Regulatory Impact Analysis:
Final Rulemaking for 2017-2025
Light-Duty Vehicle Greenhouse Gas
Emission Standards and Corporate
Average Fuel Economy Standards
&EPA
United States
Environmental Protection
Agency
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Regulatory Impact Analysis:
Final Rulemaking for 2017-2025
Light-Duty Vehicle Greenhouse Gas
Emission Standards and Corporate
Average Fuel Economy Standards
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
United States
Environmental Protection
Agency
EPA-420-R-12-016
August 2012
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MY 2017 and Later Regulatory Impact Analysis
1 TECHNOLOGY PACKAGES, COST AND EFFECTIVENESS 1-1
1.1 Overview of Technology 1-1
1.2 Technology Cost and Effectiveness 1-4
1.3 Vehicle Package Cost and Effectiveness 1-19
1.4 Use of the Lumped Parameter Approach in Determining Package Effectiveness 1-50
1.4.1 Background 1-50
1.4.2 Role of the model 1-51
1.4.3 Overview of the lumped parameter model 1-51
1.5 Lumped Parameter Model Methodology 1-54
1.5.1 Changes to the LP model for the final rulemaking 1-54
1.5.2 Development of the model 1-55
1.5.3 Baseline loss categories 1-55
1.5.4 Baseline fuel efficiency by vehicle class 1-57
1.5.5 Identification and calibration of individual technologies 1-59
1.5.6 Example build-up of LP package 1-61
1.5.7 Calibration of LP results to vehicle simulation results 1-64
1.5.8 Notable differences between LP model and Ricardo results 1-69
1.5.9 Comparison of results to real-world examples 1-71
2 EPA'S VEHICLE SIMULATION TOOL 2-1
2.1 Introduction 2-1
2.1.1 Background 2-1
2.1.2 Objective and Scope 2-1
2.2 Descriptions of EPA's Vehicle Simulation Tool 2-2
2.2.1 Overall Architecture 2-2
2.2.2 System Models 2-5
2.3 Applications of Simulation Tool for Final Rule 2-7
2.3.1 Impact of A/C on Fuel Consumption 2-7
2.3.2 Off-Cycle Credit Calculation 2-11
2.4 On-Going and Future Work 2-14
2.4.1 Simulation Tool Validation 2-14
2.4.2 Simulation Tool Upgrade 2-15
3 RESULTS OF FINAL AND ALTERNATIVE STANDARDS 3-2
3.1 Introduction 3-2
3.2 OMEGA model overview 3-3
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Table of Contents
3.3 OMEGA Model Structure 3-5
3.4 Model Inputs 3-7
3.4.1 Market Data 3-7
3.4.2 Technology Data 3-15
3.4.3 The Scenario File 3-18
3.4.4 Fuels and reference data 3-27
3.5 Analysis Results 3-29
3.5.1 Targets and Achieved Values 3-29
3.5.2 Penetration of Selected Technologies 3-41
3.5.3 Projected Technology Penetrations in Reference Case 3-43
3.5.4 Projected Technology Penetrations in Final rule case 3-49
3.5.5 Projected Technology Penetrations in Alternative Cases 3-55
3.5.6 Additional Detail on Mass Reduction Technology 3-79
3.5.7 Air Conditioning Cost 3-80
3.5.8 Stranded Capital 3-80
3.6 Per Vehicle Costs MYs 2021 and 2025 3-83
3.7 Alternative Program Stringencies 3-86
3.8 Comparative cost of advanced technologies under credit scenarios 3-89
3.9 How Many of Today's Vehicles Can Meet or Surpass the MY 2017-2025 CO2 Footprint-based
Targets with Current Powertrain Designs? 3-91
3.10 Analysis of Ferrari & Chrysler/Fiat 3-98
3.11 Cost Sensitivities 3-98
3.11.1 Overview 3-98
3.11.2 Mass Sensitivity 3-99
3.11.3 Battery Sensitivity 3-99
3.11.4 ICM Sensitivity 3-101
3.11.5 Learning Rate Sensitivity 3-102
3.11.6 Summary of Sensitivity Impacts 3-102
3.11.7 NAS report 3-103
4 PROJECTED IMPACTS ON EMISSIONS, FUEL CONSUMPTION, AND SAFETY4-109
4.1 Introduction 4-109
4.2 Analytic Tools Used 4-110
4.3 Inputs to the emissions analysis 4-111
4.3.1 Methods 4-111
4.3.2 Activity 4-113
4.3.3 Upstream Emission Factors 4-121
4.3.4 Scenarios 4-124
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MY 2017 and Later Regulatory Impact Analysis
4.3.5 Emission Results 4-133
4.3.6 Fuel Consumption Impacts 4-137
4.3.7 GHG and Fuel Consumption Impacts from Alternatives 4-139
4.4 Safety Analysis 4-140
4.5 Sensitivity Cases 4-141
4.5.1 Rebound 4-141
4.5.2 EV impacts 4-141
4.6 Inventories Used for Non-GHG Air Quality Modeling 4-142
4.6.1 Onroad Vehicles 4-143
4.6.2 Fuel Production and Distribution 4-144
4.6.3 Estimate of Emissions from Changes in Electricity Generation 4-151
4.6.4 Comparison of inventories used in air quality modeling and FRM (short tons) 4-163
5 VEHICLE PROGRAM COSTS AND FUEL SAVINGS 5-1
5.1 Technology Costs per Vehicle 5-1
5.2 Costs of the MY 2017-2025 GHG Standards 5-9
5.2.1 Technology Costs 5-9
5.2.2 Maintenance & Repair Costs 5-10
5.2.3 Vehicle Program Costs 5-24
5.3 Cost per Ton of Emissions Reduced 5-25
5.4 Reduction in Fuel Consumption and its Impacts 5-25
5.4.1 What Are the Projected Changes in Fuel Consumption? 5-25
5.4.2 What are the Fuel Savings to the Consumer? 5-26
5.5 Consumer Cost of Ownership, Payback Period and Lifetime Savings on New and Used Vehicle
Purchases 5-28
6 HEALTH AND ENVIRONMENTAL IMPACTS 6-1
6.1 Health and Environmental Impacts of Non-GHG Pollutants 6-1
6.1.1 Health Effects Associated with Exposure to Non-GHG Pollutants 6-1
6.1.2 Environmental Effects Associated with Exposure to Non-GHG Pollutants 6-14
6.2 Air Quality Impacts of Non-GHG Pollutants 6-26
6.2.1 Air Quality Modeling Methodology 6-26
6.2.2 Air Quality Modeling Results 6-38
6.3 Quantified and Monetized Non-GHG Health and Environmental Impacts 6-72
6.3.1 Quantified and Monetized Non-GHG Human Health Benefits of the 2030 Calendar Year
(CY) Analysis 6-73
6.3.2 PM-related Monetized Benefits of the Model Year (MY) Analysis 6-99
6.4 Changes in Atmospheric CO2 Concentrations, Global Mean Temperature, Sea Level Rise, and
Ocean pH Associated with the Final Rule's GHG Emissions Reductions 6-105
6.4.1 Introduction 6-105
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Table of Contents
6.4.2 Projected Change in Atmospheric CO2 Concentrations, Global Mean Surface Temperature
and Sea Level Rise 6-108
6.4.3 Projected Change in Ocean pH 6-113
6.4.4 Summary of Climate Analyses 6-115
7 OTHER ECONOMIC AND SOCIAL IMPACTS 7-1
7.1 Monetized GHG Estimates 7-3
7.2 The Benefits Due to Reduced Refueling Time 7-14
7.2.1 Relationship between tank size, fuel economy, and range 7-14
7.2.2 Calculation of benefits value 7-19
7.3 Summary of Costs and Benefits of the MYs 2017-2025 Final Rule 7-24
7.4 Summary of Costs and Benefits of the MYs 2012-2016 & 2017-2025 Final Rules 7-30
7.4.1 Model Year Lifetime Results 7-31
7.4.2 Calendar Year Results 7-34
7.4.3 Consumer Cost of Ownership Results 7-36
8 VEHICLE SALES AND EMPLOYMENT IMPACTS 8-1
8.1 Vehicle Sales Impacts 8-1
8.1.1 How Vehicle Sales Impacts were Estimated for this Rule 8-1
8.1.2 Consumer Vehicle Choice Modeling 8-2
8.1.3 Impact of the Rule on Affordability of Vehicles and Low-Income Households 8-16
8.2 Employment Impacts 8-18
8.2.1 Introduction 8-18
8.2.2 Approaches to Quantitative Employment Analysis 8-20
8.2.3 Employment Analysis of This Rule 8-24
8.2.4 Effects on Employment for Auto Dealers 8-30
8.2.5 Effects on Employment in the Auto Parts Sector 8-31
8.2.6 Effects on Employment for Fuel Suppliers 8-31
8.2.7 Effects on Employment due to Impacts on Consumer Expenditures 8-31
8.2.8 Summary 8-32
9 SMALL BUSINESS FLEXIBILITY ANALYSIS 9-1
10 ALTERNATE ANALYSIS USING 2010 MY BASELINE 10-1
10.1 Why an Alternate Analysis? 10-1
10.2 Level of the standard 10-2
10.3 Targets and Achieved Levels 10-4
10.4 Manufacturer Compliance Costs 10-8
10.5 Technology Penetrations 10-13
10.5.1 Projected Technology Penetrations in Reference Case 10-13
10.5.2 Projected Technology Penetrations in Final rule case 10-19
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10.6 GHG Impacts 10-25
10.7 Fuel Savings 10-26
10.8 Comparison to analysis using the MY 2008 based market forecast 10-27
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MY 2017 and Later Regulatory Impact Analysis
Executive Summary
The Environmental Protection Agency (EPA) and the National Highway Traffic
Safety Administration (NHTSA) are issuing a joint Notice of Final Rulemaking (FRM) to
establish standards for light-duty highway vehicles that will reduce greenhouse gas emissions
(GHG) and improve fuel economy. EPA is issuing greenhouse gas emissions standards under
the Clean Air Act, and NHTSA is issuing Corporate Average Fuel Economy standards under
the Energy Policy and Conservation Act (EPCA), as amended. These standards apply to
passenger cars, light-duty trucks, and medium-duty passenger vehicles, covering model years
(MY) 2017 through 2025. The standards will require these vehicles to meet an estimated
combined average emissions level of 163 grams of COi per mile in MY 2025 under EPA's
GHG program. These standards are designed such that compliance can be achieved with a
single national vehicle fleet whose emissions and fuel economy performance improves year
over year. The National Program will result in approximately 2 billion metric tons of COi
equivalent emission reductions and approximately 4 billion barrels of oil savings over the
lifetime of vehicles sold in model years 2017 through 2025.
Mobile sources are significant contributors to air pollutant emissions (both GHG and
non-GHG) across the country, internationally, and into the future. The Agency has
determined that these emissions cause or contribute to air pollution which may reasonably be
anticipated to endanger public health or welfare, and is therefore establishing standards to
control these emissions as required by section 202 (a) of the Clean Air Act.A The health- and
environmentally-related effects associated with these emissions are a classic example of an
externality-related market failure. An externality occurs when one party's actions impose
uncompensated costs on another party. EPA's final rule will deliver additional environmental
and energy benefits, as well as cost savings, on a nationwide basis that would likely not be
available if the rule were not in place.
Table 1 shows EPA's estimated lifetime discounted cost, benefits and net benefits for
all vehicles projected to be sold in model years 2017-2025. It is important to note that there is
significant overlap in costs and benefits for NHTSA's CAFE program and EPA's GHG
program and therefore combined program costs and benefits are not a sum of the individual
programs.
A "Technical Support Document for Endangerment and Cause or Contribute Findings for Greenhouse Gases
Under Section 202(a) of the Clean Air Act" Docket: EPA-HQ-OAR-2010-0799,
http://epa.gov/climatechange/endangernient.html. See also State of Massachusetts v. EPA. 549 U.S. 497. 533
("If EPA makes a finding of endangerment, the Clean Air Act requires the agency to regulate emissions of the
deleterious pollutant from new motor vehicles").
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Executive Summary
Table 1 EPA's Estimated 2017-2025 Model Year Lifetime Discounted Costs,
Benefits, and Net Benefits assuming the 3% discount rate SCC Valuea'b'c'd
(Billions of 2010 dollars)
Lifetime Present Value0 - 3% Discount Rate
Program Costs
Fuel Savings
Benefits
Net Benefits'1
$150
$475
$126
$451
Annualized Value6 - 3% Discount Rate
Annualized costs
Annualized fuel savings
Annualized benefits
Net benefits
$6.49
$20.5
$5.46
$19.5
Lifetime Present Value0 - 7% Discount Rate
Program Costs
Fuel Savings
Benefits
Net Benefits"
$144
$364
$106
$326
Annualized Value6 - 7% Discount Rate
Annualized costs
Annualized fuel savings
Annualized benefits
Net benefits
$10.8
$27.3
$7.96
$24.4
Notes:
a The agencies estimated the benefits associated with four different values of a one ton CO2
reduction (model average at 2.5% discount rate, 3%, and 5%; 95th percentile at 3%), which
each increase over time. For the purposes of this overview presentation of estimated costs and
benefits, however, we are showing the benefits associated with the marginal value deemed to
be central by the interagency working group on this topic: the model average at 3% discount
rate, in 2010 dollars. Section III.H provides a complete list of values for the 4 estimates.
* Note that net present value of reduced GHG 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 III.H for more detail.
c Projected results using 2008 based fleet projection analysis.
d Present value is the total, aggregated amount that a series of monetized costs or benefits that
occur over time is worth in a given year. For this analysis, lifetime present values are
calculated for the first year of each model year for MYs 2017-2025 (in year 2010 dollar terms).
The lifetime present values shown here are the present values of each MY in its first year
summed across MYs.
e Net benefits reflect the fuel savings plus benefits minus costs.
f The annualized value is the constant annual value through a given time period (the lifetime of each
MY in this analysis) 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%.
This Regulatory Impact Analysis (RIA) contains supporting documentation to the
EPA rulemaking. NHTS A has prepared its own RIA in support of its CAFE standards (see
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MY 2017 and Later Regulatory Impact Analysis
NHTSA's docket for the rulemaking, NHTSA-2010-0131). While the two sets of standards
are similar, there are also differences in the analyses that require separate discussion. This is
largely because EPA and NHTSA act under different statutes. EPA's authority comes under
the Clean Air Act, and NHTSA's authority comes under EPCA (Energy Policy and
Conservation Act of 1975) and EISA (Energy Independence and Security Act), and each
statute has somewhat different requirements and flexibilities. As a result, each agency has
followed a unique approach where warranted by these differences. Where each agency has
followed the same approach or rely on the same inputs —e.g., development of technology
costs and effectiveness—the supporting documentation is contained in the joint Technical
Support Document (joint TSD can be found in EPA's docket EPA-HQ-OAR-2010-0799).
Therefore, this RIA should be viewed as a companion document to the Joint TSD and the two
documents together provide the details of EPA's technical analysis in support of its
rulemaking.
This document contains the following;
Chapter 1: Technology Packages, Cost and Effectiveness, The details of the vehicle
technology costs and packages used as inputs to EPA's Optimization Model for Emissions of
Greenhouse gases from Automobiles (OMEGA) are presented. These vehicle packages
represent potential ways of meeting the COi stringency established by this rule and are based
on the technology costs and effectiveness analyses discussed in Chapter 3 of the Joint TSD.
This chapter also contains details on the lumped parameter model, which is a major part of
EPA's determination of the effectiveness of these packages. More detail on the effectiveness
of technologies and the Lumped Parameter model can be found in Chapter 3 of the Joint TSD.
Chapter 2: EPA's Vehicle Simulation Tool, The development and application of the EPA
vehicle simulation tool, called ALPHA (Advanced Light-Duty Powertrain and Hybrid
Analysis), are discussed. This chapter first provides a detailed description of the simulation
tool including overall architecture, systems, and components of the vehicle simulation model.
The chapter also describes applications and results of the vehicle simulation runs for
estimating impact of A/C usage on fuel consumption and calculating off-cycle credits
particularly for active aerodynamic technologies. For the result of the A/C study, the impact
of A/C usage was estimated at for cars and trucks separately using the ALPHA tool. The
result corresponds to an impact of approximately 14.0 CO2 g/mile for the (2012) fleet, which
is comparable to the 2012-2016 final rule result. For the off-cycle credits, EPA based its
analysis on manufacturer data as well as the ALPHA tool, where active grill shutters (one of
the active aerodynamic technologies considered) provide a reduction of 0-5% in aerodynamic
drag (Cd) when deployed. EPA expects that most other active aerodynamic technologies will
provide a reduction of drag in the same range as active grill shutters. Based on this analysis,
EPA will provide a credit for active aerodynamic technologies that can demonstrate a
reduction in aerodynamic drag of 3% or more.
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Executive Summary
ChapterS: Results of Final and Alternative Standards, This chapter provides the
methodology for and results of the technical assessment of the future vehicle scenarios
presented in this final rulemaking. As in the analysis of the MY 2012-2016 rulemaking,
evaluating these scenarios included identifying potentially available technologies and
assessing their effectiveness, cost, and impact on relevant aspects of vehicle performance and
utility. The wide number of technologies which are available and likely to be used in
combination required a method to account for their combined cost and effectiveness, as well
as estimates of their availability to be applied to vehicles. These topics are discussed.
Chapter 4: Projected Impacts on Emissions, Fuel Consumption, and Safety, This
chapter documents EPA's analysis of the emission, fuel consumption and safety impacts of
the final emission standards for light duty vehicles. These final standards significantly
decrease the magnitude of greenhouse gas emissions from light duty vehicles. Because of
anticipated changes to driving behavior, fuel production, and electricity generation, a number
of co-pollutants would also be affected by this rule. This analysis quantifies the program's
impacts on the greenhouse gases (GHGs) carbon dioxide (COi), methane (CH/O, nitrous oxide
(NiO) and hydrofluorocarbons (HFC-134a); program impacts on "criteria" air pollutants,
including carbon monoxide (CO), fine particulate matter (PMi.s) and sulfur dioxide (SOx) and
the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOx); and impacts on
several air toxics including benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein.
CC>2 emissions from automobiles are largely the product of fuel combustion, and
consequently, reducing CO2 emissions will also produce a significant reduction in projected
fuel consumption. EPA's projections of these impacts (in terms of gallons saved) are also
shown in this chapter. RIA Chapter 5 presents the monetized fuel savings.
In addition to the intended effects of reducing CC>2 emission, the agencies also
consider the potential of the standards to affect vehicle safety. This topic is introduced in
Preamble Section II.G. EPA's analysis of the change in fatalities due to projected usage of
mass reduction technology is shown in this chapter.
Chapter 5: Vehicle Program Costs Including Fuel Consumption Impacts, This
chapter contains the program costs and fuel savings associated with EPA's final rulemaking.
In Chapter 5, we present briefly some of the outputs of the OMEGA model (costs per vehicle)
and how we use those outputs to estimate the annual program costs which include the addition
of new technology and the potential maintenance associated with that new technology. We
also discuss repair costs and our thoughts on the difficulty associated with estimating repair
costs. In this chapter, we also present the estimated fuel savings associated with the final
standards. We present all of these program costs and the fuel savings for calendar years 2017
through 2050 and for the lifetimes of each of the model years 2017 through 2025 that are the
focus of the final rulemaking. We also present our cost per ton analysis showing the cost
incurred for each ton of GHG reduced by the program.
Also presented in Chapter 5 is our estimated consumer cost of ownership and what we
call our "payback analysis" which looks at how quickly the improved fuel efficiency of new
vehicles provides savings to buyers despite the vehicles having new technology (and new
costs). The consumer payback analysis shows that fuel savings will outweigh incremental
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MY 2017 and Later Regulatory Impact Analysis
costs in less than four years for people purchasing new 2025MY vehicles with either cash or
credit. Further, for those purchasing new vehicles with a typical five-year car loan, the fuel
savings will outweigh increased costs in the first month of ownership. We have also looked at
the payback periods for buyers of used vehicles meeting the final standards. For buyers that
purchase a 5 and/or a 10 year old vehicle meeting the final standards, the payback periods
occur in half a year or roughly one year depending on whether the vehicle is purchased with
cash or credit.
Chapter 6: Environmental and Health Impacts, This Chapter provides details on both the
climate impacts associated with changes in atmospheric CC>2 concentrations and the non-GHG
health and environmental impacts associated with criteria pollutants and air toxics.
Based on modeling analysis performed by the EPA, reductions in COi and other GHG
emissions associated with this final 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
final 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
temperature, sea level rise, and ocean pH. See Chapter 4 in this RIA for the estimated net
reductions in global emissions over time by GHG.
There are also health and environmental impacts associated with the non-GHG
emissions projected to change as a result of the final standards. To adequately assess these
impacts, we conducted full-scale photochemical air quality modeling to project changes in
atmospheric concentrations of PM2.5, ozone and air toxics in the year 2030.
Based on the magnitude of the emissions changes predicted to result from the final
vehicle standards (as shown in Chapter 4), we project that our modeling indicates that there
will be very small changes in ambient ozone and PM2.5 concentrations across most of the
country. However, there will be small decreases in ambient concentrations in some areas of
the country and small increases in ambient concentrations in other areas. The nationwide
population- weighted average change for ozone is an increase of 0.001 ppb and the nationwide
population-weighted average change for PMi.5 is a decrease of 0.007
The final rule reduces the net human health risk posed by non-GHG related pollutants.
In monetized terms, the present value of PM- and ozone-related impacts associated with the
Calendar Year analysis equals between $3.1 and $9.2 billion in benefits, depending on the
assumed discount rate (7 percent and 3 percent, respectively). The present value of PM2.5-
related benefits associated with the lifetimes of 2017-2025 model year light-duty vehicles (the
Model Year analysis) ranges between $4.3 and $5.5 billion dollars, depending on the assumed
discount rate (7% and 3%, respectively).
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Executive Summary
Chapter 7: Other Economic and Social Impacts, This Chapter presents a summary
of the total costs, total benefits, and net benefits expected under the final rule as well as an
expanded description of the agency's approach to the monetization of GHG emission
reductions and benefits from less frequent refueling. Table 2 presents a summary of all
economic impacts on an annual basis and as present values in 2012 for the years 2017 through
2050 at both 3% and 7% discount rates. Additional tables in Chapter 7 present the total value
of each category of costs and benefits from this rule over the lifetime of MY 2017-2025
vehicles as well as in select calendar years through 2050. We note that several of the cost and
benefit categories we would typically discuss in an RIA are considered joint economic
assumptions common to EPA and NHTSA and are discussed in more detail in EPA and
NHTSA's Joint TSD Chapter 4. For the reader's reference, Chapter 7 includes a summary
table with a number of the economic values discussed in the Joint TSD, including the value of
improving U.S. energy security by reducing imported oil, discount rates, the magnitude of the
VMT rebound effect, and the value of accidents, noise, and congestion associated with
additional vehicle use due to the rebound effect.
Table 2 Undiscounted Annual Monetized Net Benefits & Net Benefits of the Final
Program Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)
Technology Costs
Fuel Savings
2017
$2,470
$651
2020
$9,190
$7,430
2030
$35,900
$86,400
2040
$41,000
$155,000
2050
$46,500
$212,000
NPV, 3%a
$561,000
$1,600,000
NPV, 7%a
$247,000
$607,000
Total Annual Benefits at each assumed SCC value b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$97
$138
$171
$250
$1,120
$1,590
$1,960
$2,890
$15,300
$21,200
$25,600
$38,500
$28,500
$40,000
$48,400
$74,800
$31,300
$47,200
$58,100
$96,900
$257,000
$395,000
$515,000
$743,000
$118,000
$256,000
$376,000
$604,000
Monetized Net Benefits at each assumed SCC value0
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
-$1,690
-$1,650
-$1,610
-$1,530
-$316
$153
$524
$1,460
$68,000
$73,900
$78,300
$91,200
$146,000
$158,000
$166,000
$192,000
$201,000
$217,000
$228,000
$267,000
$1,290,000
$1,430,000
$1,550,000
$1,780,000
$478,000
$616,000
$736,000
$964,000
Notes:
a 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, 2.5 percent) is used to calculate
net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown
are undiscounted values.
* RIA Chapter 7.1 notes that SCC increases over time. For the years 2017-2050, the SCC estimates range as
follows: for Average SCC at 5%: $6-$16; for Average SCC at 3%: $26-$47; for Average SCC at 2.5%: $41-
$68; and for 95th percentile SCC at 3%: $79-$142. RIA Chapter 7.1 also presents these SCC estimates.
0 Net Benefits equal Fuel Savings minus Technology Costs plus Benefits.
Chapter 8: Vehicle Sales and Employment Impacts, Chapter 8 provides
background on analyses of the impacts of this rule on vehicle sales and employment in the
auto industry and closely related sectors. Employment effects due to the rule depend in part
on the state of the economy when the rule becomes effective. The auto industry (the directly
regulated sector) is expected to require additional labor, due both to increased vehicle
production and increased production of fuel-saving technologies. Effects on other sectors
vary: though the rule is likely to increase employment at dealerships (due to the estimated
increased sales) and parts suppliers, and through consumers' ability to use money not spent on
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fuel for other purposes, employment is expected to be reduced in fuel production and supply
sectors. These analyses provide a fuller picture of the impacts of this rule.
Chapter 9: Small Business Flexibility Analysis, Chapter 9 includes EPA's analysis
of the small business impacts due to EPA's final rulemaking. EPA is exempting domestic and
foreign businesses that meet small business size definitions established by the Small Business
Administration.
Chapter 10: Alternate Analysis Using 2010 MY Baseline, Results Using the 2010
Baseline Fleet. In this chapter, EPA presents an alternate analysis using the 2010 based fleet
as the input to the Omega model.
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MY 2017 and Later Regulatory Impact Analysis
1 Technology Packages, Cost and Effectiveness
1.1 Overview of Technology
The final program is based on the need to obtain significant GHG emissions
reductions from the transportation sector, and the recognition that there are technically
feasible, cost effective technologies to achieve such reductions in the MYs 2017-2025
timeframe at reasonable cost per vehicle and short consumer payback periods, with no
compromise to vehicle utility or safety. As in many prior mobile source rulemakings, the
decision on what standard to set is largely based on the effectiveness of the emissions control
technology, the cost (both per manufacturer and per vehicle) and other impacts of
implementing the technology, and the lead time needed for manufacturers to employ the
control technology. EPA also considers the need for reductions of greenhouse gases, the
degree of reductions achieved by the standards, and the impacts of the standards in terms of
costs, quantified and unquantified benefits, safety, and other impacts. The availability of
technology to achieve reductions and the cost and other aspects of this technology are
therefore a central focus of this rulemaking.
CC>2 is a stable compound produced by the complete combustion of fuel. Vehicles
combust fuel to perform two basic functions: 1) transport the vehicle, its passengers and its
contents, and 2) operate various accessories during the operation of the vehicle such as the air
conditioner. Technology can reduce COi emissions by either making more efficient use of
the energy that is produced through combustion of the fuel or by reducing the energy needed
to perform either of these functions.
This focus on efficiency involves a major change in focus and calls for looking at the
vehicle as an entire system. In addition to fuel delivery, combustion, and aftertreatment
technology, any aspect of the vehicle that affects the need to produce energy must also be
considered. For example, the efficiency of the transmission system, which takes the energy
produced by the engine and transmits it to the wheels, and the resistance of the tires to rolling
both have major impacts on the amount of fuel that is combusted while operating the vehicle.
Braking system drag, the aerodynamic drag of the vehicle, and the efficiency of accessories
(such as the air conditioner) all affect how much fuel is combusted.
This need to focus on the efficient use of energy by the vehicle as a system leads to a
broad focus on a wide variety of technologies that affect almost all the systems in the design
of a vehicle. As discussed below and in detail in Chapter 3 of the joint TSD, there are many
technologies that are currently available which can reduce vehicle energy consumption.
These technologies are already being commercially utilized to a limited degree in the current
light-duty fleet. These technologies include hybrid technologies that use higher efficiency
electric motors as the power source in combination with or instead of internal combustion
engines. While already commercialized, hybrid technology continues to be developed and
offers the potential for even greater efficiency improvements. There are a number of
technologies described in the MYs 2012-2016 rule (TSD and RIA) that are also common to
this rule. While significant penetration of these technologies is expected within the MY 2016
timeframe, some technologies will experience continued improvement and others will be only
partially implemented into the fleet by MY 2016. We describe those technologies for which
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Chapter 1
we expect to see further improvement or a second level of cost and effectiveness—e.g.,
engine friction reduction, improved accessories, lower rolling resistance tires—in Chapter 3
of the joint TSD and generally denote them as "level 2" versions of each technology. The
primary examples of those technologies that we expect to be only partially implemented into
the fleet by MY 2016 would be weight reduction greater than 5-10% and electrification of
powertrains to hybrid, plug-in electric and full electric, which we do not project
manufacturers as needing to utilize to meet their MYs 2012-2016 standards. There are also
other advanced technologies under development (that were not projected to be available to
meet MYs 2012-2016 standards), such as turbocharged engines with increasingly high levels
of boost and lean burn gasoline engines, both of which offer the potential of improved energy
generation through enhancements to the basic combustion process. Finally, there may be
technologies not considered for this rule that, given the long lead time, can be developed and
introduced into the market. These currently unknown technologies (or enhancements of
known technologies) could be more cost effective than those included in this analysis. The
more cost-effective a new technology is, the more likely it is that an auto manufacturer will
implement it.
The large number of possible technologies to consider and the breadth of vehicle
systems that are affected mean that consideration of the manufacturer's design and production
process plays a major role in developing the standards. Vehicle manufacturers typically
develop their many different models by basing them on a limited number of vehicle platforms.
Several different models of vehicles are produced using a common platform, allowing for
efficient use of design and manufacturing resources. The platform typically consists of
common vehicle architecture and structural components. Given the very large investment put
into designing and producing each vehicle model, manufacturers cannot reasonably redesign
any given vehicle every year or even every other year, let alone redesign all of their vehicles
every year or every other year. At the redesign stage, the manufacturer will upgrade or add all
of the technology and make all of the other changes needed so the vehicle model will meet the
manufacturer's plans for the next several years. This includes meeting all of the emissions
and other requirements that would apply during the years before the next major redesign of
the vehicle.
This redesign often involves a package of changes, designed to work together to meet
the various requirements and plans for the model for several model years after the redesign.
This typically involves significant engineering, development, manufacturing, and marketing
resources to create a new product with multiple new features. In order to leverage this
significant upfront investment, manufacturers plan vehicle redesigns with several model years
of production in mind. That said, vehicle models are not completely static between redesigns
as limited changes are often incorporated for each model year. This interim process is called
a refresh of the vehicle and generally does not allow for major technology changes although
more minor ones can be done (e.g., aerodynamic improvements, valve timing improvements).
More major technology upgrades that affect multiple systems of the vehicle (e.g.,
hybridization) thus occur at the vehicle redesign stage and not between redesigns.
Given that the regulatory timeframe of the GHG program is nine years (MY 2017
through MY 2025), and given EPA's belief that full line manufacturers (i.e., those making
small cars through large cars, minivans, small trucks and large trucks) cannot redesign, on
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MY 2017 and Later Regulatory Impact Analysis
average, their entire product line more than twice during that timeframe, we have assumed
two full redesign cycles in the MYs 2017-2025 timeframe This means that the analysis
assumes that each vehicle platform in the US fleet can undergo at least two full redesigns
during the regulatory timeframe.
As discussed below, there are a wide variety of emissions control technologies
involving several different systems in the vehicle that are available for consideration. Many
can involve major changes to the vehicle, such as changes to the engine block and heads, or
redesign of the transmission and its packaging in the vehicle. This calls for tying the
incorporation of the emissions control technology into the periodic redesign process. This
approach would allow manufacturers to develop appropriate packages of technology upgrades
that combine technologies in ways that work together and fit with the overall goals of the
redesign. It also allows the manufacturer to fit the process of upgrading emissions control
technology into its multi-year planning process, and it avoids the large increase in resources
and costs that would occur if technology had to be added outside of the redesign process.
Over the nine model years at issue in this rulemaking, MYs 2017-2025, EPA projects
that almost the entire fleet of light-duty vehicles will have gone through two redesign cycles.
If the technology to control greenhouse gas emissions is efficiently folded into this redesign
process, then by MY 2025 the entire light-duty fleet could be designed to employ upgraded
packages of technology to reduce emissions of COi, and as discussed below, to reduce
emissions of harmful refrigerants from the air conditioner.
In determining the projected technology needed to meet the standards, and the cost of
those technologies, EPA is using an approach that accounts for and builds on this redesign
process. This provides the opportunity for several control technologies to be incorporated
into the vehicle during redesign, achieving significant emissions reductions from the model at
one time. This is in contrast to what would be a much more costly approach of trying to
achieve small increments of reductions over multiple years by adding technology to the
vehicle piece by piece outside of the redesign process.
As described below, the vast majority of technology we project as being utilized to
meet the GHG standards is commercially available and already being used to a limited extent
across the fleet, although far greater penetration of these technologies into the fleet is
projected as a result of both the MYs 2012-2016 rule and this final rule. The vast majority of
the emission reductions associated with this final rule would result from the increased use of
these technologies. EPA also believes the MYs 2017-2025 standards will encourage the
development and limited use of more advanced technologies, such as plug-in hybrid electric
vehicles (PHEVs) and full electric vehicles (EVs), and is structuring the final rule to
encourage these technologies' use.
In section 1.2 below, a summary of technology costs and effectiveness is presented. In
section 1.3, the process of combining technologies into packages is described along with
package costs and effectiveness. Sections 1.4 and 1.5 discuss the lumped parameter approach
which provides background and support for determining technology and package
effectiveness.
1-3
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Chapter 1
1.2 Technology Cost and Effectiveness
EPA collected information on the cost and effectiveness of COi emission reducing
technologies from a wide range of sources. The primary sources of information were the
MYs 2012-2016 FRM, the 2010 Technical Assessment Report (TAR), tear-down analyses
done by FEV and the 2008 and 2010 Ricardo studies. In addition, we have considered
confidential data submitted by vehicle manufacturers, some of which was submitted in
response to NHTSA requests for product plans, along with confidential information shared by
automotive industry component suppliers in meetings with EPA and NHTSA staff. These
confidential data sources were used primarily as a validation of the estimates since EPA
prefers to rely on public data rather than confidential data wherever possible.
Since publication of the MYs 2012-2016 FRM, EPA has continued the work with
FEV that consists of complete system tear-downs to evaluate technologies down to the nuts
and bolts—i.e., a "bill of materials"—to arrive at very detailed estimates of the costs
associated with manufacturing them. Also, cost and effectiveness estimates were adjusted as
a result of further meetings between EPA and NHTSA staffs following publication of the
2010 TAR and into the first half of 2011 where both piece costs and fuel consumption
efficiencies were discussed in detail, and in consideration of public comments received on the
proposal. EPA and NHTSA also met with Department of Energy (DOE) along with scientists
and engineers from a number of national laboratories to discuss vehicle electrification. EPA
also reviewed the published technical literature which addressed the issue of COi emission
control, such as papers published by the Society of Automotive Engineers and the American
Society of Mechanical Engineers.1 The results of these efforts, especially the results of the
FEV tear-down and Ricardo studies were used extensively in this final rule as described in
detail in Chapter 3 of the joint TSD.
For all of the details behind the cost and effectiveness values used in this analysis the
reader is referred to Chapter 3 of the joint TSD. There we present direct manufacturing costs,
indirect costs and total costs for each technology in each MY 2017 through MY 2025. We
also describe the source for each direct manufacturing cost and how those costs change over
time due to learning, and the indirect costs and how they change over time. Note that all costs
presented in the tables that follow are total costs and include both direct manufacturing and
indirect costs.2
For direct manufacturing costs (DMC) related to turbocharging, downsizing, gasoline
direct injection, transmissions, as well as non-battery-related costs on hybrid, plug-in hybrid
and electric vehicles, the agencies have relied on costs derived from teardown studies. For
battery related DMC for HEVs, PHEVs and EVs, the agencies have relied on the BatPaC
model developed by Argonne National Laboratory for the Department of Energy. For mass
reduction DMC, the agencies have relied on several studies as described in detail in the Joint
TSD. For the majority of the other technologies considered in this final rule, the agencies
have relied on the MYs 2012-2016 final rule and sources described there for estimates of
DMC. We have also considered public comments received in response to the proposal of this
rule.
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MY 2017 and Later Regulatory Impact Analysis
For this analysis, indirect costs are estimated by applying indirect cost multipliers
(ICM) to direct cost estimates. ICMs were derived by EPA as a basis for estimating the
impact on indirect costs of individual vehicle technology changes that would result from
regulatory actions. Separate ICMs were derived for low, medium, and high complexity
technologies, thus enabling estimates of indirect costs that reflect the variation in research,
overhead, and other indirect costs that can occur among different technologies. ICMs were
also applied in the MYs 2012-2016 rulemaking. We have also included an estimate of
stranded capital that could result due to introduction of technology on a more rapid pace than
the industry norm. We describe our ICMs and the method by which they are applied to direct
costs and our stranded capital estimates in the Joint TSD Chapter 3.1.2. Stranded capital is
also discussed in this RIA at Chapter 3.5.7 and Chapter 5.1. We have also considered public
comments received in response to the proposal of this rule and responded to those comments
in section III.H of the preamble to the final rule, and in the Response to Comments Document.
Regarding learning effects, we continue to apply learning effects in the same way as
we did in both the MYs 2012-2016 final rule and in the 2010 TAR. However, we have
employed some new terminology in an effort to eliminate some confusion that existed with
our old terminology. This new terminology was described in the heavy-duty GHG final rule
(see 76 FR 57320) and in the proposal to this rule (76 FR 74929). Our previous terminology
suggested we were accounting for two completely different learning effects—one based on
volume production and the other based on time. This was not the case since, in fact, we were
actually relying on just one learning phenomenon, that being the learning-by-doing
phenomenon that results from cumulative production volumes.
As a result, we have considered the impacts of manufacturer learning on the
technology cost estimates by reflecting the phenomenon of volume-based learning curve cost
reductions in our modeling using two algorithms depending on where in the learning cycle
(i.e., on what portion of the learning curve) we consider a technology to be - "steep" portion
of the curve for newer technologies and "flat" portion of the curve for more mature
technologies. The observed phenomenon in the economic literature which supports
manufacturer learning cost reductions are based on reductions in costs as production volumes
increase with the highest absolute cost reduction occurring with the first doubling of
production. The agencies use the terminology "steep" and "flat" portion of the curve to
distinguish among newer technologies and more mature technologies, respectively, and how
learning cost reductions are applied in cost analyses.
Learning impacts have been considered on most but not all of the technologies
expected to be used because some of the expected technologies are already used rather widely
in the industry and, presumably, quantifiable learning impacts have already occurred. We
have applied the steep learning algorithm for only a handful of technologies considered to be
new or emerging technologies such as PHEV and EV batteries which are experiencing heavy
development and, presumably, rapid cost declines in coming years. For most technologies,
we have considered them to be more established and, hence, we have applied the lower flat
learning algorithm. For more discussion of the learning approach and the technologies to
which each type of learning has been applied the reader is directed to Chapter 3.2.3 of the
Joint TSD.
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Chapter 1
Fuel consumption reductions are possible from a variety of technologies whether they
be engine-related (e.g., turbocharging), transmission-related (e.g., six forward gears in place
of four), accessory-related (e.g., electric power steering), or vehicle-related (e.g., lower rolling
resistance tires). Table 1.2-1 through Table 1.2-20 present the costs associated with the
technologies we believe would be the enabling technologies for compliance with the new
standards. Note that many of these technologies are expected to have penetrated the fleet as
much as 85 to 100 percent by the 2016 MY and, as such, would represent reference case
technologies in this final rule. That is, technologies such as lower rolling resistance tires and
level 1 aerodynamic treatments are expected to exceed 85 percent penetration by MY 2016 so
they cannot be added "again" to comply with the MYs 2017-2025 standards. However, we
list all such technologies in the tables that follow for completeness and comparison to earlier
analyses.
One thing that is immediately clear from the cost tables that follow is that we have
updated our costing approach relative to the MYs 2012-2016 FRM and 2010 TAR for some
technologies in an effort to provide better granularity in our estimates. This is easily seen in
Table 1.2-1 and Table 1.2-2 where we list costs for technologies by engine configuration—in-
line or "I" versus "V"—and/or by number of cylinders. In the MYs 2012-2016 final rule, we
showed costs for a small car, large car, large truck, etc. The limitation of that approach was
that different vehicle classes can have many different sized engines. This is exacerbated when
estimating costs for turbocharged and downsized engines. For example, we project that many
vehicles in the large car class which, today, have V8 engines would have highly turbocharged
14 engines under this final rule. As such, we would not want to estimate the large car costs of
engine friction reduction (EFR)—which have always and continue to be based on the number
of cylinders—assuming (incorrectly) that all large cars have V8 engines. With our new
approach, the large cars that remain V8 would carry EFR costs for a V8, one downsized to a
V6 would carry EFR costs for a V6 and one downsized further to an 14 would carry EFR costs
for an 14. Our old approach would have applied the EFR cost for a V8 to each.
Note that Table 1.2-20 and Table 1.2-21 present costs for mass reduction technology
on each of the 19 vehicle types used in OMEGA. We present costs for only a 10% and a 20%
applied weight reduction. We use the term "applied" weight reduction to reflect the amount
of weight reduction technology—or weight reduction cost—applied to the package. We also
use the term "net" weight reduction when determining costs for hybrid, plug-in hybrid, and
full electric vehicles (see Table 1.2-7 through Table 1.2-18). The net weight reduction is the
applied weight reduction less the added weight of the hybrid and/or electric vehicle
technologies. Table 1.2-7 shows costs for P2 hybrids. For the subcompact P2 HEV with an
applied weight reduction of 10%, the net weight reduction is shown as 5%. Therefore, our
cost analysis would add the costs for 10% weight reduction for such a P2 HEV even though
the net weight reduction was only 5%. Likewise, we would add the cost of P2 HEV
technology assuming only a 5% weight reduction since that is the net weight reduction of the
vehicle. Note that the higher the net weight reduction the lower the cost for HEV and/or EV
technologies since smaller batteries and motors can be used as the vehicle gets lighter). How
we determined the necessary battery pack sizes and the resultant net weight impacts is
described in Chapter 3.3.3 of the joint TSD. We note that the approach described there is a
departure from our earlier efforts—in the MYs 2012-2016 FRM and 2010 TAR—where the
weight increase of the electrification components was not fully recognized. Importantly, that
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MY 2017 and Later Regulatory Impact Analysis
had little impact on the analysis used to support the MYs 2012-2016 rule since that rule
projected very low penetration of HEVs and no PHEV or EV penetrations.
All costs continue to be relative to a baseline vehicle powertrain system (unless
otherwise noted) consisting of a multi-point, port fuel injected, naturally aspirated gasoline
engine operating at a stoichiometric air-fuel ratio with fixed valve timing and lift paired with a
4-speed automatic transmission. This configuration was chosen as the baseline vehicle
because it was the predominant technology package sold in the United States in the baseline
model year 2008. Costs are presented in terms of their hardware incremental compliance
cost. This means that they include all potential product development costs associated with
their application on vehicles, not just the cost of their physical parts. A more detailed
description of these and the following estimates of cost and effectiveness of COi reducing
technologies can be found in Chapter 3 of the joint TSD, along with a more detailed
description of the comprehensive technical evaluation underlying the estimates.
Note that the costs presented in the tables that follow make mention of both a 2008
and a 2010 baseline. In the proposal, we used a fleet derived from a 2008 model year
baseline. In evaluating impacts for this final rule, the agencies are using a reference fleet
reflecting both a MY 2008 based market forecast and a MY 2010 based market forecast.
While costs used for both are presented here and detailed in Chapter 3 of the joint TSD, the
results of our analysis based on the MY 2008 based market forecast are presented in Chapters
3 through 9 of this RIA, while the results of our analysis based on the MY 2010 based market
forecast are presented in Chapter 10 of this RIA. The reader is directed to Section II.B of the
preamble and Chapter 1 of the joint TSD for further detail on the two baseline fleets.
Note also that all costs presented in the tables that follow are expressed in 2010 dollars
while the proposal expressed costs in 2009 dollars. We discuss this change and the factors
used to update costs to 2010 dollars in Chapter 3.1.4 of the joint TSD.
We have placed in the docket a compact disk that contains the spreadsheets used to
generate the costs presented here.3
Table 1.2-1 Costs for Engine Technologies for both the 2008 & 2010 Baselines (2010$)
Technology
Conversion to Atkinson
CCP-OHC-I
CCP-OHC-V
CCP-OHV-V
CVVL-OHC-I4
CVVL-OHC-V6
CVVL-OHC-V8
DCP-OHC-I
DCP-OHC-V
DCP-OHV-V
Deac-V6
Deac-V8
DVVL-OHC-I4
DVVL-OHC-V6
2017
$0
$46
$93
$46
$244
$448
$489
$95
$205
$104
$196
$220
$163
$236
2018
$0
$46
$91
$46
$241
$441
$482
$94
$202
$102
$193
$217
$161
$233
2019
$0
$43
$86
$43
$220
$403
$439
$86
$184
$93
$176
$198
$146
$212
2020
$0
$42
$84
$42
$216
$396
$432
$84
$181
$92
$173
$195
$144
$209
2021
$0
$42
$83
$42
$213
$390
$426
$83
$178
$90
$170
$191
$142
$206
2022
$0
$41
$82
$41
$209
$384
$419
$82
$176
$89
$168
$189
$140
$202
2023
$0
$40
$80
$40
$206
$378
$412
$80
$173
$88
$165
$186
$137
$199
2024
$0
$40
$79
$40
$203
$372
$406
$79
$170
$86
$162
$183
$135
$196
2025
$0
$39
$78
$39
$200
$367
$400
$78
$168
$85
$160
$180
$133
$193
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Chapter 1
DVVL-OHC-V8
EFR1-I3
EFR1-I4
EFR1-V6
EFR1-V8
EFR2-I3
EFR2-I4
EFR2-V6
EFR2-V8
EGR-I
EGR-V
LUB
Stoich GDI-I4
Stoich GDI-I4>I3
Stoich GDI-V6
Stoich GDI-V8
V6 OHV to V6 DOHC
V6 SOHC to V6
DOHC
V8 OHV to V8 DOHC
V8 SOHC 3V to V8
DOHC
V8 SOHC to V8
DOHC
VVTI-OHC-I
VVTI-OHC-V
VVTI-OHV-V
$338
$44
$59
$89
$118
$97
$126
$185
$244
$305
$305
$4
$277
$277
$417
$501
$682
$214
$747
$154
$247
$46
$93
$46
$333
$44
$59
$89
$118
$97
$126
$185
$244
$301
$301
$4
$273
$273
$411
$494
$666
$211
$730
$152
$243
$46
$91
$46
$303
$43
$57
$85
$113
$97
$126
$185
$244
$296
$296
$4
$248
$248
$373
$449
$604
$192
$661
$138
$221
$43
$86
$43
$298
$43
$57
$85
$113
$97
$126
$185
$244
$292
$292
$4
$244
$244
$367
$442
$590
$189
$646
$136
$218
$42
$84
$42
$294
$43
$57
$85
$113
$97
$126
$185
$244
$288
$288
$4
$240
$240
$362
$435
$576
$186
$631
$134
$214
$42
$83
$42
$289
$43
$57
$85
$113
$97
$126
$185
$244
$284
$284
$4
$236
$236
$356
$429
$562
$183
$616
$132
$211
$41
$82
$41
$285
$43
$57
$85
$113
$97
$126
$185
$244
$280
$280
$4
$233
$233
$351
$422
$554
$180
$606
$130
$208
$40
$80
$40
$280
$43
$57
$85
$113
$97
$126
$185
$244
$276
$276
$4
$229
$229
$346
$416
$545
$178
$597
$128
$205
$40
$79
$40
$276
$43
$57
$85
$113
$93
$121
$178
$234
$249
$249
$4
$226
$226
$340
$409
$537
$175
$588
$126
$202
$39
$78
$39
CCP=coupled cam phasing; CVVL=continuous variable valve lift; DCP=dual cam phasing; Deac=cylinder deactivation;
DOHC=dual overhead cam; DVVL=discrete variable valve lift; EFRl=engine friction reduction level 1; EFR2=EFR level 2;
EGR=exhaust gas recirculation; GDI=gasoline direct injection; I=inline engine; I3=inline 3 cylinder; I4=inline 4 cylinder;
LUB=low friction lube; OHC=overhead cam; OHV=overhead valve; SOHC=single overhead cam; Stoic=stoichiometric
air/fuel; V=V-configuration engine; V6=V-configuration 6 cylinder; V8=V-configuration 8 cylinder; VVTI=intake variable
valve timing; 3V=3 valves per cylinder.
All costs are incremental to the baseline case.
Table 1.2-2 Costs for Turbocharging & Downsizing for both the 2008 & 2010 Baselines
(2010$)
Technology
14 to 13 wT
14 to 13 wT
14 to 13 wT
14 DOHC to 14
DOHC wT
14 DOHC to 14
DOHC wT
14 DOHC to 14
DOHC wT
V6 DOHC to
14 wT
V6 DOHC to
14 wT
V6 DOHC to
14 wT
V6 SOHC to
BMEP
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
2017
$427
$690
$1,214
$482
$744
$1,269
$248
$510
$1,035
$331
2018
$423
$681
$1,199
$476
$734
$1,251
$250
$508
$1,026
$330
2019
$359
$654
$1,164
$421
$716
$1,226
$157
$452
$962
$251
2020
$356
$647
$1,149
$415
$707
$1,209
$159
$450
$953
$251
2021
$352
$639
$1,134
$410
$697
$1,192
$161
$449
$944
$250
2022
$348
$632
$1,120
$404
$688
$1,176
$163
$447
$935
$249
2023
$344
$624
$1,106
$399
$679
$1,160
$165
$445
$927
$248
2024
$340
$617
$1,092
$393
$670
$1,145
$167
$444
$918
$248
2025
$337
$551
$979
$388
$602
$1,031
$169
$383
$811
$247
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MY 2017 and Later Regulatory Impact Analysis
14 wT
V6 SOHC to
14 wT
V6 SOHC to
14 wT
V6 OHV to 14
DOHC wT
V6 OHV to 14
DOHC wT
V6 OHV to 14
DOHC wT
V8 DOHC to
V6 DOHC wT
V8 DOHC to
V6 DOHC wT
V8 DOHC to
14 DOHC wT
V8 SOHC to
V6 DOHC wT
V8 SOHC to
V6 DOHC wT
V8 SOHC to
14 DOHC wT
V8 SOHC 3V
to V6 DOHC
wT
V8 SOHC 3V
to V6 DOHC
wT
V8 SOHC 3V
to 14 DOHC
wT
V8 OHV to
V6 DOHC wT
V8 OHV to
V6 DOHC wT
V8 OHV to 14
DOHC wT
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
18 bar
24 bar
27 bar
$594
$1,119
$914
$1,177
$1,701
$746
$1,188
$789
$842
$1,284
$910
$806
$1,248
$864
$1,339
$1,781
$1,164
$589
$1,106
$898
$1,156
$1,674
$738
$1,174
$794
$831
$1,267
$910
$796
$1,232
$866
$1,316
$1,752
$1,152
$546
$1,056
$815
$1,110
$1,619
$635
$1,132
$716
$744
$1,241
$846
$703
$1,200
$797
$1,194
$1,691
$1,116
$542
$1,044
$799
$1,090
$1,593
$628
$1,118
$722
$733
$1,224
$845
$693
$1,184
$799
$1,172
$1,663
$1,105
$537
$1,032
$784
$1,072
$1,567
$620
$1,105
$726
$723
$1,207
$845
$684
$1,169
$800
$1,151
$1,636
$1,093
$533
$1,021
$770
$1,053
$1,542
$613
$1,092
$731
$712
$1,191
$844
$675
$1,153
$801
$1,131
$1,609
$1,082
$529
$1,010
$758
$1,038
$1,519
$606
$1,078
$728
$702
$1,175
$838
$666
$1,138
$796
$1,113
$1,586
$1,069
$524
$999
$746
$1,023
$1,498
$599
$1,066
$725
$692
$1,159
$832
$657
$1,124
$791
$1,096
$1,563
$1,056
$461
$890
$735
$949
$1,378
$592
$953
$623
$682
$1,043
$727
$648
$1,010
$688
$1,080
$1,441
$944
DOHC=dual overhead cam; I3=inline 3 cylinder; I4=inline 4 cylinder; OHV=overhead valve; SOHC=single overhead cam;
V6=V-configuration 6 cylinder; V8=V-configuration 8 cylinder; 3V=3 valves per cylinder; wT=with turbo.
All costs are incremental to the baseline case.
Table 1.2-3 Costs for Transmission Technologies for both the 2008 & 2010 Baselines
(2010$)
Technology
ASL
ASL2
5sp AT
6sp AT
6sp DCT-dry
6sp DCT-wet
6spMT
8sp AT
8sp DCT-dry
2017
$33
$34
$104
-$9
-$116
-$82
-$169
$62
-$16
2018
$32
$33
$103
-$9
-$112
-$79
-$165
$61
-$15
2019
$30
$32
$97
-$10
-$131
-$92
-$172
$55
-$15
2020
$30
$32
$95
-$9
-$127
-$89
-$167
$54
-$14
2021
$29
$31
$94
-$9
-$123
-$87
-$163
$54
-$14
2022
$29
$30
$92
-$9
-$119
-$84
-$159
$53
-$13
2023
$28
$30
$91
-$9
-$116
-$82
-$155
$52
-$13
2024
$28
$29
$89
-$8
-$112
-$79
-$151
$51
-$12
2025
$27
$27
$88
-$8
-$109
-$77
-$147
$50
-$15
1-9
-------
Chapter 1
8sp DCT-wet
HEG
TORQ
$47
$251
$30
$47
$245
$29
$46
$239
$27
$45
$233
$27
$45
$227
$27
$44
$222
$26
$44
$218
$26
$43
$215
$25
$39
$202
$25
ASL=aggressive shift logic; ASL2=aggressive shift logic level 2 (shift optimizer); AT=automatic transmission; DCT=dual
clutch transmission; HEG=high efficiency gearbox; MT=manual transmission; sp=speed; TORQ=early torque converter
lockup.
All costs are incremental to the baseline case.
Table 1.2-4 Costs for Electrification & Improvement of Accessories for both the 2008 &
2010 Baselines (2010$)
Technology
EPS/EHPS
IACC
IACC2
Stop-start (12V)
for Small car,
Standard car
Stop-start (12V)
for Large car,
Small MPV,
Large MPV
Stop-start (12V)
for Truck
2017
$109
$89
$143
$401
$454
$498
2018
$108
$88
$141
$392
$444
$487
2019
$101
$82
$133
$354
$402
$441
2020
$100
$81
$131
$346
$392
$430
2021
$98
$80
$128
$338
$383
$420
2022
$96
$78
$126
$330
$374
$410
2023
$95
$77
$124
$322
$366
$401
2024
$93
$76
$122
$315
$357
$392
2025
$92
$75
$120
$308
$349
$383
EPS=electric power steering; EHPS=electro-hydraulic power steering; IACC=improved accessories level 1; IACC2=IACC
level 2; 12V=12 volts.
All costs are incremental to the baseline case.
Table 1.2-5 Costs for Vehicle Technologies for both the 2008 & 2010 Baselines (2010$)
Technology
Aerol
Aero2
LDB
LRRT1
LRRT2
SAX
2017
$49
$213
$74
$7
$73
$98
2018
$48
$210
$74
$7
$73
$96
2019
$45
$205
$71
$6
$60
$91
2020
$45
$202
$71
$6
$60
$89
2021
$44
$199
$71
$6
$50
$88
2022
$43
$196
$71
$6
$49
$86
2023
$42
$193
$71
$6
$48
$85
2024
$42
$190
$71
$6
$47
$83
2025
$41
$176
$71
$6
$44
$82
Aerol=aerodynamic treatments level 1; Aero2=aero level 2; LDB=low drag brakes; LRRTl=lower rolling resistance tires
level 1; LRRT2=LRRT level 2; SAX=secondary axle disconnect.
All costs are incremental to the baseline case.
Table 1.2-6 Costs for Advanced Diesel Technology for both the 2008 & 2010 Baselines
(2010$)
Vehicle Class
Small car
Standard car
Large car
Small MPV
Large MPV
Truck
2017
$2,965
$2,965
$3,631
$2,971
$2,996
$4,154
2018
$2,922
$2,922
$3,578
$2,928
$2,953
$4,094
2019
$2,653
$2,653
$3,249
$2,659
$2,682
$3,718
2020
$2,612
$2,612
$3,200
$2,618
$2,641
$3,661
2021
$2,572
$2,572
$3,151
$2,578
$2,600
$3,605
2022
$2,533
$2,533
$3,103
$2,539
$2,561
$3,550
2023
$2,495
$2,495
$3,056
$2,501
$2,522
$3,496
2024
$2,457
$2,457
$3,010
$2,463
$2,484
$3,443
2025
$2,420
$2,420
$2,964
$2,426
$2,446
$3,392
All costs are incremental to the baseline case.
1-10
-------
MY 2017 and Later Regulatory Impact Analysis
Table 1.2-7 Costs for P2-Hybird Technology for the 2008 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Large MPV
Large MPV
Large MPV
Truck
Truck
Truck
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
5%
10%
15%
5%
10%
15%
5%
10%
15%
5%
10%
15%
5%
10%
15%
6%
11%
16%
2017
$3,484
$3,452
$3,419
$3,847
$3,800
$3,754
$4,481
$4,402
$4,324
$3,705
$3,664
$3,623
$4,229
$4,170
$4,110
$4,575
$4,500
$4,426
2018
$3,431
$3,398
$3,366
$3,788
$3,742
$3,696
$4,412
$4,334
$4,257
$3,648
$3,608
$3,567
$4,164
$4,106
$4,047
$4,504
$4,431
$4,357
2019
$3,025
$2,996
$2,967
$3,339
$3,298
$3,257
$3,889
$3,821
$3,752
$3,218
$3,182
$3,146
$3,670
$3,617
$3,565
$3,982
$3,916
$3,851
2020
$2,975
$2,946
$2,918
$3,284
$3,244
$3,204
$3,825
$3,757
$3,690
$3,165
$3,129
$3,093
$3,609
$3,558
$3,506
$3,916
$3,851
$3,787
2021
$2,926
$2,898
$2,870
$3,230
$3,191
$3,151
$3,762
$3,696
$3,629
$3,113
$3,078
$3,043
$3,550
$3,499
$3,449
$3,851
$3,788
$3,724
2022
$2,878
$2,851
$2,823
$3,177
$3,139
$3,100
$3,701
$3,635
$3,570
$3,062
$3,027
$2,993
$3,492
$3,442
$3,393
$3,788
$3,726
$3,663
2023
$2,832
$2,805
$2,778
$3,126
$3,088
$3,050
$3,641
$3,577
$3,513
$3,012
$2,978
$2,945
$3,436
$3,387
$3,338
$3,726
$3,665
$3,604
2024
$2,786
$2,760
$2,733
$3,076
$3,038
$3,001
$3,583
$3,519
$3,456
$2,964
$2,931
$2,897
$3,381
$3,332
$3,284
$3,666
$3,606
$3,546
2025
$2,591
$2,567
$2,542
$2,861
$2,826
$2,792
$3,332
$3,273
$3,215
$2,755
$2,724
$2,694
$3,145
$3,101
$3,057
$3,399
$3,344
$3,288
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-8 Costs for P2-Hybird Technology for the 2010 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Large MPV
Large MPV
Large MPV
Truck
Truck
Truck
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
5%
10%
15%
5%
10%
15%
5%
10%
15%
5%
10%
15%
5%
10%
15%
6%
11%
16%
2017
$3,505
$3,470
$3,435
$3,888
$3,838
$3,789
$4,567
$4,484
$4,401
$3,765
$3,721
$3,677
$4,261
$4,200
$4,138
$4,615
$4,538
$4,462
2018
$3,451
$3,417
$3,383
$3,828
$3,779
$3,731
$4,497
$4,415
$4,333
$3,707
$3,664
$3,620
$4,196
$4,135
$4,075
$4,543
$4,468
$4,393
2019
$3,043
$3,013
$2,982
$3,375
$3,331
$3,288
$3,963
$3,890
$3,818
$3,269
$3,230
$3,192
$3,696
$3,643
$3,589
$4,016
$3,950
$3,883
2020
$2,993
$2,963
$2,933
$3,319
$3,276
$3,234
$3,897
$3,826
$3,755
$3,215
$3,177
$3,139
$3,635
$3,582
$3,529
$3,950
$3,884
$3,818
2021
$2,943
$2,914
$2,885
$3,264
$3,222
$3,181
$3,833
$3,763
$3,693
$3,162
$3,125
$3,087
$3,576
$3,524
$3,472
$3,884
$3,820
$3,755
2022
$2,895
$2,867
$2,838
$3,211
$3,170
$3,129
$3,771
$3,702
$3,633
$3,111
$3,074
$3,037
$3,517
$3,466
$3,415
$3,821
$3,757
$3,693
2023
$2,849
$2,820
$2,792
$3,159
$3,119
$3,078
$3,710
$3,642
$3,574
$3,060
$3,024
$2,988
$3,461
$3,410
$3,360
$3,758
$3,696
$3,633
2024
$2,803
$2,775
$2,747
$3,108
$3,069
$3,029
$3,650
$3,584
$3,517
$3,011
$2,976
$2,940
$3,405
$3,356
$3,306
$3,698
$3,636
$3,575
2025
$2,606
$2,580
$2,555
$2,891
$2,854
$2,818
$3,396
$3,334
$3,273
$2,799
$2,767
$2,734
$3,169
$3,124
$3,078
$3,428
$3,372
$3,315
WR=weight reduction.
All costs are incremental to the baseline case.
1-11
-------
Chapter 1
Table 1.2-9 Costs for Plug-in Hybrid Technology with 20 Mile EV Range, or PHEV20,
for the 2008 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
3%
8%
13%
3%
8%
13%
2%
7%
12%
3%
8%
13%
2017
$11,041
$10,828
$10,614
$13,148
$12,793
$12,439
$17,521
$17,010
$16,499
$12,394
$12,126
$11,859
2018
$9,938
$9,743
$9,549
$11,860
$11,540
$11,219
$15,878
$15,409
$14,940
$11,159
$10,915
$10,672
2019
$9,282
$9,103
$8,924
$11,048
$10,751
$10,453
$14,710
$14,282
$13,853
$10,418
$10,194
$9,970
2020
$8,392
$8,229
$8,065
$10,009
$9,739
$9,469
$13,383
$12,989
$12,596
$9,423
$9,218
$9,013
2021
$8,345
$8,182
$8,019
$9,950
$9,682
$9,413
$13,298
$12,907
$12,516
$9,369
$9,165
$8,962
2022
$8,298
$8,136
$7,975
$9,892
$9,625
$9,358
$13,214
$12,826
$12,438
$9,316
$9,114
$8,911
2023
$8,252
$8,091
$7,931
$9,835
$9,570
$9,304
$13,132
$12,747
$12,362
$9,264
$9,063
$8,862
2024
$8,207
$8,047
$7,888
$9,779
$9,516
$9,252
$13,052
$12,670
$12,287
$9,213
$9,013
$8,814
2025
$6,804
$6,669
$6,534
$8,145
$7,924
$7,704
$10,971
$10,642
$10,314
$7,644
$7,475
$7,306
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-10 Costs for Plug-in Hybrid Technology with 20 Mile EV Range, or PHEV20,
for the 2010 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
3%
8%
13%
3%
8%
13%
2%
7%
12%
3%
8%
13%
2017
$11,251
$11,031
$10,810
$13,507
$13,138
$12,769
$18,043
$17,506
$16,969
$12,857
$12,516
$12,175
2018
$10,129
$9,929
$9,728
$12,191
$11,857
$11,523
$16,363
$15,870
$15,378
$11,583
$11,276
$10,968
2019
$9,458
$9,273
$9,088
$11,349
$11,039
$10,729
$15,146
$14,696
$14,247
$10,806
$10,520
$10,234
2020
$8,554
$8,385
$8,216
$10,287
$10,006
$9,724
$13,789
$13,375
$12,962
$9,779
$9,520
$9,260
2021
$8,505
$8,337
$8,169
$10,226
$9,946
$9,666
$13,700
$13,290
$12,879
$9,723
$9,465
$9,207
2022
$8,457
$8,290
$8,123
$10,166
$9,887
$9,609
$13,613
$13,206
$12,798
$9,667
$9,411
$9,154
2023
$8,410
$8,244
$8,078
$10,107
$9,830
$9,554
$13,528
$13,123
$12,719
$9,613
$9,358
$9,103
2024
$8,363
$8,199
$8,034
$10,049
$9,774
$9,499
$13,444
$13,043
$12,641
$9,559
$9,306
$9,052
2025
$6,939
$6,799
$6,659
$8,379
$8,148
$7,918
$11,317
$10,972
$10,626
$7,942
$7,731
$7,520
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-11 Costs for Plug-in Hybrid Technology with 40 Mile EV Range, or PHEV40,
for the 2008 Baseline (2010$)
Vehicle Class
Small car
Small car
Standard car
Standard car
Large car
Large car
Small MPV
Small MPV
Applied
WR
15%
20%
15%
20%
15%
20%
15%
20%
Net
WR
2%
7%
3%
8%
1%
6%
3%
8%
2017
$14,158
$13,853
$17,077
$16,632
$23,903
$22,998
$16,263
$15,872
2018
$12,589
$12,317
$15,199
$14,802
$21,308
$20,505
$14,447
$14,099
2019
$11,931
$11,673
$14,388
$14,013
$20,132
$19,369
$13,706
$13,377
2020
$10,669
$10,438
$12,877
$12,540
$18,044
$17,363
$12,246
$11,951
2021
$10,620
$10,391
$12,818
$12,483
$17,958
$17,280
$12,192
$11,898
2022
$10,573
$10,345
$12,760
$12,426
$17,874
$17,199
$12,139
$11,847
2023
$10,527
$10,300
$12,703
$12,371
$17,792
$17,119
$12,087
$11,796
2024
$10,482
$10,256
$12,647
$12,317
$17,711
$17,041
$12,036
$11,747
2025
$8,478
$8,294
$10,250
$9,981
$14,401
$13,861
$9,717
$9,482
WR=weight reduction.
All costs are incremental to the baseline case.
1-12
-------
MY 2017 and Later Regulatory Impact Analysis
Table 1.2-12 Costs for Plug-in Hybrid Technology with 40 Mile EV Range, or PHEV40,
for the 2010 Baseline (2010$)
Vehicle Class
Small car
Small car
Standard car
Standard car
Large car
Large car
Small MPV
Small MPV
Applied
WR
15%
20%
15%
20%
15%
20%
15%
20%
Net
WR
3%
8%
3%
8%
2%
7%
3%
8%
2017
$14,401
$14,076
$17,551
$17,082
$24,466
$23,613
$16,769
$16,358
2018
$12,806
$12,517
$15,628
$15,208
$21,821
$21,061
$14,908
$14,540
2019
$12,135
$11,862
$14,785
$14,390
$20,604
$19,886
$14,131
$13,785
2020
$10,852
$10,607
$13,238
$12,883
$18,476
$17,832
$12,634
$12,323
2021
$10,803
$10,559
$13,177
$12,824
$18,387
$17,746
$12,578
$12,268
2022
$10,755
$10,512
$13,116
$12,765
$18,300
$17,662
$12,522
$12,214
2023
$10,708
$10,467
$13,057
$12,708
$18,215
$17,580
$12,467
$12,161
2024
$10,662
$10,422
$13,000
$12,652
$18,131
$17,499
$12,414
$12,109
2025
$8,626
$8,431
$10,545
$10,261
$14,758
$14,244
$10,038
$9,789
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-13 Costs for Full Electric Vehicle Technology with 75 Mile Range, or EV75,
for the 2008 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
9%
14%
19%
2017
$14,165
$13,771
$13,378
$17,684
$17,101
$16,518
$23,296
$22,333
$21,369
$15,909
$15,453
$14,997
2018
$12,084
$11,732
$11,381
$15,244
$14,723
$14,201
$20,186
$19,320
$18,454
$13,478
$13,068
$12,658
2019
$12,078
$11,729
$11,379
$15,216
$14,697
$14,179
$20,134
$19,275
$18,415
$13,483
$13,077
$12,670
2020
$10,413
$10,097
$9,781
$13,259
$12,791
$12,322
$17,638
$16,858
$16,078
$11,539
$11,170
$10,801
2021
$10,407
$10,093
$9,780
$13,232
$12,767
$12,302
$17,589
$16,815
$16,041
$11,545
$11,178
$10,812
2022
$10,402
$10,090
$9,778
$13,206
$12,744
$12,282
$17,542
$16,774
$16,005
$11,550
$11,186
$10,822
2023
$10,398
$10,088
$9,777
$13,189
$12,729
$12,269
$17,512
$16,747
$15,982
$11,553
$11,191
$10,829
2024
$10,394
$10,085
$9,776
$13,172
$12,714
$12,256
$17,482
$16,720
$15,959
$11,556
$11,196
$10,836
2025
$7,658
$7,421
$7,184
$9,795
$9,443
$9,092
$13,057
$12,471
$11,886
$8,460
$8,183
$7,906
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-14 Costs for Full Electric Vehicle Technology with 75 Mile Range, or EV75,
for the 2010 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
10%
15%
20%
9%
14%
19%
10%
15%
20%
9%
14%
19%
2017
$14,581
$14,203
$13,824
$18,311
$17,700
$17,089
$24,054
$23,052
$22,051
$16,315
$15,834
$15,353
2018
$12,450
$12,110
$11,771
$15,806
$15,259
$14,712
$20,863
$19,963
$19,063
$13,854
$13,421
$12,989
2019
$12,442
$12,105
$11,768
$15,773
$15,230
$14,687
$20,807
$19,913
$19,020
$13,855
$13,426
$12,997
2020
$10,737
$10,430
$10,124
$13,764
$13,273
$12,781
$18,245
$17,435
$16,624
$11,886
$11,496
$11,107
2021
$10,729
$10,425
$10,122
$13,733
$13,245
$12,758
$18,193
$17,388
$16,583
$11,888
$11,501
$11,114
2022
$10,722
$10,421
$10,119
$13,703
$13,219
$12,735
$18,141
$17,343
$16,544
$11,889
$11,505
$11,121
2023
$10,718
$10,417
$10,117
$13,684
$13,202
$12,720
$18,108
$17,313
$16,518
$11,890
$11,508
$11,126
2024
$10,713
$10,414
$10,115
$13,665
$13,185
$12,705
$18,076
$17,284
$16,493
$11,891
$11,511
$11,131
2025
$7,899
$7,669
$7,439
$10,173
$9,805
$9,436
$13,512
$12,904
$12,295
$8,724
$8,431
$8,138
WR=weight reduction.
All costs are incremental to the baseline case.
1-13
-------
Chapter 1
Table 1.2-15 Costs for Full Electric Vehicle Technology with 100 Mile Range, or EV100,
for the 2008 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
4%
9%
14%
4%
9%
14%
5%
10%
15%
3%
8%
13%
2017
$17,352
$16,916
$16,480
$21,247
$20,636
$20,024
$26,749
$25,745
$24,741
$20,028
$19,490
$18,952
2018
$14,815
$14,426
$14,038
$18,304
$17,758
$17,212
$23,167
$22,267
$21,367
$17,005
$16,526
$16,046
2019
$14,806
$14,420
$14,033
$18,271
$17,728
$17,186
$23,109
$22,215
$21,322
$17,007
$16,531
$16,054
2020
$12,774
$12,427
$12,079
$15,911
$15,422
$14,932
$20,235
$19,426
$18,616
$14,589
$14,160
$13,731
2021
$12,766
$12,421
$12,075
$15,880
$15,394
$14,908
$20,181
$19,377
$18,573
$14,591
$14,165
$13,738
2022
$12,758
$12,414
$12,071
$15,850
$15,367
$14,884
$20,128
$19,329
$18,531
$14,593
$14,169
$13,746
2023
$12,752
$12,411
$12,069
$15,831
$15,350
$14,869
$20,093
$19,299
$18,504
$14,594
$14,172
$13,750
2024
$12,747
$12,407
$12,066
$15,812
$15,333
$14,854
$20,060
$19,269
$18,478
$14,596
$14,175
$13,755
2025
$9,398
$9,138
$8,878
$11,750
$11,384
$11,017
$14,977
$14,370
$13,762
$10,707
$10,385
$10,064
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-16 Costs for Full Electric Vehicle Technology with 100 Mile Range, or EV100,
for the 2010 Baseline (2010$)
Vehicle Class
Small car
Small car
Small car
Standard car
Standard car
Standard car
Large car
Large car
Large car
Small MPV
Small MPV
Small MPV
Applied
WR
10%
15%
20%
10%
15%
20%
10%
15%
20%
10%
15%
20%
Net
WR
4%
9%
14%
3%
8%
13%
4%
9%
14%
3%
8%
13%
2017
$17,837
$17,390
$16,943
$21,905
$21,294
$20,684
$27,850
$26,820
$25,790
$20,501
$19,943
$19,385
2018
$15,239
$14,841
$14,443
$18,893
$18,346
$17,800
$24,147
$23,223
$22,300
$17,439
$16,942
$16,444
2019
$15,229
$14,833
$14,437
$18,856
$18,313
$17,770
$24,083
$23,166
$22,249
$17,437
$16,943
$16,448
2020
$13,149
$12,793
$12,436
$16,440
$15,950
$15,459
$21,111
$20,280
$19,449
$14,988
$14,542
$14,096
2021
$13,139
$12,785
$12,431
$16,406
$15,918
$15,431
$21,051
$20,226
$19,401
$14,986
$14,543
$14,100
2022
$13,129
$12,777
$12,426
$16,372
$15,888
$15,404
$20,993
$20,173
$19,354
$14,984
$14,543
$14,103
2023
$13,123
$12,772
$12,422
$16,350
$15,868
$15,386
$20,955
$20,140
$19,324
$14,982
$14,544
$14,106
2024
$13,116
$12,768
$12,419
$16,329
$15,849
$15,369
$20,918
$20,106
$19,295
$14,981
$14,545
$14,108
2025
$9,676
$9,409
$9,143
$12,147
$11,779
$11,411
$15,632
$15,009
$14,385
$11,009
$10,674
$10,340
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-17 Costs for Full Electric Vehicle Technology with 150 Mile Range, or EV150,
for the 2008 Baseline (2010$)
Vehicle Class
Small car
Standard car
Large car
Small MPV
Applied
WR
20%
20%
20%
20%
Net
WR
2%
2%
3%
1%
2017
$23,024
$29,050
$34,259
$28,183
2018
$19,643
$24,946
$29,569
$23,945
2019
$19,633
$24,911
$29,508
$23,946
2020
$16,926
$21,623
$25,747
$20,555
2021
$16,916
$21,591
$25,690
$20,556
2022
$16,907
$21,559
$25,635
$20,557
2023
$16,901
$21,539
$25,599
$20,557
2024
$16,895
$21,519
$25,564
$20,558
2025
$12,448
$15,947
$19,029
$15,090
WR=weight reduction.
All costs are incremental to the baseline case.
1-14
-------
MY 2017 and Later Regulatory Impact Analysis
Table 1.2-18 Costs for Full Electric Vehicle Technology with 150 Mile Range, or EV150,
for the 2010 Baseline (2010$)
Vehicle Class
Small car
Standard car
Large car
Small MPV
Applied
WR
20%
20%
20%
20%
Net
WR
1%
1%
3%
1%
2017
$23,795
$29,822
$35,277
$28,767
2018
$20,314
$25,632
$30,469
$24,474
2019
$20,302
$25,594
$30,403
$24,471
2020
$17,515
$22,236
$26,547
$21,036
2021
$17,504
$22,200
$26,486
$21,033
2022
$17,492
$22,165
$26,426
$21,029
2023
$17,485
$22,142
$26,388
$21,027
2024
$17,478
$22,120
$26,350
$21,025
2025
$12,885
$16,406
$19,626
$15,452
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-19 Costs for EV/PHEV In-home Chargers for both the 2008 & 2010 Baselines
(2010$)
Technology
PHEV20
Charger
PHEV40
Charger
EV Charger
Charger labor
Vehicle
Class
All
Small car
Standard
car
Large car
Small
MPV
All
All
2017
$79
$414
$481
$526
$526
$1,020
2018
$66
$347
$404
$441
$441
$1,020
2019
$66
$347
$404
$441
$441
$1,020
2020
$56
$294
$342
$373
$373
$1,020
2021
$56
$294
$342
$373
$373
$1,020
2022
$56
$294
$342
$373
$373
$1,020
2023
$56
$294
$342
$373
$373
$1,020
2024
$56
$294
$342
$373
$373
$1,020
2025
$41
$216
$251
$274
$274
$1,020
EV=electric vehicle; PHEV=plug-in electric vehicle; PHEV20=PHEV with 20 mile range; PHEV40=PHEV with 40
mile range.
All costs are incremental to the baseline case.
1-15
-------
Chapter 1
Table 1.2-20 Costs for 10% and 20% Weight Reduction for the 19 Vehicle Types3 for the
2008 Baseline (2010$)
Vehicle
Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Base
Weight
2633
3094
3554
3558
3971
3651
3450
4326
4334
4671
5174
5251
3904
4157
4397
5270
4967
4959
5026
Applied
WR
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
2017
$143
$639
$168
$751
$193
$863
$193
$863
$216
$964
$198
$886
$187
$837
$235
$1,050
$235
$1,052
$254
$1,134
$281
$1,255
$285
$1,274
$212
$947
$226
$1,009
$239
$1,067
$286
$1,279
$270
$1,205
$269
$1,203
$273
$1,220
2018
$139
$624
$164
$734
$188
$843
$189
$844
$210
$942
$193
$866
$183
$818
$229
$1,026
$230
$1,028
$248
$1,108
$274
$1,227
$278
$1,245
$207
$926
$220
$986
$233
$1,043
$279
$1,250
$263
$1,178
$263
$1,176
$266
$1,192
2019
$130
$610
$153
$717
$176
$824
$176
$825
$197
$921
$181
$847
$171
$800
$214
$1,003
$215
$1,005
$231
$1,083
$256
$1,200
$260
$1,218
$193
$905
$206
$964
$218
$1,019
$261
$1,222
$246
$1,152
$246
$1,150
$249
$1,165
2020
$127
$597
$149
$701
$172
$806
$172
$807
$192
$900
$176
$828
$167
$782
$209
$981
$209
$983
$226
$1,059
$250
$1,173
$254
$1,190
$189
$885
$201
$943
$212
$997
$255
$1,195
$240
$1,126
$240
$1,124
$243
$1,140
2021
$124
$584
$146
$686
$167
$788
$168
$789
$187
$880
$172
$809
$163
$765
$204
$959
$204
$961
$220
$1,036
$244
$1,147
$247
$1,164
$184
$866
$196
$922
$207
$975
$248
$1,168
$234
$1,101
$234
$1,100
$237
$1,114
2022
$121
$571
$142
$671
$163
$771
$163
$772
$182
$861
$168
$792
$159
$748
$199
$938
$199
$940
$215
$1,013
$238
$1,122
$241
$1,139
$179
$847
$191
$902
$202
$953
$242
$1,143
$228
$1,077
$228
$1,075
$231
$1,090
2023
$119
$563
$140
$661
$161
$760
$161
$760
$179
$849
$165
$780
$156
$737
$195
$924
$196
$926
$211
$998
$234
$1,106
$237
$1,122
$176
$834
$188
$888
$199
$940
$238
$1,126
$224
$1,062
$224
$1,060
$227
$1,074
2024
$117
$555
$138
$652
$158
$749
$158
$749
$176
$836
$162
$769
$153
$727
$192
$911
$193
$913
$208
$984
$230
$1,090
$233
$1,106
$174
$822
$185
$876
$195
$926
$234
$1,110
$221
$1,046
$220
$1,045
$223
$1,059
2025
$115
$503
$135
$591
$155
$679
$156
$680
$174
$758
$160
$697
$151
$659
$189
$826
$189
$828
$204
$892
$226
$988
$230
$1,003
$171
$746
$182
$794
$192
$840
$230
$1,007
$217
$949
$217
$947
$220
$960
a See section 1.3 for details on the 19 vehicle types—what they are and how they are used.
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-21 Costs for 10% and 20% Weight Reduction for the 19 Vehicle Types3 for the
2010 Baseline (2010$)
Vehicle
Type
1
Base
Weight
2753
Applied
WR
10%
2017
$149
2018
$146
2019
$136
2020
$133
2021
$130
2022
$126
2023
$124
2024
$122
2025
$120
1-16
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MY 2017 and Later Regulatory Impact Analysis
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
3204
3651
3608
4144
3842
3517
4316
4352
4355
5381
5716
3667
4151
4591
5382
5025
5252
5224
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
10%
20%
$668
$174
$778
$198
$886
$196
$876
$225
$1,006
$209
$932
$191
$853
$234
$1,047
$236
$1,056
$237
$1,057
$292
$1,306
$310
$1,387
$199
$890
$225
$1,007
$249
$1,114
$292
$1,306
$273
$1,219
$285
$1,274
$284
$1,268
$653
$170
$760
$193
$866
$191
$856
$220
$983
$204
$911
$186
$834
$229
$1,024
$231
$1,032
$231
$1,033
$285
$1,276
$303
$1,356
$194
$870
$220
$984
$243
$1,089
$285
$1,277
$266
$1,192
$278
$1,246
$277
$1,239
$638
$159
$743
$181
$847
$179
$837
$205
$961
$190
$891
$174
$815
$214
$1,001
$216
$1,009
$216
$1,010
$267
$1,248
$283
$1,325
$182
$850
$206
$962
$228
$1,065
$267
$1,248
$249
$1,165
$260
$1,218
$259
$1,211
$624
$155
$726
$176
$828
$174
$818
$200
$939
$186
$871
$170
$797
$209
$979
$210
$987
$210
$987
$260
$1,220
$276
$1,296
$177
$831
$201
$941
$222
$1,041
$260
$1,220
$243
$1,139
$254
$1,191
$252
$1,184
$610
$151
$710
$172
$810
$170
$800
$195
$919
$181
$852
$166
$780
$203
$957
$205
$965
$205
$965
$254
$1,193
$269
$1,267
$173
$813
$196
$920
$216
$1,018
$254
$1,193
$237
$1,114
$247
$1,164
$246
$1,158
$597
$147
$695
$168
$792
$166
$782
$190
$899
$177
$833
$162
$763
$198
$936
$200
$944
$200
$944
$247
$1,167
$263
$1,240
$168
$795
$191
$900
$211
$996
$247
$1,167
$231
$1,090
$241
$1,139
$240
$1,133
$588
$145
$685
$165
$780
$163
$771
$187
$886
$174
$821
$159
$752
$195
$922
$197
$930
$197
$931
$243
$1,150
$258
$1,222
$166
$784
$188
$887
$207
$981
$243
$1,150
$227
$1,074
$237
$1,122
$236
$1,117
$580
$142
$675
$162
$769
$160
$760
$184
$873
$171
$809
$156
$741
$192
$909
$193
$917
$194
$917
$239
$1,134
$254
$1,204
$163
$772
$184
$874
$204
$967
$239
$1,134
$223
$1,059
$233
$1,106
$232
$1,100
$526
$140
$612
$160
$697
$158
$689
$181
$792
$168
$734
$154
$672
$189
$824
$190
$831
$190
$832
$235
$1,028
$250
$1,092
$160
$700
$181
$793
$201
$877
$235
$1,028
$220
$960
$230
$1,003
$228
$998
a See section 1.3 for details on the 19 vehicle types—what they are and how they are used.
WR=weight reduction.
All costs are incremental to the baseline case.
Table 1.2-22 through Table 1.2-26 summarize the COi reduction estimates of various
technologies which can be applied to cars and light-duty trucks. A more detailed discussion
of effectiveness is provided in Chapter 3 of the joint TSD.
Table 1.2-22 Engine Technology Effectiveness
Technology
Low friction lubricants
Engine friction reduction level 1
Engine friction reduction level 2
Cylinder deactivation (includes imp. oil pump, if
Absolute CO2 Reduction (% from baseline vehicle)
Small Car
0.6
2.0
3.5
n.a.
Large Car
0.8
2.7
4.8
6.5
Minivan
0.7
2.6
4.5
6.0
Small
Truck
0.6
2.0
3.4
4.7
Large
Truck
0.7
2.4
4.2
5.7
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Chapter 1
applicable)
VVT - intake cam phasing
VVT - coupled cam phasing
VVT - dual cam phasing
Discrete VVLT
Continuous VVLT
Stoichiometric Gasoline Direct Injection
Turbo+downsize (incremental to GDI-S) (18-27 bar)*
Cooled Exhaust Gas Recirculation (incremental to 24
bar TRBDS+SGDI)
Advanced diesel engine (T2B2 emissions level)
2.1
4.1
4.1
4.1
5.1
1.5
10.8-16.6
3.6
19.5
2.7
5.5
5.5
5.6
7.0
1.5
13.6-20.6
3.6
22.1
2.5
5.1
5.1
5.2
6.5
1.5
12.9-19.6
3.6
21.5
2.1
4.1
4.1
4.0
5.1
1.5
10.7-16.4
3.5
19.1
2.4
4.9
4.9
4.9
6.1
1.5
12.3-18.8
3.6
21.3
* Note: turbo downsize engine effectiveness does not include effectiveness of valvetrain improvements
Table 1.2-23 Transmission Technology Effectiveness
Technology
5-speed automatic (from 4-speed auto)
Aggressive shift logic 1
Aggressive shift logic 2
Early torque converter lockup
High Efficiency Gearbox
6-speed automatic (from 4-speed auto)
6-speed dry DCT (from 4-speed auto)
Absolute CO2 Reduction (% from baseline vehicle)
Small
Car
1.1
2.0
5.2
0.4
4.8
1.8
6.4
Large
Car
1.6
2.7
7.0
0.4
5.3
2.3
7.6
Minivan
1.4
2.5
6.6
0.4
5.1
2.2
7.2
Small
Truck
1.1
1.9
5.1
0.5
5.4
1.7
7.1
Large
Truck
1.4
2.4
6.2
0.5
4.3
2.1
8.1
Table 1.2-24 Hybrid Technology Effectiveness
Technology
12V Start-Stop
HV Mild Hybrid*
P2 Hybrid drivetrain**
Plug-in hybrid electric vehicle - 20 mile range***
Plug-in hybrid electric vehicle - 40 mile range***
Full electric vehicle (EV)
Absolute CO2 Reduction (% from baseline vehicle)
Small
Car
1.8
7.4
15.5
40
63
100
Large
Car
2.4
7.2
15.4
40
63
100
Minivan
2 2
6.9
14.6
40
63
n.a.
Small
Truck
1.8
6.8
13.4
40
63
n.a.
Large
Truck
2 2
8.0
15.7
n.a.
n.a.
n.a.
* Only includes the effectiveness related to the hybridized drivetrain (battery and electric motor) and supported accessories.
** Only includes the effectiveness related to the hybridized drivetrain (battery and electric motor) and supported accessories.
Does not include advanced engine technologies. Will vary based on electric motor size; table values are based on motor sizes
in Ricardo vehicle simulation results (ref Joint TSD, Section 3.3.1)
***Based on utility factors used for 20-mile (40%) and 40-mile (63%) range PHEV
Table 1.2-25 Accessory Technology Effectiveness
Technology
Improved high efficiency alternator & electrification of
accessories (12 volt)
Electric power steering
Improved high efficiency alternator & electrification of
accessories (42 volt)
Absolute CO2 Reduction (% from baseline vehicle)
Small
Car
1.7
1.5
3.3
Large
Car
1.3
1.1
2.5
Minivan
1.2
1.0
2.4
Small
Truck
1.3
1.2
2.6
Large
Truck
1.8
0.8
3.5
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MY 2017 and Later Regulatory Impact Analysis
Table 1.2-26 Other Vehicle Technology Effectiveness
Technology
Aero drag reduction (20% on cars, 10% on trucks)
Low rolling resistance tires (20% on cars, 10% on
trucks)
Low drag brakes
Secondary axle disconnect (unibody only)
Absolute CO2 Reduction (% from baseline vehicle)
Small
Car
4.7
3.9
0.8
1.3
Large
Car
4.7
3.9
0.8
1.3
Minivan
4.7
3.9
0.8
1.3
Small
Truck
4.7
3.9
0.8
1.3
Large
Truck
2.3
1.9
0.8
1.3
1.3 Vehicle Package Cost and Effectiveness
Individual technologies can be used by manufacturers to achieve incremental CO2
reductions. However, EPA believes that manufacturers are more likely to bundle
technologies into "packages" to capture synergistic aspects and reflect progressively larger
COi reductions with additions or changes to any given package. In addition, manufacturers
typically apply new technologies in packages during model redesigns that occur
approximately once every five years, rather than adding new technologies one at a time on an
annual or biennial basis. This way, manufacturers can more efficiently make use of their
redesign resources and more effectively plan for changes necessary to meet future standards.
Therefore, the approach taken by EPA is to group technologies into packages of
increasing cost and effectiveness. Costs for the packages are a sum total of the costs for the
technologies included. Effectiveness is somewhat more complex, as the effectiveness of
individual technologies cannot simply be summed. To quantify the COi (or fuel
consumption) effectiveness, EPA relies on its Lumped Parameter Model, which is described
in greater detail in the following section as well as in Chapter 3 of the joint TSD.
As was done in the MYs 2012-2016 rule and then updated in the 2010 TAR, EPA uses
19 different vehicle types to represent the entire fleet in the OMEGA model. This was the
result of analyzing the existing light duty fleet with respect to vehicle size and powertrain
configurations. All vehicles, including cars and trucks, were first distributed based on their
relative size, starting from compact cars and working upward to large trucks. Next, each
vehicle was evaluated for powertrain, specifically the engine size, 14, V6, and V8, then by
valvetrain configuration (DOHC, SOHC, OHV), and finally by the number of valves per
cylinder.
For the proposal, EPA used the same 19 vehicle types that were used in the 2010
TAR. However, new for this final rule are 19 new vehicle types. These new vehicle types are
conceptually identical to the vehicle types used in the proposal, but we have changed them in
an effort to group cars, MPVs (multi-purpose vehicles which are minivans, sport utility and
cross-over utility vehicles) and trucks into corresponding vehicle types. In the proposal, we
had considerable cross-over of cars mapped into truck vehicle types and vice versa. We also
wanted to better reflect towing versus non-towing in our vehicle types, a consideration that
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Chapter 1
was not really made when we developed the 19 vehicle types used up to this point. As a
result, we now have six car, or auto vehicle types that are non-towing vehicle types, six MPV
vehicle types with five of those being towing vehicle types, and seven truck (really pickup
truck) vehicle types with six of those being towing vehicle types.
EPA believes (at this time) that these 19 vehicle types broadly encompass the diversity
in the fleet as the analysis is appropriate for "average" vehicles. EPA believes that modeling
each and every vehicle in the fleet individually is cumbersome and can even give a false sense
of accuracy in the analysis of a future fleet. Each of these 19 vehicle types is mapped into one
of six vehicle classes: Small car, Standard car, Large car, Small MPV, Large MPV, and
Truck. Note that our six vehicle classes are not meant to correlate one-to-one with consumer-
level vehicle classes. For example, we have many sport utility and cross-over utility vehicles
(SUVs and CUVs) in one of our "Truck" vehicle classes. Similarly, we have some pickup
trucks placed in MPV vehicle classes. We do this to group them with respect to technology
effectiveness and some technology costs. For example, the largest MPVs are in a "Truck"
vehicle class which gives them the truck effectiveness values and truck costs because their
size, weight and use are presumably similar to large pickups. Similarly, we have placed some
smaller pickups in the "Small MPV" vehicle class since their smaller size and general use is
presumably more similar to a small MPV than to a large pickup truck. Importantly, the
vehicle class designation is not what drives credit generation for certain technologies when
applied to certain vehicles. For credits, we apply pickup truck credits to pickup trucks and not
to MPVs regardless of the vehicle class designation we use for costs and effectiveness.8
As such, the six OMEGA vehicle classes serve primarily to determine the
effectiveness levels of new technologies by determining which vehicle class is chosen within
the lumped parameter model (see sections 1.4 and 1.5 below). So, any vehicle models
mapped into a Large MPV vehicle type will get technology-specific effectiveness results for
that vehicle class. The same is true for vehicles mapped into the other vehicle classes.
Similarly, any vehicle models mapped into a Large MPV vehicle type will get technology-
specific cost results for that vehicle class. The same is true for vehicles mapped into the other
vehicle classes. This is true only for applicable technologies, i.e., those costs developed on a
vehicle class basis such as advanced diesel, hybrid and other electrified powertrains (see
Table 1.2-6 through Table 1.2-19 which show costs by vehicle class). Note that most
technology costs are not developed according to vehicle classes but are instead developed
according to engine size, valvetrain configuration, etc. (see Table 1.2-1 through Table 1.2-5
which show costs by specific technology). Lastly, note that these 19 vehicle types span the
range of vehicle footprints which served as the basis for the MYs 2012-2016 GHG standards
and the standards in this final rule. A detailed table showing the 19 vehicle types, their
baseline engines, their descriptions and some example models for each is contained in Table
1.3-1 .
B See Chapter 3 (?) for full details of the credits mentioned here.
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MY 2017 and Later Regulatory Impact Analysis
Table 1.3-1 List of 19 Vehicle Types used to Model the light-duty Fleet
Vehicle
Type#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Base Engine
14 DOHC 4v
14 DOHC 4v
V6 DOHC 4v
V6 SOHC 2v
V8 DOHC 4v
V8 OHV 2v
14 DOHC 4v
V6 DOHC 4v
V6 SOHC 2v
V6 OHV 2v
V8 DOHC 4v
V8 OHV 2v
14 DOHC 4v
V6 DOHC 4v
V6 OHV 2v
V8 DOHC 4v
V8 SOHC 2v
V8 SOHC 3v
V8 OHV 2v
Base
Trans
4sp AT
4sp AT
4sp AT
4sp AT
4sp AT
4sp AT
4sp AT
4sp AT
4spAT
4sp AT
4sp AT
4sp AT
4spAT
4sp AT
4sp AT
4sp AT
4sp AT
4spAT
4sp AT
Vehicle
Class
Small car
Standard car
Standard car
Standard car
Large car
Large car
Small MPV
Large MPV
Large MPV
Large MPV
Truck
Truck
Small MPV
Large MPV
Large MPV
Truck
Truck
Truck
Truck
Description
Subcompact car 14
Compact car 14
Midsize car V6
Midsize car V6
Large car V8
Large car V8
Small MPV 14
Midsize MPV V6
Midsize MPV V6
Midsize MPV V6
Large MPV V8
Large MPV V8
Small truck 14
Full-sized Pickup truck
V6
Full-sized Pickup truck
V6
Full-sized Pickup truck
V8
Full-sized Pickup truck
V8
Full-sized Pickup truck
V8
Full-sized Pickup truck
V8
Example Models
Ford Focus, Chevy
Aveo, Honda Fit
Ford Fusion, Chevy
Cobalt, Honda Civic
Ford Fusion, Chevy
Malibu, Honda Accord
Ford Mustang, Buick
Lacrosse, Chevy
Impala
Ford Crown Vic, Ford
Mustang, Cadillac STS
Chrysler 300, Ford
Mustang, Chevy
Corvette
Ford Escape, Honda
Element, Toyota
RAV4
Ford Edge, Chevy
Equinox, Kia Sorento
Dodge Durango, Jeep
Grand Cherokee, Ford
Explorer
Dodge Caravan, Jeep
Wrangler, Chevy
Equinox
Jeep Grand Cherokee,
Toyota 4Runner, VW
Touareg
Chrylser Aspen, Ford
Expedition, Chevy
Tahoe,
Chevy Colorado,
Nissan Frontier,
Toyota Tacoma
Ford F150, Honda
Ridgeline, Toyota
Tacoma
Dodge Dakota, Ford
Ranger, Chevy
Silverado
Nissan Titan, Toyota
Tundra
Dodge Ram, Ford
F150
Ford F150
Dodge Ram, Chevy
Silverado, GMC Sierra
Towing?
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Note: I4=inline 4 cylinder; V6/8=V-configuration 6/8 cylinder; DOHC=dual overhead cam; SOHC=single overhead cam;
OHV=overhead valve; 4v/3v/2v=4/3/2 valves per cylinder; sp=speed; AT=automatic transmission; MPV=multi-purpose
vehicle.
Note that we refer throughout this discussion of package building to a "baseline"
vehicle or a "baseline" package. This should not be confused with the baseline fleet, which is
the fleet of roughly 16 million 2008MY individual vehicles comprised of over 1,100 vehicle
1-21
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Chapter 1
models as described in Chapter 1 of the joint TSD. In this discussion, when we refer to
"baseline" vehicle we are referring to the "baseline" configuration of the given vehicle type.
So, we have 19 baseline vehicles in the context of building packages. Each of those 19
baseline vehicles is equipped with a port fuel injected engine and a 4 speed automatic
transmission. The valvetrain configuration and the number of cylinders changes for each
vehicle type to cover the diversity in the 2008 baseline fleet as discussed above. When we
apply a package of technologies to an individual vehicle model in the baseline fleet, we must
first determine which package of technologies are already present on the individual vehicle
model. From this information, we can determine the effectiveness and cost of the individual
vehicle model in the baseline fleet relative to the baseline vehicle that defines the vehicle
type. Once we have that, we can determine the incremental increase in effectiveness and cost
for each individual vehicle model in the baseline fleet once it has added the package of
interest. This process is known as the TEB-CEB process, which is short for Technology
Effective Basis - Cost Effective Basis. This process allows us to accurately reflect the level of
technology already in the 2008 baseline fleet as well as the level of technology expected in
the MYs 2017-2025 reference case (i.e., the fleet as it is expected to exist as a result of the
MY 2016 standards in the MYs 2012-2016 final rule, which reference fleet serves as the
starting point for the larger analysis supporting this final rule). But again, the discussion here
is focused solely on building packages. Therefore, while the baseline vehicle that defines the
vehicle type is relevant here, the baseline and reference case fleets of real vehicles are
relevant to the discussion presented later in Chapter 3 of this RIA.
Importantly, the effort in creating the packages attempts to maintain a constant utility
and acceleration performance for each package as compared to the baseline package. As
such, each package is meant to provide equivalent driver-perceived performance to the
baseline package. There are two possible exceptions. The first is the towing capability of
vehicle types which we have designated "non-towing." This requires a brief definition of
what we consider to be a towing vehicle versus a non-towing vehicle. Nearly all vehicles sold
today, with the exception of the smaller subcompact and compact cars, are able to tow up to
1,500 pounds provided the vehicle is equipped with a towing hitch. These vehicles require no
special OEM "towing package" of add-ons which typically include a set of more robust
brakes and some additional transmission cooling. We do not consider such vehicles to be
towing vehicles. We reserve that term for those vehicles capable of towing significantly more
than 1,500 Ibs. For example, a base model Ford Escape can tow 1,500 pounds while the V6
equipped towing version can tow up to 3,500 pounds. The former would not be considered a
true towing vehicle while the latter would. Note that all large trucks and large MPV vehicle
classes are considered towing vehicles in our analysis.
The importance of this distinction can be found in the types of hybrid and plug-in
hybrid technologies we apply to towing versus non-towing vehicle types.c For the towing
vehicle types, we apply a P2 hybrid technology with a turbocharged and downsized gasoline
direct injected engine. These packages are expected to maintain equivalent towing capacity to
c This towing/non towing distinction is not an issue for non-HEVs, EPA maintains whatever towing capability
existed in the baseline when adding/substituting technology.
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MY 2017 and Later Regulatory Impact Analysis
the baseline engine they replace. For the non-towing vehicle types, we apply a P2 hybrid
technology with an Atkinson engine that has not been downsized relative to the baseline
engine. The Atkinson engine, more correctly called the "Atkinson-cycle" engine, is used in
the current Toyota Prius and Ford Escape hybrid. We have maintained the original engine
size (i.e., no downsizing) to maintain utility as best as possible, but EPA acknowledges that
due to its lower power output, an Atkinson cycle engine cannot tow loads as well as a
standard Otto-cycle engine of the same size. However, the presence of the hybrid powertrain
would be expected to maintain towing utility for these vehicle types in all but the most severe
operating extremes. Such extremes would include towing in the Rocky Mountains (i.e, up
very long duration grades) or towing up Pike's Peak (i.e., up a shorter but very steep grade).
Under these extreme towing conditions, the battery on a hybrid powertrain would eventually
cease to provide sufficient supplemental power and the vehicle would be left with the
Atkinson engine doing all the work. A loss in utility would result (note that the loss in utility
should not result in breakdown or safety concerns, but rather loss in top speed and/or
acceleration capability). Importantly, those towing situations involving driving outside
mountainous regions would not be affected.
We do not address towing at the vehicle level. Instead, we deal with towing at the
vehicle type level. In the proposal, as a result of the discretization of our vehicle types, we
believed that some towing vehicle models had been mapped into non-towing vehicle types
while some non-towing vehicle models had been mapped into towing vehicle types. One
prime example was the Ford Escape mentioned above. We had mapped all Escapes into non-
towing vehicle types. This was done because the primary driver behind the vehicle type into
which a vehicle was mapped was the engine technology in the base engine (number of
cylinders, valvetrain configuration, etc.). Towing capacity was not an original driver in the
decision. Because of this, our model outputs in the proposal put Atkinson-HEVs on some
vehicle models that were more properly treated as towing vehicles0, and would put
turbocharged/downsized HEVs on some vehicle models that are more properly treated as non-
towing vehicles. Table 1.3-2 shows some of these vehicle models that were mapped into a
non-towing vehicle type even though they may have been towing vehicles (the right column).
The table also shows some vehicle models that were mapped into a towing vehicle type even
though they may not have been towing vehicles (the left column). The vehicles in the right
column would be expected to experience some loss of towing utility on a long grade for any
that have been converted to Atkinson-HEV although they would not have a lower tow rating.
The vehicles in the left column would be expected, when converted to HEV, to be costlier and
slightly less effective (less CO2 reduction) since they would be converted to
turbocharged/downsized HEVs rather than Atkinson-HEVs. Due to these potential flaws in
the modeling done for our proposal, we stated that we hoped to have better data on towing
capacity for the final rule analysis which could result in creating revised vehicle types to more
properly model towing and non-towing vehicles. As described above, we have indeed created
all new vehicle types and no longer treat any towing vehicles as non-towing and vice-versa.
D The Ford Escape HEV does utilize an Atkinson engine and has a tow rating of 1,500 pounds which is identical
to the base 14 (non-HEV) Ford Escape.
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Chapter 1
Table 1.3-2 Potential Inconsistencies in our Treatment of Towing & Non-towing
Vehicles in our Proposal3
Non-towing vehicles mapped into towing
vehicle types in the proposal but now mapped
into non-towing vehicle types
Towing vehicles mapped into non-towing
vehicle types in the proposal but now
mapped into towing vehicle types
Mercedes-Benz SLR
Ford Mustang
Buick Lacrosse/Lucerne
Chevrolet Impala
Pontiac G6/Grand Prix
Dodge Magnum V8
Ford Escape AWD V6
Jeep Liberty V6
Mercury Mariner AWD V6
Saturn Vue AWD V6
Honda Ridgeline 4WD V6
Hyundai Tuscon 4WD V6
Mazda Tribute AWD V6
Mitsubishi Outlander 4WD V6
Nissan Xterra V6
Subaru Forester AWD V6
Subaru Outback Wagon AWD V6
Suzuki Grand Vitara 4WD V6
Land Rover LR2 V6
Toyota Rav4 4WD V6
All of the vehicles listed here are now in appropriate vehicle types so that the potential inconsistencies no
longer exist.
The second possible exception to our attempt at maintaining utility is the electric
vehicle range. We have built electric vehicle packages with ranges of 75, 100 and 150 miles.
Clearly these vehicles would not provide the same utility as a gasoline vehicle which typically
has a range of over 300 miles. However, from an acceleration performance standpoint, the
utility would be equal if not perhaps better. We believe that buyers of electric vehicles in the
MYs 2017-2025 timeframe will be purchasing the vehicles with a full understanding of the
range limitations and will not attempt to use their EVs for long duration trips. As such, we
believe that the buyers of EVs will experience no loss of expected utility.
To prepare inputs for the OMEGA model, EPA builds "master-sets" of technology
packagesE. The master-set of packages for each vehicle type are meant to reflect both
appropriate groupings of technologies (e.g., we do not apply turbochargers unless an engine
has dual overhead cams, some degree of downsizing, direct injection and dual cam phasing)
and limitations associated with phase-in caps (see joint TSD 3.5). We then filter that list by
E We build a master-set of packages for each model year for which we run OMEGA because phase-in caps
results in different technologies being available and costs change over time resulting in different costs every
year.
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MY 2017 and Later Regulatory Impact Analysis
determining which packages provide the most cost effective groups of technologies within
each vehicle type—those that provide the best trade-off of costs versus CC>2 reduction
improvements. This is done by ranking those groupings based on the Technology Application
Ranking Factor (TARF). The TARF is the factor used by the OMEGA model to rank
packages and determine which are the most cost effective to apply. The TARF is calculated
as the net incremental cost (or savings) of a package per kilogram of COi reduced by the
package relative to the previous package. The net incremental cost is calculated as the
incremental cost of the technology package less the incremental discounted fuel savings of the
package over 5 years. The incremental CCh reduction is calculated as the incremental
COi/mile emission level of the package relative to the prior package multiplied by the lifetime
miles travelled. More detail on the TARF can be found in the OMEGA model supporting
documentation (see EPA-420-B-10-042). We also describe the TARF ranking process in
more detail below. Grouping "reasonable technologies" simply means grouping those
technologies that are complementary (e.g., turbocharging plus downsizing) and not grouping
technologies that are not complementary (e.g., dual cam phasing and coupled cam phasing).
To generate the master-set of packages for each of the vehicle types, EPA has built
packages in a step-wise fashion looking first at "simpler" conventional gasoline and vehicle
technologies, then more advanced gasoline technologies such as turbocharged (with very high
levels of boost) and downsized engines with gasoline direct injection and then hybrid and
other electrified vehicle technologies. This was done by assuming that auto makers would
first concentrate efforts on conventional gasoline engine and transmission technologies paired
with some level of mass reduction to improve CO2 emission performance. Mass reduction
varied from no mass reduction up to 20 percent as the maximum considered in this analysis.F
Once the conventional gasoline engine and transmission technologies have been fully
implemented, we expect that auto makers would apply more complex (and costly)
technologies such as the highly boosted (i.e. 24 bar and 27 bar brake mean effective pressure,
BMEP) gasoline engines and/or converting conventional gasoline engines to advanced diesel
engines in the next redesign cycle. The projected penetrations of these more advanced
technologies are presented in Chapter 3.8 of this RIA.
