-
Haul Tractors
MODEL YEARS
2010-2020
2021+
APU PENETRATION
30%
100%
11-19
-------
Table 11-15 Estimated Reductions in Road Load Factors for Alternative 5 Modeling in MOVES
VEHICLE TYPE
Combination Long-haul
Tractor-Trailers
Combination Short-haul
Tractor-Trailers1 8
Intercity Buses
Transit Buses
School Buses
Refuse Trucks
Single Unit Short-haul
Trucks
Single Unit Long-haul
Trucks
Motor Homes
MODEL
YEARS
2018-2020
2021-2023
2024+
2018-2020
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
2021-2023
2024+
REDUCTION IN
TIRE ROLLING
RESISTANCE
COEFFICIENT
5.4%
14.3%
19.2%
3.9%
14.5%
18.8%
6.5%
16.5%
0%
8.0%
0%
9.1%
0%
8.0%
4.8%
15.2%
6.5%
16.5%
3.0%
12.4%
REDUCTION IN
AERODYNAMIC
DRAG
COEFFICIENT
7.3%
24.6%
31.4%
2.3%
15.0%
19.0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
WEIGHT
REDUCTION
(LB)a
-170
936
850
-53
997
943
0
0
0
0
0
0
255
255
58
95
185
185
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
la and Alternative Ib.
11-20
-------
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 Ib. 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.
11-21
-------
Table 11-16 Summary of Costs and Benefits through MY 2029 by Alternative, Discounted at 3% (Relative to
Baseline la), Method Aa
VEHICLE SEGMENT
ALT 2
ALT 3
ALT 4
ALT 5
Discounted pre-tax fuel savings ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
11.7
5.6
88.1
105.4
18.3
18.4
138.4
175.1
22.3
24.3
151.7
198.3
24.8
38.5
196.8
260.2
Discounted Total technology costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
3.0
1.2
9.2
13.4
5.0
7.6
12.8
25.4
8.2
10.8
15.3
34.3
9.9
26.0
34.8
70.6
Discounted value of emissions reductions ($billon)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
3.1
0.6
21.5
25.2
5.0
2.6
32.7
40.3
6.1
3.5
35.1
44.7
6.8
5.7
45.1
57.7
Total costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
3.5
3.0
11.5
18.0
5.7
9.5
15.5
30.8
9.1
12.8
18.1
40.0
15.2
28.1
37.5
80.8
Total benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
17.2
12.7
142.5
172.4
27.0
31.2
217.5
275.8
33.0
39.7
236.7
309.4
36.7
60.2
304.2
401.1
Net benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
13.7
9.6
131.0
154.3
21.3
21.7
202.0
245.0
23.9
26.9
218.7
269.4
21.5
32.1
266.7
320.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
11-22
-------
Table 11-17 Summary of Program Benefits and Costs through MY 2029, Discounted at 3% (Relative to
Baseline Ib) Method Aa
VEHICLE SEGMENT
ALT 2
ALTS
ALT 4
ALT 5
Discounted pre-tax fuel savings ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
9.6
5.6
80.5
95.6
15.9
18.4
130.8
165.1
19.1
24.3
144.0
187.4
22.2
38.5
189.2
250.0
Discounted Total technology costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.5
1.2
8.9
12.5
5.0
7.6
12.5
25.0
7.2
10.8
15.0
32.9
9.7
25.9
34.4
70.0
Discounted value of emissions reductions ($billon)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.8
1.7
37.5
41.9
4.5
6.1
59.4
70.1
5.4
8.1
64.6
78.2
6.3
13.1
84.4
103.8
Total costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.8
3.0
11.2
17.0
5.5
9.5
15.2
30.3
7.8
12.8
17.7
38.4
10.4
28.0
37.2
75.7
Total benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
14.1
12.7
131.1
157.9
23.5
31.2
206.2
260.9
28.3
39.7
225.4
293.3
32.9
60.2
292.8
385.9
Net benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
11.3
9.6
119.9
140.9
18.0
21.7
191.0
230.7
20.4
26.9
207.6
254.9
22.5
32.1
255.6
310.3
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
The following two tables summarize results for each of the segments covered by today's
proposal, discounted at 7 percent.
11-23
-------
Table 11-18 Summary of Program Benefits and Costs through MY 2029, discounted at 7% (Relative to
Baseline la) Method Aa
VEHICLE SEGMENT
ALT 2
ALTS
ALT 4
ALT 5
Discounted pre-tax fuel savings ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
6.4
2.9
47.7
57.0
9.9
9.7
74.6
94.2
12.2
13.0
82.3
107.5
13.6
20.9
107.3
141.8
Discounted Total technology costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.1
0.8
6.3
9.1
3.4
5.0
8.7
17.1
5.7
7.3
10.5
23.5
6.9
17.8
23.9
48.6
Discounted value of emissions reductions ($billon)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.7
1.4
29.9
34.0
4.3
5.0
46.3
55.6
5.3
6.6
50.4
62.3
5.9
10.6
65.4
81.8
Total costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.4
1.8
7.6
11.8
3.8
6.1
10.3
20.2
6.2
8.4
12.1
26.7
10.1
19.0
25.5
54.6
Total benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
10.4
7.3
85.1
102.9
16.3
18.3
130.0
164.6
20.1
23.6
142.2
185.8
22.3
36.2
183.5
242.1
Net benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
8.1
5.5
77.5
91.1
12.4
12.2
119.7
144.4
13.9
15.2
130.1
159.1
12.2
17.2
158.0
187.5
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
11-24
-------
Table 11-19 Summary of Program Benefits and Costs through MY 2029, discounted at 7% (Relative to
Baseline Ib) Method Aa
VEHICLE SEGMENT
ALT 2
ALTS
ALT 4
ALT 5
Discounted pre-tax fuel savings ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
5.2
2.9
44.0
52.2
8.5
9.7
71.0
89.2
10.4
13.0
78.6
102.0
12.2
20.9
103.7
136.8
Discounted Total technology costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
1.7
0.8
6.0
8.5
3.4
5.0
8.4
16.8
4.9
7.3
10.3
22.5
6.7
17.8
23.7
48.2
Discounted value of emissions reductions ($billon)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
2.5
1.4
27.5
31.4
4.0
5.0
43.9
52.9
4.8
6.6
48.0
59.4
5.5
10.6
63.0
79.1
Total costs ($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
1.9
1.8
7.3
11.1
3.7
6.1
10.0
19.8
5.3
8.4
11.9
25.6
7.1
19.0
25.3
51.4
Total benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
8.6
7.3
78.9
94.8
14.1
18.3
123.7
156.2
17.1
23.6
135.9
176.6
20.0
36.2
177.3
233.5
Net benefits($billion)
HD pickups and Vans
Vocational Vehicles
Tractors/Trailers
Total
6.7
5.5
71.5
83.7
10.5
12.2
113.7
136.4
11.9
15.2
124.0
151.1
12.9
17.2
152.0
182.2
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
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.
11-25
-------
Table 11-20 Fuel Savings and GHG Emissions Reductions by Vehicle Segment, Relative to Baseline la,
Method Aa
MY 201 8 -2029 TOTAL
FUEL
REDUCTIONS
(billion gallons)
UPSTREAM &
DOWNSTREAM
GHG
REDUCTIONS
(MMT)
Alternative 2
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
5.5
2.5
37.8
45.8
67.5
33.632.2
518.8493.0
619.9592.7
Alt. 3 - Preferred Alternative
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
8.8
8.3
59.5
76.7
107.6
110.3107.0
816.4775.7
1,034.3990.4
Alt. 4
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
10.7
10.9
65.0
86.7
130.5
143139.8
892.1847.7
1,166.4118.0
Alt. 5
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
12.0
17.3
84.2
113.4
145.4
226.9221.0
1,155.1097.6
1,527.4464.1
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
11-26
-------
Table 11-21 Fuel Savings and GHG Emissions Reductions by Vehicle Segment, Relative to Baseline Ib
Method Aa
MY 201 8 -2029
TOTAL
FUEL
REDUCTIONS
(billion gallons)
UPSTREAM &
DOWNSTREAM
GHG
REDUCTIONS
(MMT)
Alternative 2
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
4.5
2.5
34.4
41.4
55.5
33.6
471.9
561.0
Alt. 3 - Preferred Alternative
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
7.8
8.3
56.1
72.2
94.1
110.3
769.4
973.8
Alt. 4
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
9.3
10.9
61.6
81.8
112.8
143.8
845.2
1,101.8
Alt. 5
HD Pickup Trucks/Vans
Vocational Vehicles
Tractors and Trailers
Total
10.8
17.3
80.7
108.8
130.5
226.9
1,108.2
1,465.6
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X.A.I.
11-27
-------
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 la), 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
Alternative la (relative
to itself)
Alt. 2 Less Stringent-
Total
Tractors
HD Pickups & Vans
Vocational Vehicles
Alt. 3 Preferred - Total
Tractors
HD Pickups & Vans
Vocational Vehicles
Alt. 4 More Stringent-
Total
Tractors
HD Pickups & Vans
Vocational Vehicles
Alt. 5 More Stringent-
Total
Tractors
HD Pickups & Vans
Vocational Vehicles
UPSTREAM
+DOWNSTREAM
GHG REDUCTIONS
(MMT)
2035
0
72
59
8
5
127
97
14
16
132
100
15
17
168
126
17
26
2050
0
101
84
11
7
183
141
19
23
184
141
19
23
232
176
22
34
FUEL REDUCTIONS
(BILLION GALLONS)
2035
0
5.2
4.2
0.7
0.3
9.3
7.0
1.1
1.2
9.7
7.2
1.2
1.3
12.4
9.0
1.4
1.9
2050
0
7.3
6.0
0.9
0.5
13.4
10.1
1.6
1.7
13.5
10.1
1.6
1.7
17.0
12.6
1.8
2.5
TECHNOLOGY
COST (MILLIONS OF
2012$)
2035
$0
$2,559
$1,855
$471
$233
$5,856
$2,888
$892
$2,076
$6,180
$2,888
$1,215
$2,077
N/A
N/A
N/A
N/A
2050
$0
$3,090
$2,279
$541
$271
$6,987
$3,548
$1,024
$2,414
$7,358
$3,548
$1,395
$2,415
N/A
N/A
N/A
N/A
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID;
dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.
for an explanation of the less
A.I
Table 11-23 presents a summary of all costs and benefits for each program alternative
relative to the Alternative la baseline case. Table 11-24 shows cost per ton of GHG reduced.