From there, auto makers needing further technology penetration to meet their
individual standards would most likely move to hybridization. For this analysis, we have
built all of our hybrid packages using the newly emerging P2 technology. This technology
and why we believe it will be the predominant hybrid technology used in the 2017-2025
timeframe is described in Chapter 3 of the joint TSD. As noted above, we have built two
types of P2 hybrid packages for analysis. The first type is for non-towing vehicle types and
uses an Atkinson-cycle engine with no downsizing relative to the baseline engine. The
F Importantly, the mass reduction associated for each of the 19 vehicle types was based on the vehicle-type sales
weighted average curb weight. Although considerations of vehicle safety are an important part of EPA's
consideration in establishing the standards, note that allowable weight reductions giving consideration to safety
is not part of the package building process so we have built packages for the full range of 0-20% weight
reduction considered in this analysis. Weight consideration for safety is handled within OMEGA as described in
Chapter 3 of this RIA.
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Chapter 1
second P2 hybrid type is for towing vehicle types and uses a turbocharged and downsized
engine (rather than an Atkinson-cycle engine) to ensure no loss of towing capacity.0
Lastly, for some vehicle types (i.e., the non-towing vehicle types), we anticipate that
auto makers would move to more advanced electrification in the form of both plug-in hybrid
(PHEV, sometimes referred to as range extended electric vehicles (REEV)) and full battery
electric vehicles (EV).
Importantly, the HEV, PHEV and EV (called collectively P/H/EV) packages take into
consideration the impact of the weight of the electrified components, primarily the battery
packs. Because these battery packs can be quite heavy, if one removes 20 percent of the mass
from a gasoline vehicle but then converts it to an electric vehicle, the resultant net weight
reduction will be less than 20 percent. We discuss this in more below where we provide
additional discussion regarding the P/H/EV packages.
Focusing first on the conventional and more advanced (higher boost, cooled EGR)
gasoline packages, the first step in creating these packages was to consider the following 12
primary categories of conventional gasoline engine technologies. These are:
1. Our "anytime technologies".11 These consist of low friction lubes, engine friction
reduction, aggressive shift logic, early torque converter lock-up (automatic
transmission only), improved accessories, electric power steering (EPS) or
electrohydraulic power steering (EHPS, used for large trucks), aerodynamic
improvements, lower rolling resistance tires, high efficiency gearbox technology
(HEG). Many of these technologies consist of two levels:
low friction lubes with engine friction reduction level 1 and with EFR level 2
(which includes low friction lubes), aggressive shift logic levels 1 & 2,
improved accessories levels 1 & 2, lower rolling resistance tires levels 1 & 2,
aerodynamic treatments levels 1 & 2.
2. Variable valve timing (VVT) consisting of coupled cam phasing (CCP, for OHV
and SOHC engines) and dual cam phasing (DCP, for DOHC engines)
0 While consistent with the proposal, this is a departure from the 2010 TAR where we built several flavors of P2
HEV packages in the same manner for each of the 19 vehicle types. We built P2 HEV packages with downsized
engines, some with turbocharged and downsized engines, some with cooled EGR, etc. We then used the TARE
ranking process (described below) to determine which packages were most cost effective. We also did not, in
the 2010 TAR, consider the weight impacts of the hybrid powertrain, which we have done in this analysis. The
effect of the changes used in this analysis has been to decrease the effectiveness of HEV packages relative to the
TAR and to increase their costs since heavier batteries and motors are now part of the packages.
H Note that the term "anytime technology," is a carryover term from the 2012-2016 rule. At this point, we
continue to use the term, but it has become merely convenient nomenclature to denote very cost effective
technologies that are relatively easy to implement and would likely be implemented very early by auto makers
when considering compliance with COi standards. This is true also of the term "other" technologies. We group
these technologies largely because they are very cost effective so will likely be implemented early in some form
and combination.
1-26
-------
MY 2017 and Later Regulatory Impact Analysis
3. Variable valve lift (VVL) consisting of discrete variable valve lift (DVVL, for
DOHC engines) and cylinder deactivation (Deac, considered for OHV and SOHC
engines)
4. Gasoline direct injection (GDI)
5. Turbocharging and downsizing (TDS, which always includes a conversion to GDI
and DCP) with and without cooled EGR. Note that 27 bar BMEP engines must
include the addition of cooled EGR in our analysis and we have applied no cooled
EGR to 18 bar BMEP engines.
6. Stop-start
7. Secondary axle disconnect (SAX)
8. Conversion to advanced diesel, which includes removal of the gasoline engine and
gasoline fuel system and aftertreatment, and replacement by a diesel engine with
diesel fuel system, a selective catalytic reduction (SCR) system and advanced fuel
and SCR controls.
9. Mass reduction consisting of 0%, 5%, 10%, 15% and 20%.
In this first step, we also considered the 6 primary transmission technologies. These
are:
10. 6 and 8 speed automatic transmissions (6sp AT/8sp AT)
11.6 and 8 speed dual clutch transmissions with wet clutch (6sp wet-DCT/8sp wet-
DCT)
12. 6 and 8 speed dual clutch transmission with dry clutch (6sp dry-DCT/8sp wet-
DCT)
In considering the transmissions, we had to first determine how each transmission
could reasonably be applied. DCTs, especially dry-DCTs, cannot be applied to every vehicle
type due to low end torque demands at launch (another example of how the standards are
developed to preserve all vehicle utility). In addition, dry-DCTs tend to be more efficient
than wet-DCTs, which are more efficient than 6sp ATs primarily due to the elimination of wet
clutches and torque converter in the dry-DCT. Further, each transmission has progressively
lower costs. Therefore, moving from wet-DCT to dry-DCT will result in lower costs and
increased effectiveness. As done in the proposal but unlike the TAR analysis, we have
limited towing vehicle types to use of automatic transmissions (both 6 and 8 speed). Like the
proposal and the TAR, we have added dry-DCTs to vehicle types in baseline 14 engines and
wet-DCTs to vehicle types with baseline V8 engines. This was done to ensure no loss of
launch performance. For the V6 baseline vehicle types, and again as was done in the proposal
and the 2010 TAR, we have added dry versus wet DCTs depending on the baseline weight of
1-27
-------
Chapter 1
the vehicle type. If the vehicle type were below 2,800 pounds curb weight, or removed
enough weight in the package such that the package weight would be below 2,800 pounds, we
added a dry-DCT. Otherwise, we added a wet-DCT. In the end, this allowed change from
wet- to dry-DCT impacted only vehicle types 3 and 4 and only in packages with 25% or 30%
weight reduction applied, neither of which we allowed for this analysis. Therefore, all V6
base engines are equipped with wet-clutch DCTs where appropriate, never dry-clutch.
Table 1.3-3 shows the vehicle types, baseline curb weights and transmissions added in
this analysis. It is important to note that these heavier towing vehicles (including pickup
trucks) have no access to the more effective technologies such as Atkinson engine, dry-DCT
transmission, PHEV, or EV (for the reasons we describe below). Together these result in a
decrease in effectiveness potential for the heavier towing vehicle types compared to the non-
towing vehicle types.
Table 1.3-3 Application of Transmission Technologies in Building OMEGA Packages
Vehicle
Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Vehicle
class
Small car
Standard car
Standard car
Standard car
Large car
Large car
Small MPV
Large MPV
Large MPV
Large MPV
Truck
Truck
Small MPV
Large MPV
Large MPV
Truck
Truck
Truck
Truck
Base
engine
14
14
V6
V6
V8
V8
14
V6
V6
V6
V8
V8
14
V6
V6
V8
V8
V8
V8
Base
weight
2,633
3,094
3,554
3,558
3,971
3,651
3,450
4,326
4,334
4,671
5,174
5,251
3,904
4,157
4,397
5,270
4,967
4,959
5,026
Mass Reduction
0% 5% 10% 15% 20%
6/8 speed dry-DCT
6/8 speed dry-DCT
6/8 speed wet-DCT
6/8 speed wet-DCT
6/8 speed wet-DCT
6/8 speed wet-DCT
6/8 speed dry-DCT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed dry-DCT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed AT
6/8 speed AT
We start building a "master-set" of packages for a given model year by building non-
electrified (i.e., gasoline and diesel) packages for each vehicle type consisting of nearly every
combination of each of the 12 primary engine technologies listed above. The initial package
for each vehicle type represents what we expect a manufacturer will most likely implement as
a first step on all vehicles because the technologies included are so attractive from a cost
effectiveness standpoint. This package consists of first level anytime technologies but no
weight reduction or transmission changes. We then add the other technologies as appropriate,
still with no weight reduction or transmission changes or HEG (we do not consider the
1-28
-------
MY 2017 and Later Regulatory Impact Analysis
addition of HEG without a simultaneous improvement in the transmission itself). We then
add HEG and a transmission improvement. The subsequent packages would iterate on nearly
all possible combinations with the result being numerous packages per vehicle type. Table
1.3-4 shows a subset of packages built for vehicle type 3, a midsized/large car with a 4 valve
DOHC V6 in the baseline. These are packages built for the 2025 MY, so costs shown
represent 2025 MY costs. Shown in this table are packages built with 5% weight reduction
only, and excluded are packages with an 8 speed transmission. So this table represents
roughly one-tenth of the packages built for vehicle type 3. Note that we have placed in the
docket a compact disk containing all of the master-sets of packages used in our final analysis.4
Table 1.3-4 A Subset of 2025 MY Non-HEV/PHEV/EV Packages Built for Vehicle Type
3 (Midsize carDOHC V6, costs in 2010$)a
TP#
3.0000
3.0129
3.0130
3.0131
3.0132
3.0133
3.0134
3.0135
3.0136
3.0137
3.0138
3.0139
3.0140
3.0141
3.0142
3.0143
3.0144
3.0145
3.0146
3.0147
MR
base
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Description
Auto 4VDV6
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL+WR5%+6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL+WR5%+6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI+WR5%+6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI+WR5%+6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +SS
+WR5% +6sp
Trans
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
2025
$0
$733
$950
$822
$1,039
$926
$1,143
$1,015
$1,232
$1,073
$1,290
$1,162
$1,379
$1,266
$1,483
$1,355
$1,572
$1,041
$1,258
$1,129
CO2%
0.0%
26.4%
31.2%
29.8%
34.3%
28.4%
32.8%
31.7%
35.9%
27.5%
32.3%
30.9%
35.3%
29.5%
33.8%
32.7%
36.8%
27.6%
32.3%
30.8%
1-29
-------
Chapter 1
3.0148
3.0149
3.0150
3.0151
3.0152
3.0153
3.0154
3.0155
3.0156
3.0157
3.0158
3.0159
3.0160
3.0161
3.0162
3.0163
3.0164
3.0165
3.0166
3.0167
3.0168
3.0169
3.0170
3.0171
3.0172
3.0173
3.0174
3.0175
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +SS
+WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI+SS+WR5%+6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI+SS+WR5%+6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$1,347
$1,234
$1,451
$1,323
$1,540
$1,381
$1,598
$1,470
$1,687
$1,574
$1,791
$1,663
$1,880
$815
$1,032
$904
$1,121
$1,008
$1,225
$1,097
$1,314
$1,155
$1,372
$1,244
$1,461
$1,348
$1,565
$1,437
35.2%
29.5%
33.8%
32.6%
36.7%
28.7%
33.3%
31.8%
36.2%
30.5%
34.8%
33.6%
37.6%
27.0%
31.8%
30.4%
34.8%
29.0%
33.3%
32.2%
36.4%
28.1%
32.8%
31.4%
35.8%
30.0%
34.3%
33.3%
1-30
-------
MY 2017 and Later Regulatory Impact Analysis
3.0176
3.0177
3.0178
3.0179
3.0180
3.0181
3.0182
3.0183
3.0184
3.0185
3.0186
3.0187
3.0188
3.0189
3.0190
3.0191
3.0192
3.0193
3.0194
3.0195
3.0196
3.0197
3.0198
3.0199
3.0200
3.0201
3.0202
3.0203
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +SS
+SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +SS
+SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+TDS18+WR5%+6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+TDS18+WR5%+6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +TDS18 +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$1,654
$1,123
$1,340
$1,211
$1,428
$1,316
$1,533
$1,405
$1,622
$1,463
$1,680
$1,552
$1,769
$1,656
$1,873
$1,745
$1,962
$1,009
$1,226
$1,070
$1,287
$1,142
$1,359
$1,203
$1,420
$1,317
$1,534
$1,378
37.3%
28.1%
32.8%
31.3%
35.7%
30.0%
34.3%
33.1%
37.1%
29.2%
33.8%
32.3%
36.7%
31.0%
35.3%
34.1%
38.1%
33.9%
37.8%
36.8%
40.4%
34.6%
38.3%
37.4%
41.0%
34.8%
38.6%
37.5%
1-31
-------
Chapter 1
3.0204
3.0205
3.0206
3.0207
3.0208
3.0209
3.0210
3.0211
3.0212
3.0213
3.0214
3.0215
3.0216
3.0217
3.0218
3.0219
3.0220
3.0221
3.0222
3.0223
3.0224
3.0225
3.0226
3.0227
3.0228
3.0229
3.0230
3.0231
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS24 +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$1,595
$1,450
$1,667
$1,511
$1,728
$1,091
$1,308
$1,152
$1,369
$1,224
$1,441
$1,285
$1,502
$1,399
$1,616
$1,460
$1,677
$1,532
$1,749
$1,593
$1,810
$1,223
$1,440
$1,284
$1,501
$1,357
$1,574
$1,417
41.1%
35.4%
39.1%
38.1%
41.6%
34.4%
38.3%
37.2%
40.9%
35.1%
38.8%
37.9%
41.4%
35.3%
39.0%
38.0%
41.6%
35.9%
39.5%
38.6%
42.1%
36.4%
40.0%
39.1%
42.5%
36.6%
40.0%
39.3%
1-32
-------
MY 2017 and Later Regulatory Impact Analysis
3.0232
3.0233
3.0234
3.0235
3.0236
3.0237
3.0238
3.0239
3.0240
3.0241
3.0242
3.0243
3.0244
3.0245
3.0246
3.0247
3.0248
3.0249
3.0250
3.0251
3.0252
3.0253
3.0254
3.0255
3.0256
3.0257
3.0258
3.0259
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+TDS24 +EGR +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$1,634
$1,531
$1,748
$1,592
$1,809
$1,665
$1,882
$1,725
$1,942
$1,305
$1,522
$1,366
$1,583
$1,439
$1,656
$1,499
$1,716
$1,613
$1,830
$1,674
$1,891
$1,747
$1,964
$1,807
$2,024
$1,472
$1,690
$1,533
42.6%
37.2%
40.7%
39.7%
43.1%
37.3%
40.7%
39.9%
43.2%
36.9%
40.4%
39.6%
42.9%
37.0%
40.5%
39.7%
43.0%
37.7%
41.1%
40.2%
43.5%
37.8%
41.2%
40.4%
43.6%
38.7%
42.1%
41.3%
1-33
-------
Chapter 1
3.0260
3.0261
3.0262
3.0263
3.0264
3.0265
3.0266
3.0267
3.0268
3.0269
3.0270
3.0271
3.0272
3.0273
3.0274
3.0275
3.0276
3.0277
3.0278
3.0279
3.0280
3.0281
3.0282
3.0283
3.0284
3.0285
3.0286
3.0287
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$1,750
$1,606
$1,823
$1,666
$1,883
$1,780
$1,997
$1,841
$2,058
$1,914
$2,131
$1,974
$2,191
$1,554
$1,771
$1,615
$1,832
$1,688
$1,905
$1,748
$1,965
$1,862
$2,079
$1,923
$2,140
$1,996
$2,213
$2,056
44.5%
38.8%
42.2%
41.4%
44.6%
39.4%
42.8%
41.9%
45.1%
39.5%
42.8%
42.0%
45.2%
39.2%
42.6%
41.7%
45.0%
39.3%
42.6%
41.9%
45.1%
39.9%
43.2%
42.3%
45.5%
40.0%
43.3%
42.5%
1-34
-------
MY 2017 and Later Regulatory Impact Analysis
3.0288
3.0289
3.0290
3.0291
3.0292
3.0293
3.0294
3.0295
3.0296
3.0297
3.0298
3.0299
3.0300
3.0301
3.0302
3.0303
3.0304
3.0305
3.0306
3.0307
3.0308
3.0309
3.0310
3.0311
3.0312
3.0313
3.0314
3.0315
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+SS +SAX +TDS27 +EGR +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$2,273
$1,901
$2,118
$1,961
$2,178
$2,034
$2,251
$2,095
$2,312
$2,209
$2,426
$2,269
$2,486
$2,342
$2,559
$2,403
$2,620
$1,983
$2,200
$2,043
$2,260
$2,116
$2,333
$2,177
$2,394
$2,291
$2,508
$2,351
45.6%
39.4%
42.7%
41.9%
45.1%
39.4%
42.6%
41.9%
45.0%
40.1%
43.4%
42.5%
45.6%
40.1%
43.2%
42.5%
45.5%
39.9%
43.2%
42.4%
45.5%
39.8%
43.0%
42.4%
45.4%
40.5%
43.8%
42.9%
1-35
-------
Chapter 1
3.0316
3.0317
3.0318
3.0319
3.0320
3.1681
3.1682
3.1683
3.1684
3.1685
3.1686
3.1687
3.1688
3.1689
3.1690
3.1691
3.1692
3.1693
3.1694
3.1695
3.1696
3.1697
3.1698
3.1699
3.1700
3.1701
3.1702
3.1703
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +MHEV +SAX +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$2,568
$2,424
$2,641
$2,485
$2,702
$1,981
$2,153
$2,070
$2,242
$2,175
$2,346
$2,263
$2,435
$2,322
$2,493
$2,411
$2,582
$2,515
$2,687
$2,604
$2,775
$2,063
$2,235
$2,152
$2,324
$2,256
$2,428
$2,345
46.0%
40.5%
43.6%
43.0%
45.9%
34.9%
38.8%
37.9%
41.5%
36.6%
40.4%
39.5%
43.1%
35.9%
39.7%
38.8%
42.4%
37.6%
41.3%
40.4%
43.9%
35.5%
39.3%
38.4%
42.0%
37.2%
40.9%
40.0%
1-36
-------
MY 2017 and Later Regulatory Impact Analysis
3.1704
3.1705
3.1706
3.1707
3.1708
3.1709
3.1710
3.1711
3.1712
3.1713
3.1714
3.1715
3.1716
3.1717
3.1718
3.1719
3.1720
3.1721
3.1722
3.1723
3.1724
3.1725
3.1726
3.1727
3.1728
3.1729
3.1730
3.1731
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG
+DCP +DVVL +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$2,517
$2,404
$2,575
$2,492
$2,664
$2,597
$2,768
$2,686
$2,857
$2,258
$2,429
$2,318
$2,490
$2,391
$2,563
$2,452
$2,623
$2,340
$2,511
$2,400
$2,572
$2,473
$2,645
$2,533
$2,705
$2,472
$2,644
$2,532
43.6%
36.5%
40.2%
39.3%
42.9%
38.1%
41.8%
40.9%
44.4%
41.4%
44.9%
43.9%
47.2%
42.0%
45.5%
44.5%
47.8%
42.0%
45.4%
44.4%
47.7%
42.5%
45.9%
45.0%
48.2%
43.7%
47.0%
46.0%
1-37
-------
Chapter 1
3.1732
3.1733
3.1734
3.1735
3.1736
3.1737
3.1738
3.1739
3.1740
3.1741
3.1742
3.1743
3.1744
3.1745
3.1746
3.1747
3.1748
3.1749
3.1750
3.1751
3.1752
3.1753
3.1754
3.1755
3.1756
3.1757
3.1758
3.1759
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$2,704
$2,605
$2,777
$2,666
$2,837
$2,554
$2,725
$2,614
$2,786
$2,687
$2,859
$2,748
$2,919
$2,721
$2,893
$2,782
$2,953
$2,854
$3,026
$2,915
$3,086
$2,803
$2,975
$2,864
$3,035
$2,936
$3,108
$2,997
49.2%
43.8%
47.1%
46.1%
49.3%
44.1%
47.4%
46.4%
49.6%
44.2%
47.5%
46.6%
49.7%
45.7%
48.9%
47.9%
51.0%
45.8%
49.0%
48.0%
51.1%
46.1%
49.3%
48.3%
51.4%
46.2%
49.4%
48.5%
1-38
-------
MY 2017 and Later Regulatory Impact Analysis
3.1760
3.1761
3.1762
3.1763
3.1764
3.1765
3.1766
3.1767
3.1768
3.1769
3.1770
3.1771
3.1772
3.1773
3.1774
3.1775
3.1776
3.2449
3.2450
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
5%
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DSL-Adv +WR5% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+DSL-Adv +WR5% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$3,168
$3,149
$3,321
$3,210
$3,382
$3,283
$3,454
$3,343
$3,515
$3,231
$3,403
$3,292
$3,464
$3,365
$3,536
$3,425
$3,597
$3,242
$3,459
51.5%
46.3%
49.4%
48.5%
51.5%
46.2%
49.4%
48.5%
51.5%
46.7%
49.9%
48.9%
51.9%
46.7%
49.9%
48.9%
51.9%
39.1%
42.5%
As stated, the packages are meant to maintain utility relative to the baseline vehicle.
Having built nearly 2500 packages for each vehicle type suggests the question "how can EPA
know that each has the same utility as the baseline vehicle for a given vehicle type?" We
believe that this is inherent in the effectiveness values used, given that they are based on the
recent Ricardo work which had maintenance of baseline performance as a constraint in
estimating technology effectiveness values. Maintaining utility is also included in the cost of
the technologies with proper consideration of engine sizing (number of cylinders), motor and
battery sizing, etc. This is discussed in more detail throughout Section 3.2 of the joint TSD.
Therefore, with the possible exception of the towing issue raised above—maintenance of
towing capacity over operating extremes for "non-towing" vehicles—we are confident that
the packages we have built for OMEGA modeling maintain utility relative to the baseline for
the "average" vehicles represented by our 19 vehicle types.
1-39
-------
Chapter 1
The next packages built are the strong HEVs (P2 HEV) and, new for this final rale, the
mild HEVs (MHEV). As done with non-electrified packages, we paired the HEV powertrain
with increasing levels of engine technologies. For non-towing vehicle types we have paired
the hybrid powertrain with an Atkinson engine. With each Atkinson engine, we include dual
cam phasing, discrete variable valve lift and stoichiometric gasoline direct injection. Since
most non-towing vehicle types are DOHC engines in the baseline, these costs were simply
added to the baseline engine to ensure that the Atkinson engine is consistent with those
modeled by Ricardo to ensure that our effectiveness values are consistent. But for those
vehicle types that are SOHC or OHV in the baseline, the package by definition included costs
associated with converting the valvetrain to a DOHC configuration. For towing vehicle types,
we have paired the hybrid powertrain with a turbocharged and downsized engine. By
definition, such engines include both dual cam phasing and stoichiometric gasoline direct
injection. Further, such engines might be 18/24/27 bar BMEP and the 24 bar BMEP engines
may or may not include cooled EGR while the 27 bar BMEP engines must include cooled
EGR as explained in Chapter 3.4.1 of the Joint TSD. As a result, we have built more HEV
packages for towing vehicle types than for non-towing types. Lastly, we built strong HEV
packages with a constant weight reduction across the board in the year of interest. For
example, in building packages for a 2016MY OMEGA ran, we built HEV packages with 10%
weight reduction as this was the maximum weight reduction (i.e., applicable phase-in cap) in
MY 2016 allowed in the analysis. This maximum allowed weight reduction was 15% for the
2021MY and 20% for MY 2025 based on the technology penetration caps set forth and
explained in Chapter 3 of the joint TSD. For MHEVs, we built packages with weight
reduction at 5%, 10% for MY 2016, 5%, 10%, 15% for MY 2021, and 5%, 10%, 15% and
20% for MY 2025. Table 1.3-5 shows the HEV packages built for vehicle type 3 which is a
non-towing vehicle type (the table shows only packages built with 20% weigh reduction and a
6 speed transmission).
Table 1.3-5 A Subset of 2025 MY Strong HEV & Mild HEV Packages Built for Vehicle
Type 3 (Midsize car DOHC V6, costs in 2010$)a
TP#
3.0000
3.1665
3.1666
3.1667
3.1668
3.1669
3.1670
3.1671
3.1672
3.2257
MR
base
20%
20%
20%
20%
20%
20%
20%
20%
20%
Description
Auto 4VDV6
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +HEV +ATKCS +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +HEV +ATKCS +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +HEV +ATKCS +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +HEV +ATKCS +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +HEV +SAX +ATKCS +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +HEV +SAX +ATKCS +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +HEV +SAX +ATKCS +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +HEV +SAX +ATKCS +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+MHEV +WR20% +6sp
Trans
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
2025
$0
$4,698
$4,870
$4,787
$4,959
$4,780
$4,952
$4,869
$5,041
$2,621
CO2%
0.0%
50.0%
53.2%
52.4%
55.3%
50.5%
53.6%
52.8%
55.7%
39.4%
1-40
-------
MY 2017 and Later Regulatory Impact Analysis
3.2258
3.2259
3.2260
3.2261
3.2262
3.2263
3.2264
3.2265
3.2266
3.2267
3.2268
3.2269
3.2270
3.2271
3.2272
3.2273
3.2274
3.2275
3.2276
3.2277
3.2278
3.2279
3.2280
3.2281
3.2282
3.2283
3.2284
3.2285
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +MHEV
+WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +MHEV
+WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +MHEV
+SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +MHEV
+SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +MHEV +SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +WR20% +6sp
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +WR20% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$2,793
$2,710
$2,882
$2,815
$2,986
$2,903
$3,075
$2,962
$3,133
$3,051
$3,222
$3,155
$3,327
$3,244
$3,415
$2,703
$2,875
$2,792
$2,964
$2,897
$3,068
$2,985
$3,157
$3,044
$3,215
$3,133
$3,304
$3,237
43.1%
42.1%
45.6%
41.0%
44.6%
43.7%
47.1%
40.3%
43.9%
43.0%
46.5%
41.9%
45.4%
44.5%
47.9%
39.9%
43.6%
42.6%
46.1%
41.5%
45.1%
44.1%
47.5%
40.8%
44.4%
43.5%
46.9%
42.4%
1-41
-------
Chapter 1
3.2286
3.2287
3.2288
3.2289
3.2290
3.2291
3.2292
3.2293
3.2294
3.2295
3.2296
3.2297
3.2298
3.2299
3.2300
3.2301
3.2302
3.2303
3.2304
3.2305
3.2306
3.2307
3.2308
3.2309
3.2310
3.2311
3.2312
3.2313
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +SAX +WR20% +6sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +SAX +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS18 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR20% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$3,409
$3,326
$3,497
$2,898
$3,069
$2,958
$3,130
$3,031
$3,203
$3,092
$3,263
$2,980
$3,151
$3,040
$3,212
$3,113
$3,285
$3,174
$3,345
$3,112
$3,284
$3,173
$3,344
$3,245
$3,417
$3,306
$3,477
$3,194
45.9%
45.0%
48.3%
45.5%
48.8%
47.8%
50.9%
46.0%
49.3%
48.3%
51.5%
46.0%
49.2%
48.2%
51.4%
46.5%
49.7%
48.8%
51.9%
47.5%
50.7%
49.7%
52.7%
47.6%
50.8%
49.8%
52.9%
48.0%
1-42
-------
MY 2017 and Later Regulatory Impact Analysis
3.2314
3.2315
3.2316
3.2317
3.2318
3.2319
3.2320
3.2321
3.2322
3.2323
3.2324
3.2325
3.2326
3.2327
3.2328
3.2329
3.2330
3.2331
3.2332
3.2333
3.2334
3.2335
3.2336
3.2337
3.2338
3.2339
3.2340
3.2341
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS24 +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR20% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$3,366
$3,255
$3,426
$3,327
$3,499
$3,388
$3,559
$3,361
$3,533
$3,422
$3,593
$3,494
$3,666
$3,555
$3,727
$3,443
$3,615
$3,504
$3,675
$3,576
$3,748
$3,637
$3,809
$3,790
$3,961
$3,850
$4,022
$3,923
51.1%
50.1%
53.1%
48.1%
51.2%
50.3%
53.3%
49.4%
52.5%
51.5%
54.4%
49.5%
52.5%
51.6%
54.5%
49.8%
52.9%
51.9%
54.8%
49.9%
53.0%
52.0%
54.9%
50.0%
53.0%
52.0%
54.9%
49.9%
1-43
-------
Chapter 1
3.2342
3.2343
3.2344
3.2345
3.2346
3.2347
3.2348
3.2349
3.2350
3.2351
3.2352
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI
+MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol +LRRT1 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL
+GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
6sp DCT-
wet
$4,094
$3,983
$4,155
$3,872
$4,043
$3,932
$4,104
$4,005
$4,176
$4,065
$4,237
53.0%
52.0%
54.9%
50.4%
53.4%
52.4%
55.3%
50.4%
53.4%
52.4%
55.3%
The last step was to build the PHEVs (also known as REEVs) and EVs for vehicle
types 1 through 7 and 13. We did not consider the other vehicle types for electrification
beyond HEVs for purposes of the current analysis, either because of their expected towing
demands or because of their high vehicle weight which would make the electrification of the
vehicle prohibitively costly. We have developed two primary types of PHEV packages and
three primary types of EV packages all of which are included in the master-set of packages.
The PHEVs consist of packages with battery packs capable of 20 miles of all electric
operation (REEV20) and packages with battery packs capable of 40 miles of all electric
operation (REEV40). For EVs, we have built packages capable of 75, 100 and 150 miles of
all electric operation, EV75, EV100 and EV150, respectively. These ranges were selected to
represent an increasing selection of ranges (and costs) that consumers would likely require
and that we believe will be available in the 2017-2025 timeframe. For each of these
packages, we have estimated specific battery-pack costs based on the net weight reduction of
the vehicle where the net weight reduction is the difference between the weight reduction
technology applied to the "glider" (i.e., the vehicle less any powertrain elements) and the
weight increase that results from the inclusion of the electrification components (batteries,
motors, etc.). The applied and net weight reductions for HEVs, PHEVs and EVs are
presented in Chapter 3 of the joint TSD, and full system costs for each depending on the net
weight reduction are presented there and are also presented in Table 1.2-7 through Table
1.2-18. We have built all EV and REEV packages with a 20% weight reduction applied (the
net weight reduction would be lower) despite the maximum allowed for a given model year
for two reasons. First, some PHEV and EV packages cannot be built unless a 20% applied
weight reduction is available because the weight of the electrification components is such that
the net weight reduction would be less than zero without the ability to apply a 20% reduction
(i.e., the vehicle would increase in weight). We did not want to build packages with net
1-44
-------
MY 2017 and Later Regulatory Impact Analysis
weight increases and we did not have the ability to properly determine their effectiveness
values even if we wanted to build them. Second, we believe it is reasonable that auto makers
would be more aggressive with respect to weight reduction on PHEVs and EVs (so as to be
able to utilize lower weight, and hence less expensive batteries) and that it is reasonable to
believe that PHEVs and EVs could achieve higher levels of weight reduction in the MY 2016
and 2021 MYs than we have considered likely for other vehicle technologies.1 Table 1.3-6
shows all of the EV and REEV packages built for this final rule.
Table 1.3-6 Full EV and Plug-in HEV (REEV) Packages Built for this Analysis (costs
shown are for the 2025MY in 2010$)
Vehicle
Type
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
TP#
1.2465
1.2466
1.2467
1.2468
1.2469
2.2465
2.2466
2.2467
2.2468
2.2469
3.2465
3.2466
3.2467
3.2468
3.2469
4.2465
4.2466
4.2467
4.2468
4.2469
5.2465
5.2466
5.2467
5.2468
MR
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
Description
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV20 +CCC +WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV40 +CCC +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
Trans
8sp DCT-
dry
8sp DCT-
dry
8sp DCT-
dry
8sp DCT-
dry
8sp DCT-
wet
8sp DCT-
wet
8sp DCT-
wet
8sp DCT-
wet
8sp DCT-
wet
8sp DCT-
wet
2025
$9,327
$11,262
$9,367
$11,061
$14,630
$10,585
$13,072
$11,363
$13,288
$18,218
$11,047
$13,534
$11,451
$13,376
$18,306
$11,223
$13,710
$11,452
$13,377
$18,306
$13,945
$17,726
$14,324
$16,200
CO2%
73.7%
83.3%
100.0%
100.0%
100.0%
74.4%
83.9%
100.0%
100.0%
100.0%
74.3%
83.8%
100.0%
100.0%
100.0%
74.7%
84.0%
100.0%
100.0%
100.0%
73.9%
83.4%
100.0%
100.0%
1 Note, as noted above, the weight reduction of a technology package has no impact on the weight reduction
allowed under our safety analysis, with the exception that it serves as an upper bound . The safety aspect to
weight reduction is not dealt with in the package building process and is instead dealt with in the TEB-CEB
process and OMEGA model itself. This is described in Chapter 3 of this RIA.
1-45
-------
Chapter 1
5
6
6
6
6
6
7
7
7
7
7
13
13
13
13
13
5.2469
6.2465
6.2466
6.2467
6.2468
6.2469
7.2465
7.2466
7.2467
7.2468
7.2469
13.2465
13.2466
13.2467
13.2468
13.2469
[20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
20%
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV20 +CCC +WR20% +8sp
Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV40 +CCC +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
MPVnt 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
MPVnt 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG
+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
SmT 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp
SmT 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP
+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
8sp DCT-
wet
8sp DCT-
wet
8sp DCT-
dry
8sp DCT-
dry
8sp DCT-
dry
8sp DCT-
dry
$21,467
$14,472
$18,253
$14,263
$16,139
$21,406
$10,255
$12,665
$10,245
$12,403
$17,429
$10,342
$12,751
$10,332
$12,490
$17,515
100.0%
74.4%
83.7%
100.0%
100.0%
100.0%
73.0%
82.9%
100.0%
100.0%
100.0%
73.0%
82.9%
100.0%
100.0%
100.0%
This master-set of packages was then ranked by TARF within vehicle type for each of
MY 2016 (using MY 2016 costs and MY 2016 penetration caps), MY 2021 (using MY 2021
costs and MY 2021 penetration caps) and MY 2025 (using MY 2025 costs and MY 2025
penetration caps). This is done by first calculating the TARF of each package relative to the
baseline package within a given vehicle type. The package with the best TARF is selected as
OMEGA package #1 for that vehicle type. The remaining packages for the given vehicle type
are then ranked again by TARF, this time relative to OMEGA package #1. The best package
is selected as OMEGA package #2, etc. We have considered penetration caps in this TARF
ranking process to ensure that the packages chosen by the ranking do not result in exceedance
of the caps. As such, if package #2 contains a technology, for example HEG, but the
penetration cap for HEG is, say 60%, then only 60% of the population of vehicles in the given
vehicle type would be allowed to migrate to package #2 with the remaining 40% left in
package #1. Importantly, the credits available to the package are included in this ranking
process/ Table 1.3-6 presents 2008 baseline data used in the TARF ranking process. Table
1.3-7 presents a ranked-set of packages for vehicle type 3 for the 2025MY.
J We have included credits for aerodynamic treatments level 2, 12V stop-start, mild HEV and strong HEV but
have not included any other off-cycle credits due to uncertainty.
1-46
-------
MY 2017 and Later Regulatory Impact Analysis
Table 1.3-7 Lifetime VMT & Baseline CO2 used for TARF Ranking Process
Vehicle
Type
l
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Description
Subcompact car 14
Compact car 14
Midsize car V6
Midsize car V6
Large car V8
Large car V8
Small MPV 14
Midsize MPV V6
Midsize MPV V6
Midsize MPV V6
Large MPV V8
Large MPV V8
Small truck 14
Full-sized Pickup
truck V6
Full-sized Pickup
truck V6
Full-sized Pickup
truck V8
Full-sized Pickup
truck V8
Full-sized Pickup
truck V8
Full-sized Pickup
truck V8
Base
engine
14 DOHC
4v
14 DOHC
4v
V6 DOHC
4v
V6 SOHC
2v
V8 DOHC
4v
V8OHV
2v
14 DOHC
4v
V6 DOHC
4v
V6 SOHC
2v
V6OHV
2v
V8 DOHC
4v
V8OHV
2v
14 DOHC
4v
V6 DOHC
4v
V6OHV
2v
V8 DOHC
4v
V8 SOHC
2v
V8 SOHC
3v
V8OHV
2v
Car/
Truck
a
c
c
c
c
c
c
c
T
T
T
T
T
T
T
T
T
T
T
T
2016MY
Lifetime
VMT
198,065
211,964
202 1MY
Lifetime
VMT
203,913
218,399
2025MY
Lifetime
VMT
208,775
223,688
Base
CO2
(g/mi)b
239.8
254.3
321.2
332.7
385.9
390.0
296.6
372.3
412.2
372.0
461.4
477.4
330.8
403.1
420.9
477.3
455.5
480.0
437.9
a Designation here matters only for lifetime VMT determination in the package building and ranking process.
b Sales weighted CO2 within vehicle type.
Table 1.3-8 Ranked-set of Packages for the 2025MY for Vehicle Type 3 (midsize car V6
DOHC)
From
Tech
Pkg#
3.0000
3.0000
3.0131
3.0195
To
Tech
Pkg#
3.0000
3.0131
3.0195
3.0196
From
Step
#
0
1
2
To
Step
#
0
1
2
3
Engine
Auto 4VDV6
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol
+LRRT1 +HEG +DCP +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aerol
+LRRT1 +HEG +DCP +GDI +TDS18 +WR5% +6sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS18 +WR5% +6sp
Trans
6sp
DCT-
wet
6sp
DCT-
wet
6sp
DCT-
wet
Weight
Red
base
5%
5%
5%
Cost
$0
$822
$1,070
$1,287
CO2 %
Reduction
0.0%
29.8%
36.8%
40.4%
1-47
-------
Chapter 1
3.0196
3.0388
3.0772
3.0804
3.0772
3.0836
3.1156
3.1220
3.2004
3.1220
3.2036
3.2196
3.2204
3.1604
3.2036
3.1604
3.2228
3.2228
3.2204
3.1612
3.2236
3.2236
3.2396
3.1628
3.0388
3.0772
3.0804
3.0836
3.1156
3.1220
3.2004
3.2036
3.2196
3.1604
3.2228
3.2204
3.2467
3.2036
3.2228
3.1612
3.2236
3.2236
3.2396
3.1628
3.2428
3.2428
3.2468
3.2020
3
4
5
6
5
7
8
9
10
9
11
12
15
13
17
13
14
18
15
19
20
21
22
23
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS18 +WR5% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS18 +WR10% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS24 +WR10% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR10% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS18 +WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS18 +WR10% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS18 +WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS18 +WR15%
+8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +SS +TDS24 +EGR +WR20%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS18 +WR20%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +SS +SAX +TDS24 +EGR
+WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +WR10% +8sp
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
5%
10%
10%
10%
15%
15%
10%
10%
15%
20%
15%
15%
20%
10%
15%
20%
15%
15%
20%
20%
20%
20%
20%
10%
$1,402
$1,519
$1,733
$1,982
$1,745
$2,209
$2,722
$3,185
$2,948
$2,506
$3,412
$3,030
$11,451
$3,185
$3,412
$2,814
$3,494
$3,494
$3,327
$2,896
$3,791
$3,791
$13,376
$2,936
42.3%
43.9%
45.7%
47.7%
45.5%
49.2%
50.2%
53.6%
51.4%
50.7%
54.7%
51.8%
100.0%
53.6%
54.7%
51.2%
55.1%
55.1%
53.0%
51.5%
56.2%
56.2%
100.0%
51.9%
1-48
-------
MY 2017 and Later Regulatory Impact Analysis
3.2020
3.2036
3.2228
3.2236
3.2396
3.1628
3.2400
3.2220
3.2036
3.2228
3.2236
3.1628
3.2400
3.2220
3.2036
3.2228
3.2236
3.1628
3.2428
3.2428
3.2428
3.2428
3.2428
3.2036
3.2228
3.2236
3.2428
3.2400
3.2469
3.2220
3.2036
3.2228
3.2236
3.2428
3.2466
3.2220
3.2036
3.2228
3.2236
3.2428
3.2465
3.1680
3.1680
3.1680
3.1680
3.1680
27
28
29
30
22
23
32
34
35
36
37
23
32
40
41
42
43
23
24
25
31
38
44
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +MHEV +SAX +TDS18
+WR20% +8sp
+IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +ATKCS +REEV40
+WR20% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15%
+8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR15% +8sp
Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +ATKCS +REEV20
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS
+WR20% +8sp
Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2
+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS
+WR20% +8sp
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
8sp
DCT-
wet
10%
15%
15%
20%
20%
20%
15%
10%
15%
15%
20%
20%
15%
10%
15%
15%
20%
20%
20%
20%
20%
20%
20%
$3,185
$3,412
$3,494
$3,791
$3,461
$18,306
$3,245
$3,185
$3,412
$3,494
$3,791
$13,534
$3,245
$3,185
$3,412
$3,494
$3,791
$11,047
$5,156
$5,156
$5,156
$5,156
$5,156
53.6%
54.7%
55.1%
56.2%
53.4%
100.0%
53.4%
53.6%
54.7%
55.1%
56.2%
83.8%
53.4%
53.6%
54.7%
55.1%
56.2%
74.3%
57.3%
57.3%
57.3%
57.3%
57.3%
1-49
-------
Chapter 1
Note that the packages shown in Table 1.3-7 do not always flow from a given package
to the next package listed. For example, step 8 actually comes from step 5 rather than from
step 7. As such, within OMEGA, the incremental cost for step 8 would be the cost for step 8
less the cost for step 5, or $1745-$1519=$227, and the incremental effectiveness improvement
would be 45.5%-43.9%=1.6%. A similar table could be shown for each of the 19 vehicle
types. We have placed in the docket a compact disk containing all of the ranked-sets of
packages used for our analysis.5
The end result of this ranking is a ranked-set of up to 50 OMEGA packages for each
vehicle type that includes the package progression that OMEGA must follow when
determining which package to employ next. The package progression is key because
OMEGA evaluates each package in a one-by-one, or linear progression. The packages must
be ordered correctly so that no single package will prevent the evaluation of the other
packages. For example, if we simply listed packages according to increasing effectiveness,
there could well be a situation where an HEV with higher effectiveness and a better TARF
than a turbocharged and downsized package with a poor TARF could never be chosen
because the turbocharged and downsized package, having a poor TARF, would never get
chosen and would effectively block the HEV from consideration. For that reason, it is
important to first rank by TARF so that the proper package progression can be determined.
These ranked-sets of packages are reformatted and used as Technology Input Files for the
OMEGA model.
1.4 Use of the Lumped Parameter Approach in Determining Package Effectiveness
1.4.1 Background
While estimating the GHG and fuel consumption reduction effectiveness of individual
vehicle technologies can often be confirmed with existing experimental and field data, it is
more challenging to predict the combined effectiveness of multiple technologies for a future
vehicle. In 2002 the National Research Council published "Effectiveness and Impact of
Corporate Average Fuel Economy (CAFE) Standards6." It was one of the first and most
authoritative analyses of potential fuel consumption-reducing technologies available to future
light-duty vehicles, and is still widely referenced to this day. However, it was criticized for
not fully accounting for system interactions ("synergies") between combinations of multiple
engine, transmission and vehicle technologies that could reduce the overall package
effectiveness.
Comments to the 2002 NRC report recommended the use of a more sophisticated
method to account for vehicle technology package synergies - that of detailed, physics-based
vehicle simulation modeling. This method simulates the function of a vehicle by physically
modeling and linking all of the key components in a vehicle (engine, transmission, accessory
drive, road loads, test cycle speed schedule, etc) and requires an intricate knowledge of the
inputs that define those components. If the inputs are well-defined and plausible, it is
generally accepted as the most accurate method for estimating future vehicle fuel efficiency.
1-50
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MY 2017 and Later Regulatory Impact Analysis
In one of the most thorough technical responses to the NRC report, Patton et al7
critiqued the overestimation of potential benefits of NRC's "Path 2" and "Path 3" technology
packages. They presented a vehicle energy balance analysis to highlight the synergies that
arise with the combination of multiple vehicle technologies. The report then demonstrated an
alternative methodology (to vehicle simulation) to estimate these synergies, by means of a
"lumped parameter" approach. This approach served as the basis for EPA's lumped
parameter model. The lumped parameter model was created for the MYs 2012-2016 light
duty vehicle GHG and CAFE standards, and has been improved to reflect updates required for
the final MYs 2017-2025 light duty GHG rule.
1.4.2 Role of the model
It is widely acknowledged that full-scale physics-based vehicle simulation modeling is
the most thorough approach for estimating future benefits of a package of new technologies.
This is especially important for quantifying the efficiency of technologies and groupings (or
packages) of technologies that do not currently exist in the fleet or as prototypes. However,
developing and running detailed vehicle simulations is very time and resource-intensive, and
generally not practical to implement over a large number of vehicle technology packages (in
our case, hundreds). As part of rulemakings EPA analyzes a wide array of potential
technology options rather than attempt to pre-select the "best" solutions. For example, in
analysis for the MYs 2012-2016 Light Duty Vehicle GHG rule8, EPA built over 140 packages
for use in its OMEGA compliance model, which spanned 19 vehicle classes and over 1100
vehicle models; for this rulemaking the number of packages has increased by another order of
magnitude over the previous rule. The lumped parameter approach was chosen as the most
practical surrogate to estimate the package effectiveness (including synergies) of many
technology combinations. However, vehicle simulation modeling was a key part of the
process to ensure that the lumped parameter model was thoroughly validated. An overview of
the vehicle simulation study (conducted by Ricardo, PLC) for this rulemaking is provided in
Section 3.3.1 of the Joint TSD. Additional details can be found in the project report9.
1.4.3 Overview of the lumped parameter model
The basis for EPA's lumped parameter analysis is a first-principles energy balance
that estimates the manner in which the chemical energy of the fuel is converted into various
forms of thermal and mechanical energy on the vehicle. The analysis accounts for the
dissipation of energy into the different categories of energy losses, including each of the
following:
• Second law losses (thermodynamic losses inherent in the combustion of fuel),
• Heat lost from the combustion process to the exhaust and coolant,
• Pumping losses, i.e., work performed by the engine during the intake and exhaust
strokes,
• Friction losses in the engine,
1-51
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Chapter 1
• Transmission losses, associated with friction and other parasitic losses of the
gearbox, torque converter (when applicable) and driveline
• Accessory losses, related directly to the parasitics associated with the engine
accessories,
• Vehicle road load (tire and aerodynamic) losses;
• Inertial losses (energy dissipated as heat in the brakes)
The remaining energy is available to propel the vehicle. It is assumed that the baseline
vehicle has a fixed percentage of fuel lost to each category. Each technology is grouped into
the major types of engine loss categories it reduces. In this way, interactions between
multiple technologies that are applied to the vehicle may be determined. When a technology is
applied, the lumped parameter model estimates its effects by modifying the appropriate loss
categories by a given percentage. Then, each subsequent technology that reduces the losses in
an already improved category has less of a potential impact than it would if applied on its
own.
Using a lumped parameter approach for calculating package effectiveness provides
necessary grounding to physical principles. Due to the mathematical structure of the model, it
naturally limits the maximum effectiveness achievable for a family of similar technologies1^
This can prove useful when computer-simulated packages are compared to a "theoretical
limit" as a plausibility check. Additionally, the reduction of certain energy loss categories
directly impacts the effects on others. For example, as mass is reduced the benefits of brake
energy recovery decreases because there is not as much inertia energy to recapture.
Figure 1.4-1 is an example spreadsheet used by EPA to estimate the package
effectiveness and the synergistic impacts of a technology package for a standard-size car.
K For example, if only 4% of fuel energy is lost (in a baseline engine) to pumping work, leveraging multiple
technologies to theoretically eliminate all pumping losses would yield an aggregate reduction of no more than
15% in fuel consumption.
1-52
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MY 2017 and Later Regulatory Impact Analysis
EPA Staff Deliberative Materials-Do Not Quote or Cite
Vehicle Energy Effects Estimator
Evaluate New
Package
Vehicle Type
Standard car
% of tractive energy
Baseline %
of fuel
Reduction
% of NEW fuel
Rated Power
" 158
0
hp
Rated
' 161
0
Torque
ft-lb
ETW
3625 Ib
SOmphRL
' 11.3 hp
0.0
Gross Indicated Energy
Brake Energy
Road load kWh
2008 Baseline
New
Indicated
Efficiency
Mech
Efficiency
36.0% 59.6%
38.0% 76.5%
Road Loads
Mass
Braking /
Inertia
23%
4.0%
0%
4.0%
0.47
Brake
Efficiency
21.5%
29.0%
Drag
Aero
Load
37%
6.4%
8%
5.9%
0.71
Drivetrain
Efficiency
80.6%
84.9%
Tires
Rolling
Load
40%
6.9%
7%
6.5%
0.77
Cycle
Efficiency
100.0%
100.0%
Gearbox,
T.C.
Trans
Losses
Total Engine Friction
Access
Losses
4.2% 1.3%
22.3% 41.7%
4.4% 0.8%
Fuel
Efficiency
Road
Loads
17.3% 100.0%
24.7% 94.2%
Friction Pumping
Losses Losses
7.9% 5.3%
15.4% 81.2%
7.1% 1.0%
Heat
Lost To
Exhaust &
Coolant
Ind Eff
Losses
Package Notes
12V Stop-Start
Stoich GDI Turbc
Irreversibilities,
etc.
Second
Law
34.0% 30.0%
n/a
32.0% | 30%
2008 Ricardo baseline
Fuel Economy
Fuel Consumption
GHG emissions
Reset LP Model
Current Results
H
30.5%
Technology
Fuel Consumption (GGFVmile)
FC Reduction \s no-techs
FE Improvement (mpgge)
FE Improvement (mpg)
](HGreduction vs 2008 Ricardo baseline
GHG reduction vs no-techs
hide pendent
FC Estimate*
Tractive
1.95
Original friction/brake ratio
Based on PMEP/IMEP »»
(GM study)
Loss Category
11% r 25%
=71.1% mech efficiency
Implementation into estimator
Check
100.0%
OK
32.0
0.031
284
Regressed baseline values
req'd fuel energy 11.95
fuel economy 30.4
fuel consumption 0.033
GHGemissions 299
Current package values
fuel economy 46.03
fuel consumption 0.022
GHGemissions 197 ^
%or LserPicklist
Level Include? (0/1) Devstatus
includes some techs
mpg (combined)
gal/mi
g/mi CO2E
assumes no techs
kWh
mpg (unadj)
gal/mi
g/miC02E
mpg (unadj)
gal/mi
g/miC02E
Vehicle mass reduction
Aero Drag Reduction
Rolling Resistance Reduction
Low Fric Lubes
EF Reduction
4V on 2V Baseline
ICP
DCP
CCP
Deac
DWL
CWL
Turbo/Downsize (gas engines only)
5-spd gearbox
6-spd gearbox
8-spd gearbox
CVT
DCTWet
DCTDry
Early upshift (formerly ASL)
Optimized shift strategy
Agg TC Lockup
High efficiency gearbox (auto)
12V SS (idle off only)
High voltage SS, with kunch (BAS)
Alternator regen on braking
EPS
Electric access ('12V)
Electric access ()iigh V)
High efficiency alternator (70%)
GDI (stoich)
GDI (stoich) w/ cooled EGR
GDI (lean)
Dies el -LNT (2008)
Diesel -SCR (2008)
5-6% per 10%
2.1% per 10%
1.5%
0.5%
3.0%
2.0%
4.0% totalWT
4.0% totalWT
6.0%
4.0%
5.0%
2.5%
5.5%
6.0%
6.7%
10.0%
2.0% '
5.5%
0.5%
2.0%
7.5% '
2.0%
2.0%
1.5%
3.0% '
1.5%
30.0%
35.0%
Hybrid drivetrain (need to select transmission style!)
Secondary axle disconnect
Low drag brakes
Atkinson cycle engine
Advanced Diesel (2020)
1.3%
0.8%
Braking/stopped, inertia, rolling resistance 0%
Aero
Rolling
Friction
Friction
Pumping, friction
Pumping
Pumping
Pumping
Pumping, friction
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Trans, pump ing
Trans
Trans
Pumping
Pumping, IE, friction
Trans
Trans
P.F.trans
B/I, P, F, trans
Access
Access
Access
Access
Access
Ind Eff
Ind. Eff, pumping
Ind Eff, P, F, trans
Ind Eff, P, F, trans
14.4% aero (cars), 9.5% aero (tru< ' 10%
9.5% rolling ' 10%
2% friction
variable % friction 1
20.5% pumping, -2.5% Me
13.5% pumping, +0.2% IE, -3.5% Me
23.5% pumping, +0.2% IE, -2.5% Me
23.5% pumping, +0.2% IE, -2.5% Me
30% pumping, -2.5% Met
27% pumping, -3% Mction 0%
33% pumping, -3% Mction
variable IE ratio, P,F 35%
% pumping
8% pumping, +0.1% IE
15% pumping, 13% trans, +0.5% IE
41% pumping, -5% trans
21 % trans (increment)
25% trans (increment)
10.5% pumping
11% pumping, 11% Met, +0.1% IE
2% trans
variable % Trans 7%
3%pu::::-::.j.~" *:'.,''. n, 2% trans
11% B/I, 3% P, 3% F, 2% trans
10% pumping
access 100%
12% access
42% access
15% access
+ 0.55% IE
+1.9% IE, 41% pumping
+1.3% IE, 41% pumping
see comment
see comment Motor kW
Inertia, trans, ace IE, F, P 0
Trans
Braking/inertia
Ind. Eff, - pumping
Ind Eff, P, F, trans
6% trans
3.5% B/I
+6% IE, -30% pumping
see comment
0
1
1
0
1
0
0
1
0
0
1
0
1
0
0
1
0
0
0
0
1
1
1
1
0
1
1
1
0
1
1
0
0
0
0
0
0
0
0
0
Plug-In
Figure 1.4-1 Sample lumped parameter model spreadsheet
1-53
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Chapter 1
The LP model has been updated from the MYs 2012-2016 final rule to support the
MYs 2017-2025 standards. Changes were made to include new technologies for 2017 and
beyond, improve fidelity for baseline attributes and technologies, and better represent hybrids
based on more comprehensive vehicle simulation modeling. Section 1.5 provides details of
the methodology used to update and refine the model.
1.5 Lumped Parameter Model Methodology
1.5.1 Changes to the LP model for the final rulemaking
The LP model was updated in conjunction with this rulemaking to provide more
flexibility to assess package effectiveness, to incorporate new technologies not previously
analyzed, and to improve the calculation methodology in an effort to increase calibration
accuracy with respect to the supporting vehicle simulation data.
Flexibility was added in several ways. First, the model now provides the user with the
capability of estimating package effectiveness for multiple vehicle classes. Second, several
compound technologies in the MYs 2012-2016 rulemaking version have been
"deconstructed" into separate components so that there is more flexibility in adding different
technology combinations. The most visible example of that is in the new model's treatment
of hybrids. In the last generation LP model, a hybrid vehicle package served as a technology
in and of itself - irrespective of engine type, ancillary technologies or road load reductions.
In the latest version the LP model offers a "hybrid drivetrain" technology which can be
combined with any engine technology and subset of road load reductions (e.g., mass
reduction, rolling resistance and aerodynamic drag reductions) and other technologies. In this
way, there is more resolution and effectiveness distinction between the many combinations of
technologies on hybrids.
The LP model also added new technologies, most stemming from the 2011 Ricardo
simulation project, which included multiple steps of transmission shift logic, more
mechanically efficient transmissions ("gearboxes"), alternator technologies, an Atkinson-
cycle engine for hybrids, highly downsized and turbocharged engines including lean-burn and
cooled EGR options, and stop-start (idle-off without launch assist). The effectiveness of some
of these technologies vary based on additional required user inputs. For example,
turbocharging and downsizing effectiveness is now based on a percentage of displacement
reduction, and hybrid effectiveness is tied to electric motor size.
EPA revisited the calculation methodology of the model with more rigor. Through
more detailed analysis of simulation data, physical trends became more apparent, such as:
• the relationship between mass reduction and rolling resistance - naturally, as
vehicle weight decreases, the normal force on the tires decreases, and should
reduce rolling resistance
• Reduced road loads (with other variables held constant) changed the required
tractive forces and usually resulted in reduced engine efficiency.
1-54
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MY 2017 and Later Regulatory Impact Analysis
• For hybrids, mass reduction was synergistic with the hybrid drivetrain, as there
was less recoverable braking energy with a lighter vehicle.
All of these trends were identified through the analysis of the simulation data and
performance metrics (detailed further in the Joint TSD, Section 3.3.1), and were incorporated
during the development of the model.
1.5.2 Development of the model
The LP model must be flexible in accommodating a wide variety of possible vehicle
and technology package combinations and also must reasonably reflect the physical system
effects of each technology added to a vehicle. Finally, its outputs must be well calibrated to
the existing vehicle simulation results for it to serve as a reliable tool for use in generating
OMEGA model inputs. To properly build the LP model with all of these requirements in
mind, several steps were needed:
• Develop a baseline energy loss distribution for each vehicle class
• Calibrate baseline fuel economy for each vehicle class based on simulation and
vehicle certification data
• Add technologies to the model and identify the significant loss categories that each
applied technology affects, and
• Assign numerical loss category modifiers for each individual technology to
achieve the estimated independent effectiveness
• Calibrate LP technology package effectiveness with simulation results
1.5.3 Baseline loss categories
In 2007, EPA contracted with PQA, who subcontracted Ricardo, LLC to conduct a
vehicle simulation modeling project in support of the MYs 2012-2016 light-duty vehicle
GHG rule. Further simulation work was conducted by Ricardo from 2010-2011 to support
EPA's analysis for the MYs 2017-2025 vehicle GHG rule. In both projects, Ricardo built
versions of its EASY5 and WAVE models to generate overall vehicle package GHG
reduction effectiveness results and corresponding 10-hz output files of the intermediate data.
EPA's detailed analysis of the Ricardo 2008 and 2010 baselineL vehicle simulation output
files for the FTP and HWFE test cycles helped quantify the distribution of fuel energy losses
L The 2008 baseline vehicles are those originally used in the 2008 Ricardo simulation project and represent
actual vehicles in production. The 2010 "baseline" vehicles (from the 2011 Ricardo report) have additional
content including stop-start, improved alternator with regenerative capability, and a six-speed automatic
transmission. For more information reference the Joint TSD, Section 3.3.1.8.
1-55
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Chapter 1
in the baseline LP model. City/highway combined cycle average data were obtained for brake
efficiency, torque converter and driveline efficiencies, accessory losses, and wheel (tractive)
energy. These values were regressed against basic vehicle parameters (power, weight, etc) to
generate curve fits for the baseline vehicle category attributes.
The distribution of energy loss categories in the baseline vehicle were estimated as
follows:
Indicated efficiency was assumed at a combined test cycle average of 36% for all
vehicles
M
Baseline engine brake efficiency was estimated as a function of (ETW, road load,
engine torque, and alternator regeneration or "regen"). These inputs were used in
a linear regression, shown in Figure 1.5-1, which fits the 2008 and 2010 Ricardo
baseline data from the output summaries.
Regression data used
ffl
c
TO
-Q
00
O
0
Cvl
OJ
TO
O
O
Cvl
Vehicle
Camry
Vue
Caravan
300
F-150
Yaris
Camry
Vue
Caravan
300
F-150
- net engine brake efficiency
Power
154
169
205
250
300
106
158
169
205
250
300
Torque
160
161
240
250
365
103
161
161
240
250
365
ETW
3625
4000
4500
4000
6000
2625
3625
4000
4500
4000
6000
SOmph RL Alt regen Net BE%
11.33
15.08
15.84
14.78
22.86
10.82
11.33
15.08
15.84
14.78
22.86
0
0
0
0
0
1
1
1
1
1
1
21.5%
24.0%
21.2%
21.3%
21.8%
25.0%
23.8%
25.8%
23.1%
23.2%
24.0%
predicted
21.5%
23.7%
21.7%
21.0%
21.9%
25.3%
23.5%
25.7%
23.7%
23.0%
23.9%
avg error
% error
0.1%
1.3%
2.3%
1.3%
0.5%
1.3%
1.3%
0.5%
2.3%
0.9%
0.8%
1.1%
Coefficients
Intercept
Torque
ETW
SOmph RL
Alt regen
0.207831
-0.00028
-6.2E-06
0.006531
0.019809
Figure 1.5-1 Regression data used to establish engine brake efficiency formula
• Pumping and friction losses are scaled based on the difference between (brake
efficiency + accessory losses) and indicated efficiency. The distribution of
pumping and friction losses was based on a combination of literature (Patton,
Heywood10 ) and prior success with values used in the LP model for the MYs
2012-2016 rule. It is assumed that pumping and friction losses for fixed valve,
naturally aspirated engines, distributed over the test cycles, average roughly 60%
and 40% of total friction, respectively.
• Accessory loss (as % of total fuel) is based on a regression of engine torque and
ETW, and comes directly from Ricardo output file data.
• Baseline driveline losses are estimated in the following manner:
M Indicated efficiency data was not included as an output in the Ricardo model. Very little data on indicated
efficiency exists in the literature. The value of 36% was assumed because it fits fairly well within the LP model,
and it is comparable to the few values presented in the Patton paper.
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MY 2017 and Later Regulatory Impact Analysis
a) Torque converter efficiency, which is a function of (engine torque/power
ratio, RL and ETW)
b) Transmission efficiency, which is calculated at 87% for 2008 vehicles
(based on the average gear efficiency values used by Ricardo in the
baseline models) For 4WD vehicles a multiplier of 96.2% is applied to
represent the rear axle efficiency
c) Losses through the TC and transmission are then determined and added to
represent driveline losses as the total % of fuel energy lost.
Baseline tractive wheel energy (the energy delivered to the wheels to actually
move the vehicle) is a simple relationship of ETW and road load.
The remaining terms (braking losses, inertia load, aero load, and rolling load)
make up the remainder of the losses and are proportioned similarly to the original
T ID m/~\/~i£il
LP model.
Reference the "input page" tab in the LP model to see the breakdown for each
predefined vehicle classN.
1.5.4 Baseline fuel efficiency by vehicle class
The new LP model estimates the basic fuel energy consumption, Efuei, for an
"unimproved" vehicle (naturally aspirated fixed valve engine with 4 speed automatic
transmission). It is calculated for each vehicle class with Equation 1.5-1:
••wheel
^1 engine •
Equation 1.5-1
To estimate the terms in the above equation, EPA regressed several known vehicle
parameters (rated engine power, rated engine torque, ETW, RL (chassis dyno road load at 50
mph)) against simulation output data. Definitions for each term and the relevant parameters
are listed below:
N For the "custom" vehicle class, values were regressed based on the following inputs: rated engine power,
torque, vehicle weight (ETW) and road load, in hp, at 50 mph (from certification data). Note that the defined
vehicle classes were validated by simulation work, while the custom vehicle data was not validated - it is for
illustrative purposes and represents a rougher estimate
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Chapter 1
1) EWheei: required wheel (or tractive) energy over the city/HW test cycle =
f(ETW, RL)
2) r|engine: net engine brake efficiency = f(torque, ETW, RL, alternator regen0 )
3) T|D/L: driveline efficiency is derived from the losses associated with the torque
converter, transmission, and final drive, where TC losses = f(torque, power,
RL, ETW) and transmission efficiency is based on vintage of the baselinep
Efuei (kWh) was then converted to fuel economy in mpg by applying the energy
content of gasoline (assumed at 33.7 kWh/gallon - for diesel it is 37.6 kWh/gallon) and
factoring in the distance traveled (10.64 miles) over the combined FTP/HWFE test cycle.
The LP model predicted baseline fuel economy for each class was then validated to
2008 baseline vehicle simulation results. Baseline unimproved vehicle FE values were first
estimated with the regression as mentioned above. From there, all other technologies
consistent with the 2008 Ricardo modeled baseline packages were added. Similarly, the
following technologies were added to the 2008 vehicles for comparison to the 2010 Ricardo
"baseline" packages: 6-speed automatic transmission, higher efficiency gearbox, 12V SS,
alternator regeneration during coastdowns, and 70% efficient alternator. The predicted LP
fuel economy values of both the 2008 baseline and 2010 vehicles all fall within roughly 2% of
the modeled data, as shown in Figure 1.5-2 below.
2008 2008 2010 2010
simulated LP model simulated LP model
Vehicle comb. comb. % FE comb. comb. % FE
Class Trans EPS Valvetrain mpg mpg error mpg mpg error
Small car 4spdauto Y ICP 41.5 41.3 -0.5% 43.4 44.1 1.7%
Standard car 5 spd auto N DCP 32.0 32.3 0.9% 34.9 34.7 -0.6%
Large car 5 spd auto N fixed 25.5 25.2 -1.0% 27.4 27.3 -0.4%
Small MPV 4spdauto Y DCP 28.8 29.1 1.1% 30.5 31.1 2.0%
Large MPV 4spdauto N fixed 23.1 23.7 2.4% 25.2 25.9 2.6%
Truck 4spdauto N CCP 17.6 17.4 -1.1% 18.6 18.6 -0.1%
2010 packages add 6spd auto trans, higher efficiency gearbox, 12V SS, alternator regen on decel, 70% efficient alternator
Figure 1.5-2 Comparison of LP model to Ricardo simulation results for 2008 and 2010
baseline vehicles
0 When the alternator regeneration technology is included, it changes the efficiency of the engine by moving the
average speed and load to a more efficient operating region. It was included in the definition of the 2010
baseline vehicle models.
p Two levels of baseline transmission efficiency were included in the simulation work, for 2008 baselines and
2010 baselines ("vintage"). Refer to the Input Page tab in the LP model for more detail.
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MY 2017 and Later Regulatory Impact Analysis
1.5.5 Identification and calibration of individual technologies
&-1-
The next step was to identify the individual technologies of interest and categorize
how they affect the physical system of the vehicle. Engineering judgment was used in
identifying the major loss categories that each individual LP model technology affected. In
some cases two or even three, loss categories were defined that were deemed significant. Not
all categories were a reduction in losses - some increased the amount of losses (for example,
increased frictional losses for various valvetrain technologies). A list of the technologies and
the categories they affect is shown in Figure 1.5-3 below. The technologies added for this
rule's version of the LP model are highlighted in bold. For a more detailed description of
each technology, refer to Section 3.4 of the Joint TSD.
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Chapter 1
Technology
Vehicle mass reduction
Aero Drag Reduction
Rolling Resistance Reduction
Low Fric Lubes
EF Reduction
4V on 2V Baseline
ICP
DCP
CCP
Deac
DVVL
CVVL
Turbo/Downsize (gas engines only)
5-spd gearbox
6-spd gearbox
8-spd gearbox
CVT
DCTWet
DCTDry
Early upshift (formerly ASL)
Optimized shift strategy
AggTC Lockup
High efficiency gearbox (auto)
12V SS (idle off only)
High voltage SS, with launch (BAS)
Alternator regen on braking
EPS
Electric access (12V)
Electric access (high V)
High efficiency alternator (70%)
GDI (stoich)
GDI (stoich) w/cooled EGR
GDI (lean)
Diesel-LNT(2008)
Diesel-SCR (2008)
Hybrid drivetrain
Secondary axle disconnect
Low drag brakes
Atkinson cycle engine
Advanced Diesel (2020)
Braking/
Inertia
Access
Losses
Friction Pumping Ind
Losses Losses Efficiency
Code:
Major
Minor
Negative ;
Figure 1.5-3 Loss categories affected by each technology
After losses were identified, EPA calibrated the loss modifiers so that each individual
technology would achieve a nominal effectiveness independent of other technologies and
consistent with the values given in Section 1.2. For example, discrete variable valve lift
(DVVL) can achieve roughly a 4-5% decrease in GHG emissions. It is coded in the LP model
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MY 2017 and Later Regulatory Impact Analysis
as a 27% reduction in pumping losses and a 3% increase (penalty) in friction losses.
Depending on the vehicle class, it reflects an effectiveness ranging from 4.1-5.6% reduction
in the LP model. Other technologies were coded in the LP model in similar fashion. In cases
where more than one loss category was affected, the majority of the effectiveness was linked
to the primary loss category, with the remainder of the effectiveness coded via the other
secondary loss categories. In some cases the LP model also reflects loss categories that are
penalized with certain technologies - for example, the increased mechanical friction
associated with advanced variable valvetrains (coded as a negative reduction in the LP
model). All technologies were calibrated on an "unimproved" vehicle (without any other
technologies present) to avoid any synergies from being accidentally incorporated. Once the
entire list of line-item technologies was coded, the next step was to compare the effectiveness
of actual (Ricardo-modeled) vehicle simulation packages to the LP model results.
1.5.6 Example build-up of LP package
The following example package for a Large Car demonstrates how synergies build as
content is added to a vehicle technology package.
505
4V DOHC 14 +EFR2 +LDB +ASL2 +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP
+GDI+TDS18
8sp DCT-
wet
12V
5%
$1,386
42.6%
• Add anytime technologies (EFR2, LDB, ASL2, IACC2, EPS)
These technologies primarily reduce accessory loads, mechanical engine friction and
pumping losses. The sum of these technologies is reflected below in Table 1.5-1Q and
provides a total of 14.9% reduction in GHG.
Table 1.5-1
% of tractive energy
Baseline % of fuel
Reduction
% of NEW fuel
Braking / Aero Rolling
Inertia Load Load
Trans
Losses
Access Friction Pumping
Losses Losses Losses
IndEff
Losses
Second
Law
23% 37%
3.9% 6.4%
4% 0%
3.8% 6.4%
2008 Baseline
New
1.1%
42%
0.6%
Indicated
Efficiency
Mech
Efficiency
Brake
Efficiency
Drivetrain
Efficiency
Cycle
Efficiency
Fuel
Efficiency
Road
Loads
36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0%
36.1% 67.9% 24.5% 81.6% 100.0% 20.0% 99.2%
8.3%
22%
6.5%
5.6% 34.0%
20%
4.5% 33.9%
30.0%
n/a
30%
85.1% Fuel Consumption
14.9% GHG reduction
Q For this table and similar subsequent tables, the "Reduction" row refers to the percentage reduction in fuel
energy for each particular loss category. Each values in that row does not translate into an absolute percentage
GHG savings, but are listed as indices between 0% (no reduction) and 100% (maximum theoretical reduction)
for each loss category. For example, in Table 1.5-1, roughly 42% of theoretical accessory losses have been
eliminated associated with the applied anytime technologies.
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Chapter 1
• Add road load reductions (Aero2, LRRT2) and 5% mass reduction
These technologies reduce braking/inertia, aerodynamic and rolling resistance loads,
with a minor degradation in indicated efficiency (because the engine is running at lower
overall loads). Combined with the technologies previously added in 1), the sum of these
technologies is shown below in Table 1.5-2 and provides a total of 24.5% reduction in GHG
compared to an unimproved vehicle.
Table 1.5-2
% of tractive
energy
Baseline % of fuel
Reduction
% of NEW fuel
2008 Baseline
New
Indicated
Efficiency
Mech
Efficiency
Braking /
Inertia
23%
3.9%
8%
3.6%
Brake
Efficiency
36.0% 58.4% 21.0%
34.7% 67.9% 23.6%
Aero
Load
37%
6.4%
17%
5.3%
Drivetrain
Efficiency
81.6%
81.6%
Rolling
Load
40%
6.9%
18%
5.6%
Cycle
Efficiency
Trans
Losses
Access
Losses
3.9% 1.1%
0% 42%
4.3% 0.6%
Fuel
Efficiency
Road
Loads
100.0% 17.1% 100.0%
100.0% 19.2% 84.8%
Friction Pumping
Losses Losses
8.3% 5.6%
22% 20%
6.2% 4.3%
75.5%
24.5%
IndEff
Losses
Second
Law
^H
34.0% 30.0%
Fuel Consumption
GHG reduction
• Add high efficiency gearbox
The high efficiency gearbox reduces transmission (driveline) losses due to the
mechanical improvements as described in Section 3.4.2.4 of the Joint TSD. Combined with
the technologies previously added, the sum of these technologies is shown below in Table
1.5-3 and provides a total of 28.5% reduction in GHG compared to an unimproved vehicle.
Table 1.5-3
% of tractive
energy
Baseline % of fuel
Reduction
% of NEW fuel
2008 Baseline
New
Indicated
Efficiency
Mech
Efficiency
Braking /
Inertia
23%
3.9%
8%
3.6%
Brake
Efficiency
36.0% 58.4% 21.0%
34.7% 67.9% 23.6%
Aero
Load
37%
6.4%
17%
5.3%
Drivetrain
Efficiency
81.6%
86.2%
Rolling
Load
Trans
Losses
Access
Losses
40%
6.9% 3.9% 1.1%
18% ^^^| 42%
5.6% 3.3% 0.6%
Cycle
Efficiency
Fuel
Efficiency
Road
Loads
100.0% 17.1% 100.0%
100.0% 20.3% 84.8%
Friction Pumping
Losses Losses
8.3% 5.6%
22% 20%
6.2% 4.3%
71.5%
28.5%
IndEff
Losses
34.0%
35.3%
Fuel Cons
GHGredu
Second
Law
30.0%
n/a
30%
a motion
ction
• Add dual cam phasing
Dual cam phasing provides significant pumping loss reductions at the expense of
increased mechanical friction due to the more complex valvetrain demands (as a result, the
"friction loss" reduction value below is actually reduced). Combined with the technologies
previously added, the sum of these technologies is shown below in Table 1.5-4 and provides a
total of 31.4% reduction in GHG compared to an unimproved vehicle.
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MY 2017 and Later Regulatory Impact Analysis
Inertia
Table 1.5-4
Braking / Aero Rolling
Load
Load
Trans
Access Friction Pumping
Losses Losses Losses Losses Losses
% of tractive energy
Baseline %
of fuel
Reduction
% of NEW fuel
Indicated
Efficiency
23%
3.9%
8%
3.6%
Mech Brake
Efficiency Efficiency
37%
6.4%
17%
5.3%
Drivetrain
Efficiency
40%
6.9%
18%
5.6%
Cycle
Efficiency
3.9%
25%
3.4%
Fuel
Efficiency
1.1%
42%
0.6%
Road
Loads
2008 Baseline
New
58.4%
70.4%
21.0%
24.6%
100.0%
100.0%
17.1%
21.2%
100.0%
84.8%
6.4%
3.3%
35.1%
Second
Law
68.6% Fuel Consumption
GHG reduction
• Add stoichiometric GDI, downsized, turbocharged engine (18-bar)
An 18-bar downsized and turbocharged engine, combined with stoichiometric gasoline
direct injection increases an engine's indicated efficiency, and drastically reduces pumping
losses. Combined with the technologies previously added, the sum of these technologies is
shown below in Table 1.5-5 and provides a total of 38.3% reduction in GHG compared to an
unimproved vehicle.
Table 1.5-5
% of tractive energy
Baseline %
of fuel
Reduction
% of NEW fuel
2008 Baseline
New
Indicated
Efficiency
Mech
Efficiency
Braking /
Inertia
23%
3.9%
8%
3.6%
Brake
Efficiency
36.0% 58.4% 21.0%
36.6% 74.7% 27.3%
Aero
Load
37%
6.4%
17%
5.3%
Drivetrain
Efficiency
81.6%
86.2%
Rolling
Load
Trans
Losses
Access
Losses
40%
6.9% 3.9% 1.1%
18% 25% 42%
5.6% 3.8% 0.6%
Cycle
Efficiency
Fuel
Efficiency
Road
Loads
100.0% 17.1% 100.0%
100.0% 23.6% 84.8%
Friction Pumping
Losses Losses
8.3% 5.6%
20% 1 67%
6.7% 1.9%
61.7%
38.3%
IndEff
Losses
Second
Law
34.0% 30.0%
n/a
314%| 30%
Fuel Consumption
GHG reduction
• Add 8-speed wet clutch DCT
An 8-speed wet clutch DCT reduces losses in several ways. The elimination of the
planetary gearset and torque converter increases the reduction in transmission losses, while
engine pumping losses are further reduced with the addition of more fixed gears (allowing for
more efficient engine operation). Combined with the technologies previously added, the sum
of these technologies is shown below in Table 1.5-6 and provides a total of 42.6% reduction
in GHG compared to an unimproved vehicle.
1-63
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Chapter 1
% of tractive energy
Baseline % of fuel
Reduction
% of NEW fuel
Table 1.5-6
23% 37%
3.9% 6.4%
8% 17%
3.6% 5.3%
2008 Baseline
New
Indicated
Efficiency
Mech
Efficiency
Brake
Efficiency
Drivetrain
Efficiency
Cycle
Efficiency
Fuel
Efficiency
Road
Loads
36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0%
37.1% 75.5% 28.0% 90.5% 100.0% 25.3% 84.8%
57.4% Fuel Consumption
42.6% GHG reduction
In summary, for this technology package, the mathematical combination of individual
effectiveness values (added without synergies) would yield a GHG reduction value of about
50%. Based on the lumped parameter model - which is calibrated to vehicle simulation
results that include synergies - this technology package would provide a GHG reduction of
42.6%. In most cases negative synergies develop between technologies addressing the same
losses, and with increasing magnitude as the level of applied technology grows. This
increasing disparity is shown below in Table 1.5-7.
Table 1.5-7: Comparison of LP-predicted to gross aggregate effectiveness
Technologies
Added
EFR2, LDB, ASL2, IACC2, EPS
Aero 2, LRRT2, MRS
HEG
DCP
GDI,TDS18
8spDCT-wet
Individual
Effectiveness
(for step)
16.4%
10.8%
5.3%
5.5%
14.9%
11.9%
Combined
Effectiveness
LP total
14.9%
24.5%
28.5%
31.4%
38.3%
42.6%
Gross
Effectiveness
total
16.4%
25.5%
29.4%
33.3%
43.2%
50.0%
1.5.7
Calibration of LP results to vehicle simulation results
The LP model includes a majority of the new technologies being considered as part of
this final rulemaking. The results from the 2011 Ricardo vehicle simulation project (Joint
TSD, Section 3.3-1) were used to successfully calibrate the predictive accuracy and the
synergy calculations that occur within the LP model. When the vehicle packages Ricardo
modeled are estimated in the lumped parameter model, the results are comparable. All of the
baselines for each vehicle class, as predicted by the LP model, fall within 3% of the Ricardo-
modeled baseline results. With a few exceptions (discussed in 1.5.8), the lumped parameter
results for the MYs 2020-2025 "nominal" technology packages are within 5% of the vehicle
simulation results. Shown below in Figure 1.5-4 through Figure 1.5-9 are Ricardo's vehicle
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MY 2017 and Later Regulatory Impact Analysis
simulation package results (for conventional stop-start and P2 hybrid packages ) compared to
the lumped parameter estimates.
Small Car Nominal Results
px ,<$> .#• ,<$-
Ricardo
LP results
Figure 1.5-4 Comparison of LP to simulation results for Small Car class
R Refer to Joint TSD, Section 3.3-1 for definitions of the baselines, "conventional stop-start" and "P2 hybrid"
vehicle architectures.
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Chapter 1
Standard Car Nominal Results
Ricardo
LP results
Figure 1.5-5 Comparison of LP to simulation results for Standard Car class
Large Car Nominal Results
60
50
40
=• 30
20
10
Illllllll
Illllllll
I Ricardo
I LP results
-------
MY 2017 and Later Regulatory Impact Analysis
Small MPV Nominal Results
60
50
40
=• 30
20
10
n • • • • • •
• • • • • • •
• • • • • • •
I Ricardo
I LP results
\ ConventionaISS
I
P2 Hybrid
Figure 1.5-7 Comparison of LP to simulation results for Small MPV class
Large MPV Nominal Results
Ricardo
LP results
Figure 1.5-8 Comparison of LP to simulation results for Large MPV class
1-67
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Chapter 1
Truck Nominal Results
Ricardo
LP results
Figure 1.5-9 Comparison of LP to simulation results for Truck class
As described in Chapter 3 of the Joint TSD, NHTSA contracted Argonne National
Laboratory (ANL) to supplement the existing Ricardo modeling with additional modeling
work for the mild hybrid pickup trucks. The recent ANL modeling results for mild hybrids
largely confirmed the effectiveness as originally predicted by the lumped parameter model,
with minor differences for small cars and large trucks.11 A comparison of the ANL results to
the original lumped parameter results (for comparable vehicle classes when modeled with a
nominal 15 kW motor size) is shown below in Table 1.5-1 and Table 1.5-2.
Table 1.5-1 ANL Effectiveness for Mild Hybrid
FC reduction
Compact
11.6%
Midsize
11.6%
Small SUV
10.2%
Midsize SUV
10.5%
Pickup
8.5%
Table 1.5-2 Lumped Parameter Model Effectiveness for Mild Hybrid
FC reduction
Small Car
14.1%
StdCar
11.8%
Small MPV
10.1%
Large MPV
10.1%
Truck
6.9%
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MY 2017 and Later Regulatory Impact Analysis
The underlying structure of the lumped parameter model was not changed to
accommodate this new information; instead, the nominal 15 kW motor sizes for small cars
and pickup truck mild hybrids were slightly adjusted (to 10 kW and 18 kW, respectively) to
reflect the updated effectiveness results provided by the ANL simulation work.
1.5.8 Notable differences between LP model and Ricardo results
1.5.8.1 Small car
At first glance, it would appear that the results for small cars predicted by the
lumped parameter model- (especially hybrids) are too high when compared to the
Ricardo vehicle simulation results. However, further investigation of the simulation
results showed that the applied road load coefficients for the small car, as modeled by
Ricardo, may have been higher than they should have been. Figure 1.5-10, below,
shows road load power (in units of horsepower, or RLHP) plotted as a function of
vehicle speed for the simulated vehicles. As expected, road load curves decrease as
the vehicle class (weight and size) decreases. The road load coefficients used by
Ricardo were all taken from certification test data. As shown, the modeled Yaris
(small car) road load curve, in purple, is actually comparable to that for a Camry (the
standard car exemplar vehicle), shown in green. By investigating the certification test
data, EPA identified a second (alternate) road load curve for an alternative Yaris
vehicle configuration, shown as a dashed line. Applying the mathematical equivalent
of this alternate road load curve to the small car in the vehicle simulation Complex
Systems tool (described in the Joint TSD, Section 3.3.1) achieved results much closer
to those predicted by the LP model. While both Yaris road load curves are based on
actual certification coefficients, it would make sense that the small car class should
exhibit lower road loads than a standard car class.
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Chapter 1
RLHP comparison
50.0
45.0
40.0
35.0
30.0
Q.
5 25.0
0£
20.0
15.0
10.0
5.0
0.0
-F150
Caravan
-Vue
-300
Camry
-Yaris
•Yaris(alt)
10
20
30 40
mph
50
60
70
Figure 1.5-10 Road load power for modeled vehicles
The LP results for the small car P2 hybrids appear to deviate further.
However, the deviation can be explained due to two main factors. Aside from the
higher road load curve employed by Ricardo, the small car P2 hybrid effectiveness
was understated due to a relatively undersized nominal motor/generator (30% smaller
than the optimal motor size of 21 kW). The percentage of available braking energy
did not match levels seen with the other vehicle classes, and fuel economy suffered
slightly as a result.
For these reasons, EPA finds the LP model estimate for the small car class to
be more appropriate for package effectiveness estimates.
1.5.8.2 Diesels
Detailed analysis of the diesel vehicle simulation results showed that the
vehicles did not operate in the most efficient operating region, either due to a potential
inconsistency in the application of the optimized shift strategy and/or due to the
apparent oversizing of the nominal diesel engines. Diesel engines appeared to have
been initially sized for rated power, not torque, which led to oversized displacement.
This conversely reduced the average transmission efficiency realized in the model test
runs. Plotting the average engine speed and load operating points for the diesel
simulation data on top of the diesel engine maps showed that there was room for
improvement in choice of selected gear, for example. EPA's LP estimate for the
Ricardo diesel packages compare well with the simulation results when optimized
shifting and early torque converter lockup (for automatic transmissions) are excluded
from the LP model. Based on this comparison which is more consistent with the
technology that appeared to be modeled, EPA is more comfortable with the LP diesel
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MY 2017 and Later Regulatory Impact Analysis
estimates which have slightly higher effectiveness estimates than the diesel package
vehicle simulation results.
1.5.9 Comparison of results to real-world examples
To validate the lumped parameter model, representations of actual late-model
production vehicles exhibiting advanced technologies were created. Shown below in Table
1.5-8 are a set of select vehicle models containing a diverse array of technologies: included
are the pertinent technologies and vehicle specifications, along with actual vehicle
certification fuel economy test data compared to the lumped parameter fuel economy
estimates. For the vehicles and technologies shown, the predicted fuel economy is within
about 3% of the actual data.
Table 1.5-8 Production vehicle certification data compared to lumped parameter predictions
Vehicle
Vehicleclass
Engine
Transmission
HEV motor (kW)
ETW (Ibs)
City/HWFE(mpg)
LP estimate (mpg)
Key technologies applied
in LP model
2011 Chevy Cruze ECO
Small Car
1.4LI4
turbo GDI
6 speed auto
n/a
3375
40.3
40.2
GDI (stoich.)
turbo (30% downsize)
ultra low R tires
active grill shutters
2011 Sonata Hybrid
Standard Car
2.4LI4
Atkinson
6 speed DCT
30
3750
52.2
51.7
P2 hybrid
aero improvements
2011 Escape Hybrid
Small MPV
2.5LI4
Atkinson
CVT
67
4000
43.9
44.0
Powersplit hybrid
2011 F-150 Ecoboost
Truck
3.5LV6
turbo GDI
6 speed auto
n/a
6000
22.6
21.9
GDI (stoich)
turbo (37% downsize)
1-71
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Chapter 1
References
1 Woldring, D., Landenfeld, T., Christie, M.J., 2007, "DI Boost: Application of a High
Performance Gasoline Direct Injection Concept." SAE Technical Paper Series No. 2007-01-
1410; Kapus, P.E., Fraidl, O.K., Prevedel, K., Fuerhapter, A., 2007, "GDI Turbo - The Next
Steps." JSAE Technical Paper No. 20075355; Hancock, D., Eraser, N., Jeremy, M., Sykes, R.,
Blaxill, H., 2008, "A New 3 Cylinder 1.21 Advanced Downsizing Technology Demonstrator
Engine." SAE Technical Paper Series No. 2008-01-0611; Lumsden, G., OudeNijeweme, D.,
Eraser, N. Blaxill, H., 2009, "Development of a Turbocharged Direct Injection Downsizing
Demonstrator Engine." SAE Technical Paper Series No. 2009-01-1503; Cruff, L., Kaiser, M.,
Krause, S., Harris, R., Krueger, U., Williams, M., 2010, "EBDI® - Application of a Fully
Flexible High Bmep Downsized Spark Ignited Engine." SAE Technical Paper Series No.
2010-01-0587; Taylor, J., Eraser, N., Wieske, P., 2010, "Water Cooled Exhaust Manifold and
Full Load EGR Technology Applied to a Downsized Direct Injection Spark Ignition Engine."
SAE Technical Paper Series No. 2010-01-0356; Roth, D.B., Keller, P, Becker, M., 2010,
"Requirements of External EGRSystems for Dual Cam Phaser Turbo GDI Engines." SAE
Technical Paper Series No. 2010-01-0588.
2 Spreadsheet files used to generate the values presented in this chapter can be found on a
compact disk placed in Docket No. EPA-HQ-OAR-2010-0799, see "LDGHG 2017-2025 Cost
Development Files."
3 See "LDGHG 2017-2025 Cost Development Files," CD in Docket No. EPA-HQ-OAR-
2010-0799.
4 See "LDGHG 2017-2025 Cost Development Files," CD in Docket No. EPA-HQ-OAR-
2010-0799.
5 See "LDGHG 2017-2025 Cost Development Files," CD in Docket No. EPA-HQ-OAR-
2010-0799.
6 National Research Council, "Effectiveness and Impact of Corporate Average Fuel Economy
(CAFE) Standards", National Academy Press, 2002.
7 Patton, et al. "Aggregating Technologies for reduced Fuel Consumption: A Review of the
Technical Content in the 2002 National Research Council Report on CAFE". SAE 2002-01-
0628. Society of Automotive Engineers, 2002.
8 See RIA Chapter 4 in support of the MYs 2012-2016 final rule (EPA-420-R-10-009, April
2010).
9 U.S. EPA, "Computer Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas
Emission Reduction in the 2020-2025 Timeframe", Contract No. EP-C-11-007, Work
Assignment 0-12, Docket EPA-HQ-OAR-2010-0799, November, 2011.
10 Heywood, J. Internal Combustion Engine Fundamentals. Figures 13-9 and 13-10, p. 723.
McGraw-Hill, 1988.
1-72
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MY 2017 and Later Regulatory Impact Analysis
11
See FRM Joint Technical Support Document 3.2.1.3 Docket No. EPA-HQ-OAR-2010-
0799
1-73
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MY 2017 and Later - Regulatory Impact Analysis
2 EPA's Vehicle Simulation Tool
2.1 Introduction
2.1.1 Background
It is well known that full-scale physics-based vehicle simulation modeling is the most
sophisticated method for estimating fuel saving benefits by a package of advanced new
technologies (short of actually building an actual prototype). For this reason, EPA has used
full vehicle simulation results generated by Ricardo, Inc. to calibrate and validate the lumped
parameter model (described in Chapter 1) to estimate technology effectiveness of many
combinations of different technologies. However, EPA only has limited access to the
Ricardo's model and proprietary data, so there has been a growing need for developing and
running detailed vehicle simulations in-house for GHG regulatory and compliance purposes
(notwithstanding that this is a very time-consuming and resource-intensive task). As a result,
over the past two years, EPA has developed full vehicle simulation capabilities in order to
support regulations and vehicle compliance by quantifying the effectiveness of different
technologies with scientific rigor over a wide range of engine and vehicle operating
conditions. This in-house vehicle simulation tool has been developed for modeling a wide
variety of light-, medium- and heavy-duty vehicle applications over various driving cycles.
The first application of this vehicle simulation tool was intended for medium- and heavy-duty
vehicle compliance and certification. This simulation tool, the "Greenhouse gas Emissions
Model" (GEM), has been peer-reviewed12 and has also recently been published.13 For the
model years 2014 to 2017 final rule for medium- and heavy-duty trucks, GEM is used both to
assess Class 2b-8 vocational vehicle and Class 7/8 combination tractor GHG emissions and to
demonstrate compliance with the vocational vehicle and combination tractor standards. See
40 CFR sections 1037.520 and 1037.810 (c).
2.1.2 Objective and Scope
Unlike in the heavy-duty program, where the vehicle simulation tool is used for GHG
certification since chassis-based certifications are not yet practical or feasible for most HD
vehicles, we intend to use the light-duty simulation tool to help with the light-duty regulatory
analysis but not for certification since it is not only feasible but also common practice to
certify light-duty vehicles based on chassis-based vehicle testing. For light-duty (LD)
vehicles, EPA had developed a simulation tool for non-hybrid and hybrid vehicles, which is
capable of simulating a wide range of conventional and advanced engines, transmissions, and
vehicle technologies over various driving cycles. It is called "Advanced Light-Duty
Powertrain and Hybrid Analysis Tool" (ALPHA). The tool evaluates technology package
effectiveness while taking into account synergy effects among vehicle components and
estimates GHG emissions for various combinations of future technologies. This LD vehicle
simulation tool, ALPHA, is capable of providing reasonably (though not absolutely) certain
predictions of the fuel economy and GHG emissions of specific vehicles to be produced in the
future. Currently, it is capable of simulating power-split and P2 hybrid vehicles as well as
non-hybrid vehicles with a Dual-Clutch Transmission (DCT), under warmed-up conditions
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Chapter 2
only. Additional simulation capabilities such as automatic transmissions, cold-start
conditions, and other hybrid architectures including PHEV and electric vehicles are being
developed by EPA for future use.
The ALPHA is a full vehicle simulator that uses the same physical principles as
commercially available vehicle simulation tools (such as Autonomie, AVL-CRUISE, GT-
Drive, Ricardo-EASY5, etc.). In order to ensure transparency of the models and free public
access, EPA has developed the tool in MATLAB/Simulink environment with a completely
open source code. For the 2017 to 2025 GHG rule, EPA used the simulation tool in a more
limited manner: to quantify the amount of GHG emissions reduced by improvements in A/C
systems and off-cycle technologies, as explained in Chapter 5 of the Joint TSD and Section
III. C of the Preamble.
2.2 Descriptions of EPA's Vehicle Simulation Tool
2.2.1 Overall Architecture
Table 2.2-1 provides a high-level architecture of ALPHA, which consists of six
systems: Ambient, Driver, Electric, Engine, Transmission, and Vehicle. With the exception
of "Ambient" and "Driver" systems, each system consists of one or more component models
which represent physical elements within the corresponding system. The definition and
function of each system and their respective component models are discussed in the next
section.
Table 2.2-1 High-Level Structure of Vehicle Simulator
System
Ambient
Driver
Electric
Engine
Transmission
Vehicle
Component Models
N/A
N/A
Accessory (electrical)
Accessory (mechanical), Cylinder
Clutch, Gear
Final Drive, Differential, Axle, Tire, Chassis
Figure 2.2-1 illustrates the overall streamline process of the vehicle simulation and
how the current tool is designed for a user to run desired vehicle simulations. Upon execution
of the main MATLAB script, it launches a Graphical User Interface (GUI) which will allow
the user to choose desired inputs such as vehicle type, engine technology type, driving cycle,
etc. while making the use of the tool much easier and straightforward. When the simulation is
run via GUI, it first initializes all necessary vehicle model parameters including engine maps,
transmission gear ratios, and vehicle road load parameters. Then, it runs the Simulink vehicle
model over the desired driving cycles. Upon completing the simulation run, it automatically
displays the simulation outputs in terms of fuel economy and GHG emissions. It also displays
a plot of the simulated vehicle speed trace, showing how closely the simulation vehicle
followed the desired speed trace. Although this first version of the vehicle simulation tool is
still in an early stage, it does provide simulation capabilities for various vehicle types, engine
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MY 2017 and Later - Regulatory Impact Analysis
and transmission technologies, and driving cycles. In the future, it will undergo upgrades and
improvements to include more technology choices and more simulation flexibilities.
2-3
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Chapter 2
GUI Inputs
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Figure 2.2-1 LD Vehicle Simulation Tool
2-4
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MY 2017 and Later - Regulatory Impact Analysis
2.2.2 System Models
In this section, detailed descriptions of the system models (Ambient, Driver, Electric,
Engine, Transmission, and Vehicle) are provided. For Electric, Engine, Transmission, and
Vehicle systems, the components within each of the systems will be described as well. These
system models remain consistent regardless of vehicle types, engine or transmission
technologies, and driving cycles.
2.2.2.1 Ambient System
This system defines surrounding environment conditions, such as pressure,
temperature, and road gradient, where vehicle operations are simulated. By default, the
environmental conditions defined in this system are in accordance with the standard SAE
practices - air temperature of 25°C, air pressure of 101.325 kPa, and air density based on the
Ideal Gas law which results in a density of 1.20 kg/m3. The road gradient is set to 0 %,
indicating a vehicle moving on a flat surface. However, these conditions are easily
reconfigurable by the user.
2.2.2.2 Driver System
The driver model utilizes two control schemes to keep the simulated vehicle speed at
the desired values: feedforward and feedback. It uses the targeted vehicle speed defined by a
desired driving cycle to first estimate vehicle's torque requirement at the wheel at any given
time. The engine power demand is then calculated based on the required wheel torque. And,
the required accelerator and braking pedal positions are determined to deliver the demanded
engine power which will drive the vehicle at the desired speed. If the simulated vehicle speed
deviates the desired target, a speed correction logic is applied via a classical proportional-
integral-derivative (PID) controller to adjust the accelerator and braking pedal positions by
necessary amount in order to maintain the targeted vehicle speed at every simulation time
step.
2.2.2.3 Electric System
The electric system was originally modeled as a system which consists of four
individual electrical components - starter, electrical energy storage such as battery, alternator,
and electrical accessory. However, for the purpose of calculating A/C credits as well as off-
cycle credits, the simulation tool has modeled the electrical system as a constant power
consumption devise as a function of the vehicle category. It basically represents the power
loss associated with the starter, alternator, and other electrical accessories. This type of
simplification was made since the purpose of the simulation was A-B comparisons only, i.e.
relative difference between case A and case B on GHG emissions.
2.2.2.4 Engine System
The engine system mainly consists of two components: Mechanical Accessory and
Cylinder, which represent torque loss and torque production by an engine, respectively.
2-5
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Chapter 2
2.2.2.4.1 Mechanical Accessory
This component is modeled as a simple power consumption source. Most vehicles run
a number of accessories that are driven by mechanical power generated from the engine
crankshaft rotation. Some of these accessories are necessary for the vehicle to run, like the
coolant pump, while others are only used occasionally at the operator's discretion, such as the
air conditioning compressor. For estimating the impact of A/C usage on fuel consumption,
the mechanical accessory is modeled as a power consumption devise which varies with engine
speed. More detailed description of the A/C compressor model is provided in the next
section.
2.2.2.4.2 Cylinder
The cylinder component is modeled based on engine torque curves at wide open
throttle (maximum torque) and closed throttle (minimum torque) as well as a steady-state fuel
map covering a wide range of engine speed and torque conditions. The engine fuel map is
represented as fueling rates pre-defined in engine speed and load conditions. This part of the
model is not physics-based, therefore does not attempt to model the in-cylinder combustion
and the corresponding torque production process. During the vehicle simulation, the
instantaneous engine torque and speed are monitored and used to select an appropriate fueling
rate based on the fuel map. This map is adjusted automatically by taking into account three
different driving modes: acceleration, brake, and coast. The fuel map, torque curves, and the
different driving modes are pre-programmed into the model for several different engine
technologies.
2.2.2.5 Transmission System
The transmission system consists of two components: Clutch and Gear. The current
version of the transmission system only models a DCT.
2.2.2.5.1 Clutch
This component represents a mechanical clutch in either a manual transmission or a
DCT. For an automatic transmission, it is replaced by a torque converter component. It is
modeled as an ideal clutch, where no dynamics during clutch slip is considered during clutch
engaging and disengaging process.
2.2.2.5.2 Gear
This component is modeled as a simple gearbox. The number of gears and
corresponding gear ratios are predefined during the preprocessing of simulation runs. Also,
torque transmitting efficiency is defined for each gear to represent the losses that occur in the
physical system. Like the clutch component, the gear is modeled as an ideal gear, where no
dynamics is considered during gear engaging and disengaging process.
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MY 2017 and Later - Regulatory Impact Analysis
2.2.2.6 Vehicle System
The vehicle system consists of five components: Final Drive, Differential, Axle, Tire,
Chassis. It basically models all components after transmission in a vehicle.
2.2.2.6.1 Final Drive and Differential
Both final drive and differential components are modeled as mechanical systems
which transmit inertia and toque from an upstream component to a downstream component
with a certain gear ratio and efficiency. The gear ratios for both components can be specified
by the user according to the simulated vehicle. The torque transmitting efficiencies are
defined by maps based on input speed and torque to the modeled component.
2.2.2.6.2 Axle
Typically, all axles are lumped together, and one axle model represents the overall
behavior of vehicle axles during vehicle simulations. In ALPHA, however, the axle
component is modeled to simulate the behavior of each individual axle used by the simulated
vehicle. The axle is treated individually in order to properly simulate all wheel drive vehicle
types.
2.2.2.6.3 Tire and Chassis
This part of the vehicle system models the body of the vehicle including tires. For the
chassis component, the coefficient of aerodynamic drag, mass of vehicle, and vehicle frontal
area are the key model parameters. For the tire component, the user specifies the
configuration of each axle on the vehicle, including the tire diameter and its rolling resistance
coefficient. However, these components will have a capability to use typical coast-down
coefficients to calculate road load, instead of tire rolling resistance and aerodynamic drag.
2.3 Applications of Simulation Tool for Final Rule
As mentioned previously, EPA used the vehicle simulation tool for the final rule to
quantify the amount of GHG emissions reduced by improvements in A/C system efficiency
(thus fixing the maximum credit potential) and to determine the default credit value for active
aerodynamics, electrical load reduction, and engine start-stop - some of the listed off-cycle
technologies (off-cycle technologies for which a credit of pre-determined amount may be
obtained). In this section, we discuss the specifics of these applications of the simulation tool.
2.3.1 Impact of A/C on Fuel Consumption
Among the simulation model systems described in the previous section, there are four
key system elements in the light-duty vehicle simulation tool which describe the overall
vehicle dynamics behavior and the corresponding fuel efficiency: electric, engine,
transmission, and vehicle. The electric system model consists of parasitic electrical load and
A/C blower fan, both of which were assumed to be constant. The engine system model is
comprised of engine torque and fueling maps. For estimating indirect A/C impact on fuel
consumption increase, two engine maps were used: baseline and EGR boost engines. These
2-7
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Chapter 2
engine maps were obtained by reverse-engineering the vehicle simulation results provided by
Ricardo Inc. For the transmission system, a Dual-Clutch Transmission (DCT) model was
used along with the gear ratios and shifting schedules used for the earlier Ricardo simulation
work. For the vehicle system, four vehicles were modeled: small, medium, large size
passenger vehicles, and a light-duty pick-up truck. The transient behavior and
thermodynamic properties of the A/C system was not explicitly simulated, in favor of a
simpler approach of capturing the compressor load based on national average ambient
conditions. We believe this simplification is justified since the goal is to capture the behavior
on the average of a fleet of vehicles (not an individual make or model).
In order to properly represent average load values to the engine caused by various A/C
compressors in various vehicle types, EPA has adopted the power consumption curves of A/C
systems, published by an A/C equipment supplier, Delphi.1 >15 Also, in an effort to
characterize an average A/C compressor load in the presence of widely varying environmental
conditions in the United States, EPA has adopted data from the National Renewable Energy
Laboratory (NREL) to estimate environmental conditions associated with typical vehicle A/C
usage.16'17'18 Based on the NREL data, EPA selected an A/C power consumption curve as a
function of engine speed that was acquired by Delphi at 27°C and 60% relative humidity as a
representative average condition. This power consumption data was taken from a fixed
displacement compressor with a displacement volume of 210 cc. The curve includes the
effect of compressor cycling as well as non-summer defrost/defog usage. In order to associate
each vehicle type with appropriate A/C compressor displacement, EPA scaled the curve based
on the displacement volume ratio. For determining indirect A/C impact on fuel consumption
increase for various vehicle types, EPA estimated A/C compressor sizes of 120 cc, 140 cc,
160 cc, and 190 cc for small, medium, large passenger cars, and light-duty pick-up truck,
respectively. By applying these ratios to the 210 cc power consumption curve, EPA created
A/C load curves for four vehicle types, as shown in Figure 2.3-1.
A/C Load Demand
5.50
5.00
4.50
4.00
P..50
i.OO
2.50
2.00
1,50
1.00
0.50
0.00
-t-
-I-
•210cc
•190cc
•160CC
•140 cc
120 cc
500 1000 1500 2000 2500 iOOO 3500 4000 4500 5000 5500 6000 6500
Engine Speed (RPM)
Figure 2.3-1 Representative A/C Compressor Load Curves
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MY 2017 and Later - Regulatory Impact Analysis
With these A/C compressor load curves, EPA ran full vehicle simulations based on the
following matrix shown below. In this matrix, the baseline engine represents a typical Spark-
Ignition (SI), Port-Fuel Injection (PFI), Naturally-Aspirated (NA) engine equipped with a
Variable Value Actuation (VVA) technology. In this technology, the valve timing (both
intake and exhaust) is continuously varied over a wide range of engine operating conditions in
order to result in optimal engine breathing efficiency. On the other hand, the EGR boost
engine uses turbocharging and cooled EGR to increase engine's Brake Mean Effective
Pressure (BMEP) level while managing combustion and exhaust temperatures. This engine
usually has a peak BMEP of 25 to 30 bars, which supports significant downsizing (e.g. about
50%) compared to the baseline engines. Table 2.3-1 provides simulation results over SC03
driving cycle with an EGR boost engine for various vehicle classes.
• Small, medium, large cars, and pick-up truck
• FTP, Highway, and SC03 driving cycles
• Baseline and EGR boost engines
• A/C off and A/C on
Table 2.3-1 Vehicle Simulation Results on CO2 Emissions over SC03 Cycle with EGR Boost
Engine
SC03 Cycle
CO2 with A/C off
CO2 Increase with A/C on
Total CO2 with A/C
Indirect A/C Fuel Use
[g/mi]
[g/mi]
[g/mi]
[%]
Small Car
196.4
11.7
208.1
5.6
Medium Car
235.7
12.0
247.7
4.8
Large Car
293.7
13.8
307.5
4.5
Truck
472.4
17.2
489.6
3.5
EPA ran the SC03 cycle simulations instead of the FTP/Highway combined cycle
simulations so that the simulation results would represent the actual A/C cycle test. EPA also
assumed the EGR boost engine during vehicle simulations because the EGR boost engine
better represents an engine technology more likely to be implemented in model years 2017 to
2025 and because the A/C impact on CCh increase in the EGR boost engine is similar to that
in the baseline engine as shown in Table 2.3-1 and Table 2.3-2. Details of this analysis which
showed impact of A/C usage on fuel consumption is relatively independent of engine
technology are provided in the next section. Moreover, EPA assumed 62% and 38% of
market penetrations for manual and automatic climate control systems, respectively. EPA
also assumed 23.9% and 35.0% of A/C on-time for manual and automatic climate control
systems, respectively. These are the same assumptions made for the 2012-2016 rule.19 In
order to come up with the overall impact of A/C usage on CCh emissions for passenger cars,
the simulation results for cars shown in Table 2.3-1 were sales-weighted for each year from
2017 to 2025. For the final result, the impact of A/C usage was estimated at 11.9 CCh g/mile
for cars and 17.2 CO2 g/mile for trucks. This corresponds to an impact of approximately 14.0
CO2 g/mile for the (2012) fleet, which is comparable to the 2012-2016 final rule result, but
still lower than the two studies by NREL17 and NESCCAF18 cited above.
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Chapter 2
2.3.1.1 Effect of Engine Technology on Fuel Consumption by A/C System
In order to continue to maintain the credit levels from the 2012-2016 rule, EPA had to
first demonstrate that the fuel economy and CO2 emissions due to A/C was relatively
insensitive to the engine technologies that may be expected to be used in 2012-2016 light duty
vehicles. If, for example, more efficient engines are able to run the A/C system more
efficiently such that the incremental increase in emissions due to A/C decreased compared to
the base engines, then credits for the same A/C technologies must decrease over time as
engines become more efficient. This would correspond to a decrease in credits proportional
(or multiplicative) to the increase in efficiency of the engine. Conversely, if the incremental
increase in emissions due to A/C remained relatively constant, then the credits available for
A/C efficiency should also remain stable. This would correspond to the credits (A/C impact)
being additive to the base emissions rate, thus being independent of engine efficiency. The
EPA based the hypothesis on the latter assumption.
In order to prove out this hypothesis, EPA carried out vehicle simulations for several
cases, including two engine technologies: baseline and EGR boost engines (a surrogate for a
future advanced efficient engine). Table 2.3-2 shows the vehicle simulation results of CC>2
emissions over the SC03 driving cycle when baseline engines are used, as opposed to the
advanced EGR boost engines. By comparing the values of CC>2 increase with A/C on in Table
2.3-1 and Table 2.3-2, it is evident that the impact of A/C usage on fuel consumption is not
highly dependent on the engine technologies. In fact, the difference in the CC>2 increase with
A/C on (2nd row in table) between the emissions from the baseline and EGR boost engines is
less than 10% for all vehicle classes.
Table 2.3-2 Vehicle Simulation Results on CO2 Emissions over SC03 Cycle with Baseline Engine
SC03 Cycle
CO2 with A/C off
CC>2 Increase with A/C on
Total CO2 with A/C
Indirect A/C Fuel Use
[g/mi]
[g/mi]
[g/mi]
[%]
Small Car
259.3
11.3
270.6
4.2
Medium Car
348.0
11.1
359.1
3.1
Large Car
425.4
12.5
437.9
2.9
Truck
628.1
16.2
644.3
2.5
Figure 2.3-2 depicts zoomed-in BSFC maps for baseline and EGR boost engines. The
circles on these maps represent average operating conditions of the engines over the FTP
(city) drive cycle. The blue circle represents a simulated average operating condition without
A/C while the red circle represents an average operating condition with A/C. As can be seen
in the figure, the engines operate at higher load levels when the A/C is on.
For the baseline engine case, the engine efficiency improves significantly (375 g/kW-h
to almost 330 g/kW-h) as it moves along the BSFC surface, whereas the improvement is
much less for the EGR boost engine as it moves from approximately 250 g/kW-h to 240
g/kW-h. However, the large improvement in engine efficiency for the baseline engine is
offset by the fact that the engine itself is less efficient than the EGR boost engine.
Conversely, the small efficiency improvement for the EGR boost engine is compensated by
the fact that the engine is much more efficient than the baseline engine. As a result, the CO2
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MY 2017 and Later - Regulatory Impact Analysis
increase seen by both engines due to A/C usage becomes similar in two different
technologies. This result allows us to approximate the A/C impact on vehicle fuel
consumption as an additive effect rather than a multiplicative effect since it is independent of
engine technologies. For the same reason, it also means that A/C credits for a given
technology can remain constant over time, which will greatly simplify the progression of
future credits.8
100
80
60
40
Figure 2.3-2 Average Engine Operating Conditions with A/C Off and A/C On over
Fueling Maps for Baseline and EGR Boost Engines
2.3.2 Off-Cycle Credit Calculation
The aerodynamics of a vehicle plays an important role in determining fuel economy.
Improving the aerodynamics of a vehicle reduces drag forces that the engine must overcome
to propel the vehicle, resulting in lower fuel consumption. The aerodynamic efficiency of a
vehicle is usually captured in a coast-down test that is used to determine the dynamometer
parameters used during both the two-cycle and five-cycle tests. This section discusses active
aerodynamic technologies that are activated only at certain speeds to improve aerodynamic
efficiency while preserving other vehicle attributes or functions. Two examples of active
aerodynamic technologies are active grill shutters and active ride height control. Active
aerodynamic features can change the aerodynamics of the vehicle according to how the
vehicle is operating, and the benefit of these vehicle attributes may not be fully captured
during the EPA test cycles.
s It also means that the last row in the above two tables are somewhat misleading as A/C impact should not be
quantified as a fraction of the total emissions, but rather an additive increment. The numbers are left onto the
tables only for comparison purposes to studies in the literature that use this convention.
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Chapter 2
EPA is limiting credits to active aerodynamic systems only (not passive). The
aerodynamic drag on the vehicle is highly dependent on the vehicle shape, and the vehicle
shape is (in turn) highly dependent on the design characteristics for that brand and model.
EPA feels that it would be inappropriate to grant off-cycle credits for vehicle aesthetic and
design qualities that are passive and fundamentally inherent to the vehicle.
2.3.2.1 Performance-Based Metrics
To evaluate technologies that reduce aerodynamic drag, the EPA conducted an
analysis of the reduction in emissions corresponding to a general reduction of aerodynamic
drag on a vehicle. Using the EPA's full vehicle simulation tool (ALPHA) described in the
previous section, the agency evaluated the change in fuel consumption for increasing
reductions in aerodynamic drag for a typically configured vehicle. The results of this analysis
form the basis for a consistent methodology that the EPA applied to technologies that provide
active aerodynamic improvements.
Vehicle aerodynamic properties impact both the combined FTP/Highway and 5-cycle
tests. However, these impacts are larger at higher speeds and have a larger impact on the 5-
cycle tests. By their nature of being "active" technologies, EPA understands that active
aerodynamic technologies will not be in use at all times. While deployment strategies for
different active aerodynamic technologies will undoubtedly vary by individual technology,
the impact of these technologies will mostly be realized at high speeds. EPA expects that the
5-cycle tests will capture the additional real-world benefits not quantifiable with the
FTP/Highway test cycles due to the higher speed in the US06 cycle. Active aero may also
depend on weather conditions. For example, active aerodynamics may operate less in hot
weather when air cooling is required to exchange heat at the condenser. Also, active grill
shutters may need to stay open during snowy conditions in order to prevent them from
freezing shut (potentially causing component failure).
Using the EPA's full vehicle simulation tool, the impact of reducing aerodynamic drag
was simulated on both the combined FTP/Highway cycle and the 5-cycle drive tests. In order
to determine the fuel savings per amount of aerodynamic drag reduction, the fuel savings on
the FTP/Highway test cycle was subtracted from the fuel savings on the 5-cycle test. This is
consistent with the approach taken for other technologies. Table 2.3-3 shows the results of the
vehicle simulation. Also, Figure 2.3-3 represents this GHG reduction metrics in a graphical
form. These results assume that the active aerodynamics affects the coefficient of drag only,
which is currently assumed to be constant over a wide range of vehicle operating speed.
However, if the coefficient of aerodynamic drag is assumed to be vehicle speed dependent,
then a different relationship could result.
This vehicle simulation tool was also used for estimating other off-cycle credits, such
as electrical load reduction and engine start-stop credits. Details of the analysis and values of
these scalable credits are described in Chapter 5 of TSD. Although this simulation tool will
not be officially used for credit compliance purposes, EPA may use the tool for the alternate
method demonstration process of credit approval. EPA encourages manufacturers to use this
simulation tool in order to estimate the credits values of their off-cycle technologies.
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MY 2017 and Later - Regulatory Impact Analysis
Table 2.3-3 Simulated GHG Reduction Benefits of Active Aerodynamic Improvements
Reduction in Aerodynamic Drag
(Cd)
1%
2%
3%
4%
5%
10%
GHG Reduction in Cars
[g/mile]
0.2
0.4
0.6
0.8
0.9
1.9
GHG Reduction in Trucks
[g/mile]
0.3
0.6
1.0
1.3
1.6
3.2
0.0
Performance Metrics for Active Aero Technology
y=
5% 10% 15%
AeroDynarnk Improvement
Figure 2.3-3 Simulated GHG Reduction Benefits of Active Aerodynamic Improvements
2.3.2.2 Active Aerodynamics
One of the active aerodynamic technologies is active grill shutters. This technology is
a new innovation that is beginning to be installed on vehicles to improve aerodynamics at
higher speeds. Nearly all vehicles allow air to pass through the front grill of the vehicle to
flow over the radiator and into the engine compartment. This flow of air is important to
prevent overheating of the engine (and for proper functioning of the A/C system), but it
creates a significant drag on the vehicle and is not always necessary. Active grill shutters
close off the area behind the front grill so that air does not pass into the engine compartment
when additional cooling is not required by the engine. This reduces the drag of the vehicle,
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Chapter 2
reduces CCh emissions, and increases fuel economy. When additional cooling is needed by
the engine, the shutters open until the engine is sufficiency cooled.
Based on manufacturer data, active grill shutters provide a reduction in aerodynamic
drag (Cd) from 0 to 5% when deployed. EPA expects that most other active aerodynamic
technologies, such as active suspension lowering will provide a reduction of drag in the same
range as active grill shutters. EPA also expects that active aerodynamic technologies may not
always be available during all operating conditions. Active grill shutters, for example, may
not be usable in very cold temperatures due to concerns that they could freeze in place and
cause overheating. Control and calibration issues, temperature limitations, air conditioning
usage, and other factors may limit the usage of grill shutters and other active aerodynamic
technologies. Therefore, EPA is providing a credit for active aerodynamic technologies
according to the performance metrics represented in Figure 2.3-3 and Table 2.3-3. It is
conceivable that some systems can achieve better performance. Manufacturers may apply for
a greater credit for better performing systems through the normal application process
described in Section IILC.S.b of the preamble to the final rule.
2.4 On-Going and Future Work
2.4.1 Simulation Tool Validation
Since the EPA's full vehicle simulation tool (ALPHA) is still in an early stage, only
the HEV version of the model has been validated to test data. The non-hybrid model has not
been fully validated against vehicle test data yet. However, EPA has attempted to compare
the EPA's simulation results to those of Ricardo's. Unfortunately, none of the Ricardo's
vehicle simulation metrics exactly matched with the simulation runs performed by the EPA's
simulation tool. For this reason, EPA used the lumped parameter model (described in Chapter
1) which had been calibrated and tuned with Ricardo's simulation results for a benchmark
comparison.
Table 2.4-1 Comparison between EPA's Full Vehicle Simulation Tool and Lumped Parameter
Model Runs
Simulation Tool
Vehicle Simulation
Lumped Parameter Model
Percent Difference
Small-Size Car
[g/mile]
211.7
220
3.8%
Mid-Size Car
[g/mile]
273.8
280
2.2%
Large-Size Car
[g/mile]
350.2
359
2.5%
Pick-up Truck
[g/mile]
532.7
520
2.4%
Using the same simulation metrics (e.g. baseline engine, DCT transmission, vehicle
types) for both ALPHA and the lumped parameter model, the results were obtained as shown
in Table 2.4-1. As shown in Table 2.4-1, it is evident that the EPA vehicle simulation tool
provides GHG estimations which are very comparable with lumped parameter model results,
and therefore with Ricardo's simulation results for various vehicle types. The differences are
all within ±5% between the two simulations. Although this benchmarking result against the
Ricardo's simulation does provide a certain level of confidence in the EPA's simulation tool,
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MY 2017 and Later - Regulatory Impact Analysis
a full validation of the tool will be performed using actual vehicle test data in the near future.
For the analysis conducted in this rule, where only a difference in COi emissions or fuel
economy is required, we believe that this is a sufficient level of validation.
2.4.2 Simulation Tool Upgrade
As mentioned previously, the EPA's full light-duty vehicle simulation tool (ALPHA)
is still in an early stage. There are a number of improvements and new additions being
planned for the simulation tool so that it will be capable of performing various different types
of simulations for a number of vehicle technologies. EPA expects that the upgraded vehicle
simulation tool can provide more capabilities for future EPA analysis.
First, an automatic transmission model will be added for the conventional (non-
hybrid) vehicle simulation tool. Although EPA expects that DCT will be a dominant
technology in transmissions in MYs 2017 to 2025, EPA must be able to simulate vehicles
with automatic transmissions which give baseline vehicle performances. Also, 8-speed
automatic transmissions with lock up will also require this model as a basis. Along with the
automatic transmission, a transmission shifting algorithm will be developed, which will help
us avoid requiring transmission shifting maps. This algorithm will automatically optimize the
shifting strategy based on torque required by the vehicle and torque produced by the engine
during simulation. Therefore, it should eliminate the need for having shifting maps for
different combinations of powertrains and vehicles.
In addition to upgrading the non-hybrid vehicle simulation tool, EPA is planning to
enhance hybrid electric vehicle (HEV) simulation capabilities. EPA has already developed
and validated power-split and P2 hybrid vehicle models. We plan to add more HEV
configurations, such as series hybrid, PHEV, electric vehicles, etc. For both non-hybrid and
hybrid simulation tools, EPA is also planning to design a Graphical User Interface (GUI) and
integrate it with the vehicle simulation tool. This GUI will allow the user to choose from
different technologies and simulation options while making the use of the tool much easier
and straightforward. These tools are expected to assist in further analysis for the final rule as
necessary.
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Chapter 2
Reference
12 "Peer Review of the Greenhouse gas Emissions Model (GEM) and EPA's Response to
Comments," Docket EPA-HQ-OAR-2010-0162-3418, Publication Number: EPA-420-R-11-
007, July 2011.
13 Lee, S., Lee, B., Zheng, H., Sze, C., Quinones, L., and Sanchez, J., "Development of
Greenhouse Gas Emissions Model for 2014-2017 Heavy- and Medium-Duty Vehicle
Compliance," SAE 2011 Commercial Vehicle Engineering Congress, Chicago, September
2011,SAE Paper 2011-01-2188.
14 Forrest, W.O., "Air Conditioning and Gas Guzzler Tax Credits," Society of Automotive
Engineers, International Congress & Exposition, Detroit, Michigan, March 2002, SAE Paper
2002-01-1958.
15 Forrest, W.O. and Bhatti, M.S., "Energy Efficient Automotive Air Conditioning System,"
Society of Automotive Engineers, International Congress & Exposition, Detroit, Michigan,
March 2002, SAE Paper 2002-01-0229.
16 Johnson, V.H., "Fuel Used for Vehicle Air Condition: A State-by-State Thermal Comfort-
Based Approach," Society of Automotive Engineers, International Congress & Exposition,
Detroit, Michigan, March 2002, SAE Paper 2002-01-1957.
17 Rugh, J.P., Hovland, V., Andersen, S.O., "Significant Fuel Savings and Emissions
Reductions by Improving Vehicle Air Conditioning," Presentation at the 15th Annual Earth
Technologies Forum and Mobile Air Conditioning Summit, April 15, 2004.
18 Northeast States Center for a Clean Air Future, "Reducing Greenhouse Gas Emissions from
Light-Duty Motor Vehicles," September, 2004.
19 EPA and DOT, "Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards: Final Rule," May 7, 2010.
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Chapter 3
3 Results of Final and Alternative Standards
3.1 Introduction
This chapter provides the methodology and results of the technical assessment of the
future vehicle scenarios presented in this final rule. All methods in this chapter pertain to
both the MY 2008 and MY 2010 based future fleet projection. We note the few places where
the methods differ between the analyses. All results in this chapter are for the MY 2008
based future fleet projection, while those for the MY 2010 based future fleet projection are
found in RIA Chapter 10. Although there are differences in the details of these cost and
technology penetration estimates, the results are largely similar between the analyses
conducted with each of the two baselines.
As in the analysis of the MYs 2012-2016 rulemaking and in the proposal, in this final
rule, our evaluation of these scenarios included identifying potentially available technologies
and assessing their effectiveness, cost, and impact on relevant aspects of vehicle performance
and utility. The wide number of technologies that are available and likely to be used in
combination required a method to account for their combined cost and effectiveness, as well
as estimates of their availability to be applied to vehicles.
Applying these technologies efficiently to the wide range of vehicles produced by
various manufacturers is a challenging task. In order to assist in this task, EPA is again using
a computerized program called the Optimization Model for reducing Emissions of
Greenhouse gases from Automobiles (OMEGA). Broadly, OMEGA starts with a description
of the future vehicle fleet, including manufacturer, sales, base CO2 emissions, footprint and
the extent to which emission control technologies are already employed. For the purpose of
this analysis, EPA uses OMEGA to analyze over 200 vehicle platforms which encompass
approximately 1300 vehicle models in order to capture the important differences in vehicle
and engine design and utility of future vehicle sales of roughly 15-17 million units annually in
the 2017-2025 timeframe. The model is then provided with a list of technologies which are
applicable to various types of vehicles, along with the technologies' cost and effectiveness
and the percentage of vehicle sales which can receive each technology during the redesign
cycle of interest. The model combines this information with economic parameters, such as
fuel prices and a discount rate, to project how various manufacturers would apply the
available technology in order to meet increasing levels of emission control. The result is a
description of which technologies are added to each vehicle platform, along with the resulting
cost. The model can also be set to account for various types of compliance flexibilities.1
EPA has described OMEGA's specific methodologies and algorithms previously in
the model documentation,20 the model is publically available on the EPA website,21 and it has
been peer reviewed.22
T While OMEGA can apply technologies which reduce COi efficiency related emissions and refrigerant leakage
emissions associated with air conditioner use, this task is currently handled outside of the OMEGA core model.
A/C improvements are highly cost-effective, and would always be added to vehicles by the model, thus they are
simply added into the results at the projected penetration levels.
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MY 2017 and Later - Regulatory Impact Analysis
No public comments were received on the use of the OMEGA model, or on the
OMEGA analytic framework used in the proposal.
3.2 OMEGA model overview
The OMEGA model evaluates the relative cost and effectiveness of available
technologies and applies them to a defined vehicle fleet in order to meet a specified
GHG emission target. Once the regulatory target (whether the target adopted in the
rule, or an alternative target) has been met, OMEGA reports out the cost and societal
benefits of doing so. The model is written in the C# programming language,
however both inputs to and outputs from the model are provided using spreadsheet and
text files. The output files facilitate additional manipulation of the results, as
discussed in the next section.
OMEGA is primarily an accounting model. It is not a vehicle simulation
model, where basic information about a vehicle, such as its mass, aerodynamic drag,
an engine map, etc. are used to predict fuel consumption or CO2 emissions over a
defined driving cycle.u Although OMEGA incorporates functions which generally
minimize the cost of meeting a specified COi target, it is not an economic simulation
model which adjusts vehicle sales in response to the cost of the technology added to
each vehicle.v
OMEGA can be used to model either a single vehicle model or any number of
vehicle models. Vehicles can be those of specific manufacturers as in this analysis or
generic fleet-average vehicles as in the 2010 Joint Technical Assessment Report
supporting the MY 2017-2025 NOT. Because OMEGA is an accounting model, the
vehicles can be described using a relatively few number of terms. The most important
of these terms are the vehicle's baseline COi emission level, the level of COi reducing
technology already present, and the vehicle's "type," which indicates the technology
available for addition to that vehicle to reduce COi emissions. Information
determining the applicable COi emission target for the vehicle must also be provided.
This may simply be vehicle class (car or truck) or it may also include other vehicle
attributes, such as footprint* In the case of this rulemaking, footprint and vehicle
class are the relevant attributes.
Emission control technology can be applied individually or in groups, often
called technology "packages." The OMEGA user specifies the cost and effectiveness
of each technology or package for a specific "vehicle type," such as midsize cars with
V6 engines or minivans. The user can limit the application of a specific technology to
a specified percentage of each vehicle's sales (i.e., a "maximum penetration cap"),
u Vehicle simulation models may be used in creating the inputs to OMEGA as discussed in Joint TSD Chapter 3
as well as Chapter 1 and 2 of the RIA.
v While OMEGA does not model changes in vehicle sales, RIA Chapter 8 discusses this topic.
w A vehicle's footprint is the product of its track width and wheelbase, usually specified in terms of square feet.
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Chapter 3
which for this rulemaking, are specified a priori by EPA and NHTSA.X The
effectiveness, cost, application limits of each technology package can also vary over
time.Y A list of technologies or packages is provided to OMEGA for each vehicle
type, providing the connection to the specific vehicles being modeled. A description
of these packages can be found in Chapter 1 of this RIA.
OMEGA is designed to apply technology in a manner similar to the way that a
vehicle manufacturer might make such decisions. In general, the model considers
three factors which EPA believes are important to the manufacturer: 1) the cost of the
technology, 2) the value which the consumer is likely to place on improved fuel
economy and 3) the degree to which the technology moves the manufacturer towards
achieving its fleetwide CO2 emission target.
Technology can be added to individual vehicles using one of three distinct
ranking approaches. Within a vehicle type, the order of technology packages is set by
the OMEGA user. The model then applies technology to the vehicle with the lowest
Technology Application Ranking Factor (hereafter referred to as the TARE).
OMEGA offers several different options for calculating TARE values. One TARE
equation considers only the cost of the technology and the value of any reduced fuel
consumption considered by the vehicle purchaser. The other two TARE equations
consider these two factors in addition to the mass of GHG emissions reduced over the
life of the vehicle. Fuel prices by calendar year, vehicle survival rates and annual
vehicle miles travelled with age are provided by the user to facilitate these
calculations.
For each manufacturer, OMEGA applies technology (subject to phase in
constraints, as discussed in Joint TSD 3) to vehicles until the sales and VMT-weighted
emission average complies with the specified standard or until all the available
technologies have been applied. The standard can be a flat standard applicable to all
vehicles within a vehicle class (e.g., cars, trucks or both cars and trucks).
Alternatively the GHG standard can be in the form of a linear or constrained logistic
function, which sets each vehicle's target as a function of vehicle footprint (vehicle
track width times wheelbase). When the linear form of footprint-based standard is
used, the "line" can be converted to a flat standard for footprints either above or below
specified levels. This is referred to as a piece-wise linear standard, and was used in
modeling the standards in this analysis.
The emission target can vary over time, but not on an individual model year
basis. One of the fundamental features of the OMEGA model is that it applies
x See TSD 3.
Y "Learning" is the process whereby the cost of manufacturing a certain item tends to decrease with increased
production volumes or over time due to experience. While OMEGA does not explicitly incorporate "learning"
into the technology cost estimation procedure, the user can currently simulate learning by inputting lower
technology costs in each subsequent redesign cycle based on anticipated production volumes or on the elapsed
time.
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MY 2017 and Later - Regulatory Impact Analysis
technology to a manufacturer's fleet over a specified vehicle redesign cycle. OMEGA
assumes that a manufacturer has the capability to redesign any or all of its vehicles
within this redesign cycle. OMEGA does not attempt to determine exactly which
vehicles will be redesigned by each manufacturer in any given model year. Instead, it
focuses on a GHG emission goal several model years in the future, reflecting the
manufacturers' capability to plan several model years in advance when determining
the technical designs of their vehicles. Any need to further restrict the application of
technology can be effected through the caps on the application of technology to each
vehicle type mentioned above.
The OMEGA model is designed to estimate the cost of complying with a
regulation in a given year. While the OMEGA design assumes that a manufacturer's
entire fleet of vehicles can be redesigned within one redesign cycle, rarely will a
manufacturer redesign exactly 20% of its vehicle sales in each of five straight model
years. The base emissions and emission reductions of the vehicles being redesigned
will vary. Thus, OMEGA inherently assumes the banking and borrowing of credits to
enable compliance with standards in the intermediate years of a redesign cycle using
the technology projected for the final year of the cycle, assuming that the intermediate
standards require gradual improvement each year. However, any credit banking or
borrowing outside of the redesign cycle is incumbent upon the user to estimate.2
Once technology has been added so that every manufacturer meets the
specified targets (or exhausts all of the available technologies), the model produces a
variety of output files. These files include information about the specific technology
added to each vehicle and the resulting costs and emissions. Average costs and
emissions per vehicle by manufacturer and industry-wide are also determined for each
vehicle class.
3.3 OMEGA Model Structure
OMEGA includes several components, including a number of pre-processors that
assist users in preparing a baseline vehicle forecast,^ creating and ranking technology
packages,88 and calculating the degree to which technology is present on baseline vehicles.
The OMEGA core model collates this information and produces estimates of changes in
vehicle cost and CO2 emission level. Based on the OMEGA core model output, the
z EPA has considered modeling credit banking as part of this analysis, but decided not to analyze the program
using this approach for two reasons. First, as the GHG standards continue indefinitely, rather than expiring in
2025, EPA wants to represent the cost of bringing vehicles into compliance with the standard, rather than the
reduced cost of a long term credit deficit. Second, properly modeling credit banking requires perfect knowledge
of future redesign cycles. The OMEGA redesign cycle approach is specifically designed to avoid this issue, and
the related uncertainty. See also Preamble Section I.C explaining the difference in the agencies' programmatic
costs estimates which result from this difference in methodology.
^ Joint TSD Chapter 1
BB RIA Chapter 1
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Chapter 3
technology penetration of the new vehicle mix and the scenario impacts (fuel savings,
emission impacts, and other monetized benefits) are calculated via post-processors. The pre-
and post- processors are Microsoft Excel spreadsheets and scripted programs (written in
Visual Basic and MATLAB), while the OMEGA core model is an executable program written
in the C# language.
OMEGA is designed to be flexible in a number of ways. Very few numerical values
are hard-coded in the model, and consequently, the model relies heavily on its input files. The
model utilizes five input files: Market, Technology, Fuels, Scenario, and Reference. Figure
3.3-1 shows the (simplified) information flow through OMEGA, and how these files interact.
Figure 3.3-1 Information Flow in the OMEGA Model
Pre-Processors
Other inputs
Vehicle Forecast
Vehicle Types
LP Model
Cost Model
Baseline Fleet
Technology
Accounting
Vehicle Platforms
Market File
Technology Packages
i
Ranking Algorithm
Technology File
Reference File
Scenario File
Fuels File
Core Model
OMEGA
Post-Processors
-Technology Penetration
-Impacts
OMEGA utilizes four basic sets of input data. The first, the market file, is a
description of the vehicle fleet. The key pieces of data required for each vehicle are its
manufacturer, COi emission level, fuel type, projected sales and footprint. The model also
requires that each vehicle be assigned to one of the 19 vehicle types, which tells the model
which set of technologies can be applied to that vehicle. Chapter 1 of the Joint TSD contains
a description of how the market forecasts were created for modeling purposes, and includes a
discussion on how EPA defined the 19 vehicle types. In addition, the degree to which each
vehicle already reflects the effectiveness and cost of each available technology in the baseline
fleet must be input. This prevents the model from adding technologies to vehicles already
having these technologies in the baseline. It also avoids the situation, for example, where the
model might try to add a basic engine improvement to a current hybrid vehicle. Section
3.4.1.2 of this Regulatory Impact Analysis (RIA) contains a detailed discussion of how EPA
accounts for technology present in the baseline fleet in OMEGA.
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MY 2017 and Later - Regulatory Impact Analysis
The second type of input data, the technology file, is a description of the technologies
available to manufacturers which consists primarily of their cost, effectiveness, compliance
credit value, and electricity consumption. This information was described in Chapter 1 of this
RIA and Chapter 3 of the Joint TSD. In all cases, the order of the technologies or technology
packages for a particular vehicle type is designated by the model user in the input files prior to
running the model. The ranking of the packages is described in Chapter 1 of the RIA.
The third type of input data describes vehicle operational data, such as annual scrap
rates and mileage accumulation rates, and economic data, such as fuel prices and discount
rates. These estimates are described in chapter 4 of the Joint TSD.
The fourth type of data describes the COi emission standards being modeled. These
include the MY 2016 standards and the MY 2017-2025 standards. As described in more
detail in Chapter 5 of the Joint TSD and briefly in section 3.5.6 below, the application of A/C
technology is evaluated in a separate analysis from those technologies which impact CO2
emissions over the 2-cycle test procedure. For modeling purposes, EPA applies this AC
credit by adjusting manufacturers' car and truck CO2 targets by an amount associated with
EPA's projected use of improved A/C systems, as discussed in Section 3.5.6, below.
The input files used in this analysis, as well as the current version of the OMEGA
model, are available in the docket (EPA-HQ-OAR-2010-0799). The following sections
describe creation of each of the input files from the data and parameters discussed in the
JointTSD and in this RIA.
3.4 Model Inputs
3.4.1 Market Data
3.4.1.1 Vehicle platforms
As discussed in Joint TSD Chapter 3 and in Chapter 1 of the RIA, vehicle
manufacturers typically develop many different models by basing them on a smaller number
of vehicle platforms. The platform typically consists of a common set of vehicle architecture
and structural components. This allows for efficient use of design and manufacturing
resources. In this analysis, EPA created over 200 vehicle platforms which were used to
capture the important differences in vehicle and engine design and utility of future vehicle
sales. The approximately sixty vehicle platforms are a result of mapping the vehicle fleet into
the 19 engine based vehicle types (Table 3.4.1) and the 10 body size and structure based
utility classes (Table Of 2) by manufacturer. As not all vehicle types match to all utility
types, and not all manufacturers make all vehicle and utility types, the number of vehicles is
less than the multiplicative maximum of the two tables.
Table 3.4-1 Vehicle Types in the MY 2017-2025 Analysis
Vehicle Description
Auto Subcompact 13 DOHC 4v
Auto Subcompact 14 SOHC/DOHC 2v/4v
Vehicle
Type
1
Vehicle
Class
Small car
3-7
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Chapter 3
Auto Subcompact Electric
Auto Compact SOHC 2v
Auto Compact SOHC/DOHC 4v
Auto Midsize SOHC/DOHC 4v
Pickup Small DOHC 4v
Auto Subcompact 15 SOHC 4v
Auto Subcompact V6 SOHC/DOHC 4v
Auto Subcompact 14 SOHC/DOHC 4v turbo/supercharged
Auto Compact Rotary
Auto Compact 15 DOHC 4v
Auto Compact V6 SOHC/DOHC 4v
Auto Compact 14 SOHC/DOHC 4v turbo/supercharged
Auto Midsize V6 SOHC/DOHC 4v
Auto Midsize 14 SOHC/DOHC 4v tubo/supercharged
Auto Large V6 SOHC/DOHC 4v
Auto Midsize 14 SOHC 4v tubo/supercharged
Auto Subcompact V6 SOHC 3v
Auto Compact V6 OHV 2v
Auto Midsize V6 SOHC 2v
Auto Midsize V6 OHV 2v
Auto Large V6 OHV 2v
Auto Subcompact V8 DOHC 4v
Auto Compact V10 DOHC 4v
Auto Compact V8 DOHC 4v turbo/supercharged
Auto Compact V8 DOHC 4v/5v
Auto Compact V6 DOHC 4v
Auto Compact V5 DOHC 4v turbo/supercharged
Auto Midsize V12 DOHC 4v
Auto Midsize V10 DOHC 4v
Auto Midsize V8 DOHC 4v/5v
Auto Midsize V8 SOHC 4v
Auto Midsize V6 DOHC 4v
Auto Midsize V7 DOHC 4v
Auto Large V16 DOHC 4v turbo/supercharged
Auto Large V12 SOHC 4v turbo/supercharged
Auto Large V12 DOHC 4v
Auto Large V10 DOHC 4v
Auto Large V8 DOHC 4v turbo/supercharged
Auto Large V8 DOHC 2v/4v
Auto Large V8 SOHC 4v
Auto Subcompact V10 OHV 2v
Auto Subcompact V8 SOHC 3v
Auto Midsize V8 SOHC 3v turbo/supercharged
Auto Midsize V8 SOHC 3v
Auto Midsize V8 OHV 2v
Auto Large V12 SOHC 3v turbo/supercharged
Auto Large V8 SOHC 3v turbo/supercharged
Auto Large V8 SOHC 2v
Auto Large V8 OHV 2v/4v
SUV Small 14 DOHC 4v
SUV Midsize SOHC/DOHC 4v
SUV Large DOHC 4v
Minivan 14 DOHC 4v
SUV Small 14 DOHC 4v turbo/supercharged
SUV Midsize V6 SOHC/DOHC 4v
2
3
4
5
6
7
8
Standard
car
Standard
car
Standard
car
Large car
Large car
Small
MPV
Large
MPV
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MY 2017 and Later - Regulatory Impact Analysis
SUV Midsize 14 SOHC/DOHC 4v turbo/supercharged
SUV Large V6 SOHC/DOHC 4v
SUV Large 15 DOHC 2v
SUV Large 14 DOHC 4v turbo/supercharged
SUV Midsize V6 SOHC 2v
SUV Large V6 SOHC 2v
SUV Small V6 OHV 2v
SUV Midsize V6 OHV 2v
SUV Large V6 OHV 2v
Minivan V6 OHV 2v
Cargo Van V6 OHV 2v
SUV Large V10 DOHC 4v turbo/supercharged
SUV Large V8 DOHC 4v turbo/supercharged
SUV Large V8 SOHC/DOHC 4v
SUV Large V6 DOHC 4v turbo/supercharged
SUV Large V8 SOHC 3v turbo/supercharged
SUV Large V8 SOHC 2v/3v
SUV Large V8 OHV 2v
Cargo Van V10 SOHC 2v
Cargo Van V8 SOHC/OHV 2v
Pickup Large DOHC 4v
Pickup Small V6 SOHC 4v
Pickup Small 15 DOHC 2v
Pickup Large V6 DOHC 2v/4v
Pickup Large 15 DOHC 2v
Pickup Small V6 SOHC 2v
Pickup Small V6 OHV 2v
Pickup Large V6 SOHC 2v
Pickup Large V6 OHV 2v
Pickup Large V8 DOHC 4v
Pickup Large V8 SOHC 2v
Pickup Large V8 SOHC/DOHC 3v turbo/supercharged
Pickup Large V8 SOHC 3v
Pickup Large V8 OHV 2v
9
10
11
12
13
14
15
16
17
18
19
Large
MPV
Large
MPV
Truck
Truck
Small
MPV
Large
MPV
Large
MPV
Truck
Truck
Truck
Truck
aI4 = 4 cylinder engine, 15 = 5 cylinder engine, V6, V7, and V8 = 6, 7, and 8 cylinder
engines, respectively, DOHC = Double overhead cam, SOHC = Single overhead cam,
OHV = Overhead valve, v = number of valves per cylinder.
Table Of 2 Vehicle Types in the Technical Assessment Analysis
Utility
Class #
1
2
3
Utility Class
Subcompact Auto
Compact Auto
Mid Size Auto
Vehicle Use l
Car
Car
Car
Footprint Criteria
Footprint <43
43<=Footprint<46
46<=Footprint<5 3
Structure Criteria
--
--
—
3-9
-------
Chapter 3
4
5
6
7
8
9
10
Large Auto
Small SUV
Large SUV
Small Pickup
Large Pickup
Cargo Van
Minivan
Car
SUV
SUV
Pickup
Pickup
Van
Van
56<=Footprint
43<=Footprint<46
46<=Footprint
Footprint < 50
50<=Footprint
—
—
--
—
—
—
--
Ladder Frame
Unibody
1. Vehicle use type is based upon analysis of EPA certification data.
3.4.1.2 Accounting for technology already on vehicles
As mentioned above, our modeling accounts for the fact that many baseline vehicles
are already equipped with one or more of the technologies discussed in Joint TSD 3. Because
of the choice to apply technologies in packages, and because vehicles are equipped with
individual technologies in a wide variety of combinations, accounting for the presence of
specific technologies in terms of their proportion of package cost and COi effectiveness
requires careful, detailed analysis.
Thus, EPA developed a method to account for the presence of the combinations of
applied technologies in terms of their proportion of the technology packages. This analysis
can be broken down into four steps
The first step in the process is to break down the available GHG control technologies
into five groups: 1) engine-related, 2) transmission-related, 3) hybridization, 4) weight
reduction and 5) other. Within each group we gave each individual technology a ranking
which generally followed the degree of complexity, cost and effectiveness of the technologies
within each group. More specifically, the ranking is based on the premise that a technology
on a baseline vehicle with a lower ranking would be replaced by one with a higher ranking
which was contained in one of the technology packages which we included in our OMEGA
modeling. The corollary of this premise is that a technology on a baseline vehicle with a
higher ranking would be not be replaced by one with an equal or lower ranking which was
contained in one of the technology packages which we chose to include in our OMEGA
modeling. This ranking scheme can be seen in an OMEGA pre-processor (the TEB/CEB
calculation macro), available in the docket (EPA-HQ-OAR-2010-0799).
In the second step of the process, we used these rankings to estimate the complete list
of technologies which would be present on each vehicle after the application of a technology
package. In other words, this step indicates the specific technology on each vehicle after a
package has been applied to it. We then used the EPA lumped parameter model to estimate
the total percentage CO2 emission reduction associated with the technology present on the
baseline vehicle (termed package 0), as well as the total percentage reduction after application
of each package. We used a similar approach to determine the total cost of all of the
technology present on the baseline vehicle and after the application of each applicable
technology package.
The third step in this process is to account for the degree to which each technology
package's incremental effectiveness and incremental cost is affected by the technology
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MY 2017 and Later - Regulatory Impact Analysis
already present on the baseline vehicle. Termed the technology effectiveness basis (TEB) and
cost effectiveness basis (CEB), respectively, the values are calculated in this step using the
equations shown in RIA chapter 3. For this final rulemaking, we also account for the credit
values using a factor termed other effectiveness basis (OEB).
The value of each vehicle's TEB for each applicable technology package is
determined as follows:
TEB,=
' TotalEffed^i ^ ( \-TotalEffect pi
\-TotalEffeavl) \\-TotalEffectp._
^ l-TotalEffeapl N
\-TotalEffedpi_v
Where
TotalEffectv,i = Total effectiveness of all of the technologies present on the baseline vehicle after
application of technology package i
TotaiEffectv,i-i = Total effectiveness of all of the technologies present on the baseline vehicle after
application of technology package i-1
TotalEffectp>i = Total effectiveness of all of the technologies included in technology package i
TotaiEffectp>i_i = Total effectiveness of all of the technologies included in technology package i-1
Equation 3.4-1 - TEB calculation
The degree to which a technology package's incremental cost is reduced by
technology already present on the baseline vehicle is termed the cost effectiveness basis, or
CEB, in the OMEGA model. The value of each vehicle's CEB for each applicable
technology package is determined as follows:
CEBj = 1 - (TotalCostv,i - TotalCostv,i-i) / (TotalCostp4 - TotalCostp4_i)
Where
TotalCosty = total cost of all of the technology present on the vehicle after addition
of package i or i-1 to baseline vehicle v
TotalCostp = total cost of all of the technology included in package i or i-1
i = the technology package being evaluated
i-1 = the previous technology package
Equation 3.4-2 - CEB calculation
3-11
-------
Chapter 3
As described above, technology packages are applied to groups of vehicles which
generally represent a single vehicle platform and which are equipped with a single engine size
(e.g., compact cars with four cylinder engine produced by Ford). Thus, the fourth step is to
combine the fractions of the CEB and TEB of each technology package already present on the
individual baseline vehicle models for each vehicle grouping. For cost, percentages of each
package already present are combined using a simple sales-weighting procedure, since the
cost of each package is the same for each vehicle in a grouping. For effectiveness, the
individual percentages are combined by weighting them by both sales and base COi emission
level. This appropriately weights vehicle models with either higher sales or CCh emissions
within a grouping. Once again, this process prevents the model from adding technology
which is already present on vehicles, and thus ensures that the model does not double count
technology effectiveness and cost associated with complying with the modeled standards.
The other effectiveness basis (OEB) was designed to appropriately account for credit
differences between technologies actually on the vehicle and technology packages applied
through the technology input file. As an example, if a baseline vehicle includes start stop
technology, and the applied package does not, the model needs to account for this different in
off-cycle credit. The OEB is an absolute credit value and is used directly in the model's
compliance calculations. Accounting for Net Mass Reduction and Safety related Mass
reduction
For this analysis, as in the proposal, EPA applied mass reduction in a manner similar to
that used by NHTSA in the CAFE model analysis. In this methodology, and in contrast to the
approach taken by EPA in the MYs 2012-2016 rule, more mass is taken out of heavier
vehicles, and less mass is taken out of lighter vehicles. This approach allows the agency to
provide costs for a technology assessment that is estimated to result in a safety neutral
compliance path (i.e., no net additional fatalities attributable to the means modeled to achieve
the standards) to the fleet. The agencies received several comments on the safety analysis;
these comments are discussed in section II.G of the preamble to the final rule. Manufacturers
may not necessarily apply mass reduction in this manner, but as shown here, EPA
demonstrates that a technically feasible and economically practicable compliance path exists
for manufacturers to meet their fleet standards without compromising safety. The limits on
mass reduction, as applied in the OMEGA model, are dependent upon both the technology
inputs discussed in TSD Chapter 3, as well as on the fatality coefficients from the 2012
Kahane report and the related adjustments for improvements in federal motor vehicle safety
standards (FMVSS) as discussed in Section II.G of the Preamble, and are subject to the same
caveats. Between the 2011 Kahane report, and the updated 2012 report used in this final
rulemaking, several relevant coefficients were updated. As noted in the proposal, adjustments
to these coefficients changes the projected amount of mass reduction projected for the fleet,
and correspondingly, changes the projected amount of other technologies. Generally, the
revisions to the Kahane coefficients led to less mass reduction technology being used in our
modeling as compared to the proposal.
Using a spreadsheet scoping tool, EPA projected the maximum amount of mass
reduction on a vehicle by vehicle basis that would result in a net fatality neutral result. Based
on the Kahane 2012 coefficients used in the analysis, reducing weight from trucks above
4,594 pounds and from minivans, reduces fatalities. By contrast, the Kahane analysis states
3-12
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MY 2017 and Later - Regulatory Impact Analysis
that removing weight from the other vehicle categories increases fatalities. The inputs used in
the OMEGA analysis are shown below (Table 3.4-3 Fatality coefficients used in OMEGA
analysis
Only the 1.56 percent risk increase in the lighter cars is statistically significant. There
are nonsignificant increases in the heavier cars and the lighter truck-based LTVs, and
nonsignificant societal benefits for mass reduction in CUVs, minivans, and the heavier truck-
based LTVs. The report concludes that judicious combinations of mass reductions that
maintain footprint and are proportionately higher in the heavier vehicles are likely to be
safety-neutral - i.e., they are unlikely to have a societal effect large enough to be detected by
statistical analyses of crash data. The primarily non-significant results are not due to a
paucity of data, but because the societal effect of mass reduction while maintaining footprint,
if any, is small. These coefficients are further discussed in Preamble Section II.G of the final
rule.
Table 3.4-3 Fatality coefficients used in OMEGA analysis
Vehicle Category
by class and
weight
PC below 3 106
PC above 3 106
LT below 4594
LT above 4594
Minivan
Kahane
Coefficients l
1 .56%
0.51%
0.52%
-0.34%
-0.37%
Base
fatalities
per billion
miles
11.091
9.313
13.241
13.032
7.499
adjustment for
new FMVSS
0.904
0.904
0.904
0.904
0.904
Change in Fatalities
per pound per mile2
1.6E-12
4.3E-13
6.2E-13
-4.0E-13
-2.5E-13
Expressed as percent change in base fatalities per 100 pound change in vehicle weight
Calculated as coefficients x base fatalities x adjustment x one billion miles /100
The mass reduction scoping tool contains the entire fleet discussed in joint TSD 1,
along with their curb weight, and their passenger car, light truck, and minivan classification
according to the criteria in the 2012 Kahane report. Using this tool, EPA determined that a
simulation of fatality neutrality could result by assuming that no MY 2008 baseline passenger
car was had its curb weight reduced below 3,200 pounds, and no light trucks were reduced
below 4,594 pounds. These values were determined iteratively, with the end product a safety
neutral analysis. By contrast, in the proposal, we assumed that no MY 2008 baseline
passenger car was reduced in weight below 3,000 pounds, and no light trucks were reduced
below 4,594 pounds; for this final rule analysis, we reduced the maximum weight reduction
for cars based on the revisions to the Kahane report.cc The OMEGA model could still select
mass reduction for vehicles above these weight limits, with the amount constrained by these
limits and the phase-in cap on mass reduction. Vehicles above these weights could have their
weight reduced through mass reduction technology in the OMEGA model. The per vehicle
The MY 2010 baseline, because it has a different distribution of weight by vehicle class, required a separate
analysis. Weight caps of 3,300 pounds (cars) and 4,100 pounds (trucks) were used.
3-13
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Chapter 3
limit on weight reduction for these vehicles was therefore determined by these specific weight
cut points, or by the maximum phase-in caps for mass reduction of 15% in 2021, 20% in
2025.. Vehicles below these weights had no net mass reduction applied.
The term "net mass reduction" is used because EPA explicitly accounted for the mass
impacts (generally increases) from converting a vehicle into a hybrid-electric, plug-in hybrid
electric, or battery electric vehicle. These weight increases were included in the proposal, but
were not included in the MYs 2012-2016 analysis or in the technical assessment report. A
table of these weight impacts is presented in Joint TSD Chapter 3. The per-vehicle limit on
weight reduction determined above is for net mass reduction, rather than the application of
total mass reduction technology.
Because the limits on net mass reduction are at the individual vehicle level, they are
reflected through modifications to the individual TEB and CEB values rather than the "caps"
in the technology file (which are discussed in the next section). EPA assumed that there was
no mass reduction technology being utilized in the baseline fleets, or in other words, that the
costs for mass reduction appropriately reflected the level of mass reduction technology
currently in the fleet.
To implement this schema, each vehicle in the baseline was assigned the following
parameters:
• Amount of mass reduction already present in baseline vehicle (assumed to be
zero in this analysis)
• Maximum amount of mass reduction allowed
• Mass penalty for adding various technologies to that vehicle
Some examples:
• A baseline vehicle is defined with a 10 percent maximum mass reduction. A
vehicle package is applied containing a 15 percent mass reduction. The
package mass reduction will be overridden resulting in a 10 percent cost and
effectiveness applied to the vehicle.
• A baseline vehicle has a 5 percent penalty for P2HEV conversion. A vehicle
package is applied containing a 10 percent mass reduction and a conversion to
P2 hybrid. Due to the 5 percent penalty for conversion, the baseline vehicle
will incur a cost of 15 percent mass reduction to result in an overall 10 percent
reduction. The resulting effectiveness due to the mass reduction will be 10
percent.
Under this system, any amount of mass reduction already in the baseline vehicle will
be subtracted from the maximum amount of mass reduction allowed. All vehicles in the
baseline fleet are assumed to have no mass reduction technology applied.
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MY 2017 and Later - Regulatory Impact Analysis
3.4.2 Technology Data
Consistent with OMEGA's redesign cycle approach, the technology input file defines
the technology packages which the model can add to the vehicle fleet. In brief, each of the 19
vehicle types has an associated list of technology packages, costs, credit values, and
effectivenesses.DD For this analysis, as discussed below, we considered the off-cycle credit
values for active aerodynamics and start-stop technology We also considered the full size
pickup truck credits - both mild and strong. Each of the 19 lists was then ordered by how
OMEGA should add them to that specific vehicle type. The order of this list is influenced by
the relative cost and effectiveness of technologies as well as their market penetration cap (or
maximum penetration rate). Market penetration caps of less than 100% restrict the model to
that fraction of a vehicle platform.EE The processes to build and rank technology packages
for the technology file are described in detail in Chapter 1 of the RIA.
For this analysis, a separate technology file was developed for each scenario
(reference and control) and model year (2021 and 2025) for which OMEGA was run. The
MY 2021 and MY 2025 costs differ due to the learning effects discussed in the Joint TSD
Chapter 3, and the technology files also differ due to the different limits on maximum
penetrations of technologies. MY 2016 was also run in order to evaluate stranded capital
costs.
OMEGA adds technology effectiveness according to the following equation in which
the subscripts t and t-1 represent the times before and after technology addition, respectively.
The numerator is the effectiveness of the current technology package and the denominator
serves to "back out" any effectiveness that is present in the baseline. AIE is the "average
incremental effectiveness" of the technology package on a vehicle type, and TEB is the
"technology effectiveness basis", which denotes the fraction of the technology present in the
baseline.
For this final rulemaking, OMEGA has been modified to additionally include the cost
and benefits of certain off-cycle credits start-stop and active aerodynamics) and the full size
pickup mild and strong HEV credit. As a result, the model separately tracks each source of
COi emissions that are used in the compliance equation. For this analysis, these sources are
the vehicle tailpipe and the credits associated with these technologies.
Equation 3.4-3- Calculation of New Tailpipe COi
CO2t_lx(l-AIE)
092, =-
1-AIExTEB
DD Given that effectiveness is expressed in percentage terms, the absolute effectiveness differs even among
vehicles of the same vehicle type, but the relative effectiveness is the same.
EE Penetration caps may reflect technical judgments about technology feasibility and availability, consumer
acceptance, lead time, and other reasons as detailed in Chapter 3 of the Joint TSD.
3-15
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Chapter 3
The tailpipe CO2 is adjusted for the usage of these credits in order to calculate
compliance CC>2. If, for example, the applied package has 1.0 grams worth of credit
associated with it, then the 1.0 gram from the credit will be subtracted from the tailpipe COi
to produce the CC>2 value that OMEGA uses in the compliance calculation. As the credits
differ on a vehicle by vehicle, rather than vehicle type by vehicle type basis, the OEB is used
in the compliance calculation rather than the credit value in the technology file.
OMEGA then adds technology cost according to the equations below, where CEB
refers to the "cost effectiveness basis", or in other words, the technology cost that is present in
the baseline. Cost can be calculated for the application of a package, or eventually, for the
average cost of a manufacturers fleet (Equation 3.4-4, Equation 3.4-5).
Equation 3.4-4- Calculation of New Cost after applying a package
A + TechCost (1 - CEB)
Equation 3.4-5 - Calculation of Average Cost for a manufacturer
TechCost* ModelSales
AvgVehicl£ostMFR =
TotalFleeSales JMFR
EPA's OMEGA model calculates the new CO2 and average vehicle cost after each
technology package has been added.
Relative to the proposal, EPA modified the methodology used to generate the
OMEGA technology input file relative to previous analyses.
As background, for both the MYs 2012-2016 rulemaking analysis and the Technical
Assessment Report supporting the MYs 2017-2025 NOT, the technology caps generally fell
into a few broad numeric categories. As an example, in the analysis supporting the MYs
2012-2016 final rulemaking, most technologies were capped at one of three levels (15%,
85%, 100%). The small number of technology caps made it relatively simple to build
packages around technologies which had a shared cap. By contrast, and as discussed in
Chapter 3 of the joint TSD, there are both more technologies and more technology cap levels
considered in this final rule. Thus, it was more difficult to construct packages with uniform
sets of caps. For the proposal, these caps were incorporated into the OMEGA modeling in
one of two ways. Major engine technologies such as turbo-charging and downsizing,
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MY 2017 and Later - Regulatory Impact Analysis
hybridization, electrification and dieselization were directly controlled through caps in the
technology file. Maximum penetration rates of other technologies were managed through
multiple runs of the TEB-CEB computation algorithm and modifications to the cost,
effectiveness, and electric conversion values in the technology file.
While this "weighting" method was used in the proposal, for this final rule, we have
implemented a package ranking scheme based solely upon calculated TARF values. This
ranking methodology is described in RIA chapter 1. In short, a list of technically reasonable
packages is fed into an algorithm which ranks the packages based on their cost-effectiveness
and the availability of space under the selected caps.23 The output is a ranked technology file.
The ranked technology files and the ranking algorithm are docketed.24
OMEGA also tracks electrical consumption of each vehicle in kWh per mile. Each
technology package is associated with an "electricity conversion percentage" which refers to
the increase in the energy consumed by the electric drivetrain relative to reduction in the
consumption of energy from liquid fuel. Electricity is a highly refined form of energy which
can be used quite efficiently to create kinetic energy. Thus, electric motors are much more
efficient than liquid fuel engines. Consequently, the electric consumption percentage input in
the Technology File for plug-in vehicles is generally well below than 100%. It may be
possible that this percentage could exceed 100% under certain circumstances, for example
when one type of plug-in vehicle is being converted into another plug-in vehicle and
electricity consumption per mile is increasing due to larger and heavier batteries, etc.
However, that was not the case for any of the technologies evaluated in this analysis.
The electric consumption for each vehicle as entered into the OMEGA technology file
(in this analysis) in the on-road energy consumption, calculated as
Equation 3.4-6 - Electricity Consumption considered in OMEGA
Electricity Consumption =
2 cycle energy consumption from the battery / (1-on road gap)/ (1-charging losses)
Where:
2 cycle energy consumption = Based on vehicle type as documented in TSD 3
On road gap for electricity = 30%
Charging losses = 10%
The actual input to the model is the "electric conversion percentage," which is
computed as a single fraction for each vehicle type. Thus, in OMEGA's calculations, the
resulting electricity consumption differs based on the starting CO2 of the vehicle.
Equation 3.4-7 - Electrical Conversion Percentage
Electric Conversion Percentage =
Electricity consumption
~~ ~ 12 gramC i gallon fuel 3409 btuper kwh .
^° 44 Grams C02 Carbon content of fuel Energy content of gasoline (btu)'
3-17
-------
Chapter 3
Where:
Electricity consumption = values from TSD 3 or RIA 1
Carbon content of fuel = 2433 for gasoline
Energy content of fuel = 115,000 btu/gallon
3.4.3 The Scenario File
3.4.3.1 Reference Scenario
In order to determine the technology costs associated with this final rulemaking, EPA
performed three separate modeling exercises. The first was to determine the costs associated
with meeting the MY 2016 GHG regulations. EPA considers the MY 2016 GHG regulations
to constitute the "reference case" for calculating the costs and benefits of this GHG rule. In
other words, absent any further rulemaking, this is the vehicle fleet EPA would expect to see
through 2016 -- the "status quo". In order to calculate the costs and benefits of this final rule
alone, EPA subtracted out any costs associated with meeting any existing standards related to
GHG emissions.
EPA assumes that in the absence of the MYs 2017-2025 GHG and CAFE standards,
the reference case fleet in MYs 2017-2025 would have fleetwide GHG emissions
performance no better than that projected to be necessary to meet the MY 2016 standards.
While it is not possible to know with certainty the future fleetwide GHG emissions
performance in the absence of more stringent standards, EPA believes that this approach is the
most reasonable assumption for developing the reference case fleet for MYs 2017-2025. A
discussion of this topic is presented in section III.D of the preamble, and is presented below
with additional figures and tables.
One critical factor supporting the final approach is that AEO2012 Early Release
projects relatively stable gasoline prices over the next 13 years. The average actualprice in
the U.S. for the first four months of 2012 for regular gasoline was $3.68 per gallon with
prices approaching $4.00 in March and April.00 The AEO2012 Early Release reference case
projects the regular gasoline price to be $3.87 per gallon in 2025, only slightly higher than the
price for the first four months of 2012.™ Accordingly, the reference fleet for MYs 2017-2025
reflects constant GHG emission standards (i.e. the MY 2016 standards continuing to apply in
each of those model years), and gasoline prices only slightly higher than today's gasoline
prices.
As discussed at proposal, these are reasonable assumptions to make for a reference
case. See 76 FR 75030-31. Based on these fuel price projections, the reference fleet for MYs
2017-2025 should correspond to a time period where there is a stable, unchanging GHG
standard, and essentially stable gasoline prices.
FF In 2012 dollars. As 2012 is not yet complete, we are not relating this value to 2010 dollars. See RIA 1 for
additional details on the conversion between dollar years.
00 http://www.eia.gov/petroleum/gasdiesel/ and click on "full history" for weekly regular gasoline prices
through May 7, 2012, last accessed on May 8, 2012.
HH http://www.eia.gov/forecasts/aeo/er/ last accessed on May 8, 2012.
3-18
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MY 2017 and Later - Regulatory Impact Analysis
EPA reviewed the historical record for similar periods when we had stable fuel
economy standards and stable gasoline prices. EPA maintains, and publishes every year, the
authoritative reference on new light-duty vehicle CO2 emissions and fuel economy.11 This
report contains very detailed data from MYs 1975-2010. There was an extended 18-year
period from 1986 through 2003 during which CAFE standards were essentially unchanged,"
and gasoline prices were relatively stable and remained below $1.50 per gallon for almost the
entire period. The 1975-1985 and 2004-2010 timeframes are not relevant in this regard due to
either rising gasoline prices, rising CAFE standards, or both. Thus, the 1986-2003 time frame
is an excellent analogue to the period out to MY 2025 during which AEO projects relatively
stable gasoline prices. EPA analyzed the Fuel Economy Trends data from the 1986-2003
timeframe (during which CAFE standards were universal rather than attribute-based),shown
in Table 3.4-4 and Table 3.4-5 and has drawn three conclusions: 1) there was a small,
industry-wide, average over-compliance with CAFE on the order of 1-2 mpg or 3-4%, 2)
almost all of this industry-wide over-compliance was from 3 companies (Toyota, Honda, and
Nissan) that routinely over-complied with the universal CAFE standards simply because they
produced smaller and lighter vehicles relative to the industry average, and 3) full line car and
truck manufacturers, such as General Motors, Ford, and Chrysler, which produced larger and
heavier vehicles relative to the industry average and which were constrained by the universal
CAFE standards, rarely over-complied during the entire 18-year period.
20 Previous OMEGA documentation for versions used in MYs 2012-2016 Final Rule (EPA-
420-B-09-035), Interim Joint TAR (EPA-420-B-10-042). Docket Nos. EPA-HQ-OAR-2010-
0799-1108 and EPA-HQ-OAR-2010-0799-1109. The documentation for OMEGA 1.4.1 is
also in the docket.
21 http://www.epa.gov/oms/climate/models.htm
22 EPA-420-R-09-016, September 2009. (Docket No. EPA-HQ-OAR-2010-0799-1135)
23 OMEGA model ranking algorithm. Available in the docket on the DVD ""FRM OMEGA
model, OMEGA inputs and outputs & GREET 2011 (DVD)"
24 OMEGA model inputs and outputs. These are available on a DVD in the docket (Docket
No. EPA-HQ-OAR-2010-0799). "FRM OMEGA model, OMEGA inputs and outputs &
GREET 2011 (DVD)"
n Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 through
2010, November 2010, available at www.epa.gov/otaq/fetrends.htm.
11 There are no EPA LD GHG emissions regulations prior to MY 2012.
3-19
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Chapter 3
Table 3.4-4 Fuel Economy Data for Selected Manufacturers, 1986-2003—Cars
Year
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Standard
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
27.5
Average 1986-2003
GM
27.0
27.2
28.1
27.4
27.3
27.2
26.7
27.3
27.5
27.3
27.9
28.2
27.6
27.4
27.6
28.1
28.5
28.6
Ford
26.7
26.5
27.0
26.9
26.3
27.2
26.7
27.8
27.1
27.6
26.3
26.9
27.3
27.2
27.1
26.8
27.1
26.7
Chrysler
28.6
27.7
28.5
28.0
27.4
27.5
27.7
27.9
26.2
28.2
27.2
27.2
28.3
27.0
27.6
27.6
27.0
28.5
Sales-
Weighted
average
27.1
27.1
27.8
27.3
27.0
27.2
26.8
27.6
27.2
27.6
27.3
27.6
27.6
27.3
27.4
27.6
27.8
27.9
Delta
-0.4
-0.4
0.3
-0.2
-0.5
-0.3
-0.7
0.1
-0.3
0.1
-0.2
0.1
0.1
-0.2
-0.1
0.1
0.3
0.4
-0.1
Vehicle
Weight
3145
3149
3157
3207
3298
3252
3329
3269
3334
3330
3388
3353
3347
3429
3448
3463
3442
3506
Toyota
32.3
32.9
32.7
31.8
30.4
30.6
28.9
29.0
29.1
30.0
29.5
29.8
30.2
30.4
30.5
31.3
30.7
32.4
Honda
33.6
32.8
31.8
31.3
30.4
30.3
30.9
32 2
32.1
32.8
31.8
32.1
32.0
30.9
31.0
32 2
32.0
32.7
Nissan
29.9
29.3
30.6
30.2
28.4
29.0
29.9
29.1
29.8
29.2
30.2
29.6
30.2
29.6
28.0
28.3
28.9
27.9
Sales-
Weighted
average
32.0
31.5
31.8
31.2
29.9
30.1
29.9
30.1
30.3
30.8
30.5
30.6
30.9
30.4
30.2
31.0
30.8
31.5
Delta
4.5
4.0
4.3
3.7
2.4
2.6
2.4
2.6
2.8
3.3
3.0
3.1
3.4
2.9
2.7
3.5
3.3
4.0
3.3
Vehicle
Weight
2706
2782
2779
2822
2943
2950
3051
3071
3084
3102
3126
3122
3249
3280
3258
3233
3303
3276
Vehicle
Weight
delta
439
368
378
385
355
303
279
198
250
228
262
230
98
148
190
230
140
230
262
Table 3.4-5 Fuel Economy Data for Selected Manufacturers, 1986-2003—Trucks
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MY 2017 and Later - Regulatory Impact Analysis
Year
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Standard
20.0
20.5
20.5
20.5
20.0
20.2
20.2
20.4
20.5
20.6
20.7
20.7
20.7
20.7
20.7
20.7
20.7
20.7
Average 1986-2003
GM
20.2
20.5
20.2
20.4
19.8
21.2
20.3
20.3
20.2
20.1
20.8
20.4
21.2
20.3
20.7
20.4
19.8
20.2
Ford
20.3
20.5
20.6
20.1
20.2
20.5
20.2
20.8
20.8
20.6
20.8
20.2
20.2
19.8
20.0
20.1
20.2
20.0
Chrysler
20.7
21.3
21.4
21.0
21.4
21.1
21.3
21.2
20.5
20.1
20.2
20.2
20.0
19.9
20.4
19.5
20.0
20.9
Sales-
Weighted
average
20.3
20.7
20.6
20.5
20.3
20.9
20.5
20.7
20.5
20.3
20.6
20.3
20.5
20.0
20.4
20.0
20.0
20.3
Delta
0.3
0.2
0.1
0.0
0.3
0.7
0.3
0.3
0.0
-0.3
-0.1
-0.4
-0.2
-0.7
-0.3
-0.7
-0.7
-0.4
-0.1
Vehicle
Weight
3917
3876
3961
4016
4102
4026
4132
4141
4204
4248
4295
4445
4376
4508
4456
4591
4686
4738
Toyota
26.1
25.9
24.4
23.2
21.8
22.4
21.9
22.1
22.0
21.2
23.1
22.6
23.4
23.0
22.0
22.3
22 2
22.0
Honda
20.2
25.5
22 2
24.7
25.5
25.2
25.0
24.7
25.3
24.8
Nissan
24.7
23.5
22.7
23.7
25.3
24.8
24.0
23.7
22.9
22.4
22.9
22.3
22.3
21.2
20.8
20.7
20.7
21.9
Sales-
Weighted
average
25.5
24.9
23.8
23.3
23.2
23.1
22.5
22.7
22.3
22.0
23.0
22.8
23.5
23.1
22 2
22.3
22.5
22.9
Delta
5.5
4.4
3.3
2.8
3.2
2.9
2.3
2.3
1.8
1.4
2.3
2.1
2.8
2.4
1.5
1.6
1.8
2.2
2.6
Vehicle
Weight
3240
3259
3352
3420
3528
3628
3620
3637
3711
3797
3678
3734
3762
3943
4098
4125
4149
4195
Vehicle
Weight
delta
677
617
609
596
574
397
512
505
494
452
617
711
614
564
359
465
537
544
547
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Chapter 3
Since the MYs 2012-2016 standards are footprint-based, every major manufacturer is
expected to be constrained by those standards in MY 2016 and manufacturers of small
vehicles will not routinely over-comply as they had with the past universal standards.KK
Thus, the historical evidence and the footprint-based design of the MY 2016 GHG emissions
and CAFE standards strongly support the use of a reference case fleet where there are no
further fuel economy improvements beyond those required by the MY 2016 standards. While
it is possible that one or two companies may over-comply, any voluntary over-compliance by
one company would generate credits that could be sold to other companies to substitute for
their more expensive compliance technologies; this ability to buy and sell credits could
eliminate any over-compliance for the overall fleet.25
Figure 3.4-1 shows that, over the 1986-2003 period discussed above, overall average
fleetwide fuel economy decreased by about 3 mpg, even with stable car CAFE standards and
very slightly increasing truck CAFE standards, as the market shifted from a market dominated
by cars in the 1980s to one split between cars and trucks in 2003.LL All projections of actual
GHG emissions and fuel economy performance in MY 2016 or any other future model year
are projections, of course, and it is plausible that actual GHG emissions and fuel economy
performance in MYs 2017-2025, absent more stringent standards, could be lower (or higher)
than projected if there are shifts in car and truck market share to truck market share, or to
higher footprint levels.
Based on the historical data discussed above, EPA believes that there is a very low
likelihood that any manufacturers will voluntarily achieve higher fuel economy than their
footprint-based targets relative to the projected fleet average 35.5 mpg level of MY 2016
standards in MYs 2017-2025, in the absence of more stringent standards. There are several
reasons for this: gasoline prices through MY 2025 are projected to be only slightly higher than
today's levels, footprint-based standards are constraining for all manufacturers, and
manufacturers may use future technology to support other vehicle attributes preferred by
consumers such as power and utility. In addition, even if some individual manufacturers were
to voluntarily over comply, it is possible that they would sell their GHG credits to other
manufacturers who might find that it is more cost-effective to purchase credits than to
continue to meet the 35.5 mpg level. EPA is aware of several automakers that have already
purchased, or are in the process of negotiating to purchase, credits for MY 2012. In this
scenario, if all credits were sold to other manufacturers, there would be no meaningful impact
on the agency's projected costs and benefits. But, the agency recognizes that it is possible
that, under certain circumstances, there might be some industry-wide over compliance. For
example, oil prices much higher than projected by AEO 2012 early release could lead to a
higher baseline due to industry-wide over compliance. But, under this higher baseline, costs
and benefits would both be lower and it is impossible to know whether net consumer and
societal benefits would be higher or lower. Both agencies assume no fuel economy
KK With the notable exception of manufacturers who only market electric vehicles or other limited product lines.
LL Note that the mpg values in this one figure are consumer label values, not the CAFE/compliance values shown
throughout this preamble. Consumer label values are typically about 20% lower than compliance values. The
trends are the same.
3-22
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MY 2017 and Later - Regulatory Impact Analysis
improvement in their primary analyses, but we note that NHTSA chose to
Figure 3.4-1 Average Fleetwide Light-Duty Vehicle Fuel Economy, Horsepower, and
Weight, 1975-2010
(fuel economy data is consumer label values, about 20% lower than compliance values)
1
&
24.0
23.i
22.i
1975
o>
S
-40%
1980
1985
1990
1995
2000
2005
2010
2015
Consistent with this discussion, for the reference case, EPA configured the OMEGA
model to determine the cost to comply with the MY 2016 standards and did not allow access
to the post-MY 2016 technology levels. This reflects the belief that manufacturers will (a)
need to comply in MY 2016, and so will not add additional technology to their vehicles
afterwards to comply with GHG standards (b) will use that new technology for attributes
other than fuel economy, since their vehicles are already compliant, (c) in the absence of
additional regulation beyond the MYs 2012-2016 rule would not develop many of the
technologies become available under the control case runs. Similarly, the air conditioning
technology usage was capped at the MY 2016 projections, as manufacturers that were already
compliant would have no need to add additional air conditioning technology (especially as the
cost of alternative refrigerants is significantly higher than the present refrigerant).
EPA ran the OMEGA model three times with the same MY 2016 technology input but
with the market data file configured to MY 2016, MY 2021, and MY 2025 sales. The model
was run three times because car/truck sales mix shifts between MYs 2016 and 2025 require
some manufacturers to add minimal additional technology to their vehicles in order to remain
in compliance. While slight additional amounts of technology are added or removed, the
compliance cost for the MY 2016 rule declines over time as a result of the learning effects
discussed in the RIA Chapter 1. To reflect this learning progression, but also that the
3-23
-------
Chapter 3
technology choices were made during MY 2016, OMEGA was ran with MY 2016 costs,
which were then post-processed to the proper cost-year.
Consistent with the proposal and the MYs 2012-2016 rale analysis, EPA did not allow
EVs and PHEVs (maximum penetration caps of zero) in the reference case. While the
penetration of EVs and PHEVs in MY 2016 will likely be non-zero, as they are being sold in
MY 2011, EPA chose not to include these technologies in the reference case assessment due
to their cost-distorting effects on the smallest companies (Table 3.4-6 ). In the OMEGA
projections, the vast majority of companies do not use EVs or PHEVs to comply with the MY
2016 standards. Six companies, some of which are intermediate or smaller volume, under the
technology restrictions set forth in this analysis, cannot comply with the MY 2016
standards.MM This finding is consistent with the MY 2012-2016 rule analysis; these
companies are BMW, Daimler, Geely-Volvo, Volkswagen, Porsche and Tata (which is
comprised of Jaguar and Land Rover vehicles in the U.S. fleet).26
As shown below, these manufacturers (other than Porsche) could comply with the MY
2016 standards by including electric vehicles and plug-in hybrids in their fleet. As reflected
in the MY 2012-2016 rule, EPA believes that it is unlikely that these manufacturers will
convert up to 10% of their fleet EVs and PHEVs by MY 2016. As an alternative to this
choice, these companies could exceed our assumed technology caps on other technologies
(such as mass reduction), make use of carry-forward credits, carry-back credits, or purchase
credits from another manufacturer. Alternatively, they could use a vehicle compliance
strategy not considered here, as discussed in section III.D of the MYs 2012-2016 rule. Thus
the compliance cost for these vehicles for the 2016 rule could potentially be greater than
presented in this analysis, which would decrease the incremental cost of the later MY
standards.NN Moreover the companies would eventually achieve the 2016 targets in the
reference case (Table 3.5-1 & Table 3.5-2).
For these manufacturers, the MY 2016 reference case results presented are those with
the fully allowable application of technology available in EPA's OMEGA modeling analysis
and not for the technology projected to enable compliance with the final MY 2016 standards.
Again, this analytic choice increases the incremental costs of the MY 2017-MY 2025 program
for these companies.
MM While OMEGA model results are presented assuming that all manufacturers must comply with the program
as proposed (to the extent that they can), some manufacturers, such as small volume manufacturers may be
eligible for additional options (and alternative standards) which have not been considered here. Under the final
rule, small volume manufacturers with U.S. sales of less than 5,000 vehicles would be able to petition EPA for
an alternative standard for MY 2017 and later. Manufacturers currently meeting the 5,000 vehicle cut point
include Lotus, Aston Martin, and McLaren. Under the MY 2016 program, the TLAAS program - which
provides additional lead time to certain intermediate sized manufacturers which meet alternative standards would
also be available, and is not modeled here.
NN Of course, any manufacturer could, in theory, also find more cost-effective methods to comply than those
shown in this analysis.
3-24
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.4-6 - MY 2016 EV+PHEV Penetrations, and additional potential additional
costs in MY 20161'2
Manufacturer
BMW
Daimler
Geely-Volvo
Porsche00
Tata/ JLR
Volkswagen
MY 2016
Shortfall
without
EV/PHEV
(g/mile)
3
19
20
46
25
14
MY 2016
Shortfall
with
EV/PHEV
(g/mile)
-
-
-
18
-
-
Reference
Cost
Delta
added by
including
EVs
($)
-$89
$1,447
$1,846
$2,195
$2,215
$803
EV+PHEV
(%ofMY
2016 Sales
if added)
3%
7%
8%
11%
9%
6%
'Please note that these are MY 2016 costs, and would be significantly lower in later MYs as a result of learning.
See RIA 1 for more details
2 For BMW, the few number of EVs that they would produce in the reference case would be more cost effective
than other technologies that they would need to use to comply, resulting in a negative cost delta.
The MY 2016 coefficients are found in 75 FR at 25409. When input to OMEGA,
these coefficients were adjusted vertically upward by 10.2 grams (cars) and 11.4 grams
(trucks) to account for external calculations relating to air conditioning costs.
No additional compliance flexibilities were explicitly modeled for the MY 2016
standards. The EPA flexible fueled vehicle credit expires before MY 2016.pp The Temporary
Leadtime Allowance Alternative Standards (TLAAS), as analyzed in RIA chapter 5 of the
MY 2012-2016 rule, is projected to have an impact of approximately 0.1 g/mile in MY 2016,
and expire afterwards. While this may have a more significant impact on specific
companies, as a result of the overall magnitude, no incentive credits are projected to be
available in the reference case modeled here. In a change from the proposal modeling, under
the reference case standards, manufacturers are allowed access to the off-cycle credit "menu."
As a result, the off-cycle credits modeled here lower costs relative to the proposal.
00 EPA analyzed Porsche and VW as separate fleets for the Final Rule. However, on August 1, 2012, VW
completed its acquisition of Porsche and thus EPA expects that the Porsche fleet will be combined with the VW
fleet for purposes of compliance with the MY 2017-2025 standards.
pp The credit available for producing FFVs will have expired, although the real world usage credits will be
available.
QQ In this final rulemaking, EPA is providing additional lead time to meet the initial model year standards for
certain intermediate volume manufacturers, as described in Preamble section III.B.8. The discussion in the text
above, however, concerns how the reference fleet is modeled in OMEGA, and in the reference fleet case, the
TLAAS ends with MY 2016.
3-25
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Chapter 3
With respect to car-truck trading, the OMEGA model facilitates the trading of car-
truck credits on a total lifetime COi emission basis, consistent with the provisions of the final
rule and the MY 2016 rule. For example, if a manufacturer over-complies with its applicable
COi standard for cars by 10 g/mi, sells 1,000,000 cars, and cars have a lifetime VMT of
195,264 miles, it generates 1,952,640 metric tons of CO2 credits. If these credits are used to
compensate for under-compliance towards the truck CC>2 standard and truck sales are
500,000, with a lifetime truck VMT of 225,865 miles, the manufacturer's truck CO2 emission
level could be as much as 17.3 g/mi COi above the standard. Car-truck trading was allowed
in the OMEGA runs without limit consistent with the trading provisions of the MYs 2012-
2016 and MYs 2017-2025 GHG rules.
3.4.3.2 Control Scenarios
Similar to the reference scenario, OMEGA runs were conducted for MYs 2021 and
2025 for the standards adopted in the final rule and for alternative scenarios. The standards for
these scenarios were derived from the coefficients discussed in Section III.B of the preamble.
The joint EPA/NHTSA development of these target curve coefficients is discussed in Joint
TSD Chapter 2. As in MYs 2012-2016, these curves were adjusted for air conditioning
through a negative additive offset based on the estimated year over year penetrations of air
conditioning shown in preamble III.C.l and below. For the OMEGA cost analysis, as we
analyzed air conditioning costs outside of the model, we re-adjusted the model input curves to
remove this projected penetration of air conditioning technology. For the MY 2021 and MY
2025 OMEGA runs, air conditioning credits were projected at 18.8 g/mi for cars and 24.4
g/mi for light trucks.
EPA's final rule incorporates several additional compliance flexibilities. See
generally Preamble section III.C for an extended discussion of these credits. EVs and PHEVs
were modeled with zero g/mile in all cases. As discussed in Section III.B of the preamble, the
cap for EVs and PHEVs at zero g/mile (i.e. the production cap relating to when upstream
emissions associated with increased electricity use is considered for compliance purposes) is
related to the standard level being finalized. As in the proposal, for purposes of this cost
modeling, we assume that this cap is never reached. The PH/EV multipliers (a regulatory
incentive, as explained in Preamble section III.C.2) were not modeled in this cost analysis, but
would reduce compliance costs in MY 2021 and earlier. The multiplier is included in EPA's
benefits analysis, as discussed in RIA chapter 4. A discussion of the potential impacts of these
credits can be found in preamble section III.B.2 and RIA chapter 4. Costs beyond MY 2025
assume no technology changes on the vehicles, and implicitly assume that the compliance
values for EVs remains at zero gram/mile.RR
As discussed previously, in a difference from the proposal, the credit for mild and
strong HEV full size pickups was modeled in this final rule analysis. Two off-cycle credits,
those for start-stop technology and active aerodynamics were also included. In a change from
RR The costs for PHEVs and EVs in this rule reflect those costs discussed in Joint TSD Chapter 3, and do not
reflect any tax incentives, as the availability of those tax incentives in this time frame is uncertain.
3-26
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MY 2017 and Later - Regulatory Impact Analysis
the proposal modeling, the impact of the off-cycle credits for start-stop technology and active
aerodynamics were modeled. This change lowers costs relative to the proposal.
Like the reference case, car-track trading was allowed without limit.
3.4.4 Fuels and reference data
Fuels data was based on AEO fuel prices, as documented in Chapter 4 of the Joint
TSD. Estimates of carbon and energy content per gallon of liquid fuel are consistent with the
MYs 2012-2016 rule analysis.
The VMT schedules used in the TARF calculation were chosen for consistency with
the EPA credit trading regulations, and is 195,264 for cars and 225,685 for tracks. It is
important to use the same VMT schedules in the numerator and denominator of the TARF
equation, or unintended errors can be introduced to the OMEGA model calculations.
Using the data and equations discussed above, the OMEGA model begins by
determining the specific COi emission standard applicable for each manufacturer and its
vehicle class (i.e., car or truck). As the reference case, the final rale, and all alternatives allow
for averaging across a manufacturer's car and track fleets, the model determines the CO2
emission standard applicable to each manufacturer's car and track sales from the two sets of
coefficients describing the piecewise linear standard functions for cars and trucks (i.e. the
respective car and truck curves) in the inputs, and creates a combined car-truck standard. This
combined standard considers the difference in lifetime VMT of cars and trucks, as indicated
in the regulations which govern credit trading between these two vehicle classes.
The model then works with one manufacturer at a time to add technologies until that
manufacturer meets its applicable standard. The OMEGA model can utilize several
approaches to determining the order in which vehicles receive technologies. For this analysis,
EPA used a "manufacturer-based net cost-effectiveness factor" to rank the technology
packages in the order in which a manufacturer is likely to apply them. Conceptually, this
approach estimates the cost of adding the technology from the manufacturer's perspective and
divides it by the mass of CO2 the technology will reduce. One component of the cost of
adding a technology is its production cost, as discussed above. However, it is expected that
new vehicle purchaser's value improved fuel economy since it reduces the cost of operating
the vehicle. Typical vehicle purchasers are assumed to value the fuel savings accrued over
the period of time which they will own the vehicle, which is estimated to be approximately
five years.88 It is also assumed that consumers discount these savings at the same rate as that
used in the rest of the analysis (3 or 7 percent).^ Any residual value of the additional
technology which might remain when the vehicle is sold is not considered for this analysis.
ss For a fuller discussion of this topic see Section III.H
TT While our costs and benefits are discounted at 3% or 7%, the decision algorithm (TARF) used in OMEGA
was run at a discount rate of 3%. Given that manufacturers must comply with the standard regardless of the
discount rate used in the TARF, this has little impact on the technology projections shown here. Further, the fuel
savings aspect of the TARF are only directly relevant when two different fuels are being compared, because the
fuel saving/delta CO2 ratio is a constant for any given vehicle on a single fuel in a single model year.
3-27
-------
Chapter 3
The CO2 emission reduction is the change in COi emissions multiplied by the percentage of
vehicles surviving after each year of use multiplied by the annual miles travelled by age.
Given this definition, the higher priority technologies are those with the lowest
manufacturer-based net cost-effectiveness value (relatively low technology cost or high fuel
savings leads to lower values).uu Because the order of technology application is set for each
vehicle, the model uses the manufacturer-based net cost-effectiveness primarily to decide
which vehicle receives the next technology addition. Initially, technology package #1 is the
only one available to any particular vehicle. However, as soon as a vehicle receives
technology package #1, the model considers the manufacturer-based net cost-effectiveness of
technology package #2 for that vehicle and so on. In general terms, the equation describing
the calculation of manufacturer-based cost effectiveness is as follows:
Equation 3.4-8 - Calculation of Manufacturer-Based Cost Effectiveness
kTechCost - AFS
CostEffManuft =
VMTregulatory
Where:
CostEffManuft= Manufacturer-Based Cost Effectiveness (in dollars per kilogram CO2),
TechCost = Marked up cost of the technology (dollars),
FS = Difference in fuel consumption due to the addition of technology times fuel price and discounted
over the payback period, or the number of years of vehicle use over which consumers value fuel savings
when evaluating the value of a new vehicle at time of purchase
dCO2 = Difference in CO2 emissions (g/mile) due to the addition of technology
VMTregulatory = the statutorily defined VMT
EPA describes the technology ranking methodology and manufacturer-based cost
effectiveness metric in greater detail in the OMEGA documentation.27 Please note that the
TARE equation does not consider attributes other than cost effectiveness, credit values, and
relative fuel savings. This distinction is significant when considering the technology
penetrations presented later in this chapter. An electric vehicle, which is approximately the
same cost as a plug-hybrid but is significantly more effective over the certification cycles, will
generally be chosen by OMEGA before the plug-in hybrid. The current TARE does not
reflect potential consumer concerns with the range limits of the electric vehicle (reflecting our
assumption that purchasers of these vehicles are aware of the vehicles' limited range). .vv As a
result of EVs greater cost-effectiveness, relatively more (although still few in an absolute
sense) are shown in the projected technology penetrations. When calculating the fuel savings
in the TARE equation, the full retail price of fuel, including taxes is used. While taxes are not
generally included when calculating the cost or benefits of a regulation, the net cost
uu To ensure a consistent approach to technology ranking, the credit value is modeled as producing fuel savings.
While credits will not actually provide fuel savings to a consumer, an increase in the denominator (increased
CO2 savings) without a corresponding change in the numerator (increased fuel savings) can provide a perverse
situation where adding credits makes a technology less desirable.
vv As the general form of the TARF is net cost change/net CO2 change, the electric vehicle attributes could be
assigned a value and incorporated into the TARF.
3-28
-------
MY 2017 and Later - Regulatory Impact Analysis
component of the manufacturer-based net cost-effectiveness equation is not a measure of the
social cost of this rule, but a measure of the private cost, (i.e., a measure of the vehicle
purchaser's willingness to pay more for a vehicle with higher fuel efficiency). Since vehicle
operators pay the full price of fuel, including taxes, they value fuel costs or savings at this
level, and the manufacturers will consider this when choosing among the technology
options.**
The values of manufacturer-based net cost-effectiveness for specific technologies will
vary from vehicle to vehicle, often substantially. This occurs for three reasons. First, both the
cost and fuel-saving component cost, ownership fuel-savings, and lifetime CCh effectiveness
of a specific technology all vary by the type of vehicle or engine to which it is being applied
(e.g., small car versus large truck, or 4-cylinder versus 8-cylinder engine). Second, the
effectiveness of a specific technology often depends on the presence of other technologies
already being used on the vehicle (i.e., the dis-synergies). Third, the absolute fuel savings and
COi reduction of a percentage an incremental reduction in fuel consumption depends on the
CO2 level of the vehicle prior to adding the technology. Chapter 1 of EPA's RIA contains
further detail on the values of manufacturer-based net cost-effectiveness for the various
technology packages.
3.5 Analysis Results
3.5.1 Targets and Achieved Values
3.5.1.1 Reference Case
Table 3.5-1 Reference Case Targets and Projected Shortfall in MY 2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Car
Target
222
228
230
234
235
230
Truck
Target
—
285
295
301
0
305
Fleet Target
(Sales Weighted)
222
243
259
250
235
256
Fleet Target
(VMT
and Sales
weighted)
222
245
261
252
235
258
Car
Achieved
346
237
227
253
399
232
Truck
Achieved
—
287
297
324
0
302
Shortfall
123
6
0
21
165
0
ww
This definition of manufacturer-based net cost-effectiveness ignores any change in the residual value of the
vehicle due to the additional technology when the vehicle is five years old. Based on historic used car pricing,
applicable sales taxes, and insurance, vehicles are worth roughly 23% of their original cost after five years,
discounted to year of vehicle purchase at 7% per annum. It is reasonable to estimate that the added technology
to improve CO2 level and fuel economy will retain this same percentage of value when the vehicle is five years
old. However, it is less clear whether first purchasers, and thus, manufacturers consider this residual value when
ranking technologies and making vehicle purchases, respectively. For this final rule, this factor was not included
in our determination of manufacturer-based net cost-effectiveness in the analyses.
3-29
-------
Chapter 3
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche**
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
232
226
223
223
218
206
220
219
226
206
219
211
208
250
206
221
217
224
280
308
283
280
291
—
276
270
294
287
280
258
272
273
—
294
296
296
247
267
241
234
235
206
230
237
247
225
227
222
219
261
206
250
233
250
248
270
243
236
237
206
231
238
249
227
229
224
221
262
206
252
235
252
247
225
222
223
223
240
224
223
222
250
248
221
209
248
0
216
225
224
306
309
285
279
279
—
262
261
302
335
319
231
265
330
—
300
329
300
19
0
0
0
0
34
0
0
0
45
31
0
0
30
0
0
14
1
xx EPA analyzed Porsche and VW as separate fleets for the Final Rule. However, on August 1, 2012, VW
completed its acquisition of Porsche and thus EPA expects that the Porsche fleet will be combined with the VW
fleet for purposes of compliance with the MY 2017-2025 standards.
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-2 Reference Case Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General
Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car
Target
222
228
229
234
235
230
232
226
223
223
218
206
220
219
227
206
219
211
208
250
206
221
217
224
Truck
Target
-
286
294
302
-
303
280
307
283
280
292
-
277
270
292
287
280
258
272
273
-
293
296
295
Fleet Target
(Sales Weighted)
222
243
257
249
235
253
246
264
240
234
234
206
230
236
246
224
227
222
219
261
206
247
233
248
Fleet Target
(VMT
and Sales
weighted)
222
245
259
251
235
255
248
267
242
235
236
206
231
238
248
226
228
223
220
261
206
250
235
250
Car
Achieved
346
237
227
254
399
232
247
225
221
223
222
240
223
223
222
250
248
220
209
248
-
215
225
224
Truck
Achieved
-
289
296
324
-
299
306
307
285
279
278
-
263
261
301
335
319
230
265
330
-
302
329
299
Shortfall
123
7
-
21
165
-
19
-
-
-
-
34
-
-
-
45
30
-
-
28
-
-
13
1
3-31
-------
Chapter 3
3.5.1.1 Final rule and Alternatives
Table 3.5-3 Final rule Targets and Projected Shortfall in MY 2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
171
175
176
180
181
177
178
174
171
171
167
157
169
168
174
157
168
161
158
193
157
170
166
172
Truck Target
-
236
246
253
-
261
231
262
234
231
243
-
227
220
248
238
230
207
222
223
-
247
248
250
Fleet Target
(Sales Weighted)
171
191
208
198
181
205
195
217
190
183
184
157
179
186
197
176
177
172
170
208
157
200
183
199
Fleet Target
(VMT
and Sales
weighted)
171
193
211
200
181
208
196
221
192
184
186
157
180
188
199
178
178
174
171
209
157
202
185
202
Car
Achieved
192
180
183
176
227
189
174
187
177
175
177
156
176
182
179
148
163
175
164
153
-
172
163
178
Truck
Achieved
-
225
239
262
-
240
237
249
221
215
214
-
198
197
238
263
257
167
199
256
-
242
259
239
Shortfall
21
-
-
-
46
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-32
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-4 Final rule Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
142
146
146
150
150
147
148
144
142
142
139
131
140
139
145
131
139
134
132
161
131
141
138
143
Truck Target
-
194
201
208
-
212
189
213
191
188
199
-
186
180
202
195
188
169
181
182
-
201
203
203
Fleet Target
(Sales Weighted)
142
159
170
163
150
167
160
177
156
151
152
131
148
153
162
144
146
142
140
171
131
163
151
163
Fleet Target
(VMT
and Sales
weighted)
142
160
172
165
150
169
162
180
158
152
153
131
149
154
163
146
147
143
141
171
131
165
152
165
Car
Achieved
142
144
154
140
168
157
138
156
145
146
145
130
145
146
149
118
132
145
133
114
-
146
131
147
Truck
Achieved
-
199
191
233
-
192
207
202
183
172
177
-
163
166
191
231
231
138
174
228
-
193
228
194
Shortfall
-
-
-
-
17
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-33
-------
Chapter 3
Table 3.5-5 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY
2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
171
175
176
180
181
177
178
174
171
171
167
157
169
168
174
157
168
161
158
193
157
170
166
172
Truck Target
-
256
267
273
-
282
250
283
253
250
263
-
245
238
267
258
249
225
241
242
-
266
268
270
Fleet Target
(Sales Weighted)
171
197
217
203
181
213
201
228
196
187
189
157
182
192
203
181
179
176
173
217
157
207
187
206
Fleet Target
(VMT
and Sales
weighted)
171
199
221
206
181
216
203
232
199
189
191
157
184
195
206
184
181
178
175
219
157
211
189
210
Car
Achieved
192
188
195
184
227
196
184
198
182
181
181
156
179
185
186
155
166
177
169
171
-
180
169
185
Truck
Achieved
-
225
248
263
-
249
237
260
231
215
222
-
204
209
244
263
257
180
199
260
-
251
259
247
Shortfall
21
-
-
-
46
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-34
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-6 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY
2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
171
175
176
180
181
177
178
174
171
171
167
157
169
168
174
157
168
161
158
193
157
170
166
172
Truck Target
-
217
227
232
-
240
212
241
215
212
223
-
208
202
228
219
212
190
204
205
-
227
228
229
Fleet Target
(Sales Weighted)
171
186
199
193
181
198
189
207
184
179
180
157
176
180
191
172
174
168
167
199
157
192
179
192
Fleet Target
(VMT
and Sales
weighted)
171
188
201
194
181
200
190
209
186
180
181
157
177
181
192
173
175
169
168
199
157
194
180
194
Car
Achieved
192
172
178
168
227
183
165
177
171
170
170
156
172
172
171
142
159
170
160
134
-
170
157
172
Truck
Achieved
-
224
225
262
-
229
236
237
216
210
214
-
193
195
232
262
257
167
199
256
-
226
259
229
Shortfall
21
-
-
-
46
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-35
-------
Chapter 3
2021
Table 3.5-7 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
190
196
197
201
202
197
199
194
190
190
187
176
188
187
194
176
187
180
177
215
176
189
185
192
Truck Target
-
236
246
253
-
261
231
262
234
231
243
-
227
220
248
238
230
207
222
223
-
247
248
250
Fleet Target
(Sales Weighted)
190
206
219
214
202
219
209
227
204
199
199
176
195
199
211
190
193
187
185
219
176
212
198
212
Fleet Target
(VMT
and Sales
weighted)
190
208
221
215
202
221
210
230
205
200
201
176
196
200
212
192
194
187
186
219
176
214
200
214
Car
Achieved
192
200
195
196
227
201
194
198
187
191
188
175
191
190
191
166
181
187
179
171
-
180
182
190
Truck
Achieved
-
226
248
263
-
255
237
258
240
228
240
-
216
216
254
264
257
187
215
260
-
258
260
251
Shortfall
1
-
-
-
25
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-36
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-8 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
151
155
156
159
160
157
158
154
151
151
148
139
149
148
154
139
148
142
140
171
139
150
147
152
Truck Target
-
236
246
253
-
261
231
262
234
231
243
-
227
220
248
238
230
207
222
223
-
247
248
250
Fleet Target
(Sales Weighted)
151
177
197
182
160
192
180
207
177
167
169
139
163
173
183
162
160
158
155
197
139
188
167
186
Fleet Target
(VMT
and Sales
weighted)
151
179
200
185
160
195
183
211
179
169
172
139
165
176
186
165
161
160
156
199
139
191
170
190
Car Achieved
192
160
178
154
227
177
155
178
163
162
161
139
160
165
166
139
143
159
146
132
-
166
144
166
Truck Achieved
-
224
223
262
-
227
236
239
211
193
204
-
183
192
224
258
255
159
198
256
-
225
257
227
Shortfall
41
-
-
-
67
-
-
-
-
-
-
-
-
-
-
5
-
-
-
-
-
-
-
-
3-37
-------
Chapter 3
Table 3.5-9 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
142
146
146
150
150
147
148
144
142
142
139
131
140
139
145
131
139
134
132
161
131
141
138
143
Truck Target
-
213
221
228
-
232
207
234
210
207
218
-
204
198
221
214
207
186
200
200
-
221
223
223
Fleet Target
(Sales Weighted)
142
164
179
168
150
173
166
187
162
155
156
131
151
159
167
149
148
146
143
179
131
170
155
170
Fleet Target
(VMT
and Sales
weighted)
142
166
181
170
150
176
168
190
164
156
158
131
152
161
170
151
149
147
145
181
131
173
157
172
Car
Achieved
142
152
163
148
168
162
149
163
149
149
148
130
149
154
153
125
135
149
138
128
-
152
137
153
Truck
Achieved
-
199
202
233
-
202
207
216
194
177
187
-
163
171
204
231
231
142
174
231
-
204
228
205
Shortfall
-
-
-
-
17
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-38
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-10 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
142
146
146
150
150
147
148
144
142
142
139
131
140
139
145
131
139
134
132
161
131
141
138
143
Truck Target
-
174
181
187
-
191
170
192
172
170
179
-
167
162
182
175
170
152
163
164
-
181
183
183
Fleet Target
(Sales Weighted)
142
153
161
158
150
161
155
167
151
147
147
131
145
147
156
140
143
138
137
162
131
156
147
156
Fleet Target
(VMT
and Sales
weighted)
142
154
163
159
150
162
155
169
152
148
148
131
145
148
157
141
144
139
138
162
131
157
148
158
Car
Achieved
142
136
143
134
168
148
130
142
140
142
141
130
142
137
141
113
129
139
129
97
-
139
125
139
Truck
Achieved
-
199
185
233
-
189
207
193
176
168
171
-
161
166
190
231
231
138
172
226
-
184
228
188
Shortfall
-
-
-
-
17
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-39
-------
Chapter 3
Table 3.5-11 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
162
166
166
171
171
168
169
164
162
162
158
149
160
159
165
149
159
153
150
183
149
161
157
163
Truck Target
-
194
201
208
-
212
189
213
191
188
199
-
186
180
202
195
188
169
181
182
-
201
203
203
Fleet Target
(Sales Weighted)
162
173
181
179
171
181
175
188
170
167
167
149
164
166
176
159
163
156
155
183
149
175
166
176
Fleet Target
(VMT
and Sales
weighted)
162
174
183
180
171
183
175
189
171
168
168
149
165
166
177
160
163
157
156
183
149
177
168
178
Car
Achieved
162
163
163
161
171
168
159
163
158
159
160
149
161
162
159
137
151
157
152
134
-
156
150
160
Truck
Achieved
-
199
206
233
-
209
207
214
196
194
193
-
180
175
211
231
231
156
175
231
-
207
228
207
Shortfall
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3-40
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-12 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Car Target
122
126
126
129
130
127
128
124
122
122
120
112
121
120
125
112
120
115
113
139
112
121
119
123
Truck Target
-
194
201
208
-
212
189
213
191
188
199
-
186
180
202
195
188
169
181
182
-
201
203
203
Fleet Target
(Sales Weighted)
122
144
158
147
130
153
146
167
143
135
136
112
131
140
147
130
129
127
125
159
112
150
135
150
Fleet Target
(VMT
and Sales
weighted)
122
146
161
149
130
156
148
170
145
137
138
112
133
142
150
132
130
129
126
160
112
153
137
152
Car
Achieved
139
123
141
120
168
139
119
146
133
129
129
111
127
128
132
103
112
130
114
97
-
137
112
133
Truck
Achieved
-
199
184
233
-
188
207
193
168
165
167
-
156
166
186
224
231
126
172
223
-
178
228
185
Shortfall
17
-
-
-
38
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
3.5.2 Penetration of Selected Technologies
On the following pages, we present OMEGA model projected penetrations of selected
technologies by manufacturer, model year, and car/truck class. These tables show results of
the reference case, the final standards, and the four alternatives which EPA examined. In
addition, we note that although the agencies have adopted technology phase-in caps for
purposes of their respective modeling analyses, no manufacturer is actually restricted by the
technology caps modeled in this analysis. However, a smaller manufacturer with only a few
vehicle platforms may only be able to pursue a single technology path. As an example, a
manufacturer with a single platform is unlikely to produce diesel, electric, and hybrid electric
vehicles, but is more likely to focus on a selected engine technology. Thus in reality,
manufacturers can use a greater (or lesser) degree of technology than we model.
Moreover, although OMEGA model results are presented assuming that all
manufacturers must comply with the base program as finalized (to the extent that they can),
some manufacturers, such as small volume manufacturers may be eligible for additional
options (including alternative case-by-case standards)which have not been considered here.
As described in the preamble, small volume manufacturers with U.S. sales of less than 5,000
3-41
-------
Chapter 3
vehicles would be able to petition EPA for an alternative standard for MY 2017 and later.
Manufacturers currently meeting the 5,000 vehicle sales cut point include Lotus, Aston
Martin, and McLaren. Intermediate volume manufacturers may be eligible for additional lead
time in the early model years of the program, this is a flexibility also not considered here. As
described in Preamble Section III.B.6, EPA is finalizing provisions to allow additional lead
time for intermediate volume manufacturers that sell less than 50,000 vehicles per year, for
the first four years of the program (MY 2017-2020).
The technology penetrations presented here are absolute, and include baseline
technologies. The analyses shown here illustrate just one single path towards compliance,
although there are many. As an example, please see the September 2010 Technical
Assessment report, where we describe technology feasibility through several different
potential compliance paths.
Table 3.5-13 Technology abbreviations
Abbreviation
Mass Tech Applied
True Mass
Mass Penalty
TDS 18/24/27
AT6/8
DCT6/8
MT
HEG
EGR
HEV
EV
PHEV
SS
LRRT2
IACC2
EFR2
DI
DSL
Meaning
Mass Technology Applied, expressed as a negative
number
Net Mass Reduced
Mass increase due to technology
turbocharged & downsized at 18/24/27 bar BMEP
Automatic transmission
Dual Clutch Transmission
Manual transmission
High Efficiency Gearbox
Cooled exhaust gas recirculation
Hybrid electric vehicle
Full electric vehicle
Plug-in HEV
12V stop-start
Lower rolling resistance tires level 2
Improved Accesssories level 2
Engine friction reduction level 2
Stoichiometric gasoline direct injection
Advanced diesel
25 Gates, Wallace E., Paul R. Portney, and Albert M. McGartland. "The Net Benefits of
Incentive-Based Regulation: A Case Study of Environmental Standard Setting." American
Economic Review 79(5) (December 1989): 1233-1242. (Docket No. EPA-HQ-OAR-2010-
0799-0833)
26
27
See 75 FR at 25457.
See OMEGA documentation at http://www.epa.gov/otaq/climate/models.htm.