11-28
-------
Table 11-23 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline
and using Method B
(Billions of 2012$, Except GHG Reductions)" b
203
5
205
0
NP
v,
3%
NP
V,
7%
Vehicle Program
Costs'
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
GHG reductions
(MMT)
Vehicle Program
Costs'
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costs'
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
Vehicle Program
Costs'
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
Alt.l
Baseline
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Alt.2 Less
Stringent
-$2.6
-$0.06
$20.9
$12.8
$31.1
71.9
-$3.1
-$0.06
$31.5
$19.9
$48.3
101.2
-$39.8
-$0.88
$280.0
$175.2
$414.5
-$19.3
-$0.42
$118.1
$105.5
$203.8
Alt.3
Preferred
-$5.9
-$0.13
$37.2
$20.5
$51.7
127.1
-$7.0
-$0.13
$57.5
$32.9
$83.2
183.4
-$86.8
-$1.80
$495.6
$279.7
$686.8
-$41.1
-$0.86
$206.7
$173.5
$338.1
Alt.4 More
Stringent
-$6.2
-$0.14
$38.7
$21.1
$53.5
132.0
-$7.4
-$0.14
$57.6
$32.9
$83.0
183.8
-$98.6
-$1.91
$517.6
$289.7
$706.8
-$48.4
-$0.92
$219.0
$180.7
$350.5
Alt. 5 More
Stringent
N/A
N/A
$49.4
$26.3
N/A
168.3
N/A
N/A
$72.7
$40.6
N/A
231.8
N/A
N/A
$664.3
$361.5
N/A
N/A
N/A
$283.0
$228.0
N/A
Notes:
a Benefits and net benefits calculated using the 3% average Social Cost of CO2 value. The net present value of
reduced CCh emissions is calculated differently than other benefits. The same discount rate used to discount the
value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present value of
SCC for internal consistency. Refer to the SCC TSD for more detail.
b For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of the
less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
' Vehicle program costs include compliance costs and R&D.
11-29
-------
Table 11-24 Annual Cost per Metric Ton of CCheq Emissions Reduced in Each Control Case Alternative
Vs. the Less Dynamic Baseline and using Method B
(Dollar Values are 2012$)a
YEAR
2035
2050
$/metric ton
w/o fuel
$/metric ton
w/ fuel
$/metric ton
w/o fuel
$/metric ton
w/ fuel
ALT.2 LESS
STRINGENT
$36
-$250
$31
-$280
ALT.3
PREFERRED
$47
-$250
$39
-$270
ALT.4
MORE
STRINGENT
$48
-$250
$41
-$270
ALT.5
MORE
STRINGENT
N/A
N/A
N/A
N/A
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A. 1
11.2.2.1 Tractors and Trailers
Table 11-25 presents a summary of all costs and benefits for each tractor/trailer program
Alternative relative to the alternative la baseline case. Table 11-26 shows cost per ton of GHG
reduced.
11-30
-------
Table 11-25 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline
and using Method B
Tractor/Trailers
(Billions of 2012$, Except GHG Reductions) *
2035
2050
NPV,
3%
NPV,
7%
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pre-tax)
Benefits
Net Benefits
ALT.l
BASELI
NE
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
ALT.2
LESS
STRING
ENT
-$1.9
-$0.03
$17.2
$10.3
$25.6
59.1
-$2.3
-$0.03
$26.1
$16.1
$39.9
83.8
-$28.8
-$0.47
$231.7
$141.7
$344.1
-$13.9
-$0.23
$98.1
$85.8
$169.8
ALT.3
PREFER
RED
-$2.9
-$0.08
$28.4
$15.7
$41.0
97.2
-$3.6
-$0.08
$44.0
$25.2
$65.5
140.9
-$43.7
-$1.19
$381.5
$215.7
$552.3
-$20.9
-$0.59
$160.1
$133.8
$272.4
ALT.4
MORE
STRINGE
NT
-$2.9
-$0.08
$29.2
$16.0
$42.2
100.0
-$3.6
-$0.08
$44.0
$25.2
$65.6
141.1
-$46.2
-$1.22
$394.5
$221.6
$568.8
-$22.7
-$0.60
$167.5
$138.1
$282.3
ALT.5
MORE
STRINGE
NT
N/A
N/A
$36.8
$19.7
N/A
125.9
N/A
N/A
$54.8
$30.7
N/A
175.7
N/A
N/A
$499.5
$274.2
N/A
N/A
N/A
$213.4
$172.4
N/A
Notes:
a Benefits and net benefits calculated using the 3% average Social Cost of CO2 value applied only to CO2
reductions. The net present value of reduced CO2 emissions is calculated differently than other benefits.
The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5
percent) is used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for
more detail.
b For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
0 Vehicle program costs include compliance costs and R&D.
11-31
-------
Table 11-26 Annual Cost per Metric Ton of CCheq Emissions Reduced in Each Control Case Alternative
Vs. the Less Dynamic Baseline and using Method B
Tractor/Trailers
(Dollar Values are 2012$)a
YEAR
2035
2050
$/metric ton
w/o fuel
$/metric ton
w/ fuel
$/metric ton
w/o fuel
$/metric ton
w/ fuel
ALT.2 LESS
STRINGENT
$32
-$260
$28
-$280
ALT.3
PREFERRED
$31
-$260
$26
-$290
ALT.4
MORE
STRINGENT
$30
-$260
$26
-$290
ALT.5
MORE
STRINGENT
N/A
N/A
N/A
N/A
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A. 1
11.2.2.2 Vocational Vehicles
Table 11-27 presents a summary of all costs and benefits for each vocational program
alternative relative to the Alternative la baseline case. Table 11-28 shows cost per ton of GHG
reduced.
11-32
-------
Table 11-27 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline
and using Method B
Vocational Vehicles
(Billions of 2012$, Except GHG Reductions)" b
2035
2050
NPV,
3%
NPV,
7%
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
ALT.l
BASELI
NE
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
ALT.2
LESS
STRING
ENT
-$0.2
-$0.02
$1.3
$1.1
$2.2
4.7
-$0.3
-$0.02
$2.0
$1.7
$3.4
6.5
-$3.6
-$0.22
$16.9
$14.8
$27.9
-$1.7
-$0.10
$6.9
$8.3
$13.4
ALT.3
PREFER
RED
-$2.1
-$0.03
$4.7
$2.6
$5.2
16.1
-$2.4
-$0.03
$7.3
$4.2
$9.0
23.2
-$29.6
-$0.42
$60.6
$34.8
$65.4
-$13.8
-$0.19
$24.7
$21.5
$32.2
ALT.4
-$2.1
-$0.04
$5.1
$2.8
$5.8
17.4
-$2.4
-$0.04
$7.3
$4.2
$9.1
23.3
-$32.8
-$0.52
$66.3
$37.4
$70.3
-$16.0
-$0.24
$27.9
$23.4
$35.0
ALT.5
MORE
STRINGE
NT
N/A
N/A
$7.6
$3.9
25.8
N/A
N/A
$10.7
$5.9
N/A
33.9
N/A
N/A
$99.9
$52.7
N/A
N/A
N/A
$42.5
$33.8
N/A
Notes:
a Benefits and net impacts calculated using the 3% average Social Cost of CCh value. The net present value
of reduced CCh emissions is calculated differently than other benefits. The same discount rate used to
discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net
present value of SCC for internal consistency. Refer to the SCC TSD for more detail.
b For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
0 Vehicle program costs include compliance costs and R&D.
11-33
-------
Table 11-28 Annual Cost per Metric Ton of CCheq Emissions Reduced in Each Control Case Alternative
Vs. the Less Dynamic Baseline and using Method B
Vocational Vehicles
(Dollar Values are 2012$)a
YEAR
2035
2050
$/metric ton
w/o fuel
$/metric ton
w/fuel
$/metric ton
w/o fuel
$/metric ton
w/fuel
ALT.2 LESS
STRINGENT
$53
-$230
$44
-$260
ALT.3
PREFERRED
$130
-$160
$110
-$210
ALT.4
$120
-$170
$110
-$210
ALT.5
MORE
STRINGENT
N/A
N/A
N/A
N/A
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for
an explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please
see Preamble Section X. A. 1
11.2.2.3 HD Pickups and Vans
Table 11-29 presents a summary of all costs and benefits for each HD pickup and van
program alternative relative to the Alternative la baseline case. Table 11-30 shows cost per ton
of GHG reduced.
11-34
-------
Table 11-29 Monetized Net Benefits Associated with Each Alternative Relative to the Less Dynamic Baseline
and using Method B
HD Pickups and Vans
(Billions of 2012$, Except GHG Reductions)" b
2035
2050
NPV,
3%
NPV,
7%
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
GHG Reductions
(MMT)
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
Vehicle Program
Costsc
Maintenance costs
Fuel Expenditures
(pretax)
Benefits
Net Benefits
ALT.l
BASELI
NE
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
ALT.2
LESS
STRING
ENT
-$0.5
-$0.01
$2.5
$1.4
$3.4
8.1
-$0.5
-$0.01
$3.5
$2.1
$5.1
10.8
-$7.5
-$0.18
$31.4
$18.7
$42.4
-$3.7
-$0.08
$13.1
$11.4
$20.7
ALT.3
PREFER
RED
-$0.9
-$0.01
$4.2
$2.2
$5.5
13.9
-$1.0
-$0.01
$6.3
$3.5
$8.7
19.3
-$13.5
-$0.18
$53.5
$29.2
$69.1
-$6.5
-$0.08
$21.9
$18.2
$33.5
ALT.4
MORE
STRINGE
NT
-$1.2
-$0.01
$4.4
$2.3
$5.5
14.6
-$1.4
-$0.01
$6.3
$3.5
$8.4
19.4
-$19.6
-$0.18
$56.8
$30.7
$67.7
-$9.7
-$0.08
$23.7
$19.3
$33.2
ALT.5
MORE
STRINGE
NT
N/A
N/A
$5.0
$2.6
N/A
16.6
N/A
N/A
$7.2
$4.0
N/A
22.1
N/A
N/A
$64.9
$34.6
N/A
N/A
N/A
$27.1
$21.8
N/A
Notes:
a Benefits and net impacts calculated using the 3% average Social Cost of CO2 value. The net present value
of reduced CO2 emissions is calculated differently than other benefits. The same discount rate used to
discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net
present value of SCC for internal consistency. Refer to the SCC TSD for more detail.
b For an explanation of analytical Methods A and B, please see Preamble Section ID; for an explanation of
the less dynamic baseline, la, and more dynamic baseline, Ib, please see Preamble Section X.A.I
0 Vehicle program costs include compliance costs and R&D.
11-35
-------
Table 11-30 Annual Cost per Metric Ton of CCheq Emissions Reduced in Each Control Case Alternative
Vs. the Less Dynamic Baseline and using Method B
HD Pickups and Vans
(Dollar Values are 2012$)a
YEAR
2035
2050
$/metric ton
w/o fuel
$/metric ton
w/ fuel
$/metric ton
w/o fuel
$/metric ton
w/ fuel
ALT.2 LESS
STRINGENT
$59
-$240
$51
-$270
ALT.3
PREFERRED
$65
-$240
$53
-$270
ALT.4
MORE
STRINGENT
$84
-$220
$73
-$250
ALT.5
MORE
STRINGENT
N/A
N/A
N/A
N/A
Note:
a For an explanation of analytical Methods A and B, please see Preamble Section ID; for an
explanation of the less dynamic baseline, la, and more dynamic baseline, Ib, please see
Preamble Section X.A.I
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).