3-42
-------
MY 2017 and Later - Regulatory Impact Analysis
3.5.3 Projected Technology Penetrations in Reference Case
Table 3.5-14 Reference Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-5%
-7%
-4%
-5%
-6%
-5%
-1%
-2%
-1%
-1%
-3%
-5%
-2%
-2%
-8%
-3%
0%
-8%
0%
-1%
-4%
-3%
g i
H S
-8%
-6%
-5%
-6%
-3%
-5%
-6%
-5%
-1%
-2%
-1%
0%
-3%
-4%
-2%
-2%
-8%
-3%
0%
-8%
0%
-1%
-4%
-3%
-S1
M ^
•3 «
S£
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
1%
0%
0%
1%
0%
0%
1%
0%
oo
t/3
e
40%
45%
56%
40%
40%
64%
52%
47%
0%
28%
7%
52%
47%
71%
22%
43%
55%
72%
70%
40%
0%
3%
46%
32%
TDS24
15%
15%
14%
15%
15%
15%
15%
11%
0%
0%
0%
15%
12%
15%
8%
15%
15%
15%
15%
15%
0%
0%
15%
8%
i^
IN
t/3
e
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
s
<
0%
12%
5%
0%
14%
22%
13%
6%
0%
14%
5%
0%
13%
14%
3%
0%
2%
2%
4%
14%
0%
5%
9%
8%
gs
<
0%
0%
1%
38%
0%
9%
4%
2%
0%
7%
2%
0%
5%
5%
1%
0%
0%
0%
2%
0%
0%
3%
0%
4%
£
U
Q
60%
48%
52%
28%
52%
36%
46%
52%
50%
37%
46%
15%
37%
42%
49%
28%
49%
42%
45%
55%
0%
50%
51%
46%
oo
u
Q
24%
26%
28%
30%
28%
19%
25%
26%
22%
20%
25%
0%
20%
22%
27%
10%
26%
22%
25%
30%
0%
11%
25%
21%
H
16%
13%
3%
0%
5%
7%
3%
6%
12%
7%
9%
85%
17%
8%
5%
56%
13%
25%
12%
0%
0%
7%
14%
8%
O
w
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
&
§
15%
15%
2%
15%
15%
10%
15%
2%
0%
0%
0%
15%
3%
15%
1%
15%
15%
15%
15%
15%
0%
0%
15%
4%
W
15%
15%
0%
15%
15%
2%
15%
0%
3%
0%
0%
15%
0%
0%
1%
15%
15%
0%
0%
15%
0%
15%
15%
6%
£
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
100%
0%
0%
0%
W
PH
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
t/3
t/3
55%
55%
0%
55%
55%
0%
57%
0%
0%
0%
0%
55%
0%
12%
0%
55%
55%
0%
0%
55%
0%
0%
55%
8%
LRRT2
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1
30%
30%
30%
30%
30%
30%
30%
30%
2%
8%
2%
30%
30%
30%
30%
30%
30%
30%
30%
30%
0%
1%
30%
19%
w
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Q
70%
75%
70%
69%
70%
79%
72%
59%
0%
28%
7%
70%
59%
85%
30%
73%
70%
85%
85%
70%
0%
8%
84%
43%
_)
%
15%
15%
0%
16%
15%
0%
13%
0%
0%
0%
0%
15%
0%
0%
0%
15%
15%
0%
0%
15%
0%
0%
15%
2%
MHEV
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
11%
0%
0%
0%
2%
0%
0%
0%
0%
0%
0%
3-43
-------
Chapter 3
Table 3.5-15 Reference Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-8%
-6%
-9%
NA
-6%
-9%
-7%
-3%
-4%
-4%
NA
-8%
-9%
-4%
-8%
-3%
-9%
-7%
-6%
NA
-2%
-8%
-5%
g i
H S
NA
-7%
-6%
-8%
NA
-6%
-8%
-7%
-3%
-4%
-4%
NA
-8%
-8%
-4%
-8%
-2%
-8%
-7%
-5%
NA
-2%
-8%
-5%
-S1
M ^
•3 «
S£
NA
1%
0%
1%
NA
0%
1%
0%
0%
0%
0%
NA
0%
0%
0%
1%
1%
0%
0%
1%
NA
0%
1%
0%
oo
t/3
e
NA
67%
22%
56%
NA
66%
68%
33%
62%
85%
84%
NA
64%
70%
65%
64%
70%
70%
70%
63%
NA
47%
67%
50%
TDS24
NA
15%
15%
13%
NA
15%
15%
15%
0%
0%
0%
NA
15%
15%
12%
15%
15%
15%
15%
15%
NA
0%
15%
9%
i^
IN
t/3
e
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
s
<
NA
70%
65%
0%
NA
59%
70%
66%
39%
59%
54%
NA
47%
51%
44%
69%
61%
17%
55%
70%
NA
47%
70%
55%
gs
<
NA
30%
28%
100%
NA
26%
30%
29%
22%
30%
30%
NA
20%
26%
24%
30%
30%
9%
30%
30%
NA
25%
30%
28%
£
U
Q
NA
0%
2%
0%
NA
4%
0%
1%
15%
0%
0%
NA
17%
7%
11%
0%
0%
33%
0%
0%
NA
5%
0%
5%
oo
u
Q
NA
0%
1%
0%
NA
2%
0%
0%
8%
0%
0%
NA
9%
4%
6%
0%
0%
18%
0%
0%
NA
3%
0%
3%
H
NA
0%
3%
0%
NA
3%
0%
0%
0%
0%
1%
NA
2%
0%
2%
1%
0%
8%
0%
0%
NA
3%
0%
2%
O
w
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
&
§
NA
15%
15%
13%
NA
15%
15%
15%
0%
0%
0%
NA
11%
15%
12%
15%
15%
15%
15%
15%
NA
0%
15%
9%
W
NA
15%
0%
15%
NA
2%
15%
0%
0%
0%
0%
NA
0%
0%
0%
15%
9%
0%
0%
15%
NA
5%
15%
3%
£
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
W
PH
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
t/3
t/3
NA
65%
0%
62%
NA
2%
68%
0%
0%
0%
0%
NA
0%
61%
0%
62%
70%
21%
82%
60%
NA
0%
67%
7%
LRRT2
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
1
NA
30%
30%
30%
NA
29%
30%
30%
11%
15%
15%
NA
30%
30%
30%
30%
30%
30%
30%
30%
NA
12%
30%
23%
w
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
Q
NA
83%
37%
69%
NA
81%
83%
48%
62%
85%
84%
NA
79%
85%
77%
92%
85%
85%
85%
75%
NA
48%
96%
61%
_)
%
NA
5%
0%
19%
NA
0%
2%
0%
0%
0%
0%
NA
0%
0%
0%
8%
0%
0%
0%
10%
NA
0%
4%
1%
MHEV
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
15%
0%
0%
6%
4%
0%
0%
NA
0%
0%
0%
3-44
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-16 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-7%
-6%
-8%
-4%
-6%
-7%
-6%
-2%
-2%
-2%
-1%
-4%
-6%
-3%
-4%
-8%
-5%
-1%
-7%
0%
-2%
-5%
-4%
0 "
£1
-8%
-6%
-6%
-7%
-3%
-6%
-6%
-6%
-2%
-2%
-2%
0%
-4%
-6%
-3%
-3%
-7%
-5%
-1%
-6%
0%
-2%
-4%
-4%
«, &
a "3
Sl
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
1%
0%
0%
1%
0%
0%
1%
0%
TDS18
40%
51%
41%
44%
40%
65%
57%
40%
19%
40%
24%
52%
50%
70%
35%
48%
57%
72%
70%
51%
0%
20%
50%
39%
TDS24
15%
15%
15%
14%
15%
15%
15%
13%
0%
0%
0%
15%
13%
15%
9%
15%
15%
15%
15%
15%
0%
0%
15%
8%
TDS27
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1
0%
27%
32%
0%
14%
35%
31%
36%
12%
23%
16%
0%
19%
27%
15%
16%
10%
6%
13%
42%
0%
21%
22%
24%
oo
%
0%
8%
13%
54%
0%
15%
12%
16%
7%
12%
9%
0%
7%
12%
8%
7%
4%
2%
7%
15%
0%
12%
6%
12%
VO
60%
35%
29%
21%
52%
25%
32%
27%
39%
30%
35%
15%
33%
30%
37%
22%
42%
40%
37%
28%
0%
32%
40%
32%
oo
24%
19%
16%
22%
28%
13%
17%
14%
18%
16%
19%
0%
18%
16%
20%
8%
22%
21%
20%
15%
0%
8%
20%
15%
H
16%
9%
3%
0%
5%
6%
2%
3%
8%
6%
7%
85%
14%
5%
4%
43%
11%
21%
10%
0%
0%
5%
11%
6%
1
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
(K
§
15%
15%
8%
14%
15%
11%
15%
8%
0%
0%
0%
15%
4%
15%
4%
15%
15%
15%
15%
15%
0%
0%
15%
6%
m
15%
15%
0%
15%
15%
2%
15%
0%
2%
0%
0%
15%
0%
0%
1%
15%
14%
0%
0%
15%
0%
12%
15%
5%
a
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
100%
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%
t/3
t/3
55%
57%
0%
57%
55%
1%
61%
0%
0%
0%
0%
55%
0%
29%
0%
57%
57%
5%
15%
58%
0%
0%
57%
7%
LRRT2
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
-------
Chapter 3
Table 3.5-17 Reference Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-5%
-7%
-4%
-5%
-6%
-5%
-1%
-2%
-1%
-1%
-3%
-4%
-2%
-2%
-8%
-4%
0%
-9%
0%
-1%
-4%
-3%
0 "
£1
-8%
-6%
-5%
-7%
-3%
-5%
-6%
-5%
-1%
-2%
-1%
0%
-3%
-4%
-2%
-2%
-8%
-3%
0%
-8%
0%
-1%
-4%
-3%
«, &
3 "3
Sl
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
1%
0%
0%
1%
0%
0%
1%
0%
TDS18
40%
45%
51%
40%
40%
64%
51%
47%
0%
28%
6%
52%
46%
71%
22%
43%
55%
72%
70%
40%
0%
3%
46%
32%
TDS24
15%
15%
14%
15%
15%
15%
15%
11%
0%
0%
0%
15%
12%
15%
8%
15%
15%
15%
15%
15%
0%
0%
15%
8%
TDS27
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1
0%
12%
4%
0%
14%
23%
13%
5%
0%
13%
4%
0%
13%
13%
3%
0%
2%
2%
3%
14%
0%
5%
9%
8%
oo
%
0%
0%
1%
39%
0%
9%
4%
2%
0%
7%
2%
0%
4%
5%
1%
0%
0%
0%
2%
0%
0%
3%
0%
4%
VO
60%
48%
52%
28%
52%
35%
46%
52%
50%
38%
52%
15%
37%
42%
49%
28%
49%
42%
45%
55%
0%
50%
51%
46%
oo
24%
26%
28%
30%
28%
19%
25%
26%
22%
21%
19%
0%
20%
22%
27%
10%
26%
22%
25%
30%
0%
11%
25%
21%
H
16%
13%
3%
0%
5%
7%
3%
6%
12%
7%
9%
85%
18%
8%
5%
56%
13%
25%
12%
0%
0%
7%
14%
8%
1
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
(K
§
15%
15%
1%
15%
15%
11%
15%
2%
0%
0%
0%
15%
3%
15%
1%
15%
15%
15%
15%
15%
0%
0%
15%
5%
m
15%
15%
0%
15%
15%
1%
15%
0%
3%
0%
0%
15%
0%
0%
1%
15%
15%
0%
0%
15%
0%
16%
15%
6%
a
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
100%
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%
t/3
t/3
55%
55%
0%
55%
55%
0%
57%
0%
0%
0%
0%
55%
0%
11%
0%
55%
55%
0%
0%
55%
0%
0%
55%
8%
LRRT2
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-18 Reference Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-8%
-6%
-9%
NA
-6%
-8%
-7%
-3%
-4%
-4%
NA
-8%
-9%
-4%
-8%
-3%
-9%
-7%
-6%
NA
-2%
-8%
-5%
-------
Chapter 3
Table 3.5-19 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-7%
-5%
-8%
-4%
-6%
-7%
-6%
-2%
-2%
-2%
-1%
-4%
-6%
-3%
-4%
-8%
-5%
-1%
-7%
0%
-2%
-5%
-4%
-------
MY 2017 and Later - Regulatory Impact Analysis
3.5.4 Projected Technology Penetrations in Final rule case
Table 3.5-20 Final rule Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-6%
-12%
-8%
-6%
-11%
-6%
-2%
-3%
-3%
-3%
-4%
-6%
-3%
-6%
-14%
-6%
-1%
-16%
0%
-3%
-7%
-5%
g i
H S
-11%
-9%
-6%
-11%
-3%
-6%
-10%
-6%
-2%
-3%
-3%
0%
-4%
-6%
-3%
-2%
-12%
-5%
0%
-13%
0%
-3%
-5%
-5%
&
M ^
•3 «
S£
6%
1%
0%
1%
5%
0%
1%
0%
0%
0%
0%
3%
0%
0%
0%
4%
2%
1%
1%
3%
0%
0%
1%
0%
oo
t/3
e
7%
52%
67%
41%
6%
74%
36%
48%
15%
41%
17%
15%
72%
71%
42%
4%
20%
71%
70%
13%
0%
23%
49%
43%
TDS24
22%
28%
21%
29%
22%
17%
30%
15%
5%
14%
5%
29%
28%
29%
19%
28%
30%
29%
30%
30%
0%
0%
30%
14%
i^
IN
t/3
e
15%
6%
1%
12%
15%
1%
13%
1%
0%
0%
0%
13%
0%
0%
0%
15%
15%
0%
0%
15%
0%
0%
12%
2%
S
<
0%
0%
1%
0%
0%
6%
3%
1%
0%
5%
2%
0%
3%
3%
1%
0%
0%
0%
1%
0%
0%
1%
0%
2%
gs
<
0%
0%
4%
7%
0%
23%
11%
5%
0%
20%
7%
0%
13%
14%
4%
0%
0%
0%
5%
0%
0%
4%
0%
7%
£
U
Q
4%
14%
21%
6%
4%
14%
9%
22%
21%
17%
21%
0%
14%
16%
21%
3%
8%
15%
16%
10%
0%
18%
11%
17%
oo
u
Q
73%
72%
72%
78%
78%
49%
66%
66%
64%
51%
62%
39%
54%
59%
69%
56%
72%
64%
68%
77%
0%
55%
71%
61%
H
7%
10%
2%
0%
2%
7%
2%
6%
12%
7%
9%
49%
16%
8%
5%
29%
8%
20%
9%
0%
0%
7%
10%
8%
O
w
59%
60%
55%
58%
59%
45%
59%
48%
10%
44%
17%
58%
55%
58%
48%
59%
58%
60%
60%
57%
0%
4%
59%
36%
A
§
30%
30%
9%
30%
30%
14%
30%
9%
0%
2%
0%
30%
28%
29%
9%
30%
30%
29%
30%
30%
0%
0%
30%
11%
W
26%
9%
0%
7%
26%
2%
13%
0%
3%
0%
0%
22%
0%
0%
1%
25%
22%
0%
0%
25%
0%
15%
1%
4%
£
16%
4%
0%
9%
16%
0%
9%
0%
0%
0%
0%
12%
0%
0%
0%
12%
12%
0%
0%
13%
100%
0%
8%
1%
W
PH
15%
0%
0%
0%
15%
0%
0%
0%
0%
0%
0%
9%
0%
0%
0%
15%
2%
0%
0%
4%
0%
0%
0%
0%
t/3
t/3
35%
36%
0%
41%
35%
4%
46%
0%
0%
0%
0%
38%
3%
7%
0%
34%
46%
5%
9%
35%
0%
0%
49%
7%
LRRT2
75%
75%
75%
75%
75%
74%
75%
75%
73%
75%
75%
75%
75%
75%
74%
75%
75%
75%
75%
75%
0%
63%
75%
72%
1
24%
57%
79%
54%
24%
72%
49%
79%
77%
75%
78%
45%
76%
75%
78%
36%
49%
74%
73%
40%
0%
63%
56%
71%
w
60%
60%
54%
60%
60%
47%
60%
28%
5%
21%
9%
57%
55%
58%
32%
59%
59%
58%
60%
57%
0%
0%
60%
29%
Q
84%
96%
89%
89%
84%
92%
91%
63%
20%
56%
23%
88%
100%
100%
61%
88%
88%
100%
100%
87%
0%
24%
92%
60%
_)
%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
MHEV
4%
21%
0%
22%
4%
6%
17%
0%
0%
1%
0%
8%
4%
6%
0%
5%
8%
19%
25%
5%
0%
0%
29%
5%
3-49
-------
Chapter 3
Table 3.5-21 Final rule Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-13%
-7%
-15%
NA
-8%
-15%
-8%
-7%
-9%
-8%
NA
-13%
-14%
-5%
-15%
-3%
-15%
-11%
-10%
NA
-4%
-14%
-7%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-22 Final rule Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-11%
-6%
-13%
-8%
-7%
-12%
-7%
-4%
-5%
-4%
-3%
-5%
-9%
-3%
-8%
-13%
-8%
-3%
-13%
0%
-3%
-8%
-6%
-------
Chapter 3
Table 3.5-23 Final rule Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-11%
-8%
-15%
-10%
-10%
-14%
-8%
-3%
-5%
-3%
-3%
-6%
-9%
-4%
-7%
-16%
-9%
-1%
-19%
0%
-3%
-8%
-6%
g i
H S
-12%
-10%
-7%
-13%
-3%
-9%
-11%
-7%
-3%
-4%
-3%
0%
-5%
-7%
-3%
-2%
-13%
-8%
0%
-14%
0%
-3%
-6%
-6%
&
M ^
•3 «
S£
8%
2%
1%
2%
8%
1%
3%
1%
0%
0%
0%
3%
1%
2%
1%
4%
3%
1%
1%
5%
0%
0%
2%
1%
oo
t/3
e
0%
6%
24%
6%
0%
21%
5%
23%
24%
25%
43%
7%
20%
19%
25%
2%
8%
10%
2%
0%
0%
48%
9%
25%
TDS24
0%
60%
72%
60%
0%
70%
46%
72%
73%
75%
57%
56%
75%
74%
74%
56%
60%
75%
75%
21%
0%
34%
73%
63%
i^
IN
t/3
e
29%
20%
3%
12%
5%
4%
26%
3%
0%
0%
0%
0%
0%
0%
0%
9%
8%
0%
0%
37%
0%
1%
2%
3%
S
<
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
gs
<
0%
0%
4%
0%
0%
29%
13%
6%
0%
24%
7%
0%
15%
16%
5%
0%
0%
0%
6%
0%
0%
4%
0%
8%
£
u
Q
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
oo
u
Q
76%
82%
94%
83%
77%
63%
71%
89%
85%
69%
84%
55%
74%
76%
91%
65%
74%
78%
79%
77%
0%
74%
79%
79%
H
1%
6%
2%
0%
0%
4%
1%
5%
12%
7%
9%
25%
10%
4%
3%
12%
4%
16%
7%
0%
0%
7%
6%
6%
O
w
77%
88%
100%
83%
77%
97%
85%
100%
97%
100%
100%
80%
98%
96%
99%
77%
79%
95%
93%
77%
0%
84%
85%
93%
A
§
29%
75%
75%
72%
5%
73%
72%
74%
73%
75%
57%
56%
75%
74%
74%
65%
69%
75%
75%
58%
0%
31%
75%
65%
W
27%
1%
0%
4%
50%
1%
5%
0%
3%
0%
0%
11%
3%
3%
1%
2%
2%
10%
16%
13%
0%
16%
0%
4%
£
23%
12%
0%
17%
23%
2%
15%
0%
0%
0%
0%
20%
2%
4%
0%
23%
21%
5%
7%
23%
100%
0%
15%
3%
W
PH
22%
0%
0%
0%
22%
0%
4%
0%
0%
0%
0%
5%
0%
0%
0%
9%
0%
0%
0%
6%
0%
0%
0%
0%
t/3
t/3
5%
33%
2%
33%
5%
15%
32%
0%
0%
0%
0%
25%
7%
11%
2%
18%
29%
0%
3%
21%
0%
0%
35%
7%
LRRT2
100%
100%
100%
100%
100%
99%
100%
100%
97%
100%
100%
100%
100%
100%
99%
100%
100%
100%
100%
100%
0%
84%
100%
96%
1
5%
38%
73%
33%
5%
61%
32%
78%
97%
90%
98%
25%
56%
46%
77%
18%
29%
68%
62%
21%
0%
84%
35%
73%
w
77%
88%
100%
83%
77%
97%
85%
100%
97%
100%
100%
80%
98%
96%
99%
77%
79%
95%
93%
77%
0%
84%
85%
93%
Q
77%
88%
99%
82%
77%
94%
85%
97%
97%
100%
100%
80%
98%
96%
99%
77%
79%
95%
93%
77%
0%
83%
85%
93%
_)
%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
MHEV
23%
49%
27%
46%
0%
35%
45%
22%
0%
10%
2%
39%
39%
47%
22%
48%
48%
17%
15%
37%
0%
0%
49%
20%
3-52
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-24 Final rule Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-17%
-10%
-20%
NA
-11%
-20%
-11%
-11%
-14%
-12%
NA
-19%
-20%
-9%
-20%
-4%
-19%
-15%
-13%
NA
-8%
-18%
-11%
-------
Chapter 3
Table 3.5-25 Final rule Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-13%
-9%
-16%
-10%
-10%
-16%
-9%
-5%
-7%
-5%
-3%
-8%
-13%
-5%
-10%
-14%
-11%
-4%
-16%
0%
-5%
-10%
-8%
-------
MY 2017 and Later - Regulatory Impact Analysis
3.5.5 Projected Technology Penetrations in Alternative Cases
Table 3.5-26 Alternative 1- (Trucks +20) Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-6%
-12%
-8%
-6%
-11%
-5%
-2%
-3%
-2%
-3%
-4%
-5%
-3%
-5%
-14%
-6%
0%
-15%
0%
-2%
-6%
-4%
g i
P S
-11%
-9%
-6%
-11%
-3%
-6%
-10%
-5%
-2%
-3%
-2%
0%
-4%
-5%
-3%
-2%
-12%
-5%
0%
-14%
0%
-2%
-5%
-4%
-S1
M ^
^ a
S£
6%
1%
0%
1%
5%
0%
1%
0%
0%
0%
0%
3%
0%
0%
0%
3%
2%
1%
0%
1%
0%
0%
1%
0%
oo
1
7%
58%
36%
52%
6%
52%
49%
37%
15%
27%
19%
15%
82%
74%
36%
10%
30%
73%
75%
34%
0%
23%
61%
38%
TDS24
22%
30%
11%
28%
22%
16%
27%
1%
0%
8%
0%
29%
18%
26%
6%
30%
30%
27%
25%
24%
0%
0%
27%
9%
i^
IN
g
15%
6%
1%
5%
15%
1%
11%
0%
0%
0%
0%
13%
0%
0%
0%
15%
15%
0%
0%
15%
0%
0%
4%
1%
£
<
0%
0%
1%
0%
0%
6%
3%
1%
0%
5%
2%
0%
3%
3%
1%
0%
0%
0%
1%
0%
0%
1%
0%
2%
oo
<
0%
0%
4%
8%
0%
23%
11%
5%
0%
20%
7%
0%
13%
14%
4%
0%
0%
0%
5%
0%
0%
4%
0%
7%
\o
4%
17%
23%
7%
4%
15%
13%
22%
21%
17%
21%
0%
16%
16%
22%
3%
9%
17%
21%
14%
0%
18%
12%
18%
oo
73%
71%
70%
79%
78%
48%
66%
66%
64%
51%
62%
39%
51%
58%
67%
55%
72%
61%
63%
78%
0%
55%
72%
61%
H
7%
10%
3%
0%
2%
7%
2%
6%
12%
7%
9%
49%
17%
9%
5%
30%
8%
21%
10%
0%
0%
7%
11%
8%
1
59%
58%
38%
59%
59%
45%
60%
12%
0%
23%
5%
58%
53%
58%
33%
58%
59%
57%
47%
60%
0%
0%
60%
25%
(K
O
W
30%
30%
1%
29%
30%
9%
30%
0%
0%
0%
0%
30%
5%
26%
0%
30%
30%
27%
25%
30%
0%
0%
30%
7%
m
26%
4%
0%
7%
26%
2%
8%
0%
3%
0%
0%
22%
0%
0%
1%
21%
12%
0%
0%
19%
0%
15%
1%
4%
m
16%
2%
0%
6%
16%
0%
6%
0%
0%
0%
0%
12%
0%
0%
0%
12%
11%
0%
0%
8%
100%
0%
6%
1%
W
PH
15%
0%
0%
0%
15%
0%
0%
0%
0%
0%
0%
9%
0%
0%
0%
12%
2%
0%
0%
0%
0%
0%
0%
0%
t/3
t/3
35%
33%
0%
38%
35%
0%
43%
0%
0%
0%
0%
38%
3%
3%
0%
36%
45%
0%
4%
38%
0%
0%
43%
6%
LRRT2
75%
75%
75%
75%
75%
74%
75%
75%
73%
75%
75%
75%
75%
75%
74%
75%
75%
75%
75%
75%
0%
63%
75%
72%
IACC2
24%
60%
79%
57%
24%
73%
52%
79%
43%
75%
63%
45%
77%
77%
78%
39%
50%
78%
78%
49%
0%
2%
59%
56%
a
&
60%
60%
17%
60%
60%
29%
59%
1%
0%
10%
0%
57%
54%
51%
7%
59%
59%
58%
60%
58%
0%
0%
60%
18%
Q
84%
98%
48%
93%
84%
69%
94%
38%
15%
35%
19%
88%
100%
100%
42%
88%
89%
100%
100%
92%
0%
24%
94%
49%
_)
%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
MHEV
4%
26%
0%
22%
4%
1%
22%
0%
0%
0%
0%
8%
3%
1%
0%
9%
19%
16%
12%
11%
0%
0%
29%
4%
3-55
-------
Chapter 3
Table 3.5-27 Alternative 1- (Trucks +20) Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-13%
-7%
-15%
NA
-7%
-15%
-7%
-6%
-9%
-7%
NA
-11%
-11%
-5%
-15%
-3%
-12%
-11%
-10%
NA
-3%
-14%
-7%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-28 Alternative 1- (Trucks +20) Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-11%
-6%
-13%
-8%
-6%
-12%
-6%
-3%
-4%
-3%
-3%
-5%
-7%
-3%
-8%
-13%
-7%
-2%
-13%
0%
-2%
-8%
-5%
-------
Chapter 3
Table 3.5-29 Alternative 2- (Trucks -20) Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-6%
-12%
-8%
-8%
-12%
-7%
-2%
-3%
-3%
-3%
-5%
-7%
-3%
-6%
-14%
-6%
-1%
-16%
0%
-3%
-7%
-5%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-30 Alternative 2- (Trucks -20) Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-13%
-9%
-15%
NA
-9%
-15%
-9%
-8%
-10%
-8%
NA
-13%
-14%
-6%
-15%
-3%
-15%
-11%
-10%
NA
-5%
-14%
-8%
-------
Chapter 3
Table 3.5-31 Alternative 2- (Trucks -20) Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-11%
-7%
-13%
-8%
-8%
-13%
-8%
-4%
-5%
-4%
-3%
-6%
-10%
-4%
-8%
-13%
-8%
-3%
-13%
0%
-4%
-8%
-6%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-32 Alternative 3- (Cars +20) Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-9%
-6%
-12%
-8%
-6%
-11%
-5%
-1%
-2%
-2%
-1%
-3%
-5%
-3%
-5%
-13%
-5%
0%
-15%
0%
-2%
-6%
-4%
-------
Chapter 3
Table 3.5-33 Alternative 3- (Cars +20) Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-13%
-7%
-15%
NA
-7%
-15%
-7%
-4%
-5%
-5%
NA
-9%
-10%
-4%
-15%
-3%
-11%
-8%
-10%
NA
-3%
-14%
-6%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-34 Alternative 3- (Cars +20) Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-6%
-12%
-8%
-6%
-12%
-6%
-2%
-3%
-2%
-1%
-4%
-7%
-3%
-7%
-12%
-6%
-1%
-13%
0%
-2%
-8%
-5%
-------
Chapter 3
Table 3.5-35 Alternative 4- (Cars -20) Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-7%
-13%
-8%
-8%
-12%
-7%
-2%
-4%
-3%
-4%
-5%
-7%
-3%
-7%
-15%
-7%
-1%
-16%
0%
-3%
-7%
-5%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-36 Alternative 4- (Cars -20) Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-13%
-8%
-15%
NA
-9%
-15%
-9%
-8%
-14%
-10%
NA
-15%
-15%
-7%
-15%
-3%
-15%
-12%
-10%
NA
-5%
-14%
-9%
-------
Chapter 3
Table 3.5-37 Alternative 4- (Cars -20) Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-11%
-7%
-13%
-8%
-9%
-13%
-8%
-4%
-6%
-4%
-4%
-7%
-10%
-4%
-9%
-13%
-9%
-3%
-13%
0%
-4%
-9%
-7%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-38 Alternative 1- (Trucks +20) Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-11%
-7%
-15%
-10%
-9%
-14%
-7%
-2%
-4%
-3%
-3%
-6%
-8%
-3%
-6%
-16%
-8%
-1%
-19%
0%
-3%
-8%
-6%
-------
Chapter 3
Table 3.5-39 Alternative 1- (Trucks +20) Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-17%
-9%
-20%
NA
-10%
-20%
-9%
-10%
-13%
-14%
NA
-19%
-18%
-7%
-20%
-4%
-20%
-15%
-13%
NA
-6%
-18%
-10%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-40 Alternative 1- (Trucks +20) Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-13%
-8%
-16%
-10%
-9%
-16%
-8%
-5%
-6%
-5%
-3%
-8%
-11%
-4%
-9%
-14%
-11%
-3%
-16%
0%
-4%
-10%
-7%
-------
Chapter 3
Table 3.5-41 Alternative 2- (Trucks -20) Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-12%
-9%
-15%
-10%
-11%
-15%
-9%
-4%
-5%
-3%
-3%
-6%
-9%
-5%
-7%
-16%
-9%
-2%
-19%
0%
-4%
-8%
-7%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-42 Alternative 2- (Trucks -20) Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-17%
-12%
-20%
NA
-12%
-20%
-13%
-14%
-18%
-14%
NA
-19%
-20%
-9%
-20%
-4%
-19%
-16%
-13%
NA
-9%
-18%
-13%
-------
Chapter 3
Table 3.5-43 Alternative 2- (Trucks -20) Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-13%
-10%
-16%
-10%
-11%
-16%
-11%
-7%
-8%
-6%
-3%
-9%
-13%
-6%
-10%
-14%
-11%
-4%
-16%
0%
-6%
-10%
-9%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-44 Alternative 3- (Cars +20) Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-11%
-7%
-15%
-10%
-8%
-13%
-7%
-2%
-3%
-3%
-2%
-4%
-7%
-3%
-5%
-15%
-8%
0%
-19%
0%
-3%
-7%
-6%
-------
Chapter 3
Table 3.5-45 Alternative 3- (Cars +20) Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-17%
-8%
-20%
NA
-9%
-20%
-9%
-9%
-10%
-9%
NA
-13%
-18%
-6%
-20%
-4%
-19%
-14%
-13%
NA
-5%
-18%
-9%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-46 Alternative 3- (Cars +20) Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-12%
-7%
-16%
-10%
-8%
-15%
-8%
-4%
-5%
-4%
-2%
-6%
-11%
-4%
-8%
-14%
-10%
-2%
-16%
0%
-4%
-9%
-7%
-------
Chapter 3
Table 3.5-47 Alternative 4- (Cars -20) Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-12%
-9%
-16%
-10%
-11%
-15%
-9%
-4%
-7%
-4%
-5%
-7%
-10%
-5%
-8%
-16%
-9%
-3%
-19%
0%
-4%
-9%
-8%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 3.5-48 Alternative 4- (Cars -20) Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-17%
-12%
-20%
NA
-12%
-20%
-13%
-15%
-20%
-17%
NA
-20%
-20%
-9%
-20%
-4%
-20%
-16%
-13%
NA
-10%
-18%
-13%
-------
Chapter 3
Table 3.5-49 Alternative 4- (Cars -20) Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-20%
-13%
-10%
-17%
-10%
-12%
-17%
-11%
-7%
-10%
-7%
-5%
-9%
-13%
-7%
-10%
-15%
-12%
-5%
-17%
0%
-6%
-11%
-9%
-------
MY 2017 and Later - Regulatory Impact Analysis
3.5.6 Additional Detail on Mass Reduction Technology
For MY 2021 and MY 2025, additional details are presented on the distribution of
mass reduction in the fleet by vehicle class. For presentation in this analysis, we aggregated
YY
the 19 vehicle types into 9 narrower vehicle classes.
Table 3.5-50 Aggregation of Vehicle types for Mass Reduction Presentation
SubAuto_4_4_4_DOHC
Auto_4_4_4_DOHC
Auto_6_6_4_DOHC
Auto_6_6_2_SOHC
Auto_8_8_4_DOHC
Auto_8_8_2_OHV
MPVnt_4_4_4_DOHC
MPVt_6_6_4_DOHC
MPVt_6_6_2_SOHC
MPVt_6_6_2_OHV
MPVt_8_8_4_DOHC
MPVt_8_8_2_OHV
Truck_4_4_4_DOHC
Truck_6_6_4_DOHC
Truck_6_6_2_OHV
Truck_8_8_4_DOHC
Truck_8_8_2_SOHC
Truck_8_8_3_SOHC
Truck_8_8_2_OHV
VehType
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
2025 sales
2,343,764
3,717,990
2,684,824
486,136
566,356
168,301
1,098,943
3,910,859
90,504
442,375
263,513
123,898
61,359
258,882
162,502
217,954
103,184
161,734
387,383
Vehicle Class
Small car
Standard car
Standard car
Standard car
Large car
Large car
Small MPV
Large MPV
Large MPV
Large MPV
Truck
Truck
Small MPV
Large MPV
Large MPV
Truck
Truck
Truck
Truck
Just to limit the size of this table.
3-79
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Chapter 3
After aggregations here are the weight reductions by vehicle class.
Subcompact car 14
Compact car 14
Midsize car V6
Large car V8
Small MPV 14
Midsize MPV V6
Large MPV V8
Full pickup V6
Full pickup V8
Reference
2021
-0.2%
-1.3%
-5.3%
-7.2%
-5.1%
-5.6%
-7.1%
-1.8%
-7.0%
2025
-0.2%
-1.3%
-5.2%
-7.2%
-5.1%
-5.7%
-7.0%
-1.7%
-7.0%
Control
2021
-0.3%
-1.6%
-7.2%
-10.6%
-8.2%
-7.6%
-9.7%
-2.0%
-7.6%
2025
-0.5%
-1.7%
-8.3%
-13.2%
-13.3%
-11.2%
-12.3%
-1.9%
-8.8%
Sales
2025
2,343,764
3,717,990
3,170,959
734,656
1,160,302
4,443,738
387,411
421,385
870,254
3.5.7
Air Conditioning Cost
As previously referenced, once the OMEGA costs were determined, the estimated air
conditioning costs, as discussed in Chapter 5 of the Joint TSD were added onto the total cost.
These costs are shown below.
Table 3.5-51 Total Costs for A/C Control Used in This Final rule (2010$)
Car/
Truck
Car
Truck
Fleet
Case
Reference
Control
Total
Reference
Control
Total
Total
2021
$68
$79
$147
$52
$95
$147
$147
2025
$64
$69
$133
$49
$84
$133
$133
3.5.8 Stranded Capital
Because the production of automotive components is capital-intensive, it is possible for
substantial capital investments in manufacturing equipment and facilities to become
"stranded" (where their value is lost, or diminished). This would occur when the capital is
rendered useless (or less useful) by some factor that forces a major change in vehicle design,
plant operations, or manufacturer's product mix, such as a shift in consumer demand for
certain vehicle types. It can also be caused by new standards that phase-in at a rate too rapid
to accommodate planned replacement or redisposition of existing capital to other activities.
The lost value of capital equipment is then amortized in some way over production of the new
technology components. A discussion of this issue is presented in Chapter 3 of the TSD. To
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MY 2017 and Later - Regulatory Impact Analysis
help ensure a conservative cost analysis for the rale (i.e., an analysis that might err on the side
of over-costing), EPA asked FEV to calculate potential stranded capital on six specific
technologies, using a set of conservative assumptions described in the TSD. EPA then
included these potential additional technology costs as a post-process to the OMEGA model
(Table 3.5-53 ). These "stranded capital" costs were not directly incorporated into the
technology inputs because they are a function of how rapidly technologies are phased in.
Costs for potential stranded capital (as shown in TSD 3) depend both on the stranded
technology and the replacing technology.
Table 3.5-52 Potential Stranded Capital Costs (2009$)
Replaced
technology
6- speed AT
6- speed AT
6- speed DCT
Conventional V6
Conventional V8
Conventional V6
New
technology
6-speed DCT
8-speed AT
8-speed DCT
DSTGDI 14
DSTGDI V6
Power- split HEV
Stranded capital cost per vehicle
when replaced technology' s production is
ended after:
3 years
$55
$48
$28
$56
$60
$111
5 years
$39
$34
$20
$40
$43
$79
8 years
$16
$14
$8
$16
$17
$32
DSTGDI=Downsized, turbocharged engine with stoichiometric gasoline direct injection.
For MY 2016, the eight year stranded capital costs were used. For MYs 2016-2021
and 2021-2021, the five year stranded capital costs were used. This properly reflects EPA's
analytic assumption that redesign schedules are evenly spread through time.
For transmissions, EPA determined the change in quantity of 6 and 8 speed automatic
and dual clutch transmissions. For each of these transmissions, manufacturers that increased
their production quantity had no stranded capital, otherwise, we applied a per piece cost
corresponding to the table above. This methodology overstates the potential stranded capital
costs, as it includes changes in production from the vehicle forecast. For engines, the
stranded capital work done by FEV does not precisely correspond to the technologies
considered in OMEGA; significantly, the pieces of "stranded" technology were often not
those that were similarly "stranded" by the OMEGA projections. As an example, OMEGA
might forecast a 24 bar BMEP turbo-charged downsized engine in MY 2021, and then 27 bar
BMEP engine technology in MY 2025. The stranded 24 bar engine, while based on a FEV
cost analysis, does not directly correspond to any technology listed above. As a result, EPA
created a projection that for each manufacturer listed the number of engines with 8, 6, 4 or 3,
as well as the number of EVs and Atkinson cycle HEVs. A decrease in any of these quantities
resulted in a $50.50 (2010$) per engine increase in cost, which is a rough average of the five
year stranded capital cost for the three engine technologies.
Total potential stranded capital determined by this analysis is shown below, and
includes all manufacturers including SVMs. These costs are not differentiated between car
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Chapter 3
and track. As the values are small, we applied these same potential stranded capital costs to
all alternatives. The highest costs are in MY 2021, reflecting the rapid technology change
during the time leading up to that MY.
Table 3.5-53 Estimated Potential Stranded Capital^ (2010$)
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General
Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
MY2016
Engine
$51
$9
$60
$11
$0
$15
$13
$18
$12
$7
$7
$30
$12
$9
$11
$16
$41
$7
$32
$12
$1
$2
$9
$13
Trans-
mission
$16
$3
$0
$6
$1
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1
$0
$0
$0
$4
$0
$0
$2
$0
Total
$67
$13
$60
$17
$1
$15
$13
$18
$12
$7
$7
$30
$12
$9
$11
$17
$41
$7
$32
$16
$1
$2
$12
$14
MY 2021
Engine
$27
$15
$10
$13
$28
$9
$14
$8
$3
$3
$26
$13
$23
$23
$4
$17
$6
$8
$6
$9
$0
$13
$15
$10
Trans-
mission
$8
$16
$14
$9
$12
$12
$16
$10
$10
$6
$17
$0
$15
$15
$9
$10
$8
$7
$8
$12
$0
$6
$10
$10
Total
$35
$31
$24
$22
$40
$21
$30
$18
$13
$9
$43
$13
$38
$39
$13
$27
$14
$15
$14
$21
$0
$19
$25
$20
MY 2025
Engine
$14
$7
$11
$6
$19
$4
$7
$9
$17
$11
$21
$8
$10
$9
$10
$12
$11
$2
$7
$16
$0
$16
$1
$10
Trans-
mission
$3
$4
$5
$4
$3
$5
$5
$5
$4
$4
$4
$2
$4
$4
$4
$3
$4
$3
$4
$4
$0
$9
$4
$5
Total
$17
$11
$16
$10
$22
$9
$12
$14
$21
$15
$25
$10
$13
$13
$14
$15
$15
$5
$11
$20
$0
$25
$5
$16
22 Note that the total potential stranded capital for Aston Martin engines is greater than $50, the cost of the
potential stranded capital. This is because the market forecast includes a decrease in sales for Aston Martin, and
a projected change in number of cylinders for every one of their engines. Also note, as described in section
III.B.5 of the preamble, small volume manufacturers with U.S. sales of less than 5,000 vehicles would be able to
petition EPA for an alternative standard for MY 2017 and later. Manufacturers currently meeting the 5,000
vehicle cut point include Lotus, Aston Martin, and McLaren. Thus, these potential stranded capital costs may be
overstated for these small volume manufacturers.
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MY 2017 and Later - Regulatory Impact Analysis
3.6 Per Vehicle Costs MYs 2021 and 2025
As described above, to get the relevant per-vehicle technology costs which are
attributable to the program alone, we must account for any cost that is incurred due to
compliance with existing vehicle programs. In order to bring the MY 2008 based market
forecast up to reference case technology levels, EPA first used OMEGA to calculate costs
reflected in the existing MY 2016 program, which is the reference case for this analysis. The
OMEGA estimates indicate that, on average, manufacturers will need to spend $783 to meet
the 2016MY standards in the 2021 MY, and $719 to meet the 2016MY standards in the
2025MY per vehicle. Reference case costs, inclusive of AC costs, are provided in Table 3.6-1
Table 3.6-1 Reference Case Costs (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
TeslaAAA
Toyota
VW
Fleet
2021
Cars
$2,632
$1,989
$811
$2,212
$2,455
$911
$2,038
$769
$110
$401
$303
$1,867
$726
$1,182
$410
$1,884
$1,913
$1,044
$1,016
$2,518
$68
$160
$1,743
$710
Trucks
$0
$2,126
$978
$2,238
$0
$1,334
$1,959
$924
$416
$670
$712
$0
$890
$1,566
$890
$1,910
$2,112
$1,191
$1,330
$2,548
$0
$417
$1,735
$917
Fleet
$2,632
$2,025
$887
$2,219
$2,455
$1,054
$2,014
$846
$205
$456
$395
$1,867
$755
$1,316
$559
$1,890
$1,941
$1,080
$1,072
$2,533
$68
$260
$1,742
$783
2025
Cars
$2,417
$1,820
$718
$2,044
$2,248
$859
$1,865
$712
$94
$376
$281
$1,715
$673
$1,076
$373
$1,728
$1,751
$1,023
$951
$2,313
$63
$149
$1,589
$655
Trucks
$0
$1,955
$909
$2,065
$0
$1,255
$1,804
$853
$397
$647
$688
$0
$835
$1,443
$818
$1,739
$1,946
$1,147
$1,244
$2,345
$0
$375
$1,574
$849
Fleet
$2,417
$1,855
$801
$2,049
$2,248
$981
$1,847
$780
$183
$430
$367
$1,715
$700
$1,198
$505
$1,730
$1,776
$1,051
$1,001
$2,328
$63
$231
$1,586
$719
AAA While costs related to air-conditioning are shown for Tesla, as a manufacturer of solely electric vehicles,
Tesla can comply with reference, control, and alternative standards without incurring additional costs from this
regulation.
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Chapter 3
EPA then used OMEGA to calculate the costs of meeting the standards in the model
years 2021 and 2025, which are shown in Table 3.6-2 . EPA has accounted for the cost to
meet the MY 2016 standards in the reference case. In other words, Table 3.6-2 contains per-
vehicle costs for the final rule (the emission "control case") that are incremental to the
reference case costs shown in Table 3.6-1 .
Table 3.6-2 Control Case Costs for the Standards MY 2021 (2010$)
Company
Aston
Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2021 Costs
Cars
$6,724
$967
$681
$1,985
$6,712
$680
$2,132
$519
$532
$773
$625
$3,739
$959
$611
$644
$4,878
$3,019
$982
$1,032
$3,916
$79
$488
$1,492
$767
Trucks
$0
$529
$796
$659
$0
$875
$734
$720
$829
$875
$908
$0
$1,246
$1,127
$904
$604
$607
$1,594
$1,210
$1,061
$0
$600
$508
$763
Fleet
$6,724
$852
$733
$1,655
$6,712
$746
$1,698
$619
$624
$794
$689
$3,739
$1,010
$791
$725
$3,871
$2,674
$1,128
$1,064
$2,495
$79
$532
$1,293
$766
2021 Sales
Cars
1,058
359,098
421,013
300,378
7,059
1,401,617
92,726
1,564,277
1,198,880
613,355
331,319
278
274,740
65,851
912,629
36,475
21,294
230,780
95,725
58,677
28,623
1,903,706
585,607
10,505,165
Truck
0
128,724
348,613
99,449
0
714,181
41,768
1,530,020
535,916
156,466
95,432
0
59,227
35,309
408,029
11,242
3,560
72,773
20,767
58,153
0
1,215,539
148,734
5,683,902
Fleet
1,058
487,822
769,626
399,827
7,059
2,115,798
134,494
3,094,297
1,734,796
769,821
426,751
278
333,967
101,160
1,320,658
47,716
24,854
303,553
116,492
116,830
28,623
3,119,245
734,341
16,189,066
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.6-3 Control Case Costs for the Standards MY 2025 (2010$)
Company
Aston
Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2025
Cars
$7,480
$2,147
$1,617
$3,011
$7,864
$1,811
$3,177
$1,518
$1,525
$1,673
$1,572
$3,566
$1,979
$1,939
$1,618
$4,807
$3,580
$1,926
$2,112
$5,077
$69
$1,239
$2,412
$1,726
Trucks
$0
$1,250
$2,388
$1,284
$0
$2,505
$1,504
$2,237
$1,923
$2,268
$1,977
$0
$2,449
$2,169
$2,391
$1,274
$964
$2,495
$1,848
$1,447
$0
$1,700
$1,237
$2,059
Fleet
$7,480
$1,910
$1,950
$2,616
$7,864
$2,025
$2,681
$1,861
$1,642
$1,792
$1,658
$3,566
$2,057
$2,015
$1,847
$4,044
$3,238
$2,054
$2,066
$3,390
$69
$1,407
$2,181
$1,836
2025 Sales
Cars
1,182
405,256
436,479
340,719
7,658
1,540,109
101,107
1,673,936
1,340,321
677,250
362,783
316
306,804
73,305
1,014,775
40,696
23,130
256,970
103,154
65,418
31,974
2,108,053
630,163
11,541,560
Truck
0
145,409
331,762
101,067
0
684,476
42,588
1,524,008
557,697
168,136
97,653
0
61,368
36,387
426,454
11,219
3,475
74,722
21,374
56,805
0
1,210,016
154,284
5,708,899
Fleet
1,182
550,665
768,241
441,786
7,658
2,224,586
143,696
3,197,943
1,898,018
845,386
460,436
316
368,172
109,692
1,441,229
51,915
26,605
331,692
124,528
122,223
31,974
3,318,069
784,447
17,250,459
EPA estimates that the additional technology required for manufacturers to meet the
GHG standards for this rule will cost on average $766/vehicle and $l,836/vehicle in the 2021
and 2025 MYs, respectively. These costs include our estimates of stranded capital and costs
associated with the A/C program as explained in sections 3.6 and 3.7 above.
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Chapter 3
The OMEGA results project that under the primary final rule approximately 1% of the
vehicles sold in MYs 2017-2025 will be EVs or PHEVs.
Table 3.6-4 Sales by Technology
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
Fraction
ICE Only
Sales
14,779,343
14,364,044
14,165,763
14,249,833
14,304,401
13,707,594
12,976,088
12,266,523
11,551,765
122,365,357
84%
MHEV +HEV
Sales
975,369
1,137,524
1,314,056
1,520,778
1,732,100
2,526,963
3,420,545
4,352,578
5,325,056
22,304,969
15%
EV+PHEV
Sales
51,609
74,842
98,839
125,327
152,565
205,216
258,856
314,986
373,638
1,655,878
1%
Total Sales
15,806,322
15,576,410
15,578,658
15,895,939
16,189,066
16,439,772
16,655,489
16,934,087
17,250,459
146,326,204
100%
3.7 Alternative Program Stringencies
Table 3.7-1 Control Case Costs for the Alternative 1 (Trucks +20) Standards (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2021
Cars
$6,724
$444
$296
$1,428
$6,712
$404
$1,373
$176
$398
$556
$514
$3,739
$834
$445
$417
$4,258
$2,671
$878
$760
$2,161
$79
$328
$1,056
$497
Trucks
$0
$529
$474
$616
$0
$505
$722
$367
$567
$875
$674
$0
$967
$510
$670
$604
$607
$822
$1,210
$562
$0
$406
$508
$492
Fleet
$6,724
$467
$377
$1,226
$6,712
$438
$1,171
$271
$450
$620
$550
$3,739
$858
$468
$495
$3,397
$2,375
$865
$840
$1,365
$79
$359
$945
$496
2025
Cars
$7,480
$1,679
$1,236
$2,441
$7,864
$1,537
$2,410
$1,196
$1,344
$1,520
$1,442
$3,566
$1,803
$1,495
$1,439
$4,341
$3,272
$1,722
$1,967
$3,801
$69
$1,020
$2,129
$1,460
Trucks
$0
$1,250
$1,832
$1,284
$0
$1,906
$1,504
$1,513
$1,452
$2,007
$1,477
$0
$2,449
$1,838
$1,748
$1,274
$964
$2,252
$1,848
$1,075
$0
$1,411
$1,237
$1,582
Fleet
$7,480
$1,566
$1,494
$2,176
$7,864
$1,650
$2,141
$1,347
$1,376
$1,617
$1,449
$3,566
$1,911
$1,609
$1,530
$3,678
$2,971
$1,842
$1,946
$2,534
$69
$1,163
$1,953
$1,500
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.7-2 Control Case Costs for the Alternative 2 (Trucks -20) Standards (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2021
Cars
$6,724
$1,569
$904
$2,704
$6,712
$962
$3,065
$916
$754
$937
$861
$3,739
$1,139
$1,153
$929
$5,579
$3,410
$1,311
$1,277
$6,220
$79
$556
$1,975
$1,062
Trucks
$0
$576
$1,460
$662
$0
$1,418
$846
$1,262
$1,035
$1,067
$910
$0
$1,467
$1,263
$1,126
$861
$607
$1,594
$1,210
$1,061
$0
$1,044
$508
$1,159
Fleet
$6,724
$1,307
$1,156
$2,196
$6,712
$1,116
$2,376
$1,087
$841
$963
$872
$3,739
$1,198
$1,192
$990
$4,468
$3,009
$1,379
$1,265
$3,652
$79
$746
$1,678
$1,096
2025
Cars
$7,480
$2,676
$2,233
$3,503
$7,864
$2,283
$3,986
$2,234
$1,718
$1,906
$1,744
$3,566
$2,155
$2,468
$2,027
$5,305
$3,719
$2,262
$2,470
$7,074
$69
$1,546
$2,857
$2,146
Trucks
$0
$1,250
$2,792
$1,284
$0
$2,631
$1,504
$2,828
$2,363
$2,504
$2,328
$0
$2,624
$2,169
$2,503
$1,274
$964
$2,495
$1,953
$1,809
$0
$2,210
$1,237
$2,434
Fleet
$7,480
$2,300
$2,474
$2,995
$7,864
$2,390
$3,250
$2,517
$1,907
$2,025
$1,868
$3,566
$2,233
$2,369
$2,168
$4,434
$3,360
$2,314
$2,381
$4,627
$69
$1,788
$2,538
$2,241
3-87
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Chapter 3
Table 3.7-3 Control Case Costs for the Alternative 3 (Cars +20) Standards (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2021
Cars
$6,724
-$258
$296
$692
$6,712
$238
$579
$187
$305
$304
$364
$2,125
$395
$275
$259
$3,315
$1,437
$462
$342
$2,161
$79
$324
$147
$298
Trucks
$0
$474
$474
$616
$0
$287
$722
$442
$377
$535
$313
$0
$491
$241
$335
$548
$532
$511
$419
$562
$0
$294
$484
$388
Fleet
$6,724
-$65
$377
$673
$6,712
$254
$623
$313
$327
$351
$353
$2,125
$412
$263
$282
$2,663
$1,308
$474
$356
$1,365
$79
$312
$215
$330
2025
Cars
$5,723
$1,068
$1,236
$1,723
$7,416
$1,212
$1,708
$1,201
$990
$1,118
$989
$2,346
$1,223
$1,133
$1,176
$3,472
$2,223
$1,369
$1,297
$3,297
$69
$857
$1,292
$1,151
Trucks
$0
$1,195
$1,659
$1,284
$0
$1,505
$1,504
$1,619
$1,382
$1,218
$1,230
$0
$1,587
$1,658
$1,407
$1,274
$964
$1,526
$1,776
$1,075
$0
$1,303
$1,237
$1,448
Fleet
$5,723
$1,102
$1,419
$1,622
$7,416
$1,302
$1,647
$1,400
$1,105
$1,138
$1,040
$2,346
$1,284
$1,307
$1,244
$2,997
$2,059
$1,405
$1,379
$2,264
$69
$1,020
$1,281
$1,249
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.7-4 Control Case Costs for the Alternative 4 (Cars -20) Standards (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
VW
Fleet
2021
Cars
$6,724
$2,597
$912
$4,014
$6,712
$1,343
$4,153
$879
$1,019
$1,311
$1,217
$5,282
$1,881
$1,625
$1,182
$5,849
$4,917
$1,915
$2,204
$6,360
$79
$708
$3,234
$1,422
Trucks
$0
$616
$1,562
$664
$0
$1,521
$925
$1,155
$1,227
$1,879
$1,359
$0
$2,100
$1,444
$1,539
$1,347
$773
$2,061
$1,276
$1,061
$0
$1,091
$766
$1,261
Fleet
$6,724
$2,075
$1,206
$3,181
$6,712
$1,403
$3,151
$1,015
$1,083
$1,426
$1,249
$5,282
$1,920
$1,562
$1,292
$4,788
$4,324
$1,950
$2,039
$3,723
$79
$857
$2,734
$1,365
2025
Cars
$7,885
$3,684
$2,332
$4,580
$7,864
$2,832
$5,049
$2,043
$2,086
$2,654
$2,425
$4,908
$3,086
$3,078
$2,524
$6,148
$5,040
$2,793
$3,301
$7,074
$69
$1,631
$4,018
$2,556
Trucks
$0
$1,250
$2,851
$1,284
$0
$2,728
$1,504
$2,828
$2,790
$2,718
$2,552
$0
$2,958
$2,186
$2,722
$2,205
$964
$3,238
$1,953
$2,214
$0
$2,565
$1,237
$2,612
Fleet
$7,885
$3,041
$2,556
$3,826
$7,864
$2,800
$3,998
$2,417
$2,293
$2,666
$2,452
$4,908
$3,064
$2,782
$2,583
$5,296
$4,507
$2,893
$3,070
$4,815
$69
$1,971
$3,471
$2,574
3.8 Comparative cost of advanced technologies under credit scenarios
As part of the analysis of the flexibility programs, EPA calculated an illustrative
example of the relative cost-effectiveness of certain advanced technologies.
Table 3.8-1 shows the cost per gram per mile of going from the MY 2016 type technologies
to MY 2021 technologies. Note that in all cases, the advanced technologies are significantly
more expensive than the average costs per vehicle from the OMEGA, even when considering
the impacts of the multiplier and advanced technology incentives.
Table 3.8-1 Gram/mile cost of advanced technologies
OMEGA projection of
Reference
Case CO2
224
MY 2021
CO2
178
Delta
g/mile
$46
Delta
CostA
$767
$per
g/mile
$ 17
3-89
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Chapter 3
average 2021 Car in
control Case
EV100 (45 sqft, VT 3,
no multiplier)
EV100 (45 sqft, VT 3,
1.5 multiplier)
OMEGA projection of
average 2021 Truck in
control Case
HEV (65 sqft, VT 16,
no credit)
HEV (65 sqft, VT 16,
20 g credit)
263
263
296
334
334
-
-
239
251
231
263
395
57
93
113
$16,877
$16,877
$763
$6,054
$6,054
$64
$43
$13
$65
$54
ANote that we use average reference case cost of $710for cars and $917for trucks, not the vehicle specific cost.
If these vehicles reference case costs were higher than average, then their costs under the final rule would be
less, and conversely if their costs were lower than averages, then their compliance costs would be greater.
The reference case COi values are determined in the case of the OMEGA projections,
from the actual OMEGA runs, and in the case of the 45 and 65 square foot vehicles from the
applicable GHG curve. In this table, the EV is assumed to have a compliance value of zero
grams per mile without the multiplier incentive. For the incentive, we simply multiplied the
delta gram per mile by 1.5. This overstates the impact of the credit, because the multiplier
would also increase the number of vehicles in a manufacturer's fleet by 1.5. The cost per
gram/mile is actually greater than shown in this illustrative table because the size of the fleet
impacts the benefit of the multiplier.
For HEVs, the technology in this example has an effectiveness of 49.8% relative to a
baseline (no technology) vehicle with a COi of 500 g/mile. This effectiveness is used to
derive the cost-effectiveness value.
HEVs and EVs, regardless of their cost-effectiveness, are more effective than the
conventional technologies, and retain that advantage despite their disadvantages on a cost-
effectiveness basis. Further in MY 2025, when the average cost per gram/mile is higher,
these technologies are relatively more cost effective.
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MY 2017 and Later - Regulatory Impact Analysis
3.9 How Many of Today's Vehicles Can Meet or Surpass the MY 2017-2025 CO2
Footprint-based Targets with Current Powertrain Designs?
As part of its evaluation of the feasibility of these standards, EPA evaluated all MY
2012 and MY 2013 vehicles sold in the U.S. today against the final CC>2 footprint-based
standard curves to determine which of these vehicles would meet or be lower than the final
MY 2017 - MY 2025 footprint-based CC>2 targets assuming air conditioning credit generation
consistent with today's final rule. Under the final MY 2017 - MY 2025 greenhouse gas
emissions standards, each vehicle will have a unique COi target based on the vehicle's
footprint (with each manufacturer having its own unique fleetwide standard). In this analysis,
EPA assumed that manufacturers would utilize all available air conditioner credits because air
conditioner improvements are considered to be among the cheapest and easiest technologies
to reduce greenhouse gas emissions, manufacturers are already investing in air conditioner
improvements, and air conditioner changes do not impact engine, transmission, or
aerodynamic designs (so utilizing air conditioning credits would not affect consideration of
cost and leadtime for use of these other technologies).EPA applied increasing air conditioner
credits over time with a phase-in of alternative refrigerant for the generation of HFC leakage
reduction credits consistent with the assumed phase-in schedule discussed in Preamble
Section III.C.l. No adjustments were made to vehicle COi performance other then this
assumption of air conditioning credit generation. Under this analysis, a wide range of these
existing vehicles would meet the MY 2017 COi targets, and a few meet even the MY 2025
CO2 targets.
Using publicly available dataBBB, EPA compiled a list of all available vehicles and
their 2-cycle COi g/mile performance (that is, the performance over the city and highway
compliance tests). Data is currently available for all MY2012 vehicles and some MY2013
vehicles. EPA gathered vehicle footprint data from EPA reports,ccc manufacturer submitted
CAFE reports, and manufacturer websites.
Table 3.9-5 shows that a significant number of vehicles sold today would meet or be
lower than the final footprint-based CC>2 targets with current powertrain designs, assuming air
conditioning credit generation consistent with this final rule. The table highlights the vehicles
with COi emissions that meet or are lower than the applicable footprint targets from MY 2017
to 2025 in green, and shows the percentage below the target for each year. The list of
vehicles includes midsize cars, minivans, sport utility vehicles, compact cars, and small
pickup trucks - all of which meet the MY 2017 target values with no technology
improvements other then air conditioning system upgrades. These vehicles utilize a wide
variety of powertrain technologies, including internal combustion, hybrid-electric, plug-in
hybrid-electric, and full electric, and operate on a variety of different fuels including gasoline,
diesel, electricity, and compressed natural gas. Nearly every major manufacturer produces
some vehicles that would meet or be lower than the MY2017 footprint CO2 target with only
simple improvements in air conditioning systems.
BBB www.fueleconomy. gov
ccc EPA's "Light Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends Report,
1975 through 2010" (Docket No. EPA-HQ-OAR-2010-0799-1126)
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Chapter 3
Vehicles that are above, but within 5%, of the targets are highlighted in yellow. This
list also includes vehicles from multiple classes, including large cars and standard pickup
trucks. Four versions of the F-150 pickup truck are within 5% of the targets through at least
2021. This includes two engine options (the 3.7L V6 and the 3.5L V6), and three wheelbase
options000
Prior to each model year, EPA receives projected sales data from each manufacturer.
Based on this data, approximately 17% of MY2012 sales will be vehicles that meet or are
below their vehicle specific MY 2017 targets, requiring only improvements in air
conditioning systems. This is more than double the percentage of sales from MY2011 that
EPA projected to meet the MY2017 targets. An additional 12% of projected MY2012 sales
will be within 5% of the MY2017 footprint CO2 target with only simple improvements to air
conditioning systems. The percentage of MY2011 and MY2012 vehicle sales that meet or are
within 5% the final MY2017-MY2025 standards are shown in Table 3.9-1 and Table 3.9-2.
Overall, nearly 30% of MY2012 vehicle sales will meet or be within 5% of the final MY2017
targets and over 40% of MY2012 sales will meet or be within 10% of the final MY2017
targets with only simple improvements to air conditioning systems, five full model years
before the standard takes effect.
Table 3.9-1 Percentage of Projected Sales Compliant with Final Targets
Model Year
2011
2012
2017
7.0%
16.8%
2018
6.2%
13.6%
2019
5.9%
8.4%
2020
5.2%
6.6%
2025
1.8%
3.1%
Table 3.9-2 Percentage of Projected Sales Within 5% of Final Targets
Model Year
2011
2012
2017
7.6%
12.2%
2018
2.5%
10.9%
2019
1.5%
7.1%
2020
1.7%
2.6%
2025
0.8%
0.3%
With improvements to air conditioning systems, the most efficient gasoline internal
combustion engines would meet the MY 2022 final footprint targets (e.g. the Ford Focus
2.0L). After MY 2022, the only current vehicles that continue to meet the footprint-based
CO2 targets (assuming improvements in air conditioning) are CNG, hybrid-electric, plug-in
hybrid-electric, and fully electric vehicles. However, the MY 2022 standards will not be in
effect for another ten years. EPA expects that gasoline vehicles will continue to improve in
that timeframe and will be able to meet the standard (using the technologies discussed in
The F-150 engine and wheelbase combinations listed in Table 3.9-5 correspond to models that are currently
available. Not all possible engine and wheelbase combinations are produced.
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MY 2017 and Later - Regulatory Impact Analysis
Chapter 3 of the Joint TSD and as discussed in Preamble Section III.D) including air
conditioner improvements. Supporting that expectation is the fact that since this rule was
proposed, the number of gasoline vehicles available in the marketplace that meet or are below
the final MY 2017 targets, assuming improvements to air conditioning systems, has more than
doubled to approximately 65 vehicles. Table 3.9-3 shows the number of currently available
MY 2012 and MY 2013 vehicles (as well as the MY 2011 and MY 2012 vehicles that were
available when the proposal for this rule was published) that meet or exceed the MY 2017
targets, assuming air conditioning improvements. Table 3.9-4 shows the number of vehicles
that are within 5% of the MY 2017 targets, also by technology.
Table 3.9-3 Number of Vehicle Models that Meet MY 2017 Targets by Technology
Model
Year
2011/2012
2012/2013
Gasoline
27
65
Diesel
1
3
CNG
1
1
HEV
27
29
PHEV
1
1
EV
3
8
FCV
0
1
Total
60
108
Table 3.9-4 Number of Vehicle Models Within 5% of 2017 Targets by Technology
Model
Year
2011/2012
2012/2013
Gasoline
38
58
Diesel
6
6
CNG
0
0
HEV
3
2
PHEV
0
0
EV
0
0
FCV
0
0
Total
47
66
Today's Toyota Prius, Prius c, Prius v, Camry Hybrid, Lexus CT200h, Ford Fusion
Hybrid, Chevrolet Volt, Nissan Leaf, Honda Civic Hybrid, Honda Insight, Mitsubishi i, and
Hyundai Sonata Hybrid all meet or surpass the footprint-based CO2 targets through MY 2025.
In fact, the current Prius, Volt, and Leaf meet the MY 2025 COi targets without air
conditioning credits.
This assessment of MY 2012 and MY 2013 vehicles also makes clear that substantial
additional technology penetration across the fleet, and lead time in which to do so, is needed
for manufacturers to meet the final standards. Notably, based on the OMEGA modeling, we
project that the MY 2017-2025 standards can primarily be achieved by advanced gasoline
vehicles - for example, in MY 2025, we project more than 80 percent of the new vehicles
could be advanced gasoline powertrains. The assessment of MY 2012 and MY 2013 vehicles
available in the market today indicates advanced gasoline vehicles (as well as diesels) can
achieve the targets for the early model years of the final standards (i.e., model years 2017-
2022) with only improvements in air conditioning systems. However, significant
improvements in technologies are needed and penetrations of those technologies must
increase substantially in order for individual manufacturers (and the fleet overall) to achieve
the standards for the early years of the program, and certainly for the later years (i.e., model
years 2021-2025). These technology improvements include: gasoline direct injection fuel
3-93
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Chapter 3
systems; downsized and turbocharged gasoline engines (including in some cases with the
application of cooled exhaust gas recirculation); continued improvements in engine friction
reduction and low friction lubricants; transmissions with an increased number of forward
gears (e.g., 8 speeds); improvements in transmission shifting logic; improvements in
transmission gear box efficiency; vehicle mass reduction; lower rolling resistance tires, and
improved vehicle aerodynamics. In many (though not all) cases these technologies are
beginning to penetrate the U.S. light-duty vehicle market.
In general, these technologies must go through the automotive product development
cycle in order to be introduced into the U.S. fleet, and in some cases additional research is
needed before the technologies CC>2 benefits can be fully realized and large-scale
manufacturing can be achieved. This topic is discussed in more detail in Chapter 3.5 of the
final Joint Technical Support Document. In that Chapter, we explain that many COi reducing
technologies should be able to penetrate the new vehicle market at high levels between now
and MY 2016, there are also many of the key technologies we project as being needed to
achieve the 2017-2025 standards which will only be able to penetrate the market at relatively
low levels (e.g., a maximum level of 30%) or less by MY 2016, and which even by MY 2021
will still be constrained. These include important powertrain technologies such as 8-speed
transmissions and second or third generation downsized engines with turbocharging.
The majority of these technologies must be integrated into vehicles during the product
redesign schedule, which is typically on a 5-year cycle. EPA discussed in the MY2012-2016
rule the significant costs and potential risks associated with requiring major technologies to be
added in-between the typical 5-year vehicle redesign schedule, (see 75 FR at 25467-68). In
addition, engines and transmissions generally have longer lifetimes than 5 years, typically on
the order of 10 years or more. Thus major powertrain technologies generally take longer to
penetrate the new vehicle fleet than can be done in a 5-year redesign cycle. As detailed in
Chapter 3.5 of the Joint TSD, EPA projects that 8-speed transmissions could increase their
maximum penetration in the fleet from 30% in MY2016 to 80% in 2021 and to 100% in
MY2025. Similarly, we project that second generation downsized and turbocharged engines
(represented in our assessment as engines with a brake-mean effective pressure of 24 bars)
could penetrate the new vehicle fleet at a maximum level of 15% in MY2016, 30% in
MY2021, and 75% in MY2025. When coupled with the typical 5 year vehicle redesign
schedule, EPA projects that it is not possible for all of the advanced gasoline vehicle
technologies we have assessed to penetrate the fleet in a single 5 year vehicle redesign
schedule.
Given the status of the technologies we project to be used to achieve the MY 2017-
2025 standards and the product development and introduction process which is fairly standard
in the automotive industry today, our assessment of the MY 2012 and MY 2013 vehicles in
comparison to the final targets supports our overall feasibility assessment, and reinforces our
assessment of the lead time needed for the industry to achieve the final standards.
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MY 2017 and Later - Regulatory Impact Analysis
Table 3.9-5 Vehicles that Meet or Exceed Final Targets With Current Powertrain Designs
Modal
Year
2012
2012
2012
2012
2012
2011
2011
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2013
2013
2012
2012
2012
2012
2012
2012
2012
2013
2013
2013
2012
2013
2013
2013
2013
2013
2012
2012
2013
2012
2013
2012
2013
Manufacturer
Azure Dynamics
Azure Dynamics
CODA
Ford
Mercedes-Benz
Mercedes-Benz
Mercedes-Benz
Mitsubishi
Nissan
Chevrolet
Toyota
Toyota
Honda
Toyota
Toyota
Toyota
Hyundai
Lexus
Ford
Lincoln
Honda
Honda
Kia
Honda
Lexus
Lexus
Ford
Chevrolet
GMC
Chevrolet
GMC
Scion
Lexus
Mazda
Mercedes-Benz
Mercedes-Benz
Honda
Hyundai
Hyundai
Hyundai
Hyundai
Lexus
Toyota
Dodge
Lincoln
Chevrolet
Kia
Hyundai
Kia
Vehicle
Transit Connect Electric Van
Transit Connect Electric Wagon
CODA
Focus FWD BEV
F-Cell
Smart fortwo (cabriolet)
Smart fortwo (coupe)
i-MiEV
Leaf
Volt
Prius
Priusc
Civic Hybrid
Priusv
Camry Hybrid LE
Camry Hybrid XLE
Sonata Hybrid
CT2COh
Fusion Hybrid FWD
MKZ Hybrid FWD
Insight
Insight
Optima Hybrid
CivicCNG
RX 450h
RX 450h AWD
Focus FWD
CIS Silverado 2WD Hybrid
CIS Sierra 2WD Hybrid
K15Silverado4WD Hybrid
K15 Sierra 4WD Hybrid
JQ
HS250h
CX-5 4WD
Smart fortwo (Convertible)
Smart fortwo (Coupe)
CR-Z
Elantra Blue
Elantra
Elantra Coupe
Elantra
GS 450h
Sienna
Ram C/V
MKT Livery FWD
Cruze ECO
Rio ECO
Veloster
Rio
Unadjusted
Fuel Economy
(mpg)
89.0
89.0
103.9
150.0
75.5
123.9
123.9
160.3
141.7
122.0
70.7
70.7
63.1
58.7
57.4
54.8
52.2
57.5
54.2
54.2
58.9
58.8
50.6
41.3
40.4
38.6
41.1
28.5
28.5
28.4
28.4
52.3
47.3
36.8
50.3
50.3
50.1
45.2
44.7
44.6
44.4
41.6
29.4
25.9
30.5
44.4
46.7
43.8
45.8
Tailpipe CO2
(g/mile)
0
0
0
0
0
0
0
0
0
56
126
126
141
151
155
162
170
155
164
164
151
151
175
163
220
230
216
311
311
313
313
170
188
241
177
177
177
197
199
199
200
214
302
343
292
200
190
203
194
Footprint
(ft2)
47.9
47.9
41.4
44.2
49.4
26.8
26.8
38.4
44.7
45.3
44.2
42.3
43.5
46.1
47.2
46.9
47.8
42.7
45.6
45.6
40.5
40.5
48.2
43.4
48.0
48.0
44.2
68.0
68.0
68.0
68.0
31.6
44.5
46.1
26.8
26.8
39.5
45.2
45.2
45.2
45.2
48.5
56.1
65.9
53.5
44.8
42.1
44.6
42.1
Powertrain
Type
EV
EV
EV
EV
Fuel Cell
EV
EV
EV
EV
PHEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
HEV
CNG
HEV
HEV
Gasoline
HEV
HEV
HEV
HEV
Gasoline
HEV
Gasoline
Gasoline
Gasoline
HEV
Gasoline
Gasoline
Gasoline
Gasoline
HEV
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Transmission
Al
Al
Al
Al
Al
Al
Al
Al
Al
CVT
CVT
CVT
CVT
CVT
CVT
CVT
A6
CVT
CVT
CVT
A7
CVT
A6
A5
A6
A6
A6
CVT
CVT
CVT
CVT
CVT
CVT
A6
A5
A5
A7
A6
M6
M6
A6
A6
A6
A6
A6
M6
A6
A6
M6
Engine
Displacement
(L)
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
1.4
1.8
1.5
1.5
1.8
2.5
2.5
2.4
1.8
2.5
2.5
1.3
1.3
2.4
1.8
3.5
3.5
2.0
6.0
6.0
6.0
6.0
1.3
2.4
2.0
1.0
1.0
1.5
1.8
1.8
1.8
1.8
3.5
2.7
3.6
2.0
1.4
1.6
1.6
1.6
Vehicle Class
Van
Van
Subcompact Cars
Compact Cars
Small Station Wagons
Two Seaters
Two Seaters
Subcompact Cars
Midsize Cars
Compact Cars
Midsize Cars
Compact Cars
Compact Cars
Midsize Station Wagons
Midsize Cars
Midsize Cars
Midsize Cars
Compact Cars
Midsize Cars
Midsize Cars
Compact Cars
Compact Cars
Midsize Cars
Subcompact Cars
Sport Utility Vehicle
Sport Utility Vehicle
Compact Cars
Standard Pick-up Truck
Standard Pick-up Truck
Standard Pick-up Truck
Standard Pick-up Truck
MinicompactCars
Compact Cars
Sport Utility Vehicle
Two Seaters
Two Seaters
Two Seaters
Midsize Cars
Midsize Cars
Midsize Cars
Midsize Cars
Midsize Cars
Minivan2WD
Minivan2WD
Sport Utility Vehicle
Midsize Cars
Compact Cars
Compact Cars
Compact Cars
Car/
Truck
T
T
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
T
T
T
T
T
T
T
C
C
T
C
C
C
C
C
C
C
C
T
T
T
C
C
C
C
Compliance
2017
100%
100%
100%
100%
100%
100%
100%
100%
100%
80%
46%
44%
38%
36%
36%
33%
30%
30%
30%
30%
29%
29%
28%
27%
23%
20%
19%
14%
14%
13%
13%
19%
17%
13%
16%
16%
15%
14%
13%
13%
12%
12%
7%
5%
6%
11%
11%
10%
9%
2018
100%
100%
100%
100%
100%
100%
100%
100%
100%
79%
44%
42%
35%
34%
34%
30%
27%
27%
27%
27%
26%
26%
25%
24%
22%
18%
18%
14%
14%
13%
13%
16%
13%
11%
12%
12%
12%
10%
9%
9%
9%
8%
5%
5%
4%
8%
7%
6%
5%
2019
100%
100%
100%
100%
100%
100%
100%
100%
100%
79%
42%
39%
33%
31%
31%
27%
24%
24%
24%
24%
23%
23%
22%
20%
21%
17%
17%
14%
14%
14%
14%
12%
9%
9%
8%
8%
8%
6%
5%
5%
4%
4%
3%
4%
3%
3%
3%
2%
1%
2020
100%
100%
100%
100%
100%
100%
100%
100%
100%
79%
40%
37%
30%
28%
28%
24%
21%
21%
21%
21%
20%
19%
19%
17%
19%
15%
15%
14%
14%
13%
13%
8%
5%
7%
4%
4%
4%
2%
1%
1%
0%
0%
1%
2%
0%
-1%
-1%
-3%
-4%
2021
100%
100%
100%
100%
100%
100%
100%
100%
100%
79%
37%
34%
27%
25%
25%
20%
17%
17%
17%
17%
16%
16%
15%
13%
13%
8%
8%
8%
8%
7%
7%
4%
1%
0%
0%
0%
-1%
-3%
-4%
-4%
-5%
-5%
2022
100%
100%
100%
100%
100%
100%
100%
100%
100%
78%
34%
31%
23%
22%
21%
17%
14%
13%
13%
13%
12%
12%
11%
9%
8%
4%
3%
3%
3%
2%
2%
-1%
-4%
2023
100%
100%
100%
100%
100%
100%
100%
100%
100%
77%
31%
28%
20%
18%
18%
13%
10%
9%
9%
9%
8%
8%
7%
5%
4%
-1%
-2%
-2%
-2%
-2%
-2%
2024
100%
100%
100%
100%
100%
100%
100%
100%
100%
75%
28%
24%
16%
14%
14%
9%
5%
5%
5%
5%
3%
3%
3%
1%
-1%
2025
100%
100%
100%
100%
100%
100%
100%
100%
100%
74%
24%
21%
12%
10%
10%
4%
1%
0%
0%
0%
-1%
-1%
-2%
-4%
3-95
-------
Chapter 3
Modal
Year
2013
2012
2012
2012
2012
2012
2012
2013
2013
2013
2012
2013
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2013
2012
2012
2012
2012
2012
2012
2012
2013
2012
2012
2013
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2013
2013
2013
Manufacturer
Hyundai
Ford
Ford
Honda
Toyota
Honda
Hyundai
Dodge
Kia
Hyundai
Volkswagen
Hyundai
Mazda
Nissan
Infiniti
BMW
Honda
Ford
Ford
Kia
Honda
Toyota
Mazda
Mercedes-Benz
BMW
Toyota
Toyota
Cadillac
Chevrolet
GMC
Ford
Fiat
Buick
Chevrolet
GMC
Ford
Chevrolet
Ford
Volkswagen
Honda
Chevrolet
Chevrolet
Ford
Chevrolet
Kia
Mini
Mini
Mazda
Buick
Chevrolet
Vehicle
Elantra Coupe
Focus SFEFWD
Focus SFEFWDFFV
Civic HF
Tacoma 2WD - Access cab
Odyssey 2WD
Veloster
Dart
Rio
Acce nt
Passat
Acce nt
Mazda3DI4-Door
Versa
M35h
528i
Civic
Focus FWD
Focus FWDFFV
Soul ECO
CR-Z
Yaris
MazdaS Dl 5- Door
S350BLUETEC4MATIC
X3xDrive28i
Tacoma 2WD - Access Cab
Tacoma 2WD - Double Cab
Escalade 2WD Hybrid
CISOOTahoe 2WD Hybrid
C1500 Yukon 2WD Hybrid
Fiesta SFEFWD
500
Lacrosse
KISOOTahoe 4WD Hybrid
K1500 Yukon 4WD Hybrid
Escape AWD
Cruze ECO
Fiesta FWD
Passat
Civic
Sonic
SonicS
Fiesta FWD
Cruze
Forte ECO
Mini Cooper
Mini CooperCoupe
CX-5 2WD
Regal
Malibu
Unadjusted
Fuel Economy
(mpg)
42.8
43.6
43.6
44.3
29.9
29.0
43.1
42.3
45.1
45.3
46.4
45.1
43.8
45.1
38.8
36.8
43.0
42.1
42.1
42.8
44.9
44.9
42.9
32.3
31.5
28.1
28.1
28.5
28.5
28.5
44.6
44.5
38.7
28.4
28.4
33.2
40.9
44.2
44.6
41.8
44.0
44.0
43.9
40.4
40.7
43.6
43.6
39.2
38.7
38.7
Tailpipe CO2
(g'mile)
208
204
204
201
297
307
206
210
197
196
220
197
203
197
229
241
207
211
211
208
198
198
207
315
283
317
317
311
311
311
199
200
230
313
313
268
217
201
228
212
202
202
202
220
218
204
204
227
230
230
Footprint
(ft2)
45.2
44.2
44.2
43.4
54.0
55.9
44.6
45.6
42.1
41.7
47.2
41.7
43.1
41.5
49.1
51.6
43.4
44.2
44.2
43.3
39.5
39.9
43.1
56.6
48.8
55.9
55.9
54.8
54.8
54.8
39.3
34.7
48.0
54.8
54.8
45.3
44.8
39.3
47.2
43.4
41.0
41.0
39.3
44.8
44.4
36.7
38.8
46.1
46.8
46.6
Powertrain
Type
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Gasoline
Gasoline
Gasoline
HEV
HEV
HEV
Gasoline
Gasoline
Gasoline
HEV
HEV
Gasoline
Gasoline
Gasoline
Diesel
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Transmission
A6
A6
A6
A5
M5
A6
M6
M6
A6
M6
M6
A6
A6
CVT
A7
A8
A5
A6
A6
A6
M6
M5
A6
A7
A8
A4
A4
CVT
CVT
CVT
A6
M5
A6
CVT
CVT
A6
A6
A6
A6
M5
M6
M6
M5
M6
A6
M6
M6
M6
A6
A6
Engine
Displacement
0-)
1.8
2.0
2.0
1.8
2.7
3.5
1.6
1.4
1.6
1.6
2.0
1.6
2.0
1.6
3.5
2.0
1.8
2.0
2.0
1.6
1.5
1.5
2.0
3.0
2.0
2.7
2.7
6.0
6.0
6.0
1.6
1.4
2.40
6.00
6.0
1.6
1.4
1.6
2.0
1.8
1.4
1.4
1.6
1.4
2.0
1.6
1.6
2.0
2.4
2.4
Vehicle Class
Midsize Cars
Compact Cars
Compact Cars
Compact Cars
Small Pick-up Truck
Minivan2WD
Compact Cars
Midsize Cars
Compact Cars
Compact Cars
Midsize Cars
Compact Cars
Compact Cars
Compact Cars
Midsize Cars
Midsize Cars
Compact Cars
Compact Cars
Compact Cars
Small Station Wagons
Two Seaters
Compact Cars
Midsize Cars
Large Cars
Sport Utility Vehicle
Small Pick-up Truck
Small Pick-up Truck
Sport Utility Vehicle
Sport Utility Vehicle
Sport Utility Vehicle
SubcompactCars
MinicompactCars
Midsize Cars
Sport Utility Vehicle
Sport Utility Vehicle
Sport Utility Vehicle
Midsize Cars
SubcompactCars
Midsize Cars
Compact Cars
Compact Cars
SubcompactCars
SubcompactCars
Midsize Cars
Midsize Cars
MinicompactCars
Two Seaters
Sport Utility Vehicle
Midsize Cars
Midsize Cars
Car/
Truck
C
c
C
c
T
T
C
C
C
C
C
C
C
C
C
c
c
c
c
c
c
c
c
T
T
T
T
T
T
T
C
C
C
T
T
T
C
C
C
C
C
C
C
C
C
C
C
C
C
C
Compliance
2017
9%
9%
9%
9%
5%
5%
8%
8%
8%
7%
7%
7%
7%
7%
6%
6%
6%
5%
5%
5%
5%
5%
5%
3%
2%
2%
2%
2%
2%
2%
4%
4%
4%
1%
1%
1%
4%
3%
3%
3%
3%
3%
3%
2%
2%
2%
2%
2%
2%
1%
2018
5%
5%
5%
5%
3%
3%
4%
4%
4%
3%
3%
3%
3%
2%
2%
2%
2%
1%
1%
1%
1%
1%
1%
1%
0%
0%
0%
-1%
-1%
-1%
0%
0%
0%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-3%
-3%
-3%
2019
0%
0%
0%
0%
1%
1%
0%
0%
-1%
-1%
-1%
-2%
-2%
-2%
-2%
-3%
-3%
-4%
-4%
-4%
-4%
-4%
-4%
-1%
-2%
-2%
-2%
-2%
-2%
-2%
-5%
-5%
-5%
-3%
-3%
-3%
2020
-4%
-4%
-4%
-4%
-1%
-1%
-5%
-5%
-3%
-5%
2021
2022
2023
2024
2025
3-96
-------
MY 2017 and Later - Regulatory Impact Analysis
Modal
Year
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2013
2012
2012
2012
Manufacturer
BMW
Chevrolet
Mazda
Toyota
Audi
Mazda
BMW
Ford
Ford
Ford
Ford
Cadillac
GMC
Chevrolet
Chevrolet
GMC
GMC
Honda
Toyota
Toyota
Nissan
Ford
Subaru
Toyota
Toyota
Suzuki
Honda
Volkswagen
Mazda
Toyota
Volkswagen
Porsche
Vehicle
528ixDrive
Cruze
MazdaS Dl 4- Door
Yaris
A6
MazdaS Dl 5- Door
328i
F150 Pickup 2WD (157 in]
F150 Pickup 2WD (163 in]
F150 Pickup 2WDFFV (145 in]
F150 Pickup 2WD (145 in]
Escalade 4WD Hybrid
KISOOYukin Denali Hybrid 4WD
Equinox AWD
Equinox AWD
Terra in AWD
Terra in AWD
Odyssey 2WD
Tacoma 2WD - Access Cab
Tacoma2WD- Double Cab
Quest
Transit Connect Wagon FWD
Outback Wagon AWD
VenzaAWD
Sienna
Equator 2WD
CR-V 4WD
Jetta
CX-5 2WD
Camry
Jetta
Panamera S Hybrid
Unadjusted
Fuel Economy
(mpg)
35.3
40.1
41.3
43.1
35.4
41.1
39.4
24.0
24.0
24.5
24.0
28.0
28.0
30.8
30.8
30.8
30.8
27.5
28.1
28.1
27.2
31.1
31.9
30.2
26.9
27.3
33.0
46.1
38.5
37.7
46.1
34.4
Tailpipe CO2
(g'mile)
252
222
215
206
251
216
225
371
371
363
371
317
317
289
289
289
289
324
317
317
326
286
279
294
331
325
269
221
231
236
221
259
Footprint
(ft2)
51.6
44.8
43.1
39.9
50.9
43.1
45.0
73.0
75.8
67.5
67.5
54.8
54.8
48.8
48.8
48.8
48.8
55.9
54.0
54.0
55.9
47.9
45.7
48.8
56.1
55.0
44.1
43.9
46.1
47.1
43.9
52.1
Powertrain
Type
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Gasoline
Gasoline
Diesel
Gasoline
Transmission
A8
A6
M6
A4
CVT
M6
A8
A6
A6
A6
A6
CVT
CVT
A6
A6
A6
A6
A5
A4
A4
CVT
A4
CVT
A6
A6
M5
A5
A6
A6
A6
M6
A8
Engine
Displacement
(L)
2.0
1.4
2.0
1.5
2.0
2.0
2.0
3.5
3.5
3.7
3.5
6.0
6.0
2.4
2.4
2.4
2.4
3.5
2.7
2.7
3.5
2.0
2.5
2.70
3.5
2.5
2.4
2.0
2.0
2.5
2.0
3.0
Vehicle Class
Midsize Cars
Midsize Cars
Compact Cars
Compact Cars
Midsize Cars
Midsize Cars
Compact Cars
Standard Pick-up Truck
Standard Pick-up Truck
Standard Pick-up Truck
Standard Pick-up Truck
Sport Utility Vehicle
Sport Utility Vehicle
Sport UtilityVehicle
Sport UtilityVehicle
Sport Utility Vehicle
Sport Utility Vehicle
Minivan
Small Pick-up Truck
Small Pick-up Truck
Minivan
Van
Sport Utility Vehicle
Sport Utility Vehicle
Minivan
Small Pick-up Truck
Sport Utility Vehicle
Compact Cars
Sport UtilityVehicle
Midsize Cars
Compact Cars
Large Cars
Car/
Truck
C
c
C
c
c
c
c
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
C
C
C
C
C
Compliance
2017
1%
1%
1%
1%
1%
0%
0%
-3%
-3%
-1%
-3%
0%
0%
0%
0%
0%
0%
0%
-1%
-1%
-1%
-1%
-2%
-2%
-2%
-2%
-2%
0%
0%
0%
0%
0%
2018
-3%
-3%
-4%
-4%
-4%
-4%
-4%
-3%
-3%
-1%
-3%
-3%
-3%
-3%
-3%
-3%
-3%
-3%
-4%
-4%
-4%
-3%
-5%
-5%
-5%
-5%
-4%
-5%
-5%
-5%
-5%
-5%
2019
-3%
-3%
-1%
-3%
-4%
-4%
-5%
-5%
-5%
-5%
-5%
2020
-4%
-4%
-2%
-5%
2021
-4%
-4%
2022
2023
2024
2025
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3.10 Analysis of Ferrari & Chrysler/Fiat
Note that in the primary analyses, Ferrari is shown as a separate entity, but in this side-
analysis, it is combined with other Fiat-owned companies for purposes of GHG compliance.
Ferrari could be combined with other Fiat-owned companies for purposes of GHG
compliance at the manufacturer's discretion (assuming Ferrari meets the criteria for
demonstrating operational independence but Fiat-owned companies decide to aggregate
anyway, or assuming that Ferrari does not meet the operational independence criteria). We
conducted an OMEGA run to evaluate a scenario where Ferrari's compliance would be
included with other Fiat-owned companies, including Chrysler. Unlike Ferrari under the
scenario in which Ferrari was modeled as a stand-alone company, Chrysler/Fiat would
comply, even with the Ferrari vehicles included. In preamble Section III.B., EPA describes
the provisions we are finalizing on the concept of allowing companies that are able to
demonstrate "operational independence" to be eligible for small volume manufacturer (SVM)
alternative standards. If Ferrari were to qualify for these operational independence
provisions, they would likely petition for an alternative standard under the SVM provisions,
rather than comply as part of Chrysler/Fiat.
Under the assumptions made in the main analysis, where Ferrari is shown as a separate
entity, and complies with the promulgated CC>2 curves, under the MY 2025 OMEGA
projections, Ferrari falls short of its 2025 target (150 grams/mile CO2) by seventeen grams.
EE Under this scenario, Ferrari would produce a fleet consisting of almost entirely HEVs
(50%), EVs (23%) and PHEVs (22%) with a MY 2025 compliance cost of approximately
$7,900 relative to the MY 2016 standards.
If Ferrari is included in the Chrysler/Fiat GHG compliance fleet, Chrylser/Fiat' s
baseline (no technology added) COiin 2025 is 2 grams higher ( 345.6 vs 347.6). For
Chrysler/Fiat, the cost of complying with the reference case standards would increase by
approximately $58, and the cost of complying with the standards would increase by $104 for
a net average increase in MY 2025 compliance costs of $46 per vehicle for Chrysler/Fiat. Net
program costs would not change significantly.
3.11 Cost Sensitivities
3.11.1 Overview
We have conducted several sensitivity analyses on a variety of input parameters. We
have run the OMEGA model to generate 2025MY results for each of these sensitivities. We
have looked at different levels of mass reduction costs, battery pack costs, indirect cost
multipliers, and learning rates. These sensitivities are summarized in
EEE Assuming that Ferrari complied with the primary proposed standards.
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Table 3.11-1, followed by a discussion of the methods, with the results in Table 3.11-10
Additional sensitivities with regard to benefits are shown in RIA Chapter 4.
Table 3.11-1 Summary of Cost Sensitivities
Sensitivity parameter
Mass reduction direct
manufacturing costs
Battery pack direct
manufacturing costs
Indirect cost multipliers
Learning ratesa
Low side sensitivity
40% lower
10% lower for P2HEVs
20% lower for PHEV/EV
Low side of 95% confidence
interval of modified Delphi
survey results
P- value of 30% on steep
portion of the curve; cost
reductions of 4%/3%/2%
per year for each 5 year
increment on the flat portion
of the learning curve
High side sensitivity
40% higher
10% higher for P2HEVs
20% higher for PHEV/EV
High side of 95%
confidence interval of
modified Delphi survey
results
P- value of 10% on steep
portion of the curve; cost
reductions of 2%/l%/0%
per year for each 5 year
increment on the flat portion
of the learning curve
a Higher learning rates results in lower costs, hence the low side sensitivity uses the higher learning rates
while the high side sensitivity uses the lower learning rates.
3.11.2 Mass Sensitivity
For the mass reduction cost sensitivity, we adjusted the mass reduction DMC cost
equation by +/-40%. That cost equation is shown in Table 3.11-2 along with the cost equation
used for each side of the mass reduction cost sensitivity.
Table 3.11-2 Mass Reduction Cost Sensitivities
Sensitivity parameter
Low side
Primary case
High side
Mass reduction DMC equation used
DMC=$2.60x, where x=% mass reduction
DMC=$4.33x, where x=% mass reduction
DMC=$6.06x, where x=% mass reduction
As mass reduction is a relatively cost effective technology, even with higher costs,
OMEGA still chooses a relatively similar, but somewhat diminished degree of mass
reduction. By contrast, even with lower costs, mass reduction is still limited by the
constraints given by the safety analysis.. These impacts would be greater on manufacturers
that use more mass reduction technology, and less on those that use less.
3.11.3 Battery Sensitivity
For the battery pack cost sensitivities, we decreased/increased the battery pack DMCs
by the amounts shown in Table 3.11-3. As presented in Chapter 3 of the joint TSD, we have
developed linear regressions for our battery pack costs. These linear regressions provide
battery pack DCM as a function of net weight reduction of the vehicle. Table 3.11-3 and
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Table 3.11-5 show the linear regressions used for our low side and high side sensitivity
analyses, respectively, while Table 3.11-4 presents the linear regressions used for our primary
analysis (as presented in Chapter 3 of the joint TSD).
Table 3.11-3 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net
Weight Reduction used for Low Side Sensitivity (2010$)
Vehicle
Class
Small car
Standard
car
Large car
Small MPV
Large MPV
Truck
P2HEV
-$163x+$653
-$216x+$721
-$332x+$843
-$202x+$701
-$272x+$788
-$330x+$909
PHEV20
-$688x+$2,026
-$l,235x+$2,370
-$l,505x+$2,987
-$858x+$2,268
PHEV40
-$l,214x+$2,917
-$l,756x+$3,511
-$3,760x+$4,808
-$l,565x+$3,397
EV75
-$l,488x+$4,105
-$2,203x+$4,818
-$3,485x+$6,180
-$l,649x+$4,797
EV100
-$l,734x+$4,892
-$2,367x+$5,645
-$3,718x+$6,904
-$2,119x+$5,834
EV150
-$l,636x+$6,464
-$2,042x+$7,803
-$2,272x+$8,896
-$15x+$8,087
Notes:
"x" in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack
with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-
$163)x(15%)+$653=$629.
The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not
believe these vehicle classes would use the technologies.
Table 3.11-4 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net
Weight Reduction used for the Primary Analysis (2010$)
Vehicle
Class
Small car
Standard
car
Large car
Small MPV
Large MPV
Truck
P2HEV
-$181x+$726
-$240x+$801
-$369x+$937
-$224x+$779
-$303x+$876
-$367x+$l,010
PHEV20
-$861x+$2,533
-$l,543x+$2,962
-$l,881x+$3,734
-$l,073x+$2,835
PHEV40
-$l,517x+$3,646
-$2,195x+$4,389
-$4,700x+$6,010
-$l,957x+$4,247
EV75
-$l,859x+$5,131
-$2,754x+$6,023
-$4,356x+$7,725
-$2,061x+$5,997
EV100
-$2,168x+$6,115
-$2,958x+$7,056
-$4,647x+$8,630
-$2,649x+$7,293
EV150
-$2,045x+$8,080
-$2,552x+$9,753
-$2,840x+$ll,120
-$19x+$10,109
Notes:
"x" in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack
with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-
$ 18 l)x( 15 %)+$726=$699.
The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not
believe these vehicle classes would use the technologies.
Table 3.11-5 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net
Weight Reduction used for the High Side Sensitivity (2010$)
Vehicle
Class
Small car
Standard
car
Large car
Small MPV
Large MPV
Truck
P2HEV
-$200x+$798
-$264x+$881
-$406x+$l,031
-$247x+$857
-$333x+$963
-$404x+$l,lll
PHEV20
-$l,033x+$3,039
-$l,852x+$3,555
-$2,257x+$4,480
-$l,287x+$3,402
PHEV40
-$l,821x+$4,376
-$2,633x+$5,266
-$5,639x+$7,212
-$2,348x+$5,096
EV75
-$2,231x+$6,157
-$3,305x+$7,227
-$5,227x+$9,269
-$2,473x+$7,196
EV100
-$2,601x+$7,338
-$3,550x+$8,467
-$5,577x+$10,357
-$3,179x+$8,752
EV150
-$2,455x+$9,696
-$3,063x+$ll,704
-$3,407x+$13,344
-$23x+$12,131
Notes:
"x" in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack
with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-
$200)x(15%)+$798=$768.
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The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not
believe these vehicle classes would use the technologies.
In the high case, the penetration of EVs decreased slightly, and MHEVs declined
slightly as companies and MY 2025 TDS 24, start stop and HEV penetrations increased
slightly. In the low cost case, the MY 2025 penetration of EVs increased, while the HEV
penetration decreased. In general, these shifts were slight, as this ralemaking doesn't rely
heavily on strong hybrids, EVs, or other battery technology vehicles. In general, changing
the battery costs shifted the choice between HEVs and EVs. As both EVs and HEVs are less
cost effective (in this set of inputs) than conventional technologies, the penetrations of non-
battery dependent technologies was generally little changed.
3.11.4 ICM Sensitivity
For the ICM sensitivity, we looked at the 95% confidence intervals of the survey
responses gathered as part of the modified Delphi process used to generate our low, medium
and high2 complexity ICMs. We discuss this modified Delphi process in Chapter 3 of the
joint TSD and provide details in a memorandum to the docket (EPA-HQ-OAR-2010-
0799).FFF In that memorandum, the survey responses from each respondent are presented for
each element of the ICM along with average responses, standard deviations and other
statistical measures. Using these, we calculate the ICM elements at the low side of the 95%
confidence interval and at the high side. Table 3.11-6 and Table 3.11-8 show the ICMs used
for the low side and high side sensitivity analyses, respectively, while Table 3.11-7 shows
the ICMs used for our primary analysis. For the Highl ICM, since it was generated using a
consensus approach rather than blind surveys, we have scaled the ICM elements using the
same ratios as resulted from the 95% confidence intervals for the High2 ICM.
Table 3.11-6 ICMs used for the Low Side Sensitivity
Complexity
Low
Medium
Highl
High2
Near term
Warranty
0.004
0.037
0.043
0.048
Non-warranty
0.113
0.225
0.361
0.479
Long term
Warranty
0.001
0.025
0.027
0.041
Non-warranty
0.090
0.148
0.217
0.272
Summed
Near term
1.118
1.262
1.404
1.528
Long term
1.091
1.174
1.243
1.313
Table 3.11-7 ICMs used for the Primary Analysis
Complexity
Low
Medium
Highl
High2
Near term
Warranty
0.012
0.045
0.065
0.074
Non-warranty
0.230
0.343
0.499
0.696
Long term
Warranty
0.005
0.031
0.032
0.049
Non-warranty
0.187
0.259
0.314
0.448
Summed
Near term
1.242
1.387
1.564
1.770
Long term
1.193
1.290
1.345
1.497
"Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies;
Helfand, G., and Sherwood, T., Memorandum dated August 2009.
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Chapter 3
Table 3.11-8 ICMs used for the High Side Sensitivity
Complexity
Low
Medium
Highl
High2
Near term
Warranty
0.019
0.052
0.087
0.099
Non-warranty
0.347
0.461
0.637
0.914
Long term
Warranty
0.010
0.037
0.037
0.057
Non-warranty
0.284
0.369
0.411
0.623
Summed
Near term
1.366
1.513
1.723
2.012
Long term
1.294
1.406
1.447
1.680
3.11.5 Learning Rate Sensitivity
For the learning rate sensitivity, we increased the learning effects for the low side case
and decreased the learning effects for the high side case. This sounds counterintuitive, but we
have done this because the increased learning rates result in lower technology costs so,
therefore, are more appropriate for the low side sensitivity. The reverse is true when
decreasing the learning rates. For our primary analysis, as described in Chapter 3 of the joint
TSD, we have used a 20% p-value for technologies on the steep portion of the learning curve
and then have used learning rates of 3% per year for five years, 2% per year for 5 years, then
1% per year for 5 years for technologies on the flat portion of the learning curve. Table
3.11-9 shows how we have adjusted these learning rates for both the low and high side
sensitivities.
Table 3.11-9 Learning Rates used for our Learning Rate Sensitivity
Sensitivity
Low side
Primary case
High side
Steep learning rate
30%
20%
10%
Flat learning rate
4%, 3%, 2%
3%, 2%, 1%
2%, 1%, 0%
3.11.6 Summary of Sensitivity Impacts
The average per-vehicle impacts of the sensitivity runs are shown in Table 3.11-10.
Note that the majority of these impacts are less than $150 relative to the primary analysis
costs. The ICM impacts are larger. For those sensitivities that change technology costs,
generally, an increase in the cost of a single technology will provide a smaller incremental
change in total cost than a equivalent decrease in cost of a single technology. This is due to
the TARF function in the model which attempts to minimize incremental cost. By contrast,
learning and ICM changes, because they affect every technology, tend to produce more
symmetrical increases and decreases.
Table 3.11-10 Summary of Per-vehicle Cost Impacts of Sensitivity Analyses in MY 2025
relative to Primary Analysis (2010$)
Sensitivity Title
Primary Case
Mass Cost High
Reference
Case Change
—
$21
Control Case
Change
--
$65
Impact
--
$44
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MY 2017 and Later Regulatory Impact Analysis
Mass Cost Low
Battery Cost High
Battery Cost Low
ICM High
ICM Low
Learning Rate High
Learning Rate Low
-$24
$2
-$2
$110
-$114
$48
-$44
-$87
$72
-$74
$316
-$317
$177
-$159
-$63
$69
-$72
$206
-$203
$129
-$115
3.11.7 NAS report
As in the proposal, we note that EPA has decided not to base a sensitivity case on the
2010 National Academy of Science Report "Assessment of Technologies for Improving Light-
Duty Vehicle Fuel Economy, Assessment for Fuel Economy Technologies for Light-Duty
Vehicles" (The National Academies Press, June 2010).
As discussed in detail in Chapter 3 of the Joint Technical Support Document for this
final rule, EPA and NHTSA have utilized the best available information in order to estimate
the cost and effectiveness for a large number of technologies which can be used to reduce
GHG emissions and improve fuel efficiency.
In 2007, NHTSA commissioned the National Academy of Science to perform an
assessment of, among other things, the cost and effectiveness of technologies for improving
the fuel economy of light-duty vehicles. The 2010 NAS Committee published their results of
their assessment in June of 2010. EPA has reviewed this report in detail and for the reasons
discussed below, we have not relied upon this report as a primary assessment for our cost and
effectiveness estimates for this final rule, and we have also not used the report to perform a
sensitivity assessment based on the 2010 NAS report for the same reasons.
Our principal reasons are twofold. First, the 2010 NAS Committee focused their
report on the near-term, specifically the 2010-2015 time frame, and not on the time frame of
this final rule, which is 2017 to 2025. Second, on a range of topics EPA and NHTSA have
relied upon newer information for cost and effectiveness estimates.
With respect to the time frame of interest, in the Summary of the NAS 2010 report
(pages S-l and S-2), the NAS Committee discusses that their costs estimates are for the 2010-
2015 time frame. In contrast, our costs are deemed valid for a given model year and then
learned down from there using our learning curve effect (for years prior to the given model
year, learning effects are backed out resulting in higher costs for earlier years). The 2010
NAS Report also discusses that there are longer-term technologies which are in the 5 to 15
year time horizon which are not the focus of the NAS 2010 report. There are a number of
specific examples where this difference in time frame is relevant to any potential comparison
between the 2010 NAS report and the EPA and NHTSA assessment for this final rule. For
example, there are a number of technologies that EPA and NHTSA discuss in Chapter 3 of the
Joint TSD which are not a single, discrete piece of hardware, but rather a continuum of
improvements where the level of improvement can change given the potential time horizon.
The 2010 NAS Committee considered at least six of these technologies: low friction
lubricants, engine friction reduction, improved accessories, lower rolling resistance tires,
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Chapter 3
aerodynamic drag improvement, and improved internals for automatic transmissions. The
2010 NAS report provides cost and effectiveness estimates for one increment of improvement
for each of these technologies applicable to the 2010-2015 time frame. This is similar to the
approach utilized by NHTSA and EPA for the MYs 2012-2016 rulemaking. However, for the
MYs 2017-2025 final rules, where EPA and NHTSA are using a baseline set (or sets
considering we use both a 2008MY and 2010MY baseline) of vehicles, the agencies estimate
that for each these technologies two increments of improvement can be implemented across
the fleet between promulgation of the final rule and MY 2025. Using the NAS Report
estimates for these technologies thus, without basis, would not consider the further projected
incremental improvements in these technologies.
A second example of the importance of the time frame is evaluation of the
effectiveness of gasoline direct injection with turbocharging and downsizing. The 2010 NAS
Committee considered one level of downsizing in the 2010-2015 time frame, and EPA and
NHTSA took a similar approach for the MYs 2012-2016 rule. But, for the MYs 2017-2025
final rule, based on data in the literature, our discussions with the auto companies and
automotive suppliers, and a 2011 Ricardo study commissioned by EPA, in the longer term
additional levels of downsizing are achievable, including in some cases with the use of cooled
exhaust gas recirculation, that provide additional CCVfuel consumption reductions. Those
additional levels of downsizing were not considered by the 2010 NAS Committee in their
assessment of near-term costs and effectiveness.
In addition to the difference in time frames being considered by the 2010 NAS report
and this final rule, a second significant difference between the two assessments were the
additional studies and information available to EPA and NHTSA which were not reviewed by
2010 NAS Committee. In many cases this was due to the additional two years EPA and
NHTSA had available (while the NAS Committee's report was published in 2010, the bulk of
their assessments occurred between 2007 and 2009), and in other areas this new information
was the result of the many confidential meetings EPA and NHTSA had with auto companies
and auto suppliers over the past two years.
The additional publically available studies which EPA and NHTSA utilized included
new studies on the costs for mass reduction, lithium-ion battery packs, 8 speed automatic
transmissions, 8 speed dual-clutch transmissions, hybrid electric vehicle, plug-in hybrid
electric vehicle, and all electric vehicles. EPA and NHTSA also utilized new reports dealing
with the use of indirect cost multipliers for estimating indirect manufacturing costs. EPA and
NHTSA also are using a number of new studies which were not available to the 2010 NAS
Committee for the estimation of the effectiveness of a large number of the 2017-2025
technologies; these include peer reviewed papers in the literature as well as the 2011 Ricardo
study (discussed in detail in Chapter 3 of the Joint TSD). A partial list of the studies and data
sources regarding technology feasibility, costs, lead time, and effectiveness considered by
EPA which were not reviewed by the 2010 NAS Committee or were published after they
completed their work, or was obtained confidentially from automotive suppliers includes:
2011 Ricardo Report "Computer Simulation of Light-duty Vehicle Technologies for
Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe"28, this report has
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MY 2017 and Later Regulatory Impact Analysis
been peer reviewed and the peer review report and the response to peer review
comments are available in the EPA docket EPA-HQ-OAR-2010-0799.
Argonne National Laboratories 2011 Report "Modeling the Performance and Cost of
Lithium-Ion Batteries for Electric-Drive Vehicles"29 and the accompanying Battery
Performance and Cost Model, which estimates lithium-ion battery pack cost for the
2020 time frame. This report was peer reviewed and revised in 2011, and the model,
report, and peer review report are available in the EPA docket EPA-HQ-OAR-2010-
0799.
2011 FEV Report "Light-Duty Technology Cost Analysis Power-split and P2 HEV
Case Studies."30 This report was peer reviewed, and a copy of the report, the peer
review report, and the response to peer review comments report are available in the
EPA docket EPA-HQ-OAR-2010-0799.
2011 FEV Report "Light-Duty Technology Cost Analysis: Advanced 8-speed
Transmissions"31. A copy of this report is available in the EPA docket EPA-HQ-
OAR-2010-0799.
2010 Lotus Engineer Study "An Assessment of Mass Reduction Opportunities for a
2017 - 2020 Model Year Vehicle Program"32, this report has been peer reviewed, and
a copy of the report and the peer review report are available in the EPA docket EPA-
HQ-OAR-2010-0799.
EPA vehicle fuel economy certification data from MY2011 and MY2012 vehicles,
including for example the MY2011 Ford F-150 with the 3.5L Ecoboost engine,
MY2011 Sonata Hybrid, MY2012 Infiniti M35h hybrid, and several other advanced
technology production vehicles.
"EBDI - Application of a Fully Flexible High BMEP Downsized Spark Ignited
Engine." Society of Autmotive Engineers (SAE) Technical Paper No. 2010-01-0587,
Cruff, L., Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010.33
"Water Cooled Exhaust Manifold and Full Load EGR Technology Applied to a
Downsized Direct Injection Spark Ignition Engine." SAE Technical Paper Series No.
2010-01-0356. Taylor, J., Fraser, N., Wieske, P., 2010.34
"Requirements of External EGR Systems for Dual Cam Phaser Turbo GDI Engines."
SAE Technical Paper Series No. 2010-01-0588. Roth, D.B., Keller, P, Becker, M.,
2010.35
"Doing More with Less - The Fuel Economy Benefits of Cooled EGR on a Direct
Injected Spark Ignited Boosted Engine," SAE Technical Paper Series, No. 2010-01-
0589. Kaiser, M., Krueger, U., Harris, R., Cruff, L., 2010.36 EPA-HQ-OAR-2010-
0799
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"Using indirect cost multipliers to estimate the total cost of adding new technology in
the automobile industry," International Journal of Production Economics Rogozhin,
A.,et al, 2009.37
"Documentation of the Development of Indirect Cost Multipliers for Three
Automotive Technologies," EPA Technical Memorandum, Helfand, G., Sherwood, T.,
August 2009.38
Confidential business information regarding the development status, effectiveness and
costs for a large number of technologies obtained by EPA in meetings during 2010
and 2011 with more than a dozen worldwide automotive suppliers involved in the
development and production of a wide range of technologies, including but not limited
to fuel injection systems, transmissions, turbochargers, lower mass automotive
components, tires, and automotive lithium-ion batteries.
Technologies not considered by the NAS Committee which have been launched into
production recently by auto makers, such as the 2013 Dodge Ram pickup truck which
includes active ride height and active grill shutters that can improve aerodynamics,
and the 2013 Audi A3 which in Europe includes a 1.4 liter, four cylinder gasoline
engine with cylinder deactivation - a technology in production previously for six and
eight cylinder engines only.39
With the exception of the confidential business information and copyrighted
information, copies of the reports and studies listed above are available in the EPA docket for
this final rule, EPA-HQ-OAR-2010-0799. Information on how to obtain copies of the SAE
papers is also available in the EPA docket, or they can be order from SAE on-line at
http://papers.sae.org/.
For the reasons described above, EPA has elected not to perform a sensitivity
assessment based on the 2010 NAS Report, nor have we used the 2010 NAS Report as our
primary basis for assessing the costs and effectiveness of technologies for the proposal or this
final rule.
EPA requested comment on our overall approach for basing our assessment on
technology feasibility, lead time, costs and effectiveness on the full range of information
described in the Joint Technical Support Document (which includes consideration of the
2010 NAS Study), as opposed to an alternative approach in which EPA would base our
technology feasibility, lead time, costs and effectiveness primarily on the 2010 NAS Study
and place lower weighting or no weighting on the additional information which has become
available since the 2010 NAS Study (including those data sources, studies and reports listed
above). EPA received public comment from the Delphi Corportation recommending that "the
National Research Council technology cost estimates and implementation cadence data be
included in the agencies' analyses and be considered a primary source of information." This
comment is discussed in the Response to Comment document, section 12.3.
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EPA also requested comment specifically on EPA's use of the 2011 Ricardo study
(listed above), and any ways to improve our estimates of technology effectiveness, including
the use of full vehicle simulation modeling as was used in the 2011 Ricardo study or
alternative approaches. We also requested comment on the 2011 Ricardo Study and the
Ricardo response to comments report with respect to the peer review conducted on the
Ricardo report. We received comments from the ICCT, and several other organizations, these
comments are discussed in section 12 of the Response to Comments document. These
documents are all available in the EPA docket for this rulemaking (EPA-HQ-OAR-2010-
0799). Significant additional detail regarding the 2011 Ricardo study and how it was used to
inform EPA's estimates of technology effectiveness is contained in Chapter 3 of the Joint
Technical Support Document.
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References
28 2011 Ricardo Report "Computer Simulation of Light-duty Vehicle Technologies for
Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe" (Docket No. EPA-HQ-
OAR-2010-0799-1144)
29 Argonne National Laboratories 2011 Report "Modeling the Performance and Cost of
Lithium-Ion Batteries for Electric-Drive Vehicles" (Docket No. EPA-HQ-OAR-2010-0799-
0031)
30 2011 FEV Report "Light-Duty Technology Cost Analysis Power-split and P2 HEV Case
Studies" (Docket No. EPA-HQ-OAR-2010-0799-1102)
31 2011 FEV Report "Light-Duty Technology Cost Analysis: Advanced 8-speed
Transmissions" (Docket No. EPA-HQ-OAR-2010-0799-1103)
32 2010 Lotus Engineer Study "An Assessment of Mass Reduction Opportunities for a 2017 -
2020 Model Year Vehicle Program" (EPA-HQ-OAR-2010-0799-0036)
33 "EBDI - Application of a Fully Flexible High BMEP Downsized Spark Ignited Engine."
Society of Automotive Engineers (SAE) Technical Paper No. 2010-01-0587, Graff, L.,
Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010
34 "Water Cooled Exhaust Manifold and Full Load EGR Technology Applied to a Downsized
Direct Injection Spark Ignition Engine." SAE Technical Paper Series No. 2010-01-0356.
Taylor, J., Fraser, N., Wieske, P., 2010 (Docket No. EPA-HQ-OAR-2010-0799-1200)
35 "Requirements of External EGR Systems for Dual Cam Phaser Turbo GDI Engines." SAE
Technical Paper Series No. 2010-01-0588. Roth, D.B., Keller, P, Becker, M., 2010. (Docket
No. EPA-HQ-OAR-2010-0799-1201)
36 "Doing More with Less - The Fuel Economy Benefits of Cooled EGR on a Direct Injected
Spark Ignited Boosted Engine," SAE Technical Paper Series, No. 2010-01-0589. Kaiser, M.,
Krueger, U., Harris, R., Cruff, L., 2010
37 "Using indirect cost multipliers to estimate the total cost of adding new technology in the
automobile industry," International Journal of Production Economics Rogozhin, A.,et al.,
2009 (Docket No. EPA-HQ-OAR-2010-0799-0067)
38 "Documentation of the Development of Indirect Cost Multipliers for Three Automotive
Technologies," EPA Technical Memorandum, Helfand, G., Sherwood, T., August 2009
(Docket No. EPA-HQ-OAR-2010-0799-0064)
39 See "2013 Ram 1500 unveiled with eight-speed auto, start/stop, air suspension" at
autoblog.com or "2013-ram-1500-unveiled-with-.pdf" in Docket No. EPA-HQ-OAR-2010-
0799. See also "2013 Audi A3 Euro-Spec" at caranddriver.com or "2013-audi-a3-euro-spec-
firs.pdf' in Docket No. EPA-HQ-OAR-2010-0799.
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4 Projected Impacts on Emissions, Fuel Consumption, and
Safety
4.1 Introduction
This chapter documents EPA's analysis of the emission, fuel consumption and safety
impacts of the emission standards for light duty vehicles. Light duty vehicles include
passenger vehicles such as cars, sport utility vehicles, vans, and pickup trucks. Such vehicles
are used for both commercial and personal uses and are significant contributors to the total
United States (U.S.) GHG emission inventory.
This chapter documents the analysis using the MY 2008 based market forecast. The
analysis using the MY 2010 based market forecast is documented in RIA Chapter 10. The
methods are generally identical between the two analyses; in places where they are not, a note
is placed in this chapter.
Mobile sources represent a significant share of U.S. GHG emissions and include light-
duty vehicles, light-duty trucks, medium-duty passenger vehicles, heavy-duty trucks,
airplanes, railroads, marine vessels and a variety of other sources. In 2010, mobile sources
emitted 30% of all U.S. GHGs, and have been the source of the largest absolute increase in
U.S. GHGs since 1990. Transportation sources, which do not include certain off highway
sources such as farm and construction equipment, account for 27% of U.S. GHG emissions,
and motor vehicles (CAA section 202(a)), which include light-duty vehicles, light-duty
trucks, medium-duty passenger vehicles, heavy-duty trucks, buses, and motorcycles, account
for 23% of total U.S. GHGs.