11-36
-------
11.3.1 Tractor Technology
Table 11-31 Alternative 2 2021MY Technology Adoption Rates for Tractors
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Alternative 2 Engine Technology Package
100%
100%
100%
100%
100%
100%
100%
100%
100%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
10%
80%
10%
0%
10%
80%
10%
0%
0%
0%
45%
35%
20%
0%
0%
10%
80%
10%
0%
10%
80%
10%
0%
0%
0%
45%
35%
20%
0%
0%
10%
80%
10%
0%
10%
80%
10%
0%
0%
0%
45%
35%
20%
0%
0%
Steer Tires
Base
Level 1
Level 2
Level 3
15%
60%
25%
0%
15%
60%
25%
0%
5%
65%
30%
0%
15%
60%
25%
0%
15%
60%
25%
0%
5%
65%
30%
0%
15%
60%
25%
0%
15%
60%
25%
0%
15%
65%
20%
0%
Drive Tires
Base
Level 1
Level 2
Level 3
15%
60%
25%
0%
15%
60%
25%
0%
5%
65%
30%
0%
15%
60%
25%
0%
15%
60%
25%
0%
5%
65%
30%
0%
15%
60%
25%
0%
15%
60%
25%
0%
5%
60%
35%
0%
Extended Idle Reduction
APU
Other
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
30%
0%
30%
0%
30%
0%
Transmission Type
Manual
AMT
Auto
Dual Clutch
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
45%
40%
10%
5%
Driveline
Axle
Lubricant
6x2 or 4x2
Axle
Downspeed
20%
20%
20%
20%
20%
0%
20%
10%
0%
20%
10%
0%
20%
20%
0%
20%
10%
0%
20%
10%
0%
20%
10%
0%
Accessory Improvements
A/C
Electric
Access.
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
11-37
-------
Other Technologies
Predictive
Cruise
Control
Automated
Tire
Inflation
System
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
11-38
-------
Table 11-32 Alternative 2 2024MY Technology Adoption Rates for Tractors
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Alternative 2 Engine Technology Package
100%
100%
100%
100%
100%
100%
100%
100%
100%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
10%
70%
20%
0%
10%
70%
20%
0%
0%
0%
35%
45%
20%
0%
0%
10%
70%
20%
0%
10%
70%
20%
0%
0%
0%
35%
45%
20%
0%
0%
10%
70%
20%
0%
10%
70%
20%
0%
0%
0%
35%
45%
20%
0%
0%
Steer Tires
Base
Level 1
Level 2
Level 3
10%
60%
30%
0%
10%
60%
30%
0%
5%
60%
35%
0%
10%
60%
30%
0%
10%
60%
30%
0%
5%
60%
35%
0%
10%
60%
30%
0%
10%
60%
30%
0%
10%
60%
30%
0%
Drive Tires
Base
Level 1
Level 2
Level 3
10%
60%
30%
0%
10%
60%
30%
0%
5%
60%
35%
0%
10%
60%
30%
0%
10%
60%
30%
0%
5%
60%
35%
0%
10%
60%
30%
0%
10%
60%
30%
0%
5%
50%
45%
0%
Extended Idle Reduction
APU
Other
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
40%
10%
40%
10%
40%
10%
Transmission Type
Manual
AMT
Auto
Dual Clutch
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
20%
50%
20%
10%
Driveline
Axle
Lubricant
6x2 or 4x2
Axle
Downspeed
40%
0%
40%
0%
40%
0%
40%
20%
0%
40%
20%
0%
40%
20%
0%
40%
20%
0%
40%
20%
0%
40%
20%
0%
Accessory Improvements
A/C
Electric
Access.
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Other Technologies
Predictive
Cruise
Control
Automated
40%
20%
40%
20%
40%
20%
40%
20%
40%
20%
40%
20%
40%
20%
40%
20%
40%
20%
11-39
-------
Tire
Inflation
System
11-40
-------
Table 11-33 Alternative 4Adoption Rates for 2021 MY
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Alternative 4 2021MY Engine Technology Package
100%
100%
100%
100%
100%
100%
100%
100%
100%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
65%
30%
5%
0%
65%
30%
5%
0%
0%
35%
30%
25%
10%
0%
0%
65%
30%
5%
0%
65%
30%
5%
0%
0%
35%
30%
25%
10%
0%
0%
65%
30%
5%
0%
65%
30%
5%
0%
0%
35%
30%
25%
10%
0%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
5%
35%
45%
15%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
80%
80%
80%
Transmission Type
Manual
AMT
Auto
Dual Clutch
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
25%
40%
30%
5%
Driveline
Axle
Lubricant
6x2 Axle
Downspeed
Direct Drive
20%
30%
50%
20%
30%
50%
20%
30%
50%
20%
10%
30%
50%
20%
10%
30%
50%
20%
20%
30%
50%
20%
10%
30%
50%
20%
10%
30%
50%
20%
30%
30%
50%
Accessory Improvements
A/C
Electric
Access.
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Other Technologies
Predictive
Cruise
Control
Automated
Tire
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
11-41
-------
[Inflation
System
1
11-42
-------
Table 11-34 Alternative 4 Adoption Rates for 2024 MY
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
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
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
5%
20%
50%
25%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
100%
100%
100%
Transmission Type
Manual
AMT
Auto
Dual Clutch
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
0%
50%
30%
10%
Driveline
Axle Lubricant
6x2 Axle
Downspeed
Direct Drive
40%
60%
50%
40%
60%
50%
40%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
Accessory Improvements
A/C
Electric Access.
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
Other Technologies
Predictive
Cruise Control
Automated Tire
Inflation
System
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
40%
11-43
-------
Table 11-35 Alternative 5 Adoption Rates for 2021 MY
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Alternative 5 2021 MY Engine Technology Package
Additional
Waste Heat
Recovery
100%
100%
100%
100%
100%
100%
100%
10%
100%
10%
100%
10%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
0%
50%
40%
10%
0%
50%
40%
10%
0%
0%
20%
20%
35%
20%
5%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
20%
40%
35%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
30%
40%
25%
5%
20%
40%
35%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
100%
100%
100%
Transmission Type
Manual
AMT
Auto
Dual Clutch
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
0%
50%
40%
10%
Driveline
Axle
Lubricant
6x2 Axle
Downspeed
Direct Drive
40%
60%
50%
40%
60%
50%
40%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
30%
60%
50%
Accessory Improvements
A/C
Electric
Access.
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
Other Technologies
Predictive
Cruise
40%
40%
40%
40%
40%
40%
40%
40%
40%
11-44
-------
Control
Automated
Tire Inflation
System
Hybrid
Powertrain
with
Electrified
Accessories
Weight
Reduction
(Ibs)
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
40%
20%
1,200
Table 11-36 Alternative 5 Adoption Rates for 2024 MY
CLASS 7
Day Cab
Low
Roof
Mid
Roof
High
Roof
CLASS 8
Day Cab
Low
Roof
Mid
Roof
High
Roof
Sleeper Cab
Low
Roof
Mid
Roof
High
Roof
Alternative 5 2024 MY Engine Technology Package
Additional
Waste Heat
Recovery
100%
100%
100%
100%
100%
100%
100%
10%
100%
10%
100%
10%
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
0%
40%
50%
10%
0%
40%
50%
10%
0%
0%
15%
15%
35%
25%
10%
0%
40%
50%
10%
0%
40%
50%
10%
0%
0%
15%
15%
35%
25%
10%
0%
40%
50%
10%
0%
40%
50%
10%
0%
0%
15%
15%
35%
25%
10%
Steer Tires
Base
Level 1
Level 2
Level 3
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
Drive Tires
Base
Level 1
Level 2
Level 3
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
5%
20%
40%
35%
Extended Idle Reduction
APU
N/A
N/A
N/A
N/A
N/A
N/A
100%
100%
100%
Transmission Type
Manual
AMT
Auto
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
0%
50%
40%
11-45
-------
Dual Clutch
10%
10%
10%
10%
10%
10%
10%
10%
10%
Driveline
Axle
Lubricant
6x2 Axle
Downspeed
Direct Drive
40%
60%
50%
40%
60%
50%
40%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
40%
20%
60%
50%
40%
20%
60%
50%
40%
60%
60%
50%
Accessory Improvements
A/C
Electric
Access.