Light-duty vehicles emit carbon dioxide, methane, nitrous oxide and
hydrofluorocarbons. Carbon dioxide (CCh) is the end product of fossil fuel combustion.
During combustion, the carbon stored in the fuels is oxidized and emitted as CC>2 and smaller
amounts of other carbon compounds. Methane (CELO emissions are a function of the methane
content of the motor fuel, the amount of hydrocarbons passing uncombusted through the
engine, and any post-combustion control of hydrocarbon emissions (such as catalytic
converters). Nitrous oxide or NiO (and nitrogen oxide or NOx) emissions from vehicles and
their engines are closely related to air-fuel ratios, combustion temperatures, and the use of
pollution control equipment. For example, some types of catalytic converters installed to
reduce motor vehicle NOx, carbon monoxide (CO) and hydrocarbon (HC) emissions can
promote the formation of NiO. Hydrofluorocarbons (HFC) are progressively replacing
chlorofluorocarbons (CFC) and hydrochlorofluorocarbons (HCFC) in vehicle air conditioning
systems as CFCs and HCFCs are being phased out under the Montreal Protocol and Title VI
of the CAA. There are multiple emissions pathways for HFCs with emissions occurring
during charging of cooling and refrigeration systems, during operations, and during
decommissioning and disposal.
This rule will significantly decrease the magnitude of these emissions. Because of
anticipated changes to driving behavior, fuel production, and electricity generation, a number
of co-pollutants would also be affected by this rule. This analysis quantifies the program's
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impacts on the greenhouse gases (GHGs) carbon dioxide (CCh), methane (CfLO, nitrous oxide
(N2O) and hydrofluorocarbons (HFC-134a); program impacts on "criteria" air pollutants,
including carbon monoxide (CO), fine particulate matter (PM^s) and sulfur dioxide (SCh) and
the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOx); and impacts on
several air toxics including benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein.
CC>2 emissions from automobiles are largely the product of fuel combustion, and
consequently, reducing CO2 emissions will also produce a significant reduction in projected
fuel consumption. EPA's projections of these impacts are also shown in this chapter.
In addition to the intended effects of reducing CO2 emissions, the agencies also
consider the potential of the standards to affect vehicle safety. This topic is discussed in
Preamble Section II.G. EPA's analysis of the change in light duty vehicle related fatalities
due to projected usage of mass reduction technology is shown in this chapter.
This chapter primarily describes the methods used by EPA in its analysis. Detailed
discussion of the inputs, such as VMT, emission factors, and safety coefficients are found in
Chapter 4 of the Joint TSD.
4.2 Analytic Tools Used
As in the MYs 2012-2016 rule, EPA used its Optimization Model for reducing
Emissions of Greenhouse gases from Automobiles (OMEGA) post-processor to project the
impacts of this rule. Broadly speaking, the OMEGA core model is used to predict the most
likely paths by which manufacturers would meet tailpipe CO2 emission standards. OMEGA
applies technologies with varying degrees of cost and effectiveness to a defined vehicle fleet
in order to meet a specified GHG emission target and calculates the costs and benefits of
doing so. The projections of impacts in OMEGA are conducted in a Microsoft Excel
Workbook (the benefits post-processor), (for more detail, see RIA chapter 3) The OMEGA
benefits post-processor produces a national scale analysis of the impacts (emission reductions,
monetized co-benefits, safety impacts) of the analyzed program.
The benefits post-processor incorporates the inputs discussed (many extensively) in the
Joint Technical Support Document. Specifically, Joint TSD Chapter 1 discusses the
development of the vehicle fleet, Joint TSD Chapter 2 discusses the attribute based curves
which define the CO2 targets, Joint TSD Chapter 3 discusses the technologies which may be
available to meet those targets,000 and Joint TSD Chapter 4 discusses other relevant inputs
(such as vehicle sales, vehicle miles traveled (VMT), and survival schedules).
The remainder of this chapter provides a summary of the discussion of the TSD inputs,
additional data on methodology and inputs, and the results of the analysis.
GGG Specifically, the power consumption of plug-in hybrid and battery electric vehicles are discussed in Joint
TSD Chapter 3 and used in this analysis. Mass reduction, an input to the mass-safety analysis, is also discussed
therein.
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4.3 Inputs to the emissions analysis
4.3.1 Methods
EPA estimated greenhouse impacts from several sources including: (a) the impact of
the standards on tailpipe COi emissions, (b) projected improvements in the efficiency of
vehicle air conditioning systems, HHH (c) reductions in direct emissions of the potent
greenhouse gas refrigerant HFC-134a from air conditioning systems, (d) "upstream" emission
reductions from gasoline extraction, production and distribution processes as a result of
reduced gasoline demand associated with this rule, and (e) "upstream" emission increases
from power plants as electric powertrain vehicles increase in prevalence as a result of this rule
(Table 4.3-17).ni EPA additionally accounted for the greenhouse gas impacts of additional
vehicle miles travelled (VMT) due to the "rebound" effect discussed in Section III.H.
Our estimates of non-GHG emission impacts from the GHG program are broken down
by the three drivers of these changes: a) "downstream" emission changes, reflecting the
estimated effects of VMT rebound (discussed in Sections III.F and III.H) and decreased
consumption of motor vehicle fuel; b) "upstream" emission reductions due to decreased
extraction, production and distribution of motor vehicle gasoline; c) "upstream" emission
increases from power plants as electric powertrain vehicles increase in prevalence as a result
of this rule. For all criteria and air toxic pollutants, the overall impact of the program is small
compared to total U.S. inventories across all sectors.
As discussed in preamble section III.C.2, although electric vehicles have zero tailpipe
emissions, EPA assumes that manufacturers will plan for these vehicles in their regulatory
compliance strategy for criteria pollutant and air toxics emissions, and will not over-comply
with those standards for non-GHG air pollutants. Since the Tier 2 emissions standards are
fleet-average standards, we assume that if a manufacturer introduces EVs into its fleet, that it
would correspondingly compensate through changes to vehicles elsewhere in its fleet, rather
than produce an overall lower fleet-average emissions level.40 Consequently, EPA assumes
neither tailpipe pollutant benefit (other than CO2) nor an evaporative emission benefit from
the introduction of electric vehicles into the fleet.
Two basic elements feed into OMEGA's calculation of vehicle tailpipe emissions.
These elements are vehicle miles traveled (VMT) and emission rates.
Total Emissions = VMT miies * Emission rate grams/mile
Equation 9 - Emissions
111111 While EPA anticipates that the efficiency of the majority of mobile air conditioning systems will be
improved in response to the MY 2012-2016 rulemaking, the agency expects that the remainder will be improved
as a result of this action.
mThe increased emissions from power plants includes feedstock gathering. This includes GHG emissions from
the extraction of fuel for power plants, including coal and natural gas.
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Chapter 4
This equation is adjusted in calculations for various emissions, but provides the basic
form used throughout this analysis. As an example, in an analysis of a single calendar year,
the emission equation is repeatedly applied to determine the contribution of each model year
in the calendar year's particular fleet. Appropriate VMT and emission factors by age are
applied to each model year within the calendar year, and the products are then summed.
Similarly, to determine the emissions of a single model year, appropriate VMT and emission
factors by age are applied to each calendar year between when the model year fleet is
produced and projected to be scrapped
Tailpipe SOi emissions, which are largely controlled by the sulfur content of the fuel,
is an exception to this basic equation. As discussed in TSD 4, decreasing the quantity of fuel
consumed decreases tailpipe SOi emissions proportionally to the decrease in fuel combusted.
4.3.1.1
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 4.3-1). When expressed in CC>2 equivalent (CC>2 EQ)
terms, each gas is weighted by its heat trapping ability relative to that of carbon dioxide.
Table 4.3-1 Global Warming Potentials for the Inventory GHGsJJJ, 41
Gas
CO2
CH4
N2O
HFC (R134a)
Global Warming potential
(CO2 Equivalent)
1
25
298
1430
4.3.1.2 Years considered
This analysis presents the projected impacts of this rule in calendar years 2020, 2030,
2040 and 2050. We also present the emission impacts over the estimated full lifetime of MYs
JJJ As with the MY 2012-2016 Light Duty rule and the MY 2014-2018 Medium and Heavy Duty rule, the GWPs
used in this rule are consistent with 100-year time frame values in the 2007 Intergovernmental Panel on Climate
Change (IPCC) Fourth Assessment Report (AR4). At this time, the 100-year GWP values from the 1995 IPCC
Second Assessment Report are used in the official U.S. GHG inventory submission to the United Nations
Framework Convention on Climate Change (UNFCCC) per the reporting requirements under that international
convention. The UNFCCC recently agreed on revisions to the national GHG inventory reporting requirements,
and will begin using the 100-year GWP values from AR4 for inventory submissions in the future.
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2017-2025 vehicles.KKK The program was quantified as the difference in mass emissions
between a control case under final standards and a reference case as described in Section
4.3.4.
4.3.2 Activity
4.3.2.1 Vehicle Sales
Vehicle sales projections from MY 2012 through MY 2025 were developed jointly by
NHTSA and EPA and are discussed in Chapter 1 of the Joint TSD. For MYs between 2025
and 2035, EPA used the Volpe Center run of the NEMs model (discussed in Joint TSD
Chapter 1) in order to project the sales of cars and trucks by "pre-MY 2011" definitions. 23
percent of "pre-MY 2011" defined trucks were then converted to cars (Table 4.3-2),
consistent with the percent that changed in MY 2025 within the reference fleet forecast. This
action reflects the assumption that the vehicle mix within the car and truck classes stops
changing after MY 2025. These same methods and sales projections were used at proposal.
Table 4.3-2 MY 2011 and later Car and Truck Definitions
LLL
CAR DEFINITION
Passenaer Car - Vehicles defined pre-MY 201 las Cars + 2
wheel drive SUVs below 6,000 GVW
TRUCK DEFINITION
fleet
As the NEMS analysis only goes through 2035, and this analysis goes through 2050,
sales from 2035-2050, the sales of cars and trucks were each projected to grow at the average
annual rates of sales growth from 2017-2035 (1.16%).
4.3.2.2 Survival schedulesMMM
TSD 4 documents the survival schedule used in this rule.
The agencies' analyses of fuel savings and related benefits from adopting more
stringent fuel economy and GHG standards for MYs 2017-2025 passenger cars and light
trucks begin by estimating the resulting changes in fuel use over the entire lifetimes of
affected cars and light trucks. The change in total fuel consumption by vehicles produced
during each of these model years is calculated as the difference in their total lifetime fuel use
over the entire lifetimes of these vehicles as compared to a reference case.
KKK The "full lifetime" is the time span between sales and scrappage for a given MY, and includes estimates of
sales, scrappage, and VMT accumulation by year. For a given vehicle, it is the mileage between when it is
driven for its first and last miles.
'•'•'• While the formal definitions are lengthy, brief summaries of the classifications are shown here.
MMM ^ iengthier discussion of both survival and mileage schedules are provides in Joint TSD Chapter 4.
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Chapter 4
The first step in estimating lifetime fuel consumption by vehicles produced during a
model year is to calculate the number of those vehicles expected to remain in service during
each future calendar year after they are produced and sold.NNN This number is calculated by
multiplying the number of vehicles originally produced during a model year by the proportion
expected to remain in service at the age they will have reached during each subsequent
calendar year, often referred to as a "survival rate."
The proportions of passenger cars and light trucks expected to remain in service at
each age are estimated from R.L. Polk vehicle registration data for calendar years 1970-2010,
and are shown in Table 4.3-3.42 Note that these survival rates were calculated against the pre-
MY 2011 definitions of cars and light trucks, and are not projected to change over time in the
analysis. The rates are applied to vehicles based on their regulatory class (passenger car or
light truck) regardless of fuel type or level of technology.
The survival and annual mileage estimates reported in this section's tables reflect the
convention that vehicles are defined to be of age 1 during the calendar year that coincides
with their model year. Thus for example, model year 2017 vehicles will be considered to be
of age 1 during calendar year 2017. This convention is used in order to account for the fact
that vehicles produced during a model year typically are first offered for sale in June through
September of the preceding calendar year (for example, sales of a model year typically begin
in June through September of the previous calendar year, depending on manufacturer). Thus,
virtually all of the vehicles produced during a model year will be in use for some or all of the
calendar year coinciding with their model year, and they are considered to be of age 1 during
that year.000
NNN Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they
are produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year
2000, age 2 during calendar year 2001, and to reach their maximum age of 30 years during calendar year 2029.
NHTS A considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles
originally produced during a model year remain in service. Applying these conventions to vehicle registration
data indicates that passenger cars have a maximum age of 30 years, while light trucks have a maximum lifetime
of 37 years. See Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, "Vehicle Survivability and
Travel Mileage Schedules," DOT HS 809 952, 8-11 (January 2006). Available at http://www-
nrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed Sept. 9, 2011). For the Final Rule, the survivability schedules
developed by Lu were updated using national vehicle registration data collected by R.L. Polk for calendar years
2006-2010.
000 A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for
a small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to
various ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the DOT's Center
for Statistical Analysis
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Table 4.3-3 Survival Rates
VEHICLE AGE
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
ESTIMATED
SURVIVAL
FRACTION
CARS
1.0000
0.9878
0.9766
0.9614
0.9450
0.9298
0.9113
0.8912
0.8689
0.8397
0.7999
0.7556
0.7055
0.6527
0.5946
0.5311
0.4585
0.3832
0.3077
0.2414
0.1833
0.1388
0.1066
0.0820
0.0629
0.0514
0.0420
0.0337
0.0281
0.0235
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
ESTIMATED
SURVIVAL
FRACTION
LIGHT TRUCKS
1.0000
0.9776
0.9630
0.9428
0.9311
0.9152
0.8933
0.8700
0.8411
0.7963
0.7423
0.6916
0.6410
0.5833
0.5350
0.4861
0.4422
0.3976
0.3520
0.3092
0.2666
0.2278
0.2019
0.1750
0.1584
0.1452
0.1390
0.1250
0.1112
0.1028
0.0933
0.0835
0.0731
0.0619
0.0502
0.0384
0.0273
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Chapter 4
4.3.2.3 VMT
The second step in estimating lifetime fuel use by the cars or light trucks produced
during a future model year is to calculate the total number of miles that they will be driven
during each year of their expected lifetimes. To estimate total miles driven, the number of
cars and light trucks projected to remain in use during each future calendar year is multiplied
by the average number of miles a surviving car or light truck is expected to be driven at the
age it will have reached in that year. Estimates of average annual miles driven by cars and
light trucks of various ages were developed by NHTSA from the Federal Highway
Administration's 2009 National Household Travel Survey. This updates the schedules of
annual miles driven that were used in the NPRM, which were based on the previous National
Household Travel Survey, conducted in 2001. Additionally, the agencies have accounted for
the higher usage of fleet vehicles, which include rental vehicles as well as those owned by
corporations and government agencies. These represent about 20% of new vehicle sales, are
not represented in the NHTS, and are driven much more intensively (on average) than
household vehicles for the first several years of their lives before being absorbed into the
household vehicle population.ppp The updated mileage schedules are reported in Table 4.3-4.
These estimates represent the average number of miles driven by a surviving light duty
vehicle at each age over its estimated full lifetime. To determine the number of miles a
typical vehicle produced during a given model year is expected to be driven at a specific age,
the average annual mileage for a vehicle of that model year and age is multiplied by the
corresponding survival rate for vehicles of that age. Further details are available in TSD 4.
ppp Using the Annual Energy Outlook 2012, early release version of the National Energy Modeling System,
developed and maintained by the U.S. Energy Information Administration, the proportion of fleet vehicles and
their typical usage were calculated and then averaged into the household mileage accumulation schedules
developed using the 2009 NHTS. [NHTSA's documentation needed.]
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Table 4.3-4CY 2009 Mileage Schedules based on NHTS Data
VEHICLE AGE
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
ESTIMATED
VEHICLE MILES
TRAVELED
CARS
14,700
14,252
14,025
13,593
13,324
13,064
12,809
1 1 ,378
11,087
10,806
10,535
10,273
10,021
9,779
9,547
9,324
9,111
8,908
8,714
8,530
8,356
8,192
8,037
7,892
7,757
7,632
7,516
7,410
7,314
7,227
7,151
7,083
7,026
6,979
6,941
6,912
6,894
ESTIMATED
VEHICLE MILES
TRAVELED
LIGHT TRUCKS
15,974
15,404
14,841
14,435
14,038
13,650
12,590
12,192
11,810
1 1 ,443
11,091
10,755
10,434
10,129
9,839
9,564
9,305
9,061
8,833
8,620
8,423
8,241
8,075
7,923
7,788
7,668
7,563
7,473
7,399
7,341
7,298
7,270
7,258
7,246
7,233
7,221
7,209
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Chapter 4
4.3.2.4 Adjusting vehicle use for years after 2009
The estimates of annual miles driven by passenger cars and light trucks at each age
were also adjusted to reflect projected future growth in average use for vehicles of all ages.
Increases in the average number of miles that cars and trucks are driven each year have been
an important source of historical growth in total car and light truck use, and are expected to be
a continued source of future growth in total light-duty vehicle travel as well. As an
illustration of the importance of growth in average vehicle use, the total number of miles
driven by passenger cars increased 35 percent from 1985 through 2005, equivalent to a
compound annual growth rate of 1.5 percent.43 During that same time, however, the total
number of passenger cars registered in the U.S. grew by only about 0.3 percent annually.QQQ
Thus growth in the average number of miles that automobiles are driven each year accounted
for the remaining 1.2 percent (= 1.5 percent - 0.3 percent) annual growth in total automobile
RRR
use.
In the U.S., overall change in VMT is attributable to factors such as employment rate,
vehicle ownership rates, demographic trends, the cost of driving, and other macroeconomic
factors. Rather than independently developing estimates of these factors, the agencies have
used the DOT Volpe Center NEMSSSS run which considers many of these factors, as a
benchmark of total VMT levels in each future year. The VMT projections produced by this
NEMS run are highly similar to those shown in AEO 2012 Early Release. The AEO 2012
Early Release Reference Case projection of total car and light truck use and of the number of
cars and light trucks in use suggest that their average annual use will continue to increase
from 2010 through 2035, although at a slower rate of increase than shown in AEO 201 l.TTT
In calendar year 2030, total VMT projected in AEO 2012 Early Release is 10% lower than
that projected in AEO 2011.
In order to develop reasonable estimates of future growth in the average number of
miles driven by cars and light trucks of all ages in the reference case, the agencies calculated
the average rate of growth in the mileage schedules necessary for total car and light truck
travel to closely correspond to AEO 2012 Early Release Reference Case. The growth rate in
average annual car and light truck use produced by this calculation is approximately 0.6
QQQ A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for
a small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to
various ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the NHTSA's
Center for Statistical Analysis.
RRR See supra note k below.
sss This is the version of NEMS that is used in AEO 2012 Early Release, and modified by the Volpe center to
hold new vehicle fuel economy constant after 2016. See TSD 1 for additional details. This version produces
VMT estimates that are highly similar to those in the AEO 2012 Early Release
TTT The agencies note that VMT growth has slowed, and because the impact of VMT is an important element in
our benefit estimates, we will continue to monitor this trend to see whether this is a reversal in trend or
temporary slowdown. See the 2009 National Household Travel Survey (http://nhts.ornl.gov/2009/pub/stt.pdf)
and National transportation Statistics
(http://www.bts.gov/publications/national_transportation_statistics/html/table_04_09.html)
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MY 2017 and Later Regulatory Impact Analysis
percent per year.uuu When the 0.6% annual growth rate is combined with the MY 2010 base
sales projection (TSD 1), as well as the VMT, and survival schedules derived for this rule the
estimated total vehicle usage in the EPA's reference cases closely approximates that
contained in AEO 2012 ER. Thus, a growth rate is applied to the mileage figures reported in
Table 4.3-4 (after adjusting vehicle populations for expected vehicle survival rates) to
estimate average annual mileage during each calendar year analyzed and during the expected
lifetimes of model year 2017-25 cars and light trucks in the reference case.vvv
EPA developed the reference case VMT using the single growth factor discussed
above; this growth factor reflects driver responsiveness to changes in fuel prices, fuel
efficiency, and other factors consistent with the AEO 2012 ER Reference Case.*** To
develop EPA's policy case VMT, EPA applied the elasticity of annual vehicle use with
respect to fuel cost per mile corresponding to the 10 percent fuel economy rebound effect
used in this analysis (i.e., an elasticity of annual vehicle use with respect to fuel cost per mile
driven of -0.10;) to the percentage change in cost-per-mile travel between each future year's
vehicle under a policy case and a reference case in the same year. In other words, if the per-
mile fuel cost of a MY 2025 vehicle under the policy case was 30% less than its counterpart
under the reference case, the change in VMT would be 3%.xxx Thus, in the EPA analysis,
VMT associated with the rebound effect only reflects the impact of the EPA program relative
to the reference case.YYY The following equation summarizes in mathematical form how EPA
captured the change in VMT due to increased fuel efficiency in the policy case (i.e., the
EPA's approach for incorporating the rebound effect):
uuu It was not possible to estimate separate growth rates in average annual use for cars and light trucks, because
of the significant reclassification of light truck models as passenger cars discussed previously.
vw As indicated previously, a vehicle's age during any future calendar year is uniquely determined by the
difference between that calendar year and the model year when it was produced.
**w This approach is consistent with the MYs 2012-2016 rule, but represents a slight difference from our
approach in the NPRM where we first accounted for changes in fuel cost-per-mile compared to 2009 before
applying a growth factor to meet levels in AEO 2011. The use of a single growth factor ensures consistency
with the AEO projections about future micro and macroeconomic trends and underlying assumptions about
consumer responsiveness to those trends.
xxx Under the equation: percent difference in VMT = (rebound effect * (FCreference case - FCpolicy
case)/FCreference case) and the rebound effect = 10%. A 30% change in fuel costs, multiplied by a 10%
rebound effect would result in 3% additional driving.
YYYThis approach is consistent with the MYs 2012-2016 rule, but represents a slight difference from our
approach in the NPRM where rebound VMT was estimated based on the difference between FCPM in our policy
case and the FCPM in the calendar year of our baseline VMT (i.e., 2001 NHTS). As discussed in our draft RIA,
the NPRM approach implicitly assumes drivers are comparing their current fuel costs to fuel costs from a distant
past when making decisions about the amount of miles to drive. Additionally, the NPRM approach implicitly
assumes that factors in the years between a future calendar year and one in the distant past have no influence on
VMT levels in future calendar year (which contrasts with AEO assumptions that the previous year VMT is a
factor in current year VMT). The FRM approach of estimating rebound VMT based on the difference between
policy case FCPM and reference case FCPM in the same calendar year better captures the likely real-world
driver response to changes in fuel costs. Finally, this approach allows EPA to vary the rebound effect in the
policy case while holding the reference case VMT constant in the sensitivity analyses in section 4.5.1 to ensure
that we are capturing the effect of our standards alone.
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Chapter 4
Percent difference in VMT = (elasticity of VMT with respect to FCPM * (FCPMreference case - FCPMpolicy
case/'^ *— ^-IV-l-reference case/
Where FCPM = fuel cost per mile
EPA made adjustments to vehicle use to account for projected changes in future fuel
prices, fuel efficiency, and other factors that influence growth in average vehicle use during
each future calendar year. Because the effects of fuel prices and other factors influencing
growth in average vehicle use differ for each year, these adjustments result in different VMT
schedules for each future year. The net impact resulting from these adjustments is continued
growth over time in the average number of miles that vehicles of each age are driven,
although at slower rates than those observed from 1985 - 2005. zzz
VMT equationAAAA
The following equation summarizes in mathematical form the adjustments that are
made to the values of average miles driven by vehicle age derived from the 2009 NHTS to
derive the estimates of average miles driven by vehicles of each model year during future
calendar years that are used in this analysis.
VMTcalendaryear Xiagey = (Vy) * (1 + GRYS * (1 - fl * (FCPMtiy - FCPMXiy)/FCPMty
Where:
Vy = Average miles driven in CY 2009 (from NHTS A analysis of 2009 NHTS data) by a vehicle of age
y during 2009
GR = Secular Growth Rate
YS = Years since 2009)
R= elasticity of VMT with respect to FCPM (-0.10). (Note that this term has no impact on the reference
case because FCPM xy = FCPMty
FCPMjj, = Fuel cost per mile of a analyzed vehicle of age y in calendar year x
FCPMt>y = this variable represents the fuel cost per mile of a reference case vehicle of age y in calendar
year t (Note: in the reference case, this variable is identical to FCPMx,y.)
In turn, fuel cost per mile of an age y vehicle in calendar year x is determined by the
following equation, which can be extended for any number of fuels:
222 Observed aggregate VMT in recent years has actually declined (about 0.4% per year over the past decade),
but it is unclear if the underlying cause is general shift in behavior or a response to a set of temporary economic
conditions.
AAAA While both agencies applied the VMT calculation described above in the NPRM, for the final rule, in the
EPA baseline calculation, the rebound effect is in effect embedded in the growth rate. Under the regulatory
alternatives, the rebound effect is based solely on the percentage increase in fuel economy over the relevant
baseline model year. NHTS A continued to follow the NPRM approach because of its requirement to produce an
Environmental Impact Statement for the rule, and the need for consistent results among the alternative scenarios
it considers.
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MY 2017 and Later Regulatory Impact Analysis
FCPMcalendaryearx ~ ECy * EPX + GCy * GPX + DCy * DPX
Where:
ECy= Electricity consumption of age y vehicle (in KWh) per mile
EPX = Electricity Price (in $ per KWh) during calendar year x
GCy = Gasoline Consumption of age y vehicle (in gallons) per mile
GPX = Gasoline Price (in $ per gallon) during calendar year x
DC, = Diesel Consumption of age y vehicle (in gallons) per mile
DPX = Diesel Price (in $ per gallon) during calendar year x
Since the proposal, EPA has made some adjustments to the modeling of VMT to
improve consistency with the CAFE model and with the analysis used to collect the VMT and
survival rate data. The OMEGA model benefits processor now separately tracks the VMT
schedules of classic cars, cars that would be trucks under the pre-MY 2011 CAFE regulations,
and post-MY 2011 trucks. VMT and survival rates are mapped according to the pre-MY
2011 CAFE regulation definitions. This adjustment changes the mapping of VMT, but has
little effect on total VMT.
Table 4.3-5 Survival Weighted Per-Vehicle Reference VMT used in the Agencies'
Analyses
BBBB
FRM
NPRM
MY 2021
Cars
204,161
204,688
Light
Trucks
218,399
242,576
MY 2025
Cars
209,037
210,898
Light
Trucks
223,688
249,713
The net effect of all of the changes results in slightly lower VMT schedules than those
used in the proposal analysis, with a greater impact on the light truck schedules.
4.3.3 Upstream Emission Factors
As documented in Joint TSD Chapter 4, emission factors for this analysis were derived
from several sources. Tailpipe emission factors other than COi were derived from MOVES
2010a, with the complete documentations for these calculations provided in the Joint TSD.44
As in the proposal, upstream emission factors for petroleum product production, transport and
distribution were derived from EPA's "Impact spreadsheet" based on Argon National Labs
Greet 1.8.45'46 Electricity related emission factors for were derived from EPA's Integrated
Due to the differences in VMT mapping between proposal and this final rulemaking, the car VMT shown
here are not directly comparable between NPRM and FRM.
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Chapter 4
Planning Model (IPM), as discussed later in this document. These emission factors were used
as inputs to the OMEGA post-processor.47
Several modifications were made to the analysis of upstream emission factors since
proposal. These revisions are discussed later in the following sections
4.3.3.1 Updates to the Gasoline Production and Transport Emission Rates
As discussed in section 4.6.2, EPA made a number of updates to the upstream
emission rates as a part of the process leading up to the air quality analysis. These updates led
to changes in the inventory analysis from the emission rates inputs used in the proposal, and
have provided improved consistency with the national emission inventory (NEI). No changes
were made to the upstream GHG emission rates. We received no comments on the gasoline
production and distribution rates used in this rulemaking.
The gasoline production and transport sector is composed of four distinct components:
• Domestic crude oil production and transport
• Petroleum production and refining emissions
• Production of energy for refinery use
• Gasoline transport, storage and distribution.
The emission factors associated with on-road combustion emissions were updated
based on the HD GHG rule MOVES runs.48'cccc Category 3 Ocean going vessel emission
rates were also updated for consistency with the EPA 2010 Category 3 vessel rule.
Refinery related emissions were updated to reconcile the emission totals with those in
the national emission inventory. For some pollutants, such as NOx, this change was a
significant reduction in the emission rate related to "upstream" gasoline. For others
emissions, there was little change in the rate.
As discussed in section 4.6.2, we also made adjustments to the feedstock mix for
refinery use to be consistent with the IPM runs conducted for this rule. See section 4.6.3 for
more details on the IPM analysis.
As in the NPRM, we assumed CY 2030 upstream emission rates for this analysis.
cccc According to the EPA modified version of GREET 1.8, combustion emissions account for approximately
150 grams of CO2 per mmbtu of fuel produced. As the total estimate of CO2 emission per mmbtu is 18,792
grams, the 10% reduction in HD emissions has an impact of less than 0.1% on the total emissions from
producing a gallon of fuel. As such, these changes had no meaningful impact on the GHG emission rates for
fuel production.
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MY 2017 and Later Regulatory Impact Analysis
Table 4.3-6 Comparison of NPRM and FRM Gasoline Production Emission Rates.
CO
NOx
PM2.5
SOx
VOC
1,3 -Butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
Naphthalene
CH4
N2O
C02
g/mmbtu
NPRM
4.3
13.3
1.8
8.2
44.6
0.001
0.005
0.001
0.096
0.036
0.003
106.6
0.3
18792.2
FRM
2.7
6.5
1.0
4.4
44.2
0.001
0.005
0.001
0.090
0.038
0.011
106.6
0.3
18777.2
4.3.3.2 Updates to the Electricity Generation Emission Rates
An updated analysis of emissions from electricity generation using the IPM model is
presented in section 4.6.3 of this chapter.
For this rulemaking, we conducted an Integrated Planning Model (IPM) analysis of the
electricity sector in order to gauge the impacts of additional electric charging upon the power
grid. This analysis is discussed in section 4.6.3.3 below. Because the IPM analysis was
conducted with a specific electricity demand (that of the NPRM) and in specific years, for the
FRM inventory analysis, we developed emission factors that could be extrapolated to
additional scenarios for use as inputs to the OMEGA model.
In general, IPM runs in a single year are considered indicative of the surrounding
decade. In other words, the 2030 results can be considered inclusive of the five years before
and after 2030. As such, the 2030 impacts are an appropriate representation of the electrical
grid in the time period surrounding 2030, which is a time when significant vehicles subject to
this rule will be on the road.
The 2030 IPM results were post-processed to develop gram per kwh emission factors
for use in the OMEGA model. The total emissions reported above were divided by the
incremental power demand in 2030. For those emissions that IPM does not generate, we
relied upon the NEI for air toxic emissions49 and eGrid for N2O and CFU.50
IPM includes the emissions from the power generation, however, there are additional
emissions attributable to feedstock generation, or the gathering and transport of fuel to the
power plant. Emission factors from the version of GREET 1.8c (as modified for the EPA
upstream analysis discussed above) were used to generate feedstock emission factors. As
discussed in preamble III.C.2, and later in this chapter, the incremental mix of generation for
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Chapter 4
the additional load was approximately 80% natural gas, 15% coal, and 5% other. The natural
gas and coal emission factors from GREET were weighted in this ratio as used to generate the
emission factors from GREET for the criteria pollutants and air toxics. For GHGs, additional
EPA analysis was conducted to properly determine the appropriate impact of feedstock
gathering. This analysis is also presented in III.C.2.
We also used the retail electricity price projections from this IPM run in our analysis
of electricity fuel costs to drivers.
Table 4.3-7 Emission factors used in analysis of electricity generation
CY
VOC
CO
NOx
PM2.5
S02
C02
N2O
CH4
1,3 -butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
IPM (g/kwh)
8.28E-03
2.89E-01
1.13E-01
5.81E-03
1.90E-01
4.45E+02
6.76E-03
8.60E-03
O.OE+00
5.5E-05
2.8E-05
1.3E-04
3.0E-05
Feedstock (g/kwh)
4.69E-02
5.01E-02
1.27E-01
6.51E-02
4.69E-02
3.55E+01
6.81E-04
3.31E+00
O.OOE+00
9.47E-06
3.15E-05
1.41E-03
7.51E-06
Total
(g/kwh)
5.52E-02
3.39E-01
2.41E-01
7.09E-02
2.37E-01
4.80E+02
7.44E-03
3.32E+00
O.OOE+00
6.40E-05
5.95E-05
1.54E-03
3.79E-05
4.3.4 Scenarios
4.3.4.1 Air conditioning
HFC-134a (refrigerant) emission factors were applied on a gram per mile basis, and are
consistent with the Interim Joint TAR analysis of the on-road HFC impact per mile of 11.5
gram/mile for cars and 13.0 gram/mile for trucks. For this analysis, the per-mile impact of
HFC reduction was determined by multiplying the fractional phase in of the credit by the
Interim Joint TAR assessment of the g/mile impact. Relative to the NPRM estimates, the
TAR estimates of HFC-134a leakage are smaller. See TSD 5 for a detailed discussion of the
TAR estimates of HFC-134a emissions, and why the total reductions estimated here may be
conservative in this regard. As VMT is increasing and the impact the vehicle HFC-134a
control programs are calculated on a gram/mile basis, this analysis implicitly assumes that a
vehicle driven more miles will have its HFC-134a reservoir refilled more times.
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MY 2017 and Later Regulatory Impact Analysis
Table 4.3-8 - A/C Credits
MY 2017
MY 2018
MY2019
MY2020
MY2021
MY2022
MY2023
MY2024
MY2025
Reference
Car
Indirect
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4-'
-------
Chapter 4
control case, two off-cycle credits were made available to vehicle manufacturers in our
OMEGA modeling of the reference case (active aerodynamics and start-stop technology).
These credits are considered environmentally neutral in our analysis, and are not modeled as
having an impact on emissions or fuel savings.
Consistent with the MYs 2012-2016 rule analysis, EPA did not allow EVs and PHEVs
(maximum penetration caps of zero) in the reference case. While the penetration of EVs and
PHEVs in MY 2016 will like be non-zero, as they are being sold in MY 2011, EPA chose not
to include these technologies in the reference case assessment due to their cost-distorting
effects on the smallest companies. For further discussion see RIA Chapter 3.
As discussed above, no credits with environmental disbenefit are projected to be used
after MY 2016 in the reference case. As manufacturers must comply with the EPA
programEEEE, the projected emission rates are simply the footprint of the projected fleet
against the standard curves.FFFF COi emission rates for MY 2016, 2021 and 2025 were taken
from fleet projections against the curves. Two cycle COi emission rates for the reference case
are shown below, and continue changing on a fleet basis due to mix shifts (Table 4.3-9). As
no EVs were modeled, there is no increase in electricity consumption in the reference case.
The air conditioning impacts as discussed in Section 4.3.4.1 were also incorporated.
Table 4.3-9 - Reference Case Two Cycle CO2
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Car
234
234
234
234
234
234
234
234
234
Truck
308
308
308
308
308
308
307
307
307
Fleet
262
261
261
260
260
260
259
259
258
4.3.4.3 Control Case
MY 2017-2025 CO2 emission estimates were derived from the curves that determine
the targets and from projected credit usage on an industry wide basis. These values slightly
differ from those produced by the OMEGA modeling, which includes credit transfer between
car and truck fleet, but the results should be environmentally equivalent due to the VMT- and
sales-weighted components of that transfer. A/C refrigerant and efficiency credit estimates are
discussed in Section4.3.4.1, while the methodology used to estimate the impact of the
EV/PHEV/FCV multiplier credit and pickup related credits are discussed in following
EEEE
js no Option for voluntary non-compliance (fine payment) under the EPA program.
FFFF These reference case rates are slightly more stringent than those modeled in the proposal, which were based
on OMEGA model runs and had a slight shortfall (approximately 1 gram) relative to the standard.
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MY 2017 and Later Regulatory Impact Analysis
sections. These estimates of total credit usage are summarized in Table 4.3-10 through Table
4.3-12. In the impacts modeling, off-cycle credits are modeled as environmentally neutral, or
in other words, the credits were modeled so that the environmental benefits of the credits are
larger than their 2 cycle values by the on-road gap.
The following three tables, Table 4.3-10 through Table 4.3-12, summarize EPA's
projections of overall projected CC>2 emissions averages for passenger cars, light trucks, and
the overall fleet combining passenger cars and light trucks for projected MYs 2017-2025
under the emission control case - ie the final rule. It is important to emphasize that these
projections are based on technical assumptions by EPA about various matters, including the
mix of cars and trucks, as well as the mix of vehicle footprint values, in the fleet in varying
years. It is of course possible that the actual CO2 emissions values, as well as the actual
utilization of incentives and credits, will be either higher or lower than the EPA
projections.0000
In each of these tables, the column "Projected CC>2 Compliance Target" represents our
projected fleetwide average COi compliance target value based on the CCVfootprint curve
standards as well as the projected mixes of cars and trucks and vehicle footprint distributions.
The columns under "Incentives" represent the projected emissions impact of the
TTTTTTTT
advanced technology multiplier incentive , as well as the incentives for use of advanced
technologies (both so-called 'game changing' technologies, and technologies providing
comparable emission reductions) on pickup trucks. Also shown under incentives is the
projected impact of the flexibilities provided to intermediate volume manufacturers
(additional lead time to meet the early model year standards). These incentives allow
manufacturers to meet their compliance targets with COi emissions levels slightly higher than
otherwise required , but do not reflect actual real-world COi emissions reductions. As such
they reduce the emissions reductions that the main CO2 standards would otherwise be
expected to achieve.
The column "Projected Achieved CCh" is the sum of the CO2 Compliance Target and
the values in the "Incentive" columns. This Achieved CC>2 value is a better reflection of the
CO2 emissions benefits of the standards, since it accounts for the incentive programs.
One incentive that is not reflected in these tables is the 0 gram per mile compliance
value for EV/PHEV/FCVs. The 0 gram per mile value accurately reflects the tailpipe CO2
gram per mile achieved by these vehicles; however, fuel use from these vehicles will impact
the overall GHG reductions associated with the standards due to fuel production and
distribution-related upstream GHG emissions which are projected to be greater than the
upstream GHG emissions associated with gasoline from oil. The combined impact of the 0
GGGG
All EPA projections in the this chapter are relative to the MY 2008 market forecast; see the EPA
Regulatory Impact Analysis Chapter 10 for projections relative to a 2010-based reference fleet.
HHHH -j,^ acjvancecj technology multiplier incentive applies to EVs, PHEVs, FCVs, and CNG vehicles. The
projections reflect the use of EVs and PHEVs for MYs 2017-2021. It is, of course, possible that there will be
FCVs and CNG vehicles during this timeframe as well.
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Chapter 4
gram per mile compliance value for EV/PHEV/FCVs and the advanced technology multiplier
on overall program GHG emissions is discussed in more detail below in Preamble Section
III.C.2.
The columns under "Credits" quantify the projected COi emissions credits that we
project manufacturers will generate through improvements in air conditioner leakage
(including refrigerant substitution) and efficiency, as well as certain off-cycle technologies.
These credits reflect real world emissions reductions, so they do not raise the levels of the
Achieved COi values, but they do allow manufacturers to meet their compliance targets with
2-cycle test COi emissions values higher than otherwise apply. For the off-cycle credit
program, values are projected for two technologies—active aerodynamics and stop-start
systems—EPA is not quantifying the use of additional off-cycle technologies at this time
because of a lack of information with respect to the likely use of additional off-cycle
technologies. The off-cycle credits, like A/C credits, reflect real world reductions, so they
would not change the Achieved COi values.
In the MYs 2012-2016 rule, we estimated the impact of the Temporary Leadtime
Allowance Alternative Standards credit in MY 2016 to be 0.1 gram/mile. Due to the small
magnitude, we have not included this flexibility in the following tables for the MY 2016 base
year.
The column "Projected 2-cycle CCh" is the projected fleetwide 2-cycle COi emissions
values that manufacturers would have to achieve in order to be able to comply with the
standards. This value is the sum of the projected fleetwide credit, incentive, and Compliance
Target values. Table 4.3-10 EPA Projections for Fleetwide Tailpipe Emissions Compliance
with COi Standards - Passenger Cars1111
(grams per mile)
Model
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Projected
C02
Compliance
Target
212
202
191
182
172
164
157
150
143
IncentivesJJJJ
Advanced
Technology
Multiplier
0.6
1.1
1.6
1.5
1.2
0.0
0.0
0.0
0.0
Intermediate
Volume
Provisions
0.1
0.3
0.1
0.1
0.0
0.0
0.0
0.0
0.0
Projected
Achieved
C02
213
203
193
183
173
164
157
150
143
Credits
Off
Cycle
Credit
0.5
0.6
0.7
0.8
0.8
0.9
1.0
1.1
1.4
A/C
Refrigerant
7.8
9.3
10.8
12.3
13.8
13.8
13.8
13.8
13.8
A/C
Efficiency
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
Projected
2-cycle
C02
226
218
210
201
193
184
177
170
163
nn Projected results using 2008-based fleet projection analysis. These values differ slightly from those shown in
the proposal because of revisions to the MY 2008-based fleet and updates to the analysis.
JJJJ An incentive not reflected in this table is the 0 gram per mile compliance value for EV/PHEV/FCVs. See text
for explanation.
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MY 2017 and Later Regulatory Impact Analysis
Table 4.3-11 EPA Projections for Fleetwide Tailpipe Emissions Compliance with COi
Standards - Light TrucksKKKK
(grams per mile)
Model
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Projected
C02
Compliance
Target
295
286
277
269
249
237
225
214
203
IncentivesLLLL
Pickup
Mild HEV +
Strong HEV
0.1
0.2
0.3
0.4
0.5
0.6
0.6
0.7
0.8
Intermediate
Volume
Provisions
0.2
0.3
0.2
0.2
0.0
0.0
0.0
0.0
0.0
Projected
Achieved
C02
295
287
278
270
250
238
226
214
204
Credits
Off Cycle
Credit
0.9
1.0
1.2
1.4
1.5
2.2
2.9
3.6
4.3
A/C
Refrigerant
7
11
13.4
15.3
17.2
17.2
17.2
17.2
17.2
A/C
Efficiency
5
5
7.2
7.2
7.2
7.2
7.2
7.2
7.2
Projected
2-cycle
C02
308
304
299
294
276
264
253
242
233
Table 4.3-12 EPA Projections for Fleetwide Tailpipe Emissions Compliance with CO2
Standards -
Combined Passenger Cars and Light TrucksMMMM (grams per mile)
Model
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Projected
CO2
Compliance
Target
243
232
222
213
199
190
180
171
163
Incentives™^
Advanced
Technology
Multiplier
0.4
0.7
1.0
1.0
0.8
0.0
0.0
0.0
0.0
Pickup
Mild
HEV +
Strong
HEV
0.0
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.3
Intermediate
Volume
Provision
0.1
0.3
0.1
0.1
-
-
-
-
-
Projected
Achieved
C02
243
234
223
214
200
190
181
172
163
Credits
Off
Cycle
Credit
0.6
0.8
0.9
1.0
1.1
1.4
1.7
1.9
2.3
A/C
Refrigerant
7.5
9.9
11.7
13.4
15.0
15.0
15.0
14.9
14.9
A/C
Efficiency
5.0
5.0
5.8
5.8
5.8
5.8
5.8
5.7
5.7
Projected
2-cycle
C02
256
249
242
234
222
212
203
194
186
KKKK projectecj resuits using 2008-based fleet projection analysis. These values differ slightly from those shown
in the proposal because of revisions to the MY 2008-based fleet and updates to the analysis.
LLLL An incentive not reflected in this table is the 0 gram per mile compliance value for EV/PHEV/FCVs. See
text for explanation.
MMMM projectecj results using 2008-based fleet projection analysis. These values differ slightly from those shown
in the proposal because of revisions to the MY 2008-based fleet and updates to the analysis.
one jnceritjve not reflected in this table is the 0 gram per mile compliance value for EV/PHEV/FCVs.
See text for explanation.
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Chapter 4
Mild and Strong HEV Pickup Credits
Between MY 2017 and MY 2025, full-size pickup sales vary as a fraction of the fleet
sales as well as a fraction of light truck sales in the MY 2008 based market forecast.
Table 4.3-13 Pickup Trucks as a Fraction of the Fleet
Model Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Projected Sales of
Full Size Pickup Trucks
1,218,829
1,163,965
1,110,802
1,134,230
1,100,818
1,082,815
1,026,579
993,161
983,954
Pickup Trucks
(of Trucks)
21%
21%
20%
20%
19%
19%
18%
17%
17%
Pickup Trucks
(of Fleet)
8%
8%
7%
7%
7%
7%
6%
6%
6%
Trucks
(of fleet)
37%
36%
36%
35%
35%
35%
34%
34%
33%
Based on these fleet fractions, and the credit available, the maximum potential credit
can be calculated.
Table 4.3-14 Maximum Potential Impact of Pickup Credits on Truck Fleet
Model
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Mild
HEV
Credit
10.0
10.0
10.0
10.0
10.0
0.0
0.0
0.0
0.0
Mild
HEV
Maximum
Potential
Impact
(Trucks)
2.1
2.1
2.0
2.0
1.9
0.0
0.0
0.0
0.0
Strong
HEV
Credit
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
20.0
Strong HEV
Maximum
Potential
Impact
(Trucks)
4.2
4.1
4.0
4.0
3.9
3.8
3.6
3.5
3.4
Not every pickup truck will get these credits. Unlike in the proposal, where we post-
processed these credits, we calculated these credits directly in the OMEGA model, based on
the cost effectiveness of the full size pickup HEV packages (with full consideration of
credits). See the earlier tables Table 4.3-10 through Table 4.3-12 used for the estimated
values.
4-130
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MY 2017 and Later Regulatory Impact Analysis
In the OMEGA runs conducted for this final rulemaking, we did not model the fleet
minimums for either strong or mild HEV. This change would mildly overstate the impact of
the credit. However, total usage of pickup truck credits had less than 1 gram of impact on the
truck fleet achieved values in any given MY (Table 4.3-11).
Consumption of Electricity
Based on the OMEGA model outputs, we estimated electricity consumption and
emission impacts from the consumption of electricity due to the electric vehicles and plug-in
electric hybrids. EPA accounts for all electricity consumed by the vehicle. For calculations
of GHG emissions from electricity generation, the total energy consumed from the battery is
divided by 0.9 to account for charging losses, and by 0.93 to account for losses during
transmission. Both values were discussed in the MYs 2012-2016 rule as well as in the Interim
Joint TAR, and the final rule (and proposal) is unchanged from those analyses. The estimate
of charging losses is based upon engineering judgment and manufacturer CBI. The estimate
of transmission losses is consistent, although not identical to the 8% estimate used in GREET,
as well as the 6% estimate in eGrid 2010.51'52 The upstream emission factor is applied to
total electricity production, rather than simply power consumed at the wheel. 00° It is
assumed that electrically powered vehicles drive the same drive schedule as the rest of the
fleet.pppp
Table 4.3-15 Average Electricity Consumption
Model
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Average 2 cycle
Electricity
Consumption for the
fleet (kwh/mile)
Cars
0.001
0.001
0.002
0.002
0.003
0.004
0.004
0.005
0.006
Trucks
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
oooo gy contrast, consumer electricity costs would not include the power lost during transmission. While
consumers indirectly pay for this lost power through higher rates, this power does not appear on their electric
meter.
PPPP
The validity of this assumption will depend on the use of electric vehicles by their purchasers.
4-131
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Chapter 4
EV/PHEV/FCVs multipliers
As discussed in Section III.B of the preamble, the compliance cap for EVs and PHEVs
at zero g/mile is related to the standard level. For purposes of this modeling, we assume that
this cap is never reached. This does not imply that EPA has finalized a cap based on this
criteria. A discussion of the potential impacts of these credits can be found in preamble
section III.C.2 and Section 4.5.2 of the RIA. Costs beyond MY 2025 assume no technology
changes on the vehicles, and implicitly assume EVs used for compliance receive their tailpipe
measurement of zero gram/mile.QQQQ Upstream emissions from electric vehicles, regardless
of the zero-gram mile credit, are always modeled in this analysis.
For the analysis of impacts, we assumed the following penetration of electric vehicles,
where the MY 2021 and MY 2025 values come from OMEGA, with the earlier and later
values interpolated. As modeled, 2016 EV penetrations were set at 0% of the fleet.
PHEV sales, as projected by OMEGA, are not significant.
RRRR
ssss
Table 4.3-16 - EV Fraction of the MY Fleets
Model Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Cars
0.3%
0.5%
0.8%
1.1%
1.4%
1.8%
2.2%
2.6%
3.0%
Truck
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.2%
0.2%
0.3%
EV
multiplier
2
2
2
1.75
1.5
0
0
0
0
The EV multiplier credit was calculated by following formula
Equation 10 - Impact of EV multiplier
GHG Target with multiplier = (GHG Target without multiplier * (Total MY Sales + Multiplier *
Number of EV sales))/Total sales
QQQQ -pjjg COS(S for pHEVs and EVs in this rule reflect those costs discussed in Joint TSD Chapter 3, and do not
reflect any tax incentives, as the availability of those tax incentives in this time frame is uncertain.
RRRR while the actual real world penetration of electric vehicles will be greater than 0% in 2016, for purposes of
this rulemaking, we do not model any EVs or PHEVs in the reference case, as they are generally not needed for
compliance. For further details, see EPA RIA Chapter 3.
ssss pjease noje jjjaj jjjg OMEGA technology projection for EVs and PHEVs does not include the multiplier
provision. Including that provision would presumably increase EV penetration in the MYs 2017-2021
timeframe.
4-132
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MY 2017 and Later Regulatory Impact Analysis
So for MY 2021, which had car sales of 10.5 million and a car GHG target of 172.8,
the formula would yield
Equation 11 - Impact of EV multiplier: example
GHG Target with multiplier =
(172.1 * (10.5 million+ 1.5 * 1.4% EV sales * 10.5 million sales))/10.5 million sales
= 173.2 or a delta of 1.2 grams.
4.3.5 Emission Results
4.3.5.1 Calendar Year Analyses
Table 4.3-17 Detailed Impacts of Program on GHG Emissions (MMT CO2eq)
Calendar Year:
Net Delta*
Net CO2
Net other GHG
Downstream
CO2 (excluding A/C)
A/C - indirect CO2
A/C - direct HFCs
CH4 (rebound effect)
N2O (rebound effect)
Gasoline Upstream
CO2
CH4
N2O
Electricity Upstream
CO2
CH4
N2O
2020
-27
-23
-4
-22
-18
-1
-3
0
0
-5
-5
-1
0
1
1
0
0
2030
-271
-247
-25
-223
-201
-3
-19
0
0
-57
-50
-7
0
9
7
1
0
2040
-455
-417
-38
-374
-341
-4
-28
0
0
-96
-84
-12
0
15
13
2
0
2050
-569
-522
-47
-467
-428
-5
-35
0
0
-121
-105
-15
-1
19
16
3
0
4-133
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Chapter 4
Table 4.3-18 Impacts of Program on GHG Emissions in all CYs
CY
2017
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
CO2
(MMT)
-2
-7
-14
-23
-37
-54
-75
-99
-125
-151
-176
-200
-224
-247
-268
-289
-309
-327
-344
-361
-376
-391
-404
-417
-429
-440
-451
-462
-472
-482
-492
-502
-512
-522
HFC134a
(MMT CO2eq)
0
-1
-2
-3
-5
-7
-8
-10
-12
-13
-15
-16
-17
-19
-20
-21
-22
-23
-24
-25
-26
-27
-27
-28
-29
-29
-30
-31
-31
-32
-33
-33
-34
-35
CH4
(MMT CO2eq)
0
0
0
-1
-1
-1
-2
-2
-3
-4
-4
-5
-5
-6
-6
-7
-7
-8
-8
-8
-9
-9
-9
-10
-10
-10
-10
-11
-11
-11
-11
-12
-12
-12
N2O
(MMT CO2eq)
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.
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Total (MMT CO2eq)
-2
-8
-16
-27
-43
-63
-85
-111
-140
-167
-195
-221
-247
-271
-295
-317
-338
-358
-377
-394
-411
-427
-441
-455
-468
-480
-492
-504
-515
-526
-537
-548
-558
-569
4-134
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MY 2017 and Later Regulatory Impact Analysis
Table 4.3-19 Annual Criteria Pollutant Emission Impacts of Program (short tons)
Total
Downstream
Fuel Production
and Distribution
Electricity
Pollutant
VOC
CO
NOX
PM2.5
SOX
VOC
CO
NOX
PM2.5
SOX
VOC
CO
NOX
PM2.5
SOX
VOC
CO
NOX
PM2.5
SOX
CY 2020
Impacts
(Short Tons)
-11,712
14,164
-904
-136
-1,270
249
14,414
498
40
-420
-12,043
-749
-1,757
-280
-1,198
81
499
355
104
348
% of Total
US Inventory
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
CY 2030
Impacts
(Short Tons)
-123,070
224,875
-6,509
-1,254
-13,377
4,835
227,250
8,281
568
-4,498
-128,823
-8,009
-18,795
-3,000
-12,813
917
5,634
4,005
1,179
3,933
% of Total
US Inventory
-1.0%
0.4%
-0.1%
0.0%
-0.2%
0.0%
0.4%
0.1%
0.0%
-0.1%
-1.0%
0.0%
-0.2%
-0.1%
-0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
4-135
-------
Chapter 4
Table 4.3-20 Annual Air Toxic Emission Impacts of Program (short tons)
Total
Downstream
Fuel Production
and Distribution
Electricity
Pollutant
1,3- Butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
1,3- Butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
1,3- Butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
1,3- Butadiene
Acetaldehyde
Acrolein
Benzene
Formaldehyde
CY 2020
Impacts
(Short Tons)
1
3
0
-16
-7
1
4
0
8
3
0
-1
0
-24
-10
0
0
0
0
0
% of Total
US Inventory
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
CY 2030
Impacts
(Short Tons)
25
57
2
-101
-43
28
70
3
160
66
-2
-14
_2
-261
-110
0
1
1
0
1
% of Total
US Inventory
0.2%
0.1%
0.0%
0.0%
0.0%
0.2%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
-0.1%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
4.3.5.2 Model Year Lifetime Analyses
Table 4.3-21 Projected Net GHG Deltas (MMTCO2eq per model year lifetime)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
Downstream
-25
-58
-89
-124
-178
-222
-262
-304
-347
-1,610
Upstream
(Gasoline)
-6
-14
-21
-29
-43
-55
-66
-78
-90
-402
Electricity
i
2
3
4
5
7
9
11
14
57
Total
CO2e
-30
-70
-108
-149
-216
-270
-320
-371
-423
-1,956
4-136
-------
MY 2017 and Later Regulatory Impact Analysis
Table 4.3-22 Projected Net Non-GHG Deltas
Criteria Emission Impacts of Program (short tons)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
VOC
-12,972
-28,424
-44,042
-61,383
-90,206
-115,470
-138,798
-163,022
-187,348
-841,664
CO
36,172
78,396
120,847
167,694
243,828
311,003
372,813
436,805
500,822
2,268,380
NOx
-258
-625
-991
-1,404
-2,366
-2,861
-3,216
-3,561
-3,871
-19,151
PM2.5
-102
-245
-384
-539
-884
-1,075
-1,219
-1,358
-1,484
-7,292
SQ2TTTT
-1,446
-3,247
-5,047
-7,042
-10,672
-13,471
-15,947
-18,479
-20,972
-96,322
Model Year Lifetime Air Toxic Emissions (short tons)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Benzene
7
14
21
29
38
48
58
69
79
364
1,3 Butadiene
5
12
18
25
36
46
55
64
73
332
Formaldehyde
2
3
5
6
8
10
11
13
14
72
Acetaldehyde
13
27
42
58
84
107
128
150
171
778
Acrolein
1
1
2
2
3
4
5
6
7
33
4.3.6 Fuel Consumption Impacts
The fuel consumption analyses relied on the same set of fleet and activity inputs as the
emission analysis. Because the OMEGA modeled penetrations of diesel technology are small
(<1% in MY 2025), EPA modeled the entire fleet as using petroleum gasoline, and used a
conversion factor of 8887 grams of CC^per gallon petroleum gasoline in order to determine
the quantity of fuel savings. The term petroleum gasoline is used here to mean fuel with
115,000 BTU/gallon. This is different than retail fuel, which is typically blended with ethanol
and has a lower energy content.uuuu This topic is further discussed in Joint TSD 4. A brief
memorandum discussing the differences in the agencies' calendar year analyses have been
placed in the EPA docket.
TTTT -
Note that one source of SO2 emission reductions are a result of the reduction in gasoline fuel use. Existing
EPA regulations require that highway gasoline fuel must not contain more than SOppm sulfur, and the average
content must be 30ppm sulfur.
sjmjjarjy assumes a value of 10,180 grams of CO2 per gallon of diesel fuel.
4-137
-------
Chapter 4
Table 4.3-23 Calendar Year Fuel Consumption Impacts
CY
2017
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
Sum 2017-2050
Fuel Delta
(Billion Gallons
petroleum gasoline)
0
-1
-1
-2
-3
-5
-7
-9
-12
-14
-16
-19
-21
-23
-25
-27
-29
-30
-32
-34
-35
-36
-38
-39
-40
-41
-42
-43
-44
-45
-46
-47
-48
-49
-903
Fuel Delta (Billion
Barrels petroleum
gasoline)
0.0
0.0
0.0
-0.1
-0.1
-0.1
-0.2
-0.2
-0.3
-0.3
-0.4
-0.4
-0.5
-0.5
-0.6
-0.6
-0.7
-0.7
-0.8
-0.8
-0.8
-0.9
-0.9
-0.9
-1.0
-1.0
-1.0
-1.0
-1.0
-1.1
-1.1
-1.1
-1.1
-1.2
-22
Electricity Delta
(Billion kwh)
0
0
1
1
2
3
4
6
7
9
10
12
14
15
17
18
19
20
22
23
24
25
26
27
27
28
29
29
30
31
31
32
33
33
607
4-138
-------
MY 2017 and Later Regulatory Impact Analysis
Table 4.3-24 Model Year Fuel Consumption Impacts
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Fuel Delta
( Billion
Gallons
petroleum
gasoline)
-3
-5
-9
-12
-17
-22
-27
-31
-36
-163
Fuel Delta
(Billion
Barrels
petroleum
gasoline)
-0.1
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.9
-3.9
Electricity Delta
(Billion kwh)
2
3
5
7
9
13
16
20
24
100
4.3.7 GHG and Fuel Consumption Impacts from Alternatives
Table 4.3-25 Calendar Year Impacts of Alternative Scenarios
Scenario
Primary
A - Cars +20
g/mile
B - Cars -20
g/mile
C - Trucks +20
g/mile
D - Trucks -20
g/mile
GHG Delta
(MMT2 CO2eq)
2020
-27
-19
-34
-27
-36
2030
-271
-223
-311
-249
-294
2040
-455
-382
-514
-420
-484
2050
-569
-480
-641
-526
-604
Fuel Savings
(B. Gallons petroleum
gasoline)
2020
_2
-1
-3
_2
-3
2030
-23
-18
-28
-21
-25
2040
-39
-32
-46
-36
-42
2050
-49
-40
-58
-45
-53
4-139
-------
Chapter 4
Table 4.3-26 Model Year Lifetime Impacts of Alternative Scenarios
(Summary of MY 2017-MY2025)
Primary
A - Cars +20 g/mile
B - Cars -20 g/mile
C - Trucks +20 g/mile
D - Trucks -20 g/mile
Total
CO2e
-1,956
-1,537
-2,314
-1,781
-2,231
Fuel Delta
(bgal
petroleum
gasoline)
-163
-122
-200
-146
-189
Fuel Delta
(b. barrels
petroleum
gasoline)
-3.9
-2.9
-4.8
-3.5
-4.5
4.4 Safety Analysis
As described in Preamble Section II.G and RIA Chapter 3, EPA used the OMEGA
model to conduct a similar analysis of the impacts of mass reduction on vehicle safety. After
applying these percentage increases to the estimated weight reductions per vehicle size by
model year assumed in the OMEGA model, Table 6-6 shows the results of EPA's safety
analysis separately for each model year. These are estimated increases or decreases in
fatalities over the lifetime of the model year fleet. A positive number means that fatalities are
projected to increase; a negative number means that fatalities are projected to decrease. For
details, see the EPA RIA Chapter 3.
Table 4.4-1 - Summary of Fatality Analysis
Reference
Case
Control
Case
Delta
Passenger
cars
Light
trucks
Total
Passenger
cars
Light
trucks
Total
Passenger
cars
Light
trucks
Total
MY
2017
61
-105
-44
65
-110
-45
5
-5
-1
MY
2018
61
-101
-40
71
-112
-41
9
-11
-1
MY
2019
63
-99
-36
78
-115
-38
14
-16
-2
MY
2020
66
-99
-33
86
-121
-35
20
-22
-2
MY
2021
69
-100
-31
95
-128
-34
26
-29
-3
MY
2022
71
-100
-29
101
-141
-40
30
-40
-10
MY
2023
73
-100
-27
108
-152
-45
35
-52
-18
MY
2024
76
-100
-24
115
-164
-49
40
-64
-25
MY
2025
78
-101
-22
123
-178
-55
45
-77
-32
Total
618
-905
-286
842
-1,222
-381
223
-317
-94
4-140
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MY 2017 and Later Regulatory Impact Analysis
4.5 Sensitivity Cases
4.5.1 Rebound
EPA conducted a sensitivity analysis regarding the GHG and fuel savings benefits of
the program under different rebound rates.
As discussed in TSD 4, the rebound effect refers to the increase in vehicle use that
results if an increase in fuel efficiency lowers the cost per mile of driving, which can
encourage people to drive slightly more. The rebound effect is measured directly by
estimating the change in vehicle use, often expressed in terms of vehicle miles traveled
(VMT), with respect to changes in vehicle fuel efficiency.vvvv However, it is a common
practice in the literature to measure the rebound effect by estimating the change in vehicle use
with respect to the fuel cost per mile driven, which depends on both vehicle fuel efficiency
and fuel prices.**** When expressed as a positive percentage, these two parameters give the
ratio of the percentage increase in vehicle use that results from a percentage increase in fuel
efficiency or reduction in fuel cost per mile, respectively. For example, the 10 percent
rebound effect we assume in this final rulemaking means that a 10 percent decrease in fuel
cost per mile is expected to result in a 1 percent increase in VMT.
xxxx
Table 4.5-1 - Rebound Sensitivity Results
Rebound
Rate
0%
5%
10%
15%
20%
MY Lifetime
2017-2025
GHG Benefits
(MMT CO2e)
2,115
2,035
1,956
1,877
1,798
Fuel Savings
(B. Gallons)
176
169
163
156
149
CY 2030
GHG Benefits
(MMT CO2e)
292
282
271
261
250
Fuel Savings
(B. Gallons)
25
24
23
22
21
4.5.2 EV impacts
In section III.C.2 of the preamble, as in the NPRM, EPA presents an analysis of the
GHG impacts of the EV zero gram/mile and EV/PHEV multiplier impacts on the cumulative
fuei efficiency is mOre often measured in terms of fuel consumption (gallons per mile) rather than
fuel economy (miles per gallon) in rebound estimates.
**** Fuel cost per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per
gallon (or multiplied by fuel consumption in gallons per mile), so this figure declines when a vehicle's fuel
efficiency increases.
xxxx
Please note that increasing VMT by 1% in response to a 10% decrease in fuel cost per mile is not
equivalent to decreasing the benefits from the rule by 1% due to the decreased fuel consumption and GHG
emissions in the control case. To a lesser extent, the issue is also complicated due to compliance strategies that
do not directly impact fuel cost per mile, such as HFC emission reduction strategies, and the use of electric
vehicles for compliance, which do not reduce cost per mile to the same extent that gasoline technologies do.
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GHG savings from the fleet. This projection of the impact of the EV/PHEV/FCV incentives
on the overall program GHG emissions reductions assumes that EPA would have finalized
exactly the same standard if the 0 gram per mile compliance value were not allowed for any
EV/PHEV/FCVs. While EPA has not analyzed such a scenario, not allowing a 0 gram per
mile compliance value would change the technology mix and cost projected for the standard.
To conduct this analysis, EPA first ran the OMEGA model post-processor assuming
that no vehicles operated on wall electricity. The OMEGA scenario results were drawn from
the primary analysis, but were adjusted as for a different ratio of EVs and PHEVs, as
discussed below. The sensitivity scenario, involving 2 million EVs and PHEVs sold from
2022-2025, was modeled through the same method as the proposal. The EV phase in schedule
from the primary scenario was multiplied by -1.82 in order to produce the phase-in
corresponding to 2.0 million EVs sold in 2022-2025. 2 cycle performance was then adjusted
accordingly for the multiplier credits and electricity usage was included in the accounting.
As in the proposal, for this analysis, we assumed that 50% of the plug-in vehicles
would be PHEVs, and subtracted 25% from the total impacts of the EVs and PHEVs in order
to approximate the lesser reliance of PHEVs on electric power.
If EPA established the exact same tailpipe standards, and provided no additional
flexibilities, the program impacts would be estimated at 2,032 MMT between MY 2017 and
MY 2025 if there were no electric vehicles or plug-in electric vehicles used for compliance.
Scenario
No EV/PHEVs
EPA OMEGA
model
Projection
Sensitivity
Scenario
Cumulative
EV/PHEV sales
MYs 2017-2025
0
1.5 million
2.8 million
Cumulative
EV/PHEV sales
MYs 2022-2025
0
1.1 million
2.0 million
Cumulative
Decrease in
GHG Emission
Reductions MYs
2017-2025
0
56 MMT
101 MMT
Percentage
Decrease in
GHG
Emission
Reductions
MYs 2017-
2025
0
2.7%
5.0%
4.6 Inventories Used for Non-GHG Air Quality Modeling
Because air quality analysis requires emission inventories with greater geographical
resolution than the national inventories described above, these air quality inventories were
developed separately. For this analysis, we needed three air quality inventories: a 2005
baseline inventory, a 2030 reference inventory and a 2030 control inventory. As described
above, the sectors that are impacted by the rule are the "downstream" emissions from light-
duty onroad vehicles affected directly by the regulations and the "upstream" emissions that
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are affected by changes in fuel usage. Other sectors are not changed by the rule and are
described in a technical support document.53
4.6.1 Onroad Vehicles
As summarized in Section 4.3.1, non-GHG emissions from light duty vehicles are
affected by rebound VMT and by reduced fuel consumption. For the air quality inventories
(except refueling as described below), we modeled these effects using existing air quality
inventories created for the Heavy Duty Greenhouse Gas rule signed August 9, 2011.54 This
allowed us to account for the impacts of the HDGHG rule in both our control and reference
case. In particular, for the 2005 base case, we used the 2005 base case emissions from the
HDGHG analysis and for the 2030 reference case, we used the control case from the HDGHG
analysis. For the 2030 control case for this rule, we modified the HDGHG control case to
account for rebound and fuel consumption effects.
To model the effect of rebound on non-GHG emissions, we started with the VMT
changes by model year and vehicle type as predicted in the VMT equation in Section4.3.2.3.
For each model year and vehicle type, the multiplicative change in VMT due to rebound was
multiplied by emissions by model year (from a 2030 national default run of the
MOVES2010a model) to estimate the predicted new emissions for each pollutant. These
original emissions and the new emissions were summed across all model years and the ratio
of the two totals was computed for each pollutant and vehicle type. This ratio was then
applied to the grid-level reference inventory for running emissions, start emissions, brake
wear and tire wear. No rebound effect was applied to vapor venting, permeation or liquid leak
emissions.
Similarly, the effect of reduced fuel consumption on emissions of sulfate and sulfur
dioxide was estimated by model year and applied to MOVES 201 Ob results by model year.
The emissions were summed and the ratio of the resulting emissions was applied to the grid-
level reference inventories for these pollutants.
The effect of reduced fuel consumption on refueling emissions was calculated
separately. A modified draft version of MOVES2010b was run to generate reference and
control refueling emissions at the national level, The reference case emissions were generated
using VMT and energy consumption estimates from the analysis for the rule. The calculated
effects of these changes at the national level were then applied to county-level emissions
calculated by running a draft version of MOVES2010b at the county-month level.
These impacts are summarized in Table 4.6-1 below.
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Table 4.6-1 Air Quality Inventory Impacts for Vehicle Emissions (2030)
Pollutant
PM2.5
PM10
NOX
VOC
CO
SOX
NH3
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Reference
58100
121011
1609568
935118
18023434
22742
95331
9677
746
19284
3064
15713
Control
56794
120504
1618449
941090
18278399
18625
96909
9752
750
19483
3098
15785
Difference
-1306
-507
8881
5972
254965
-4117
1578
75
3
199
34
72
Percent
Difference
-2.2%
-0.4%
0.6%
0.6%
1.4%
-18.1%
1.7%
0.8%
0.5%
1.0%
1.1%
0.5%
4.6.2 Fuel Production and Distribution
In addition to the effects of improved fuel economy on emissions from vehicles and
equipment, and EGU emissions associated with increases in electric vehicles, there are
reductions in emissions associated with domestic crude production and transport, petroleum
refineries, production of energy for refinery use, vapor losses from transfer and storage of
gasoline and gasoline/ethanol blends, and combustion emissions associated with transport of
gasoline from refineries to bulk terminals and bulk terminals to service stations. The air
quality inventories for this rule account for all these impacts except for combustion emissions
associated with transport of crude oil.
4.6.2.1 Domestic Crude Production and Losses During Transport to Refineries
To obtain the reference case inventory, we applied adjustments to emissions from the
version 4 2005-based EPA air quality modeling platform,YYYY to account for the impacts of
medium- and heavy-duty greenhouse gas emissions and fuel efficiency standards. The
YYYY -p^e ^ quaijty mO(jeiing platform represents a structured system of connected modeling-related tools and
data that provide a consistent and transparent basis for assessing the air quality response to projected changes in
emissions. The 2005-based CMAQ modeling platform was developed by the U.S. EPA's Office of Air Quality
Planning and Standards in collaboration with the Office of Research and Development and is intended to support
a variety of regulatory and research model applications and analyses.
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platform assumed implementation of 2012-2016 light-duty vehicle fuel economy standards.
The control case inventory reflects the emission standards being finalized in this rule.2222
Consistent with the regulatory impact analysis for the recent EPA final rule
establishing greenhouse gas emissions standards for medium- and heavy-duty engines and
vehicles,55 we assumed 50% of the change in gasoline and diesel supply was projected to
come from domestic refineries, and (b) 10% of the change in crude being used by domestic
refineries would be domestic crude. Using the assumption that 1.0 gallon less of gasoline
equates to approximately 1.0 gallon less crude throughput, the reduction in crude extraction
and transport from this rule would equal about 5% of the change in gasoline volume. Since
the reduction in fuel consumption is estimated at 6.02 billion gallons for the medium- and
heavy-duty greenhouse gas rule and 31.6 billion gallons for this rule, the reduction in crude
production is about 0.3 billion gallons for the medium and heavy-duty rule and 1.58 billion
gallons for this rule. To generate the emission inventory adjustment factors for air quality
modeling these reductions were applied to the projected crude supply of 230 billion gallons to
US refineries in 2030, per AEO 2011.56 Thus, the adjustment factors are 0.13% and 0.68%
for the two rules, respectively.
4.6.2.2 Petroleum Production and Refining Emissions
•&
The petroleum refinery inventory in the modeling platform was adjusted to account for
the impacts of ethanol production due to EISA and medium- and heavy-duty greenhouse gas
emissions and fuel efficiency standards. The impacts spreadsheet, originally developed for
the RFS2 rule, was used to develop these adjustments.57'58 This spreadsheet uses emission
factors and changes in fuel volumes and energy throughput to estimate total nationwide
emission impacts on refinery emissions associated with gasoline and diesel production. This
spreadsheet estimated that refinery emissions associated with gasoline and diesel production
would decrease by 12% as a result of the greenhouse gas emissions standards and fuel
efficiency standards for medium- and heavy-duty engines and vehicles, and another 21% as a
result of the standards in this rule. 76% of refinery emissions in the modeling platform were
estimated to be the result of gasoline and diesel production, based on petroleum refinery
output estimates from Energy Information Administration and emission rates associated with
producing various refinery products obtained from GREET 20II.59'60 The impacts of
decreased production were applied only to the portion of refinery emissions associated with
gasoline and diesel production. They were also assumed to be spread evenly across all U. S.
refineries.
4.6.2.3 Production of Energy for Refinery Use
The fuel efficiency standards being finalized in this rule not only impact on-site
refinery emissions, but also emissions upstream of refineries associated with producing the
2222 The reference case inventories for these sources do not account for the increased ethanol production impacts
of EISA. However, these sources are a minor portion of gasoline and diesel related air emissions, and do not
meaningfully impact the delta between the cases.
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energy they use. Refineries rely on upstream energy from residual oil, natural gas, coal and
electricity.
GREET l.S.c was used to adjust emission factors for refinery upstream emissions in
the impacts spreadsheet, with adjustments to emission rates for electricity to reflect the
incremental mix of EGU energy feedstocks assumed in the IPM analysis discussed in Section
4.6.3. Table 4.6-2 summarizes the emission rates for refinery upstream emissions in the
impacts spreadsheet, and percent of emission rates attributable to each type of input energy.
Table 4.6-2 Refinery Energy Use
Pollutant
VOC
CO
NOX
PM10
PM2.5
sox
Emission
Rate
(g/mmBTU)
0.622
1.069
2.960
4.445
1.158
2.398
Percent of Emission Rates from Energy Feedstocks
Residual Oil
5
5
7
0
1
4
Natural Gas
45
38
40
1
2
28
Coal
26
5
11
85
81
8
Electric
24
52
42
14
16
60
Table 4.6-3 presents the emission impacts for upstream refinery emissions. Along
with nationwide emissions for sources associated with producing these energy feedstocks in
the modeling platform, these impacts were used to develop nationwide scalars which were
applied to county and facility level emission estimates. The scalars used are given in Table
4.6-3 as well. It should be noted that the emission totals in the platform that scalars were
applied to reflect only point and nonpoint sources directly associated with producing these
energy feedstocks, and do not include emissions upstream of the feedstocks or from nonroad
equipment used in mining or natural gas extraction that may be accounted for in the impacts
spreadsheet.
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Table 4.6-3 Upstream Refinery Emission Impacts in Tons and Inventory Scalars
Pollutant
VOC
CO
NOX
PM10
PM25
sox
Residual Oil
Production
Impact
-51
-93
-351
-36
-17
-179
Scalar
0.9992
0.9990
0.9943
0.9997
0.9990
0.9913
Natural Gas
Production
Impact
-474
-676
-2010
-51
-39
-1168
Scalar
0.9883
0.9758
0.9583
0.9199
0.9353
0.9753
Coal Production
Impact
-270
-97
-551
-6366
-1584
-325
Scalar
0.9026
0.9817
0.9136
0.7195
0.7874
0.8947
Electricity
Production
Impact
-252
-932
-2070
-1027
-309
-2482
Scalar
0.9951
0.9991
0.9989
0.9966
0.9987
0.9988
4.6.2.4 Gasoline Transport, Storage and Distribution Emissions
Non-Combustion Emissions
VOC and benzene emissions are produced by transfer and storage activities associated
with distribution of gasoline. These are referred to as Stage I emissions. Stage I distribution
begins at the point the fuel leaves the production facility and ends when it is loaded into the
storage tanks at dispensing facilities. It does not include emissions associated with refueling
vehicles.
There are five types of facilities that make up this distribution chain for gasoline.
Bulk gasoline terminals are large storage facilities that are either collocated at refineries or
receive gasoline directly from the refineries via pipelines, barges, or tankers. Gasoline from
the bulk terminal storage tanks is loaded into cargo tanks (tank trucks or railcars) for
distribution to smaller intermediate storage facilities (bulk plants), or directly to gasoline
dispensing facilities (retail public service stations and private service stations). When ethanol
is blended into gasoline it usually occurs in the pipes which supply cargo tanks at bulk
terminals.
Bulk plants are intermediate storage and distribution facilities that normally receive
gasoline or gasoline/ethanol blends from bulk terminals via tank trucks or railcars. Gasoline
and gasoline/ethanol blends from bulk plants are subsequently loaded into tank trucks for
transport to local dispensing facilities.
Gasoline and gasoline/ethanol blend dispensing facilities include both retail public
outlets and private dispensing operations such as rental car agencies, fleet vehicle refueling
centers, and various government motor pool facilities. Dispensing facilities receive gasoline
and gasoline/ethanol blends via tank trucks from bulk terminals or bulk plants. Inventory
estimates for this source category only include the delivery of gasoline at dispensing facilities
and does not include the vehicle or equipment refueling activities.
Emissions from a version of the platform inventory adjusted to account for ethanol
production impacts of EISA were used to develop the reference and control case inventories.
Emissions were first partitioned into a refinery to bulk terminal component (RBT), a bulk
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Chapter 4
plant storage (EPS) component, and a bulk terminal to gasoline dispensing pump (BTP)
component. One set of scalars was applied to RBT/BPS emissions and another to BTP
emissions. These scalars are provided in Table 4.6-4. The scalars for BTP emissions reflect
the change in total gasoline plus ethanol volume in gasoline and gasoline/ethanol blends.
However, it does not account for changes in gasoline/ethanol blends used. Impacts were
assumed to be spread evenly across the U. S.
Table 4.6-4 Scalars Applied to Base Inventory (2005 Platform with EISA Impacts) to
Obtain Reference and Control Case Gasoline Storage, Transport and Distribution
Emissions
Process
Refinery to Bulk Terminal/
Bulk Plant Storage
Bulk Terminal to Pump
Reference Case (Impacts of
Medium- and Heavy-Duty
Greenhouse Gas Rule)
0.9972
0.9976
Control Case (Impacts of
Medium- and Heavy-Duty
Greenhouse Gas Rule Plus
this Rule)
0.7944
0.8234
Combustion Emissions
In addition to non-combustion emissions associated with storage, transport and
distribution, there are combustion emissions associated with transport of gasoline by pipeline,
commercial marine vessel, rail, and tanker truck. Overall impacts of the rule on combustion
emissions associated with transport were estimated using the impacts spreadsheet. The
overall impacts were allocated to transport mode using nationwide emission fractions from
GREET l.S.c. GREET provides emission fractions by transport mode for conventional
gasoline, Federal reformulated gasoline, and California reformulated gasoline. These were
weighted together using fuel sales volumes developed for highway vehicle modeling based on
data from the Energy Information Administration.61
Emission impacts by transport mode were then applied to total emissions from
transport sources to develop scaling factors. However, SOX emission impacts for heavy-duty
trucks, commercial marine vessels and locomotives were unreasonably high relative to the
total inventory; thus, we estimated scalars for this pollutant based on the average scalars for
other pollutants.
For pipelines, due to the difficulty in isolating the emissions along pipelines from
pumps and other equipment by SCC, we assigned impacts to refinery and bulk terminal SCCs.
Rail transport impacts were assigned to emissions from Class I and II line-haul locomotives,
commercial marine impacts to Cl and C2 marine vessels, and tanker truck impacts to Class 8
heavy-duty diesel vehicle emissions. Emission inventory impacts and inventory scalars are
given in Table 4.6-5.
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Table 4.6-5 Gasoline Transport Combustion Emission Impacts in Tons and Inventory
Scalars
Pollutant
VOC
CO
NOx
PM10
PM2.5
sox
Commercial
Marine Vessels
Impact
-159
-532
-1989
-76
-63
-32
Scalar
0.9776
0.9962
0.9933
0.9926
0.9936
0.9907
Pipelines
Impact
-287
-1447
-6137
-241
-130
-1292
Scalar
0.9959
0.9864
0.9192
0.9895
0.9932
0.9890
Rail
Impact
-46
-377
-859
-30
-22
-1
Scalar
0.9966
0.9979
0.9976
0.9958
0.9969
0.9969
Truck
Impact
-84
-241
-609
-61
-31
-172
Scalar
0.9979
0.9988
0.9985
0.9936
0.9968
0.9971
4.6.2.5 Fuel Production and Distribution Summaries
Table 4.6-6 provides 2030 air quality inventory impacts for fuel production and
distribution. Table 4.6-7 and Table 4.6-8 provide the percentage of these impacts by source
category. These impacts do not include combustion emission reductions for tanker trucks;
those impacts are reflected in the highway vehicle inventory totals. They also do not include
impacts on emissions from production of electricity used at refineries.
Table 4.6-6 Air Quality Inventory Impacts for Fuel Production and Distribution (2030)
Pollutant
PM2.5
PM10
NOX
VOC
CO
SOX
NH3
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Tons
-3663
-8509
-18391
-149398
-13918
-14748
0
-22
-1
-1503
-2
-720
Percent of Total Upstream
Inventory
0.2
0.4
0.3
1.7
0.1
0.3
0
0.05
0.02
1.3
0.04
0.2
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Chapter 4
Table 4.6-7 Percent
and
Contribution by Source Category to Reduction in Fuel Production
Distribution Emissions by Source Category in 2030
Source
Crude Oil
Production and
Transport
Petroleum
Production and
Refining
Production of
Energy for Refinery
Use
Gasoline Transport,
Storage and
Distribution, Non-
Combustion
Gasoline Transport,
Storage and
Distribution,
Combustion from
Locomotive and
Marine Engines
voc
0.9
4.8
0.3
93.9
0.1
CO
0
89.3
4.0
0
6.7
NOX
0
75.0
8.6
0
16.4
PM10
0
30.4
68.5
0
1.1
PM2.5
0
56.6
41.1
0
2.3
sox
0
89.7
10.1
0
0.2
NH3
0
0
0
0
0
Table 4.6-8 Percent Contribution by Source Category to Reduction in Fuel Production
and Distribution Emissions by Source Category in 2030 (continued)
Source
Crude Oil Production
and Transport
Petroleum Production
and Refining
Production of Energy
for Refinery Use
Gasoline Transport,
Storage and
Distribution, Non-
Combustion
Gasoline Transport,
Storage and
Distribution,
Combustion from
Locomotive and
Marine Engines
Combustion
1,3-
Butadiene
0
90.4
1.1
0
8.5
Acetaldehyde
0.1
10.8
12
0
77.1
Acrolein
0
24.8
1.2
0
74.0
Benzene
0.7
26.5
0.6
71.8
0.3
Formaldehyde
0.1
93.2
1.5
0
5.3
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4.6.3 Estimate of Emissions from Changes in Electricity Generation
4.6.3.1 The IPM model
As is typical in EPA air quality modeling, we used the Integrated Planning Model
(IPM) to estimate upstream emissions from electric power plants. In this case, we ran two
scenarios, with a reference scenario based upon the IPM Final Mercury and Air Toxics
Standards (MATS)62 and another with additional load associated with electric vehicle
charging in the FRM. This differs from the NPRM, where we estimated impacts based on
national average emissions.63
While this section is not intended to be a thorough discussion of the IPM, additional
information can be seen at the EPA IPM website, and in the model documentation.64
EPA uses IPM to analyze the projected impact of environmental policies on the
electric power sector in the 48 contiguous states and the District of Columbia. IPM is a multi-
regional, dynamic, deterministic linear programming model of the U.S. electric power sector.
It provides forecasts of least-cost capacity expansion, electricity dispatch, and emission
control strategies for meeting energy demand and environmental, transmission, dispatch, and
reliability constraints. IPM can be used to evaluate the cost and emissions impacts of
proposed policies to limit emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), carbon
dioxide (CO2), and mercury (Hg) from the electric power sector. The model is used by EPA
for rulemaking purposes and has been used to support analysis for the Cross-State Air
Pollution Rule (CSAPR),65, as well as the proposed EGU GHG NSPS66, Final Mercury and
Air Toxics Standards (MATS),67 and Climate Change and Multi-Pollutant Legislative
Proposals.68
IPM generates optimal decisions under the assumption of perfect foresight,
determining the least-cost method of meeting energy and peak demand requirements over a
specified period (e.g. 2010 to 2030). In its solution, the model considers a number of key
operating or regulatory constraints (e.g. emission limits, transmission capabilities, renewable
generation requirements, fuel market constraints) that are placed on the power, emissions, and
fuel markets.
IPM represents the U.S. electric power grid through 32 model regions that are
geographical entities with distinct characteristics (See Figure 4.6-9). For example, the model
regions representing the U.S. power market correspond broadly to regions and sub-regions
constituting the North American Electric Reliability Council (NERC) regions as well as with
the organizational structures of the Regional Transmission Organizations (RTOs) and
Independent System Operators, which handle dispatch on most of the U.S. grid. In some
cases, these NERC regions are further subdivided in IPM into sub-regions to provide higher
resolution. For instance, NERC depicts much of the Western U.S. as a single region, the
Western Electricity Coordinating Council (WECC), whereas IPM depicts this region as
several distinct sub-regions, such as Northern California, Southern California, the Pacific
Northwest, and Arizona and New Mexico. Each of these IPM region have its own set of
unique electric power generations characteristics and electricity transmission limitations. For
instance, unlike neighboring regions, much electricity generated in the Pacific Northwest
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comes from hydroelectric power plants. As such, this region is modeled differently than
Southern California, which imports much of its electricity from Arizona and Nevada.
However, electricity transmission from Nevada to Southern California is limited by number
and size of high voltage electric power lines strung across the adjacent mountain ranges.
Figure 4.6-9 IPM regions comprising the U.S.
EPA Base Case v4.0 U.S. Regions
ICF20081222SYK003
4.6.3.2 Dispatch method as compared to a national average method
IPM estimates the electric demand, generation, transmission, and distribution within
each region as well as the inter-regional transmission grid. All existing utility power
generation units, including renewable resources, are modeled, as well as independent power
producers and cogeneration facilities that sell electricity to the grid.
To accomplish this, the model incorporates detailed representations of new and
existing resource options, including fossil generating options (coal steam, gas-fired simple
cycle combustion turbines, combined cycles, and oil/gas steam), nuclear generating options,
and renewable and non-conventional (e.g., fuel cells) resources. Renewable resource options
include wind, geothermal, solar thermal, solar photovoltaic and biomass. With these inputs,
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IPM selects the least-cost method for meeting energy and peak demand subject to the
constraints specified above, providing estimates of the associated electric power plant
emissions and costs. The least cost method may include generating power from existing
plants, or making the decision to dispatch (or build) new plants.
This least-cost approach to estimating upstream emissions from electric power plants
differs from the approach used in the NPRM, which was based on national average
emissions.69 There are several shortcomings are associated with the use of national average
emissions. First, it is not a least-cost approach. Electric utilities typically employ least-cost
approaches to dispatch electric power plants to meet the electric power demands of the
ratepayer. With a least-cost approach like IPM, the model selects from thousands of
candidate electric power plants to meet the demand imposed by charging electric vehicles.
Some selections may be above the national average for emissions - such as coal-fired electric
power plants - while others may be considerably below the national average for emissions,
such as wind turbines. Regardless of the relative emissions, the selection made by IPM is the
least-cost selection and, thereby, minimizing costs to the ratepayer. Not using a least-cost
approach is economically inefficient since it does not minimize costs to the ratepayer and
implies higher-than-necessary electricity prices.
Secondly, demand for electricity varies with time; daytime peaks in electricity demand
are considerably higher than nighttime demand. Likewise, the electric power plants tasked to
meet this demand will vary considerably with time as will the emissions from these plants
(See Figure 4.7-2). Base Load plants, such as coal and nuclear, have limited ability and/or
incentive to vary electric power output. Therefore, these plants typically run at full capacity
with little variation in emissions. Renewable electric power plants, such as wind turbines,
also run at full capacity whenever available, but have no emissions. Depending upon the time
of day, it is possible that emissions associated with a charging PHEV/EVs may be non-
existent, as in the case of nuclear or wind power plants, or high, as in the case of coal-fired
electric power plants. Similarly, the ability to vary electric power output for Intermediate
Load and Peak Load power plants vary considerably with time as does their associated
emissions. As such, a national average emissions approach fails to capture the time-varying
nature of electricity demand.
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Chapter 4
Figure 4.6-10 Time-varying nature of electric power generation
6AM Noon Midnight Noon 6AM
Thirdly, a national average emissions approach fails to capture the location-varying
nature of electricity generation; the location of electric power plants - and their associated
emissions - varies with the availability of fuel sources. Coal-fired electric power plants in
Appalachia, for instance, will have considerable higher emissions that electricity generated in
hydroelectric power plants in the Pacific Northwest. This is relevant because PHEV/EV
distribution - as with fuel sources - are not uniform across the nation. As such, PHEV/EVs in
one region of the nation will be typically charged from electric power plants within that
region. A location-varying approach to estimating upstream emissions from electric power
plants can capture the impact of such variations whereas a national average approach, by
definition, cannot.
Finally, a national average emissions approach fails to capture regional constraints
within the high-voltage transmission system. Despite the interconnected nature of much of the
U.S. electric power grid, electricity generated in one region may not necessarily leave the
region in which it was generated due to the location and capacity of high-voltage transmission
lines. If it does, the flow of that power is greatly limited by the location and capacity of high-
voltage transmission lines, called "regional interties". For instance, southern California
(designated as "CA-S" in Figure 4.7-3) is connected to southern Nevada ("SNV"), Arizona
("AZNM"), the Northwest Power Pool - East ("NWPE"), and northern California (CA-N) by
way of regional interties. However, the availability of unused generation capacity, coupled
with congestion on the regional interties, can severely limit the importation of electricity. For
these reasons, electricity is often imported to southern California from southern Nevada. As
such, the electric power plant emissions associated with electricity consumed in southern
California is physically deferred to southern Nevada. In the U.S. electric power grid, there
exist several similar examples of deferred electric power plant emissions. For instance, the
electric power plant emissions from the regions that comprise New York City are deferred to
other regions simply because there is not enough generation capacity within New York City
to meet its demand.
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Figure 4.6-11 Regional constraints in high-voltage transmission for southern California
Where Will Electricity to Charge Vehicles in Southern California
PowerCome From in 2025?
4.6.3.3 Estimating Nationwide Power Estimates for PHEV/EVs
To estimate upstream electric power plant emissions associated with electric vehicle
charging, we used OMEGA to estimate the nationwide PHEV/EV energy requirements for a
total of eight OMEGA vehicle types considered likely candidates for electrification as electric
vehicles or as PHEVs years 2011-2025 and 2025-2050. For years 2011-2025, we interpolated
from a PHEV/EV fleet size of zero vehicles in 2011 to a 2025 sales volume size estimated by
OMEGA using the NPRM estimates (approximately 3%).70 We used the NPRM modeling of
electricity demand as an input to the IPM modeling. For 2025-2050, this method assumed
that the degree of PHEV/EV technology penetration stabilized at 2025 levels, so that electric
vehicle fleet growth between the years 2025-2050 are attributed to the turnover of electrified
and non-electrified portions of the vehicle fleet.
In the NPRM analysis, eight OMEGA NPRM vehicle types - subcompact (type 1),
small car (type 2 & 3), large car (type 5 & 6), minivan and small truck (types 4 & 8), and
minivan and and large truck (types 7 & 15) - were considered potential candidates as electric
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vehicles or as PHEVs. The energy demand associated with each of these vehicles is discussed
in joint TSD 3. Larger vehicles, such as pickup trucks with heavy-duty towing packages
capable of hauling large travel trailers or power boats, were not considered potential
candidates for electrification in the NPRM analysis, and were not projected as a portion of the
electric vehicle demand. As such, we did not make an attempt to model the electric power
consumption of these vehicle types. The OMEGA output yielded estimates for nationwide
electric power demand associated with charging vehicles (See Table 4.6-12).
Table 4.6-12 OMEGA NPRM projections for incremental PHEV/EV charging loads
IPM Model Run Year
2017
2020
2030
2040
2050
Total Electricity for
PHEV/EV Charging
(kwh)
123,800,453
1,265,251,557
26,126,391,091
45,558,246,553
54,971,493,663
4.6.3.4
Distribution of Nationwide Power Estimates to IPM Regions
We distributed the nationwide estimates of PHEV/EV energy requirements across
each of the 32 IPM model regions on the basis of publically-available annual HEV sales for
2006-2009 from Polk and Wards.71 EPA judged this a reasonable proxy for the initial
distribution of electric vehicles. These vehicles are unlikely to be evenly distributed with
population, given their particular attributes, and the HEV distribution offers likely parallels.
There was little PHEV/EV sales data when our modeling efforts started, and as of 2012, few
models are on the market. As such, we used annual HEV sales data to provide a reasonable
state-by-state basis for a distribution of EVs and PHEVs across the country.
However, it was necessary to apportion the sales across IPM regions. If a state
resides completely within an IPM region, all of the annual HEV sales for 2006-2009 were
attributed to that particular IPM region. For instance, all of Minnesota resides within the IPM
region MRO (Midwest Regional Planning Organization). As such, all annual HEV sales for
2006-2009 for the state of Minnesota were attributed to the MRO region for modeling
purposes.
However, state boundaries did not necessarily coincide with IPM region boundaries.
In cases in which a state resides in more than one IPM region, the vehicles were assumed to
be located in the counties that comprised the state's top Metropolitan Statistical Areas (MSA)
as of 2008. These vehicles were allocated based upon the number of counties in the MSA that
resides in each of the IPM regions. For instance, Chicago-Joliet-Naperville is the top MSA in
Illinois. It consists of 14 counties and spans the IPM regions of COMD (Commonwealth
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Edison), RFCO (Reliability First Corporation), and WUMS (Wisconsin-Upper Michigan).
Nine of the fourteen counties fall into the COMD region, four counties fall into the RFCO
region, and one county falls into the WUMS region. The annual HEV sales for 2006-2009 for
the Chicago-Joliet-Naperville MSA was then apportioned based upon the county's population
density. In this case, 9/14th of the MSA's vehicle sales attributed to the COMD region, 4714th
of the MSA's vehicle sales attributed to the RFCO region, and l/14th of the MSA's vehicle
sales attributed to the WUMS region. Similar proportions were developed for each of the
remaining IPM regions. These proportions were applied to the nationwide PHEV/EV energy
requirements developed in OMEGA.
Table 4.6-13 Distribution of nationwide power estimates to IPM regions
IPM Region
AZNM
CA-N
CA-S
COMD
ENTG
ENTG
ERCT
FRCC
GWAY
LILC
MACE
MACS
MACW
MECS
MRO
NENG
NWPE
NYC
PNW
RFCO
RFCP
RMPA
SNV
SOU
SPPN
SPPS
TVA
TVAK
UPNY
VACA
VAPW
Average Annual HEV
Sales (2006-2009)
9,793
32,401
42,654
6,016
5,539
5,539
14,562
17,727
3,266
1,673
19,023
6,637
4,834
7,477
7,218
15,652
2,264
4,182
16,515
10,976
4,813
7,387
1,623
7,393
2,689
3,267
3,575
2,051
2,904
12,483
9,740
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4.6.3.5
Generation of PHEV/EV Charging Profiles
The charging of PHEV/EV varies by time of day. However, very little historic data on
the time of day that electric vehicle owners charged their vehicles was available when this
analysis was first started. As such, we developed an electric vehicle charging profile which
varies by time of day assuming that 25% of the charging will occur between the hours of 6:00
AM and 7:00 PM. We term this period the "on-peak" period. Charging rate during this time
is assumed to be uniform; that is, the same amount of charging is expected to occur for the
one-hour period starting at, say, 6:00 AM as would be expected to occur for a one-hour period
starting at 4:00 PM. The remaining 75% of PHEV/EV charging is expected to occur between
the hours of 7:00 PM and 6:00 AM. We term this period as the "off-peak" period and this
charging profile is distributed as a Gaussian-like distribution (See Figure 1-6). In this way,
both the on-peak and off-peak charging profiles were mathematically defined; should
charging profiles based upon historical data be found to differ from this profile, the impact of
these real-life deviations could be better diagnosed.
Figure 4.6-14 PHEV/EV Charging Profile by Time of Day
w 10%
0-00 3:00 6:00 9-00 12-00 15:00 12:00 21-0
Time of Day {hours)
Subsequent to our analysis, electric vehicle and charging infrastructure data for DOE's
"EV Project" became available. This actual charging data was found to be were largely
consistent (within a few percent) with both of the on-peak and off-peak charging profiles
developed for our analysis.72 These charging profiles are input into IPM, which uses the
profiles to estimate associated incremental emissions and price impacts.
4.6.3.6
Results
IPM uses these charging profiles and total demand to estimate associated incremental
emissions and price impacts associated with electric power plant emissions. In these IPM
runs,73 natural gas is generally projected to offset coal as the primary fuel used in electric
power plants in future years. As such, the expected fuel mix for all national electric power
plant generation is expected to change significantly over the years analyzed.
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Correspondingly, the electric power plant generation resulting from electric vehicle charging
is expected to similarly shift towards natural gas and away from coal-fired generation (Figure
4.6-15).
Figure 4.6-15 Fuel mix for electric power plants providing electricity to charging
vehicles
2020 2030 2040
Model Run Year
2050
For instance, the use of coal to fuel electric power plants for all end-users is expected
to decrease from approximately 45% in 2020AAAAA to 41% in 2050 while the use of nuclear
power is expected to decrease from roughly 20% in 2020 to just over 5% in 2050. During this
time, the use of natural gas to fuel electric power plants is expected in increase from roughly
20% in 2020 to over 41% in 2050 (See Figure 4.6-15).
The fuel mix for electric power plants that produce electricity for electric vehicle
charging is expected to be similar to the overall trend towards natural gas. During this time,
the use of natural gas to fuel all electric power plants is expected in increase from roughly
42% in 2020 to over 48% in 2050. In 2030, it is 80% natural gas, 14% coal, and 6%wind and
other sources, with similar numbers in 2040
AAAAA
mcremen]-ai power demand in 2020 is very small, near negligible, and should not
be ascribed significance.
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Chapter 4
Figure 4.6-16 Projected fuel mix for PHEV/EV charging in 2030
Figure 4.6-17 Projected fuel mix for all electric power in 2030
In this analysis, the overall portion of electricity consumed by charging PHEV/EVs is
projected to be small; as compared to all electric expected to be generated, the portion of
electricity earmarked for electric vehicle charging is expected to constitute 0% in 2020, 0.6%
in 2030, 0.9% in 2040, and just over 1% in 2050 (see Figure 1-10).
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Figure 4.6-18 Electric power consumption for PHEV/EVs charging compared to total
U.S. electric power consumption in 2030
0.6%
In 2030, based on the NPRM OMEGA runs, COi emissions related to electric vehicle
charging are expected to be 12 MMT. As compared to electricity generated in coal-fired
power plants for purposes of electric vehicle charging, mercury emissions are expected to be
virtually negligible, as natural gas is virtually mercury-free. NOx emissions are estimated to
be 3 M Tons while SO2 emissions are estimated to be 6 M Tons nationwide. In both of these
cases, the contribution of emissions from electric power plants related to electric vehicle
charging will be on the order of a few tenths of a percent as compared to overall emissions of
these pollutants. PM10 and PM2.5 emissions from electric power plants fueled by natural
gas- are also expected to decrease relative to coal-fired power plants, in this case, on the order
of 67 tons and 172 tons, respectively (Table 4.6-19).
Table 4.6-19 Electric power plant emissions due to charging PHEV/EVs
CO2 [MM Tonnes]
NOx [M Tons]
SO2 [M Tons]
Hg [Tons]
PM 2.5 [Tons]
PM 10 [Tons]
2020
0
-2
-4
0
15
16
2030
12
3
6
0
172
67
2040
17
1
4
0
322
360
2050
29
2
6
0
846
837
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Chapter 4
4.6.3.7
Air Quality Inventory
More detailed emission data was post-processed out of the emission data for 2030.
This more detailed data, which includes VOC and CO, was not available for all years, and
these inventories were used for air quality modeling. Due to the difficulty related to
geographic apportionment, the air quality modeling analyses did not consider the feedstock
gathering aspects of the power generation.
Table 4.6-20 - Air Quality Inventory.
Annual CO (Tons)
Annual NOx (Tons)
Annual VOC (Tons)
Annual SO2 (Tons)
Annual Primary PM10 (Tons)
Annual Primary PM25 (Tons)
Calendar year
2030 Impacts
8,544
2,528
245
5,612
67
172
% Impact relative to total IPM
emissions
0.8%
0.1%
0.5%
0.3%
0.0%
0.1%
4.6.3.8 Costs
EPA has prepared a memo to the docket on the cost analysis contained from the
IPM runs.74
4.6.3.9 Additional impacts from reduction in refinery electricity consumption
In addition to the impacts from electric vehicles and plug-in electric vehicles, there are
additional impacts on electric power plant emissions from reductions in energy used to supply
petroleum refineries. These impacts were accounted for in air quality inventories as well, and
methods used to estimate these impacts are discussed in Section 4.7.2.3. Table 4-43 presents
total air quality inventory impacts on electric power plants emissions when these impacts are
included (Table 4.6-21).
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Table 4.6-21 - Total Air Quality Inventory Impacts on Electric Power Plants from
Electric and Electric Plug-in Vehicles, and Reductions in Production of Electricity for
Refinery Use.
Pollutant
PM2.5
PM10
NOX
VOC
CO
SOX
NH3
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Tons
-136
-923
459
-7
7618
3131
541
0
0
-12
0
112
Percent of Total Upstream
Inventory
-0.06
-0.31
0.02
1.7
0.72
0.15
1.10
0
0
0.24
0
1.25
4.6.4 Comparison of inventories used in air quality modeling and FRM (short tons)
A comparison of the inventories used for AQ modeling and this FRM analysis is
shown below (Table 4.6-22). The AQ modeling and FRM inventories are highly similar, with
some updates made to the FRM modeling (such as the updates due to AEO 2012 ER, reduced
number of EVs from the FRM technology analysis, and inclusion of updated power plant
feedstock gathering emission factors) which were not included in the AQ inventories due to
the lead time required for the air quality modeling.
BBBBB
Table 4.6-22 - Comparison of Inventories
Pollutant
CO
NOX
PM2_5
SO2
VOC
AQ
Reference
43,939,504
9,160,190
2,888,030
4,870,847
10,805,700
Control
44,190,468
9,150,007
2,883,315
4,854,965
10,658,356
Delta
250,963
-10,183
-4,715
-15,882
-147,344
FRM
Delta
224,875
-6,509
-1,254
-13,377
-123,070
BBBBB
djfference between the FRM and AQ deltas for two air toxics, benzene and formaldehyde, are larger,
and are attributable to modeling artifacts in the AQ inventories. This difference would not have a significant
effect on our AQ modeling results, as this rule does not have a significant impact on the ambient level of these
air toxics.
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Chapter 4
References
""Historically, manufacturers have reduced precious metal loading in catalysts in order to reduce costs.
See http://www.platinum.matthey.com/media-room/our-view-on-.-.-./thrifting-of-precious-metals-in-
autocatalysts/ Accessed 11/08/2011. Alternatively, manufacturers could also modify vehicle
calibration. (Docket No. EPA-HQ-OAR-2010-0799-0956)
41 Intergovernmental Panel on Climate Change. Chapter 2. Changes in Atmospheric Constituents and
in Radiative Forcing. September 2007. http://www.ipcc.ch/pdf/assessment-report/ar4/wgl/ar4-wg 1 -
chapter2.pdf. Docket ID: EPA-HQ-OAR-2009-0472-0117
42 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, "Vehicle Survivability and Travel
Mileage Schedules," DOT HS 809 952, 8-11 (January 2006). Available at http://www-
nrd.nhtsa.dot.gov/pdf/nrd-30/NCSA/Rpts/2006/809952.pdf (last accessed Sept. 9, 2011). ( Docket
No. EPA-HQ-OAR-2010-0799-1139)
43 FHWA, Highway Statistics, Summary to 1995, Table vm201at
http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw , and annual editions 1996-2005, Table VM-
1 at http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm (last accessed Feb. 15, 2010). (EPA-HQ-
OAR-2010-0799-1141)
44EPA MOVES 2010a. August 2010. http://www.epa.gov/otaq/models/moves/index.htm. (Docket
No. EPA-HQ-OAR-2010-0799-1105)
45 Craig Harvey, EPA, "Calculation of Upstream Emissions for the GHG Vehicle Rule." 2009. Docket
ID: EPA-HQ-OAR-2009-0472-0216 (Docket No. EPA-HQ-OAR-2010-0799-1120)
46 Argonne National Laboratory. The Greenhouse Gases, Regulated Emissions, and Energy Use in
Transportation (GREET) Model versions 1.7 and 1.8.
http://www.transportation.anl.gov/modeling simulation/GREET/. Docket ID: EPA-HQ-OAR-2010-
0799-1105
47 OMEGA Benefits post-processor. The FRM OMEGA inputs and outputs are available on a DVD in
the docket (EPA-HQ-OAR-2010-0799). DVD title: "FRM OMEGA model, OMEGA inputs and
outputs & GREET 2011 (DVD)."