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
30%
Other Technologies
Predictive
Cruise
Control
Automated
Tire
Inflation
System
Hybrid
Powertrain
with
Electrified
Accessories
Weight
Reduction
(Ibs)
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
40%
40%
40%
1,200
11-46
-------
11.3.2 Trailer Technology
Table 11-37 Alternative 1 Trailer Adoption Rates
TECHNOLOGY
Model Year
LONG BOX
DRY & REFRIGERATED VANS
2018
2021
2024
2027
2040a
SHORT BOX,
NON-AERO BOX,
& NON-BOX TRAILERS
2018+
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
45%
-
20%
30%
5%
-
-
-
0.2
41%
-
20%
34%
5%
-
-
-
0.3
38%
-
20%
37%
5%
-
-
-
0.3
35%
-
20%
40%
5%
-
-
0.3
20%
20%
55%
5%
-
-
-
0.4
100%
-
-
-
-
-
-
-
0.0
Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
50%
50%
-
5.6
47%
53%
-
5.5
43%
57%
-
5.5
40%
60%
-
5.5
25%
75%
-
5.3
100%
-
-
6.0
Tire Inflation
ATI
Avg % Reduction
50%
0.8%
53%
0.8%
57%
0.9%
60%
0.9%
75%
1.1%
0%
0.0%
Weight Reduction (pounds)
Weight
0
0
0
0
0
0
Note:
a Considered in Alternative Ib only
11-47
-------
Table 11-38 Alternative 2 Trailer Adoption Rates
TECHNOLOGY
Model Year
LONG BOX
DRY & REFRIGERATED VANS
2018
2021
2024
SHORT BOX,
NON-AERO BOX,
& NON-BOX TRAILERS
2018+
Aerodynamics
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
5%
30%
60%
5%
-
-
-
0.4
5%
20%
70%
5%
-
-
-
0.4
5%
10%
75%
10%
-
-
-
0.5
100%
-
-
-
-
-
-
-
0.0
Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
5.2
10%
90%
5.2
5%
95%
4.8
100%
-
-
6.0
Tire Inflation
ATI
Avg % Reduction
85%
1.3%
90%
1.4%
95%
1.4%
0%
0.0%
Weight Reduction (pounds)
Weight
0
0
0
0
Table 11-39 Alternative 3 Long Box Trailer Adoption Rates
TECHNOLOGY
Model Year
LONG BOX
DRY VANS
2018
2021
2024
2027
LONG BOX
REFRIGERATED VANS
2018
2021
2024
2027
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
5%
-
30%
60%
5%
-
-
-
0.4
-
-
5%
55%
10%
30%
-
-
0.7
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
10%
50%
40%
-
1.1
5%
-
30%
60%
5%
-
-
-
0.4
-
-
5%
55%
10%
30%
-
-
0.7
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
20%
60%
20%
-
1.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
95%
1.4%
Weight Reduction (pounds)
Weight
0
0
0
0
0
0
0
0
11-48
-------
Table 11-40 Alternative 3 Short Box Trailer Adoption Rates
TECHNOLOGY
Model Year
SHORT BOX
DRY VANS
2018
2021
2024
2027
SHORT BOX
REFRIGERATED VANS
2018
2021
2024
2027
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
100%
-
-
-
-
-
-
-
0.4
5%
95%
-
-
-
-
-
-
0.7
-
70%
30%
-
-
-
-
-
0.8
-
30%
60%
10%
-
-
-
-
1.1
100%
-
-
-
-
-
-
-
0.4
5%
95%
-
-
-
-
-
-
0.7
-
70%
30%
-
-
-
-
-
0.8
-
55%
40%
5%
-
-
-
-
1.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
5%
-
95%
4.8
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
95%
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
Model Year
NON-AERO BOX
& NON-BOX TRAILERS
2018
2021
2024
2027
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
-
100%
-
5.1
-
100%
-
5.1
-
-
100%
4.7
-
-
100%
4.7
Tire Inflation System
ATI
Avg ATI Reduction (%)
100%
1.5%
100%
1.5%
100%
1.5%
100%
1.5%
Weight Reduction (pounds)
Weight
0
0
0
0
11-49
-------
Table 11-42 Alternative 4 Long Box Trailer Adoption Rates
TECHNOLOGY
Model Year
LONG BOX
DRY VANS
2018
2021
2024
LONG BOX
REFRIGERATED VANS
2018
2021
2024
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
5%
-
30%
60%
5%
-
-
-
0.4
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
10%
50%
40%
-
1.1
5%
-
30%
60%
5%
-
-
-
0.4
-
-
-
25%
10%
65%
-
-
0.8
-
-
-
-
20%
60%
20%
-
1.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
Weight Reduction (pounds)
Weight
0
0
0
0
0
0
Table 11-43 Alternative 4 Short Box Trailer Adoption Rates
TECHNOLOGY
Model Year
SHORT BOX
DRY VANS
2018
2021
2024
SHORT BOX
REFRIGERATED VANS
2018
2021
2024
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
100%
-
-
-
-
-
-
-
0.4
-
70%
30%
-
-
-
-
-
0.8
-
30%
60%
10%
-
-
-
-
1.1
100%
-
-
-
-
-
-
-
0.4
-
70%
30%
-
-
-
-
-
0.8
-
55%
40%
5%
-
-
-
-
1.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
Weight Reduction (pounds)
Weight
0
0
0
0
0
0
11-50
-------
Table 11-44 Alternative 4 Non-Aero Box and Non-Box Trailer Adoption Rates
TECHNOLOGY
Model Year
NON-AERO BOX
& NON-BOX TRAILERS
2018
2021
2024
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
-
100%
-
5.1
-
100%
-
5.1
-
-
100%
4.7
Tire Inflation System
ATI
Avg ATI Reduction (%)
100%
1.5%
100%
1.5%
100%
1.5%
Weight Reduction (pounds)
Weight
0
0
0
Table 11-45 Alternative 5 Box Trailer Adoption Rates
TECHNOLOGY
Model Year
LONG BOX
DRY VANS
2018
2021
2024
LONG BOX
REFRIGERATED VANS
2018
2021
2024
SHORT BOX DRY &
REFRIGERATED VANS
2018
2021
2024
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
5%
-
5%
55%
10%
25%
-
-
0.6
-
-
-
15%
85%
-
-
0.9
-
-
-
-
-
10%
90%
-
1.4
5%
-
5%
55%
10%
25%
-
-
0.6
0%
-
-
15%
-
85%
-
-
0.9
0%
-
-
-
-
100%
-
-
1.0
100%
-
-
-
-
-
-
-
0.0
-
65%
25%
-
-
-
-
-
0.1
-
10%
90%
-
-
-
-
-
0.3
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
Weight Reduction (pounds)
Weight
0
0
0
0
0
0
0
0
0
11-51
-------
Table 11-46 Alternative 5 Non-Box Trailer Adoption Rates
TECHNOLOGY
Model Year
CONTAINER
CHASSIS
2018
2021
2024
FLATBED
2018
2021
2024
TANKS
2018
2021
2024
NON-AERO BOX
& OTHER NON-
BOX
2018
2021
2024
Aerodynamic Technologies
Bin I
Bin II
Bin III
Bin IV
BinV
Bin VI
Bin VII
Bin VIII
Avg Delta CdA (m2)
100%
-
-
-
-
-
-
-
0.0
80%
-
20%
-
-
-
-
-
0.1
30%
-
55%
15%
-
-
-
-
0.2
100%
-
-
-
-
-
-
-
0.0
80%
-
20%
-
-
-
-
-
0.1
30%
-
55%
15%
-
-
-
-
0.2
100%
-
-
-
-
-
-
-
0.0
67%
-
33%
-
-
-
-
-
0.1
0%
-
-
100%
-
-
-
-
0.5
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
100%
-
-
-
-
-
-
-
0.0
Trailer Tire Rolling Resistance
Level 1 tires
Level 2 tires
Level 3 tires
Avg CRR (kg/ton)
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
15%
85%
-
5.2
5%
95%
-
5.1
5%
-
95%
4.8
-
100%
-
5.1
-
100%
-
5.1
-
-
100%
4.7
Tire Inflation System
ATI
Avg ATI Reduction (%)
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
85%
1.3%
95%
1.4%
95%
1.4%
100%
1.5%
100%
1.5%
100%
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
HFC Leakage
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Steer Tire CRR
15% better Steer Tire CRR
Neutral-idle
2021
100%
40%
50%
20%
50%
80%
0%
43%
2024
100%
75%
30%
10%
70%
60%
30%
80%
11-52
-------
Table 11-48 Alternative 3 Technology Adoption Rates for Vocational Subcategories: LHD, MHD, HHD
Multipurpose & Urban
TECHNOLOGY
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Strong Hybrid
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
5%
100%
75%
4%
50%
20%
50%
0%
80%
0%
0%
0%
70%
5%
5%
15%
4%
2024
5%
100%
75%
7%
20%
10%
50%
30%
30%
0%
60%
0%
85%
15%
15%
30%
4%
2027
5%
100%
75%
18%
10%
0%
25%
50%
20%
15%
30%
50%
30%
70%
5%
70%
5%
Note:
a Idle reduction values in MY 2027 are for multipurpose vehicles. For Urban vehicles, Neutral Idle is 25% and Stop-
Start is 75% in MY 2027.
11-53
-------
Table 11-49 Alternative 3 Technology Adoption Rates for Vocational Subcategories: LHD & MHD Regional
TECHNOLOGY
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
5%
100%
75%
50%
20%
50%
0%
80%
0%
0%
0%
70%
5%
5%
15%
7%
2024
5%
100%
75%
20%
10%
50%
30%
30%
0%
60%
0%
85%
15%
15%
30%
7%
2027
5%
100%
75%
10%
0%
25%
50%
20%
15%
30%
50%
30%
70%
5%
70%
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.
11-54
-------
Table 11-50 Alternative 3 Technology Adoption Rates for Vocational Subcategories: HHD Regional
TECHNOLOGY
HFC Leakage
Axle 6x2
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Automatic Transmission
Stop-start
Automated Manual Transmission
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
100%
45%
75%
50%
20%
50%
0%
80%
0%
0%
0%
22%
0%
22%
22%
15%
5%
2024
100%
60%
75%
20%
10%
50%
30%
30%
0%
60%
0%
33%
15%
33%
33%
30%
5%
2027
100%
60%
75%
10%
0%
25%
50%
20%
15%
30%
50%
25%
70%
25%
25%
70%
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.
11-55
-------
Table 11-51 Alternative 4 Technology Adoption Rates for Vocational Subcategories: LHD, MHD, HHD
Multipurpose & Urban
Technology
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Strong Hybrid
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
11%
100%
75%
9%
50%
20%
50%
0%
80%
0%
0%
0%
88%
12%
10%
25%
4%
2024
10%
100%
75%
18%
10%
0%
25%
50%
20%
15%
30%
50%
30%
70%
10%
70%
5%
Note:
1 Idle reduction values in MY 2024 are for multipurpose vehicles. For Urban vehicles, Neutral Idle is 25% and Stop-
Start is 75% in MY 2024.
11-56
-------
Table 11-52 Alternative 4 Technology Adoption Rates for Vocational Subcategories: LHD & MHD Regional
TECHNOLOGY
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
11%
100%
75%
50%
20%
50%
0%
80%
0%
0%
0%
88%
12%
10%
25%
7%
2024
10%
100%
75%
10%
0%
25%
50%
20%
15%
30%
50%
30%
70%
10%
70%
8%
Note:
1 Weight reduction values are given for LHD. For MHD, adoption rates are 6% in MY 2021 & 7% in MY 2024.
11-57
-------
Table 11-53 Alternative 4 Technology Adoption Rates for Vocational Subcategories: HHD Regional
TECH
HFC Leakage
Axle 6x2
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Automatic Transmission
Stop-start
Automated Manual Transmission
Dual Clutch Transmission
Transjmproved
200 Pounds Lightweighting
2021
100%
60%
75%
50%
20%
50%
0%
80%
0%
0%
0%
15%
0%
15%
15%
25%
5%
2024
100%
60%
75%
10%
0%
25%
50%
20%
15%
30%
50%
25%
70%
25%
25%
70%
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.
11-58
-------
Table 11-54 Alternative 5 Technology Adoption Rates for Vocational Subcategories: LHD Regional, LHD &
HHD Multipurpose, LHD & HHD Urban
TECH
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Electric Vehicle
Strong Hybrid
Baseline Drive Tire CRR
Baseline Steer Ti re CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
2021
16%
100%
100%
3%
27%
50%
20%
50%
0%
80%
0%
0%
0%
60%
40%
10%
35%
2024
11%
100%
100%
3%
36%
0%
0%
10%
45%
0%
45%
20%
80%
40%
60%
5%
60%
Notes:
1 Strong Hybrid is zero for the LHD Regional subcategory
11-59
-------
Table 11-55 Alternative 5 Technology Adoption Rates for Vocational Subcategories: MHD Regional, MHD
Multipurpose, & MHD Urban
TECHNOLOGY
Add Two Trans Gears
HFC Leakage
Axle - Low Friction Lubes
Electric Vehicle
Strong Hybrid
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Neutral-idle
Stop-start
Dual Clutch Transmission
Transjmproved
1,000 Pounds Lightweighting
2021
16%
100%
100%
3%
27%
50%
20%
50%
0%
80%
0%
0%
0%
60%
40%
10%
35%
28%
2024
11%
100%
100%
3%
36%
0%
0%
10%
45%
0%
45%
20%
80%
40%
60%
5%
60%
42%
Note:
1 Strong Hybrid is zero for the MHD Regional subcategory
11-60
-------
Table 11-56 Alternative 5 Technology Adoption Rates for Vocational Subcategories: HHD Regional
TECHNOLOGY
HFC Leakage
Axle_disconnect
Axle - Low Friction Lubes
Baseline Drive Tire CRR
Baseline Steer Tire CRR
5% better Drive Tire CRR
10% better Drive Tire CRR
10% better Steer Tire CRR
15% better Drive Tire CRR
15% better Steer Tire CRR
20% better Steer Tire CRR
Automatic Transmission
Stop-start
Automated Manual Transmission
Dual Clutch Transmission
Transjmproved
2021
100%
61%
100%
50%
20%
50%
0%
80%
0%
0%
0%
33%
40%
33%
33%
29%
2024
100%
61%
100%
0%
0%
10%
45%
0%
45%
20%
80%
33%
60%
33%
33%
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.