48 Heavy-Duty Vehicle Greenhouse Gas Emissions Inventory for Air Quality Modeling Technical
Support Document (51 pp, 477K, EPA-420-R-11-008, August 2011).
http://www.epa.gov/oms/climate/documents/420r 11008 .pdf
49 EPA. The 2008 National Emissions Inventory.
http://www.epa.gov/ttnchiel/net/2008inventory.html
50 EPA. eGrid 2010.
http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010Vl_l_year07_SummaryTables.pdf
. (EPA-HQ-OAR-2010-0799-0832)
51 Argonne National Laboratory's The Greenhouse Gases, Regulated Emissions, and Energy Use in
Transportation (GREET) Model, Version l.Sc.O, available at
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http://www.transportation.anl.gov/modeling_simulation/GREET/). EPA Docket EPA-HQ-OAR-2009-
0472. (Docket No. EPA-HQ-OAR-2010-0799-1105)
52 EPA. eGrid 2010, http://www.epa.gov/cleanenergv/energv-resources/egrid/index.html (Docket No.
(EPA-HQ-O AR-2010-0799-0832)
53 Emission Inventory TSD
54 U.S. EPA. Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency
Standards for Medium- and Heavy-Duty Engines and Vehicles: Regulatory Impact Analysis. Report
No. EPA-420-R-11 -901, August 2011. http://www.epa.gov/otaq/climate/documents/420rl 1901 .pdf
55 U. S. EPA and NHTSA. Final Rulemaking to Establish Greenhouse Gas Emissions Standards and
Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles: Regulatory Impact
Analysis. Report No. EPA-420-R-11-901, August 2011.
http://www.epa.gov/otaq/climate/documents/420rl 1901 .pdf
56 U. S. Energy Information Administration. Annual Energy Outlook 2011 with Projections to 2035.
Report No. DOE/EIA-0383. April 2011. http://205.254.135.7/forecasts/aeo/pdf/0383(2011).pdf
57 U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2)
Regulatory Impact Analysis. Assessment and Standards Division, Office of Transportation and Air
Quality, Ann Arbor, MI. Report No. EPA-420-R-10-006, February, 2010.
http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm
58 U.S. EPA. 2009. "Impact Calculations RFS-Docket.xls." Docket EPA-HQ-OAR-2011-0135
59 U. S. Energy Information Administration. 2012.
http://www.eia.gov/dnav/pet/pet cons psup dc nus mbbl a.htm
60 U. S. Department of Energy. 2011. GREET 1 2011. Argonne National Laboratory.
http://greet.es.anl.gov/
61 U. S. Energy Information Administration. Annual Energy Outlook 2011 with Projections to 2035.
Report No. DOE/EIA-0383. April 2011. http://205.254.135.7/forecasts/aeo/pdf/0383(2011).pdf
62IPM Final Mercury and Air Toxics Standards (MATS) base case.
http://www.epa.gov/airmarkets/progsregs/epa-ipm/toxics.html, http://www.gpo.gov/fdsys/pkg/FR-
2012-02-16/pdf/2012-806.pdf
63 See Joint Draft TSD Chapter 4.
64 Integrated Planning Model (IPM). http://www.epa.gov/airmarkt/progsregs/epa-ipm/
65 IPM Analysis of the Cross-State Air Pollution Rule, http://www.epa.gov/airmarkt/progsregs/epa-
ipm/
66 IPM Analysis of the Proposed GHG New Source Performance Standards for Electric Generating
Units (EGU GHG NSPS). http://www.epa.gov/airmarkt/progsregs/epa-
ipm/proposedEGU_GHG_NSPS.html
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67IPM Analysis of the Final Mercury and Air Toxics Standards (MATS).
http://www.epa.gov/airmarkt/progsregs/epa-ipm/toxics.html
68 IPM Analyses of Climate Change Legislative Proposals.
http://www.epa.gov/airmarkt/progsregs/epa-ipm/ipmanalyses.html
69 See draft Joint TSD 4
70 NPRM EPA DRIA Chapter 4
71 Wards State Vehicle Registration Data for 2006-2009 and R.L. Polk HEV Sales Data for 2006-
2009.
72 American Recovery and Reinvestment Act (ARRA) - Light-Duty Electric Drive Vehicle and
Charging Infrastructure Testing, http://avt.inel.gov/evproject.shtml
73
IPM Output for Reference Case and IPM Output for FRM Policy Case
Docket memo from Ari Kahan and Zoltan Jung. "Cost Analysis of Electric Vehicles in the
Integrated Planning Model for the Light Duty Greenhouse Gas 2017+ rulemaking"
74
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5 Vehicle Program Costs and Fuel Savings
In this chapter, EPA presents our estimate of the costs associated with the final vehicle
program. The presentation here summarizes the vehicle level costs associated with the new
technologies expected to be added to meet the MYs 2017-2025 GHG standards, including
hardware costs to comply with the A/C credit program. The analysis summarized here
provides our estimate of incremental costs on a per vehicle basis and on an annual total
basis.75
The presentation here summarizes the outputs of the OMEGA model that were
discussed in some detail in Chapter 3 of this RIA. For details behind the analysis, such as the
OMEGA model inputs and the estimates of costs associated with individual technologies, the
reader is directed to Chapter 1 of this RIA, and Chapter 3 of the Joint TSD. Note that the cost
analysis is based on a fixed vehicle fleet, as discussed in Chapter 1 of the Joint TSD. For the
cost analysis, then, the implicit demand elasticities are zero.
New for this final rule relative to the proposal are the inclusion of maintenance costs
associated with the new technologies and a discussion of potential repair costs. In the
proposal, we requested comment on maintenance and repair costs and received comments
from two commenters (see Chapter 5.2.2 below).
5.1 Technology Costs per Vehicle
To develop technology costs per vehicle, EPA has used the same methodology as that
used in the MYs 2012-2016 final rule, the 2010 TAR and the proposal for this rule.
Individual technology direct manufacturing costs have been estimated in a variety of ways—
vehicle and technology tear down, models developed by outside organizations, and literature
review—and indirect costs have been estimated using the updated and revised indirect cost
multiplier (ICM) approach that was first developed for the MYs 2012-2016 final rule.ccccc
All of these individual technology costs are described in detail in Chapter 3 of the joint TSD.
Also described there are the ICMs used in this rule and the ways the ICMs have been updated
and revised since the MYs 2012-2016 final rule which results in considerably higher indirect
costs in this rule than estimated in the MYs 2012-2016 final rule. Further, we describe in
detail the adjustments to technology costs to account for manufacturing learning and the cost
reductions that result from that learning. We note here that learning impacts are applied only
to direct manufacturing costs. This approach differs from the MYs 2012-2016 final rule
which applied learning to both direct and indirect costs. Learning effects in this final rule are
applied exactly as was done in the proposal. Lastly, we have included costs associated with
stranded capital (i.e., capital investments that are not fully recaptured by auto makers because
they would be forced to update vehicles on a more rapid schedule than they may have
intended absent this final rule). Again, this is detailed in Chapter 3 of the joint TSD.
approach was updated for the proposal and has not changed for this final rule.
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Chapter 5
EPA used the technology costs to build GHG and fuel consumption reducing packages
of technologies for each of 19 different vehicle types meant to fully represent the range of
baseline vehicle technologies in the marketplace (i.e., number of cylinders, valve train
configuration, vehicle class, etc.). This package building process as well as the process we
use to determine the most cost effective packages for each of the 19 vehicle types is detailed
in Chapter 1 of this RIA. These packages are then used as inputs to the OMEGA model to
estimate the most cost effective means of compliance with the final standards giving due
consideration to the timing required for manufacturers to implement the needed technologies.
That is, we assume that manufacturers cannot add the full suite of needed technologies in the
first year of implementation. Instead, we expect them to add technologies to vehicles during
the typical 4 to 5 year redesign cycle. As such, we expect that every vehicle can be
redesigned to add significant levels of new technology every 4 to 6 years. Further, we do not
expect manufacturers to redesign vehicles at a pace more rapid than the standard industry four
to five year cycle.
We then ran the OMEGA model for the 2021 and 2025 MYs as described in detail in
Chapter 3 of this RIA. The control case OMEGA cost outputs for the 2021 and 2025 MYs
were presented there and are repeated here in Table 5.1-1.
Table 5.1-1 2021MY & 2025MY Control Case OMEGA Costs, including AC-Related
Costs but no Stranded Capital (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
202 1MY
Car
$6,688
$936
$657
$1,962
$6,672
$659
$2,102
$501
$519
$764
$583
$3,727
$921
$572
$631
$4,851
$3,005
$967
$1,018
$3,895
$79
$469
$1,467
$748
Truck
$0
$499
$772
$637
$0
$854
$705
$702
$816
$866
$866
$0
$1,208
$1,089
$890
$577
$593
$1,579
$1,196
$1,040
$0
$581
$484
$744
Combined
$6,688
$821
$709
$1,633
$6,672
$725
$1,668
$600
$611
$785
$646
$3,727
$972
$752
$711
$3,844
$2,659
$1,114
$1,049
$2,474
$79
$512
$1,268
$746
2025MY
Car
$7,463
$2,137
$1,601
$3,002
$7,843
$1,803
$3,166
$1,505
$1,505
$1,658
$1,548
$3,556
$1,966
$1,926
$1,604
$4,793
$3,566
$1,921
$2,101
$5,058
$70
$1,215
$2,408
$1,711
Truck
$0
$1,240
$2,372
$1,275
$0
$2,497
$1,492
$2,223
$1,903
$2,253
$1,953
$0
$2,436
$2,157
$2,378
$1,259
$950
$2,491
$1,837
$1,427
$0
$1,675
$1,232
$2,044
Combined
$7,463
$1,900
$1,934
$2,607
$7,843
$2,017
$2,670
$1,847
$1,622
$1,777
$1,634
$3,556
$2,044
$2,003
$1,833
$4,029
$3,224
$2,050
$2,056
$3,370
$70
$1,383
$2,176
$1,821
Note: Results correspond to the 2008 baseline fleet.
5-2
-------
MY 2017 and Later - Regulatory Impact Analysis
To get the costs per vehicle for the intervening years 2017-2020 and 2022-2024, we
have interpolated costs based on target COi levels for each individual company. For this final
rule, those target COi levels, excluding AC impacts, were presented in Chapter 3 of this RIA
and are repeated here for cars in Table 5.1-2 and for trucks in Table 5.1-3.
Table 5.1-2 Target CO2 Levels, excluding AC, by MY for Cars (g/mi)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Prosche
Spyker
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
2016
232.7
238.5
243.0
243.1
245.1
239.5
242.6
238.6
233.1
232.4
227.8
216.3
229.0
229.7
236.0
216.3
229.0
221.2
217.8
260.2
216.3
231.8
227.3
234.5
2017
223.3
228.9
230.6
234.1
235.2
230.6
232.4
227.4
223.6
223.6
219.8
207.5
220.6
220.0
227.1
216.3
219.7
212.2
217.8
260.2
207.5
222.4
218.1
225.3
2018
214.3
219.7
221.0
224.8
225.7
221.2
223.0
218.1
214.5
214.5
210.8
199.2
211.8
211.1
217.9
216.3
210.9
203.6
217.8
260.2
199.2
213.4
209.3
216.3
2019
205.6
210.8
211.9
215.9
216.6
212.2
214.0
209.2
205.8
205.9
202.3
191.1
203.3
202.5
209.0
199.2
202.4
195.4
200.7
239.7
191.1
204.7
200.9
207.4
2020
197.4
202.3
204.0
207.2
207.9
204.0
205.4
200.8
197.5
197.5
194.0
183.4
195.3
194.4
200.7
191.1
194.2
187.6
192.5
230.0
183.4
196.5
192.8
199.2
2021
189.4
194.2
195.3
198.6
199.5
195.8
197.1
192.5
189.5
189.5
186.1
176.0
187.5
186.6
192.7
176.0
186.4
180.0
177.3
211.8
176.0
188.5
185.0
190.9
2022
181.8
186.4
186.9
190.6
191.5
187.9
189.2
184.7
181.9
181.8
178.5
169.0
180.1
179.0
185.0
169.0
178.9
172.8
170.1
203.3
169.0
181.0
177.5
183.2
2023
174.4
178.8
179.4
183.1
183.8
180.3
181.5
177.4
174.7
174.6
171.4
162.1
172.9
171.9
177.6
162.1
171.7
165.9
163.3
195.1
162.1
173.7
170.4
175.9
2024
167.4
171.6
171.8
175.9
176.4
173.0
174.1
170.0
167.6
167.5
164.4
155.6
165.8
164.9
170.4
155.6
164.8
159.2
156.7
187.2
155.6
166.7
163.5
168.7
2025
160.7
164.7
164.9
168.7
169.3
166.1
167.1
163.1
160.8
160.7
157.7
149.3
159.0
158.2
163.6
149.3
158.1
152.8
150.4
179.7
149.3
159.9
156.9
161.9
Note: Results correspond to the 2008 baseline fleet.
5-3
-------
Chapter 5
Table 5.1-3 Target CO2 Levels, excluding AC, by MY for Trucks (g/mi)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
2016
0.0
294.1
306.6
305.8
0.0
316.6
291.7
324.4
292.2
289.9
300.9
0.0
282.4
280.7
306.7
298.4
291.0
268.9
283.4
284.0
0.0
306.2
304.2
308.6
2017
0.0
295.1
305.1
310.8
0.0
315.8
290.4
321.0
292.1
288.9
300.9
0.0
283.8
277.9
305.1
298.4
289.6
263.5
283.4
284.0
0.0
304.5
306.8
306.8
2018
0.0
289.1
300.5
306.3
0.0
311.4
283.7
316.4
287.1
283.2
296.7
0.0
276.9
271.5
300.2
298.4
283.0
257.4
283.4
284.0
0.0
299.2
301.4
302.3
2019
0.0
284.4
295.9
301.1
0.0
307.4
278.9
311.4
282.4
278.5
292.0
0.0
272.3
267.0
295.1
291.7
278.2
253.2
274.1
275.2
0.0
294.3
296.2
297.7
2020
0.0
277.7
288.8
294.6
0.0
303.4
272.3
305.8
275.4
271.8
284.7
0.0
266.6
260.6
288.6
286.7
271.5
247.0
269.4
270.4
0.0
288.8
289.7
292.0
2021
0.0
260.6
270.8
277.0
0.0
285.2
255.3
286.2
258.5
254.9
267.2
0.0
250.9
244.3
272.4
262.4
254.6
231.6
246.6
247.4
0.0
271.0
272.2
273.8
2022
0.0
249.2
258.9
265.0
0.0
272.7
244.0
273.3
247.0
243.6
255.4
0.0
240.5
233.5
260.4
250.8
243.3
221.4
235.7
236.4
0.0
259.0
260.4
261.6
2023
0.0
238.4
247.3
253.5
0.0
259.9
233.2
260.7
236.3
233.0
244.3
0.0
230.3
223.3
248.4
239.8
232.6
211.7
225.3
225.9
0.0
247.2
249.2
249.7
2024
0.0
228.0
236.2
242.6
0.0
247.3
223.0
248.7
225.6
222.5
233.4
0.0
219.9
213.4
236.7
229.2
222.4
202.3
215.4
215.9
0.0
235.9
238.0
238.1
2025
0.0
218.0
225.7
232.0
0.0
236.1
213.1
237.5
215.7
212.8
223.2
0.0
210.1
204.0
226.0
219.1
212.6
193.4
205.9
206.4
0.0
225.3
227.5
227.5
Note: Results correspond to the 2008 baseline fleet.
Interpolating the costs shown in Table 5.1-1 by COi targets shown in
Table 5.1-2 and Table 5.1-3 is straight forward enough, but the costs shown in Table
5.1-1 include our estimated AC-related costs (see Chapter 5 of the joint TSD). Because 2-
cycle COi targets do not include AC-related GHG controls, we first backed out the AC-
related costs prior to conducting the interpolations. The non-AC Costs were interpolated first
between 2016MY costs (set to $0 for the Control case) and 2021MY costs, and were
interpolated again between 2021MY and 2025MY costs. Also included in this step was a
scalar that was applied to costs in an effort to estimate the effects of learning on costs for the
intervening years. This scalar was generated by simply averaging package costs year-over-
year using the ranked-set of packages used for our 2021MY OMEGA runs and the ranked-set
of OMEGA packages for our 2025MY OMEGA runs. We note that ranked-sets of packages
and how they were developed is described in detail in Chapter 1 of this RIA. These averaged
package costs were then expressed as a percentage of the 2021MY costs and then 2025MY
costs, respectively. The former scalar was used for the interpolations between 2016 and 2021
model years while the latter scalar was used for the interpolations between 2021 and 2025
model years. These scalars are shown in Table 5.1-4.
5-4
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 5.1-4 Scalars Applied to Interpolated Costs to Reflect Learning Effects
Sealer
Costs as % of 2021
Costs as % of 2025
2017
118%
133%
2018
114%
129%
2019
105%
120%
2020
102%
116%
2021
100%
114%
2022
113%
2023
111%
2024
110%
2025
100%
Note that scalars exclude AC-related costs.
AC-related costs as presented in Chapter 5 of the joint TSD were then added back in
to the interpolated costs by year. Note that the same cost for AC was used for each
manufacturer as we do not have unique AC-related costs by manufacturer.
The final step was to include our estimates of stranded capital. The stranded capital
costs used were based on those presented in Chapter 3 of this RIA where we presented
estimates of stranded capital for the 2016, 2021 and 2025 MYs. To estimate stranded capital
for the intervening years, we have done straight line interpolations to arrive at the stranded
capital costs shown in
Table 5.1-5. Note that the same stranded capital costs were used for both cars and
trucks except that no truck stranded capital costs were included for those manufacturers with
no truck sales (Aston Martin, Ferrari, Lotus and Tesla).
Table 5.1-5 Interpolated Estimates of Stranded Capital Costs (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
2017
$60
$16
$53
$18
$9
$16
$16
$18
$12
$7
$14
$26
$17
$15
$12
$19
$36
$8
$28
$17
$1
$5
$14
$14
2018
$54
$20
$45
$19
$16
$17
$20
$18
$12
$8
$21
$23
$22
$21
$12
$21
$30
$10
$25
$18
$1
$9
$17
$16
2019
$48
$23
$38
$20
$24
$19
$23
$18
$13
$8
$28
$20
$28
$27
$13
$23
$25
$11
$21
$19
$1
$12
$19
$17
2020
$41
$27
$31
$21
$32
$20
$26
$18
$13
$8
$36
$16
$33
$33
$13
$25
$20
$13
$18
$20
$0
$16
$22
$18
2021
$35
$31
$24
$22
$40
$21
$30
$18
$13
$9
$43
$13
$38
$39
$13
$27
$14
$15
$14
$21
$0
$19
$25
$20
2022
$31
$26
$22
$19
$35
$18
$25
$17
$15
$11
$38
$12
$32
$32
$14
$24
$14
$12
$14
$21
$0
$21
$20
$19
2023
$26
$21
$20
$16
$31
$15
$21
$16
$17
$12
$34
$11
$26
$26
$14
$21
$15
$10
$13
$20
$0
$22
$15
$18
2024
$21
$16
$18
$13
$26
$12
$16
$15
$19
$14
$29
$11
$20
$19
$14
$18
$15
$7
$12
$20
$0
$24
$10
$17
2025
$17
$11
$16
$10
$22
$9
$12
$14
$21
$15
$25
$10
$13
$13
$14
$15
$15
$5
$11
$20
$0
$25
$5
$16
Note: Results correspond to the 2008 baseline fleet.
5-5
-------
Chapter 5
The end results are presented in Table 5.1-6 for cars, Table 5.1-7 for tracks and Table
5.1-8 for the combined fleet.
Table 5.1-6 Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs -- Cars (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Teslauuuuu
Toyota
Volkswagen
Fleet
2017
$1,778
$261
$256
$495
$1,720
$180
$577
$164
$151
$200
$153
$987
$244
$170
$171
$44
$810
$262
$54
$43
$26
$130
$395
$206
2018
$3,296
$475
$390
$944
$3,250
$334
$1,054
$272
$266
$373
$295
$1,831
$460
$303
$316
$61
$1,488
$482
$65
$58
$41
$238
$729
$374
2019
$4,438
$643
$491
$1,288
$4,403
$455
$1,414
$358
$359
$510
$409
$2,469
$634
$409
$430
$2,210
$2,000
$652
$496
$1,777
$57
$325
$986
$510
2020
$5,588
$804
$577
$1,633
$5,565
$564
$1,771
$435
$443
$640
$516
$3,107
$795
$508
$535
$3,120
$2,511
$815
$679
$2,506
$66
$404
$1,238
$634
2021
$6,724
$967
$681
$1,985
$6,712
$680
$2,132
$519
$532
$773
$625
$3,739
$959
$611
$644
$4,878
$3,019
$982
$1,032
$3,916
$79
$488
$1,492
$767
2022
$7,173
$1,351
$993
$2,358
$7,280
$1,042
$2,511
$834
$843
$1,066
$928
$3,811
$1,288
$1,026
$954
$5,018
$3,286
$1,295
$1,388
$4,392
$77
$726
$1,816
$1,079
2023
$7,546
$1,700
$1,257
$2,687
$7,763
$1,369
$2,851
$1,114
$1,124
$1,329
$1,200
$3,850
$1,591
$1,407
$1,234
$5,114
$3,513
$1,578
$1,711
$4,809
$72
$942
$2,104
$1,357
2024
$7,854
$2,024
$1,520
$2,981
$8,174
$1,676
$3,162
$1,389
$1,396
$1,578
$1,459
$3,866
$1,877
$1,765
$1,501
$5,176
$3,709
$1,842
$2,009
$5,176
$71
$1,147
$2,369
$1,622
2025
$7,480
$2,147
$1,617
$3,011
$7,864
$1,811
$3,177
$1,518
$1,525
$1,673
$1,572
$3,566
$1,979
$1,939
$1,618
$4,807
$3,580
$1,926
$2,112
$5,077
$69
$1,239
$2,412
$1,726
Note: Results correspond to the 2008 baseline fleet; MY 2017-2018 costs for Porsche, Suzuki and Tata-JLR
reflect AC and stranded capital even though EPA assumed for purposes of this analysis that these companies
would use the intermediate volume manufacturer provisions allowing the MY 2016 standards to continue
through MY 2018. However, for Porsche, we note that this analysis was already completed before EPA learned
that, as of August 1, 2012, Volkswagen purchased 100% ownership of Porsche and, thus, EPA expects that in
actuality the Porsche fleet will be combined with the Volkswagen fleet for purposes of compliance with the MYs
2017-2025 standards.
DDDDD while costs related to air-conditioning are shown for Tesla, as a manufacturer of solely electric vehicles,
Tesla can comply with reference, control, and alternative standards without incurring additional costs from this
regulation.
5-6
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 5.1-7 Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs -- Trucks (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
2017
$0
$5
$89
-$91
$0
$42
$43
$84
$18
$35
$16
$0
-$40
$106
$59
$21
$60
$263
$31
$20
$0
$36
-$20
$57
2018
$0
$136
$223
$55
$0
$207
$218
$209
$184
$224
$176
$0
$292
$354
$232
$67
$202
$577
$71
$65
$0
$165
$102
$196
2019
$0
$220
$324
$186
$0
$327
$321
$307
$307
$346
$315
$0
$477
$492
$367
$191
$283
$742
$389
$330
$0
$258
$194
$304
2020
$0
$310
$455
$316
$0
$426
$438
$400
$460
$497
$492
$0
$683
$672
$521
$266
$372
$978
$526
$457
$0
$342
$283
$415
2021
$0
$529
$796
$659
$0
$875
$734
$720
$829
$875
$908
$0
$1,246
$1,127
$904
$604
$607
$1,594
$1,210
$1,061
$0
$600
$508
$763
2022
$0
$1,095
$1,222
$1,851
$0
$1,235
$2,024
$1,057
$978
$1,259
$1,062
$0
$1,430
$1,112
$1,184
$3,964
$2,510
$1,482
$1,323
$3,314
$0
$873
$1,476
$1,186
2023
$0
$1,189
$1,713
$1,695
$0
$1,764
$1,892
$1,551
$1,365
$1,683
$1,443
$0
$1,851
$1,552
$1,689
$3,067
$2,001
$1,919
$1,567
$2,708
$0
$1,220
$1,433
$1,562
2024
$0
$1,274
$2,166
$1,541
$0
$2,272
$1,759
$2,008
$1,739
$2,086
$1,805
$0
$2,270
$1,965
$2,165
$2,205
$1,511
$2,326
$1,792
$2,121
$0
$1,543
$1,386
$1,914
2025
$0
$1,250
$2,388
$1,284
$0
$2,505
$1,504
$2,237
$1,923
$2,268
$1,977
$0
$2,449
$2,169
$2,391
$1,274
$964
$2,495
$1,848
$1,447
$0
$1,700
$1,237
$2,059
Note: Results correspond to the 2008 baseline fleet; MY 2017-2018 costs for Porsche, Suzuki and Tata-JLR
reflect AC and stranded capital even though EPA assumed for purposes of this analysis that these companies
would use the intermediate volume manufacturer provisions allowing the MY 2016 standards to continue
through MY 2018. However, for Porsche, we note that this analysis was already completed before EPA learned
that, as of August 1, 2012, Volkswagen purchased 100% ownership of Porsche and, thus, EPA expects that in
actuality the Porsche fleet will be combined with the Volkswagen fleet for purposes of compliance with the MYs
2017-2025 standards; negative entries are due to shifts in compliance values due to the sales projections used
(see Chapter 1 of the Joint TSD for details on our sales projections).
5-7
-------
Chapter 5
Table 5.1-8 Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs - Combined Fleet (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Prosche
Spyker-Saab
Subaru
Suzuki
Tata-JLR
Tesla
Toyota
Volkswagen
Fleet
2017
$1,778
$193
$180
$349
$1,720
$133
$412
$125
$110
$166
$123
$987
$193
$148
$136
$39
$703
$262
$50
$31
$26
$94
$311
$154
2018
$3,296
$386
$314
$723
$3,250
$291
$794
$241
$241
$343
$269
$1,831
$430
$321
$290
$62
$1,304
$505
$66
$61
$41
$210
$602
$311
2019
$4,438
$531
$416
$1,014
$4,403
$412
$1,075
$333
$343
$477
$388
$2,469
$606
$438
$411
$1,734
$1,754
$673
$477
$1,057
$57
$299
$825
$438
2020
$5,588
$673
$521
$1,305
$5,565
$517
$1,357
$418
$448
$611
$511
$3,107
$775
$565
$531
$2,447
$2,205
$854
$651
$1,486
$66
$380
$1,044
$557
2021
$6,724
$852
$733
$1,655
$6,712
$746
$1,698
$619
$624
$794
$689
$3,739
$1,010
$791
$725
$3,871
$2,674
$1,128
$1,064
$2,495
$79
$532
$1,293
$766
2022
$7,173
$1,283
$1,092
$2,242
$7,280
$1,102
$2,366
$940
$883
$1,105
$957
$3,811
$1,312
$1,055
$1,022
$4,790
$3,185
$1,337
$1,377
$3,891
$77
$780
$1,749
$1,115
2023
$7,546
$1,565
$1,454
$2,460
$7,763
$1,491
$2,567
$1,322
$1,194
$1,400
$1,251
$3,850
$1,634
$1,455
$1,369
$4,672
$3,315
$1,655
$1,686
$3,832
$72
$1,043
$1,972
$1,425
2024
$7,854
$1,826
$1,799
$2,652
$8,174
$1,860
$2,746
$1,684
$1,497
$1,679
$1,532
$3,866
$1,942
$1,831
$1,697
$4,534
$3,422
$1,951
$1,972
$3,756
$71
$1,291
$2,176
$1,718
2025
$7,480
$1,910
$1,950
$2,616
$7,864
$2,025
$2,681
$1,861
$1,642
$1,792
$1,658
$3,566
$2,057
$2,015
$1,847
$4,044
$3,238
$2,054
$2,066
$3,390
$69
$1,407
$2,181
$1,836
Note: Results correspond to the 2008 baseline fleet; MY 2017-2018 costs for Porsche, Suzuki and Tata-JLR
reflect AC and stranded capital even though EPA assumed for purposes of this analysis that these companies
would use the intermediate volume manufacturer provisions allowing the MY 2016 standards to continue
through MY 2018. However, for Porsche, we note that this analysis was already completed before EPA learned
that, as of August 1, 2012, Volkswagen purchased 100% ownership of Porsche and, thus, EPA expects that in
actuality the Porsche fleet will be combined with the Volkswagen fleet for purposes of compliance with the MYs
2017-2025 standards.
These costs per vehicle are then carried forward for future MYs to arrive at the costs
presented in Table 5.1-9, including costs associated with the air conditioning program and
estimates of stranded capital.
Table 5.1-9 Industry Average Vehicle Costs Associated with the Final Standards (2010$)
Model Year
$/car
$/truck
Combined
2017
$206
$57
$154
2018
$374
$196
$311
2019
$510
$304
$438
2020
$634
$415
$557
2021
$767
$763
$766
2022
$1,079
$1,186
$1,115
2023
$1,357
$1,562
$1,425
2024
$1,622
$1,914
$1,718
2025
$1,726
$2,059
$1,836
2030
$1,710
$2,044
$1,818
2040
$1,710
$2,044
$1,816
2050
$1,710
$2,044
$1,816
Note: Results correspond to the 2008 baseline fleet.
5-8
-------
MY 2017 and Later - Regulatory Impact Analysis
5.2 Costs of the MY 2017-2025 GHG Standards
5.2.1 Technology Costs
The costs presented here represent the costs for newly added technology to comply
with the program incremental to the costs of the MYs 2012-2016 standards. Together with
the projected increases in car and track sales, the increases in per-car and per-track average
costs shown in Table 5.1-9 above result in the total annual technology costs presented in
Table 5.2-1 below. Note that the costs presented in Table 5.2-1 do not include the fuel
savings that consumers would realize as a result of driving a vehicle with improved fuel
economy. Those impacts are presented in Chapter 5.4 below. Similarly, the costs presented
in Table 5.2-1 do not include the maintenance costs that we have estimated in this final rale.
Maintenance costs, presented below, were not included in the proposal. Note also that the
costs presented here represent costs estimated to occur presuming that the MY 2025 standards
would continue in perpetuity. In other words, the standards do not apply only to 2017-2025
model year vehicles - they do, in fact, apply to all 2025 and later model year vehicles.
Table 5.2-1 Undiscounted Annual Technology Costs & Costs Discounted back to 2012 at
3% and 7% Discount Rates (2010 dollars)
Calendar
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Sales
Cars
9,987,667
9,905,364
9,995,696
10,291,562
10,505,165
10,735,777
10,968,003
11,258,138
11,541,560
12,535,870
14,097,092
15,822,370
Tracks
5,818,655
5,671,046
5,582,962
5,604,377
5,683,902
5,703,996
5,687,486
5,675,949
5,708,899
5,986,092
6,505,226
7,301,371
$/unit
$/car
$206
$374
$510
$634
$767
$1,079
$1,357
$1,622
$1,726
$1,710
$1,710
$1,710
$/truck
$57
$196
$304
$415
$763
$1,186
$1,562
$1,914
$2,059
$2,044
$2,044
$2,044
$Million/year
Cars
$2,060
$3,700
$5,100
$6,530
$8,060
$11,600
$14,900
$18,300
$19,900
$21,400
$24,100
$27,100
$336,000
$149,000
Tracks
$334
$1,110
$1,700
$2,320
$4,340
$6,760
$8,880
$10,900
$11,800
$12,200
$13,300
$14,900
$186,000
$81,900
Combined
$2,440
$4,850
$6,820
$8,860
$12,400
$18,300
$23,700
$29,100
$31,700
$33,700
$37,400
$42,000
$521,000
$231,000
Note: Results correspond to the 2008 baseline fleet.
Note that costs are estimated to decrease slightly in years beyond 2025. This
represents the elimination of stranded capital that is included in the costs for 2017 through
2025. These costs are described in detail in Chapter 3 of the Joint TSD.
5.2-2.
Looking at these costs by model year gives us the technology costs as shown in Table
5-9
-------
Chapter 5
Table 5.2-2 Model Year Lifetime Present Value Technology Costs, Discounted back to
the 1st Year of each MY at 3% and 7% Discount Rates (millions of 2010 dollars)
NPVat
3%
7%
Car
Truck
Fleet
Car
Truck
Fleet
2017
$2,030
$330
$2,400
$1,990
$323
$2,360
2018
$3,650
$1,100
$4,780
$3,580
$1,080
$4,690
2019
$5,020
$1,670
$6,720
$4,930
$1,640
$6,590
2020
$6,430
$2,290
$8,730
$6,320
$2,250
$8,570
2021
$7,940
$4,280
$12,200
$7,800
$4,200
$12,000
2022
$11,400
$6,670
$18,100
$11,200
$6,540
$17,700
2023
$14,700
$8,750
$23,400
$14,400
$8,590
$23,000
2024
$18,000
$10,700
$28,700
$17,700
$10,500
$28,100
2025
$19,600
$11,600
$31,200
$19,300
$11,400
$30,600
Sum
$88,800
$47,400
$136,000
$87,200
$46,500
$134,000
Note: Results correspond to the 2008 baseline fleet.
5.2.2 Maintenance & Repair Costs
New for this final rale are consideration and quantification of maintenance costs
associated with the new technologies added to comply with the standards. To make clear, we
distinguish maintenance from repair costs as follows: maintenance costs are those costs that
are required to keep a vehicle properly maintained and, as such, are usually recommended by
auto makers to be conducted on a regular, periodic schedule. Examples of maintenance costs
are oil and air filter changes, tire replacements, etc. Repair costs are those costs that are
unexpected and, as such, occur randomly and uniquely for every driver, if at all. Examples of
repair costs would be parts replacement following an accident, turbocharger replacement
following a mechanical failure, etc.
5.2.2.1 Maintenance Costs
In the joint TSD (see Chapter 3.6), we present our estimates for maintenance cost
impacts along with how we derived them. For most technologies that we expect will be added
to comply with the final standards, we expect no impact on maintenance costs. In other words,
the new technologies have identical maintenance intervals and identical costs per interval as
the technologies they will replace. However, for a few technologies, we do expect some
maintenance cost changes. As detailed in the Joint TSD, those technologies expected to result
in a change in maintenance costs are low rolling resistance tires levels 1 and 2 since they cost
more than traditional tires and must be replaced at similar intervals, diesel fuel filters since
they must be replaced more frequently and at higher cost than gasoline fuel filters, and several
items for full EVs reflecting both reduced costs (oil changes, air filter changes, engine coolant
flushes, spark plug replacements, etc.) since they do not need to be done on full EVs and
increased costs (related to battery maintenance). Table 5.2-3 presents the maintenance costs
and maintenance intervals used in this analysis for those technologies expected to result in
expenditure changes.
5-10
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MY 2017 and Later - Regulatory Impact Analysis
Table 5.2-3 Maintenance Event Costs & Intervals (2010 dollars)
New Technology
Low rolling resistance tires level 1
Low rolling resistance tires level 2
Diesel fuel filter replacement
EV oil change
EV air filter replacement
EV engine coolant replacement
EV spark plug replacement
EV/PHEV battery coolant replacement
EV/PHEV battery health check
Reference Case
Standard tires
Standard tires
Gasoline vehicle
Gasoline vehicle
Gasoline vehicle
Gasoline vehicle
Gasoline vehicle
Gasoline vehicle
Gasoline vehicle
Cost per
Maintenance
Event
$6.44
$43.52
$49.25
-$38.67
-$28.60
-$59.00
-$83.00
$117.00
$38.67
Maintenance
Interval
(mile)
40,000
40,000
20,000
7,500
30,000
100,000
105,000
150,000
15,000
Note that many of the maintenance event costs for EVs are negative. The negative
values represent savings since EVs do not incur these costs while their gasoline counterparts
do. Note also that the MYs 2012-2016 rule is expected to result in widespread use of low
rolling resistance tires level 1 (LRRT1) on the order of 85 percent penetration. Therefore, as
the MYs 2017-2025 rule results in increasing use of low rolling resistance tire level 2
(LRRT2), there is a corresponding decrease in the use of LRRT1. As such, as LRRT2
maintenance costs increase with increasing market penetration, LRRT1 maintenance costs
decrease. There is further discussion of this point below.
Using the maintenance costs and intervals presented in Table 5.2-3, we can estimate
the annual maintenance cost increases/decreases associated with each of these technologies
relative to their reference cases counterparts. We have done this by using the VMT schedules
discussed in Chapter 4 of the joint TSD to determine when the maintenance events would
occur on the average vehicle. These maintenance intervals by mileage throughout the average
2017MY car lifetime are shown in Table 5.2-4.
5-11
-------
Chapter 5
Table 5.2-4 Maintenance Intervals for the Average 2017MY Car (Events/CY)
CY
2017
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
2051
2052
2053
2054
2055
2056
MY
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
Age
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
38
39
40
Cumulative
VMT (with
rebound)
15,692
30,772
45,473
59,579
73,244
86,486
99,160
110,378
121,074
131,168
140,558
149,235
157,170
164,353
170,782
176,433
181,264
185,279
188,503
191,044
192,993
194,488
195,661
196,582
197,316
197,933
198,461
198,902
199,277
199,604
199,736
199,856
199,960
200,049
200,122
200,178
200,218
200,218
200,218
200,218
LRRT1
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.
0.
0.
0.
0.
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
LRRT2
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.
0.
0.
0.
0.
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Diesel
fuel
filter
0.8
0.8
0.7
0.7
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.1
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
EVoil
change
2.1
2.0
2.0
.9
.8
.8
.7
.5
.4
.3
.3
.2
.1
.0
0.9
0.8
0.6
0.5
0.4
0.3
0.3
0.2
0.2
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
EV
air
filter
0.5
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
EV
engine
coolant
0.2
0.2
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
EV
spark
plugs
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
EV
battery
coolant
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
EV
battery
health
1.0
1.0
1.0
0.9
0.9
0.9
0.8
0.7
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.1
0.1
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: Results correspond to the 2008 baseline fleet.
Note that the table presents fractional maintenance intervals. Obviously, a given car
cannot undergo a fractional maintenance interval. However, some cars will undergo the
maintenance while others will not and, on average, the intervals would occur as shown.
Similar tables could be shown for a 2017MY truck which, because the VMT is higher, would
show more maintenance intervals. Tables for 2018 through 2025MY cars and trucks would
also differ as the VMT schedule changes by MY. However, since the information is very
5-12
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MY 2017 and Later - Regulatory Impact Analysis
similar and conceptually identical, we have not shown all of those tables but have placed them
in the docket.76
Importantly, the maintenance intervals shown are generated using a survival adjusted
VMT schedule, so the maintenance intervals are adjusted by survival rates. Further, the VMT
used includes rebound miles driven since the costs we are estimating here are societal costs
(in Section 5.5 we exclude rebound miles since the maintenance costs considered there are
private costs). Further, including rebound miles helps to ensure that our estimates remain
conservative since more miles means more maintenance and, therefore, more costs.
EEEEE
Using the information shown in Table 5.2-4, we can easily calculate the maintenance
costs using the cost per event information presented in Table 5.2-3. However, we also need to
consider the penetrations of each technology. For example, our OMEGA modeling predicts
that no gasoline sales will be converted to diesel sales making the diesel fuel filter
maintenance costs essentially moot for our maintenance analysis. Similarly, our EV
penetration rates are on the order of 1-3% so, while an EV could provide considerable
maintenance savings relative to a gasoline vehicle, those savings have little impact in our
analysis because so few gasoline sales are expected to be converted to EVs. Note that PHEVs
would be expected to incur the battery coolant and battery health check costs, as do EVs, but
would not see the savings that EVs see since most of the typical gasoline maintenance would
probably be required on a PHEV. The penetration rates used in this analysis are those
presented in Chapter 3.8 of this RIA and are shown in Table 5.2-5 for the relevant
technologies.
Table 5.2-5 Fleet Mix and Penetration Rates used for Maintenance Costs
MY
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Fleet Mix
Car
63%
64%
64%
65%
65%
65%
66%
66%
67%
Truck
37%
36%
36%
35%
35%
35%
34%
34%
33%
LRRT1
Car
85.0%
0.6%
-13.9%
-28.3%
-42.7%
-57.2%
-63.2%
-69.2%
-75.2%
-81.2%
Truck
85.0%
0.2%
-14.6%
-29.3%
-44.1%
-58.9%
-65.2%
-71.6%
-77.9%
-84.3%
LRRT2
Car
0.0%
14.4%
28.9%
43.3%
57.7%
72.2%
78.2%
84.2%
90.2%
96.2%
Truck
0.0%
14.8%
29.6%
44.3%
59.1%
73.9%
80.2%
86.6%
92.9%
99.3%
Diesel
Car
0.0%
-0.4%
-0.8%
-1.2%
-1.7%
-2.1%
-2.1%
-2.1%
-2.1%
-2.1%
Truck
0.0%
-0.1%
-0.2%
-0.3%
-0.4%
-0.5%
-0.5%
-0.5%
-0.5%
-0.6%
EV
Car
0.0%
0.2%
0.4%
0.7%
0.9%
1.1%
1.5%
1.9%
2.3%
2.7%
Truck
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.2%
0.2%
0.3%
PHEV
Car
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
Truck
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Note: The penetration rates shown reflect results of our OMEGA runs and represent our estimated response to the 2017-2025
GHG standards, not necessarily the true fleet penetration; results correspond to the 2008 baseline fleet.
Now, using the maintenance event costs, the maintenance intervals and the technology
penetration rates, we can estimate the maintenance cost changes resulting from the new
standards. For a 2017MY car, those costs are shown in Table 5.2-6.
Of course, more miles means more savings in the case of EVs. However, since EV penetration rates are
quite low in our analysis which minimizes their influence.
5-13
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Chapter 5
Table 5.2-6 Sales Weighted Maintenance Costs for a 2017MY Car (2010 dollars)
CY
2017
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
2051
2052
2053
2054
2055
2056
MY
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
Age
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
38
39
40
Cumulative
VMT (with
rebound)
15,692
30,772
45,473
59,579
73,244
86,486
99,160
110,378
121,074
131,168
140,558
149,235
157,170
164,353
170,782
176,433
181,264
185,279
188,503
191,044
192,993
194,488
195,661
196,582
197,316
197,933
198,461
198,902
199,277
199,604
199,736
199,856
199,960
200,049
200,122
200,178
200,218
200,218
200,218
200,218
LRRT1
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
LRRT2
$2.47
$2.37
$2.31
$2.21
$2.15
$2.08
$1.99
$1.76
$1.67
$1.59
$1.47
$1.36
$1.25
$1.13
$1.01
$0.89
$0.76
$0.63
$0.51
$0.40
$0.31
$0.24
$0.19
$0.14
$0.12
$0.10
$0.08
$0.07
$0.06
$0.05
$0.02
$0.02
$0.02
$0.01
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
Diesel fuel
filter
-$0.16
-$0.15
-$0.15
-$0.14
-$0.14
-$0.14
-$0.13
-$0.11
-$0.11
-$0.10
-$0.10
-$0.09
-$0.08
-$0.07
-$0.07
-$0.06
-$0.05
-$0.04
-$0.03
-$0.03
-$0.02
-$0.02
-$0.01
-$0.01
-$0.01
-$0.01
-$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
EV
(all items)
-$0.14
-$0.14
-$0.13
-$0.13
-$0.12
-$0.12
-$0.11
-$0.10
-$0.10
-$0.09
-$0.08
-$0.08
-$0.07
-$0.06
-$0.06
-$0.05
-$0.04
-$0.04
-$0.03
-$0.02
-$0.02
-$0.01
-$0.01
-$0.01
-$0.01
-$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
PHEV
(all items)
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
Total
$2.19
$2.10
$2.05
$1.96
$1.91
$1.85
$1.77
$1.56
$1.48
$1.41
$1.30
$1.21
$1.11
$1.00
$0.90
$0.79
$0.67
$0.56
$0.45
$0.35
$0.27
$0.21
$0.16
$0.13
$0.10
$0.09
$0.07
$0.06
$0.05
$0.05
$0.02
$0.02
$0.01
$0.01
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
Note: Results correspond to the 2008 baseline fleet.
Again, similar tables could be generated for tracks and for each MY. Note the small
costs for LRRT1. This is because the MYs 2017-2025 rule is expected to result in a very low
penetration of LRRT1. Table 5.2-5 shows only a 1% penetration rate for the 2017MY after
which the penetration starts to fall as LRRT2 replaces LRRT1. The analogous information
for a 2025MY car makes this clear, as shown in Table 5.2-7.
5-14
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 5.2-7 Sales Weighted Maintenance Costs for a 2025MY Car (2010 dollars)
CY
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
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
MY
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
Age
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
38
39
40
Cumulative VMT
(with rebound)
16,906
33,154
48,996
64,197
78,924
93,196
106,862
118,950
130,479
141,359
151,483
160,840
169,397
177,144
184,078
190,173
195,381
199,707
203,178
205,912
208,008
209,612
210,870
211,856
212,640
213,298
213,860
214,329
214,727
215,073
215,210
215,333
215,440
215,532
215,607
215,664
215,705
215,705
215,705
215,705
LRRT1
-$2.21
-$2.13
-$2.07
-$1.99
-$1.92
-$1.87
-$1.79
-$1.58
-$1.51
-$1.42
-$1.32
-$1.23
-$1.12
-$1.01
-$0.91
-$0.80
-$0.68
-$0.57
-$0.45
-$0.36
-$0.27
-$0.21
-$0.16
-$0.13
-$0.10
-$0.09
-$0.07
-$0.06
-$0.05
-$0.05
-$0.02
-$0.02
-$0.01
-$0.01
-$0.01
-$0.01
-$0.01
$0.00
$0.00
$0.00
LRRT2
$17.70
$17.00
$16.50
$15.90
$15.40
$14.90
$14.30
$12.70
$12.00
$11.30
$10.60
$9.81
$8.95
$8.11
$7.25
$6.38
$5.45
$4.53
$3.64
$2.86
$2.19
$1.68
$1.32
$1.03
$0.82
$0.69
$0.59
$0.49
$0.42
$0.36
$0.14
$0.13
$0.11
$0.10
$0.08
$0.06
$0.04
$0.00
$0.00
$0.00
Diesel fuel
filter
-$0.87
-$0.84
-$0.82
-$0.79
-$0.76
-$0.74
-$0.71
-$0.63
-$0.60
-$0.56
-$0.52
-$0.48
-$0.44
-$0.40
-$0.36
-$0.32
-$0.27
-$0.23
-$0.18
-$0.14
-$0.11
-$0.08
-$0.07
-$0.05
-$0.04
-$0.03
-$0.03
-$0.02
-$0.02
-$0.02
-$0.01
-$0.01
-$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
EV
(all items)
-$1.87
-$1.80
-$1.75
-$1.68
-$1.63
-$1.58
-$1.51
-$1.34
-$1.28
-$1.20
-$1.12
-$1.04
-$0.95
-$0.86
-$0.77
-$0.68
-$0.58
-$0.48
-$0.38
-$0.30
-$0.23
-$0.18
-$0.14
-$0.11
-$0.09
-$0.07
-$0.06
-$0.05
-$0.04
-$0.04
-$0.02
-$0.01
-$0.01
-$0.01
-$0.01
-$0.01
$0.00
$0.00
$0.00
$0.00
PHEV
(all items)
$0.07
$0.07
$0.07
$0.06
$0.06
$0.06
$0.06
$0.05
$0.05
$0.05
$0.04
$0.04
$0.04
$0.03
$0.03
$0.03
$0.02
$0.02
$0.01
$0.01
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
Total
$12.82
$12.30
$11.93
$11.51
$11.15
$10.77
$10.35
$9.20
$8.66
$8.16
$7.68
$7.10
$6.48
$5.88
$5.24
$4.62
$3.94
$3.28
$2.64
$2.07
$1.58
$1.22
$0.96
$0.75
$0.59
$0.50
$0.43
$0.35
$0.30
$0.26
$0.10
$0.09
$0.08
$0.07
$0.06
$0.04
$0.03
$0.00
$0.00
$0.00
Note: Results correspond to the 2008 baseline fleet.
Doing this for all model years and adding up costs across given calendar years
matched with the appropriate sales provides the annual maintenance costs shown in Table
5.2-8.
5-15
-------
Chapter 5
Table 5.2-8 Undiscounted Sales Weighted Annual Maintenance Costs & Costs
Discounted back to 2012 at 3% and 7% Discount Rates (millions of 2010 dollars)
CY
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
LRRT1
Car
$0
-$3
-$11
-$22
-$37
-$55
-$75
-$97
-$121
-$234
-$396
-$493
-$4,140
-$1,600
Truck
$0
-$2
-$7
-$13
-$22
-$32
-$42
-$52
-$64
-$119
-$193
-$247
-$2,070
-$807
LRRT2
Car
$25
$73
$146
$250
$381
$526
$685
$862
$1,050
$1,940
$3,190
$3,950
$34,100
$13,400
Truck
$16
$45
$88
$146
$221
$299
$379
$462
$554
$976
$1,540
$1,970
$17,000
$6,710
Diesel
Car
-$2
-$5
-$10
-$16
-$25
-$34
-$42
-$52
-$61
-$103
-$160
-$195
-$1,770
-$706
Truck
$0
-$1
-$1
-$2
-$3
-$4
-$5
-$6
-$8
-$13
-$20
-$25
-$223
-$89
EV
Car
-$1
-$4
-$8
-$14
-$22
-$32
-$46
-$63
-$84
-$183
-$331
-$417
-$3,350
-$1,270
Truck
$0
$0
$0
$0
$0
$0
-$1
-$2
-$3
-$9
-$17
-$22
-$163
-$60
PHEV
Car
$0
$0
$1
$1
$1
$2
$3
$3
$4
$8
$13
$16
$135
$53
Truck
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
Car
$22
$61
$118
$199
$298
$408
$525
$654
$792
$1,430
$2,320
$2,860
$24,900
$9,830
Truck
$16
$43
$80
$131
$196
$262
$331
$402
$479
$836
$1,310
$1,680
$14,500
$5,760
Vehicle
$37
$103
$199
$330
$494
$670
$856
$1,060
$1,270
$2,260
$3,630
$4,540
$39,500
$15,600
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
We can also look at the costs on a model year basis by looking at the net present value
of costs and savings over the full lifetime of each model year of vehicles. The net present
value lifetime costs and savings for each MY 2017-2025 are shown in Table 5.2-9 using a 3%
discount rate and in Table 5.2-10 using a 7% discount rate.
Table 5.2-9 Model Year Lifetime Present Value Maintenance Costs and Savings,
Discounted to the 1st Year of each MY at 3% (millions of 2010 dollars)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Tires
$/c
$25
$47
$69
$92
$115
$125
$135
$146
$157
$911
$/t
$27
$50
$74
$97
$123
$134
$146
$157
$169
$975
Diesel
$/c
-$2
-$3
-$5
-$7
-$8
-$9
-$9
-$9
-$9
-$60
$/t
$0
-$1
-$1
-$2
-$2
-$2
-$2
-$2
-$2
-$15
EV
$/c
-$1
-$3
-$4
-$6
-$7
-$10
-$13
-$16
-$19
-$80
$/t
$0
$0
$0
$0
$0
-$1
-$1
-$2
-$2
-$6
PHEV
$/c
$0
$0
$0
$0
$0
$1
$1
$1
$1
$4
$/t
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
$/c
$22
$41
$60
$80
$99
$107
$114
$122
$130
$775
$/t
$26
$49
$72
$95
$121
$131
$142
$153
$164
$954
$/veh
$24
$44
$65
$85
$107
$115
$124
$132
$141
$836
$Million per MY
$/c
$222
$406
$600
$819
$1,040
$1,150
$1,250
$1,380
$1,490
$8,360
$/t
$153
$279
$404
$534
$686
$747
$810
$867
$936
$5,420
$/veh
$375
$684
$1,000
$1,350
$1,730
$1,890
$2,060
$2,240
$2,430
$13,800
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
Table 5.2-10 Model Year Lifetime Present Value Maintenance Costs and Savings,
Discounted to the 1st Year of each MY at 7% (millions of 2010 dollars)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Tires
$/c
$20
$36
$54
$71
$89
$97
$106
$113
$122
$707
$/t
$21
$38
$56
$75
$94
$102
$112
$121
$129
$747
Diesel
$/c
-$1
-$3
-$4
-$5
-$7
-$7
-$7
-$7
-$7
-$46
$/t
$0
-$1
-$1
-$1
-$2
-$2
-$2
-$2
-$2
-$12
EV
$/c
-$1
-$2
-$3
-$5
-$6
-$8
-$10
-$12
-$15
-$62
$/t
$0
$0
$0
$0
$0
$0
-$1
-$1
-$2
-$4
PHEV
$/c
$0
$0
$0
$0
$0
$0
$0
$1
$1
$3
$/t
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
$/c
$17
$32
$47
$62
$77
$83
$89
$94
$101
$601
$/t
$20
$38
$56
$73
$92
$100
$109
$118
$126
$731
$/veh
$18
$34
$50
$66
$82
$89
$96
$102
$109
$646
$Million per MY
$/c
$172
$314
$465
$634
$812
$887
$977
$1,060
$1,160
$6,480
$/t
$118
$214
$310
$411
$523
$570
$620
$669
$718
$4,150
$/veh
$290
$528
$775
$1,050
$1,330
$1,460
$1,600
$1,730
$1,880
$10,600
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
5-16
-------
MY 2017 and Later - Regulatory Impact Analysis
5.2.2.2 Repair Costs
For repair costs, EPA has found it much more difficult to find transparent data upon
which to base any estimated cost differences. Because repairs occur randomly and uniquely
for individual vehicle owners, we have no clear schedules to compare as was done above for
maintenance costs. While it is reasonable to assume that more expensive vehicles are more
expensive to repair, we have no certain methodology of quantifying those costs.
Repair costs can be broken down into two primary types: those resulting from
accidents or collisions, and those resulting from component failures. Some
accidents/collisions result in the "totaling" of the vehicle. In those cases, our primary
analyses already include in our benefit-cost analyses the cost associated with losing more
expensive vehicles, since the new vehicle sales estimates include sales to replace totaled
vehicles, and we apply marginal per vehicle costs to all new vehicle sales. In some other
cases, accidents/collisions may not result in a repair. Especially as vehicles age, owners may
decide that non-vital repairs are no longer justifiable. As a result, the accidents and collisions
of interest to us are actually a subset of those that occur, since we would not want to include
those that result in a "totaled" determination, or those that result in no additional cost of
repair. For that subset of accidents and collisions, the key question is whether repair costs
would increase or decrease as a result of this rule. We do not include those costs here,
because we lack data on the effects of this rule on repair costs. For instance, it is possible that
lighter-weight body components may be either more or less expensive to repair in the case of
dents than current body components. In the absence of such data, we acknowledge this
omission from our cost estimates. We note that our payback analysis includes increased costs
associated with insurance premiums (higher insurance premiums for a higher priced vehicle),
to reflect the out-of-pocket costs that vehicle buyers will face. The insurance premiums do
not provide good measures of the increased repair costs for use in the benefit-cost analysis,
though, because they include costs associated with "totaled" vehicles that, as noted, are
already accounted for in the vehicle sales estimates.
The other type of repair costs, those for component failures, is similarly difficult to
estimate. Our ICMs include a warranty factor that is generally higher than the average
warranty level for some initial number of years. This increased level of warranty cost is
meant to cover probable increases in warranty expenses incurred by auto makers as they
introduce new technologies. Increased warranty expenses are typical in any industry when a
new product or new technology is introduced. No matter what level of pre-production testing
is done, not all failure modes can be predicted or accurately captured in that testing. As such,
failure rates are generally higher than "typical" during some period following first
introduction. Following this period of higher than normal warranty costs, our ICM warranty
factor is reduced to reflect the "working out" of failure issues and a return to a normal level of
warranty expense (i.e., suppliers and auto makers learn from experience and reduce costs).
Importantly, our ICM factors continue to consider warranty costs indefinitely, they are not
assumed to be $0 at any point in time.
For out-of-warranty repair costs, it could be argued that vehicles meeting the new
standards will certainly be more complex than those meeting the reference case standards
(e.g., turbocharged vehicles have a turbocharger and, by definition, their intake and exhaust
5-17
-------
Chapter 5
systems are more complex than those on naturally aspirated engines). Increased complexity
generally implies increased chances for failures. In an effort to shed light on this possibility,
we searched for a reliable source of data that would show how vehicle repair rates differed for
vehicles with traditional technology versus those with the types of technologies we project
can be employed to comply with the new standards. Unfortunately, after a thorough search it
was determined that there currently is no reliable source for data or a study on failure rates
and changes in repair cost for the new technologies being forecast to be used in this rule.
EPA received only one commentFFFFF on this issue. NADA commented that the
agencies should account for the cost of ownership and referred to the calculator provided on
its website. Based on EPA's review of this tool, it appears that the NADA calculator
considers the first 5 years of ownership. However, based on a search of several vehicles
shown in Table 5.2-11, we have found no significant increase in repair when comparing
hybrids with non-hybrid versions of the same vehicles. We also found no significant
difference in repair cost when comparing vehicles with a manual transmission to one with an
automatic transmission, or when comparing a vehicle with turbo charged engine vs. a
naturally aspirated engine. There was a $455 dollar difference between the diesel vs. gasoline
engine equipped vehicles. Though we did a thorough search of the NADA site, we were not
able to determine the underlying data on which these projections are made. This means that
the difference in repair cost could be due to factors other than powertrain components such as
radio, lights, electric windows, or brakes, to name but a few examples.
FFFFF ,,,-pkg benefits analysis used in the proposal uses an oversimplified pay-back method that overstates
potential fuel economy savings. Instead, for purposes of calculating any "pay-back," real-world finance,
opportunity, and additional maintenance costs should be accounted for. In other words, the final rule should
evaluate its potential impact on a vehicle's total cost of ownership. An example of such a calculator is found at
http://www.nadaguides.com/Cars/Cost-to-Own. NADA would welcome the opportunity to discuss further with
EPA and NHTSA how prospective purchasers of new light-duty customers would be better served by a total cost
of ownership approach to understanding a given vehicle's future costs of operation."
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MY 2017 and Later - Regulatory Impact Analysis
Table 5.2-11 NADA Repair Cost Data Technology Being Compared
Hybrid FWD to Non-Hybrid
AWD
Hybrid to Non-Hybrid
Hybrid to Non-Hybrid
Hybrid to Non-Hybrid
Turbo Diesel to Standard Gas
6 Speed Manual Trans (Base) to 6
Speed Auto Trans
Hybrid to Non-Hybrid to Turbo
Downsized Engine
Turbo Charged Engine to
Natually Aspirated Engine
Vehicle
2012 Ford Fusion HEV FWD
2012 Ford Fusion SEL AWD
2012 Honda Civic Hybrid
2012 Honda Civic LX
2012 Toyota Camry Hybrid LE
2012 Toyota Camry Auto LE
2012 Ford Escape XLT FWD
2012 Ford Escape Hybrid FWD
2012 Volkswagen Touareg TDI
Sport
2012 Volkswagen Touareg VR6
Sport
2012 Kia Sorento 14 Base
2012 Kia Sorento 14 LX
2012 Hyundai Sonata 2.0T Auto
Limited
2012 Hyundai Sonata 2.4L Auto
Hybrid
2012 Hyundai Sonata 2.4L Auto
Limited
2012 Ford Taurus SHO (Turbo)
AWD
2012 Ford Taurus SEL AWD
Estimated 5 Year
Repair Cost
$2,691
$2,620
$2,157
$2,133
$2,133
$2,133
$2,275
$2,275
$3,298
$2,843
$1,071
$1,071
$1,142
$1,071
$1,071
$2,843
$2,843
Repair Cost
Difference
$71
$24
$0
$0
$455
$0
$71
$0
$0
While we did not find specific repair data on the projected technologies, data are
available on vehicle reliability which we believe provides a reasonable basis to project no net
increase in future failure rates. Both J. D. Power and Consumer Reports have annual
dependability/reliability studies. We have examined these sources in detail.
The J.D. Power and Associates Vehicle Dependability Study (YDS) provides
information about long-term vehicle quality after three years of ownership, when most
vehicles reach the end of the warranty period and owners assume responsibility for repair
costs. Owners rate vehicles based on problems experienced during the previous 12 months in
a variety of categories, including ride/handling/braking, engine and transmission, and a broad
range of vehicle quality problems. The YDS study has been an industry benchmark since
1990. The information we found is presented in Table 5.2-12.
Consumer Reports puts out an "Annual Auto Survey," which is sent to Consumer
Reports' print and Web subscribers and conducted by the Consumer Reports National
Research Center. Respondents report on their vehicles in any of the trouble spots during the
previous 12 months, and each year's survey is independent of the previous year's survey.
Consumer Reports' most recent survey covered model year 2005 through 2010 models and
focused on problems that the respondents considered serious because of cost, failure, safety,
or downtime. At the time of their latest survey most 2010 models were less than 6 months old
and were driven an average of 3,000 miles, while the 2005 models were about 5 years old.
5-19
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Chapter 5
Both the J. D. Power and the Consumer Reports surveys show positive results for
vehicles with advanced technologies, specifically hybrid vehicles. We were not able to find a
source for projecting failure rates for individual technologies.
Table 5.2-12 J. D. Power Vehicle Dependability Survey Data 2000 to 2009 Model Year
Vehicles
JD Powers
Survey
Report
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
Vehicle
Model Year
Covered by
Survey
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
Industry
Average
Repairs per
100 Vehicles
132
151
155
170
206
216
227
237
269
273
300
JD Powers Vehicle Dependability Study
Industry Average Repairs per 100 Vehicles
2001
2002
2003
2004
2005
2006
2007
2008
2009
Vehicle Model Year
Figure 5-1 J. D. Power VDS Data 2000 Model Year to 2009 Model Year
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MY 2017 and Later - Regulatory Impact Analysis
For J. D. Power's YDS results we present here the industry average repairs per 100
vehicles with data starting in the 2000 model year (published in 2003) and ending in the 2009
model year (published in 2012). Table 5.2-12 and Figure 5-1 show the YDS results for 2000-
2009 model years. One can see that there is a distinct trend toward decreased problems
reported per 100 vehicles for model years 2000 to 2009. The repairs per 100 vehicles metric
has roughly halved in the decade spanning 2003 to 2012 (i.e., for model years 2000 through
2009). This trend occurred concurrently with an increasing frequency of complex
technologies added to vehicles. This complexity includes improvements in powertrain,
safety, and many consumer related electronic features. Table 5.2-13 and Table 5.2-14 show
Engine Characteristics and Transmission Characteristics, respectively, that have been added
to 2000 to 2009 model year vehicles. The data in these tables are based on the EPA's 2010
Trends Report. The two tables show increased penetration in some of the more complex
engine technologies such as GDI, VVT, CD (cylinder deactivation), Multi-Valve, Gasoline
Hybrid, Turbocharged engines. There is also a significant penetration of advanced
transmissions (CVTs and 6 speeds). All of these advanced technologies have been added
while reliability has improved significantly as shown in FIGURE. The data definitely show
that vehicle reliability has improved dramatically even as manufacturers are moving toward
increasingly complex powertrains. While we do not have specific data on the change in other
attributes, EPA is confident that 2009MY vehicles are also more complex than 2000MY
vehicles in their use of navigation systems, entertainment systems, power-seats, and several
safety related features (e.g. number of airbags and electronic stability control systems).
J.D. Power also stated in a February 15, 2012, press release that the Toyota Prius (a
hybrid only vehicle) had the lowest problems per 100 score (80). The vehicle with the next
closest score (93) in its segment was the Toyota Corolla, which happens to be the closest
vehicle from Toyota to being a gasoline-only equivalent of the Prius.
Table 5.2-13 Engine Characteristics of MY 2000 to MY 2009 Light Duty Vehicles
Cars and Trucks
Powertrain
Fuel Injection Metering Method
Gasoline
Gasoline Hybrid
Diesel
Gasoline Direct Injection
Port Fuel Injection
Throttle Body Injection
Diesel
Multi-Valve
Variable Valve Timing
Cylinder Deactivation
Boosted (Turbocharged or Supercharged)
2000 Model Year
99.90%
0.00%
0.10%
-
99.80%
0.00%
0.10%
44.80%
15.00%
-
1.70%
2009 Model Year
97.20%
2.30%
0.50%
4.2%
95.20%
-
0.50%
83.60%
72.00%
7.40%
3.50%
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Chapter 5
Table 5.2-14 Transmission and Drive Characteristics of MY 2000 to MY 2009 Light
Duty Vehicles
Cars and Trucks
Manual
CVT
4 Gears or Fewer
5 Gears
6 Gears
7 Gears or More
2000 Model Year
9.7%
0.0%
83.8%
15.8%
0.5%
-
2009 Model Year
4.7%
9.5%
31.5%
31.6%
24.7%
2.6%
We also looked at information from Consumer Reports. Here we looked at both the
April 2011 and the December 2011 monthly publications. The April issues covered reliability
of individual models based on customer surveys, while the December issue analyzed and
predicted future reliability of vehicles based on past trends.
In the April issue, it is clear that hybrid models consistently have equal or greater
powertrain (engine and transmission) reliability than their non-hybrid counterparts. Hybrid
models shown for which there exists a non-hybrid counterpart are the Ford Escape, Honda
Civic, Lexus RX, Mercury Mariner, Nissan Altima, Toyota Highlander, and Toyota Camry.
Each of the hybrid models has a significantly more complex powertrain than its non-hybrid
counterpart while having equal or better reliability history.
In the December 2011 issue, Consumer Reports predicts future reliability rating in
vehicle categories such as family cars, small hatchbacks, small SUVs, etc. In every category
in which a hybrid was offered, the hybrid's reliability was the best or at least in the top 5
vehicles in the category. No hybrid was in the "not recommended" category for reliability.
The Ford Fusion Hybrid was the family car with the best predicted reliability. The Toyota
Prius was the fuel-efficient hatchback with the best predicted reliability of any other vehicle
with sufficient data. The only vehicle that scored higher was also a hybrid, but did not have
sufficient data to warrant mentioning.
Also in the 2011 issue was the first mention of Ford's EcoBoost engines. The
EcoBoost engine is an example of a turbocharged and downsized engine with GDI. This type
of engine is one of the most complex gasoline technologies used in the automotive industry,
and our modeling projects widespread use in both the car and truck fleets to meet the
standards. See preamble Tables 111-49 and 111-52. The Ford F150 with EcoBoost is a
"recommended" vehicle by Consumer Reports. This means that Consumer Reports expects
the vehicle to have above-average reliability. It is worth mentioning that the Ford Flex with
EcoBoost is not recommended. EPA checked the Consumer Reports websiteGGGGG to
determine if its concern was with the EcoBoost engine or other systems. The website showed
' The data is available on its website (http://www.consumerreports.org/) to subscribers.
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MY 2017 and Later - Regulatory Impact Analysis
the reliability of the EcoBoost engine is much better than average and there are other
problems with that model that gave them reason to give it a "not recommended" rating.
Another source for information on turbo charged engines is Paul Tan's Automotive
NewsHHHHH. The site has an article on turbo charged engine failure rates that cites some data
from an aftermarket warranty company in UK called Warranty Direct. The article states that
(based on the Warranty Direct data) turbo charged engines are expected to have higher failure
rates and repair costs than non turbo charged engines. It also states: "Of course, data such as
this benefits companies like Warranty Direct, which sell extended warranty coverage which
you can buy for your car when your manufacturer warranty expires. So there is a hidden
motive in them delivering this message to the public. But if it is backed by data, it could
warrant a little worry." The article hasn't verified that its source (Warranty Direct) has data to
back up its numbers. If their numbers are really just based on the warranty claims it pays, it
could simply be that more customers who have Turbo Charged vehicles elect their coverage.
Since the article has not verified the data, they do not know the years the vehicle data are
from, the types of vehicles (SUV, passenger cars, etc.), nor do they know the average age or
average mileage of the vehicles being compared. At best, the data from Warrant Direct is
speculative on the future failure rates of downsized engines based on past turbo charged
engine vehicles, which were typically designed for performance versions of vehicles that are
typically made in limited production vs. high production turbo downsized engines.
Furthermore, we believe that the evidence presented here suggests that the warranty
portion of some of our indirect cost multipliers (ICM) may be slightly overstated. In
developing our ICMs, warranty costs were generally estimated to increase over normal
practice due to the move to new and, more significantly, more complex technologies. This
may, in fact, not be the case; perhaps the warranty portion of the ICM should be lower than
"normal" or, at least, on par with it. We have not made such a change for the final rule in
order to keep costing methodology conservative (i.e., err on the side of estimating increased
costs), but we intend to consider this in the future.
Over the last ten years, vehicle powertrain complexity has been on a steady rise.
Vehicle manufacturers have stepped up efforts to improve powertrain quality, in part due to
On Board Diagnostics (OBD). OBD has made powertrain issues more visible to consumers,
and correcting these issues has made manufacturers' warranty due to OBD components more
visible. Almost every engine, transmission or hybrid component failure will cause the check
engine indicator to light. In response to the increased warranty, manufacturers have increased
their internal requirements for powertrain durability and now qualify most powertrain/OBD
components to last 15 years or 150,000 miles. Due to the expense of paying for replacement
parts for the most costly powertrains, such as hybrids or turbo downsized engines, we expect
manufacturers will continue to improve quality. Also, with the industry making its most
reliable vehicles in its history, reliability is the price of entry into a marketplace that will no
longer accept less. Due to improved reliability of powertrains, the expected repair costs for
powertrain systems are expected to decrease in the future, though in our analysis EPA has
taken a conservative estimate of zero incremental costs. Furthermore, we believe that there is
based automotive news site, http://paultan.org/
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Chapter 5
evidence to show that EPA should consider adjusting the indirect cost multipliers based on
these findings. We believe that there is evidence to show that the agency should adjust the
maintenance and repair portion of the ICM such that it does not increase with added
complexity. The agency will consider this for the mid-term evaluation.
5.2.3 Vehicle Program Costs
Annual costs of the vehicle program are the annual technology costs shown in Table
5.2-1 and the annual maintenance costs shown in Table 5.2-8. Those results are shown in
Table 5.2-15.
Table 5.2-15 Undiscounted Annual Program Costs & Costs Discounted back to 2012 at
3% and 7% Discount Rates (2010 dollars)
Calendar Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Car
$2,080
$3,760
$5,220
$6,730
$8,360
$12,000
$15,400
$18,900
$20,700
$22,900
$26,400
$29,900
$361,000
$159,000
Truck
$350
$1,150
$1,780
$2,450
$4,530
$7,030
$9,210
$11,300
$12,200
$13,100
$14,600
$16,600
$200,000
$87,700
Total
Annual
Costs
$2,470
$4,950
$7,020
$9,190
$12,900
$19,000
$24,600
$30,200
$32,900
$35,900
$41,000
$46,500
$561,000
$247,000
Note: Results correspond to the 2008 baseline fleet.
Model year lifetime costs of the vehicle program are the MY lifetime technology costs
shown in Table 5.2-2 and the MY lifetime maintenance costs shown in Table 5.2-9 and Table
5.2-10. Those results are shown in Table 5.2-16.
Table 5.2-16 Model Year Lifetime Present Value Vehicle Program Costs
Discounted to the 1st Year of each MY at 3% & 7% (millions of 2010 dollars)
NPV
at
3%
7%
MY-»
Cars
Trucks
Combined
Cars
Trucks
Combined
2017
$2,250
$483
$2,770
$2,170
$441
$2,650
2018
$4,050
$1,370
$5,460
$3,890
$1,290
$5,220
2019
$5,620
$2,070
$7,720
$5,400
$1,950
$7,370
2020
$7,250
$2,820
$10,100
$6,950
$2,660
$9,610
2021
$8,990
$4,960
$14,000
$8,610
$4,720
$13,300
2022
$12,600
$7,410
$19,900
$12,100
$7,110
$19,200
2023
$15,900
$9,560
$25,400
$15,400
$9,210
$24,600
2024
$19,400
$11,600
$30,900
$18,700
$11,200
$29,900
2025
$21,100
$12,500
$33,600
$20,400
$12,100
$32,500
Sum
$97,200
$52,800
$150,000
$93,600
$50,600
$144,000
Note: Results correspond to the 2008 baseline fleet.
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MY 2017 and Later - Regulatory Impact Analysis
5.3 Cost per Ton of Emissions Reduced
EPA has calculated the cost per ton of GHG reductions associated with the GHG
standards on a CC^eq basis using the costs and the emissions reductions described in Chapter
3. These values are presented in Table 5.3-1 for cars, trucks and the combined fleet. The cost
per metric ton of GHG emissions reductions has been calculated in the years 2020, 2030,
2040, and 2050 using the annual vehicle compliance costs and emission reductions for each of
those years. The value in 2050 represents the long-term cost per ton of the emissions reduced.
EPA has also calculated the cost per metric ton of GHG emission reductions including the
savings associated with reduced fuel consumption (presented below in Section 5.4). These
cost effectiveness estimates are similar to the highly cost effective MYs 2012-2016 standards
($50 per ton CO2e in 2030, see 75 FR 25515 (Table III.H.3-1); the delta becomes less in 2040
and 2050 ); the increase in cost effectiveness reflects the extra model years of the program.
This latter calculation does not include the other benefits associated with this program such as
those associated with energy security benefits as discussed later in Chapter 7. By including
the fuel savings, the cost per ton is generally less than $0 since the estimated value of fuel
savings considerably outweighs the program costs.
Table 5.3-1 Annual Cost per Metric Ton of CO2eq Reduced (2010 dollars)
Cars
Trucks
Combined
Calendar
Year
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
Undiscounted
Annual Costs
($millions)
$6,730
$22,900
$26,400
$29,900
$2,450
$13,100
$14,600
$16,600
$9,190
$35,900
$41,000
$46,500
Undiscounted Annual
Pre-tax Fuel Savings
($millions)
$6,000
$56,700
$102,000
$138,000
$1,430
$29,700
$53,400
$73,700
$7,430
$86,400
$155,000
$212,000
Annual CO2eq
Reduction
(mmt)
21
179
300
374
6
92
155
196
27
271
455
569
$/ton
(w/o fuel
savings)
$316
$128
$88
$80
$430
$142
$94
$85
$340
$132
$90
$82
$/ton
(w/ fuel
savings
$34
-$189
-$252
-$289
$179
-$180
-$251
-$292
$65
-$186
-$251
-$291
Note: Results correspond to the 2008 baseline fleet.
5.4 Reduction in Fuel Consumption and its Impacts
5.4.1 What Are the Projected Changes in Fuel Consumption?
The final CC>2 standards will result in significant improvements in the fuel efficiency
of affected vehicles. Drivers of those vehicles will see corresponding savings associated with
reduced fuel expenditures. EPA has estimated the impacts on fuel consumption for both the
tailpipe COi standards and the A/C credit program. While gasoline consumption would
decrease under the final GHG standards, electricity consumption would increase slightly due
to the small penetration of EVs and PHEVs (<1% in MY 2021 and 2% in MY 2025). The
fuel savings includes both the gasoline consumption reductions and the electricity
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Chapter 5
consumption increases. Note that the total number of miles that vehicles are driven each year
is different under the control case than in the reference case due to the "rebound effect,"
which is described in Chapter 4.2.5 of the joint TSD. EPA also notes that consumers who
drive more than our average estimates for vehicle miles traveled (VMT) will experience more
fuel savings; consumers who drive less than our average VMT estimates will experience less
fuel savings.
The expected impacts on fuel consumption are shown in Table 5.4-1 . The gallons
reduced and kilowatt hours increased (kWh) as shown in the tables reflect impacts from the
final CO2 standards, including the A/C credit program, and include the increased fuel
consumption resulting from the rebound effect.
Table 5.4-1 Fuel Consumption Impacts of the Final Standards and A/C Credit Programs
Calendar Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
Total
Petroleum-based
Gasoline Reference
(million gallons)
128,136
126,732
125,458
124,513
123,886
123,530
123,431
123,596
124,074
129,995
150,053
177,323
5,464,349
Petroleum-based
Gasoline Reduced
(million gallons)
197
620
1,265
2,149
3,435
5,055
6,967
9,158
11,620
22,986
38,901
48,743
903,298
Electricity Increased
(million kWh)
125
370
739
1,242
1,881
2,743
3,830
5,148
6,704
14,026
24,661
30,943
564,873
Note: The electricity increase shown is that needed to charge EVs/PHEVs, not that generated by power plants;
results correspond to the 2008 baseline fleet.
5.4.2 What are the Fuel Savings to the Consumer?
Using the fuel consumption estimates presented in Section 5.4.1, EPA can calculate
the monetized fuel savings associated with the final standards. To do this, we multiply
reduced fuel consumption in each year by the corresponding estimated average fuel price in
that year, using the reference case taken from the AEO 2012 Early Release.11 l AEO is a
nm In the Executive Summary to AEO 2012 Early Release, the Energy Information Administration describes the
reference case. They state that, "Projections.. .in the Reference case focus on the factors that shape U.S. energy
markets in the long term, under the assumption that current laws and regulations remain generally unchanged
throughout the projection period. The AEO2012 Reference case provides the basis for examination and
discussion of energy market trends and serves as a starting point for analysis of potential changes in U.S. energy
policies, rules, or regulations or potential technology breakthroughs."
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MY 2017 and Later - Regulatory Impact Analysis
standard reference used by NHTSA and EPA and many other government agencies to
estimate the projected price of fuel. The agencies also used AEO as the source of fuel price
projections for the 2012-2016 rulemaking.
However, these estimates do not account for the significant uncertainty in future fuel
prices. AEO also provides a "low" fuel price case and a "high" fuel price case. The
monetized fuel savings would be understated if actual fuel prices are higher, or overstated if
fuel prices are lower, than estimated/™1 In addition, since future fuel prices are not known
with certainty, there could be a distribution of possible fuel price outcomes, as opposed to sets
of known higher price- and lower price-pathways.
EPA's assessment uses both the pre-tax and post-tax gasoline prices. Since the post-
tax gasoline prices are the prices paid at fuel pumps, the fuel savings calculated using these
prices represent the savings consumers would see. The pre-tax fuel savings are those savings
that society would see. Assuming no change in gasoline tax rates, the difference between
these two columns represents the reduction in fuel tax revenues that will be received by state
and federal governments - about $85 million in 2017 and $4.7 billion by 2025. These results
are shown in Table 5.4-2 . Note that in Chapter 7 of this RIA, the overall benefits and costs of
the final standards are presented and only the pre-tax fuel savings are presented there.
Table 5.4-2 Undiscounted Annual Fuel Savings & Fuel Savings Discounted back to 2012
at 3% and 7% Discount Rates (millions of 2010 dollars)
Calendar
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Gasoline
Savings
(pre-tax)
$662
$2,110
$4,370
$7,540
$12,200
$17,900
$24,700
$32,800
$42,300
$87,900
$158,000
$216,000
$1,630,000
$617,000
Gasoline
Savings
(taxed)
$747
$2,360
$4,920
$8,440
$13,600
$20,000
$27,600
$36,500
$47,000
$97,000
$172,000
$233,000
$1,780,000
$677,000
Electricity
Costs
$11.5
$34.1
$67.9
$114
$175
$258
$366
$499
$658
$1,450
$2,800
$3,800
$28,100
$10,600
Total Fuel
Savings
(pre-tax)
$651
$2,070
$4,310
$7,430
$12,000
$17,700
$24,400
$32,300
$41,700
$86,400
$155,000
$212,000
$1,600,000
$607,000
Total Fuel
Savings
(taxed)
$735
$2,330
$4,850
$8,320
$13,400
$19,700
$27,200
$36,000
$46,300
$95,500
$169,000
$229,000
$1,750,000
$666,000
Note: Annual values represent undiscounted values; net present values represent annual costs discounted to
2012; results correspond to the 2008 baseline fleet.
jjjjj
id not conduct an uncertainty analysis on the future price of fuel, NHTSA has conducted both a
sensitivity analysis on fuel prices and a probabilistic uncertainty analysis where fuel price is one of the uncertain
parameters (See Chapters X and XII of NHTSA's FRIA). Because the agencies' analyses are generally
consistent and feature similar parameters, the results of NHTSA's sensitivity and uncertainty analyses are
indicative of the uncertainty present in EPA's results.
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Chapter 5
Looking at these fuel savings by model year gives us the savings as shown in Table
5.4-3.
Table 5.4-3 Model Year Lifetime Present Value Fuel Savings Discounted to the 1st
of each MY at 3% & 7% (millions of 2010 dollars)
Year
NPV
at
3%
7%
Car
Truck
Total
Car
Truck
Total
2017
$6,770
$275
$7,050
$5,200
$209
$5,410
2018
$12,800
$2,700
$15,500
$9,870
$2,050
$11,900
2019
$19,300
$4,950
$24,300
$14,900
$3,750
$18,700
2020
$26,600
$7,480
$34,100
$20,500
$5,670
$26,200
2021
$34,400
$16,000
$50,400
$26,500
$12,100
$38,600
2022
$43,000
$21,800
$64,800
$33,100
$16,600
$49,700
2023
$50,800
$27,600
$78,400
$39,100
$21,000
$60,100
2024
$59,400
$31,200
$90,600
$45,700
$23,900
$69,600
2025
$68,200
$39,300
$108,000
$52,400
$29,800
$82,200
Sum
$321,000
$151,000
$472,000
$247,000
$115,000
$362,000
Note: Results correspond to the 2008 baseline fleet.
As shown in Table 5.4-2 and Table 5.4-3, the agencies are projecting that consumers
would realize very large fuel savings as a result of these standards. These calculations are
based on the assumption, discussed in Preamble Section III.D.l.a, that the fuel economy of
vehicles would be constant at MY 2016 levels in the absence of the rule. As discussed further
in Chapter 8.1.2 of this RIA, it is a conundrum from an economic perspective that these large
fuel savings have not been provided by automakers and purchased by consumers. A number
of behavioral and market phenomena may lead to this disparity between the fuel economy that
makes financial sense to consumers. See also preamble section III.H.l. Regardless of how
consumers make their decisions on how much fuel economy to purchase, EPA expects that, in
the aggregate, they will gain these fuel savings, which will result in actual money in
consumers' pockets. Importantly, roughly 70% of discounted fuel savings occur within the
first 10 years of a vehicle's lifetime and 90% occur within the first 15 years, at both 3% and
7% discount rates.
5.5 Consumer Cost of Ownership, Payback Period and Lifetime Savings on New and
Used Vehicle Purchases
Here we look at the cost of owning a new vehicle complying with the standards and
the payback period - the point at which savings exceed costs. For example, a new 2025 MY
vehicle is estimated to cost roughly $1,800 more (on average, and relative to the reference
case vehicle) due to the addition of new GHG reducing/fuel economy improving technology.
This new technology will result in lower fuel consumption and, therefore, savings in fuel
expenditures. But how many months or years would pass before the fuel savings exceed the
cumulative costs?
Table 5.5-1 presents our estimate of increased costs associated with owning a new
2025MY vehicle.77 The table uses annual miles driven (vehicle miles traveled, or VMT) and
survival rates consistent with the emission and benefits analyses presented in Chapter 4 of the
Joint TSD. The control case includes fuel savings associated with A/C controls. Newly
included in this final rule compared to the proposal, are estimated maintenance costs that
5-28
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MY 2017 and Later - Regulatory Impact Analysis
owners of these vehicles will likely incur (as explained above). Further, this 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, since the focus is meant to be on those factors consumers think
about most while in the showroom considering a new car purchase and those factors that
result in more or fewer dollars in their pockets. To estimate the cumulative vehicle costs, we
have included not only the sales tax on the new car purchase but also the increased insurance
premiums that would result from the more valuable vehicle (see Chapter 4.2.13 of the Joint
TSD for details on how sales tax and increased insurance premiums were estimated).
Car/truck fleet weighting is handled as described in Chapter 1 of the Joint TSD. The
cumulative discounted costs are presented for both 3% and 7% discount rates with lifetime
discounted costs shown in the last 2 rows of the table, again at both 3% and 7% discount
rates.
Table 5.5-1 Increased Costs on a 2025 MY New Vehicle Purchase via Cash (2010
dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 3%
NPV, 7%
Increased
Purchase
Costs2
-$1,937
$0
$0
$0
$0
$0
$0
$0
4
-$1,937
-$1,937
Increased
Insurance
Costs b
-$34
-$33
-$31
-$29
-$28
-$26
-$25
-$23
1
-$313
-$254
Increased
Maintenance
Costs
-$14
-$13
-$13
-$12
-$12
-$11
-$11
-$10
1
-$139
-$109
Total
Increased
Costs
-$1,984
-$46
-$44
-$41
-$39
-$38
-$35
-$33
1
-$2,389
-$2,300
Cumulative
Discounted
Increased Costs
at 3%
-$1,984
-$2,029
-$2,070
-$2,108
-$2,143
-$2,175
-$2,205
-$2,232
1
-$2,389
Cumulative
Discounted
Increased Costs
at 7%
-$1,984
-$2,027
-$2,065
-$2,099
-$2,129
-$2,156
-$2,179
-$2,200
1
-$2,300
a Increased vehicle cost due to the rule is $1,836; the value here includes nationwide average sales tax of 5.46.
b See 4.2.13 of the Joint TSD for information on how increased insurance costs were estimated.
However, most people purchase a new vehicle using credit rather than paying cash up
front. A common car loan today is a five year, 60 month loan. As discussed in TSD Chapter
4.2.13, the national average interest rate for a 4 or 5 year new car loan is estimated to be 5.35
percent in 2025. For the credit purchase, the increased costs would look like that shown in
Table 5.5-2 .
5-29
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Chapter 5
Table 5.5-2 Increased Costs on a 2025 MY New Vehicle Purchase via Credit (2010
dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
I
NPV, 3%
NPV, 7%
Increased
Purchase
Costs"
-$452
-$452
-$452
-$452
-$452
$0
$0
$0
I
-$2,131
-$1,982
Increased
Insurance
Costs b
-$34
-$33
-$31
-$29
-$28
-$26
-$25
-$23
I
-$313
-$254
Increased
Maintenance
Costs
-$14
-$13
-$13
-$12
-$12
-$11
-$11
-$10
I
-$139
-$109
Total
Increased
Costs
-$500
-$497
-$495
-$493
-$491
-$38
-$35
-$33
I
-$2,583
-$2,345
Cumulative
Discounted
Increased Costs
at 3%
-$500
-$982
-$1,449
-$1,900
-$2,337
-$2,369
-$2,399
-$2,425
I
-$2,583
Cumulative
Discounted
Increased Costs
at 7%
-$500
-$964
-$1,397
-$1,799
-$2,174
-$2,201
-$2,224
-$2,245
I
-$2,345
a This uses the same increased cost as Table 5.5-1 but spreads it out over 5 years assuming a 5 year car loan at
5.35 percent.
b See 4.2.13 of the Joint TSD for information on how increased insurance costs were estimated.
The above discussion covers costs, but what about the fuel savings side? Of course,
fuel savings are the same whether a vehicle is purchased using cash or credit. Table 5.5-3
shows the fuel savings for a 2025MY vehicle (excluding rebound driving).
Table 5.5-3 Fuel Savings for a 2025MY Vehicle (2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 3%
NPV, 7%
Fuel
Price
$3.87
$3.91
$3.94
$3.96
$4.00
$4.04
$3.96
$3.96
I
Miles
Driven
16,779
16,052
15,539
14,902
14,424
13,941
13,106
11,866
4
Reference Fuel
$2,407
$2,325
$2,265
$2,183
$2,134
$2,082
$1,912
$1,739
1
$25,261
$19,354
Control Fuel
$1,702
$1,644
$1,601
$1,543
$1,508
$1,471
$1,350
$1,229
1
$17,859
$13,680
Fuel
Savings
$705
$681
$664
$640
$626
$611
$562
$510
1
$7,402
$5,674
Cumulative
Discounted
Fuel Savings
at 3%
$695
$1,347
$1,964
$2,541
$3,089
$3,608
$4,072
$4,480
1
$7,402
Cumulative
Discounted
Fuel Savings
at 7%
$682
$1,298
$1,859
$2,365
$2,827
$3,248
$3,610
$3,917
1
$5,674
Note: Fuel prices include taxes; miles driven exclude rebound miles.
We can now compare the cumulative discounted costs to the cumulative discounted
fuel savings to determine the point at which savings begin to exceed costs. This comparison
is shown in Table 5.5-4 for the 3% discounting case (see Table 5.5-5 for the 7% discounting
case).
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MY 2017 and Later - Regulatory Impact Analysis
Table 5.5-4 Payback Period for 2025MY Cash & Credit Purchases - 3% discount rate
(2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 3%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$1,984
-$2,029
-$2,070
-$2,108
-$2,143
-$2,175
-$2,205
-$2,232
4
-$2,389
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$500
-$982
-$1,449
-$1,900
-$2,337
-$2,369
-$2,399
-$2,425
1
-$2,583
Cumulative
Discounted
Fuel Savings
$695
$1,347
$1,964
$2,541
$3,089
$3,608
$4,072
$4,480
1
$7,402
Cumulative
Discounted Net
Savings -
Cash purchase
-$1,290
-$682
-$106
$433
$946
$1,433
$1,867
$2,249
1
$5,013
Cumulative
Discounted Net
Savings -
Credit purchase
$195
$365
$515
$641
$752
$1,239
$1,673
$2,055
1
$4,819
Table 5.5-4 shows that, somewhere early in the 4th year of ownership (3.2 years), the
savings have started to outweigh the costs of the cash purchase. More interestingly, the
savings immediately outweigh costs for the credit purchase case and, in fact, this is true even
in the first month of ownership, when the increased costs are $42 and the first month's fuel
savings are $59 and, presumably, no maintenance costs have yet been incurred (none of these
values are shown since the tables present annual values).78 So, for a new car purchaser who
does not keep the vehicle for the full lifetime, the increased costs will pay back within 4 years.
When considering the vehicle over its full life, the payback period could be considered as that
point at which the savings outweigh the full lifetime costs, which occurs somewhat later since
the costs associated with future years are being included.KKKKK For this case, referring again
to Table 5.5-4, we want the point at which the cumulative discounted fuel savings exceed the
discounted full lifetime costs of $2,389 or $2,583 for cash and credit purchases, respectively.
Those payback periods would be 3.7 years for the cash purchase and 4.1 years for the credit
purchase. Note that the full lifetime net savings amount to $5,013 for the cash purchase and
$4,819 for the credit purchase.79 These very large net savings may not be realized by many
individual owners since very few people keep vehicles for their full lifetime. However, those
savings would be realized in combination by all owners of the vehicle. Figure 5-2 shows this
information for the cash purchase, while Figure 5-3 shows the analogous information for the
credit purchase.
KKKKK
of the full lifetime costs are what we estimated in the draft RIA. In this final RIA, we
have focused on a payback period defined as a "breakeven" point - the point at which cumulative savings equal
cumulative costs or, said another way, the point at which owners start to save more than they spend.
5-31
-------
Chapter 5
$8,000
$7,000 -
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
Payback of Full Lifetime Costs
Breakeven Payback
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Year of Ownership
^^Costs ^^Fuel Savings
Figure 5-2 Cumulative 3% Discounted Costs & Fuel Savings for a 2025MY New Vehicle
Purchase via Cash (2010 dollars)
$8,000
$7,000 -
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
Payback of Full Lifetime Costs
Breakeven Payback
$o I
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Year of Ownership
Costs Fuel Savings
Figure 5-3 Cumulative 3% Discounted Costs & Fuel Savings for a 2025MY New Vehicle
Purchase via Credit (2010 dollars)
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MY 2017 and Later - Regulatory Impact Analysis
Table 5.5-5 shows the same information using a 7 percent discount rate. Here, the fuel
savings begin to outweigh the costs in just under 4 years for the cash purchase (3.4 years) and
within the first year for the credit purchase. For the full lifetime owner, the payback period to
recover full lifetime increased costs would be 3.9 years for the cash purchase and 4.0 years for
the credit purchase. The full lifetime net savings would be $3,375 for the cash purchase and
$3,330 for the credit purchase.80
Table 5.5-5 Payback Period for 2025MY Cash & Credit Purchases - 7% discount rate
(2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 7%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$1,984
-$2,027
-$2,065
-$2,099
-$2,129
-$2,156
-$2,179
-$2,200
I
-$2,300
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$500
-$964
-$1,397
-$1,799
-$2,174
-$2,201
-$2,224
-$2,245
I
-$2,345
Cumulative
Discounted
Fuel Savings
$682
$1,298
$1,859
$2,365
$2,827
$3,248
$3,610
$3,917
4
$5,674
Cumulative
Discounted Net
Savings -
Cash purchase
-$1,302
-$729
-$206
$266
$697
$1,092
$1,431
$1,717
1
$3,375
Cumulative
Discounted Net
Savings -
Credit purchase
$183
$334
$462
$565
$653
$1,047
$1,386
$1,672
1
$3,330
These payback periods are even more dramatic for the purchaser of a used 2025MY
vehicle. For this analysis, we have estimated annual depreciation of 20 percent per year and
have discounted all values back to the year of purchase by the purchaser of the used vehicle
(so present values of a 2025MY vehicle bought 5 years into its lifetime would be discounted
to 2030). We have assumed that the used car purchaser incurs the same maintenance and
insurance costs as the new car purchaser, but shifted by the number equal to the age of the
used car. The used car purchaser also reaps the fuel savings for the remainder of the vehicle's
lifetime with appropriate discounting. Importantly, for the credit purchase case we have
assumed a 3 year loan at interest rates 4 percent higher than those for the new car purchase (or
9.35%). The results for a 2025MY used car purchase 5 years into its lifetime are shown in
Table 5.5-6 with 3% discounting.81
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Chapter 5
Table 5.5-6 Payback Period for Cash & Credit Purchases of a 5 Year Used
2025MY Vehicle - 3% discount rate (2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 3%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$654
-$673
-$689
-$704
-$717
-$729
-$740
-$749
4
-$790
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$272
-$535
-$789
-$804
-$818
-$830
-$840
-$849
1
-$891
Cumulative
Discounted
Fuel Savings
$602
$1,140
$1,613
$2,055
$2,460
$2,827
$3,156
$3,450
1
$5,000
Cumulative
Discounted Net
Savings -
Cash purchase
-$52
$467
$924
$1,351
$1,743
$2,098
$2,417
$2,701
1
$4,210
Cumulative
Discounted Net
Savings -
Credit purchase
$330
$604
$824
$1,251
$1,643
$1,998
$2,316
$2,600
1
$4,109
As shown in the table, the payback period for the cash purchase case is just over 1
year (1.1 years). In the credit purchase case, the payback occurs within the first month where
monthly savings are roughly $23 during the life of the 3 year loan, after which savings would
be even higher.
The results for a 2025MY used car purchase 5 years into its lifetime are shown in
Table 5.5-7 with 7% discounting.
82
Table 5.5-7 Payback Period for Cash & Credit Purchases of a 5 Year Used
2025MY Vehicle - 7% discount rate (2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
1
NPV, 3%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$654
-$672
-$687
-$700
-$712
-$722
-$730
-$737
1
-$764
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$272
-$525
-$761
-$774
-$786
-$795
-$804
-$811
1
-$838
Cumulative
Discounted
Fuel Savings
$591
$1,099
$1,529
$1,916
$2,258
$2,556
$2,813
$3,033
1
$3,994
Cumulative
Discounted Net
Savings -
Cash purchase
-$64
$427
$842
$1,216
$1,546
$1,834
$2,083
$2,296
1
$3,230
Cumulative
Discounted Net
Savings -
Credit purchase
$319
$574
$769
$1,142
$1,473
$1,760
$2,009
$2,223
1
$3,157
As shown in the table, the payback period for the cash purchase case is just over 1
year (1.1 years). In the credit purchase case, the payback occurs within the first month where
monthly savings are roughly $21 during the life of the 3 year loan, after which savings would
be even higher.
We also looked at a 10 year old used car purchase. The results are shown in Table
5.5-8 and Table 5.5-9 using 3% and 7% discounting, respectively.83
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MY 2017 and Later - Regulatory Impact Analysis
Table 5.5-8 Payback Period for Cash & Credit Purchases of a 10 Year Used
2025MY Vehicle - 3% discount rate (2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
4
NPV, 3%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$218
-$227
-$234
-$241
-$247
-$251
-$256
-$259
4
-$271
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$93
-$182
-$267
-$274
-$279
-$284
-$288
-$292
1
-$304
Cumulative
Discounted
Fuel Savings
$425
$807
$1,147
$1,446
$1,708
$1,934
$2,126
$2,285
1
$2,944
Cumulative
Discounted Net
Savings -
Cash purchase
$208
$580
$913
$1,205
$1,461
$1,683
$1,870
$2,026
1
$2,673
Cumulative
Discounted Net
Savings -
Credit purchase
$333
$625
$880
$1,172
$1,428
$1,650
$1,837
$1,993
1
$2,640
As shown in Table 5.5-8, the payback period for the cash purchase case is under 1
year (0.5 years). In the credit purchase case, the payback occurs within the first month where
monthly savings are roughly $24 during the life of the 3 year loan, after which savings would
be even higher.84
Table 5.5-9 Payback Period for Cash & Credit Purchases of a 10 Year Used
2025MY Vehicle - 7% discount rate (2010 dollars)
Year of
Ownership
1
2
3
4
5
6
7
8
1
NPV, 3%
Cumulative
Discounted
Increased Costs -
Cash purchase13
-$218
-$226
-$233
-$239
-$244
-$248
-$251
-$254
1
-$262
Cumulative
Discounted
Increased Costs -
Credit purchase13
-$93
-$178
-$257
-$263
-$268
-$272
-$276
-$278
1
-$286
Cumulative
Discounted
Fuel Savings
$418
$778
$1,087
$1,349
$1,570
$1,753
$1,903
$2,023
1
$2,435
Cumulative
Discounted Net
Savings -
Cash purchase
$200
$552
$854
$1,110
$1,326
$1,505
$1,652
$1,769
1
$2,173
Cumulative
Discounted Net
Savings -
Credit purchase
$325
$600
$830
$1,086
$1,302
$1,481
$1,627
$1,745
1
$2,149
As shown in the Table 5.5-9, the payback period for the cash purchase case is under 1
year (0.5 years). In the credit purchase case, the payback occurs within the first month where
monthly savings are roughly $23 during the life of the 3 year loan, after which savings would
be even higher.
85
Note that throughout this consumer payback discussion, the analysis reflects the
average number of vehicle miles traveled per year. Drivers who drive more miles than the
average would incur fuel-related savings more quickly and, therefore, the payback would
come sooner. Drivers who drive fewer miles than the average would incur fuel related
savings more slowly and, therefore, the payback would come later.
5-35
-------
Chapter 5
Note also that the insurance costs and sales taxes included here in the cost of
ownership analysis have not been included in the benefit-cost analysis because those costs are
transfer payments and have no net impact on the societal costs of interest in a benefit-cost
analysis. Likewise, the fuel savings presented here include taxes since those are the cost
incurred by drivers. However, fuel taxes are not included in the benefit-cost analysis since,
again, they are transfer payments. Lastly, in this cost of ownership analysis, we have not
included rebound miles in determining maintenance costs or fuel savings, and we have not
included other private benefits/costs such as the value of driving rebound miles or reduced
time spent refueling, since we do not believe that consumers consider such impacts in their
daily lives. In the benefit-cost analysis, we include rebound miles in estimating maintenance
costs and fuel savings, and we include the other private benefits/costs listed here.
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MY 2017 and Later - Regulatory Impact Analysis
References
75 Spreadsheet files used to generate the values presented in this chapter can be found on a
compact disk placed in Docket No. EPA-HQ-OAR-2010-0799, see "LDGHG 2017-2025 Cost
Development Files."
76 See "GHGLD_2017-2025_MaintenanceCosts.xlsx" on "LDGHG 2017-2025_Cost
Development Files," CD in Docket No. EPA-HQ-OAR-2010-0799.
77 See "2008_OwnershipCost_Payback.xlsx" on "LDGHG 2017-2025_Cost Development
Files," CD in Docket No. EPA-HQ-OAR-2010-0799..
78 Ibid.
79 Ibid.
80 Ibid.
81 Ibid.
oo
82 Ibid.
83 Ibid.
84 Ibid.
85 Ibid.
5-37
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MY 2017 and Later - Regulatory Impact Analysis
6 Health and Environmental Impacts
6.1 Health and Environmental Impacts of Non-GHG Pollutants
6.1.1 Health Effects Associated with Exposure to Non-GHG Pollutants
In this section we 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 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
(l^m, or 10"6 meter) in diameter (for reference, a typical strand of human hair is 70 um in diameter
and a grain of salt is about 100 j^m). Atmospheric particles can be grouped into several classes
according to their aerodynamic and physical sizes, including ultrafine particles (<0.1 jim),
accumulation mode or 'fine' particles (< 1 to 3 j^m), and coarse particles (>1 to 3 j^m). For
regulatory purposes, fine particles are measured as PM2.5 and inhalable or thoracic coarse
particles are measured as PMio-2.5, corresponding to their size (diameter) range in micrometers
and referring to total particle mass under 2.5 and between 2.5 and 10 micrometers, respectively.
The EPA currently has standards that measure PM2.5 and PMi0.LLLLL
Particles span many sizes and shapes and consist of hundreds of different chemicals.
Particles are emitted directly from sources and are also formed through atmospheric chemical
reactions; the former are often referred to as "primary" particles, and the latter as "secondary"
particles. Particle pollution also varies by time of year and location and is affected by several
weather-related factors, such as temperature, clouds, humidity, and wind. A further layer of
complexity comes from particles' ability to shift between solid/liquid and gaseous phases, which
is influenced by concentration and meteorology, especially temperature.
Fine particles are produced primarily by combustion processes and by transformations of
gaseous emissions (e.g., SOx, NOx and volatile organic compounds (VOCs)) in the atmosphere.
The chemical and physical properties of PM2.5 may 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.86
LLLLL Reguiatory 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.
6-1
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Chapter 6
6.1.1.2 Participate Matter Health Effects
This section provides a summary of the health effects associated with exposure to ambient
concentrations of PM.MMMMM The information in this section is based on the information and
conclusions in the Integrated Science Assessment (ISA) for Particulate Matter (December 2009)
prepared by EPA's Office of Research and Development (ORD).NNNNN
The ISA concludes that ambient concentrations of PM are associated with a number of
adverse health effects.00000 The ISA characterizes the weight of evidence for different health
effects associated with three PM size ranges: PM2.5, PMio-2.5, and UFPs. The discussion below
highlights the ISA's conclusions pertaining to these three size fractions of PM, considering
variations in health effects associated with both short-term and long-term exposure periods.
6.1.1.2.1 Effects Associated with Short-term Exposure to PM2.5
The ISA concludes that cardiovascular effects and mortality are causally associated with
short-term exposure to PM2.5.87 It also concludes that respiratory effects are likely to be causally
associated with short-term exposure to PM2.5, including respiratory emergency department (ED)
visits and hospital admissions for chronic obstructive pulmonary disease (COPD), respiratory
infections, and asthma; and exacerbation of respiratory symptoms in asthmatic children.
6.1.1.2.2 Effects Associated with Long-term Exposure to PM2.5
The ISA concludes that there are causal associations between long-term exposure to
PM2.5 and cardiovascular effects, such as the development/progression of cardiovascular disease
(CVD), and premature mortality, particularly from cardiovascular causes.88 It also concludes that
long-term exposure to PM^s is likely to be causally associated with respiratory effects, such as
reduced lung function growth, increased respiratory symptoms, and asthma development. The
ISA characterizes the evidence as suggestive of a causal relationship for associations between
long-term PM2.5 exposure and reproductive and developmental outcomes, such as low birth
weight and infant mortality. It also characterizes the evidence as suggestive of a causal
relationship between PM2.5 and cancer incidence, mutagenicity, and genotoxicity.
6.1.1.2.3 Effects Associated with PMio-2.5
The ISA summarizes evidence related to short-term exposure to PMio-2.5- PMio-2.5 is the
fraction of PMio particles that is larger than PM2.5.89 The ISA concludes that available evidence
is suggestive of a causal relationship between short-term exposures to PMio-2.5 and cardiovascular
MMMMM personaj exposure includes contributions from many different types of particles, from many sources, and in
many different environments. Total personal exposure to PM includes both ambient and nonambient components
and collectively these components may contribute to adverse health effects.
NNNNN -p^e PSA is available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546
ooooo -j,^ j<^ 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 1.5 of the ISA.
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effects. It also concludes that the available evidence is suggestive of a causal relationship
between short-term exposures to PMi0-2.5 and respiratory effects, including respiratory-related ED
visits and hospitalizations. The ISA also concludes that the available literature suggests a causal
relationship between short-term exposures to PMi0-2.5 and mortality. Data are inadequate to draw
Qfl
conclusions regarding health effects associated with long-term exposure to PMio-2.5-
6.1.1.2.4 Effects Associated with Ultrafine Particles
The ISA concludes that the evidence is suggestive of a causal relationship between short-
term exposures to UFPs and cardiovascular effects, including changes in heart rhythm and
vasomotor function (the ability of blood vessels to expand and contract).91
The ISA also concludes that there is suggestive evidence of a causal relationship between
short-term UFP exposure and respiratory effects. The types of respiratory effects examined in
epidemiologic studies include respiratory symptoms and asthma hospital admissions, the results
of which are not entirely consistent. There is evidence from toxicological and controlled human
exposure studies that exposure to UFPs may increase lung inflammation and produce small
asymptomatic changes in lung function. Data are inadequate to draw conclusions regarding
health effects associated with long-term exposure to UFPs.92
6.1.1.3 Background on Ozone
Ground-level ozone pollution is typically formed by the reaction of 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 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 (NOi); 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
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concentrations of NOx, VOC, and ozone, all of which change with time and location. When
NOx levels are relatively high and VOC levels relatively low, NOx forms inorganic nitrates (i.e.,
particles) but relatively little ozone. Such conditions are called "VOC-limited." Under these
conditions, VOC reductions are effective in reducing ozone, but NOx reductions can actually
increase local ozone under certain circumstances. Even in VOC-limited urban areas, NOx
reductions are not expected to increase ozone levels if the NOx reductions are sufficiently large.
Rural areas are usually NOx-limited, due to the relatively large amounts of biogenic VOC
emissions in such areas. Urban areas can be either VOC- or NOx-limited, or a mixture of both,
in which ozone levels exhibit moderate sensitivity to changes in either pollutant.
6.1.1.4 Ozone Health Effects
Exposure to ambient ozone contributes to a wide range of adverse health effects.ppppp
These health effects are well documented and are critically assessed in the EPA ozone air quality
criteria document (ozone AQCD) and EPA staff paper.93'94 We are relying on the data and
conclusions in the ozone AQCD and staff paper, regarding the health effects associated with
ozone exposure.
Ozone-related health effects include lung function decrements, respiratory symptoms,
aggravation of asthma, increased hospital and emergency room visits, increased asthma
medication usage, and a variety of other respiratory effects. Cellular-level effects, such as
inflammation of lungs, have been documented as well. In addition, there is suggestive evidence
of a contribution of ozone to cardiovascular-related morbidity and highly suggestive evidence
that short-term ozone exposure directly or indirectly contributes to non-accidental and
cardiopulmonary-related mortality, but additional research is needed to clarify the underlying
mechanisms causing these effects. In a report on the estimation of ozone-related premature
mortality published by the National Research Council (NRC), a panel of experts and reviewers
concluded that short-term exposure to ambient ozone is likely to contribute to premature deaths
and that ozone-related mortality should be included in estimates of the health benefits of reducing
ozone exposure.95 People who appear to be more susceptible to effects associated with exposure
to ozone include children, asthmatics and the elderly. Those with greater exposures to ozone, for
instance due to time spent outdoors (e.g., children and outdoor workers), are also of concern.
Based on a large number of scientific studies, EPA has identified several key health
effects associated with exposure to levels of ozone found today in many areas of the country.
Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to ambient ozone concentrations
have been linked to lung function decrements, respiratory symptoms, increased hospital
admissions and emergency room visits for respiratory problems.96'97'98'"'100'101 Repeated
exposure to ozone can increase susceptibility to respiratory infection and lung inflammation and
can aggravate preexisting respiratory diseases, such as asthma.102'103'104'105'106 Repeated
exposure to sufficient concentrations of ozone can also cause inflammation of the lung,
ppppp 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 individuals breathing route and rate.