11-61
-------
Table 11-57 Fleet Profile for Model Year 2030 under Method A using the Dynamic Baseline
Total Vehicles Sales
Van Share
Pickup Share
Gasoline Share
Diesel Share
Gasoline Van Share
Diesel Van Share
Gasoline Pickup Share
Diesel Pickup Share
Overall
Fleet
755,787
39.3%
60.7%
56.4%
43.6%
32.3%
6.9%
24.1%
36.6%
Daimler
24,188
100.0%
0.0%
0.0%
100.0%
0.0%
100.0%
0.0%
0.0%
Fiat
110,622
20.6%
79.4%
40.1%
59.9%
17.2%
3.3%
22.8%
56.6%
Ford
335,385
41.4%
58.6%
57.8%
42.2%
34.0%
7.4%
23.7%
34.9%
General
Motors
272,441
35.9%
64.1%
64.3%
35.7%
35.9%
0.0%
28.4%
35.7%
Nissan
13,152
100.0%
0.0%
100.0%
0.0%
100.0%
0.0%
0.0%
0.0%
Table 11-58 presents the total penetration rates for select technologies in 2030 for the entire fleet. Table 11-59
through
11-62
-------
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
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
25%
29%
10%
19%
14%
38%
33%
100%
0%
0%
24%
22%
0%
0%
0%
Alternative
2
2% per year
56%
94%
29%
19%
17%
38%
67%
100%
23%
36%
50%
23%
0%
0%
0%
Alternative
3 (2.5% per
year)
56%
94%
21%
19%
25%
38%
96%
100%
78%
38%
92%
63%
0%
0%
8%
Alternative
4
(3.5% per
year)
49%
87%
21%
19%
31%
38%
96%
100%
78%
65%
78%
58%
3%
12%
13%
Alternative
5
(4% per
year)
49%
87%
21%
19%
32%
53%
97%
100%
75%
65%
76%
73%
14%
34%
16%
11-63
-------
Table 11-59 Technology Penetration Rates for the Daimler in Model Year 2030 under Method A using the
Dynamic Baseline
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
0%
44%
0%
0%
0%
44%
0%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
2
2% per year
0%
44%
0%
0%
0%
44%
0%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
3 (2.5% per
year)
0%
44%
0%
0%
0%
44%
44%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
4
(3.5% per
year)
0%
44%
0%
0%
0%
44%
44%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
5
(4% per
year)
0%
44%
0%
0%
0%
44%
100%
100%
0%
0%
0%
0%
0%
0%
0%
11-64
-------
Table 11-60 Technology Penetration Rates for the Fiat in Model Year 2030 under Method A using the
Dynamic Baseline
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
0%
18%
13%
17%
0%
57%
48%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
2
2% per year
40%
97%
23%
17%
17%
57%
65%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
3 (2.5% per
year)
40%
97%
23%
17%
17%
57%
88%
100%
100%
100%
100%
63%
0%
0%
3%
Alternative
4
(3.5% per
year)
40%
97%
23%
17%
17%
57%
88%
100%
100%
100%
100%
63%
0%
0%
3%
Alternative
5
(4% per
year)
40%
97%
23%
17%
17%
57%
88%
100%
79%
100%
83%
72%
0%
0%
12%
11-65
-------
Table 11-61 Technology Penetration Rates for the Ford in Model Year 2030 under Method A using the
Dynamic Baseline
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
0%
0%
0%
34%
16%
35%
59%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
2
2% per year
58%
93%
18%
34%
16%
35%
100%
100%
0%
0%
30%
0%
0%
0%
0%
Alternative
3 (2.5% per
year)
58%
93%
0%
34%
34%
35%
100%
100%
59%
0%
90%
66%
0%
0%
2%
Alternative
4
(3.5% per
year)
41%
76%
0%
34%
34%
35%
100%
100%
59%
59%
59%
59%
0%
0%
12%
Alternative
5
(4% per
year)
41%
76%
0%
34%
34%
69%
100%
100%
59%
59%
59%
59%
32%
8%
18%
11-66
-------
Table 11-62 Technology Penetration Rates for the General Motors in Model Year 2030 under Method A
using the Dynamic Baseline
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
64%
64%
18%
0%
18%
36%
0%
100%
0%
0%
66%
61%
0%
0%
0%
Alternative
2
2% per year
64%
100%
47%
0%
18%
36%
36%
100%
64%
100%
100%
63%
0%
0%
0%
Alternative
3 (2.5% per
year)
64%
100%
47%
0%
18%
36%
100%
100%
100%
64%
100%
69%
0%
0%
19%
Alternative
4
(3.5% per
year)
64%
100%
47%
0%
36%
36%
100%
100%
100%
64%
100%
63%
9%
34%
20%
Alternative
5
(4% per
year)
64%
100%
47%
0%
36%
36%
100%
100%
100%
64%
100%
100%
0%
82%
18%
11-67
-------
Table 11-63 Technology Penetration Rates for the Nissan in Model Year 2030 under Method A using
Dynamic Baseline
the
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
Turbo Machinery
Improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic drag
reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start engine
systems
Mild hybrid
Strong hybrid
Alternative
la
(0% per
year)
100%
100%
100%
100%
0%
0%
0%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
2
2% per year
100%
100%
49%
100%
51%
0%
0%
100%
0%
0%
0%
0%
0%
0%
0%
Alternative
3 (2.5% per
year)
100%
100%
49%
100%
51%
0%
51%
100%
100%
0%
100%
0%
0%
0%
0%
Alternative
4
(3.5% per
year)
100%
100%
49%
100%
51%
0%
51%
100%
100%
100%
100%
0%
0%
0%
0%
Alternative
5
(4% per
year)
100%
100%
49%
100%
100%
0%
51%
100%
100%
100%
100%
28%
0%
27%
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.
11-68
-------
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
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
8 speed
transmission
Low rolling
resistance tires
Aerodynamic
drag reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start
engine systems
Mild hybrid
Strong hybrid
ALTERNATIVE
1A
(0% PER YEAR)
42%
42%
8%
0%
0%
44%
0%
0%
0%
42%
41%
0%
0%
0%
ALTERNATIVE
2
2% PER YEAR
100%
100%
56%
56%
0%
86%
100%
42%
42%
86%
41%
0%
0%
1%
ALTERNATIVE
3 (2.5% PER
YEAR)
100%
100%
56%
56%
0%
100%
100%
100%
56%
100%
86%
0%
0%
25%
ALTERNATIVE
4
(3.5% PER
YEAR)
100%
100%
56%
56%
0%
100%
100%
100%
100%
100%
86%
20%
18%
48%
ALTERNATIVE
5
(4% PER YEAR)
100%
100%
56%
56%
0%
100%
100%
100%
100%
100%
92%
7%
20%
66%
11-69
-------
Table 11-65 Technology Penetration Rates for Gasoline Vans using Method B
Low friction
lubricants
Engine friction
reduction
Cylinder
deactivation
Variable valve
timing
Gasoline direct
injection
8 speed
transmission
Low rolling
resistance tires
Aerodynamic
drag reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start
engine systems
Mild hybrid
Strong hybrid
ALTERNATIVE
1A
(0% PER YEAR)
45%
45%
23%
40%
42%
0%
8%
0%
0%
3%
0%
0%
0%
0%
ALTERNATIVE
2
2% PER YEAR
100%
100%
23%
40%
52%
95%
60%
40%
0%
41%
20%
0%
0%
0%
ALTERNATIVE
3 (2.5% PER
YEAR)
100%
100%
23%
40%
77%
97%
100%
53%
8%
55%
7%
0%
0%
7%
ALTERNATIVE
4
(3.5% PER
YEAR)
100%
100%
23%
40%
97%
97%
60%
53%
13%
53%
20%
2%
18%
0%
ALTERNATIVE
5
(4% PER YEAR)
100%
100%
23%
40%
100%
97%
100%
53%
13%
53%
42%
0%
38%
4%
11-70
-------
Table 11-66 Technology Penetration Rates for Diesel Pickups using Method B
Low friction
lubricants
Engine friction
reduction
Turbo
machinery
improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic
drag reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start
engine systems
Mild hybrid
Strong hybrid
ALTERNATIVE
1A
(0% PER YEAR)
0%
0%
75%
61%
0%
0%
0%
35%
33%
0%
0%
0%
ALTERNATIVE
2
2% PER YEAR
0%
0%
100%
97%
100%
35%
35%
53%
35%
0%
0%
0%
ALTERNATIVE
3 (2.5% PER
YEAR)
0%
0%
100%
97%
100%
100%
58%
100%
100%
0%
0%
0%
ALTERNATIVE
4
(3.5% PER
YEAR)
0%
0%
100%
97%
100%
100%
100%
100%
100%
11%
36%
0%
ALTERNATIVE
5
(4% PER YEAR)
0%
0%
100%
97%
100%
100%
100%
100%
100%
31%
47%
0%
11-71
-------
Table 11-67 Technology Penetration Rates for Diesel Vans using Method B
Low friction
lubricants
Engine friction
reduction
Turbo
machinery
improvements
8 speed
transmission
Low rolling
resistance tires
Aerodynamic
drag reduction
Mass reduction
and materials
Electric power
steering
Improved
accessories
Stop/start
engine systems
Mild hybrid
Strong hybrid
ALTERNATIVE
1A
(0% PER YEAR)
0%
0%
0%
0%
7%
0%
0%
0%
0%
0%
0%
0%
ALTERNATIVE
2
2% PER YEAR
0%
0%
20%
47%
80%
0%
0%
11%
0%
0%
0%
0%
ALTERNATIVE
3 (2.5% PER
YEAR)
0%
0%
20%
67%
54%
7%
7%
21%
7%
0%
0%
0%
ALTERNATIVE
4
(3.5% PER
YEAR)
0%
0%
20%
67%
54%
7%
7%
12%
7%
0%
0%
0%
ALTERNATIVE
5
(4% PER YEAR)
0%
0%
20%
93%
100%
7%
7%
12%
7%
0%
0%
0%
11-72
-------
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
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
305
314
328
MHD (Class 6-7)
194
196
192
HHD (Class 8)
205
207
195
2021-2023 Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
29.9468
30.8574
32.1726
MHD (Class 6-7)
19.0615
19.2642
18.8587
HHD (Class 8)
20.0969
20.2999
19.1834
2024 and Later Model Year CCh Grams per Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
296
304
317
MHD (Class 6-7)
187
189
185
HHD (Class 8)
198
200
190
2024 and Later Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
29.0602
29.8789
31.1068
MHD (Class 6-7)
18.3233
18.5280
18.2209
HHD (Class 8)
19.4746
19.6796
18.6547
11-73
-------
Table 11-69 Alternative 2 Phase 2 Gasoline (SI) Vocational Vehicle Standards
2021-2023 Model Year CO2 Grams per Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
330
339
353
MHD (Class 6-7)
210
212
207
HHD (Class 8)
221
223
211
2021-2023 Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
37.0853
38.1283
39.7508
MHD (Class 6-7)
23.5770
23.8092
23.3446
HHD (Class 8)
24.8811
25.1137
23.7185
2024-2026 Model Year CO2 Grams per Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
325
334
348
MHD (Class 6-7)
205
207
204
HHD (Class 8)
217
219
208
2024-2026 Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
36.5703
37.6252
39.1490
MHD (Class 6-7)
23.0998
23.3344
22.9826
HHD (Class 8)
24.4215
24.6563
23.3648
11-74
-------
Table 11-70 Alternative 2 Tractor Standards
2021 MODEL YEAR CO2 GRAMS PER TON-MILE
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
102
112
114
Class 8
82
88
90
Sleeper Cab
Class 8
73
81
80
2021 Model Year Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
10.0196
11.0020
11.1984
Class 8
8.055
8.6444
8.8409
Sleeper Cab
Class 8
7.1709
7.9568
7.8585
2024 Model Year CO2 Grams per Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
97
107
109
Class 8
77
84
85
Sleeper Cab
Class 8
68
76
74
2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
9.5285
10.5108
10.7073
Class 8
7.5639
8.2515
8.3497
Sleeper Cab
Class 8
6.6798
7.4656
7.2692
Table 11-71 Alternative 2 Trailer Standards
MODEL
YEAR
oni o onon
on ii ono'?
on 1/1 im^
SUBCATEGORY
LENGTH
EPA Standard
(CO2 Grams per Ton-Mile)
Voluntary NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
DRY VAN
LONG
83
8.1532
82
8.0550
81
7.9568
SHORT
147
14.4401
147
14.4401
147
14.4401
REFRIGERATED VAN
LONG
84
8.2515
84
8.2515
83
8.1532
SHORT
151
14.8330
151
14.8330
151
14.8330
11-75
-------
Table 11-72 Alternative 2 HD Pickup and Van Standard Target Curve Coefficients
Diesel Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0416
0.0408
0.0400
0.0392
0.0384
0.0376
b
320
313
307
301
295
289
c
0.0004086
0.0004008
0.0003929
0.0003851
0.0003772
0.0003694
d
3.143
3.075
3.016
2.957
2.898
2.839
Gasoline Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0440
0.0431
0.0422
0.0414
0.0406
0.0398
b
339
332
325
319
312
306
c
0.0004951
0.0004850
0.0004749
0.0004658
0.0004568
0.0004478
d
3.815
3.736
3.657
3.590
3.511
3.443
Note:
a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only.
11-76
-------
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 Ton-
Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
288
297
309
MHD
(Class 6-
7)
183
185
181
HHD
(Class 8)
193
196
185
2021-2023 Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
purpose
Regional
LHD
(Class 2b-
5)
28.3890
29.0766
30.1572
MHD
(Class 6-
7)
17.9764
18.0747
17.6817
HHD
(Class 8)
18.8605
18.9587
17.9764
2024 and Later Model Year CCh Grams per
Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
272
280
292
MHD
(Class 6-
7)
172
174
170
HHD
(Class 8)
182
183
174
2024 and Later Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
26.7191
27.5049
28.6837
MHD
(Class 6-
7)
16.8959
17.0923
16.6994
HHD
(Class 8)
17.8782
17.9764
17.0923
11-77
-------
Table 11-74 Alternative 4 Phase 2 Gasoline (SI) Vocational Vehicle Standards
2021-2023 Model Year CO2 Grams per Ton-
Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
313
323
336
MHD
(Class 6-
7)
199
201
197
HHD
(Class 8)
210
212
201
2021-2023 Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
purpose
Regional
LHD
(Class 2b-
5)
35.2200
36.3452
37.8080
MHD
(Class 6-
7)
22.3923
22.6173
22.1672
HHD
(Class 8)
23.6300
23.8551
22.6173
2024 and Later Model Year CCh Grams per
Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
299
308
321
MHD
(Class 6-
7)
189
191
187
HHD
(Class 8)
196
198
188
2024 and Later Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
33.6446
34.6574
36.1202
MHD
(Class 6-
7)
21.2670
21.4921
21.0420
HHD
(Class 8)
22.0547
22.2797
21.1545
11-78
-------
Table 11-75 Alternative 4 Tractor Standards
Low Roof
Mid Roof
High Roof
DAY CAB
Class 7
94
104
106
Class 8
76
82
84
SLEEPER CAB
Class 8
68
76
75
2021 Model Year Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
9.2338
10.2161
10.4126
Class 8
7.4656
8.055
8.2515
Sleeper Cab
Class 8
6.6798
7.4656
7.3674
2024 Model Year CCh Grams per Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
87
96
96
Class 8
70
76
76
Sleeper Cab
Class 8
62
69
67
2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
8.5462
9.4303
9.4303
Class 8
6.8762
7.4656
7.4656
Sleeper Cab
Class 8
6.0904
6.7780
6.5815
Table 11-76 Alternative 4 Trailer Standards
MODEL
YEAR
2018-2020
2021-2023
2024 - 2026
SUBCATEGORY
LENGTH
EPA Standard
(CO2 Grams per Ton-Mile)
Voluntary NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
DRY VAN
LONG
83
8.1532
80
7.8585
77
7.5639
SHORT
144
14.1454
142
13.9489
140
13.7525
REFRIGERATED VAN
LONG
84
8.2515
81
7.9568
80
7.8585
SHORT
147
14.4401
145
14.2436
144
14.1454
11-79
-------
Table 11-77 Alternative 4 HD Pickup and Van Standard Target Curve Coefficients
Diesel Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0416
0.0402
0.0388
0.0374
0.0361
0.0348
b
320
308
298
287
277
267
c
0.0004086
0.0003949
0.0003811
0.0003674
0.0003546
0.0003418
d
3.143
3.026
2.927
2.819
2.721
2.623
Gasoline Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0440
0.0425
0.0410
0.0395
0.0381
0.0368
b
339
327
315
304
294
283
c
0.0004951
0.0004782
0.0004613
0.0004445
0.0004287
0.0004141
d
3.815
3.680
3.545
3.421
3.308
3.184
Note:
a Phase 1 primary phase-in coefficients. Alternative phase-in coefficients are different in MY2018 only.
11-80
-------
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
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
292
300
313
MHD (Class 6-7)
185
186
183
HHD (Class 8)
194
196
185
2021-2023 Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
32.8863
33.8112
35.2499
MHD (Class 6-7)
20.7809
20.9856
20.5761
HHD (Class 8)
21.8523
22.0566
20.8312
2024 and Later Model Year CCh Grams per Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
278
286
298
MHD (Class 6-7)
175
177
174
HHD (Class 8)
185
186
177
2024 and Later Model Year Gallons of Fuel per 1,000 Ton-Mile
Urban
Multi-Purpose
Regional
LHD (Class 2b-5)
31.2817
32.1840
33.4874
MHD (Class 6-7)
19.7042
19.9043
19.6042
HHD (Class 8)
20.7621
20.9617
19.8637
11-81
-------
Table 11-79 Alternative 5 Phase 2 Gasoline (SI) Vocational Vehicle Standards
2021-2023 Model Year CO2 Grams per Ton-
Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
292
300
313
MHD
(Class 6-
7)
185
186
183
HHD
(Class 8)
194
196
185
2021-2023 Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
purpose
Regional
LHD
(Class 2b-
5)
32.8863
33.8112
35.2499
MHD
(Class 6-
7)
20.7809
20.9856
20.5761
HHD
(Class 8)
21.8523
22.0566
20.8312
2024 and Later Model Year CCh Grams per
Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
278
286
298
MHD
(Class 6-
7)
175
177
174
HHD
(Class 8)
185
186
177
2024 and Later Model Year Gallons of Fuel per
1,000 Ton-Mile
Urban
Multi-
Purpose
Regional
LHD
(Class 2b-
5)
31.2817
32.1840
33.4874
MHD
(Class 6-
7)
19.7042
19.9043
19.6042
HHD
(Class 8)
20.7621
20.9617
19.8637
11-82
-------
Table 11-80 Alternative 5 Tractor Standards
Low Roof
Mid Roof
High Roof
DAY CAB
Class 7
87
96
98
Class 8
70
75
77
SLEEPER CAB
Class 8
63
71
70
2021 Model Year Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
8.5462
9.4303
9.6267
Class 8
6.8762
7.3674
7.5639
Sleeper Cab
Class 8
6.1886
6.9745
6.8762
2024 Model Year CCh Grams per Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
82
91
92
Class 8
65
71
72
Sleeper Cab
Class 8
58
64
63
2024 Model Year and Later Gallons of Fuel per 1,000 Ton-Mile
Low Roof
Mid Roof
High Roof
Day Cab
Class 7
8.0550
8.9391
9.0373
Class 8
6.3851
6.9745
7.0727
Sleeper Cab
Class 8
5.6974
6.2868
6.1886
Table 11-81 Alternative 5 Trailer Standards
MODEL
YEAR
2018-2020
2021-2023
2024 - 2026
SUBCATEGORY
LENGTH
EPA Standard
(CO2 Grams per Ton-Mile)
Voluntary NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
EPA Standard
(CO2 Grams per Ton-Mile)
NHTSA Standard
(Gallons per 1,000 Ton-Mile)
DRY VAN
LONG
82
8.0550
79
7.7603
76
7.4656
SHORT
144
14.1454
142
13.9489
140
13.7525
REFRIGERATED VAN
LONG
83
8.1532
81
7.9568
80
7.8585
SHORT
147
14.4401
145
14.2436
143
14.0472
11-83
-------
Table 11-82 Alternative 5 HD Pickup and Van Standard Target Curve Coefficients
Diesel Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0416
0.0400
0.0384
0.0368
0.0354
0.0339
b
320
307
295
283
271
261
c
0.0004086
0.0003929
0.0003772
0.0003615
0.0003477
0.0003330
d
3.143
3.016
2.898
2.780
2.662
2.564
Gasoline Vehicles
Model Year
201 8-2020 a
2021
2022
2023
2024
2025 and later
a
0.0440
0.0422
0.0405
0.0389
0.0374
0.0359
b
339
325
312
300
288
276
c
0.0004951
0.0004749
0.0004557
0.0004377
0.0004208
0.0004040
d
3.815
3.657
3.511
3.376
3.241
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
2021-
2023
2024 and
Later
STANDARDS
EPA Standard
(CCh Grams
per Ton-Mile)
NHTSA
Standard
(Gallons per
100 Ton-Mile)
EPA Standard
(CO2 Grams
per Ton-Mile)
NHTSA
Standard
(Gallons per
100 Ton-Mile)
ALTERNATIVE 2
MHD
481
4.7250
471
4.6267
HHD
455
4.4695
445
4.3713
ALTERNATIVE 4
MHD
477
4.6857
466
4.5776
HHD
451
4.4303
441
4.3320
ALTERNATIVE 5
MHD
474
4.6562
464
4.5580
HHD
448
4.4008
438
4.3026
11-84
-------
Table 11-84 presents the alternative CCh 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
HEAVY-
DUTY
DIESEL
MEDIUM
HEAVY-
DUTY
DIESEL
HEAVY
HEAVY-
DUTY
DIESEL
Alternative 2
2021-2023
2024 and
Later
CO2 Standard (g/bhp-hr)
Fuel Consumption Standard
(gallon/1 00 bhp-hr)
CC-2 Standard (g/bhp-hr)
Fuel Consumption (gallon/100
bhp-hr)
571
5.6090
559
5.4912
571
5.6090
559
5.4912
550
5.4028
539
5.2947
Alternative 4
2021-2023
2024 and
Later
CC-2 Standard (g/bhp-hr)
Fuel Consumption Standard
(gallon/1 00 bhp-hr)
CC-2 Standard (g/bhp-hr)
Fuel Consumption (gallon/100
bhp-hr)
562
5.5206
553
5.4322
562
5.5206
553
5.4322
541
5.3143
533
5.2358
Alternative 5
2021-2023
2024 and
Later
CC-2 Standard (g/bhp-hr)
Fuel Consumption Standard
(gallon/1 00 bhp-hr)
CO2 Standard (g/bhp-hr)
Fuel Consumption (gallon/100
bhp-hr)
559
5.4912
550
5.4028
559
5.4912
550
5.4028
538
5.2849
530
5.2063
11-85
-------
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. l(a).]" Forty
Most Asked Questions Concerning CEQ's National Environmental Policy Act Regulations, 46 FR 18026 (1981)
(emphasis added).