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MY 2017 and Later - Regulatory Impact Analysis
impairment of lung defense mechanisms, and possibly irreversible changes in lung structure,
which over time could affect premature aging of the lungs and/or the development of chronic
respiratory illnesses, such as emphysema and chronic bronchitis.107'108'109'no
Children and outdoor workers tend to have higher ozone exposure because they typically
are active outside, working, playing and exercising, during times of day and seasons (e.g., the
summer) when ozone levels are highest.111 For example, summer camp studies have reported
statistically significant reductions in lung function in children who are active outdoors.112'113'114'
115, lie, 117, ii8,119 purmer, children are more at risk of experiencing health effects from ozone
exposure than adults because their respiratory systems are still developing. These individuals (as
well as people with respiratory illnesses, such as asthma, especially asthmatic children) can
experience reduced lung function and increased respiratory symptoms, such as chest pain and
cough, when exposed to relatively low ozone levels during prolonged periods of moderate
exertion.120'121'122'123
6.1.1.5 Background on Nitrogen Oxides and Sulfur Oxides
Sulfur dioxide (SOi), 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. Nitrogen dioxide (NCh) is a member of the nitrogen oxide (NOx) family of
gases. Most NOi is formed in the air through the oxidation of nitric oxide (NO) emitted when
fuel is burned at a high temperature. SO2 andNO2 can dissolve in water droplets and further
oxidize to form sulfuric and nitric acid which react with ammonia to form sulfates and nitrates,
both of which are important components of ambient PM. The health effects of ambient PM are
discussed in Section 6.1.1.2. NOx along with non-methane hydrocarbons (NMHC) are the two
major precursors of ozone. The health effects of ozone are covered in Section 6.1.1.4.
6.1.1.6 Health Effects of SO2
This section provides an overview of the health effects associated with SO2. Additional
information on the health effects of SOi can be found in the EPA Integrated Science Assessment
for Sulfur Oxides.124 Following an extensive evaluation of health evidence from epidemiologic
and laboratory studies, the U.S. EPA has concluded that there is a causal relationship between
respiratory health effects and short-term (from 5 minutes to 24 hours) 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 laboratory studies involving controlled human exposures to SO2, respiratory
effects have consistently been observed following 5-10 min exposures at SO2 concentrations >
0.4 ppm in asthmatics engaged in moderate to heavy levels of exercise, with more limited
evidence of respiratory effects among exercising asthmatics exposed to concentrations as low as
0.2-0.3 ppm. A clear concentration-response relationship has been demonstrated in these studies
following exposures to SO2 at concentrations between 0.2 and 1.0 ppm, 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
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Chapter 6
ranging from 12 to 75 ppb. Important new multicity studies and several other studies have found
an association between 24-hour average ambient 862 concentrations and respiratory symptoms in
children, particularly those with asthma. Generally consistent associations also have been
observed between ambient SOi 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
that although copollutant adjustment has varying degrees of influence on the SOi effect
estimates, the effect of SOi 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 SOi on respiratory endpoints occur independent of the effects of other ambient air
pollutants. In addition, this epidemiologic evidence is plausible and coherent given the
consistency of the effects observed in the epidemiologic and controlled human exposure studies
along with toxicological evidence related to the mode of action of 862 on the human respiratory
system.
Consistent associations between short-term exposure to 862 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 862 on respiratory morbidity, uncertainty remains with respect to the interpretation of these
associations due to potential confounding by various copollutants. The U.S. EPA has therefore
concluded that the overall evidence is suggestive of a causal relationship between short-term
exposure to 862 and mortality. Significant associations between short-term exposure to 862 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 862 exposure and cardiovascular
morbidity.
6.1.1.7 Health Effects of NO2
Information on the health effects of NC>2 can be found in the EPA Integrated Science
Assessment (ISA) for Nitrogen Oxides.125 The EPA has concluded that the findings of
epidemiologic, controlled human exposure, and animal toxicological studies provide evidence
that is sufficient to infer a likely causal relationship between respiratory effects and short-term
NC>2 exposure. The ISA concludes that the strongest evidence for such a relationship comes from
epidemiologic studies of respiratory effects including symptoms, emergency department visits,
and hospital admissions. Based on both short- and long-term studies, the ISA concludes that
associations of NO2 with respiratory health effects are stronger among a number of groups; these
include individuals with preexisting pulmonary conditions (e.g., asthma or COPD), children and
older adults. The ISA also draws two broad conclusions regarding airway responsiveness
following NC>2 exposure. First, the ISA concludes that NC>2 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 asthmatics to NC>2
concentrations as low as 0.26 ppm. Second, exposure to NO2 has been found to enhance the
inherent responsiveness of the airway to subsequent nonspecific challenges in controlled human
exposure studies of asthmatic subjects. Small but significant increases in non-specific airway
hyperresponsiveness were reported following 1-hour exposures of asthmatics to 0.1 ppm NC>2.
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MY 2017 and Later - Regulatory Impact Analysis
Enhanced airway responsiveness could have important clinical implications for asthmatics since
transient increases in airway responsiveness following NOi exposure have the potential to
increase symptoms and worsen asthma control. Together, the epidemiologic and experimental
data sets form a plausible, consistent, and coherent description of a relationship between NOi
exposures and an array of adverse health effects that range from the onset of respiratory
symptoms to hospital admission.
Although the weight of evidence supporting a causal relationship is somewhat less certain
than that associated with respiratory morbidity, NC>2 has also been linked to other health
endpoints. These include all-cause (non- accidental) mortality, hospital admissions or emergency
department visits for cardiovascular disease, and decrements in lung function growth associated
with chronic exposure.
6.1.1.8 Health Effects of Carbon Monoxide
Information on the health effects of carbon monoxide (CO) can be found in the EPA
Integrated Science Assessment (ISA) for Carbon Monoxide.126 The ISA concludes that ambient
concentrations of CO are associated with a number of adverse health effects. QQQQQ This section
provides a summary of the health effects associated with exposure to ambient concentrations of
RRRRR
Human clinical 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, 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 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 inconsistent neural and behavioral effects following low-level CO
exposures. The ISA concludes the evidence is suggestive of a causal relationship with both
short- and long-term exposure to CO and central nervous system effects.
QQQQQ -pjjg jg^ 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.
RRRRR personaj exposure includes contributions from many sources, and in many different environments. Total
personal exposure to CO includes both ambient and nonambient components; and both components may contribute
to adverse health effects.
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Chapter 6
A number of epidemiologic and animal toxicological studies cited in the ISA have
evaluated associations between CO exposure and 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 associations between perinatal CO exposure and decrements in
birth weight, as well as other developmental outcomes. The ISA concludes these studies are
suggestive of a causal relationship between long-term exposures to CO and developmental
effects and birth outcomes.
Epidemiologic studies provide evidence of effects on respiratory morbidity such as
changes in pulmonary function, respiratory symptoms, and hospital admissions associated with
ambient CO concentrations. A limited number of epidemiologic studies considered co-pollutants
such as ozone, SOi, 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 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 ISA concludes that the epidemiologic evidence is suggestive of a causal
relationship between short-term exposures to 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 co-pollutant models
contributes to the uncertainty as to whether CO is acting alone or as an indicator for other
combustion-related pollutants. The ISA also concludes that there is not likely to be a causal
relationship between relevant long-term exposures to CO and mortality.
6.1.1.9 Health Effects of Air Toxics
Motor 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 air toxics.127
These compounds include, but are not limited to, benzene, 1,3-butadiene, formaldehyde,
acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. These compounds
were identified as national or regional risk drivers or contributors in the 2005 National-scale Air
Toxics Assessment (NATA) and have significant inventory contributions from mobile sources.
Although the 2005 NATA did not quantify cancer risks associated with exposure to diesel
exhaust, EPA has concluded that diesel exhaust ranks with the other emissions that the 2005
NATA suggests pose the greatest relative risk. According to NATA for 2005, mobile sources
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MY 2017 and Later - Regulatory Impact Analysis
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.sssss
Noncancer health effects can result from chronic,TTTTT subchronic,uuuuu or acutevvvvv
inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and
respiratory effects as well as effects on the immune and reproductive systems. According to the
2005 NATA, about three-fourths of the U.S. population was exposed to an average chronic
concentration of air toxics that has the potential for adverse noncancer respiratory health effects.
This will continue to be the case in 2030, even though toxics concentrations will be lower.128
The NATA modeling framework has a number of limitations which prevent its use as the
sole basis for setting regulatory standards. These limitations and uncertainties are discussed on
the 2005 NATA website.129 Even so, this modeling framework is very useful in identifying air
toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the
decision making process.
6.1.1.9.1 Benzene
The 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.1 °'131'132 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. 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.133'134
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.135'136 The most sensitive noncancer effect observed in humans, based on current data, is
the depression of the absolute lymphocyte count in blood.137'138 In addition, published 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.139'140'141'142 EPA's IRIS program has not yet evaluated these new data.
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.
TTTTT chro^ exposure is defined in the glossary of the Integrated Risk Information (IRIS) database
(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately
10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal
species).
uuuuu rjefmecj jn the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an
organism.
vww Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.
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Chapter 6
6.1.1.9.2 1,3-Butadiene
EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.143'144 The
IARC has determined that 1,3-butadiene is a human carcinogen and the U.S. DHHS has
characterized 1,3-butadiene as a known human carcinogen.1 5'146>147 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. 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.148
6.1.1.9.3 Formaldehyde
In 1991, EPA concluded that formaldehyde is a carcinogen based on nasal tumors in
animal bioassays.149 An Inhalation Unit Risk 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.150'151'152
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.153'154'155 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.156 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.1 7 Finally, a study of embalmers
reported formaldehyde exposures to be associated with an increased risk of myeloid leukemia but
not brain cancer.158
Health effects of formaldehyde in addition to cancer were reviewed by the Agency for
Toxics Substances and Disease Registry in 1999159 and supplemented in 2010,16° and by the
World Health Organization.161 These organizations reviewed the literature concerning effects on
the eyes and respiratory system, the primary point of contact for inhaled formaldehyde, including
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.
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 2010.162 The draft assessment reviewed more recent research from animal and
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MY 2017 and Later - Regulatory Impact Analysis
human studies on cancer and other health effects. The NRC released their review report in April
2011163 (http://www.nap.edu/catalog.php?record_id=13142). The EPA is currently revising the
draft assessment in response to this review.
6.1.1.9.4 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.164
Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11th
Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the
IARC.165'166 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.167 In short-term (4 week) rat studies, degeneration of olfactory
epithelium was observed at various concentration levels of acetaldehyde exposure.168'169 Data
from these studies were used by EPA to develop an inhalation reference concentration. Some
asthmatics have been shown to be a sensitive subpopulation to decrements in functional
expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation.170 The
agency is currently conducting a reassessment of the health hazards from inhalation exposure to
acetaldehyde.
6.1.1.9.5 Acrolein
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.171 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.172 Evidence available from 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.173 Lesions to the lungs and upper respiratory tract of rats,
rabbits, and hamsters have been observed after subchronic exposure to acrolein.1 4 Acute
exposure effects in animal studies report bronchial hyperresponsiveness.175 In one study, the
acute respiratory irritant effects of exposure to 1.1 ppm acrolein were more pronounced in mice
with allergic airway disease by comparison to non-diseased mice which also showed decreases in
respiratory rate.176 Based on these animal data 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 determined in 2003 that the human carcinogenic potential of acrolein could not be
determined because the available data were inadequate. No information was available on the
carcinogenic effects of acrolein in humans and the animal data provided inadequate evidence of
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Chapter 6
carcinogenicity.177 The IARC determined in 1995 that acrolein was not classifiable as to its
1 "78
carcinogenicity in humans.
6.1.1.9.6 Polycyclic Organic Matter (POM)
The term polycyclic organic matter (POM) defines a broad class of compounds that
includes the polycyclic aromatic hydrocarbon compounds (PAHs). One of these compounds,
naphthalene, is discussed separately below. POM compounds are formed primarily from
combustion and are present in the atmosphere in gas and particulate form. Cancer is the major
concern from exposure to POM. Epidemiologic studies have reported an increase in lung cancer
in humans exposed to diesel exhaust, coke oven emissions, roofing tar emissions, and cigarette
smoke; all of these mixtures contain POM compounds.179'180 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.181 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.182 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).183'184 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.
6.1.1.9.7 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
1 S^
system. Chronic (long term) exposure of workers and rodents to naphthalene has been
reported to cause cataracts and retinal damage.186 EPA released an external review draft of a
reassessment of the inhalation carcinogenicity of naphthalene based on a number of recent animal
carcinogenicity studies.187 The draft reassessment completed external peer review.188 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.189
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.190
Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal
cell changes and growth in respiratory and nasal tissues.191
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6.1.1.9.8 Other Air Toxics
In addition to the compounds described above, other compounds in gaseous hydrocarbon
and PM emissions from vehicles will be affected by the vehicle standards. 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.™™™
6.1.1.10 Exposure and Health Effects Associated with Traffic-Related Air Pollution
Populations who live, work, or attend school near major roads experience elevated
exposure to a wide range of air pollutants, as well as higher risks for a number of adverse health
effects. While the previous sections of this RIA have focused on the health effects associated
with individual criteria pollutants or air toxics, this section discusses the mixture of different
exposures near major roadways, rather than the effects of any single pollutant. As such, this
section emphasizes traffic-related air pollution, in general, as the relevant indicator of exposure
rather than any particular pollutant.
Concentrations of many traffic-generated air pollutants are elevated for up to 300-500
meters downwind of roads with high traffic volumes.192 Numerous sources on roads contribute
to elevated roadside concentrations, including exhaust and evaporative emissions, and
resuspension of road dust and tire and brake wear. Concentrations of several criteria and
hazardous air pollutants are elevated near major roads. Furthermore, different semi-volatile
organic compounds and chemical components of particulate matter, including elemental carbon,
organic material, and trace metals, have been reported at higher concentrations near major roads.
Populations near major roads experience greater risk of certain adverse health effects.
The Health Effects Institute published a report on the health effects of traffic-related air
pollution.193 It concluded that evidence is "sufficient to infer the presence of a causal
association" between traffic exposure and exacerbation of childhood asthma symptoms. The HEI
report also concludes that the evidence is either "sufficient" or "suggestive but not sufficient" for
a causal association between traffic exposure and new childhood asthma cases. A review of
asthma studies by Salam et al. (2008) reaches similar conclusions.194 The HEI report also
concludes that there is "suggestive" evidence for pulmonary function deficits associated with
traffic exposure, but concluded that there is "inadequate and insufficient" evidence for causal
associations with respiratory health care utilization, adult-onset asthma, COPD symptoms, and
allergy. A review by Holguin (2008) notes that the effects of traffic on asthma may be modified
by nutrition status, medication use, and genetic factors.195
The HEI report also concludes that evidence is "suggestive" of a causal association
between traffic exposure and all-cause and cardiovascular mortality. There is also evidence of an
association between traffic-related air pollutants and cardiovascular effects such as changes in
heart rhythm, heart attack, and cardiovascular disease. The HEI report characterizes this
integratecj Ri^ information System (IRIS) database is available at: www.epa.gov/iris
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Chapter 6
evidence as "suggestive" of a causal association, and an independent epidemiological literature
review by Adar and Kaufman (2007) concludes that there is "consistent evidence" linking traffic-
related pollution and adverse cardiovascular health outcomes.196
Some studies have reported associations between traffic exposure and other health effects,
such as birth outcomes (e.g., low birth weight) and childhood cancer. The HEI report concludes
that there is currently "inadequate and insufficient" evidence for a causal association between
these effects and traffic exposure. A review by Raaschou-Nielsen and Reynolds (2006)
concluded that evidence of an association between childhood cancer and traffic-related air
pollutants is weak, but noted the inability to draw firm conclusions based on limited evidence.197
There is a large population in the U.S. living in close proximity of major roads.
According to the Census Bureau's American Housing Survey for 2007, approximately 20 million
residences in the U.S., 15.6% of all homes, are located within 300 feet (91 m) of a highway with
4+ lanes, a railroad, or an airport.198 Therefore, at current population of approximately 309
million, assuming that population and housing are similarly distributed, there are over 48 million
people in the U.S. living near such sources. The HEI report also notes that in two North
American cities, Los Angeles and Toronto, over 40% of each city's population live within 500
meters of a highway or 100 meters of a major road. It also notes that about 33% of each city's
population resides within 50 meters of major roads. Together, the evidence suggests that a large
U.S. population lives in areas with elevated traffic-related air pollution.
People living near roads are often socioeconomically disadvantaged. According to the
2007 American Housing Survey, a renter-occupied property is over twice as likely as an owner-
occupied property to be located near a highway with 4+ lanes, railroad or airport. In the same
survey, the median household income of rental housing occupants was less than half that of
owner-occupants ($28,921/$59,886). Numerous studies in individual urban areas report higher
levels of traffic-related air pollutants in areas with high minority or poor populations.199'200'201
Students may also be exposed in situations where schools are located near major roads.
In a study of nine metropolitan areas across the U.S., Appatova et al. (2008) found that on
average greater than 33% of schools were located within 400 m of an Interstate, US, or state
highway, while 12% were located within 100 m.202 The study also found that among the
metropolitan areas studied, schools in the Eastern U.S. were more often sited near major
roadways than schools in the Western U.S.
Demographic studies of students in schools near major roadways suggest that this
population is more likely than the general student population to be of non-white race or Hispanic
ethnicity, and more often live in low socioeconomic status locations.203'204'205 There is some
inconsistency in the evidence, which may be due to different local development patterns and
measures of traffic and geographic scale used in the studies.202
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.
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6.1.2.1 Visibility Degradation
Visibility can be defined as the degree to which the atmosphere is transparent to visible
light.206 Visibility impairment is caused by light scattering and absorption by suspended particles
and gases. Visibility is important because it has direct significance to people's enjoyment of
daily activities in all parts of the country. Individuals value good visibility for the well-being it
provides them directly, where they live and work, and in places where they enjoy recreational
opportunities. Visibility is also highly valued in significant natural areas, such as national parks
and wilderness areas, and special emphasis is given to protecting visibility in these areas. For
more information on visibility see the final 2009 PM ISA.207
EPA is pursuing a two-part strategy to address visibility impairment. First, EPA
developed the regional haze program (64 FR 35714) which was put in place in July 1999 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. Second, EPA has 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 that control their visibility impact
effectiveness such as dry chemical composition and relative humidity (i.e., an indicator of the
water composition of the particles), and has set secondary PM2.5 standards to address these areas.
The existing annual primary and secondary PM2.5 standards have been remanded and are being
addressed in the currently ongoing PM NAAQS review. Figure 6.1-1 shows the location of the
156 Mandatory Class I Federal areas.
"-/
f
4
. 1 \
r' ~~^*™ ,. x
^%,
* Rainbow Lake, VA and BradweH Bay, PL are Class 1 Areas
where tisibtiMy is not an important air quality related salue
Figure 6.1-1 Mandatory Class I Federal Areas in the U.S.
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Chapter 6
6.1.2.1.1 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., 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 6.1-1). 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
scene measurements at some of the sites. Aerosol measurements are taken for PMio and PMi.5
mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon,
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. 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 either a nephelometer to
measure light scattering, some sites also include an aethalometer for light absorption, or at a few
sites using 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
9ns
fine soil, by season.
6.1.2.2 Plant and Ecosystem Effects of Ozone
There are a number of environmental or public welfare effects associated with the
ce of ozone in the ambient air.209 In this section wi
including trees, agronomic crops and urban ornamentals.
presence of ozone in the ambient air.209 In this section we discuss the impact of ozone on plants,
The Air Quality Criteria Document for Ozone and related Photochemical Oxidants notes
that, "ozone affects vegetation throughout the United States, impairing crops, native vegetation,
and ecosystems more than any other air pollutant."210 Like carbon dioxide (CO2) and other
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MY 2017 and Later - Regulatory Impact Analysis
gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in leaves in
a process called "uptake."211 Once sufficient levels of ozone (a highly reactive substance), or its
reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular
components and functions, including enzyme activities, lipids, and cellular membranes,
disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns.212'213 If enough
tissue becomes damaged from these effects, a plant's capacity to fix carbon to form
carbohydrates, which are the primary form of energy used by plants, is reduced,214 while plant
respiration increases. With fewer resources available, the plant reallocates existing resources
away from root growth and storage, above ground growth or yield, and reproductive processes,
toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have
shown that plants stressed in these ways may exhibit a general loss of vigor, which can lead to
secondary impacts that modify plants' responses to other environmental factors. Specifically,
plants may become more sensitive to other air pollutants, more susceptible to disease, insect
attack, harsh weather (e.g., drought, frost) and other environmental stresses. Furthermore, there
is evidence that ozone can interfere with the formation of mycorrhiza, essential symbiotic fungi
associated with the roots of most terrestrial plants, by reducing the amount of carbon available
for transfer from the host to the symbiont.21 '216
This ozone damage may or may not be accompanied by visible injury on leaves, and
likewise, visible foliar injury may or may not be a symptom of the other types of plant damage
described above. When visible injury is present, it is commonly manifested as chlorotic or
necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Because ozone damage
can consist of visible injury to leaves, it can also reduce the aesthetic value of ornamental
vegetation and trees in urban landscapes, and negatively affects scenic vistas in protected natural
areas.
Ozone can produce both acute and chronic injury in sensitive species depending on the
concentration level and the duration of the exposure. Ozone effects also tend to accumulate over
the growing season of the plant, so that even lower concentrations experienced for a longer
duration have the potential to create chronic stress on sensitive vegetation. Not all plants,
however, are equally sensitive to ozone. Much of the variation in sensitivity between individual
plants or whole species is related to the plant's ability to regulate the extent of gas exchange via
leaf stomata (e.g., avoidance of ozone uptake through closure of stomata)217'218'219 Other
resistance mechanisms may involve the intercellular production of detoxifying substances.
Several biochemical substances capable of detoxifying ozone have been reported to occur in
plants, including the antioxidants ascorbate and glutathione. After injuries have occurred, plants
may be capable of repairing the damage to a limited extent.220
Because of the differing sensitivities among plants to ozone, ozone pollution can also
exert a selective pressure that leads to changes in plant community composition. Given the range
of plant sensitivities and the fact that numerous other environmental factors modify plant uptake
and response to ozone, it is not possible to identify threshold values above which ozone is
consistently toxic for all plants. The next few paragraphs present additional information on
ozone damage to trees, ecosystems, agronomic crops and urban ornamentals.
Assessing the impact of ground-level ozone on forests in the United States involves
understanding the risks to sensitive tree species from ambient ozone concentrations and
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Chapter 6
accounting for the prevalence of those species within the forest. As a way to quantify the risks to
particular plants from ground-level ozone, scientists have developed ozone-exposure/tree-
response functions by exposing tree seedlings to different ozone levels and measuring reductions
in growth as "biomass loss." Typically, seedlings are used because they are easy to manipulate
and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone
within the leaves of seedlings and mature trees are identical, though the magnitude of the effect
r)'~) 1
may be higher or lower depending on the tree species.
Some of the common tree species in the United States that are sensitive to ozone are black
cherry (Primus serotind), tulip-poplar (Liriodendron tulipifera), and eastern white pine (Pinus
strobus). Ozone-exposure/tree-response functions have been developed for each of these tree
species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus ponderosa).
Other common tree species, such as oak (Quercus spp.) and hickory (Carya spp.), are not nearly
as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive species and
the level of ozone at particular locations, it is possible to estimate a "biomass loss" for each
species across their range.
Ozone also has been conclusively shown to cause discernible injury to forest trees.222'223
In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the
greatest potential for regional-scale forest impacts. Studies have demonstrated repeatedly that
ozone concentrations commonly observed in polluted areas can have substantial impacts on plant
function.224'225
Because plants are at the base of the food web in many ecosystems, changes to the plant
community can affect associated organisms and ecosystems (including the suitability of habitats
that support threatened or endangered species and below ground organisms living in the root
zone). Ozone impacts at the community and ecosystem level vary widely depending upon
numerous factors, including concentration and temporal variation of tropospheric ozone, species
composition, soil properties and climatic factors.22 In most instances, responses to chronic or
recurrent exposure in forested ecosystems are subtle and not observable for many years. These
injuries can cause stand-level forest decline in sensitive ecosystems.227'228'229 It is not yet possible
to predict ecosystem responses to ozone with much certainty; however, considerable knowledge
of potential ecosystem responses has been acquired through long-term observations in highly
damaged forests in the United States.
Air pollution can have noteworthy cumulative impacts on forested ecosystems by
affecting regeneration, productivity, and species composition.230 In the U.S., ozone in the lower
atmosphere is one of the pollutants of primary concern. Ozone injury to forest plants can be
diagnosed by examination of plant leaves. Foliar injury is usually the first visible sign of injury
to plants from ozone exposure and indicates impaired physiological processes in the leaves. 31
However, not all impaired plants will exhibit visible symptoms.
Laboratory and field experiments have also shown reductions in yields for agronomic
crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and
wheat). The most extensive field experiments, conducted under the National Crop Loss
Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN
results show that "several economically important crop species are sensitive to ozone levels
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MY 2017 and Later - Regulatory Impact Analysis
typical of those found in the United States."232 In addition, economic studies have shown
reduced economic benefits as a result of predicted reductions in crop yields associated with
observed ozone levels.233'234'235
Urban ornamentals represent an additional vegetation category likely to experience some
degree of negative effects associated with exposure to ambient ozone levels. It is estimated that
more than $20 billion (1990 dollars) are spent annually on landscaping using ornamentals, both
by private property owners/tenants and by governmental units responsible for public areas.236
This is therefore a potentially costly environmental effect. However, in the absence of adequate
exposure-response functions and economic damage functions for the potential range of effects
relevant to these types of vegetation, no direct quantitative analysis has been conducted.
6.1.2.2.1 Data on Visible Foliar Injury Due to Ozone in the U.S.
In the U.S. the national-level visible foliar injury indicator is based on data from the U.S.
Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) program.
As part of its Phase 3 program, formerly known as Forest Health Monitoring, FIA examines
ozone injury to ozone-sensitive plant species at ground monitoring sites in forest land across the
country. For this indicator, forest land does not include woodlots and urban trees. Sites are
selected using a systematic sampling grid, based on a global sampling design.237'238 At each site
that has at least 30 individual plants of at least three ozone-sensitive species and enough open
space to ensure that sensitive plants are not protected from ozone exposure by the forest canopy,
FIA looks for damage on the foliage of ozone-sensitive forest plant species. Because ozone injury
is cumulative over the course of the growing season, examinations are conducted in July and
August, when ozone injury is typically highest. Monitoring of ozone injury to plants by the
USDA Forest Service has expanded over time from monitoring sites in 10 states in 1994 to
nearly 1,000 monitoring sites in 41 states in 2002.
There is considerable regional variation in ozone-related visible foliar injury to sensitive
plants in the U.S. The U.S. EPA has developed an environmental indicator based on data from
the USDA FIA program which examines ozone injury to ozone-sensitive plant species at ground
monitoring sites in forest land across the country. The data underlying the indicator in Figure
6.1-2 is based on averages of all observations collected in 2002, the latest year for which data are
publicly available at the time the study was conducted, and is broken down by U.S. EPA Region.
Ozone damage to forest plants is classified using a subjective five-category biosite index based
on expert opinion, but designed to be equivalent from site to site. Ranges of biosite values
translate to no injury, low or moderate foliar injury (visible foliar injury to highly sensitive or
moderately sensitive plants, respectively), and high or severe foliar injury, which would be
expected to result in tree-level or ecosystem-level responses, respectively.239'240
The highest percentages of observed high and severe foliar injury, those which are most
likely to be associated with tree or ecosystem-level responses, are primarily found in the Mid-
Atlantic and Southeast regions. In EPA Region 3 (which comprises the States of Pennsylvania,
West Virginia, Virginia, Delaware, Maryland and Washington D.C.), 12% of ozone-sensitive
plants showed signs of high or severe foliar damage, and in Regions 2 (States of New York, New
Jersey), and 4 (States of North Carolina, South Carolina, Kentucky, Tennessee, Georgia, Florida,
Alabama, and Mississippi) the values were 10% and 7%, respectively. The sum of high and
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Chapter 6
severe ozone injury ranged from 2% to 4% in EPA Region 1 (the six New England States),
Region 7 (States of Missouri, Iowa, Nebraska and Kansas), and Region 9 (States of California,
Nevada, Hawaii and Arizona). The percentage of sites showing some ozone damage was about
45% in each of these EPA Regions.
Degree of injury:
Region 1
(.54 sites)
Region 2
;42 sites)
Region 3
{111 sites)
Region 4
; 227 nes;
Region 5
f 180 sites}
Region 6
(59 sites)
Region 7
(63 sites)
Region 3
(72 sites'i
Region 9
(SO sites)
Region 10
f57 sites'i
-Coverage:
kicaJed in A
:TataJs may
rounding.
Gate stturc
2006
None Low Moderate High Severe
Percent nl monitoring sites in each category:
68.5 1&.7 11.1
61.5 21.4 71 7
E5.9 mO 144 7
75.3 ML17.0'
75.fi 1EJ 5
Ml
35.7 9.5
100.0
76.3 12.5 3.8
-G.7
124
24.5
-:;..[
-4.G
.1
-5.1
'•'32
•-1.6
fr13
lu
• :o.c
M5 monitiJiing sites, EPA RB9' Dns
1 Kates.
not arid to 100% due ta
**
^Pcfll
o- ti'Cni Pnn-et ^crirt™
^
r
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Figure 6.1-2 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002
ab
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MY 2017 and Later - Regulatory Impact Analysis
6.1.2.2.2 Indicator Limitations
The categories for the biosite index are subjective and may not necessarily be directly
related to biomass loss or physiological damage to plants in a particular area. Ozone may have
other adverse impacts on plants (e.g., reduced productivity) that do not show signs of visible
foliar injury.241 The presence of diagnostic visible ozone injury on indicator plants does provide
evidence that ozone is having an impact in an area. However, absence of ozone injury in an area
does not necessarily mean that there is no impact from ozone exposure.
Field and laboratory studies were reviewed to identify the forest plant species in each
region that are sensitive to ozone air pollution and exhibit diagnostic injury. Other forest plant
species, or even genetic variants of the same species, may not show symptoms at ozone levels
that cause effects on the selected ozone-sensitive species.
Because species distributions vary regionally, different ozone-sensitive plant species were
examined in different parts of the country. These target species could vary with respect to ozone
sensitivity, which might account for some of the apparent differences in ozone injury among
regions of the U.S. Ozone damage to foliage may be reduced under conditions of low soil
moisture, but most of the variability in the index (70%) was explained by ozone concentration.242
Though FIA has extensive spatial coverage based on a robust sample design, not all
forested areas in the U.S. are monitored for ozone injury. Even though the biosite data have been
collected over multiple years, most biosites were not monitored over the entire period, so these
data cannot provide more than a baseline for future trends.
6.1.2.3 Particulate Matter Deposition
Particulate matter contributes to adverse effects on vegetation and ecosystems, and to
soiling and materials damage. These welfare effects result predominately from exposure to
excess amounts of specific chemical species, regardless of their source or predominant form
(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 2005 PM Staff Paper as
well as the Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological
Criteria.243'244'215
6.1.2.3.1 Deposition of Nitrogen and Sulfur
Nitrogen and sulfur interactions in the environment are highly complex. Both nitrogen
and sulfur are essential, and sometimes limiting, nutrients needed for growth and productivity.
Excesses of nitrogen or sulfur can lead to acidification, nutrient enrichment, and eutrophication
of aquatic ecosystems.246
The process of acidification affects both freshwater aquatic and terrestrial ecosystems.
Acid deposition causes acidification of sensitive surface waters. The effects of acid deposition
on aquatic systems depend largely upon the ability of the ecosystem to neutralize the additional
acid. As acidity increases, aluminum leached from soils and sediments, flows into lakes and
streams and can be toxic to both terrestrial and aquatic biota. The lower pH concentrations and
higher aluminum levels resulting from acidification make it difficult for some fish and other
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Chapter 6
aquatic organisms to survive, grow, and reproduce. Research on effects of acid deposition on
forest ecosystems has come to focus increasingly on the biogeochemical processes that affect
uptake, retention, and cycling of nutrients within these ecosystems. Decreases in available base
cations from soils are at least partly attributable to acid deposition. Base cation depletion is a
cause for concern because of the role these ions play in acid neutralization, and because calcium,
magnesium and potassium are essential nutrients for plant growth and physiology. Changes in
the relative proportions of these nutrients, especially in comparison with aluminum
concentrations, have been associated with declining forest health.
At current ambient levels, risks to vegetation from short-term exposures to dry deposited
particulate nitrate or sulfate are low. However, when found in acid or acidifying deposition, such
particles do have the potential to cause direct leaf injury. Specifically, the responses of forest
trees to acid precipitation (rain, snow) include accelerated weathering of leaf cuticular surfaces,
increased permeability of leaf surfaces to toxic materials, water, and disease agents; increased
leaching of nutrients from foliage; and altered reproductive processes—all which serve to
weaken trees so that they are more susceptible to other stresses (e.g., extreme weather, pests,
pathogens). Acid deposition with levels of acidity associated with the leaf effects described
above are currently found in some locations in the eastern U.S.247 Even higher concentrations of
acidity can be present in occult depositions (e.g., fog, mist or clouds) which more frequently
impacts higher elevations. Thus, the risk of leaf injury occurring from acid deposition in some
areas of the eastern U.S. is high. Nitrogen deposition has also been shown to impact ecosystems
in the western U.S. A study conducted in the Columbia River Gorge National Scenic Area
(CRGNSA), located along a portion of the Oregon/Washington border, indicates that lichen
communities in the CRGNSA have shifted to a higher proportion of nitrophilous species and the
nitrogen content of lichen tissue is elevated.248 Lichens are sensitive indicators of nitrogen
deposition effects to terrestrial ecosystems and the lichen studies in the Columbia River Gorge
clearly show that ecological effects from air pollution are occurring.
Some of the most significant detrimental effects associated with excess nitrogen
deposition are those associated with a condition known as nitrogen saturation. Nitrogen
saturation is the condition in which nitrogen inputs from atmospheric deposition and other
sources exceed the biological requirements of the ecosystem. The effects associated with
nitrogen saturation include: (1) decreased productivity, increased mortality, and/or shifts in plant
community composition, often leading to decreased biodiversity in many natural habitats
wherever atmospheric reactive nitrogen deposition increases significantly above background and
critical thresholds are exceeded; (2) leaching of excess nitrate and associated base cations from
soils into streams, lakes, and rivers, and mobilization of soil aluminum; and (3) fluctuation of
ecosystem processes such as nutrient and energy cycles through changes in the functioning and
species composition of beneficial soil organisms.249
In the U.S. numerous forests now show severe symptoms of nitrogen saturation. These
forests include: the northern hardwoods and mixed conifer forests in the Adirondack and Catskill
Mountains of New York; the red spruce forests at Whitetop Mountain, Virginia, and Great
Smoky Mountains National Park, North Carolina; mixed hardwood watersheds at Fernow
Experimental Forest in West Virginia; American beech forests in Great Smoky Mountains
National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern California
and the southwestern Sierra Nevada in Central California; the alpine tundra/subalpine conifer
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MY 2017 and Later - Regulatory Impact Analysis
forests of the Colorado Front Range; and red alder forests in the Cascade Mountains in
Washington.
Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or
oceans) either from direct atmospheric deposition, surface runoff, or leaching from nitrogen
saturated soils into ground or surface waters can contribute to conditions of severe water oxygen
depletion; eutrophication and algae blooms; altered fish distributions, catches, and physiological
states; loss of biodiversity; habitat degradation; and increases in the incidence of disease.
Atmospheric deposition of nitrogen is a significant source of total nitrogen to many
estuaries in the United States. The amount of nitrogen entering estuaries that is ultimately
attributable to atmospheric deposition is not well-defined. On an annual basis, atmospheric
nitrogen deposition may contribute significantly to the total nitrogen load, depending on the size
and location of the watershed. In addition, episodic nitrogen inputs, which may be ecologically
important, may play a more important role than indicated by the annual average concentrations.
Estuaries in the U.S. that suffer from nitrogen enrichment often experience a condition known as
eutrophication. Symptoms of eutrophication include changes in the dominant species of
phytoplankton, low levels of oxygen in the water column, fish and shellfish kills, outbreaks of
toxic algae, and other population changes which can cascade throughout the food web. In
addition, increased phytoplankton growth in the water column and on surfaces can attenuate light
causing declines in submerged aquatic vegetation, which serves as an important habitat for many
estuarine fish and shellfish species.
Severe and persistent eutrophication often directly impacts human activities. For
example, losses in the nation's fishery resources may be directly caused by fish kills associated
with low dissolved oxygen and toxic blooms. Declines in tourism occur when low dissolved
oxygen causes noxious smells and floating mats of algal blooms create unfavorable aesthetic
conditions. Risks to human health increase when the toxins from algal blooms accumulate in
edible fish and shellfish, and when toxins become airborne, causing respiratory problems due to
inhalation. According to a NOAA report, more than half of the nation's estuaries have moderate
to high expressions of at least one of these symptoms - an indication that eutrophication is well
9M"1
developed in more than half of U.S. estuaries.
6.1.2.3.2 Deposition of Heavy Metals
Heavy metals, including cadmium, copper, lead, chromium, mercury, nickel and zinc,
have the greatest potential for impacting forest growth.251 Investigation of trace metals near
roadways and industrial facilities indicate that a substantial load of heavy metals can accumulate
on vegetative surfaces. Copper, zinc, and nickel have been documented to cause direct toxicity
to vegetation under field conditions. Little research has been conducted on the effects associated
with mixtures of contaminants found in ambient PM. While metals typically exhibit low
solubility, limiting their bioavailability and direct toxicity, chemical transformations of metal
compounds occur in the environment, particularly in the presence of acidic or other oxidizing
species. These chemical changes influence the mobility and toxicity of metals in the
environment. Once taken up into plant tissue, a metal compound can undergo chemical changes,
exert toxic effects on the plant itself, accumulate and be passed along to herbivores or can re-
enter the soil and further cycle in the environment. Although there has been no direct evidence
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of a physiological association between tree injury and heavy metal exposures, heavy metals have
been implicated because of similarities between metal deposition patterns and forest decline.
This hypothesized relationship/correlation was further explored in high elevation forests in the
northeastern U.S. These studies measured levels of a group of intracellular compounds found in
plants that bind with metals and are produced by plants as a response to sublethal concentrations
of heavy metals. These studies indicated a systematic and significant increase in concentrations
of these compounds associated with the extent of tree injury. These data strongly imply that
metal stress causes tree injury and contributes to forest decline in the northeastern United
States.252 Contamination of plant leaves by heavy metals can lead to elevated soil levels. Trace
metals absorbed into the plant frequently bind to the leaf tissue, and then are lost when the leaf
drops. As the fallen leaves decompose, the heavy metals are transferred into the soil.253'254 Upon
entering the soil environment, PM pollutants can alter ecological processes of energy flow and
nutrient cycling, inhibit nutrient uptake, change ecosystem structure, and affect ecosystem
biodiversity. Many of the most important effects occur in the soil. The soil environment is one
of the most dynamic sites of biological interaction in nature. It is inhabited by microbial
communities of bacteria, fungi, and actinomycetes. These organisms are essential participants in
the nutrient cycles that make elements available for plant uptake. Changes in the soil
environment that influence the role of the bacteria and fungi in nutrient cycling determine plant
and ultimately ecosystem response.255
The environmental sources and cycling of mercury are currently of particular concern due
to the bioaccumulation and biomagnification of this metal in aquatic ecosystems and the potent
toxic nature of mercury in the forms in which is it ingested by people and other animals.
Mercury is unusual compared with other metals in that it largely partitions into the gas phase (in
elemental form), and therefore has a longer residence time in the atmosphere than a metal found
predominantly in the particle phase. This property enables mercury to travel far from the primary
source before being deposited and accumulating in the aquatic ecosystem. The major source of
mercury in the Great Lakes is from atmospheric deposition, accounting for approximately eighty
percent of the mercury in Lake Michigan. 56'257 Over fifty percent of the mercury in the
Chesapeake Bay has been attributed to atmospheric deposition.258 Overall, the National Science
and Technology Council identifies atmospheric deposition as the primary source of mercury to
aquatic systems.259 Forty-four states have issued health advisories for the consumption of fish
contaminated by mercury; however, most of these advisories are issued in areas without a
mercury point source.
Elevated levels of zinc and lead have been identified in streambed sediments, and these
elevated levels have been correlated with population density and motor vehicle use.260'261 Zinc
and nickel have also been identified in urban water and soils. In addition, platinum, palladium,
and rhodium, metals found in the catalysts of modern motor vehicles, have been measured at
elevated levels along roadsides.262 Plant uptake of platinum has been observed at these locations.
6.1.2.3.3 Deposition of Polycyclic Organic Matter
Polycyclic organic matter (POM) is a byproduct of incomplete combustion and consists
of organic compounds with more than one benzene ring and a boiling point greater than or equal
to 100 degrees centigrade.263 Polycyclic aromatic hydrocarbons (PAHs) are a class of POM that
contains compounds which are known or suspected carcinogens.
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Major sources of PAHs include mobile sources. PAHs in the environment may be present
as a gas or adsorbed onto airborne particulate matter. Since the majority of PAHs are adsorbed
onto particles less than 1.0 jam in diameter, long range transport is possible. However, studies
have shown that PAH compounds adsorbed onto diesel exhaust particulate and exposed to ozone
have half lives of 0.5 to 1.0 hours.264
Since PAHs are insoluble, the compounds generally are particle reactive and accumulate
in sediments. Atmospheric deposition of particles is believed to be the major source of PAHs to
the sediments of Lake Michigan.265'266 Analyses of PAH deposition in Chesapeake and
Galveston Bay indicate that dry deposition and gas exchange from the atmosphere to the surface
water predominate.267'268 Sediment concentrations of PAHs are high enough in some segments of
Tampa Bay to pose an environmental health threat. EPA funded a study to better characterize the
sources and loading rates for PAHs into Tampa Bay.269 PAHs that enter a water body through
gas exchange likely partition into organic rich particles and can be biologically recycled, while
dry deposition of aerosols containing PAHs tend to be more resistant to biological recycling.270
Thus, dry deposition is likely the main pathway for PAH concentrations in sediments while
gas/water exchange at the surface may lead to PAH distribution into the food web, leading to
increased health risk concerns.
Trends in PAH deposition levels are difficult to discern because of highly variable
ambient air concentrations, lack of consistency in monitoring methods, and the significant
influence of local sources on deposition levels.271 Van Metre et al. noted PAH concentrations in
urban reservoir sediments have increased by 200-300% over the last forty years and correlate
with increases in automobile use.272
Cousins et al. estimate that more than ninety percent of semi-volatile organic compound
(SVOC) emissions in the United Kingdom deposit on soil.273 An analysis of PAH concentrations
near a Czechoslovakian roadway indicated that concentrations were thirty times greater than
background.274
6.1.2.3.4 Materials Damage and Soiling
The effects of the deposition of atmospheric pollution, including ambient PM, on
materials are related to both physical damage and impaired aesthetic qualities. The deposition of
PM (especially sulfates and nitrates) can physically affect materials, adding to the effects of
natural weathering processes, by potentially promoting or accelerating the corrosion of metals, by
degrading paints, and by deteriorating building materials such as concrete and limestone. Only
chemically active fine particles or hygroscopic coarse particles contribute to these physical
effects. In addition, the deposition of ambient PM can reduce the aesthetic appeal of buildings
and culturally important articles through soiling. Particles consisting primarily of carbonaceous
compounds cause soiling of commonly used building materials and culturally important items
such as statues and works of art.
6.1.2.4 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
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(VOCs), some of which are considered air toxics, have long been suspected to play a role in
vegetation damage.275 In laboratory experiments, a wide range of tolerance to VOCs has been
observed.276 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. 7?
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.278'279'280 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 4 of this RIA presents the projected emissions changes due to the vehicle
standards. 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 the
standards is to address greenhouse gas emissions, the GHG standards will also impact emissions
of criteria pollutants and air toxics. Sections 6.2.1 and 6.2.2 describe the air quality modeling
methodology and results.
6.2.1 Air Quality Modeling Methodology
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.
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 - local, regional, national, and global. This section provides detailed
information on the photochemical model used for our air quality analysis (the Community Multi-
scale Air Quality (CMAQ) model), atmospheric reactions and the role of chemical mechanisms
in modeling, and model uncertainties and limitations. Further discussion of the modeling
methodology is included in the Air Quality Modeling Technical Support Document (AQM TSD)
found in the docket for this rulemaking (EPA-HQ-OAR-2010-0799). Results of the air quality
modeling are presented in Section 6.2.2.
6.2.1.1 Modeling Methodology
A national-scale air quality modeling analysis was performed to estimate future year
annual PM2.5 concentrations, 24-hour PM2.5 concentrations, 8-hour ozone concentrations, air
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toxics concentrations, visibility levels and nitrogen and sulfur deposition levels. The 2005-based
CMAQ modeling platform was used as the basis for the air quality modeling of the future
reference case and the future control scenario for this final rulemaking. This platform represents
a structured system of connected modeling-related tools and data that provide a consistent and
transparent basis for assessing the air quality response to projected changes in emissions. The
base year of data used to construct this platform includes emissions and meteorology for 2005.
The platform was developed by the U.S. EPA's Office of Air Quality Planning and Standards in
collaboration with the Office of Research and Development and is intended to support a variety
of regulatory and research model applications and analyses.
The CMAQ modeling system is a non-proprietary, publicly available, peer-reviewed,
state-of-the-science, three-dimensional, grid-based Eulerian air quality grid model designed to
estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations,
acid deposition, and air toxics, over regional and urban spatial scales for given input sets of
meteorological conditions and emissions.281'282'283 The CMAQ model version 4.7 was most
recently peer-reviewed in February of 2009 for the U.S. EPA.284 The CMAQ model is a well-
known and well-respected tool and has been used in numerous national and international
applications.285'286'287 This 2005 multi-pollutant modeling platform used CMAQ version
"Y"Y"Y"Y"Y
4.7.1 with a minor internal change made by the U.S. EPA CMAQ model developers
intended to speed model runtimes when only a small subset of toxics species are of interest.
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. We used CMAQ v4.7.1 which reflects updates to version 4.7 to improve the
underlying science. These include aqueous chemistry mass conservation improvements,
improved vertical convective mixing and lowered CB05 mechanism unit yields for acrolein from
1,3-butadiene tracer reactions which were updated to be consistent with laboratory
measurements. Section 6.2.1.2 of this RIA discusses the chemical mechanism and Secondary
Organic Aerosol (SOA) formation.
6.2.1.1.1 Model Domain and Configuration
The CMAQ modeling domain encompasses all of the lower 48 States and portions of
Canada and Mexico. The modeling domain is made up of a large continental U.S. 36 kilometer
(km) grid and two 12 km grids (an Eastern US and a Western US domain), as shown in Figure
6.2-1. The modeling domain contains 14 vertical layers with the top of the modeling domain at
about 16,200 meters, or 100 millibars (mb).
xxxxx CMAQ version 4.7.1 was released in June 2010. It is available from the Community Modeling and Analysis
System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org. The air quality modeling
for these final standards was initiated prior to February 2012, when CMAQ 5.0 was publically released. CMAQ
4.7.1 was used since it was the most current version of the model available at the time the air quality modeling
started.
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Chapter 6
| 12km West Domain Boundary
V \T
Figure 6.2-1 Map of the CMAQ Modeling Domain
6.2.1.1.2 Model Inputs
The key inputs to the CMAQ model include emissions from anthropogenic and biogenic
sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological
input files were derived from simulations of the Pennsylvania State University/National Center
for Atmospheric Research Mesoscale Model288 for the entire year of 2005 over model domains
that are slightly larger than those shown in Figure 6.2-1. This model, commonly referred to as
MM5, is a limited-area, nonhydrostatic, terrain-following system that solves for the full set of
physical and thermodynamic equations which govern atmospheric motions.289 The meteorology
for the national 36 km grid and the two 12 km grids were developed by EPA and are described in
more detail within the AQM TSD. The meteorological outputs from MM5 were processed to
create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor
(MCIP) version 3.4, for example: horizontal wind components (i.e., speed and direction),
temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical
layer.
290
The lateral boundary and initial species concentrations are provided by a three-
dimensional global atmospheric chemistry model, the GEOS-CHEM model.291 The global
GEOS-CHEM model simulates atmospheric chemical and physical processes driven by
assimilated meteorological observations from the NASA's Goddard Earth Observing System
(GEOS). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitude-
longitude) and 20 vertical layers. The predictions were used to provide one-way dynamic
boundary conditions at three-hour intervals and an initial concentration field for the 36 km
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MY 2017 and Later - Regulatory Impact Analysis
CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used
as the initial/boundary state for all subsequent 12 km finer grid modeling.
The emissions inputs used for the 2005 base year and each of the future year base cases
and control scenarios are summarized in Chapter 4 of this RIA.
6.2.1.1.3 CMAQ Evaluation
An operational model performance evaluation for ozone, PM2.5 and its related speciated
components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate and sulfate
deposition, and specific air toxics (formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and
acrolein) was conducted using 2005 state/local monitoring data in order to estimate the ability of
the CMAQ modeling system to replicate base year concentrations. Model performance statistics
were calculated for observed/predicted pairs of daily/monthly/seasonal/annual concentrations.
Statistics were generated for the following geographic groupings: domain wide, Eastern vs.
Western (divided along the 100th meridian), and each Regional Planning Organization (RPO)
region.YYYYY The "acceptability" of model performance was judged by comparing our results to
those found in recent regional PM2.5 model applications for other, non-EPA studies.22222
Overall, the performance for the 2005 modeling platform is within the range or close to that of
these other applications. The performance of the CMAQ modeling was evaluated over a 2005
base case. The model was able to reproduce historical concentrations of ozone and PM2.5 over
land with low bias and error results. Model predictions of annual formaldehyde, acetaldehyde
and benzene showed relatively small bias and error results when compared to observations. The
model yielded larger bias and error results for 1,3 butadiene and acrolein based on limited
monitoring sites. A more detailed summary of the 2005 CMAQ model performance evaluation is
available within the AQM TSD found in the docket for this rule.
6.2.1.1.4 Model Simulation Scenarios
As part of our analysis for this rulemaking, the CMAQ modeling system was used to
calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual and
seasonal (summer and winter) air toxics concentrations, visibility levels, and annual nitrogen and
sulfur deposition total levels for each of the following emissions scenarios:
- 2005 base year
- 2030 reference case projection
- 2030 control case projection
YYYYY
Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU),
Midwest Regional Planning Organization - Lake Michigan Air Directors Consortium (MWRPO-LADCO),
Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air
Partnership (CENRAP), and Western Regional Air Partnership (WRAP).
zzzzz T[jese other modeling studies represent a wide range of modeling analyses which cover various models, model
configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.
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Chapter 6
The emission inventories used in the air quality (and benefits) modeling are different from
the final rule inventories due to the considerable length of time required to conduct the modeling.
However, the air quality modeling inventories are generally consistent with the final emission
inventories, so the air quality modeling adequately reflects the effects of the rule. The emission
inventories used for air quality modeling are discussed in Chapter 4 of this RIA. The emissions
modeling TSD, found in the docket for this rule (EPA-HQ-OAR-2010-0799), contains a detailed
discussion of the emissions inputs used in our air quality modeling.
We use the predictions from the model in a relative sense by combining the 2005 base-
year predictions with predictions from each future-year scenario and applying these modeled
ratios to ambient air quality observations to estimate daily and annual PM2.5 concentrations, and
8-hour ozone concentrations for each of the 2030 scenarios. The ambient air quality observations
are average conditions, on a site-by-site basis, for a period centered around the model base year
(i.e., 2003-2007).
The projected daily and annual PM2.5 design values were calculated using the Speciated
Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference Method mass
construction methodology that results in reduced nitrates (relative to the amount measured by
routine speciation networks), higher mass associated with sulfates (reflecting water included in
Federal Reference Method measurements), and a measure of organic carbonaceous mass that is
derived from the difference between measured PM25 and its non-carbon components. This
characterization of PM2.5 mass also reflects crustal material and other minor constituents. The
resulting characterization provides a complete mass balance. It does not have any unknown mass
that is sometimes presented as the difference between measured PM2.5 mass and the characterized
chemical components derived from routine speciation measurements. However, the assumption
that all mass difference is organic carbon has not been validated in many areas of the U.S. The
SMAT methodology uses the following PM2.5 species components: sulfates, nitrates, ammonium,
organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5
l^g/m3). More complete details of the SMAT procedures can be found in the report "Procedures
for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the (Revised)
Speciated Modeled Attainment Test (SMAT)".292 For this latest analysis, several datasets and
techniques were updated. These changes are fully described within the technical support
document for the Final Transport Rule AQM TSD.293 The projected 8-hour ozone design values
were calculated using the approach identified in EPA's guidance on air quality modeling
attainment demonstrations.294
Additionally, we conducted an analysis to compare the absolute and percent differences
between the 2030 control case and the 2030 reference cases for annual and seasonal
formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein, as well as annual nitrate and
sulfate deposition. These data were not compared in a relative sense due to the limited
observational data available.
6.2.1.2 Chemical Mechanisms in Modeling
This rule presents inventories for NOx, VOC, CO, PM2.5, SO2, NH3 and five air toxics:
benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. The air toxics are explicit
model species in the CMAQv4.7 model with carbon bond 5 (CB05) mechanisms.295 In addition
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MY 2017 and Later - Regulatory Impact Analysis
to direct emissions, photochemical processes mechanisms are responsible for formation of some
of these compounds in the atmosphere from precursor emissions. For some pollutants such as
PM, formaldehyde, and acetaldehyde, many photochemical processes are involved. CMAQ
therefore also requires inventories for a large number of other air toxics and precursor pollutants.
Methods used to develop the air quality inventories can be found in Chapter 4 of the RIA.
In the CB05 mechanism, the chemistry of thousands of different VOCs in the
atmosphere is represented by a much smaller number of model species which characterize the
general behavior of a subset of chemical bond types; this condensation is necessary to allow the
use of complex photochemistry in a fully 3-D air quality model.296
Complete combustion of ethanol in fuel produces carbon dioxide (CO2) and water (H2O).
Incomplete combustion of ethanol results in the production of other air pollutants, such as
acetaldehyde and other aldehydes, and the release of unburned ethanol. Ethanol is also present in
evaporative emissions. In the atmosphere, ethanol from unburned fuel and evaporative emissions
can undergo photodegradation to form aldehydes (acetaldehyde and formaldehyde) and
peroxyacetyl nitrate (PAN), and also plays a role in ground-level ozone formation. Mechanisms
for these reactions are included in CMAQ. Additionally, alkenes and other hydrocarbons are
considered because any increase in acetyl peroxy radicals due to ethanol increases might be
counterbalanced by a decrease in radicals resulting from decreases in other hydrocarbons.
CMAQ includes 63 inorganic reactions to account for the cycling of all relevant oxidized
nitrogen species and cycling of radicals, including the termination of NO2 and formation of nitric
acid (HNO3) without PAN
NO2 + -OH + M -> HNO3 + M k = 1.19 x 10"11 cn^molecule'Y1 297
The CB05 mechanism also includes more than 90 organic reactions that include alternate
pathways for the formation of acetyl peroxy radical, such as by reaction of ethene and other
alkenes, alkanes, and aromatics. Alternate reactions of acetyl peroxy radical, such as oxidation
of NO to form NO2, which again leads to ozone formation, are also included.
Atmospheric reactions and chemical mechanisms involving several key formation
pathways are discussed in more detail in the following sections.
6.2.1.2.1 Acetaldehyde
Acetaldehyde is the main photodegradation product of ethanol, as well as other precursor
hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the atmosphere,
acetaldehyde can react with the OH radical and O2 to form the acetyl peroxy radical
[CH3C(O)OO-].BBBBBB When NOX is present in the atmosphere this radical species can then
AAAAAA All rate coefficients are listed at 298 K and, if applicable, 1 bar of air.
BBBBBB Acetaldehyde is not the only source of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl
compounds (methylglyoxal, biacetyl, and others) also form acetyl radicals, which can further react to form
peroxyacetyl nitrate (PAN).
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Chapter 6
further react with nitric oxide (NO), to produce formaldehyde (HCHO), or with nitrogen dioxide
(NO2), to produce PAN [CH3C(O)OONO2]. An overview of these reactions and the
corresponding reaction rates are provided below.cccccc
CH3CHO + -OH -> CH3C-O + H2O k = 1.5 x 10"11 cm3molecule"1s"1 298
CH3C-O + O2 + M -> CH3C(O)OO- + M
CH3C(O)OO- + NO -> CH3C(O)O- + NO2 k = 2.0 x 10"11 cm3molecule"1s"1 2"
CH3C(O)O- -> -CH3 + CO2
•CH3 + O2 + M -> CH3OO- + M
CH3OO- + NO -> CH3O- + NO2
CH3O- + O2 -> HCHO + HO2
CH3C(O)OO- + NO2 + M -> CH3C(O)OONO2 + M k = 1.0 x 10"11 cm3molecule"1s"1 30°
Acetaldehyde can react with the NO3 radical, ground state oxygen atom (O3P) and
chlorine, although these reactions are much slower. Acetaldehyde can also photolyze (hv), which
predominantly produces -CH3 (which reacts as shown above to form CH3OO-) and HCO (which
rapidly forms HO2 and CO):
CH3CHO + hv +2 O2 -> CH3OO- +HO2 + CO X = 240-380 nm301
As mentioned above, CH3OO- can react in the atmosphere to produce formaldehyde
(HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, the
most important reactions of formaldehyde are photolysis and reaction with the OH, with
atmospheric lifetimes of approximately 3 hours and 13 hours, respectively.302 Formaldehyde can
also react with NO3 radical, ground state oxygen atom (O3P) and chlorine, although these
reactions are much slower. Formaldehyde is removed mainly by photolysis whereas the higher
aldehydes, those with two or more carbons such as acetaldehyde, react predominantly with OH
radicals. The photolysis of formaldehyde is an important source of new hydroperoxy radicals
(HO2), which can lead to ozone formation and regenerate OH radicals.
HCHO + hv + 2 O2 -> 2 HO2 + CO X = 240-360 nm303
HO2 + NO -> NO2+ OH
Photolysis of HCHO can also proceed by a competing pathway which makes only stable
products: H2 and CO.
cccccc ^jj rate coefficjents are listed at 298 K and, if applicable, 1 bar of air.
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CB05 mechanisms for acetaldehyde formation warrant a detailed discussion given the
increase in vehicle and engine exhaust emissions for this pollutant and ethanol, which can form
acetaldehyde in the air. Acetaldehyde is represented explicitly in the CB05 chemical
mechanism304'305 by the ALD2 model species, which can be both formed from other VOCs and
can decay via reactions with oxidants and radicals. The reaction rates for acetaldehyde, as well
as for the inorganic reactions that produce and cycle radicals, and the representative reactions of
other VOCs have all been updated to be consistent with recommendations in the literature.306
The decay reactions of acetaldehyde are fewer in number and can be characterized well
because they are explicit representations. In CB05, acetaldehyde can photolyze in the presence
of sunlight or react with molecular oxygen (O3(P)), hydroxyl radical (OH), or nitrate radicals.
The reaction rates are based on expert recommendations,307 and the photolysis rate is from
IUPAC recommendations.
In CMAQ v4.7, the acetaldehyde that is formed from photochemical reactions is tracked
separately from that which is due to direct emission and transport of direct emissions. In CB05,
there are 25 different reactions that form acetaldehyde in molar yields ranging from 0.02 (ozone
reacting with lumped products from isoprene oxidation) to 2.0 (cross reaction of acylperoxy
radicals, CXOs). The specific parent VOCs that contribute the most to acetaldehyde
concentrations vary spatially and temporally depending on characteristics of the ambient air, but
alkenes in particular are found to play a large role. The IOLE model species, which represents
internal carbon-carbon double bonds, has high emissions and relatively high yields of
acetaldehyde. The OLE model species, representing terminal carbon double bonds, also plays a
role because it has high emissions although lower acetaldehyde yields. Production from
peroxyproprional nitrate and other peroxyacylnitrates (PANX) and aldehydes with 3 or more
carbon atoms can in some instances increase acetaldehyde but because they also are a sink of
radicals, their effect is smaller. Thus, the amount of acetaldehyde (and formaldehyde as well)
formed in the ambient air as well as emitted in the exhaust (the latter being accounted for in
emission inventories) is affected by changes in these precursor compounds due to the addition of
ethanol to fuels (e.g., decreases in alkenes would cause some decrease of acetaldehyde, and to a
larger extent, formaldehyde).
The reaction of ethanol (CHsCHiOH) with OH is slower than some other important
reactions but can be an important source of acetaldehyde if the emissions are large. Based on
kinetic data for molecular reactions, the only important chemical loss process for ethanol (and
other alcohols) is reaction with the hydroxyl radical (-OH).308 This reaction produces
acetaldehyde (CH3CHO) with a 90 percent yield.309 The lifetime of ethanol in the atmosphere
can be calculated from the rate coefficient, k, and due to reaction with the OH radical, occurs on
the order of a day in polluted urban areas or several days in unpolluted areas.DDDDDD
In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. It
assumes the majority of the reaction occurs through H-atom abstraction of the more weakly-
bonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy
DDDDDDAII rate coefficients w& listed at 298 K and, if applicable, 1 bar of air.
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radical (HO2), and the remainder of the reaction occurs at the -CHS and -OH groups, creating
formaldehyde (HCHO), oxidizing NO to NO2 (represented by model species XO2) and creating
glycoaldehyde, which is represented as ALDX:
CH3CHOH + OH -> HO2 + 0.90 CH3CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO2
6.2.1.2.2 Secondary Organic Aerosols
Secondary organic aerosol (SOA) chemistry research described below has led to
implementation of new pathways for SOA in CMAQ 4.7, based on recommendations of Edney et
al. and the recent work of Carlton et al.310'311 In previous versions of CMAQ, all SOA was
semivolatile and resulted from the oxidation of compounds emitted entirely in the gas-phase. In
CMAQ v4.7, parameters in existing pathways were revised and new formation mechanisms were
added. Some of the new pathways, such as low-NOx oxidation of aromatics and particle-phase
oligomerization, result in nonvolatile SOA.
•'•&'•
Organic aerosol can be classified as either primary or secondary depending on whether it
is emitted into the atmosphere as a particle (primary organic aerosol, POA) or formed in the
atmosphere (SOA). SOA precursors include volatile organic compounds (VOCs) as well as low-
volatility compounds that can react to form even lower volatility compounds. Current research
suggests SOA contributes significantly to ambient organic aerosol (OA) concentrations, and in
Southeast and Midwest States may make up more than 50 percent (although the contribution
varies from area to area) of the organic fraction of PM2.5 during the summer (but less in the
winter).312'313 A wide range of laboratory studies conducted over the past twenty years show that
anthropogenic aromatic hydrocarbons and long-chained alkanes, along with biogenic isoprene,
monoterpenes, and sesquiterpenes, contribute to SOA formation.314'31 >316>317>318 Modeling
studies, as well as carbon isotope measurements, indicate that a significant fraction of SOA
results from the oxidation of biogenic hydrocarbons.319'320 Based on parameters derived from
laboratory chamber experiments, SOA chemical mechanisms have been developed and integrated
into air quality models such as the CMAQ model and have been used to predict OA
321
concentrations.
Over the past 10 years, ambient OA concentrations have been routinely measured in the
U.S. and some of these data have been used to determine, by employing source/receptor methods,
the contributions of the major OA sources, including biomass burning and vehicular gasoline and
diesel exhaust. Since mobile sources are a significant source of VOC emissions, currently
accounting for almost 40 percent of anthropogenic VOC,322 mobile sources are also an important
source of SOA, particularly in populated areas.
Toluene is an important contributor to anthropogenic SOA. Mobile sources are the most
significant contributor to ambient toluene concentrations as shown by analyses done for the 2005
National Air Toxics Assessment (NATA)323 and the Mobile Source Air Toxics (MSAT) Rule.324
The 2005 NATA indicates that onroad and nonroad mobile sources accounted for almost 60
percent (1.46 (^g/m3) of the total average nationwide ambient concentration of toluene (2.48
l^g/m3), when the contribution of the estimated "background" is apportioned among source
sectors.
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The amount of toluene in gasoline influences the amount of toluene emitted in vehicle
exhaust and evaporative emissions, although, like benzene, some toluene is formed in the
combustion process. In turn, levels of toluene and other aromatics in gasoline are potentially
influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of
ethanol, it greatly reduces the need for and levels of other high-octane components such as
aromatics including toluene (which is the major aromatic compound in gasoline). Since toluene
contributes to SOA and the toluene level of gasoline is decreasing, it is important to assess the
effect of these reductions on ambient PM.
In addition to toluene, other mobile-source hydrocarbons such as benzene, xylene, and
alkanes form SOA. Similar to toluene, the SOA produced by benzene and xylene from low-NOx
pathways is expected to be less volatile and be produced in higher yields than SOA from high-
NOx conditions.325 Alkanes form SOA with higher yields resulting from the oxidation of longer
chain as well as cyclic alkanes.326
It is unlikely that ethanol would form directly from SOA or affect SOA formation
indirectly through changes in the radical populations from increasing ethanol exhaust.
Nevertheless, scientists at the U.S. EPA's Office of Research and Development recently directed
experiments to investigate ethanol's SOA forming potential.327 The experiments were conducted
under conditions where peroxy radical reactions would dominate over reaction with NO (i.e.,
irradiations performed in the absence of NOx and OH produced from the photolysis of hydrogen
peroxide). This was the most likely scenario under which SOA formation could occur, since a
highly oxygenated C4 organic would be potentially made. As expected, no SOA was produced.
From these experiments, the upper limit for the aerosol yield would have been less than 0.01
percent based on scanning mobility particle sizer (SMPS) data. Given the expected negative
result based on these initial smog chamber experiments, these data were not published.
In general, measurements of organic aerosol represent the sum of POA and SOA and the
fraction of aerosol that is secondary in nature can only be estimated. One of the most widely
applied method of estimating total ambient SOA concentrations is the EC tracer method using
ambient data which estimates the OC/EC ratio in primary source emissions.328'329 SOA
concentrations have also been estimated using OM (organic mass) to OC (organic carbon) ratios,
which can indicate that SOA formation has occurred, or by subtracting the source/receptor-based
total primary organic aerosol (POA) from the measured OC concentration.330 Aerosol mass
spectrometer (AMS) measurements along with positive matrix factorization (PMF) can also be
used to identify surrogates for POA and SOA in ambient as well as chamber experiments. Such
methods, however, may not be quantitatively accurate and provide no information on the
contribution of individual biogenic and anthropogenic SOA sources, which is critical information
needed to assess the impact of specific sources and the associated health risk. These methods
assume that OM containing additional mass from oxidation of OC comes about largely (or
solely) from SOA formation. In particular, the contributions of anthropogenic SOA sources,
including those of aromatic precursors, are required to determine exposures and risks associated
with replacing fossil fuels with biofuels.
Upon release into the atmosphere, numerous VOC compounds can react with free radicals
in the atmosphere to form SOA. While this has been investigated in the laboratory, there is
relatively little information available on the specific chemical composition of SOA compounds
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Chapter 6
themselves from specific VOC precursors. This absence of compositional data from the
precursors has largely prevented the identification of aromatically-derived SOA in ambient
samples which, in turn, has prevented observation-based measurements of the aromatic and other
SOA contributions to ambient PM levels.
As a first step in determining the ambient SOA concentrations, EPA has developed a
tracer-based method to estimate such concentrations.331'332 The method is based on using mass
fractions of SOA tracer compounds, measured in smog chamber-generated SOA samples, to
convert ambient concentrations of SOA tracer compounds to ambient SOA concentrations. This
method consists of irradiating the SOA precursor of interest in a smog chamber in the presence of
NOx, collecting the SOA produced on filters, and then analyzing the samples for highly polar
compounds using advanced analytical chemistry methods. Employing this method, candidate
tracers have been identified for several VOC compounds which are emitted in significant
quantities and known to produce SOA in the atmosphere. Some of these SOA-forming
compounds include toluene, a variety of monoterpenes, isoprene, and p-caryophyllene, the latter
three of which are emitted by vegetation and are more significant sources of SOA than toluene.
Smog chamber work can also be used to investigate SOA chemical formation
mechanisms.333'334'335'336
Although these concentrations are only estimates, due to the assumption that the mass
fractions of the smog chamber SOA samples using these tracers are equal to those in the ambient
atmosphere, there are presently no other means available for estimating the SOA concentrations
originating from individual SOA precursors. Among the tracer compounds observed in ambient
PM2.5 samples are two tracer compounds that have been identified in smog chamber aromatic
SOA samples.337 To date, these aromatic tracer compounds have been identified, in the
laboratory, for toluene and m-xylene SOA. Additional work is underway by the EPA to
determine whether these tracers are also formed by benzene and other alkylbenzenes (including
o-xylene, p-xylene, 1,2,4-trimethylbenzene, and ethylbenzene).
One caveat regarding this work is that a large number of VOCs emitted into the
atmosphere, which have the potential to form SOA, have not yet been studied in this way. It is
possible that these unstudied compounds produce SOA species which are being used as tracers
for other VOCs. This means that the present work could overestimate the amount of SOA
formed in the atmosphere by the VOCs studied to date. This approach may also estimate entire
hydrocarbon classes (e.g., all methylsubstituted-monoaromatics or all monoterpenes) and not
individual precursor hydrocarbons. Thus the tracers could be broadly representative and not
indicative of individual precursors. This is still unknown. Also, anthropogenic precursors play a
role in formation of atmospheric radicals and aerosol acidity, and these factors influence SOA
formation from biogenic hydrocarbons. This anthropogenic and biogenic interaction, important
to EPA and others, needs further study. The issue of SOA formation from aromatic precursors is
an important one to which EPA and others are paying significant attention.
The aromatic tracer compounds and their mass fractions have also been used to estimate
monthly ambient aromatic SOA concentrations from March 2004 to February 2005 in five U.S.
Midwestern cities.338 The annual tracer-based SOA concentration estimates were 0.15, 0.18,
0.13, 0.15, and 0.19 ^ig carbon/m3 for Bondville, IL, East St. Louis, IL, Northbrook, IL,
Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations occurring in the
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summer. On average, the aromatic SOA concentrations made up 17 percent of the total SOA
concentration. Thus, this work suggests that we are finding ambient PM levels on an annual
basis of about 0.15 (^g/m3 associated with present toluene levels in the ambient air in these
Midwest cities. Based on preliminary analysis of recent laboratory experiments, it appears the
toluene tracer could also be formed during photooxidation of some of the xylenes.339
Over the past decade a variety of modeling studies have been conducted to predict
ambient SOA levels. While early studies focused on the contribution of biogenic monoterpenes,
additional precursors, such as sesquiterpenes, isoprene, benzene, toluene, and xylene, have been
implemented in atmospheric models such as GEOS-Chem, PMCAMx, and CMAQ.340'341'342'343'
344, 45,346 §tu(jjes have inc[icated that ambient OC levels may be underestimated by current model
parameterizations.347 While the treatment of new precursors has likely reduced the
model/measurement bias, underestimates can persist.348 In general, modeling studies focus on
comparing the sum of the POA and SOA concentrations with ambient OC or estimated OA
concentrations. Without a method to attribute measured OC to different sources or precursors,
identifying causes of the underestimates in modeled OC via model/measurement comparisons
can be challenging. Oxidation of low-volatility organic compounds as well as particle-phase
reactions resulting from acidity have been explored as potential missing sources of OC in
models.349'350
6.2.1.2.3 Ozone
As mentioned above, the addition of ethanol to fuels has been shown to contribute to
PAN formation and this is one way for it to contribute therefore to ground-level ozone formation
downwind of NOx sources. PAN is a reservoir and carrier of NOx and is the product of acetyl
radicals reacting with NOi in the atmosphere. One source of PAN is the photooxidation of
acetaldehyde (Section 6.2.1.2.1), but many VOCs have the potential for forming acetyl radicals
and therefore PAN or a PAN-type compound.EEEEEE PAN can undergo thermal decomposition
with a lifetime of approximately 1 hour at 298K or 148 days at 250K.FFFFFF
CH3C(O)OONO2 + M -> CH3C(O)OO- + NO2 + M k = 3.3 x 10"4 s"1351
The reaction above shows how NO2 is released in the thermal decomposition of PAN,
along with a peroxy radical which can oxidize NO to NO2. NOi can also be formed in
photodegradation reactions where NO is converted to NO2 (see OH radical reaction of
acetaldehyde in Section 6.2.1.2.1). In both cases, NOi further photolyzes to produce ozone (Os).
NO2 + hv -> NO + O(3P) X = 300-800 nm352
O(3P) + O2 + M -> O3 + M
EEEEEE Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, p-
xylene, and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal andbiacetyl, which are also strong generators of
acetyl radicals (Smith, D.F., T.E. Kleindienst, C.D. Mclver (1999) Primary product distribution from the reaction of
OH with m-, p-xylene and 1,2,4- and 1,3,5-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.).
FFFFFF ^jj rate coefficjents are listed at 298 K and, if applicable, 1 bar of air.
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Chapter 6
The temperature sensitivity of PAN allows it to be stable enough at low temperatures to
be transported long distances before decomposing to release NC>2. NOi can then participate in
ozone formation in regions remote from the original NOx source.353 A discussion of CB05
mechanisms for ozone formation can be found in Yarwood et al. (2005).354
Another important way that ethanol fuels contribute to ozone formation is by increasing
the formation of new radicals through increases in formaldehyde and acetaldehyde. As shown in
Section 6.2.1.2.1, the photolysis of both aldehydes results in two molecules of either hydroperoxy
radical or methylperoxy radical, both of which oxidize NO to NC>2 leading to ozone formation.
6.2.1.3 Modeling Uncertainties and Limitations
All the results presented below must be interpreted with the understanding that there are
uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of the modeling
process. While it is beyond the scope of this RIA to include a comprehensive discussion of all
limitations and uncertainties associated with air quality modeling, several sources of uncertainty
that impact analyses for this rule are discussed.
A source of uncertainty is the photochemical mechanisms in CMAQ 4.7.1. Pollutants
such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3-butadiene can be formed
secondarily through atmospheric chemical processes. Since secondarily formed pollutants can
result from many different reaction pathways, there are uncertainties associated with each
pathway. Simplifications of chemistry must be made in order to handle reactions of thousands of
chemicals in the atmosphere. Mechanisms for formation of ozone, PM, acetaldehyde and
peroxyacetyl nitrate (PAN) are discussed in Section 6.2.1.2.
For PM, there are a number of uncertainties associated with SOA formation. As
mentioned in Section 6.2.1.2.2, a large number of VOCs emitted into the atmosphere, which
have the potential to form SOA, have not yet been studied in detail. In addition, the amount of
ambient SOA that comes from benzene is uncertain. Simplifications to the SOA treatment in
CMAQ have also been made in order to preserve computational efficiency. These
simplifications are described in release notes for CMAQ 4.7 on the Community Modeling and
Analysis System (CMAS) website.355
6.2.2 Air Quality Modeling Results
6.2.2.1 Ozone
As described in Section 6.1.1.4, exposure to ozone causes adverse health effects, and the
EPA has set national standards to provide requisite protection against those health effects. In this
section, we present information on current and model-projected future ozone levels.
6.2.2.1.1 Current Levels of Ozone
Figure 6.2-2 shows a snapshot of measured ozone concentrations in 2010. The highest
ozone concentrations were located in California.
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MY 2017 and Later - Regulatory Impact Analysis
Concentration Range (ppm)
• 0.025 - 0,059 {81 Sites)
O 0.060 - 0.075 {835 Sites)
O 0.076 - 0.095 {279 Sites)
• 0.096-0.120 {18 Sites)
Puerto Rico
Alaska
Figure 6.2-2 Ozone Concentrations (average of annual fourth highest daily maximum 8-
hour concentration) in ppm for 2010356
The primary and secondary NAAQS for ozone are 8-hour standards set at 0.075 ppm. The most
recent revision to the ozone standards was in 2008; the previous 8-hour ozone standards, set in
1997, had been set at 0.08 ppm. In 2004, the U.S. EPA designated nonattainment areas for the
1997 8-hour ozone NAAQS (69 FR 23858, April 30, 2004).GGGGGG As of July 20, 2012, there
were 43 8-hour ozone nonattainment areas for the 1997 ozone NAAQS, composed of 237 full or
partial counties, with a total population of over 129 million. Nonattainment areas for the 1997 8-
hour ozone NAAQS are pictured in Figure 6.2-3. Nonattainment designations for the 2008 ozone
standards were finalized on April 30, 2012 and May 31, 2012.357 These designations include 46
areas, composed of 227 full or partial counties, with a population of over 123 million.
Nonattainment areas for the 2008 ozone NAAQS are pictured in Figure 6.2-9. As of July 20,
2012, 140 million people are living in ozone nonattainment areas.
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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Chapter 6
8-Hour Ozone Nonattainment Areas (1997 Standard)
Nonattainment areas are indicated by color \
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary,
Sbr Ozone Classifications
^| Extreme
| | Severe 17
I | Severe 15
| | Se'icus
| | Moderate
I | Marginal
^ Forrryef Subpart 1
The following multi-state nonattainment area, Chicago-Gary-Lake County. IL-IN 8-hr Ozone area, has some
states in the area that have been redesignated. but it is not considered a maintenance area until all states
in the area are redesignated-The counties for this area are displayed as nonattainment areas:
Figure 6.2-3 1997 8-hour Ozone Nonattainment Areas
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MY 2017 and Later - Regulatory Impact Analysis
8-Hour Ozone Nonattainment Areas (2008 Standard)
Nonattainment areas are indicated by color
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
S-hour Ozone Classification
^^| Extreme
I | Severe 15
| | Serious
| | ModHate
I | Marginal
Figure 6.2-4 2008 8-hour Ozone Nonattainment Areas
States with ozone nonattainment areas are required to take action to bring those areas into
attainment in the future. The attainment date assigned to an ozone nonattainment area is based
on the area's classification. Most ozone nonattainment areas are required to attain the 1997 8-
hour ozone NAAQS in the 2007 to 2013 time frame.HHHHHH Once an ozone nonattainment area
has attained the NAAQS they are then required to maintain it thereafter. 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.
6.2.2.1.2 Projected Levels of Ozone
In the following sections, we describe projected ozone levels in the future with and
without the vehicle standards. Our modeling indicates that there will be very small changes in
ozone across most of the country. In addition, ozone concentrations in some areas will decrease
and ozone concentrations in some other areas will increase. The impacts of the standards on
ozone are a function of VMT increases from rebound, upstream reductions in petroleum
HHHHHH
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.
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Chapter 6
consumption from crude oil production and transport, and gasoline production, distribution and
transport, and changes in location and amount of electricity generation. Information on the air
quality modeling methodology is contained in Section 6.2.1 and additional detail can be found in
the air quality modeling technical support document (AQM TSD).
Projected Levels of Ozone without this Final Action
EPA has already adopted many emission control programs that are expected to reduce
ambient ozone levels. These control programs include the New Marine Compression-Ignition
Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine
Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 8, 2008), the
Locomotive and Marine Rule (73 FR 25098, May 6, 2008), the Clean Air Interstate Rule (70 FR
25162, May 12, 2005), the Clean Air Nonroad Diesel Rule (69 FR 38957, June 29, 2004), and the
Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control
Requirements (66 FR 5002, January 18, 2001). As a result of these and other federal, state and
local programs, 8-hour ozone levels are expected to improve in the future. However, even with
the implementation of all current state and federal regulations, there are projected to be counties
that would have projected design values above the level of the ozone NAAQS well into the
future.
The air quality modeling projects that in 2030, with all current controls in effect but
excluding the emissions changes expected to occur as a result of this final action, at least 10
counties, with a projected population of over 30 million people, would have projected design
values above the level of the 2008 8-hour ozone standard of 75 ppb. Since the emission changes
from this final action go into effect during the period when some areas are still working to attain
the ozone NAAQS, the projected emission changes will impact state and local agencies in their
effort to attain and maintain the ozone standard. In the following section we discuss the
projected ozone impacts associated with the vehicle standards.
Projected Levels of Ozone with this Final Action
This section summarizes the results of our modeling of ozone air quality impacts in the
future with the vehicle standards. Specifically, we compare a 2030 reference scenario, a scenario
without the vehicle standards, to a 2030 control scenario which includes the vehicle standards.
Our modeling indicates that there will be very small changes in ambient ozone concentrations
across most of the country. However, there will be small decreases in ozone design value
concentrations in some areas of the country and small increases in ozone design value
concentrations in other areas.mm
Figure 6.2-5 presents the changes in 8-hour ozone design value concentrations in 2030
between the reference case and the control case. The ozone impacts are related to downstream
emissions changes from VMT rebound and upstream emissions changes in electrical power
nnn An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour
ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 40
CFR part 50.
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MY 2017 and Later - Regulatory Impact Analysis
generation and fuel production. In some areas the ozone impact is a result of a combination of
the various emissions changes but in other areas the impact is likely mainly the result of one of
the types of emissions changes. Some of the ozone increases and decreases are related mainly to
upstream emissions changes in electricity generation. For example, the projected increases in
Las Vegas, Dayton, and Little Rock are due mainly to increased demand for electricity from
electric vehicles and the projected decrease in ozone in northeast West Virginia is due mainly to
reductions in power plant emissions.JJJJJJ Some of the ozone decreases are mainly related to
upstream emissions reductions from reduced refinery demand as fuel production decreases (e.g.
the Gulf Coast) and some of the ozone increases are mainly related to increased emissions of
NOx from the VMT rebound effect (e.g., Knoxville and Atlanta).
Difference in 8-hr Ozone DV- 2030ct_ldghg_ctl2 minus 2030ctjdgng_ref
Figure 6.2-5 Projected Change in 2030 8-hour Ozone Design Values Due to the Final
Standards
As can be seen in Figure 6.2-5, the majority of the ozone design value impacts are
between + 0.3 ppb and -0.3 ppb. However, there are two counties that will experience 8-hour
ozone design value decreases of more than 0.3 ppb: Garrett County, Maryland, and Harris
County, Texas. The maximum projected decrease in an 8-hour ozone design value is 0.47 ppb in
Section 4.7.3.1 has more information on the Integrated Planning Model (IPM) analysis which was done to
project future electricity demand and plant locations.
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Chapter 6
Garrett County, Maryland and is likely related to the projected reductions in power plant
emissions in northeast WV. There is also one county, Pulaski County in Arkansas, with a
projected design value increase greater than 0.3 ppb. The projected increase in Pulaski County is
0.37 ppb. There are 10 counties, most of them in California, that are projected to have 8-hour
ozone design values above the 2008 NAAQS in 2030 with the vehicle standards in place. Table
6.2-1 below presents the changes in design values for these counties.
Table 6.2-1 Change in Ozone Design Values (ppb) for Counties Projected to be Above the
2008 Ozone NAAQS in 2030
County Name
San Bernardino Co, California
Riverside Co, California
Los Angeles Co, California
Kern Co, California
Harris Co, Texas
Tulare Co, California
Orange Co, California
Fresno Co, California
Suffolk Co, New York
Brazoria Co, Texas
Change in 8 -hour
Ozone Design
Value (ppb'
-0.20
-0.23
-0.13
0.05
-0.31
-0.02
-0.12
-0.04
-0.06
-0.30
Population in
2030a
2,784,490
2,614,198
10,742,722
981,806
5,268,889
528,663
4,431,071
1,196,950
1,705,822
364,257
Note:
a Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001.
Population by Single Year of Age CD.
Table 6.2-2 shows the average change in 2030 8-hour ozone design values for: (1) all
counties with 2005 baseline design values, (2) counties with 2005 baseline design values that
exceeded the 2008 ozone standard, (3) counties with 2005 baseline design values that did not
exceed the 2008 standard, but were within 10% of it, (4) counties with 2030 design values that
exceeded the 2008 ozone standard, and (5) counties with 2030 design values that did not exceed
the standard, but were within 10% of it. Counties within 10% of the standard are intended to
reflect counties that although not violating the standards, will also be impacted by changes in
ozone as they work to ensure long-term maintenance of the ozone NAAQS. The average
modeled future-year 8-hour ozone design values are projected to increase by 0.01 ppb in 2030.
Average design values in those counties that are projected to be above the 2008 ozone standard in
2030 will decrease by 0.14 ppb due to the vehicle standards.
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Table 6.2-2 Average Change in Projected 8-hour Ozone Design Value
Averagea
All
All, population-weighted
Counties whose 2005 base year is above the 2008
8-hour ozone standard
Counties whose 2005 base year is above the 2008
8-hour ozone standard, population-weighted
Counties whose 2005 base year is within 10 percent
of the 2008 8-hour ozone standard
Counties whose 2005 base year is within 10 percent
of the 2008 8-hour ozone standard, population-
weighted
Counties whose 2030 control case is above the 2008
8-hour ozone standard
Counties whose 2030 control case is above the 2008
8-hour ozone standard, population-weighted
Counties whose 2030 control case is within 10% of
the 2008 8-hour ozone standard
Counties whose 2030 control case is within 10% of
the 2008 8-hour ozone standard, population-
weighted
Number
of US
Counties
675
393
201
10
40
2030
Population"
261,439,344
194,118,748
44,436,103
30,618,868
29,661,201
Change in
2030 design
value (ppb)
0.01
0.00
0.02
0.00
0.02
0.01
-0.14
-0.16
-0.02
0.00
Notes:
a Averages are over counties with 2005 modeled design values
b Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by
Single Year of Age CD.
Ground-level ozone pollution is formed by the reaction of VOCs and NOx in the
atmosphere in the presence of heat and sunlight. The science of ozone formation, transport, and
accumulation is complex.358 The projected ozone impacts which are seen in the air quality
modeling for this final action are a result of the emissions changes due to the vehicle standards
combined with the photochemistry involved, the different background concentrations of VOCs
and NOx in different areas of the country, and the different meteorological conditions in different
areas of the country.
When VOC levels are relatively high, relatively small amounts of NOx enable ozone to
form rapidly. Under these conditions, VOC reductions have little effect on ozone and while NOx
reductions are highly effective in reducing ozone, conversely NOx increases lead to increases in
ozone. 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. Rural areas
are usually NOx -limited, due to the relatively large amounts of biogenic VOC emissions in such
areas.
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 "NOx-saturated."
Under these conditions, VOC reductions are effective in reducing ozone, but NOx reductions can
actually increase local ozone under certain circumstances.
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6.2.2.2 Participate Matter
As described in Section 6.1.1.2, exposure to PMi.5 causes adverse health effects, and the
EPA has set national standards to provide requisite protection against those health effects. In this
section, we present information on current and model-projected future PMi.5 levels.
6.2.2.2.1
Current Levels of Particulate Matter
Figure 6.2-6 and Figure 6.2-7 respectively show a snapshot of annual and 24-hour PMi.5
concentrations in 2010. In 2010, the highest annual average PM2.5 concentrations were in
California, Indiana, Pennsylvania, and Hawaii and the highest 24-hour PMi.5 concentrations were
in California and Alaska.
Annual
Concentration Range (|jg/m3)
• 3.1 -12.0 (680 Sites)
O 12.1-15.0 (148 Sites)
O 15.1-18.0 (5 Sites)
• 18.1 -225(1 Site)
Puerto Rico
Alaska
Figure 6.2-6 Annual Average PM2.s Concentrations in |ug/m3 for 2010
,359
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MY 2017 and Later - Regulatory Impact Analysis
24-hour
Concentration Range
• 6-15 (8 7 Sites.)
O 16 - 35 (704 Sites)
O 36-55 (42 Sites)
• 55-56(1 Srte)
Puerto Rico
Alaska
Figure 6.2-7 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in
ug/m3 for 2010360
There are two NAAQS for PM2.5: an annual standard (15.0 (^g/m) and a 24-hour standard
(35 |^g/m3). The most recent revisions to these standards were in 1997 and 2006. In June 2012,
EPA proposed to revise the PM2.5 NAAQS and is scheduled to issue final revisions in December
2012 under a court-ordered schedule. The proposed changes include revising the annual PM2.5
standard to a level between 12 and 13 |^g/m3, and establishing a distinct secondary PM2.5 standard
for the protection of visibility, particularly in urban areas.
In 2005 the U.S. EPA designated nonattainment areas for the 1997 PM2.5 NAAQS (70 FR
19844, April 14, 2005). As of July 20, 2012, over 91 million people lived in the 35 areas that are
designated as nonattainment for the 1997 PM2.5 NAAQS. These PM2.5 nonattainment areas are
comprised of 191 full or partial counties. Nonattainment areas for the 1997 PM2.5 NAAQS are
pictured in Figure 6.2-8. On October 8, 2009, the EPA issued final nonattainment area
designations for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13, 2009). These
designations include 32 areas, composed of 121 full or partial counties, with a population of over
74 million. Nonattainment areas for the 2006 PM2.5 NAAQS are pictured in Figure 6.2-9. In
total, there are 50 PM2.5 nonattainment areas with a population of over 105 million people.
States with PM2.5 nonattainment areas will be required to take action to bring those areas
into attainment in the future. The 1997 PM2.5 nonattainment areas are required to attain the 1997
PM2.5 NAAQS in the 2009 to 2015 time frame and then maintain the 1997 PM2.5 NAAQS
thereafter.361 The 2006 24-hour PM2.5 nonattainment areas will be required to attain the 2006 24-
hour PM2.5 NAAQS in the 2014 to 2019 time frame and then maintain the 2006 24-hour PM2.5
NAAQS thereafter.362
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PM-2.5 Nonattainment Areas (1997 Standard)
Nonattainment areas are indicated by color.
When only a portion of a county is shown in color.
it indicates that only that part of the county is within
a nonattainment area boundary.
Figure 6.2-8 1997 PM2.s Nonattainment Areas
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MY 2017 and Later - Regulatory Impact Analysis
PM-2.5 Nonattainment Areas (2006 Standard)
-V2011
1=55,
Nonattainment areas are indicated by color.
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
Figure 6.2-9 2006 PM2.5 Nonattainment Areas
As of July 20, 2012, over 29 million people live in the 46 areas that are designated as
nonattainment for the PMio NAAQS. There are 39 full or partial counties that make up the
nonattainment areas. Nonattainment areas for the PMio NAAQS are pictured in Figure 6.2-10
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Chapter 6
Counties Designated Nonattainment for PM-10
Ead.eRiver.AK
(Moderate)
Newark Co., NY
(Moderate)
12/2010
Juneau, AK
(Moderate)
Classification
BB Serious
I I Moderate
Classification colors are shown for whole counties and
denote the highest area classification that the county is in
Figure 6.2-10 PMio Nonattainment Areas
6.2.2.2.2 Projected Levels of PM2.5
In the following sections we describe projected PM2.5 levels in the future, with and
without the standards. Our modeling indicates that there will be very small changes in PM2.5
across most of the country. The impacts of the standards on PM2.5 are a function of VMT
increases from rebound, upstream reductions in petroleum consumption from crude oil
production and transport, and gasoline production, distribution and transport, and changes in
location and amount of electricity generation. Information on the air quality modeling
methodology is contained in Section 6.2.1. Additional detail can be found in the air quality
modeling technical support document (AQM TSD).
Projected Levels of PM2.5 without this Final Action
EPA has already adopted many mobile source emission control programs that are
expected to reduce ambient PM levels. These control programs include the New Marine
Compression-Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30,
2010), the Marine Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October
8, 2008), the Locomotive and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6,
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2008), the Clean Air Nonroad Diesel (69 FR 38957, June 29, 2004), the Heavy Duty Engine and
Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, January
18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control
Requirements (65 FR 6698, February 10, 2000). As a result of these and other federal, state and
local programs, the number of areas that fail to meet the PM2.5 NAAQS in the future is expected
to decrease. However, even with the implementation of all current state and federal regulations,
there are projected to be counties that would have projected design values above the level of the
PM2.5 NAAQS well into the future.
The air quality modeling conducted projects that in 2030, with all current controls in
effect but excluding the emissions changes expected to occur as a result of this final action, at
least 4 counties, with a projected population of nearly 7 million people, would have projected
design values above the level of the annual standard of 15 (^g/m and at least 21 counties, with a
projected population of over 31 million people, would have projected design values above the
level of the 2006 24-hour standard of 35 (ag/m3. Since the emission changes from this final
action go into effect during the period when some areas are still working to attain the PM2.5
NAAQS, the projected emission changes will impact state and local agencies in their effort to
attain and maintain the PM2.5 standard. In the following section we discuss the PMi.5 impacts
associated with the vehicle standards.
Projected Annual Average PM2.5 Design Values with this Final Action
This section summarizes the results of our modeling of annual average PM2.5 air quality
impacts in the future due to the vehicle standards finalized in this action. Specifically, we
compare a 2030 reference scenario (a scenario without the vehicle standards) to a 2030 control
scenario which includes the vehicle standards. Our modeling indicates that the majority of the
modeled counties will experience small changes of between 0.05 (^g/m3 and -0.05 (^g/m3 in their
annual PMi.5 design values due to the vehicle standards.
Figure 6.2-11 presents the changes in annual PM2.5 design values in 2030.
KKKKKK
KKKKKK ^n annuaj pM2 5 design value is the concentration that determines whether a monitoring site meets the annual
NAAQS for PM2.5. The full details involved in calculating an annual PM2.5 design value are given in appendix N of
40 CFR part 50.
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Chapter 6
= 0.0510<0.15 0
'0.15to<0.25 0
:0.25to<0.50 0
'0.50 0
Difference in Annual PM2.5 DV - 2030ct_ldghg_ctl2 minus 2030ct_ldghg_ref
Figure 6.2-11 Projected Change in 2030 Annual PM2.5 Design Values Due to the Final
Standards
Figure 6.2-11, eight counties will experience decreases larger than 0.05 (^g/m3. These
counties are in the Gulf Coast and in Missouri. The maximum projected decrease in an annual
PMi.5 design value is 0.16 (^g/m3 in West Baton Rouge County, Louisiana. The decreases in
annual PM2.5 design values in the Gulf Coast are likely due to emission reductions related to
lower fuel production. Additional information on the emissions reductions that are projected
with this final action is available in Section 4.7.
There are 4 counties, all in California, that are projected to have annual PMi.5 design
values above the NAAQS in 2030 with the vehicle standards in place. Table 6.2-3 below
presents the changes in design values for these counties.
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Table 6.2-3 Change in Annual PM2.5 Design Values (|ug/m3) for Counties Projected to be
Above the Annual PM2.5 NAAQS in 2030
County Name
Riverside County, California
San Bernardino County, California
Kern County, California
Tulare County, California
Change in
Annual
PM2.5
Design
Value
(|ag/m3)
-0.01
0
-0.02
-0.01
Population
in 2030a
2,614,198
2,784,489
981,806
528,662
a Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by Single
Year of Age CD.
Average changes in 2030 annual PM2.5 design values for a variety of metrics are all
between 0.00 and -0.03 |^g/m3 illustrating the small decrease in annual PM2.5 design values in
2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with
2005 baseline design values that exceeded the annual PM2.5 standard, (3) counties with 2005
baseline design values that did not exceed the standard, but were within 10% of it, (4) counties
with 2030 design values that exceeded the annual PM2.5 standard, and (5) counties with 2030
design values that did not exceed the standard, but were within 10% of it.
Projected 24-hour Average PM2.5 Design Values with this Final Action
This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts
in the future due to the vehicle standards. Specifically, we compare a 2030 reference scenario (a
scenario without the vehicle standards) to a 2030 control scenario which includes the vehicle
standards. Our modeling indicates that the majority of the modeled counties will experience
changes of between -0.05 (Jg/m3 and 0.05 (^g/m3 in their 24-hour PM2.5 design values. Figure
6.2-12 presents the changes in 24-hour PM2.5 design values in 2030.LLLLLL
LLLLLL ^ 24-hour PM2.s design value is the concentration that determines whether a monitoring site meets the 24-
hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.s design value are given in appendix
Nof40CFRpart50.
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Chapter 6
Legend
^^ <= -0.50 ug/m3
^H > -°-50 Io <= -°-25
^B >-0.25to<=-0.15
| | > -0.15 10 <= -0.05
| -0.05 to < 0.05
=0.05to<0.15
| = 0.15 to < 0.25
__ =0.25to<0.50
• =0.50
Difference in Daily PM2.5 DV- 2030ct_ldghg_ctl2 minus 2030ct_ldghg_ref
Figure 6.2-12 Projected Change in 2030 24-hour PM2.s Design Values Due to the Final
Standards
As shown in Figure 6.2-12, design value concentrations will increase more than 0.05
3 in six counties and design value concentrations will decrease more than 0.05 (^g/m3 in 23
counties. The decreases in 24-hour PMi.5 design values in some counties are likely due to
emission reductions related to lower fuel production. The maximum projected decrease in a 24-
hour PMi.5 design value is 0.76 (^g/m3 in East Baton Rouge County, Louisiana. The increases in
24-hour PM2.5 design values in some counties are likely due to increased emissions from the
VMT rebound effect or increased electricity generation. The maximum projected increase in a
24-hour PM2.5 design value is 0.14 |^g/m3 in El Paso County, Colorado. Additional information
on the emissions changes that are projected with this final action is available in Section 4.7.
There are 21 counties, mainly in California, that are projected to have 24-hour PM2.5
design values above the NAAQS in 2030 with the vehicle standards in place. Table 6.2-4 below
presents the changes in design values for these counties.
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Table 6.2-4 Change in 24-hour PM2.5 Design Values (ng/m ) for Counties Projected to be
Above the 24-hour PM2.5 NAAQS in 2030
County Name
Kern Co, California
Riverside Co, California
Fresno Co, California
San Bernardino Co, California
Sacramento Co, California
Kings Co, California
Los Angeles Co, California
Tulare Co, California
Lane Co, Oregon
Cache Co, Utah
Allegheny Co, Pennsylvania
Stanislaus Co, California
Lake Co, Montana
Orange Co, California
Klamath Co, Oregon
Salt Lake Co, Utah
Ravalli Co, Montana
Butte Co, California
Missoula Co, Montana
Pierce Co, Washington
Lincoln Co, Montana
Change in 24-
hour PM2.5
Design Value
(Hg/m3)
0.02
-0.01
-0.02
-0.27
0
-0.03
0.03
-0.03
0
0.04
-0.01
-0.02
0
0.05
0
0.02
0
0
0.01
0.03
0
Population in 2030a
981,806
2,614,198
1,196,950
2,784,490
1,856,971
195,067
10,742,722
528,663
460,993
141,446
1,234,931
688,246
40,126
4,431,071
77,200
1,431,946
63,914
287,236
141,264
1 ,082,579
20,454
Note:
a Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by Single
Year of Age CD.
Average changes in 2030 24-hour PM2.5 design values for a variety of metrics are all
between 0.00 and -0.01 ug/m3 illustrating the small decrease in 24-hour PM2.5 design values in
2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with
2005 baseline design values that exceeded the 24-hour PMi.5 standard, (3) counties with 2005
baseline design values that did not exceed the standard, but were within 10% of it, (4) counties
with 2030 design values that exceeded the 24-hour PMi.5 standard, and (5) counties with 2030
design values that did not exceed the standard, but were within 10% of it.
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Chapter 6
6.2.2.3 Air Toxics
According to the National Air Toxics Assessment (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.MMMMMM'NNNNNN'363 According to the 2005 NATA, about three-fourths of the U.S.
population was exposed to an average chronic concentration of air toxics that has the potential for
adverse noncancer respiratory health effects. In 2007 EPA finalized vehicle and fuel controls to
reduce mobile source air toxics.364 In addition, over the years, EPA has implemented a number
of mobile source and fuel controls resulting in VOC reductions, which also reduce air toxic
emissions. Modeling from the Mobile Source Air Toxics (MSAT) rule suggests that the mobile
source contribution to ambient benzene concentrations is projected to decrease over 40% by
2015, with a decrease in ambient benzene concentration from all sources of about 25%.
Although benzene is used as an example, the downward trend is projected for other air toxics as
well. See the RIA for the final MSAT rule for more information on ambient air toxics
projections.365
6.2.2.3.1 Current Levels 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.366 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 U.S. EPA's 2007 Mobile Source Air
Toxics (MSAT) Rule. 6? In order to identify and prioritize air toxics, emission source types and
locations which are of greatest potential concern, U. S. EPA conducts the National-Scale Air
Toxics Assessment (NATA). The most recent NATA was conducted for calendar year 2005, and
was released in March 2011.368 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
MMMMMM 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.
NNNNNN NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide
(ASPEN), to estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this final
action were modeled with CMAQ 4.7.1.
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Figure 6.2-13 and Figure 6.2-14 depict estimated tract-level carcinogenic risk and
noncancer respiratory hazard from the assessment. The respiratory hazard is dominated by a
single pollutant, acrolein.
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.00000°'PP|I'PP'369 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.
2005 NATA Estimated Tract Level Total Cancer Risk
*, j:
Figure 6.2-13 Tract Level Average Carcinogenic Risk, 2005 NATA
oooooo 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
PPPPPP NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN),
to estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this final action were
modeled with CMAQ 4.7.1.
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Chapter 6
2005 NATA Estimated Tract Level Total Respiratory Hazard Index
Figure 6.2-14 County Level Average Noncancer Hazard Index, 2005 NATA
6.2.2.3.2 Projected Levels of Air Toxics
In the following sections, we describe results of our modeling of air toxics levels in the
future with the finalized standards. Although there are a large number of compounds which are
considered air toxics, we focused on those which were identified as national and regional-scale
cancer and noncancer risk drivers in past NATA assessments and were also likely to be
significantly impacted by the standards. These compounds include benzene, 1,3-butadiene,
formaldehyde, acetaldehyde, and acrolein. Information on the air quality modeling methodology
is contained in Section 6.2.1. Additional detail, including seasonal concentration maps, can be
found in the air quality modeling technical support document (AQM TSD) in the docket for this
rule.
It should be noted that EPA has adopted many mobile source emission control programs
that are expected to reduce ambient air toxics levels. These control programs include the Heavy-
duty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine
Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098,
May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air
Nonroad Diesel Rule (69 FR 38957, June 29, 2004), Heavy-Duty Engine and Vehicle Standards
and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier
2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698,
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Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the
future is expected to decrease. The reference case and control case scenarios include these
controls.
Our modeling indicates that national average ambient concentrations of the modeled air
toxics change less than 1 percent across most of the country due to the final standards. Because
overall impacts are relatively small in future years, we concluded that assessing exposure to
ambient concentrations and conducting a quantitative risk assessment of air toxic impacts was not
warranted. However, we did develop population metrics, including the population living in areas
with changes in concentrations of various magnitudes.
Acetaldehyde
Our air quality modeling results show that this rule does not have substantial impacts on
ambient concentrations of acetaldehyde. Figure 6.2-15 shows nationwide changes in ambient
acetaldehyde in 2030 are between + 1 percent, with decreases up to 10 percent in a few urban
areas. Reductions in ambient acetaldehyde in 2030 range between 0.001 and 0.01 |^g/m3 across
much of the country with decreases as high as 0.1 |^g/m3 in urban areas; these changes are mainly
associated with reductions from upstream sources including fuel production, refining, storage and
transport (Figure 6.2-15).
Figure 6.2-15 Changes in Acetaldehyde Ambient Concentrations in 2030 due to the Final
Standards: Percent Changes (left) and Absolute Changes in |ug/m3 (right)
Formaldehyde
Our air quality modeling results do not show substantial impacts on ambient
concentrations of formaldehyde as a result of the final standards. In 2030, annual percent
changes in ambient concentrations of formaldehyde are less than 1 percent across much of the
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Chapter 6
country, with a decrease ranging from 2.5 to 10 percent in Oklahoma (Figure 6.2-16). Ambient
formaldehyde reductions in 2030 generally range from 0.001 to 0.1 |^g/m3 and are associated with
upstream reductions in fuel production, refining, storage and transport (Figure 6.2-16).
Decreases in Oklahoma are over 0.3 j^g/m3 and due to reductions in emissions from refineries in
that area. Increases in ambient formaldehyde concentrations range between 0.001 to 0.1 |^g/m3 in
areas associated with increased emissions from power plants.
Figure 6.2-16 Changes in Formaldehyde Ambient Concentrations in 2030 due to the Final
Standards: Percent Changes (left) and Absolute Changes in ^g/m3 (right)
Benzene
Our air quality modeling results do not show substantial impacts on ambient
concentrations of benzene as a result of this rule. In 2030, percent changes in ambient
concentrations of benzene are + 1 percent nationwide (Figure 6.2-17); a few areas, mainly in the
Gulf Coast region, are projected to have benzene reductions from 1 to 10 percent, likely due to
decreases in refinery emissions. Absolute changes in ambient benzene in 2030 are generally +
0.001 |^g/m3 in the western half of the U.S. with decreases up to 0.01 |^g/m3 across the eastern
half of the U.S due to upstream reductions in fuel production, refining, storage and transport
(Figure 6.2-17).
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MY 2017 and Later - Regulatory Impact Analysis
Figure 6.2-17 Changes in Benzene Ambient Concentrations in 2030 due to the Final
Standards: Percent Changes (left) and Absolute Changes in j^g/m3 (right)
1,3-Butadiene
Our modeling also shows that this rule does not have a significant impact on ambient 1,3-
butadiene concentrations in 2030. Figure 6.2-18 shows that ambient concentrations of 1,3-
butadiene generally range between + 1 percent across the country in 2030. Some areas have 1,3-
butadiene increases on the order of 1 to 2.5 percent; however, as shown in the map on the right,
all changes in absolute concentrations are between + 0.001 ^ig/m3 nationwide (Figure 6.2-18).
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Chapter 6
Figure 6.2-18 Changes in 1,3-Butadiene Ambient Concentrations in 2030 due to the Final
Standards: Percent Changes (left) and Absolute Changes in ^g/m3 (right)
Acrolein
Our air quality modeling results do not show substantial impacts on ambient
concentrations of acrolein as a result of this rule. In 2030, percent changes in ambient acrolein
concentrations are generally + 1 percent nationwide (Figure 6.2-19). Parts of the Midwest and
Texas have decreases in ambient acrolein concentrations generally between 1 and 10 percent and
increases of similar magnitude in a few urban areas; however, all absolute changes in ambient
acrolein concentrations are between + 0.001 ^ig/m3 in 2030 (Figure 6.2-19).
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MY 2017 and Later - Regulatory Impact Analysis
Figure 6.2-19 Changes in Acrolein Ambient Concentrations in 2030 due to the Final
Standards: Percent Changes (left) and Absolute Changes in |ug/m3 (right)
Population Metrics
To assess the impact of this rule's projected changes in air quality, we developed
population metrics that show the population experiencing changes in annual ambient
concentrations across the modeled air toxics. As shown in Table 6.2-5, over 98 percent of the
U.S. population is projected to experience a less than one percent change in formaldehyde and
1,3-butadiene. Over 83 percent of the U.S. population is projected to experience a less than one
percent change in acetaldehyde, benzene and acrolein, and over 12 percent are projected to
experience a 1 to 5 percent decrease in these pollutants.