3 http://energv.gov/eere/vehicles/vehicle-technologies-office-21st-century-truck
4 http://www.epa.gov/smartwav/
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 "CCh emission rates" for tractors and vocational vehicles reflect changes in CCh 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 Ibs 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 Ibs 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 Ibs 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 Ibs 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.
11-86
-------
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 Ibs 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.
11-87
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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.
12-1
-------
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 Ib. 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 post-
2017. 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
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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
Alternative Fuel
Engine Converters
HD Pick-up Trucks & Vans
Vocational Chassis,
Class 7 & 8 Tractors
Trailers
HD Engines
NAICS
CODE
333999
811198
336111
336120
336212
333924
336310
NAICS
DESCRIPTION
Misc. General Purpose Machinery
All Other Auto Repair & Maintenance
Automobile Manufacturing
Heavy-Duty Truck Manufacturing
Truck Trailer Manufacturing
Ind. Truck, Trailer & Stacker Machinery
Motor Vehicle Gasoline Engine & Engine
Parts
SBA SIZE
THRESHOLD
500 employees
$7.0M (annual receipts)
1,000 employees
1,000 employees
500 employees
750 employees
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.
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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 heavy-
duty 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 CCh 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 CCh, 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
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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 CCh
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 CI-
engines 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 CCh 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.
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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 CCh 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
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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 pre-
approved 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-to-
date 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.
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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-
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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 CCh 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
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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.
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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
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Chapter 13: Natural Gas Vehicles and Engines
13.1 Detailed Life-Cycle Analysis
We conducted a review to assess the lifecycle impacts of natural gas used by the heavy-
duty 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
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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
Field Production
NG Processing
Transmission and Storage
Subtotal without Distribution
Distribution
Total with Distribution
METHANE EMISSIONS
(GIGAGRAMS)
1858
892
2071
4821
1231
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
Production
NG Processing
Transportation and Storage
Distribution
Total
CARBON DIOXIDE EMISSIONS
(GIGAGRAMS)
13,659
21,469
63
37
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
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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
Methane Emissions
Teregram CC^eq.
Natural Gas Production (dry)
trillion cubic ft
2012
145
24.1
2020
140
26.6
2025
151
29.3
As Table 13-3 shows, methane emissions from natural gas facilities are expected to
increase from 145 teregram CCheq. in 2012 to 151 teregram CCh 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.
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Table 13-4 Process Energy Demand by the Natural Gas System (BTU/million BTU)
FUEL TYPE
Natural Gas
Diesel
Electricity
Gasoline
Resid
Totals
PRODUCTION
Conv
Wells
22,016
2816
256
256
256
25,600
Shale
Wells
20,955
2680
244
244
244
24,367
Weighted
Avg
21,307
2725
248
248
248
24,777
NATURAL
GAS
PROCESSING
26,123
272
816
0
0
27,211
TRANSMISSION/
DISTRIBUTION
0
0
0
0
0
0
TOTAL
47,430
2997
1064
248
248
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
Natural Gas
Diesel
Electricity
Gasoline
Resid
GCO2/BTU
0.0398
0.0555
0.1549
0.0535
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.
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Table 13-6 Summary of Year 2025 Emissions from the Natural Gas System (grams/million BTU)
CNG Analysis (includes CH4
emissions from the distribution
system)
LNG Analysis (does not include
CH4 emissions from the
distribution system)
METHANE EMISSIONS
242
192
CARBON DIOXIDE
3881
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)
Emissions
METHANE
6.9
CARBON DIOXIDE
3988
NITROUS OXIDE
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.
13-5
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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. Lhe 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)
Direct Emissions
Indirect Emissions
Total Emissions
METHANE
0
48
48
CARBON DIOXIDE
15,175
970
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.
13-6
-------
Table 13-9 Boil-Off Emissions Estimate for LNG Transportation to Retail (g/million BTU)
Fuel Use (Diesel Fuel)
Methane Boil Off
Emissions
Total
METHANE
0.45
0.43
0.88
CARBON DIOXIDE
378
0
378
NITROUS OXIDE
0.009
0
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)
LNG Retail Boil-Off
Emissions
METHANE
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)
CNG
LNG
METHANE
249
251
CARBON DIOXIDE
7869
20,405
NITROUS OXIDE
0.07
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
13-7
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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 technology6 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
13-8
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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
Methane Standard
Estimated Emissions
g/bhp-hr
g/million BTU
PRE-2014
None
2-5
214-534
20 14 AND LATER
g/bhp-hr
0.05
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
13-9
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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.0 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.
13-10
-------
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 CFU which translates to 132 - 400K grams of CCh-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 CCh-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
Boil-off Scenarios
PERCENT FULL
(INITIAL)
90
50
10
PERCENT FULL
(FINAL)
83.2
46.2
9.3
LIQUID LOSS
(GALS)
13.6
7.6
1.5
TOTAL MASS
LOSS (LBS)
38.7
24.8
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 Ibs.
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.
13-11
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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.
140,000
120,000
100,000
ro
£ 80,000
l_
°- 60,000
£ 40,000
Q
% 20,000
^ 0
2015 2010 2005 2000 1995
Truck Year
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
13-12
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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)
20 14 and Later
Pre-2014
Direct
Indirect
Total
Direct
Indirect
Total
METHANE
5.3
0.1
5.4
374
6
380
CARBON
DIOXIDE
60,702
3
60,705
60,702
189
60,891
NITROUS
OXIDE
2
0
2
2
0
2
Table 13-15 Estimated Tailpipe and Boil-Off Emissions for LNG Trucks (g/mmbtu)
20 14 and Later
assuming low
Venting and Boil-
Off Emissions
20 14 and Later
Assuming High
Venting and
Boil-Off
Emissions
Pre-2014
Assuming Low
Venting and
Boil-Off
Emissions
Direct
Indirect
Total
Direct
Indirect
Total
Direct
Indirect
Total
METHANE
5.3
0.07
40.4
5.3
0.07
739
374
4.8
413
CARBON
DIOXIDE
60,702
5.5
60.707
60,702
5.5
60,707
60,702
386
61,088
NITROUS
OXIDE
2
0
2
2
0
2
2
0
2
13-13
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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)
Well to Tank
Tank to Wheels
Well to Wheels
CARBON
DIOXIDE
15,838
78,308
94,146
METHANE
98
1
99
NITROUS
OXIDE
0.3
2
2.3
TOTALS
CO2EQ*
18,377
78,929
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
13-14
-------
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
Pre-
2014
CNG
Truck
20 14 or
Later
Truck
EMISSION
CATEGOR
Y
Well to
Tank
Tank to
Wheels
Well to
Wheels
Well to
Tank
Tank to
Wheels
Well to
Wheels
CARBO
N
DIOXID
E
7869
60,891
68,760
7869
60,704
68574
METHAN
E
249
380
628
249
5
254
NITROU
S OXIDE
0.07
2
2
0.07
2
2
TOTA
L
CO2
EQ.*
14,107
70,980
85087
14,107
61,436
75,544
THERMAL
EFFICIENC
Y 5% AND
15%
CO2EQ.*
705
2116
3548
10647
4254
12,763
705
2116
3072
9215
3777
11,331
TOTALS
INCLUDIN
G
THERMAL
EFFICIENC
Y IMPACT
CO2EQ. *
14,812
16,223
74,528
81,627
89,379
97,850
14,812
16,223
64,508
70,651
79,321
86,875
Note:
*The CCheq. 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.
13-15
-------
120,000
= 100,000
CO
E
^ 80,000
01
O 60,000
.22
V)
£ 20,000
IQThermal High
Thermal Low
CH4:&N2O
HCO2
Diesel CNG
Tailpipe Tailpipe
Diesel CNG pre- CNG 2014
2014 and later
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.
13-16
-------
Table 13-18 Full Lifecycle Analysis of an LNG Truck (g/million BTU)
TRUCK
TYPE
Pre-
2014
LNG
Trucks
assumm
g low
Venting
and
Boil-Off
Emissio
ns
2014
and
Later
LNG
Trucks
assumin
g low
Venting
and
Boil-Off
Emissio
ns
2014
and
Later
LNG
Trucks
with
High
Venting
and
Boil-Off
Emissio
ns
EMISSION
CATEGOR
Y
Well to
Tank
Tank to
Wheels
Well to
Wheels
Well to
Tank
Tank to
Wheels
Well to
Wheels
Well to
Tank
Tank to
Wheels
Well to
Wheels
CARBO
N
DIOXID
E
20,405
61,088
81,494
20,405
60,707
81,113
20,405
60,707
81,113
METHAN
E
251
413
665
251
40
291
251
990
990
NITROU
S
OXIDE
0.01
2
2
0.01
2
2
0.01
2
2
TOTALS
*
CO2EQ.
26,693
72,896
98,732
26,693
62314
89,024
26,693
86,037
112,720
THERMAL
EFFICIENC
Y5%AND
15%
CO2EQ.*
1334
4004
3645
10,934
4935
14,807
1334
4004
3116
9347
4450
13,350
1334
4004
3035
9106
4370
13,109
TOTALS
INCLUDIN
G
THERMAL
EFFICIENC
Y IMPACT
CO2EQ.*
28,027
30,697
76,542
83,831
103,668
113,540
28,027
30,697
65,429
71,661
93.475
102,375
28,027
30,697
89,072
95,143
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.
13-17
-------
140,000
IT 120,000
CO
1 100,000
HThermal High
Thermal Low
CH4:&N2O
HCO2
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.
13-18
-------
120,000
100,000
80,000
60,000
40,000
20,000
0
^+.
>+<
+4
+4
+4
+4
+4
+4
+4
+4
+4
+4
+4
++
**
++
**
*+
**<
(DThermal High
Thermal Low
CH4:&N2O
QCO2
Diesel Nat Gas Diesel Nat Gas Nat Gas Nat Gas
Tailpipe Tailpipe Low Avg High
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.
13-19
-------
Table 13-19 Summary of GWPs
Methane (CHO
Nitrous Oxide
(N2O)
PRIMARY
ANALYSIS
100 Year
25
298
SENSITIVITY ANALYSES
20 Year
72
289
500 Year
7.6
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.
140,000
HThermal High
Thermal Low
CH4:&N2O
HCO2
Diesel CNG Diesel CNG pre-CNG 2014
Tailpipe Tailpipe 2014 and later
Figure 13-5 Full Lifecycle Analysis of a CNG Truck
(Projected Upstream Methane Emissions in 2025, Methane GWP of 72)
13-20
-------
"5"
&
E
u-
01
fM
O
u
jjs
U)
c
_g
'u>
U)
JE
200,000
180,000
160,000
140,000
120,000
100,000
80,000
60,000 ;j{
40,000 j+j
20,000 ;+;
0 ^
X.
^
1
X
HThermal High
111 Thermal Low
CH4:&N2O
HCO2
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)
120,000
= 100,000
00
E
^ 80,000
CT
01
O 60,000
.22
= 40,000
*U)
.12
£ 20,000
0
++
++
++
++
++
++
++
++
++
++
++
++
++
++
HThermal High
Thermal Low
CH4:&N2O
HCO2
Diesel CNG
Tailpipe Tailpipe
Diesel
CNG pre- CNG 2014
2014 and later
Figure 13-7 Full Lifecycle Analysis of a CNG Truck
(Projected Upstream Methane Emissions in 2025, Methane GWP of 7.6)
13-21
-------
120,000 -
£ 100,000
E
-t 80,000
HThermal High
Thermal Low
CH4:&N2O
HCO2
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 (CCheq) 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 CCheq emissions as a diesel truck, while our analysis estimates
that CNG engines emit 81 percent of the CCheq emissions as a diesel truck. The most likely
explanation for CARB's lower estimated CCheq 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 CCheq emissions, as a
diesel truck while our analysis estimates LNG trucks emit 96 percent of the CCheq 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,
13-22
-------
which is close to CARS'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 CCh-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.
13-23
-------
Rgure 33. Near-Term Class 8 Natural Gas Penetration Forecasts
16% -i
14% -
12% -
10% -
0%
ACT Research
PACCAR-High
Frost SiSullivan- High
Frost &Sullivan- Low
NPC-Reference
PACCAR-Low
Frost & Sullivan- Fcst
2012
2013
2014
2015
2016
2017
Source: Citi Research
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 heavy-
duty 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
13-24
-------
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.
13-25
-------
Rgure 34. Long-Term Class 8 Natural Gas Penetration Forecasts
60%-
50%-
40%-
30%-
20%-
10%-
0%
Citi Penetration
E stirnate
for 2020
4-
ACT Research
NPC-High
^NPC - Reference
PACCAR-High
PACCAR-Low
^Frost& Sullivan-High
^Frost & Sullivan- Fcst
Frost & Sullivan- Low
2012 2016 2020 2024 2028 2032 2036 2040 2044 2048
Source: Citi Research
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
13-26
-------
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.
TO
GO
f/V
a
0
u
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
CNG LNG Diesel
Raw Fuel Cost/Crude Oil .> Liquifaction/Refining o Distribution and Marketing Taxes
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
13-27
-------
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
9.00%
5 8.00% -»~NG percent of
S 7 nno/ HD Demand
o7.00/o _*_NG Price DGE
'o 6.00%
c 5cOO% Diesel Price
8
2.00%
£
3 1.00%
z 0.00%
2010 2015
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
' Early LNG Adopters Experience Mixed Results; Truck News, October 1, 2013
13-28
-------
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.
12.00%
10.00%
8.00%
CD
CuO
(D
£ 6.00%
u
m
Medium HD
4.00% / -flight HD
2.00%
0.00%
2005 2010 2015 2020 2025 2030 2035 2040 2045
Year
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.
13-29
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50 100 150 200
Annual Miles Traveled (thosand miles)
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.
13-30
-------
Table 13-20 Payback Analysis
Miles per Year
Miles per Gallon
Incremental NG Truck Cost
Incremental NG Maintenance
Cost per year
Diesel Fuel Price ($/gal)
Natural Gas Price ($/gal DGE)
Diesel Fuel Cost per Year
Natural Gas Fuel Cost Per Year
Lower NG Efficiency (%)
Vehicle NG Use (%)
Simple Payback (years)
Discounted Payback (years)
CASE1
20 14 DUEL
FUELED
120,000
6.7
55,000
970
3.80
2.64
68,263
60,062
5%
50%
6.7
9.1
CASE 2
2014HPDI
120,000
6.7
70,000
1613
3.80
2.64
56,886
43,978
5%
95%
4.5
5.5
CASE 3
2020 HPDI
120,000
7.1
65,000
1935
4.16
2.66
70,608
50,614
5%
95%
3.3
3.7
CASE 4
2030 HPDI
120,000
7.2
60,000
1935
5.65
2.83
94,167
53,810
5%
95%
1.5
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
' FPInnovations estimates that natural gas truck maintenance costs are $0.0 I/per kilometer more than diesel trucks.
13-31
-------
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.
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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 paniculate 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.
(D
W
o
U
CNG
I Raw Fuel Cost/Crude Oil
LNG
Liquifaction/Refining
Diesel DME
Distribution and Marketing BTaxes
Figure 13-16 Relative Retail Cost of DME to CNG, LNG and Diesel Fuel ($/gal dge)
DME is estimated to cost $3.507 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
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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'sGWP.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
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(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 72°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 CCh 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 over-
complying with CCh 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 CCh 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 CCh emission credits
(generated from the same averaging set) based on the relative GHG equivalents. To establish
the GHG equivalents used by the CCh 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
CCh equivalency of methane and to seek comment on whether the methane GWP used to
establish the GHG equivalency value for the CCh 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 CCh Credits Program can be estimated by
the cost to the manufacturer for reducing CCh emissions. If for example the GWP of methane is
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increased from 25 to 34, then the number of CCh 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 CCh for the difference in CCh credits. See chapter 7 for a discussion of our
estimated cost per ton of CCh 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 technology11 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
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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.1 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 GHG-
potent 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, fora 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 Ibs over two
days, and close to 50 Ibs 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.
1 As per confidential discussions with OEM's, June 27, 2014, and November 20, 2014.
1 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"
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Boil Off Rate for a 90%-Filled 115 gallon LNG Tank with 18 Watts of
Heat Input
?.oo
Each point represents
a venting event
012345678 91011121314151617181920212223242526272829303132333435
u
Days from Filled to 90%
Figure 13-17 Boil Off Rate for a 90% Filled 115 gallon LNG Tank with 18 Watts of Heat Input
ANG Control of 115 gallon LNG Tank
with 18 watts of Heat Input (35 days to boil off LNG tank)
60.0
24 36 48 60 72
Duration of Venting Control (hours)
84
96
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 CCh
using a burner. Another potential option would be to convert the methane captured in a canister
to CCh 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.
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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.
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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(0)).
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 boil-
off 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.
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References
1 US Greenhouse Gas Emissions and Sinks: 1990 -2012, US Environmental Protection Agency, April 15, 2014
(EPA430-R-14-003).
2 United States Climate Action Report 2014, First Biennial Report of the United States of America, Under the
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
Standards for Modified and Reconstructed Power Plants, US Environmental Protection Agency, June 2014, Table
3A-4. (EPA-542/R-14-002)
4 GREET 1 Model, Argonne National Laboratory, 2012.
5 Inventory of US Greenhouse Gas Emissions and Sinks: 1990 - 2012, Annex 2, Tables A-34 and A-35, April 15,
2014.
6 GREET 1 Model, Argonne National Laboratory, 2012.
7 Detailed California-Modified GREET Pathway for Liquefied Natural Gas (LNG) from North American and
Remote Natural Gas Sources, Version 1.0, July 20, 2009.
8 Detailed California-Modified GREET Pathway for Liquefied Natural Gas (LNG) from North American and
Remote Natural Gas Sources, Version 1.0, July 20, 2009.
9 Detailed California-Modified GREET Pathway for Liquid Natural Gas (LNG) from North American and Remote
Natural Gas Sources, Version 1.0, California Air Resources Board, July 20, 2009.
10 The emission standards were required by the rulemaking named "Greenhouse Gas Emissions Standards and Fuel
Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles" published September 15, 2011.
11 Conversation with Timothy J. Skone P.E., National Energy Technology Laboratory, Department of Energy, June
2014.
12 Low Carbon Fuel Standard Reconsideration: CA-GREET Model Update, California Air Resources Board,
August 22, 2014.
13 Jaffe, Amy Myers, Exploring the role of Natural Gas in U.S. Trucking, NextSTEPS Program, UC Davis Institute
of Transportation Studies, February 18, 2015.
14 Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase
Two: First Report, National Research Council, National Academy of Sciences, 2014.
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16 Annual Energy Outlook 2014 with projections to 2040, US Energy Information Administration, April 2014.
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17 Natural Gas Adoption Increases Slowly, ACT Reports, HOT Truckinginfo, www.truckinginfo.com. September
17, 2014.
18 Advancing Technology for America's Transportation Future, Summary Report, National Petroleum Council,
August 1,2012.
19 Energy 2020: Trucks, Trains and Automobiles, Citi GPS: Global Perspectives and Solutions, June 2013.
20 Issues in Focus: Section 6" Heavy-Duty Natural Gas Vehicles, Energy Information Administration, Annual
Energy Outlook 2012, June 25, 2012.
21 Conversation with Nicholas Chase, Energy Information Administration, October 2014.
22 Annual Energy Outlook 2014 with projections to 2040, US Energy Information Administration, April 2014.
(www. eia. gov/forecasts/aeo)
23 U.S. Department of Commerce, Bureau of the Census, 2002 Vehicle Inventory and Use Survey, Microdata, CD,
2005. (Additional resources: www.census.gov/svsd/www/tiusview.html)
24 Good, D.A et al, Lifetimes and global warming potentials for dimethyl ether and for fluorinated ethers:
CH3OCF3, CHF2OCHF2, CHF2OCF3, Journal of Geophysical Res, Vol 103, Pages 28,181 - 28,186, November
20, 1998.
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