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Chapter 6
Table 6.2-5 Percent of Total Population Experiencing Changes in Annual Ambient
Concentrations of Toxic Pollutants in 2030 as a Result of the Final Standards
Percent Change
<-100
>-100to<-50
> -50 to < -10
>-10to<-5
> -5 to < -2.5
>-2.5to<-1
> -1 to < 1
> 1 to < 2.5
> 2.5 to < 5
> 5 to < 1 0
> 10 to < 50
>50to< 100
> 100
Acetaldehyde
0.0%
1 .5%
15.3%
83.1%
Formaldehyde
0.0%
0.1%
1 .2%
98.7%
Benzene
0.8%
1 .8%
13.0%
84.4%
0.0%
1 ,3-Butadiene
0.0%
0.2%
99.2%
0.6%
0.0%
Aero le in
0.2%
2.0%
10.3%
86.1%
0.9%
0.0%
0.0%
6.2.2.4 Deposition of Nitrogen and Sulfur
6.2.2.4.1
Current Levels of Nitrogen and Sulfur Deposition
Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen
and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show
that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years. The
data show that reductions were more substantial for sulfur compounds than for nitrogen
compounds. In the eastern U.S., where data are most abundant, total sulfur deposition decreased
by about 44 percent between 1990 and 2007, while total nitrogen deposition decreased by 25
percent over the same time frame.370 These numbers are generated by the U.S. national
monitoring network and they likely underestimate nitrogen deposition because neither ammonia
nor organic nitrogen is measured. Although total nitrogen and sulfur deposition has decreased
over time, many areas continue to be negatively impacted by deposition. Deposition of inorganic
nitrogen and sulfur species routinely measured in the U.S. between 2005 and 2007 were as high
as 9.6 kilograms of nitrogen per hectare (kg N/ha) averaged over three years and 20.8 kilograms
of sulfur per hectare (kg S/ha) averaged over three years.371
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MY 2017 and Later - Regulatory Impact Analysis
A. 1iti-1891
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3? moiiiiosniy sias ;TI lift}.1901
me 72 f!«)il-;I.Orii!.J rj*;s in ZOilS-JOO?
NADP ftXiS- U.S. (PA.
Figure 6.2-20 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2005 -2007
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Chapter 6
,
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'
— & ' Siitfi uf lirulK indiudlH iliis rtfaUve iita
Hrtlnrs in nlrnlts intilpsle Ihe hrpjtkHfiw/r rvf icilsl ntrn^cn rlfifinsllifMi-
Dry nitrogen deposiuon • Wet nitrogen deposition
Figure 6.2-21 Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2005-
2007
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MY 2017 and Later - Regulatory Impact Analysis
6.2.2.4.2 Projected Levels of Nitrogen and Sulfur Deposition
Our air quality modeling projects increases in nitrogen deposition in some localized areas
across the U.S. along with a few areas of decreases in nitrogen deposition. Figure 6.2-22 shows
that for nitrogen deposition the vehicle standards will result in annual percent increases of more
than 2 percent in some areas. The increases in nitrogen deposition are likely due to projected
upstream emissions increases in NOx from increased electricity generation and increased driving
due to the rebound effect. Figure 6.2-22 also shows that for nitrogen deposition the vehicle
standards will result in annual percent decreases of more than 2 percent in a few areas in West
Virginia and New Mexico. The decreases in nitrogen deposition are likely due to projected
upstream emissions decreases in NOx from changes in the location of electricity generation. The
remainder of the country will experience only minimal changes in nitrogen deposition, ranging
from decreases of less than 0.5 percent to increases of less than 0.5 percent.
Percent Change in Annual Nitrogen Deposition -
2030ct_ldghg_ctl2 minus 2030ct_ldghg_ref
Figure 6.2-22 Percent Change in Annual Total Nitrogen Deposition as a Result of the Final
Standards
Our air quality modeling projects both increases and decreases in sulfur deposition in
localized areas across the U.S. Figure 6.2-23 shows that for sulfur deposition the vehicle
standards will result in annual percent decreases of more than 2% in some areas. The decreases
in sulfur deposition are likely due to projected upstream emissions decreases from changes in the
location of electricity generation and from reduced gasoline production. Figure 6.2-23 also
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Chapter 6
shows that for sulfur deposition the vehicle standards will result in annual percent increases of
more than 2% in some areas. The increases in sulfur deposition are likely due to projected
upstream emissions increases from increased electricity generation. The remainder of the
country will experience only minimal changes in sulfur deposition, ranging from decreases of
less than 0.5% to increases of less than 0.5%.
Percent Change in Annual Sulfur Deposition --
2030ctjdghg_c«2 minus 2030ct_ldghg_rel
Figure 6.2-23 Percent Change in Annual Total Sulfur Deposition as a Result of the Final
Standards
6.2.2.5 Visibility Degradation
6.2.2.5.1
Current Visibility Levels
As of August 30, 2011, approximately 101 million people live in nonattainment areas for
the PMi.5 NAAQS. Thus, at least these populations would likely be experiencing visibility
impairment, as well as many thousands of individuals who travel to these areas. While visibility
trends have improved in most Class I areas, the recent data show that these areas continue to
Q-y^-j
suffer from visibility impairment. Calculated from light extinction efficiencies from Trijonis et
al. (1987, 1988), annual average visual range under natural conditions in the East is estimated to
be 150 km + 45 km (i.e., 65 to 120 miles) and 230 km + 35 km (i.e., 120 to 165 miles) in the
West.373'374'375 In summary, visibility impairment is experienced throughout the U.S., in multi-
state regions, urban areas, and remote Mandatory Class I Federal areas.
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MY 2017 and Later - Regulatory Impact Analysis
6.2.2.5.2
Projected Visibility Levels
Air quality modeling conducted for the final action was used to project visibility
conditions in 139 mandatory class I federal areas across the U.S. in 2030. The results show that
all the modeled areas will continue to have annual average deciview levels above background in
2030.QQQQQQ The results also indicate that the majority of the modeled mandatory class I federal
areas will see very little change in their visibility. Some mandatory class I federal areas will see
improvements in visibility due to the vehicle standards and a few mandatory class I federal areas
will see visibility decreases. The average visibility at all modeled mandatory class I federal areas
on the 20% worst days is projected to improve by 0.003 deciviews, or 0.03%, in 2030. The
greatest projected improvement in visibility, 0.1% improvement (0.02 DV) in 2030 due to the
vehicle standards, occurs in Sipsey Wilderness in AL, Agua Tibia Wilderness in CA, and Alpine
Lake Wilderness in WA. The following seven areas will see small degradations in visibility in
2030 as a result of the heavy-duty standards: Wolf Island GA, 0.03 deciview degradation; Joshua
Tree National Monument CA, 0.02 deciview degradation, San Gorgonio Wilderness CA, 0.01
deciview degradation; Upper Buffalo Wilderness AR, 0.01 deciview degradation; San Jacinto
Wilderness CA, 0.01 deciview degradation; Okefenokee GA, 0.01 deciview degradation; and
Hells Canyon Wilderness OR, 0.01 deciview degradation. Table 6.2-6 contains the full visibility
results from 2030 for the 138 analyzed areas.
Table 6.2-6 Visibility Levels (in Deciviews) for Class I Areas on the 20% Worst Days
Class 1 Area
(20% worst days)
Sipsey Wilderness
Caney Creek Wilderness
Upper Buffalo Wilderness
Chiricahua NM
Chiricahua Wilderness
Galiuro Wilderness
Grand Canyon NP
Mazatzal Wilderness
Mount Baldy Wilderness
Petrified Forest NP
Pine Mountain Wilderness
Saguaro NM
Sierra Ancha Wilderness
Superstition Wilderness
Sycamore Canyon Wilderness
State
AL
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
2005
Base
29.88
26.69
26.97
12.89
12.89
12.89
11.86
13.95
11.32
13.56
13.95
14.39
14.45
14.15
15.45
2030
LDGHG
Reference
20.54
19.84
20.17
12.08
12.08
12.09
10.92
12.46
10.74
12.65
12.42
13.43
13.28
12.85
14.67
2030
LDGHG
Control
20.52
19.84
20.18
12.07
12.07
12.09
10.91
12.45
10.74
12.65
12.41
13.43
13.28
12.85
14.67
Natural
Background
11.39
11.33
11.28
6.92
6.91
6.88
6.95
6.91
6.95
6.97
6.92
6.84
6.92
6.88
6.96
QQQQQQ -pjjg ievei of visibility impairment in an area is based on the light-extinction coefficient and a unitless
visibility index, called a "deciview", which is used in the valuation of visibility. The deciview metric provides a
scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic
conditions, the average person can generally perceive a change of one deciview. The higher the deciview value, the
worse the visibility. Thus, an improvement in visibility is a decrease in deciview value.
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Chapter 6
Agua Tibia Wilderness
Ansel Adams Wilderness (Minarets)
Caribou Wilderness
Cucamonga Wilderness
Desolation Wilderness
Emigrant Wilderness
Hoover Wilderness
John Muir Wilderness
Joshua Tree NM
Kaiser Wilderness
Kings Canyon NP
Lassen Volcanic NP
Lava Beds NM
Mokelumne Wilderness
Pinnacles NM
Point Reyes NS
Redwood NP
San Gabriel Wilderness
San Gorgonio Wilderness
San Jacinto Wilderness
San Rafael Wilderness
Sequoia NP
South Warner Wilderness
Thousand Lakes Wilderness
Ventana Wilderness
Yosemite NP
Black Canyon of the Gunnison NM
Eagles Nest Wilderness
Flat Tops Wilderness
Great Sand Dunes NM
La Garita Wilderness
Maroon Bells-Snowmass Wilderness
Mesa Verde NP
Mount Zirkel Wilderness
Rawah Wilderness
Rocky Mountain NP
Weminuche Wilderness
West Elk Wilderness
Everglades NP
Okefenokee
Wolf Island
Craters of the Moon NM
Sawtooth Wilderness
Mammoth Cave NP
Acadia NP
Moosehorn
Roosevelt Campobello International Park
Isle Royale NP
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
FL
GA
GA
ID
ID
KY
ME
ME
ME
Ml
22.36
15.24
13.65
18.44
12.87
16.87
11.61
15.24
18.90
15.24
23.73
13.65
14.13
12.87
17.90
22.40
18.55
18.44
21.43
21.43
19.43
23.73
14.13
13.65
17.90
16.87
10.00
8.82
8.82
11.82
10.00
8.82
12.14
9.72
9.72
12.85
10.00
8.82
22.48
27.21
27.21
14.06
14.97
32.00
22.75
21.19
21.19
21.31
18.41
14.39
12.68
15.64
12.10
15.94
11.07
14.34
16.39
14.11
22.19
12.66
13.19
12.08
15.42
21.00
17.66
15.54
19.27
18.10
17.40
21.68
13.31
12.65
16.37
15.95
9.21
8.05
8.32
11.20
9.49
8.27
11.31
9.20
9.15
12.15
9.46
8.21
18.43
20.28
20.12
12.94
14.70
22.29
18.34
17.58
17.57
18.19
18.39
14.39
12.67
15.64
12.09
15.94
11.06
14.34
16.41
14.10
22.19
12.66
13.19
12.07
15.42
21.00
17.66
15.53
19.28
18.11
17.39
21.67
13.31
12.64
16.37
15.95
9.21
8.05
8.31
11.20
9.49
8.26
11.31
9.19
9.14
12.15
9.46
8.21
18.43
20.29
20.15
12.94
14.70
22.29
18.33
17.58
17.56
18.19
7.17
7.12
7.29
7.17
7.13
7.14
7.12
7.14
7.08
7.13
7.13
7.31
7.49
7.14
7.34
7.39
7.81
7.17
7.10
7.12
7.28
7.13
7.32
7.32
7.32
7.14
7.06
7.08
7.07
7.10
7.06
7.07
7.09
7.08
7.08
7.05
7.06
7.07
11.15
11.45
11.42
7.13
7.15
11.53
11.45
11.36
11.36
11.22
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MY 2017 and Later - Regulatory Impact Analysis
Seney
Boundary Waters Canoe Area
Voyageurs NP
Hercules-Glades Wilderness
Anaconda-Pintler Wilderness
Bob Marshall Wilderness
Cabinet Mountains Wilderness
Gates of the Mountains Wilderness
Glacier NP
Medicine Lake
Mission Mountains Wilderness
Red Rock Lakes
Scapegoat Wilderness
Selway-Bitterroot Wilderness
ULBend
Linville Gorge Wilderness
Shining Rock Wilderness
Lostwood
Theodore Roosevelt NP
Great Gulf Wilderness
Presidential Range-Dry River Wilderness
Brigantine
Bandelier NM
Bosque del Apache
Carlsbad Caverns NP
Gila Wilderness
Pecos Wilderness
Salt Creek
San Pedro Parks Wilderness
Wheeler Peak Wilderness
White Mountain Wilderness
Jarbidge Wilderness
Wichita Mountains
Crater Lake NP
Diamond Peak Wilderness
Eagle Cap Wilderness
Gearhart Mountain Wilderness
Hells Canyon Wilderness
Kalmiopsis Wilderness
Mount Hood Wilderness
Mount Jefferson Wilderness
Mount Washington Wilderness
Mountain Lakes Wilderness
Strawberry Mountain Wilderness
Three Sisters Wilderness
Cape Romain
Badlands NP
Wind Cave NP
Ml
MN
MN
MO
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
ND
ND
NH
NH
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NM
NV
OK
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
SC
SD
SD
25.05
20.20
19.62
26.95
17.11
16.13
14.31
11.94
19.62
18.21
16.13
11.19
16.13
17.11
15.49
29.66
28.54
19.61
17.88
21.43
21.43
28.68
11.97
13.81
16.51
13.12
9.60
18.27
10.42
9.60
13.01
12.26
23.63
13.21
13.21
17.34
13.21
19.00
16.38
14.68
15.80
15.80
13.21
17.34
15.80
27.43
16.82
15.95
20.80
16.56
16.61
21.00
16.69
15.63
13.65
11.48
18.73
17.17
15.50
10.62
15.59
16.74
15.00
20.08
19.49
17.64
16.02
16.46
16.39
20.96
10.51
12.40
14.48
12.41
8.85
16.19
9.63
8.66
12.05
11.92
18.27
12.49
12.39
16.31
12.61
17.57
15.36
13.03
14.78
14.78
12.42
16.37
14.87
19.70
14.91
14.21
20.80
16.56
16.61
21.00
16.68
15.63
13.65
11.47
18.73
17.17
15.49
10.62
15.59
16.74
15.00
20.07
19.48
17.64
16.02
16.46
16.39
20.95
10.51
12.40
14.47
12.40
8.85
16.18
9.62
8.65
12.05
11.92
18.26
12.49
12.39
16.31
12.61
17.58
15.36
13.03
14.78
14.77
12.42
16.37
14.87
19.70
14.90
14.21
11.37
11.21
11.09
11.27
7.28
7.36
7.43
7.22
7.56
7.30
7.39
7.14
7.29
7.32
7.18
11.43
11.45
7.33
7.31
11.31
11.33
11.28
7.02
6.97
7.02
6.95
7.04
6.99
7.03
7.07
6.98
7.10
11.07
7.71
7.77
7.34
7.46
7.32
7.71
7.77
7.81
7.89
7.57
7.49
7.87
11.36
7.30
7.24
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Chapter 6
Great Smoky Mountains NP
Joy ce-Kilmer-Slickrock Wilderness
Big Bend NP
Guadalupe Mountains NP
Arches NP
Bryce Canyon NP
Canyonlands NP
Capitol Reef NP
James River Face Wilderness
Shenandoah NP
Lye Brook Wilderness
Alpine Lake Wilderness
Glacier Peak Wilderness
Goat Rocks Wilderness
Mount Adams Wilderness
Mount Rainier NP
North Cascades NP
Olympic NP
Pasayten Wilderness
Dolly Sods Wilderness
Otter Creek Wilderness
Bridger Wilderness
Fitzpatrick Wilderness
Grand Teton NP
North Absaroka Wilderness
Teton Wilderness
Washakie Wilderness
Yellowstone NP
TN
TN
TX
TX
LIT
UT
LIT
UT
VA
VA
VT
WA
WA
WA
WA
WA
WA
WA
WA
WV
WV
WY
WY
WY
WY
WY
WY
WY
30.56
30.56
17.21
16.51
10.77
11.62
10.77
10.86
28.93
29.42
24.11
16.99
13.29
12.67
12.67
17.07
13.29
15.83
15.35
29.94
29.94
10.73
10.73
11.19
11.30
11.19
11.30
11.19
21.28
20.97
15.35
14.47
9.98
10.95
10.12
10.39
19.62
19.58
16.87
15.06
12.18
11.35
11.39
15.36
12.15
14.31
14.36
19.65
19.73
10.29
10.29
10.57
10.90
10.68
10.90
10.61
21.27
20.97
15.34
14.46
9.97
10.95
10.11
10.39
19.62
19.58
16.86
15.04
12.17
11.34
11.39
15.35
12.14
14.31
14.36
19.64
19.72
10.29
10.28
10.56
10.90
10.68
10.90
10.61
11.44
11.45
6.93
7.03
6.99
6.99
7.01
7.03
11.24
11.25
11.25
7.86
7.80
7.82
7.78
7.90
7.78
7.88
7.77
11.32
11.33
7.08
7.09
7.09
7.09
7.09
7.09
7.12
6.3 Quantified and Monetized Non-GHG Health and Environmental Impacts
This section presents EPA's analysis of the non-GHG, or co-pollutant, health and
environmental impacts that can be expected to occur as a result of the final light-duty vehicle
GHG rule. GHG emissions are predominantly the byproduct of fossil fuel combustion processes
that also produce criteria and hazardous air pollutants. The vehicles that are subject to the final
standards are also significant sources of mobile source air pollution such as direct PM, NOx,
VOCs and air toxics. The standards will affect exhaust emissions of these pollutants from
vehicles. They will also affect emissions from upstream sources related to changes in fuel
consumption and electricity generation. Changes in ambient ozone, PMi.5, and air toxics that will
result from the standards are expected to affect human health in the form of premature deaths and
other serious human health effects, as well as other important public health and welfare effects.
It is important to quantify the health and environmental impacts associated with the final
standards because it allows us to more accurately assess the net costs and benefits of the
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MY 2017 and Later - Regulatory Impact Analysis
standards. Moreover, co-pollutant impacts tend to accrue in the near term, while effects from
reduced climate change mostly accrue over a time frame of several decades or longer.
This section is split into two sub-sections: the first presents the PM- and ozone-related
health and environmental impacts associated with final rule in calendar year (CY) 2030; the
second presents the PM-related benefits-per-ton values used to monetize the PM-related co-
benefits associated with the model year (MY) analysis (i.e., over the lifetimes of the MY 2017-
2025 vehicles) of the final rule.RRRRRR
Though EPA is characterizing the changes in emissions associated with toxic pollutants,
we were not able to quantify or monetize the human health effects associated with air toxic
pollutants for this final rule analysis due to data and methodological limitations. Please refer to
Chapter 4 of this RIA for more information about the air toxics emissions impacts associated
with the final standards.
6.3.1 Quantified and Monetized Non-GHG Human Health Benefits of the 2030 Calendar Year
(CY) Analysis
This section presents EPA's analysis of the criteria pollutant-related health and
environmental impacts that will occur as a result of the final standards. Light-duty vehicles and
fuels are significant sources of mobile source air pollution such as direct PM, NOx, SOx, VOCs
and air toxics. The impact that improved fuel economy will have on rebound driving will affect
exhaust and evaporative emissions of these pollutants from vehicles. In addition, increased fuel
savings associated with improved fuel economy achieved under the standards will affect
emissions from upstream sources (see Chapter 4 for a complete description of emission impacts
associated with the final standards). Emissions of NOx (a precursor to ozone formation and
secondarily-formed PM2.5), SOx (a precursor to secondarily-formed PM2.5), VOCs (a precursor to
ozone formation and, to a lesser degree, secondarily-formed PM2.5) and directly-emitted PM2.5
contribute to ambient concentrations of PM2.5 and ozone. Exposure to ozone and PM2.5 is linked
to adverse human health impacts such as premature deaths as well as other important public
health and environmental effects.
The analysis in this section aims to characterize the benefits of the final standards by
answering two key questions:
1. What are the health and welfare effects of changes in ambient particulate matter
(PM2.s) and ozone air quality resulting from reductions in precursors including NOx and SO2?
2. What is the economic value of these effects?
RRRRRR gpA typically analyzes rule impacts (emissions, air quality, costs and benefits) in the year in which they
occur; for this analysis, we selected 2030 as a representative future year. We refer to this analysis as the "Calendar
Year" (CY) analysis. EPA also conducted a separate analysis of the impacts over the model year lifetimes of the
2017 through 2025 model year vehicles. We refer to this analysis as the "Model Year" (MY) analysis. In contrast to
the CY analysis, the MY lifetime analysis shows the lifetime impacts of the program on each MY fleet over the
course of its lifetime.
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Chapter 6
For the final rale, we have quantified and monetized the health and environmental
impacts in 2030, representing impacts associated with a year when the standards are fully
implemented and reflects a limited degree of fleet turnover. Overall, we estimate that the final
standards will lead to a net decrease in PM2.5-related health impacts in 2030. The decrease in
population-weighted national average PM2.5 exposure results in a net decrease in adverse PM-
related human health impacts (the decrease in national population-weighted annual average PM2.5
is 0.0065 |^g/m3 in 2030).ssssss We estimate that there is a very small increase in population-
weighted national average ozone exposure, which results in a very small net increase in ozone-
related health impacts (population-weighted maximum 8-hour average ozone increases by 0.0009
ppb in 2030).
Using the most conservative premature mortality estimates (Pope et al., 2002 for PM2.5
and Bell et al., 2004 for ozone),rr'uuuuuu we estimate that by 2030, implementation of the
final standards will reduce approximately 110 premature mortalities annually and yield
approximately $0.95 billion in total annual benefits. The upper end of the range of avoided
premature mortality estimates associated with the standards (based on Laden et al., 2006 for
PM2.5 and Levy et al., 2005 for ozone^vvvv''^w^fw^ results in approximately 280 premature
mortalities avoided in 2030 and yields approximately $2.6 billion in total benefits. Thus, even
taking the most conservative premature mortality assumptions, the health impacts of the
standards presented in this rale are substantial.
6.3.1.1 Overview
This analysis reflects the impacts of the final MY 2017-2025 standards in 2030 compared
to a future-year reference scenario without the standards in place. Overall, we estimate that the
final rule will lead to a net decrease in population-weighted national average PMi.5 exposure,
which results in a net decrease in adverse PM-related human health and environmental impacts
(the decrease in national population weighted annual average PM2.5 is 0.0065 ^ig/m3 in 2030).
The air quality modeling also projects a very small net increase in ozone concentrations
(population weighted maximum 8-hour average ozone increases by 0.0009 ppb in 2030). The
national, population- weighted PM2.s and ozone air quality metrics presented in this Chapter
represent an average for the entire, gridded U.S. CMAQ domain. These are different than the population-weighted
PM2.s and ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air
quality monitor.
TTTTTT pop^ c A ^ ni) R T Bumett M j Thun E E CallC; D Krewski, K. Ito, and G.D. Thurston. (2002). Lung
Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the
American Medical Association, 287, 1132-1141.
uuuuuu Bell M L ^ et ^ ,-2004). Ozone and short-term mortality in 95 US urban communities, 1987-2000 Journal of
the American Medical Association, 292(19), 2372-2378.
vwwv Laden> F ^ j Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution
and Mortality. American Journal of Respiratory and Critical Care Medicine. 173, 667-672.
wwwwww Levv> J.I., S.M. Chemerynski, and J.A. Sarna
metaregression analysis. Epidemiology. 16(4), 458-68.
wwwwww Levv> J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes
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MY 2017 and Later - Regulatory Impact Analysis
small increase in population-weighted national average ozone exposure results in a very small
increase in net ozone-related health and environmental impacts.
We base our analysis of the final rule's impact on human health and the environment on
peer-reviewed studies of air quality and human health effects.376'377 Our benefits methods are
also consistent with recent rulemaking analyses such as the final Transport Rule,378 the final
2012-2016 MY Light-Duty Vehicle Rule, and the final Portland Cement National Emissions
Standards for Hazardous Air Pollutants (NESHAP) RIA.380 To model the ozone and PM air
quality impacts of the final standard, we used the Community Multiscale Air Quality (CMAQ)
model (see Section 6.2). The modeled ambient air quality data serves as an input to the
Environmental Benefits Mapping and Analysis Program version 4.0 (BenMAP).^0000^
BenMAP is a computer program developed by the U.S. EPA that integrates a number of the
modeling elements used in previous analyses (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.
The range of total monetized ozone- and PM-related health impacts in 2030 is presented
in Table 6.3-1. We present total benefits (the sum of morbidity-related benefits and mortality-
related benefits) based on the PM- and ozone-related premature mortality function used. The
benefits ranges therefore reflect the addition of each estimate of ozone-related premature
mortality (across six selected studies, each with its own row) to each estimate of PM-related
premature mortality (based on either Pope et al., 2002 or Laden et al., 2006), along with all
morbidity-related benefits. These estimates represent EPA's preferred approach to characterizing
a best estimate of benefits. As is the nature of RIAs, the assumptions and methods used to
estimate air quality benefits evolve to reflect the Agency's most current interpretation of the
scientific and economic literature.
Table 6.3-1: Estimated 2030 Monetized PM-and Ozone-Related Health Benefits3
2030 Total Ozone and PM Benefits - PM Mortality Derived from American Cancer Society Analysis and
Six-Cities Analysis2
Premature Ozone
Mortality Function
Multi-city analyses
Meta-analyses
Reference
Bell et al., 2004
Huang et al., 2005
Schwartz, 2005
Bell et al., 2005
Total Benefits
(Billions, 2010$, 3%
Discount Rate)b'c
Total: $1.0 -$2.6
PM: $1.1 -$2.6
Ozone: -$0.006
Total: $1.0 -$2.6
PM: $1.1 -$2.6
Ozone: -$0.006
Total: $1.0 -$2.6
PM: $1.1 -$2.6
Ozone: -$0.009
Total: $1.0 -$2.6
Total Benefits
(Billions, 2010$, 7%
Discount Rate) b'c
Total: $0.92 - $2.3
PM: $0.95 - $2.3
Ozone: -$0.006
Total: $0.92 - $2.3
PM: $0.95 - $2.3
Ozone: -$0.006
Total: $0.92 - $2.3
PM: $0.95 - $2.3
Ozone: -$0.009
Total: $0.92 - $2.3
Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/
benmodels.html.
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Ito et al., 2005
Levy etal., 2005
PM: $1.1 -$2.6
Ozone: -$0.019
Total: $1.0 -$2.6
PM: $1.1 -$2.6
Ozone: -$0.026
Total: $1.0 -$2.6
PM: $1.1 -$2.6
Ozone: -$0.027
PM: $0.95 - $2.3
Ozone: -$0.019
Total: $0.92 - $2.3
PM: $0.95 - $2.3
Ozone: -$0.026
Total: $0.92 - $2.3
PM: $0.95 - $2.3
Ozone: -$0.027
Notes:
"Total includes premature mortality-related and morbidity-related ozone and PM2.sbenefits. Range was developed
by adding the estimate from the ozone premature mortality function to the estimate of PM2.s-related premature
mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).
* Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed
listing of unquantified health and welfare effects is provided in Table 6.3-2.
c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA's Guidelines for Preparing
Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation
and computation. Totals may not sum due to rounding.
The benefits in Table 6.3-1 include all of the human health impacts we are able to
quantify and monetize at this time. However, the full complement of human health and welfare
effects associated with PM, ozone, and other criteria pollutants remain unquantified because of
current limitations in methods or available data. We have not quantified a number of known or
suspected health effects linked with ozone, PM, and other criteria pollutants for which
appropriate health impact functions are not available or which do not provide easily interpretable
outcomes (e.g., changes in heart rate variability). Additionally, we are unable to quantify a
number of known welfare effects, including reduced acid and particulate deposition damage to
cultural monuments and other materials, and environmental benefits due to reductions of impacts
of eutrophication in coastal areas. These are listed in Table 6.3-2. As a result, the health benefits
quantified in this section are likely underestimates of the total benefits attributable to the final
standards.
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Table 6.3-2: Human Health and Welfare Effects of Pollutants Affected by the Final
Standards
Pollutant/
Effect
Quantified and monetized in primary estimate
Unquantified
PM: health3
Premature mortality based on cohort study
estimates'3 and expert elicitation estimates
Hospital admissions: respiratory and
cardiovascular
Emergency room visits for asthma
Nonfatal heart attacks (myocardial
infarctions)
Lower and upper respiratory illness
Minor restricted activity days
Work loss days
Asthma exacerbations (among asthmatic
populations
Respiratory symptoms (among asthmatic
populations)
Infant mortality
Low birth weight, pre-term birth and other
reproductive outcomes
Pulmonary function
Chronic respiratory diseases other than chronic
bronchitis
Non-asthma respiratory emergency room visits
UVb exposure (+/-)c
PM: welfare
Visibility in Class I areas in SE, SW, and CA
regions
Household soiling
Visibility in residential areas
Visibility in non-class I areas and class 1 areas in
NW, NE, and Central regions
UVb exposure (+/-)c
Global climate impacts0
Ozone: health
Premature mortality based on short-term
study estimates
Hospital admissions: respiratory
Emergency room visits for asthma
Minor restricted activity days
School loss days
Chronic respiratory damage
Premature aging of the lungs
Non-asthma respiratory emergency room visits
UVb exposure (+/-)c
Ozone: welfare
Decreased outdoor worker productivity
Yields for:
-Commercial forests
-Fruits and vegetables, and
-Other commercial and noncommercial crops
Damage to urban ornamental plants
Recreational demand from damaged forest
aesthetics
Ecosystem functions
UVb exposure (+/-)c
Climate impacts
CO: health
Behavioral effects
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Chapter 6
Pollutant/
Effect
Nitrate
Deposition:
welfare
Sulfate
Deposition:
welfare
HC/Toxics:
healthd
HC/Toxics:
welfare
Quantified and monetized in primary estimate
Unquantified
Commercial fishing and forestry from acidic
deposition effects
Commercial fishing, agriculture and forestry
from nutrient deposition effects
Recreation in terrestrial and estuarine
ecosystems from nutrient deposition effects
Other ecosystem services and existence values
for currently healthy ecosystems
Coastal eutrophication from nitrogen deposition
effects
Commercial fishing and forestry from acidic
deposition effects
Recreation in terrestrial and aquatic ecosystems
from acid deposition effects
Increased mercury methylation
Cancer (benzene, 1,3-butadiene, formaldehyde,
acetaldehyde)
Anemia (benzene)
Disruption of production of blood components
(benzene)
Reduction in the number of blood platelets
(benzene)
Excessive bone marrow formation (benzene)
Depression of lymphocyte counts (benzene)
Reproductive and developmental effects (1,3-
butadiene)
Irritation of eyes and mucus membranes
(formaldehyde)
Respiratory irritation (formaldehyde)
Asthma attacks in asthmatics (formaldehyde)
Asthma-like symptoms in non-asthmatics
(formaldehyde)
Irritation of the eyes, skin, and respiratory tract
(acetaldehyde)
Upper respiratory tract irritation and congestion
(acrolein)
Direct toxic effects to animals
Bioaccumulation in the food chain
Damage to ecosystem function
Odor
a In addition to primary economic endpoints, there are a number of biological responses that have been associated
with PM health effects including morphological changes and altered host defense mechanisms. The public health
impact of these biological responses may be partly represented by our quantified endpoints.
b Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk
estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of
this issue).381 While some of the effects of short term exposure are likely to be captured by the cohort estimates, there
may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in
the primary analysis.
c May result in benefits or disbenefits.
d Many of the key hydrocarbons related to this action are also hazardous air pollutants listed in the CAA.
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While there will be impacts associated with air toxic pollutant emission changes that
result from the final standards, we do not attempt to monetize those impacts. This is primarily
because 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). The EPA 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.382 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,383 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."384 EPA continues to work to address these limitations; however,
we did not have the methods and tools available for national-scale application in time for the
analysis of the final standards.YYYYYY
6.3.1.2 Human Health Impacts
Table 6.3-3 and Table 6.3-4 present the annual PM2.5 and ozone health impacts in the 48
contiguous U.S. states associated with the final standards. For each endpoint presented in Table
6.3-3 and Table 6.3-4, we provide both the point estimate and the 90 percent confidence interval.
Using EPA's preferred estimates, based on the American Cancer Society (ACS) and Six-
Cities studies and no threshold assumption in the model of mortality, we estimate that the final
standards will result in between 110 and 280 cases of avoided PMi.s-related premature deaths
annually in 2030. As a sensitivity analysis, when the range of expert opinion is used, we estimate
between 36 and 370 fewer premature mortalities in 2030.
The range of ozone impacts is based on changes in risk estimated using several sources of
ozone-related mortality effect estimates. This analysis presents six alternative estimates for the
association based upon different functions reported in the scientific literature, derived from both
YYYYYY jn Apn^ 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.
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Chapter 6
the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (Bell et al, 2004; Huang
et al., 2005; Schwartz, 2005) and from a series of recent meta-analyses (Bell et al., 2005, Ito et
al., 2005, and Levy et al., 2005). This approach is not inconsistent with recommendations
provided by the NRC in their recent report (NRC, 2008) on the estimation of ozone-related
mortality risk reductions, "The committee recommends that the greatest emphasis be placed on
estimates from new systematic multicity analyses that use national databases of air pollution and
mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of
previously published studies."385 For ozone-related premature mortality in 2030, we estimate a
range of between 1 to 3 additional premature mortalities.
Following these tables, we also provide a more comprehensive presentation of the
distributions of incidence generated using the available information from empirical studies and
expert elicitation.
Table 6.3-5 presents the distributions of the reduction in PMi.s -related premature
mortality based on the C-R distributions provided by each expert, as well as that from the data-
derived health impact functions, based on the statistical error associated with the ACS study
(Pope et al., 2002) and the Six-Cities study (Laden et al., 2006). The 90 percent confidence
interval for each separate estimate of PM-related mortality is also provided.
In 2030, the effect estimates of nine of the twelve experts included in the elicitation panel
fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one
expert falls below this range, while two of the experts are above this range. Although the overall
range across experts is summarized in these tables, the full uncertainty in the estimates is
reflected by the results for the full set of 12 experts. The twelve experts' judgments as to the
likely mean effect estimate are not evenly distributed across the range illustrated by arraying the
highest and lowest expert means.
Table 6.3-3: Estimated PM2.5-Related Health Impacts3
Health Effect
Premature Mortality - Derived from epidemiology literature
Adult, age 30+, ACS Cohort Study (Pope et al., 2002)
Adult, age 25+, Six-Cities Study (Laden et al., 2006)
Infant, age <1 year (Woodruff et al., 1997)
Chronic bronchitis (adult, age 26 and over)
Non-fatal myocardial infarction (adult, age 18 and over)
Hospital admissions - respiratory (all ages)c
Hospital admissions - cardiovascular (adults, age >18)d
Emergency room visits for asthma (age 18 years and younger)
Acute bronchitis, (children, age 8-12)
2030 Annual Reduction in
Incidence (5th - 95th percentile)
110
(30 - 190)
280
(130-440)
0
(0-1)
76
(1 - 150)
130
(32 - 230)
20
(8 - 32)
50
(33 - 60)
72
(34-110)
160
(-42 - 370)
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MY 2017 and Later - Regulatory Impact Analysis
Lower respiratory symptoms (children, age 7-14)
Upper respiratory symptoms (asthmatic children, age 9-18)
Asthma exacerbation (asthmatic children, age 6-18)
Work loss days
Minor restricted activity days (adults age 18-65)
2,100
(770 - 3,400)
1,600
(260 - 2,900)
3,500
(-120-9,700)
14,000
(12,000-16,000)
81,000
(65,000 - 96,000)
Notes:
a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United
States.
b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002)
and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and
should not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf,
(1997).zzzzzz
c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD),
pneumonia and asthma.
d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart
disease, dysrhythmias, and heart failure.
Table 6.3-4: Estimated Ozone-Related Health Impacts3
Health Effect
Premature Mortality, All agesb
Multi-City Analyses
Bell et al. (2004) - Non-accidental
Huang et al. (2005) - Cardiopulmonary
Schwartz (2005) - Non-accidental
Meta-analyses:
Bell et al. (2005) - All cause
Ito et al. (2005) - Non-accidental
Levy et al. (2005) - All cause
Hospital admissions- respiratory causes (adult, 65 and older)c
Hospital admissions -respiratory causes (children, under 2)
Emergency room visit for asthma (all ages)
Minor restricted activity days (adults, age 18-65)
2030 Annual Reduction in Incidence
(5th - 95th percentile)
-1
(-4-3)
-1
(-5 - 4)
-1
(-6-4)
-2
(-10-6)
-3
(-11-6)
-3
(-10-4)
-6
(-30 - 15)
-3
(-12-6)
-1
(-18-15)
-930
(-18,000-16,000)
222222 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. "The Relationship Between Selected Causes of
Postneonatal Infant Mortality and Particulate Air Pollution in the United States." Environmental Health
Perspectives 105(6):608-612.
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Chapter 6
School absence
days
-850
(-6,700-5,100)
Notes:
a Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two significant
digits. Estimates represent incidence within the 48 contiguous U.S.
b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative
studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005); Bell et al. (2005); Ito et al. (2005); Levy et al. (2005).
The estimates of ozone-related premature mortality should therefore not be summed.
c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for
COPD and pneumonia.
Table 6.3-5: Results of Application of Expert Elicitation: Annual Reductions in Premature
Mortality in 2030 Associated with the Final Standards
Source of Mortality
Estimate
Pope et al. (2002)
Laden et al. (2006)
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
2030 Incidence
5th Percentile
30
130
6
-23
-4
23
150
120
0
-38
19
19
0
8
Mean
110
280
300
220
230
160
370
200
130
170
220
180
36
140
95th Percentile
190
440
590
530
530
280
60
290
260
430
430
440
190
330
6.3.1.3 Monetized Estimates of Human Health and Environmental Impacts
Table 6.3-6 presents the estimated monetary value of changes in the incidence of ozone
and PMi.s-related health and environmental effects. Total aggregate monetized benefits are
presented in Table 6.3-7. All monetized estimates are presented in 2010$. Where appropriate,
estimates account for growth in real gross domestic product (GDP) per capita between 2000 and
2030.AAAAAAA The monetized value of PM2.5-related mortality also accounts for a twenty-year
Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for
most goods (such as environmental protection) will increase if real incomes increase. Benefits are therefore adjusted
by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time.
For growth between 2000 and 2030, this factor is 1.23 for long-term mortality, 1.27 for chronic health impacts, and
1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the
reader to the PM NAAQS regulatory impact analysis.9 Note that similar adjustments do not exist for cost-of-illness-
based unit values. For these, we apply the same unit value regardless of the future year of analysis.
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MY 2017 and Later - Regulatory Impact Analysis
segmented cessation lag.BBBBBBB TO discount the value of premature mortality that occurs at
different points in the future, we apply both a 3 and 7 percent discount rate. We also use both a 3
and 7 percent discount rate to value PM-related nonfatal heart attacks (myocardial
infarctions).ccccccc
In addition to omitted benefits categories such as air toxics and various welfare effects,
not all known PM2.5- and ozone-related health and welfare effects could be quantified or
monetized. The estimate of total monetized health benefits of the final standards is thus equal to
the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus the
sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits in
2030 for the final standards, using the ACS and Six-Cities PM mortality studies and the range of
ozone mortality assumptions, is between $1.0 and $2.6 billion, assuming a 3 percent discount
rate, or between $0.92 and $2.3 billion, assuming a 7 percent discount rate. As the results
indicate, total benefits are driven primarily by the reduction in PMi.s-related premature fatalities
each year and represent the benefits of the final standards anticipated to occur annually when the
program is fully implemented.
The next largest benefit is for reductions in chronic illness (chronic bronchitis and
nonfatal heart attacks), although this value is more than an order of magnitude lower than for
premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor
restricted activity days, and work loss days account for the majority of the remaining benefits.
The remaining categories each account for a small percentage of total benefit; however, they
represent a large number of avoided incidences affecting many individuals. A comparison of the
incidence table to the monetary benefits table reveals that there is not always a close
correspondence between the number of incidences avoided for a given endpoint and the monetary
value associated with that endpoint. For example, there are many more work loss days than PM-
related premature mortalities, yet work loss days account for only a very small fraction of total
monetized benefits. This reflects the fact that many of the less severe health effects, while more
common, are valued at a lower level than the more severe health effects. Also, some effects,
such as hospital admissions, are valued using a proxy measure of willingness-to-pay (e.g., cost-
of-illness). As such, the true value of these effects may be higher than that reported here.
BBBBBBB gasecj jn p^ on prjor g^g advice, EPA has typically assumed that there is a time lag between changes in
pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses,
this term is often referred to as "cessation lag". The existence of such a lag is important for the valuation of
premature mortality incidence because economic theory suggests that benefits occurring in the future should be
discounted. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is
consistent with the most recent recommendation by the EPA's Science Advisory Board. Refer to: EPA - Science
Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on
Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency
Administrator, December.
ccccccc Nonfata[ rnyocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost
earnings and direct medical costs over a 5-year period following a nonfatal MI.
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Chapter 6
Table 6.3-6: Estimated Monetary Value of Changes in Incidence of Health and Welfare
Effects (millions of 2010$) a'b
PM2.5-Related Health Effect
Premature Mortality -
Derived from Epidemiology
Studies0'
Chronic bronchitis (adults, 26
Adult, age 30+ - ACS study
(Pope etal., 2002)
3% discount rate
7% discount rate
Adult, age 25+ - Six-Cities study
(Laden etal., 2006)
3% discount rate
7% discount rate
Infant Mortality, <1 year -
(Woodruff etal. 1997)
and over)
Non-fatal acute myocardial infarctions
3% discount rate
7% discount rate
Hospital admissions for respiratory causes
Hospital admissions for cardiovascular causes
Emergency room visits for asthma
Acute bronchitis (children, age 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthma, 9-11)
Asthma exacerbations
Work loss days
Minor restricted-activity days
(MRADs)
2030
(5ffi and 95ffi Percentile)
$980
($110 -$2,600)
$880
($97 - $2,400)
$2,500
($340 - $6,300)
$2,300
($3 10 -$5,700)
$3.8
(-$3.9 -$15)
$42
($0.4 -$140)
$14
($2.3 - $36)
$12
($1.8 -$30)
$0.32
($0.13 -$0.51)
$0.73
($0.07 - $1.4)
$0.03
($0.01 - $0.05)
$0.08
(-$0.02 - $0.21)
$0.04
($0.01 - $0.09)
$0.05
($0.009 -$0.12)
$0.20
(-$0.007 - $0.58)
$2.2
($1.9 - $2.6)
$5.6
($3.2 -$8.1)
Premature Mortality, All ages - Bell et al., 2004
Derived from Multi-city analyses
Premature Mortality, All ages
Derived from Meta-analyses
Huang et al., 2005
Schwartz, 2005
Bell et al., 2005
-$5.8
(-$45 - $27)
-$6.2
(-$60 - $41)
-$8.7
(-$71 - $44)
-$19
(-$120 -$38)
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MY 2017 and Later - Regulatory Impact Analysis
Ito et al., 2005
Levy etal., 2005
Hospital admissions- respiratory causes (adult, 65 and older)
Hospital admissions- respiratory causes (children, under 2)
Emergency room visit for asthma (all
Minor restricted activity days (adults,
ages)
age 18-65)
School absence days
-$26
(-$140 -$58)
-$27
(-$120 -$38)
-$0.16
(-$0.77 - $0.39)
-$0.03
(-$130 - $0.07)
-$0.0003
(-$0.007 - $0.006)
-$0.06
(-$1.3 -$1.1)
-$0.08
(-$0.65 - $0.49)
Notes:
a Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary benefits are rounded to
two significant digits for ease of presentation and computation. PM and ozone benefits are nationwide.
b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis
year (2030).
c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results
reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for
preparing economic analyses.
Table 6.3-7: Total Monetized Ozone and PM-related Benefits Associated with the Final
Standards in 2030
Total Ozone and PM Benefits (billions, 2010$) -
PM Mortality Derived from the ACS and Six-Cities Studies
3% Discount Rate
Ozone
Mortality
Function
Multi-city
Meta-analysis
Reference
Bell et al.,
2004
Huang et al.,
2005
Schwartz,
2005
Bell et al.,
2005
Ito et al.,
2005
Levy etal.,
2005
Mean Total
Benefits
$1.0 -$2.6
$1.0 -$2.6
$1.0 -$2.6
$1.0 -$2.6
$1.0 -$2.6
$1.0 -$2.6
7% Discount Rate
Ozone
Mortality
Function
Multi-city
Meta-analysis
Reference
Bell etal.,
2004
Huang etal.,
2005
Schwartz,
2005
Bell etal.,
2005
Ito etal.,
2005
Levy et al.,
2005
Mean Total
Benefits
$0.95 - $2.3
$0.94 - $2.3
$0.94 - $2.3
$0.93 - $2.3
$0.92 - $2.3
$0.92 - $2.3
Total Ozone and PM Benefits (billions, 2010$) -
PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)
3% Discount Rate
Ozone
Mortality
Function
Multi-city
Reference
Bell et al.,
2004
Huang et al.,
2005
Mean Total
Benefits
$0.39 - $3.4
$0.39 - $3.4
7% Discount Rate
Ozone
Mortality
Function
Multi-city
Reference
Bell etal.,
2004
Huang etal.,
2005
Mean Total
Benefits
$0.35 -$3.1
$0.35 -$3.1
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Meta-analysis
Schwartz,
2005
Bell et al.,
2005
Ito et al.,
2005
Levy etal.,
2005
$0.39 - $3.4
$0.38 - $3.4
$0.37 - $3.4
$0.37 - $3.4
Meta-analysis
Schwartz,
2005
Bell etal.,
2005
Ito etal.,
2005
Levy et al.,
2005
$0.35 -$3.1
$0.34 -$3.1
$0.33 - $3.0
$0.33 - $3.0
6.3. 1.4 Methodology
6.3. 1.4. 1 Human Health Impact Functions
Health impact functions measure the change in a health endpoint of interest, such as
hospital admissions, for a given change in ambient ozone or PM concentration. Health impact
functions are derived from primary epidemiology studies, meta- analyses of multiple
epidemiology studies, or expert elicitations. A standard health impact function has four
components: (1) an effect estimate from a particular study; (2) a baseline incidence rate for the
health effect (obtained from either the epidemiology study or a source of public health statistics
such as the Centers for Disease Control); (3) the size of the potentially affected population; and
(4) the estimated change in the relevant ozone or PM summary measures.
A typical health impact function might look like:
where yo is the baseline incidence (the product of the baseline incidence rate times the
potentially affected population), p is the effect estimate, and Ax is the estimated change in the
summary pollutant measure. There are other functional forms, but the basic elements remain the
same. The following subsections describe the sources for each of the first three elements: size of
the potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence
rates. We also describe the treatment of potential thresholds in PM-related health impact
functions. Section 8.2 describes the ozone and PM air quality inputs to the health impact
functions.
6.3. 1 .4. 1 . 1 Potentially Affected Populations
The starting point for estimating the size of potentially affected populations is the 2000
U.S. Census block level dataset.386 Benefits Modeling and Analysis Program (BenMAP)
incorporates 250 age/gender/race categories to match specific populations potentially affected by
ozone and other air pollutants. The software constructs specific populations matching the
populations in each epidemiological study by accessing the appropriate age-specific populations
from the overall population database. BenMAP projects populations to 2030 using growth
factors based on economic projections.387
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6.3.1.4.1.2 Effect Estimate Sources
The most significant quantifiable benefits of reducing ambient concentrations of ozone
and PM are attributable to reductions in human health risks. EPA's Ozone and PM Criteria
Documents388'389 and the World Health Organization's 2003 and 2004390'391 reports outline
numerous human health effects known or suspected to be linked to exposure to ambient ozone
and PM. EPA recently evaluated the ozone and PM literature for use in the benefits analysis for
the final 2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature
in this analysis; for more information on the studies that underlie the health impacts quantified in
this RIA, please refer to those documents.
It is important to note that we are unable to separately quantify all of the possible PM and
ozone health effects that have been reported in the literature for three reasons: (1) the possibility
of double counting (such as hospital admissions for specific respiratory diseases versus hospital
admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect
relationships that are based on clinical studies to the potentially affected population; or (3) the
lack of an established concentration-response (CR) relationship. Table 6.3-8 lists the health
endpoints included in this analysis.
Table 6.3-8: Health Impact Functions Used in BenMAP to Estimate Impacts of PM^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
PM2.5
PM2.5
Multi-city
Bell et al (2004) (NMMAPS study)392 - Non-
accidental
Huang et al (2005)393 - Cardiopulmonary
Schwartz (2005)394 - Non-accidental
Meta-analvses:
Bell et al (2005)395 - All cause
Ito et al (2005)396 - Non-accidental
Levy et al (2005)397 - All cause
Pope et al. (2002)398
Laden et al. (2006)399
Expert Elicitation (lEc, 2006)400
Woodruff etal. (1997)401
All ages
>29 years
>25 years
>24 years
Infant (<1 year)
Chronic Illness
Chronic bronchitis
Nonfatal heart attacks
PM2.5
PM2.5
Abbey etal. (1995)402
Peters etal. (200 1)403
>26 years
Adults (>18 years)
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ENDPOINT
POLLUTANT
STUDY
STUDY POPULATION
Hospital Admissions
Respiratory
Cardiovascular
Asthma-related ER
visits
Asthma-related ER
visits (cont'd)
03
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5
03
PM2.5
Pooled estimate:
Schwartz (1995) - ICD 460-519 (all resp)404
Schwartz (1994a; 1994b) - ICD 480-486
, • N 405 406
(pneumonia) '
Moolgavkar et al. (1997) - ICD 480-487
407
(pneumonia)
Schwartz (1994b) - ICD 491-492, 494-496
(COPD)
Moolgavkar et al. (1997) - ICD 490-496
(COPD)
Burnett etal. (2001)408
Pooled estimate:
Moolgavkar (2003)— ICD 490-496 (COPD)409
Ito (2003)— ICD 490-496 (COPD)410
Moolgavkar (2000)— ICD 490-496 (COPD)411
Ito (2003)— ICD 480-486 (pneumonia)
Sheppard (2003)— ICD 493 (asthma)412
Pooled estimate:
Moolgavkar (2003)— ICD 390-429 (all
cardiovascular)
Ito (2003)— ICD 410-414, 427-428 (ischemic
heart disease, dysrhythmia, heart failure)
Moolgavkar (2000)— ICD 390-429 (all
cardiovascular)
Pooled estimate:
Peeletal(2005)413
Wilson etal(2005)414
Norrisetal. (1999)415
>64 years
<2 years
>64 years
20-64 years
>64 years
<65 years
>64 years
20-64 years
All ages
All ages
0-18 years
Other Health Endpoints
Acute bronchitis
Upper respiratory
symptoms
Lower respiratory
symptoms
Asthma exacerbations
Work loss days
School absence days
Minor Restricted
Activity Days
(MRADs)
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5
03
03
PM2.5
Dockeryetal. (1996)416
Pope etal. (1991)417
Schwartz andNeas (2000)418
Pooled estimate:
Ostro et al. (2001)419 (cough, wheeze and
shortness of breath)
Vedal et al. (1998)420 (cough)
Ostro (1987)421
Pooled estimate:
Gilliland et al. (2001)422
Chen et al. (2000)423
Ostro and Rothschild (1989)424
Ostro and Rothschild (1989)
8-12 years
Asthmatics, 9-11
years
7-14 years
6-18 years2
18-65 years
5-17 yearsb
18-65 years
18-65 years
Notes:
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a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998)
study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended
the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age
group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan
for EPA's Second Prospective Analysis -Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SAB-
COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits
of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.
* Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent
advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school
absences for all school-aged children based on the biological similarity between children aged 5 to 17.
In selecting epidemiological studies as sources of effect estimates, we applied several
criteria to develop a set of studies that is likely to provide the best estimates of impacts in the
U.S. To account for the potential impacts of different health care systems or underlying health
status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due
to the potential for confounding by co-pollutants, we give preference to effect estimates from
models including both ozone and PM over effect estimates from single-pollutant models.425'426
6.3.1.4.1.3 Baseline Incidence Rates
Epidemiological studies of the association between pollution levels and adverse health
effects generally provide a direct estimate of the relationship of air quality changes to the relative
risk of a health effect, rather than estimating the absolute number of avoided cases. For example,
a typical result might be that a 100 ppb decrease in daily ozone levels might, in turn, decrease
hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to
convert this relative change into a number of cases. A baseline incidence rate is the estimate of
the number of cases of the health effect per year in the assessment location, as it corresponds to
baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate
must be multiplied by the corresponding population number. For example, if the baseline
incidence rate is the number of cases per year per 100,000 people, that number must be
multiplied by the number of 100,000s in the population.
Table 6.3-9 summarizes the sources of baseline incidence rates and provides average
incidence rates for the endpoints included in the analysis. Table 6.3-10 presents the asthma
prevalence rates used in this analysis. For both baseline incidence and prevalence data, we used
age-specific rates where available. We applied concentration-response functions to individual
age groups and then summed over the relevant age range to provide an estimate of total
population benefits. In most cases, we used a single national incidence rate, due to a lack of
more spatially disaggregated data. Whenever possible, the national rates used are national
averages, because these data are most applicable to a national assessment of benefits. For some
studies, however, the only available incidence information comes from the studies themselves; in
these cases, incidence in the study population is assumed to represent typical incidence at the
national level. Regional incidence rates are available for hospital admissions, and county-level
data are available for premature mortality. We have projected mortality rates such that future
mortality rates are consistent with our projections of population growth.427
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Table 6.3-9: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact
Functions, General Population
Endpoint
Mortality
Hospitalizations
Asthma ER Visits
Chronic Bronchitis
Nonfatal Myocardial
Infarction (heart
attacks)
Asthma Exacerbations
Acute Bronchitis
Lower Respiratory
Symptoms
Upper Respiratory
Symptoms
Work Loss Days
School Loss Days
Minor Restricted-
Activity Days
Parameter
Daily or annual mortality
rate projected to 2020
Daily hospitalization rate
Daily asthma ER visit rate
Annual prevalence rate per
person
Aged 18-44
Aged 45-64
Aged 65 and older
Annual incidence rate per
person
Daily nonfatal myocardial
infarction incidence rate
per person, 18+
Incidence among asthmatic
African- American children
daily wheeze
daily cough
daily dyspnea
Annual bronchitis
incidence rate, children
Daily lower respiratory
symptom incidence among
children13
Daily upper respiratory
symptom incidence among
asthmatic children
Daily WLD incidence rate
per person (18-65)
Aged 18-24
Aged 25-44
Aged 45-64
Rate per person per year,
assuming 180 school days
per year
Daily MRAD incidence
rate per person
Rates
Value
Age-, cause-, and
county-specific rate
Age-, region-, state-,
county- and cause-
specific rate
Age-, region-, state-,
county- and cause-
specific rate
0.0367
0.0505
0.0587
0.00378
Age-, region-, state-,
and county- specific
rate
0.076
0.067
0.037
0.043
0.0012
0.3419
0.00540
0.00678
0.00492
9.9
0.02137
Source
CDC Wonder (2006-2008)428
U.S. Census bureau
2007 HCUP data, files"'429
2007 HCUP data, files"
1999 NHIS (American Lung
Association, 2002, Table 4)430
Abbey et al. (1993, Table 3)
2007 HCUP data, files"; adjusted by
0.93 for probability of surviving after
28 days (Rosamond et al., 1999)
Ostroetal. (2001)
American Lung Association (2002,
Table II)431
Schwartz et al. (1994, Table 2)
Pope etal. (1991, Table 2)
1996 HIS (Adams, Hendershot, and
Marano, 1999, Table 41);432 U.S.
Bureau of the Census (2000)433
National Center for Education
Statistics (1996)434 and 1996 HIS
(Adams et al., 1999, Table 47);
Ostro and Rothschild (1989, p. 243)
Notes:
" Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level
hospital and emergency department discharges for a variety of ICD codes.
* Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.
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Table 6.3-10: Asthma Prevalence Rates Used for this Analysis
Population Group
All Ages
< 18
5-17
18-44
45-64
65+
African American, 5 to 17
African American, <18
Asthma Prevalence Rates
Value
0.0780
0.0941
0.1070
0.0719
0.0745
0.0716
0.1776
0.1553
Source
American Lung Association (2010, Table 7)
American Lung Association (2010, Table 9)
American Lung Association13
Notes:
aSeeftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.
* Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).435
6.3.1.4.2 Economic Values for Health Outcomes
Reductions in ambient concentrations of air pollution generally lower the risk of future
adverse health effects for a large population. Therefore, the appropriate economic measure is
willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health
effect that would occur with certainty (Freeman, 1993).436 Epidemiological studies generally
provide estimates of the relative risks of a particular health effect that is avoided because of a
reduction in air pollution. We converted those to units of avoided statistical incidence for ease of
presentation. We calculated the value of avoided statistical incidences by dividing individual
WTP for a risk reduction by the related observed change in risk. For example, suppose a
pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000
to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then
the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk).
WTP estimates generally are not available for some health effects, such as hospital
admissions. In these cases, we used the cost of treating or mitigating the effect as a primary
estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing
the risk of a health effect, because they reflect the direct expenditures related to treatment, but not
the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).437'438 We
provide unit values for health endpoints (along with information on the distribution of the unit
value) in Table 6.3-11. All values are in constant year 2010 dollars, adjusted for growth in real
income out to 2030 using projections provided by Standard and Poor's. Economic theory argues
that WTP for most goods (such as environmental protection) will increase if real income
increases. Many of the valuation studies used in this analysis were conducted in the late 1980s
and early 1990s. Because real income has grown since the studies were conducted, people's
willingness to pay for reductions in the risk of premature death and disease likely has grown as
well. We did not adjust cost of illness-based values because they are based on current costs.
Similarly, we did not adjust the value of school absences, because that value is based on current
wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 PM
NAAQS RIA.439 For details on valuation estimates for ozone-related endpoints, see the 2008
Ozone NAAQS RIA.440
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Table 6.3-11: Unit Values for Economic Valuation of Health Endpoints (2010$)
Health Endpoint
Central Estimate of
Value Per Statistical
Incidence
2000
Income
Level
2030
Income
Level
Derivation of Distributions of Estimates
Premature Mortality
(Value of a Statistical
Life)
5,000,000
$9,900,000
EPA currently recommends a central VSL of $6.3m (2000$) based on a
Weibull distribution fitted to 26 published VSL estimates (5 contingent
valuation and 21 labor market studies). The underlying studies, the
distribution parameters, and other useful information are available in
Appendix B of EPA's current Guidelines for Preparing Economic
Analyses (U.S. EPA, 2000).
Chronic Bronchitis
(CB)
$450,000
$550,000
The WTP to avoid a case of pollution-related CB is calculated as where
x is the severity of an average CB case, WTP13 is the WTP for a severe
case of CB, and $ is the parameter relating WTP to severity, based on
the regression results reported in Krupnick and Cropper (1992). The
distribution of WTP for an average severity-level case of CB was
generated by Monte Carlo methods, drawing from each of three
distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9
probability of being each of the first nine deciles of the distribution of
WTP responses in Viscusi et al. (1991); (2) the severity of a pollution-
related case of CB (relative to the case described in the Viscusi study)
is assumed to have a triangular distribution, with the most likely value
at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant
in the elasticity of WTP with respect to severity is normally distributed
with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and
Cropper [1992]). This process and the rationale for choosing it is
described in detail in the Costs and Benefits of the Clean Air Act, 1990
to 2010 (U.S. EPA, 1999).
Nonfatal Myocardial
Infarction (heart
attack)
3% discount rate
Age 0-24
Age 25-44
Age 45-54
Age 55-65
Age 66 and over
7% discount rate
Age 0-24
Age 25-44
Age 45-54
Age 55-65
Age 66 and over
$89,373
$100,690$!
06,053
$185,785
$89,373
$89,373
$100,690
$106,053
$185,785
$89,373
5,547
$98,680
$103,481
$174,866
$88,548
5,547
$98,680
$103,481
$174,866
$88,548
No distributional information available. Age-specific cost-of-illness
values reflect lost earnings and direct medical costs over a 5-year
period following a nonfatal MI. Lost earnings estimates are based on
Cropper and Krupnick (1990). Direct medical costs are based on
simple average of estimates from Russell et al. (1998) and Wittels et al.
(1990).
Lost earnings:
Cropper and Krupnick (1990). Present discounted value of 5 years of
lost earnings:
age of onset: at 3% at 7%
25-44 $8,774 $7,855
45-54 $12,932 $11,578
55-65 $74,746 $66,920
Direct medical expenses: An average of:
1. Wittels et al. (1990) ($102,658—no discounting)
2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate;
$21,113 at 7% discount rate)
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Hospital Admissions
Chronic Obstructive
Pulmonary Disease
(COPD)
$17,996
$17,996
No distributional information available. The COI estimates (lost
earnings plus direct medical costs) are based on ICD-9 code-level
information (e.g., average hospital care costs, average length of
hospital stay, and weighted share of total COPD category illnesses)
reported in Agency for Healthcare Research and Quality (2000)
(www.ahrq.gov).
Asthma Admissions
$11,957
$11,957
No distributional information available. The COI estimates (lost
earnings plus direct medical costs) are based on ICD-9 code-level
information (e.g., average hospital care costs, average length of hospital
stay, and weighted share of total asthma category illnesses) reported in
Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).
All Cardiovascular
$30,256
$30,256
No distributional information available. The COI estimates (lost
earnings plus direct medical costs) are based on ICD-9 code-level
information (e.g., average hospital care costs, average length of hospital
stay, and weighted share of total cardiovascular category illnesses)
reported in Agency for Healthcare Research and Quality (2000)
(www.ahrq.gov).
All respiratory (ages
65+)
$25,413
$25,413
No distributions available. The COI point estimates (lost earnings plus
direct medical costs) are based on ICD-9 code level information (e.g.,
average hospital care costs, average length of hospital stay, and
weighted share of total COPD category illnesses) reported in Agency
for Healthcare Research and Quality, 2000 (www.ahrq.gov).
All respiratory (ages
0-2)
$10,943
$10,943
No distributions available. The COI point estimates (lost earnings plus
direct medical costs) are based on ICD-9 code level information (e.g.,
average hospital care costs, average length of hospital stay, and
weighted share of total COPD category illnesses) reported in Agency
for Healthcare Research and Quality, 2000 (www.ahrq.gov).
Emergency Room
Visits for Asthma
$405
$405
No distributional information available. Simple average of two unit
COI values:
(1) $311.55, from Smith et al. (1997) and
(2) $260.67, from Stanford et al. (1999).
Respiratory Ailments Not Requiring Hospitalization
Upper Respiratory
Symptoms (URS)
$32
$34
Combinations of the three symptoms for which WTP estimates are
available that closely match those listed by Pope et al. result in seven
different "symptom clusters," each describing a "type" of URS. A
dollar value was derived for each type of URS, using mid-range
estimates of WTP (lEc, 1994) to avoid each symptom in the cluster and
assuming additivity of WTPs. In the absence of information
surrounding the frequency with which each of the seven types of URS
occurs within the URS symptom complex, we assumed a uniform
distribution between $9.2 and $43.1.
Lower Respiratory
Symptoms (LRS)
$20
$21
Combinations of the four symptoms for which WTP estimates are
available that closely match those listed by Schwartz et al. result in 11
different "symptom clusters," each describing a "type" of LRS. A
dollar value was derived for each type of LRS, using mid-range
estimates of WTP (lEc, 1994) to avoid each symptom in the cluster and
assuming additivity of WTPs. The dollar value for LRS is the average
of the dollar values for the 11 different types of LRS. In the absence of
information surrounding the frequency with which each of the 11 types
of LRS occurs within the LRS symptom complex, we assumed a
uniform distribution between $6.9 and $24.46.
Asthma
Exacerbations
$55
$57
Asthma exacerbations are valued at $45 per incidence, based on the
mean of average WTP estimates for the four severity definitions of a
"bad asthma day," described in Rowe and Chestnut (1986). This study
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Acute Bronchitis
Work Loss Days
(WLDs)
Minor Restricted
Activity Days
(MRADs)
School Absence Days
$452
Variable
(U.S.
median =
$137)
$64
$95
$494
Variable
(U.S.
median =
$137)
$69
$95
surveyed asthmatics to estimate WTP for avoidance of a "bad asthma
day," as defined by the subjects. For purposes of valuation, an asthma
exacerbation is assumed to be equivalent to a day in which asthma is
moderate or worse as reported in the Rowe and Chestnut (1986) study.
The value is assumed have a uniform distribution between $15.6 and
$70.8.
Assumes a 6-day episode, with the distribution of the daily value
specified as uniform with the low and high values based on those
recommended for related respiratory symptoms in Neumann et al.
(1994). The low daily estimate of $10 is the sum of the mid-range
values recommended by lEc 1994 for two symptoms believed to be
associated with acute bronchitis: coughing and chest tightness. The
high daily estimate was taken to be twice the value of a minor
respiratory restricted-activity day, or $1 10.
No distribution available. Point estimate is based on county-specific
median annual wages divided by 50 (assuming 2 weeks of vacation)
and then by 5 — to get median daily wage. U.S. Year 2000 Census,
compiled by Geolytics, Inc.
Median WTP estimate to avoid one MRAD from Tolley et al. (1986).
Distribution is assumed to be triangular with a minimum of $22 and a
maximum of $83, with a most likely value of $52. Range is based on
assumption that value should exceed WTP for a single mild symptom
(the highest estimate for a single symptom — for eye irritation — is
$16.00) and be less than that for a WLD. The triangular distribution
acknowledges that the actual value is likely to be closer to the point
estimate than either extreme.
No distribution available
6.3.1.4.3 Processing Air Quality Modeling Data for Health Impacts Analysis
In Section 6.2, we summarized the methods for and results of estimating air quality for
the standards. These air quality results are in turn associated with human populations to estimate
changes in health effects. For the purposes of this analysis, we focus on the health effects that
have been linked to ambient changes in ozone and PM2.5 related to emission reductions estimated
to occur due to the implementation of the standards. We estimate ambient PM2.5 and ozone
concentrations using the Community Multiscale Air Quality model (CMAQ). This section
describes how we converted the CMAQ modeling output into full-season profiles suitable for the
health impacts analysis.
6.3.1.4.3.1 General Methodology
First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell
from the standard CMAQ output files. For ozone, these model predictions are used in
conjunction with the observed concentrations obtained from the Aerometric Information
Retrieval System (AIRS) to generate ozone concentrations for the entire ozone
The predicted changes in ozone concentrations from the future-year base
season.
DDDDDDD,EEEEEEE
' The ozone season for this analysis is defined as the 5-month period from May to September.
Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at
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case to future-year control scenario serve as inputs to the health and welfare impact functions of
the benefits analysis (i.e., BenMAP).
To estimate ozone-related health effects for the contiguous United States, full-season
ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we
generated full-season ozone profiles for each location in two steps: (1) we combined monitored
observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid
of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these
full-season hourly ozone profiles to an ozone measure of interest, such as the daily 8-hour
maximum™^0000000
For PM2.5, we also use the model predictions in conjunction with observed monitor data.
CMAQ generates predictions of hourly PM species concentrations for every grid. The species
include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a
primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several
secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the
primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5
were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5
species concentrations. A gridded field of PM2.5 concentrations was created by interpolating
Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5
species concentrations were created by interpolating EPA speciation network (ESPN) ambient
data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid.
The procedures for determining the RRFs are similar to those in EPA's draft guidance for
modeling the PM2.5 standard (EPA, 2001).441 The guidance recommends that model predictions
be used in a relative sense to estimate changes expected to occur in each major PM2.5 species.
The procedure for calculating future-year PM2.5 design values is called the "Speciated Modeled
Attainment Test (SMAT)." EPA used this procedure to estimate the ambient impacts of the final
standards.
Table 6.3-12 provides those ozone and PM2.5 metrics for grid cells in the modeled domain
that enter the health impact functions for health benefits endpoints. The population-weighted
average reflects the baseline levels and predicted changes for more populated areas of the nation.
This measure better reflects the potential benefits through exposure changes to these populations.
least nine hourly observations per day [8 am to 8 pm] during the ozone season).
FFFFFFF - -km grid squares contain the population data used in the health benefits analysis model, BenMAP.
approac[j js a generalization of planar interpolation that is technically referred to as enhanced Voronoi
Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for
download at http://www.epa.gov/air/benmap.
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Table 6.3-12: Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality
Metrics for Health Benefits Endpoints Associated with the Final Standards
Statistic51
2030
Baseline
Change6
Ozone Metric: National Population-Weighted Average (ppb)c
Daily Maximum 8-Hour Average
Concentration
42.3735
0.0009
PM2.5 Metric: National Population-Weighted Average (jjg/m3)
Annual Average Concentration
8.1135
-0.0065
" Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects
estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of the
"ozone season" (i.e., May through September). Note that the national, population-weighted PM2.s
and ozone air quality metrics presented in this chapter represent an average for the entire, gridded
U.S. CMAQ domain. These are different than the population-weighted PM2.s and ozone design
value metrics presented in Chapter 7, which represent the average for areas with a current air
quality monitor.
b The change is defined as the control-case value minus the base-case value; a negative value
therefore indicates a reduction and a positive value an increase.
c Calculated by summing the product of the projected CMAQ grid-cell population and the
estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population.
Emissions and air quality modeling decisions are made early in the analytical process.
For this reason, the emission control scenarios used in the air quality and benefits modeling are
slightly different than the final emission inventories estimated for the final standards. Please
refer to Section 6.2.1 for more information about the inventories used in the air quality modeling
that supports the health impacts analysis.
6.3.1.4.4 Methods for Describing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous models,
there are likely to be many sources of uncertainty and this analysis is no exception. As outlined
both in this and preceding chapters, many inputs were used to derive the estimate of benefits for
the final standards, including emission inventories, air quality models (with their associated
parameters and inputs), epidemiological health effect estimates, estimates of values (both from
WTP and COI studies), population estimates, income estimates, and estimates of the future state
of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be
uncertain and, depending on its role in the benefits analysis, may have a disproportionately large
impact on estimates of total benefits. For example, emissions estimates are used in the first stage
of the analysis. As such, any uncertainty in emissions estimates will be propagated through the
entire analysis. When compounded with uncertainty in later stages, small uncertainties in
emission levels can lead to large impacts on total benefits.
The National Research Council (NRC) (2002, 2008)442'443 highlighted the need for EPA to
conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these
estimates to decision makers in ways that foster an appropriate appreciation of their inherent
uncertainty. In general, the NRC concluded that EPA's general methodology for calculating the
benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties.
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Since the publication of these reports, EPA's Office of Air and Radiation (OAR) continues to
make progress toward the goal of characterizing the aggregate impact of uncertainty in key
modeling elements on both health incidence and benefits estimates in two key ways: Monte Carlo
analysis and expert-derived concentration-response functions. In this analysis, we use both of
these two methods to assess uncertainty quantitatively, as well as provide a qualitative
assessment for those aspects that we are unable to address quantitatively.
First, we used Monte Carlo methods for characterizing random sampling error associated
with the concentration response functions from epidemiological studies and random effects
modeling to characterize both sampling error and variability across the economic valuation
functions. Monte Carlo simulation uses random sampling from distributions of parameters to
characterize the effects of uncertainty on output variables, such as incidence of premature
mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around
the estimated health impact and dollar benefits. The reported standard errors in the
epidemiological studies determined the distributions for individual effect estimates.
Second, because characterization of random statistical error omits important sources of
uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may
exist), we also incorporate the results of an expert elicitation on the relationship between
premature mortality and ambient PM2.5 concentration (Roman et al., 2008).444 Use of the expert
elicitation and incorporation of the standard errors approaches provide insights into the likelihood
of different outcomes and about the state of knowledge regarding the benefits estimates.
However, there are significant unquantified uncertainties present in upstream inputs including
emission and air quality. Both approaches have different strengths and weaknesses, which are
fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006).
In benefit analyses of air pollution regulations conducted to date, the estimated impact of
reductions in premature mortality has accounted for 85 to 95 percent of total monetized benefits.
Therefore, it is particularly important to attempt to characterize the uncertainties associated with
reductions in premature mortality. The health impact functions used to estimate avoided
premature deaths associated with reductions in ozone have associated standard errors that
represent the statistical errors around the effect estimates in the underlying epidemiological
studies. In our results, we report credible intervals based on these standard errors, reflecting the
uncertainty in the estimated change in incidence of avoided premature deaths. We also provide
multiple estimates, to reflect model uncertainty between alternative study designs.
For premature mortality associated with exposure to PM, we follow the same approach
used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of
premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation
study. Even these multiple characterizations, including confidence intervals, omit the
contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates,
populations exposed and transferability of the effect estimate to diverse locations. Furthermore,
the approach presented here does not yet include methods for addressing correlation between
input parameters and the identification of reasonable upper and lower bounds for input
distributions characterizing uncertainty in additional model elements. As a result, the reported
confidence intervals and range of estimates give an incomplete picture about the overall
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uncertainty in the estimates. This information should be interpreted within the context of the
larger uncertainty surrounding the entire analysis.
In 2006 the EPA requested an NAS study to evaluate the extent to which the
epidemiological literature to that point improved the understanding of ozone-related mortality.
The NAS found that short-term ozone exposure was likely to contribute to ozone-related
mortality (NRC, 2008) and issued a series of recommendations to EPA, including that the
Agency should:
1. Present multiple short-term ozone mortality estimates, including those based on multi-city
analyses such as the National Morbidity, Mortality and Air Pollution Study (NMMAPS)
as well as meta-analytic studies.
2. Report additional risk metrics, including the percentage of baseline mortality attributable
to short-term exposure.
3. Remove reference to a no-causal relationship between ozone exposure and premature
mortality.
The quantification and presentation of ozone-related premature mortality in this chapter is
responsive to these NRC recommendations.
Some key sources of uncertainty in each stage of both the PM and ozone health impact
assessment are the following:
• gaps in scientific data and inquiry;
• variability in estimated relationships, such as epidemiological effect estimates,
introduced through differences in study design and statistical modeling;
• errors in measurement and projection for variables such as population growth rates;
• errors due to mis specification of model structures, including the use of surrogate
variables, such as using PMi0 when PM2.5 is not available, excluded variables, and
simplification of complex functions; and
• biases due to omissions or other research limitations.
In Table 6.3-13 we summarize some of the key uncertainties in the benefits analysis.
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Table 6.3-13: Primary Sources of Uncertainty in the Benefits Analysis
1. Uncertainties Associated with Impact Functions
The value of the ozone or PM effect estimate in each impact function.
- Application of a single impact function to pollutant changes and populations in all locations.
- Similarity of future-year impact functions to current impact functions.
Correct functional form of each impact function.
- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source
epidemiological study.
- Application of impact functions only to those subpopulations matching the original study population.
2. Uncertainties Associated with CMAQ-Modeled Ozone and PM Concentrations
- Responsiveness of the models to changes in precursor emissions from the control policy.
- Projections of future levels of precursor emissions, especially ammonia and crustal materials.
- Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from urban to
rural areas.
3. Uncertainties Associated with PM Mortality Risk
- Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.
- Direct causal agents within the complex mixture of PM have not been identified.
- The extent to which adverse health effects are associated with low-level exposures that occur many times in the
year versus peak exposures.
- The extent to which effects reported in the long-term exposure studies are associated with historically higher
levels of PM rather than the levels occurring during the period of study.
Reliability of the PM2 5 monitoring data in reflecting actual PM2 5 exposures.
4. Uncertainties Associated with Possible Lagged Effects
The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels
that would occur in a single year is uncertain as well as the portion that might occur in subsequent years.
5. Uncertainties Associated with Baseline Incidence Rates
- Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not
accurately represent the actual location-specific rates.
Current baseline incidence rates may not approximate well baseline incidence rates in 2030.
- Projected population and demographics may not represent well future-year population and demographics.
6. Uncertainties Associated with Economic Valuation
Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore
have uncertainty surrounding them.
Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of
differences in income or other factors.
7. Uncertainties Associated with Aggregation of Monetized Benefits
- Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or
unmonetized benefits are not included.
6.3.2 PM-related Monetized Benefits of the Model Year (MY) Analysis
As described in Chapter 4, the final standards will reduce emissions of several criteria and
toxic pollutants and precursors. In the MY analysis, EPA estimates the economic value of the
human health benefits associated with reducing PMi.5 exposure. Due to analytical limitations,
this analysis does not estimate benefits related to other criteria pollutants (such as ozone, NOi or
SCh) or toxics pollutants, nor does it monetize all of the potential health and welfare effects
associated with PM2.5.
The MY analysis uses a "benefit-per-ton" method to estimate a selected suite of PM2.5-
related health benefits described below. These PM2.5-related benefit-per-ton estimates provide
the total monetized human health benefits (the sum of premature mortality and premature
morbidity) of reducing one ton of directly emitted PM^s, or one ton of a pollutant that contributes
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to secondarily-formed PM2.5 (such as NOx and SOx) from a specified source. Ideally, the human
health benefits would be estimated based on changes in ambient PM2.5 concentrations and
population exposure, as determined by complete air quality and exposure modeling. However,
conducting such detailed modeling for the model year analysis was not possible within the
timeframe for the final rule. Note that EPA conducted full-scale photochemical air quality
modeling for the calendar year analysis. Please refer to Chapter 6.2 for a description of EPA's
air quality modeling results and to Chapter 6.3.1 for a description of the quantified and monetized
PM- and ozone-related health impacts of the FRM.
Due to analytical limitations, the estimated benefit-per-ton values do not include
comparable benefits related to reductions in other ambient concentrations of criteria pollutants
(such as ozone, NO2 or SO2) or toxic air pollutants, nor do they monetize all of the potential
health and welfare effects associated with PM2.5 or the other criteria pollutants. As a result,
monetizing PM-related health impacts alone underestimates the benefits associated with
reductions of the suite of non-GHG pollutants that would be reduced by the final standards.
The dollar-per-ton estimates used to monetize reductions in emissions that contribute to
ambient concentrations of PM2.5 are provided in Table 6.3-14.
Table 6.3-14 PM2.5-related Benefits-per-ton Values (2010$)a
Year
All Sources'1
S02
Upstream (Non-EGU)
Sources'1
NOX
Direct PM2.5
Mobile Sources
NOX
Direct PM2.5
Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated
Using a 3 Percent Discount Rate0
2015
2020
2030
2040
$30,000
$33,000
$38,000
$45,000
$4,900
$5,400
$6,400
$7,600
$230,000
$250,000
$290,000
$340,000
$5,100
$5,600
$6,700
$8,000
$280,000
$310,000
$370,000
$440,000
Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated
Using a 7 Percent Discount Rate0
2015
2020
2030
2040
$27,000
$30,000
$35,000
$41,000
$4,500
$4,900
$5,800
$6,900
$210,000
$230,000
$270,000
$310,000
$4,600
$5,100
$6,100
$7,300
$250,000
$280,000
$330,000
$400,000
Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 3
Percent Discount Rate0
2015
2020
2030
2040
$73,000
$80,000
$94,000
$110,000
$12,000
$13,000
$16,000
$19,000
$560,000
$620,000
$720,000
$840,000
$12,000
$14,000
$16,000
$20,000
$680,000
$750,000
$900,000
$1,100,000
Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 7
Percent Discount Rate0
2015
2020
2030
2040
$66,000
$72,000
$84,000
$99,000
$11,000
$12,000
$14,000
$17,000
$510,000
$560,000
$650,000
$760,000
$11,000
$12,000
$15,000
$18,000
$620,000
$680,000
$810,000
$960,000
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a Total dollar-per-ton estimates include monetized PM2.5-related premature mortality and morbidity endpoints.
Range of estimates are a function of the estimate of PM2.5-related premature mortality derived from either the ACS
study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).
b Dollar-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA extrapolated
exponentially based on the growth between 2020 and 2030.
c The dollar-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.
d Note that the dollar-per-ton value for SO2 is based on the value for Stationary (Non-EGU) sources; no SO2 value
was estimated for mobile sources.
As Table 6.3-14 indicates, EPA projects that the per-ton values for reducing emissions of
criteria pollutants from both vehicle use and stationary sources such as fuel refineries and storage
facilities will increase over time.HHHHHHH These projected increases reflect rising income levels,
which are assumed to 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 in older age groups with the highest mortality risk.445'1111111
For certain PM2.5-related pollutants (such as direct PM2.5 and NOx), EPA estimates
different per-ton values for reducing mobile source emissions than for reductions in emissions of
the same pollutant from stationary sources such as fuel refineries and storage facilities. These
reflect differences in the typical geographic distributions of emissions of each pollutant by
different sources, their contributions to ambient levels of PM2.5, and resulting changes in
population exposure. EPA applies these separate values to its estimates of changes in emissions
from vehicle use and from fuel production and distribution to determine the net change in total
economic damages from emissions of those pollutants.
The benefit per-ton technique has been used in previous analyses, including the 2012-
2016 Light-Duty Greenhouse Gas Rule,446 the Ozone National Ambient Air Quality Standards
(NAAQS) RIA,447 the Portland Cement National Emissions Standards for Hazardous Air
Pollutants (NESHAP) RIA,448 and the final NO2 NAAQS.449 Table 6.3-15 shows the quantified
and monetized PM2.5-related co-benefits that are captured in these benefit-per-ton estimates, and
also lists other effects that remain un-quantified and are thus excluded from the estimates.
HHHHHHH ^s wg cjjscuss jn jjjg emissions chapter of EPA's DRIA (Chapter 4), the rule would yield emission
reductions from upstream refining and fuel distribution due to decreased petroleum consumption.
nnm 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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Table 6.3-15 Human Health and Welfare Effects of PM2.5
Quantified and Monetized Un-quantified Effects
in Primary Estimates Changes in:
Adult premature mortality Subchronic bronchitis cases
Bronchitis: chronic and acute Low birth weight
Hospital admissions: respiratory and Pulmonary function
cardiovascular Chronic respiratory diseases other than
Emergency room visits for asthma chronic bronchitis
Nonfatal heart attacks (myocardial Non-asthma respiratory emergency room
infarction) visits
Lower and upper respiratory illness Visibility
Minor restricted-activity days Household soiling
Work loss days
Asthma exacerbations (asthmatic
population)
Infant mortality
Consistent with the NO2 NAAQS,JJJJJJJ the benefits estimates utilize concentration-
response functions as reported in the epidemiology literature. Readers interested in reviewing the
complete methodology for creating the benefit-per-ton estimates used in this analysis can consult
the Technical Support Document (TSD)450 accompanying the final ozone NAAQS RIA.
Readers can also refer to Fann et al. (2009)451 for a detailed description of the benefit-per-ton
methodology.KKKKKKK
As described 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., NOi emitted from mobile
sources; direct PM emitted from stationary sources). Our estimate of total PMi.5 benefits is
therefore based on the total direct PM2.5 and PM2.5-related precursor emissions (NOx, SOx, and
VOCs) controlled from each source and multiplied by the respective per-ton values of reducing
emissions from that source.
The benefit-per-ton coefficients in this analysis were derived using modified versions of
the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically,
this analysis uses the benefit-per-ton estimates first applied in the Portland Cement NESHAP
RIA, which incorporated concentration-response functions directly from the epidemiology
jjjjjjj Although we summarize the main issues in this chapter, we encourage interested readers to see benefits chapter
of the NO2 NAAQS for a more detailed description of recent changes to the PM benefits presentation and preference
for the no-threshold model.
KKKKKKK -j^g values included in this report are different from those presented in the article cited above. Benefits
methods change to reflect new information and evaluation of the science. Since publication of the June 2009 article,
EPA has made two significant changes to its benefits methods: (1) We no longer assume that a threshold exists in
PM-related models of health impacts, which is consistent with the findings reported in published research; and (2)
We have revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an estimate of $5.5
million (year 2000$) used in the June 2009 report. Please refer to the following website for updates to the dollar-per-
ton estimates: http://www.epa.gov/air/benmap/bpt.html
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studies, without any adjustment for an assumed threshold. Removing the threshold assumption is
a key difference between the method used in this analysis to estimate PM co-benefits and the
methods used in analyses prior to EPA's Portland Cement NESHAP.LLLLLLL As a consequence,
the benefit-per-ton estimates used in this analysis include incremental benefits of reductions in
PM2.5 concentrations down to their lowest modeled levels. This approach is also consistent with
EPA's analysis of the 2012-2016 Light-Duty Vehicle Greenhouse Gas rule.
Reductions in PM-related mortality provide the majority of the monetized value in each
benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients that
underlie the benefits-per-ton estimates are drawn from epidemiology studies that examine two
large population cohorts: the American Cancer Society cohort (Pope et al., 2002)452 and the
Harvard Six Cities cohort (Laden et al., 2006).453 The concentration-response (C-R) function
developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in
Pope et al. (2002), has previously been used by EPA to generate its primary benefits estimate.
The extended analysis of the Harvard Six Cities cohort, as reported by Laden et al (2006), was
published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS and has been used
as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 co-benefits estimates in analyses
completed since the PM2.5 NAAQS.
These studies provide logical choices for co-equal anchor points when presenting PM-
related benefits because, while both studies are well designed and peer-reviewed, there are
strengths and weaknesses inherent in each. Although EPA's primary method of characterizing
PM-related premature mortality is to use both studies to generate a co-equal range of benefits
estimates, EPA has chosen to present only the benefit-per-ton value derived from the ACS study
in its summary tables of total Model Year costs and benefits (See RIA Chapter 7). This decision
was made to provide the reader with summary tables that are easier to understand and interpret
and does not convey any preference for one study over the other. We note that this is also the
more conservative of the two estimates - PM-related benefits would be approximately 245
percent (or nearly two-and-a-half times) larger had we used the per-ton benefit values based on
the Harvard Six Cities study instead. See RIA Chapter 7.3 for the monetized PM-related health
impacts of the Model Year analysis.
As is the nature of benefits analyses, assumptions and methods evolve over time to reflect
the most current interpretation of the scientific and economic literature. For a period of time
LLLLLLL gasecj on a review of (ne current body of scientific literature, EPA estimates PM-related mortality without
applying an assumed concentration threshold. EPA's Integrated Science Assessment for Particulate Matter (U.S.
Environmental Protection Agency. 2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA-
600-R-08-139F. National Center for Environmental Assessment - RTF Division. December), which was reviewed
by EPA's Clean Air Scientific Advisory Committee (U.S. Environmental Protection Agency - Science Advisory
Board. 2009. Review of EPA's Integrated Science Assessment for Particulate Matter (First External Review Draft,
December 2008). EPA-COUNCIL-09-008. May.; U.S. Environmental Protection Agency Science Advisory Board .
2009. Consultation on EPA's Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan
for Health Risk and Exposure Assessment. EPA-COUNCIL-09-009. May), concluded that the scientific literature
consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-
response relationship while recognizing potential uncertainty about the exact shape of the concentration-response
function. This assumption is incorporated into the calculation of the PM-related benefits-per-ton values.
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(2004-2008), EPA's Office of Air and Radiation (OAR) valued mortality risk reductions using a
value of statistical life (VSL) estimate derived from a limited analysis of some of the available
studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent
with two meta-analyses of the wage-risk literature.
The $1 million value represented the lower end of the interquartile range from the Mrozek
and Taylor (2002)454 meta-analysis of 33 studies. The $10 million value represented the upper
end of the interquartile range from the Viscusi and Aldy (2003)455 meta-analysis of 43 studies.
The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4
million estimated in the Kochi et al. (2006)456 meta-analysis. However, the Agency neither
changed its official guidance on the use of VSL in rulemakings nor subjected the interim estimate
to a scientific peer-review process through the Science Advisory Board (SAB) or other peer-
review group.
Until updated guidance is available, EPA determined that a single, peer-reviewed estimate
applied consistently best reflects the Science Advisory Board Environmental Economics
Advisory Committee (SAB-EEAC) advice it has received. Therefore, EPA has decided to apply
the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic
Analyses (U.S. EPA, 2000)457 while they continue efforts to update their guidance on this
issue.MMMMMMM This approach calculates a mean value across VSL estimates derived from 26
labor market and contingent valuation studies published between 1974 and 1991. The mean VSL
across these studies is $6.3 million (2000$). The dollar-per-ton estimates used in this analysis are
based on this revised VSLNNNNNNN
The benefit-per-ton estimates are subject to a number of assumptions and uncertainties.
• They do not reflect local variability in population density, meteorology, exposure,
baseline health incidence rates, or other local factors that might lead to an overestimate or
underestimate of the actual benefits of controlling fine particulates in specific locations.
Please refer to Chapter 6.3.1 for the description of the agency's quantification and
monetization of PM- and ozone-related health impacts for the final standards.
• 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 PMi.5 released from engines and other industrial sources. At the
present time, however, no clear scientific grounds exist for supporting differential effects
estimates by particle type.
• 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
MMMMMMM In the update of fae Economic Guidelines (U.S. EPA, 2011), EPA retained the VSL endorsed by the SAB
with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near
future. The update of the Economic Guidelines is available on the Internet at
http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf.
NNNNNNN -p^ value differs from the Department of Transportation's most recent estimate of the value of preventing
transportation-related fatalities, which is $6.1 million when expressed in today's (2011) dollars.
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benefits from reducing fine particles in areas with varied initial concentrations of PMi.5,
including both regions that are in attainment with fine particle standard and those that do
not meet the standard, down to the lowest modeled concentrations.
• There are several health benefits categories that EPA was unable to quantify due to
limitations associated with using benefits-per-ton estimates, several of which could be
substantial. Because NOx and VOC emissions are also precursors to ozone, changes in
NOx and VOC would also impact ozone formation and the health effects associated with
ozone exposure. Benefits-per-ton estimates for ozone 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 6.3.1 for a
description of the unquantified co-pollutant benefits associated with this rulemaking.
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 the localized impacts
associated with the rulemaking may vary significantly. 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. For this final rule, EPA conducted a national-scale
air quality modeling analysis for 2030 to analyze the impacts of the standards on PM2.5, ozone,
and selected air toxics.
6.4 Changes in Atmospheric COi Concentrations, Global Mean Temperature, Sea Level
Rise, and Ocean pH Associated with the Final Rule's GHG Emissions Reductions
6.4.1 Introduction
The impact of GHG emissions on the climate has been reviewed in the NPRM, as well as
in the MYs 2012-2016 light-duty rulemaking and the heavy-duty GHG rulemaking. See 76 FR at
75096; 75 FR at 25491; 76 FR at 57294. This section briefly discusses again the issue of climate
impacts noting the context of 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 domestic emissions).458
The 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")459 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
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address the scientific issues the 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.
Based on these assessments, the Administrator determined that greenhouse gases cause
warming; that levels of greenhouse gases are increasing in the atmosphere due to human activity;
the climate is warming; recent warming has been attributed to the increase in greenhouse gases;
and that warming of the climate threatens human health and welfare. The Administrator further
found that emissions of well-mixed greenhouse gases from new motor vehicles and engines
contribute to the air pollution that endangers public health and welfare. Specifically, the
Administrator found under section 202 (a) of the Act that six greenhouse gases (carbon dioxide,
methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride) taken in
combination endanger both the public health and the public welfare of current and future
generations, and further found that the combined emissions of these greenhouse gases from new
motor vehicles and engines contribute to the greenhouse gas air pollution that endangers public
health and welfare. The D.C. Circuit recently emphatically upheld the reasonableness of all of
these conclusions. See Coalition for Responsible Regulation v. EPA,No. 09-1322 (June 26,
2012) (D.C. Circuit) slip op. p. 30 (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.")
More recent assessments have reached similar conclusions to those of the assessments
upon which the Administrator relied. In May 2010, the NRC published its comprehensive
assessment, "Advancing the Science of Climate Change."460 It concluded that "climate change is
occurring, is caused largely by human activities, and poses significant risks for—and in many
cases is already affecting—a broad range of human and natural systems." Furthermore, the NRC
stated that this conclusion is based on findings that are "consistent with the conclusions of recent
assessments by the U.S. Global Change Research Program, the Intergovernmental Panel on
Climate Change's Fourth Assessment Report, and other assessments of the state of scientific
knowledge on climate change." These are the same assessments that served as the primary
scientific references underlying the Administrator's Endangerment Finding. Another NRC
assessment, "Climate Stabilization Targets: Emissions, Concentrations, and Impacts over
Decades to Millenia", was published in 2011. This report found that climate change due to
carbon dioxide emissions will persist for many centuries. The report also estimates a number of
specific climate change impacts, finding that every degree Celsius (C) of warming could lead to
increases in the heaviest 15% of daily rainfalls of 3 to 10%, decreases of 5 to 15% in yields for a
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number of crops (absent adaptation measures that do not presently exist), decreases of Arctic sea
ice extent of 25% in September and 15% annually averaged, along with changes in precipitation
and streamflow of 5 to 10% in many regions and river basins (increases in some regions,
decreases in others). The assessment also found that for an increase of 4 degrees C nearly all land
areas would experience summers warmer than all but 5% of summers in the 20th century, that for
an increase of 1 to 2 degrees C the area burnt by wildfires in western North America will likely
more than double, that for an increase of 3 degrees C the sea level will rise 1.6 to 3.3 feet by
2100, and that coral bleaching and erosion will increase due both to warming and ocean
acidification,. The assessment notes that many important aspects of climate change are difficult
to quantify but that the risk of adverse impacts is likely to increase with increasing temperature,
and that the risk of abrupt climate changes can be expected to increase with the duration and
magnitude of the warming.
In the 2010 report cited above, the NRC stated that some of the largest potential risks
associated with future climate change may come not from relatively smooth changes that are
reasonably well understood, but from extreme events, abrupt changes, and surprises that might
occur when climate or environmental system thresholds are crossed. Examples cited as
warranting more research include the release of large quantities of GHGs stored in permafrost
(frozen soils) across the Arctic, rapid disintegration of the major ice sheets, irreversible drying
and desertification in the subtropics, changes in ocean circulation, and the rapid release of
destabilized methane hydrates in the oceans.
On ocean acidification, the same report noted the potential for broad, "catastrophic"
impacts on marine ecosystems. Ocean acidity has increased 25 percent since pre-industrial times,
and is projected to continue increasing. By the time atmospheric CO2 content doubles over its
preindustrial value, there would be virtually no place left in the ocean that can sustain coral reef
growth. Ocean acidification could have dramatic consequences for polar food webs including
salmon, the report said.
Importantly, these recent NRC assessments represent another independent and critical
inquiry of the state of climate change science, separate and apart from the previous IPCC and
USGCRP assessments.
Based on modeling analysis performed by the EPA, reductions in CO2 and other GHG
emissions associated with this final rule will affect future climate change. Since GHGs are well-
mixed in the atmosphere and have long atmospheric lifetimes, changes in GHG emissions will
affect atmospheric concentrations of greenhouse gases and future climate for decades to
millennia, depending on the gas. This section provides estimates of the projected change in
atmospheric CO2 concentrations based on the emission reductions estimated for this 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 temperature, sea
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level rise, and ocean pH. See Chapter 4 in this RIA for the estimated net GHG emissions
reductions over time.0000000
6.4.2 Projected Change in Atmospheric COi Concentrations, Global Mean Surface
Temperature and Sea Level Rise
To assess the impact of the emissions reductions from the finalrule, EPA estimated
changes in projected atmospheric CC>2 concentrations, global mean surface temperature and sea-
level rise to 2100 using the GCAM (Global Change Assessment Model, formerly MiniCAM),
integrated assessment modelppppppp'461 coupled with the MAGICC (Model for the Assessment of
Greenhouse-gas Induced Climate Change) simple climate model.QQQQQQQ'462'463 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
this rule, a simple climate model such as MAGICC is appropriate for estimating the atmospheric
and climate response.
6.4.2.1 Methodology
Emissions reductions associated with this rule were evaluated with respect to a reference
case. An emissions scenario was developed by applying the estimated emissions reductions from
the rule to the GCAM reference (no climate policy) scenario (used as the basis for the
Representative Concentration Pathway RCP4.5).464 Specifically, the annual upstream and
downstream CCh, N2O, CH4, HFC-134a, NOx, CO, and SOi emissions reductions estimated from
this rule were applied as net reductions to the GCAM global baseline net emissions for each
to jjjjjjjjg constraints, the modeling analysis in this section was conducted with preliminary estimates of
the emissions reductions projected from the final rule, which were highly similar to the final estimates presented in
Chapter 4 of this RIA. For example, the final projected CO2 emissions reductions for most years in the 2017-2050
time period were roughly one-tenth of a percent smaller than the preliminary estimates. The preliminary emissions
reduction projections are available in the docket (see "Emissions for MAGICC modeling" in Docket EPA-HQ-OAR-
2010-0799).
PPPPPPP QCAM 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.
ooooooo
MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single
framework. The framework allows the user to determine changes in greenhouse-gas concentrations, global-mean
surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O), reactive gases (CO, NOx, VOCs), the halocarbons (e.g. HCFCs, MFCs, PFCs) and sulfur
dioxide (SO2). MAGICC emulates the global-mean temperature responses of more sophisticated coupled
Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy.
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MY 2017 and Later - Regulatory Impact Analysis
substance. RRRRRRRxhe emissions reductions past calendar year 2050 for all emissions were scaled
with total U.S. road transportation fuel consumption from the GCAM reference scenario. Road
transport fuel consumption past 2050 does not change significantly and thus emissions reductions
remain relatively constant from 2050 through 2100.
The GCAM reference scenario465 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
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 CC>2 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-COi GHG and non-GHG 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 v2,466 the change in atmospheric COi 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 rule. To capture
some of the uncertainty in the climate system, the changes in projected atmospheric COi
concentrations, global mean temperature and sea level were estimated across the most current
Intergovernmental Panel on Climate Change (IPCC) range of climate sensitivities, 1.5°C to
6 QoC sssssss The range as illustratecj in chapter 10, Box 10.2, Figure 2 of the IPCC's Working
RRRRRRR j-^ JQ jjjjjjjjg constraints, the modeling analysis in this section was conducted with preliminary estimates of
the emissions reductions projected from the final rule, which were highly similar to the final estimates presented in
Chapter 4 of this RIA. For example, the final projected CO2 emissions reductions for most years in the 2017-2050
time period were roughly one-tenth of a percent smaller than the preliminary estimates. The preliminary emissions
reduction projections are available in the docket (see "Emissions for MAGICC modeling" in Docket EPA-HQ-OAR-
2010-0799), and the files used as inputs for the MAGICC model are also available (see "MAGICC Input File
(policy)" and "MAGICC Input File (reference)").
sssssss jn 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 IPCC
states that climate sensitivity is "likely" to be in the range of 2°C to 4.5°C, "very unlikely" to be less than 1.5°C, and
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Chapter 6
Group I is approximately consistent with the 10-90% probability distribution of the individual
cumulative distributions of climate sensitivity.467 Other uncertainties, such as uncertainties
regarding the carbon cycle, ocean heat uptake, reference emissions scenarios, or aerosol forcing,
were not addressed.
MAGICC calculates the forcing response at the global scale from changes in atmospheric
concentrations of CC>2, CH4, NiO, HFCs, and tropospheric ozone. It also includes the effects of
temperature changes on stratospheric ozone and the effects of CH4 emissions on stratospheric
water vapor. Changes in CH4, NOx, VOC, and CO emissions affect both O3 concentrations and
CH4 concentrations. MAGICC includes the relative climate forcing effects of changes in sulfate
concentrations due to changing SO2 emissions, including both the direct effect of sulfate particles
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 SOi) presented in Chapter 4 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 standards. See generally,
EPA, Response to Comments to the Endangerment Finding Vol. 9 section 9.1.6.1468, 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 Carbon469. 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 COi concentration, global mean temperature, and
sea level rise specifically attributable to the impacts of the rule, the emissions reductions from
this rule were applied to the GCAM reference emissions scenario. As a result of the emissions
reductions from the rule relative to the reference case, by 2100 the concentration of atmospheric
COi is projected to be reduced by approximately 3.2 to 3.6 parts per million by volume (ppmv),
the global mean temperature is projected to be reduced by approximately 0.007-0.018°C, and
global mean sea level rise is projected to be reduced by approximately 0.07-0.16 cm
"values substantially higher than 4.5°C cannot be excluded." IPCC WGI, 2007, Climate Change 2007 - The Physical
Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the IPCC, http://www.ipcc.ch/.
TTTTTTT More compiete results from the MAGICC modeling can be found in the docket (see " Supporting Document
for MAGICC Analysis" in Docket EPA-HQ-OAR-2010-0799).
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MY 2017 and Later - Regulatory Impact Analysis
sea level rise, the calculations in MAGICC do not include the possible effects of accelerated ice
flow in Greenland and/or Antarctica.
Figure 6.4-1 provides the results over time for the estimated reductions in atmospheric
COi concentration associated with the rule compared to the reference case. Figure 6.4-2 provides
the estimated change in projected global mean temperatures associated with the rule. Figure 6.4-
3 provides the estimated reductions in global mean sea level rise associated with the rule. The
range of reductions in global mean temperature and sea level rise due to uncertainty in climate
sensitivity is larger than that for COi concentrations because COi concentrations are only weakly
coupled to climate sensitivity through the dependence on temperature of the rate of ocean
absorption of COi, whereas the magnitude of temperature change response to COi changes (and
therefore sea level rise) is more tightly coupled to climate sensitivity in the MAGICC model.
Change in CO2 Concentration
(Final Standard - Reference)
2000
2020
2040
2060
2080
2100
Figure 6.4-1 Projected Reductions in Atmospheric COi Concentrations (parts per million
by volume) from the MY 2017-2025 Standards (climate sensitivity (CS) cases ranging from
1.5-6.0°C)
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Chapter 6
Change in Global Mean Temperature
(Final Standard - Reference)
-0,02
2000
2020
2040
2060
2080
2100
Figure 6.4-2 Projected Reductions in Global Mean Surface Temperatures from MY 2017-
2025 Standards (climate sensitivity (CS) cases ranging from 1.5-6.0°C)
Change in Global Mean Sea Level Rise
(Final Standard - Reference)
-0,2
2000
2020
2040
2060
2080
2100
Figure 6.4-3 Projected Reductions in Global Mean Sea Level Rise from the MY 2017-2025
Standards (climate sensitivity (CS) cases ranging from 1.5-6.0°C)
The results in Figure 6.4-2 and Figure 6.4-3 show reductions in the projected global mean
temperature and sea level respectively, across all climate sensitivities. The projected reductions
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MY 2017 and Later - Regulatory Impact Analysis
are small relative to the overall expected increase in temperature (1.8 - 4.8 °C) and sea level rise
(23 - 56 cm) projected by the baseline GCAM reference case simulated by MAGICC from 1990
to 2100. However, this is to be expected given the magnitude of emissions reductions expected
from the rule in the context of global emissions. 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 business-as-usual
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.470 If other uncertainties besides
climate sensitivity were included in the analysis, the resulting ranges of projected changes would
likely be slightly larger.
6.4.3 Projected Change in Ocean pH
For this rule, 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 (COi) resulting from the emissions
reductions associated with the rule.uuuuuuu EPA used the program developed for CO2 System
Calculations CO2SYS,471 version 1.05, a program which performs calculations relating
parameters of the carbon dioxide (COi) 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 CO2 system
[total alkalinity (TA), total inorganic CO2 (TC), pH, and either fugacity (fCOi) or partial pressure
of COi (pCOi)] 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 DOS
version (Lewis and Wallace, 1998)472 of the program to compute pH for three scenarios: the
reference scenario at a climate sensitivity of 3 degrees for which the CO2 concentrations was
calculated to be 784.868 in 2100, the rule relative to the baseline with a CO2 concentration of
781.503, and a calculation for 1990 with a CO2 concentration of 353.633.
Using the set of seawater parameters detailed below, the EPA calculated pH levels for the
three scenarios. The pH of the emissions standards relative to the reference scenario pH was
+0.0017 pH units (more basic). For comparison, the difference between the reference scenario in
2100 and the pH in 1990 was -0.30 pH units (more acidic).
The CO2S YS 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 rule's emissions reduction
uuuuuuu j~jue jo jjjjjjjjg constraints, the modeling analysis in this section was conducted with preliminary estimates of
the COi emissions reductions projected from the final rule, which were highly similar to the final estimates presented
in Chapter 4 of this RIA. The final projected COi emissions reductions for most years in the 2017-2050 time period
were roughly one-tenth of a percent smaller than the preliminary estimates. The preliminary COi emissions
reduction projections are available in the docket (see "Emissions for MAGICC modeling" in Docket EPA-HQ-OAR-
2010-0799).
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Chapter 6
case. EPA used the following inputs, with justification and references for these inputs provided
in brackets:
1) Input mode: Single-input [This simply means that the program calculates pH for
one set of input variables at a time, instead of a batch of variables. The choice has no affect on
results].
(1987)
2)
474
473
Choice of constants: Mehrbach et al. (1973) , refit by Dickson and Millero
3) Choice of fCO2 or pCO2: pCO2 [pCO2 is the partial pressure of CO2 and can be
converted to fugacity (fCO2) if desired]
4)
Choice of KSO4: Dickson (1990)475 [Lewis and Wallace (1998)476 recommend
using the equation of Dickson (1990) for this dissociation constant. The model also allows the
use of the equation of Khoo et al. (1977).477 Switching this parameter to Khoo et al. (1977)
instead of Dickson (1990) had no effect on the calculated result].
5) Choice of pH scale: Total scale [The model allows pH outputs to be provided on
the total scale, the seawater scale, the free scale, and the National Bureau of Standards (NBS)
scale. The various pH scales can be interrelated using equations provided by Lewis and Wallace
(1998)].
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).478 Based on the projected
atmospheric CCh concentration reductions that would result from this rule (3.37 ppmv for a
climate sensitivity of 3.0), the modeling program calculates an increase in ocean pH of
approximately 0.0017 pH units in 2100. Thus, this analysis indicates the projected decrease in
atmospheric COi concentrations from the standards yields an increase in ocean pH (i.e., a
reduction in the expected acidification of ocean pH in the reference case). Table 6.4-1 contains
the projected changes in ocean pH based the change in atmospheric COi concentrations that were
derived from the MAGICC modeling.
Table 6.4-1 Impact of the MY 2017-2025 Standards On Ocean pH
CLIMATE
SENSITIVITY
3.0
DIFFERENCE
INCO2IN2100
-3.37 ppm
YEAR
2100
PROJECTED
pH CHANGE
+0.0017
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6.4.4 Summary of Climate Analyses
EPA's analysis of the impact of the emissions reductions from this rule 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 rule alone show small differences in climate
effects (CO2 concentration, global mean temperature, sea level rise, and ocean pH), in
comparison to the total projected changes, they yield results that are repeatable and directionally
consistent within the modeling frameworks used. The results are summarized in Table 6.4-2,
Impact of MY 2017-2025 GHG Standards On Projected Changes in Global Climate.
These projected reductions are proportionally representative of changes to U.S. GHG
emissions in the transportation sector. While not formally estimated for this rule, a reduction in
projected global mean temperature change, sea level rise, and ocean acidification 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
increase less in the emissions reduction scenario than in the reference (no climate policy) case,
and the ocean does not become as acidic as it does in the reference case. The benefits of GHG
emissions reductions can be characterized both qualitatively and quantitatively, some of which
can be monetized (see Chapter 7). There are substantial uncertainties in modeling the global risks
of climate change, which complicates quantification and cost-benefits assessments. Changes in
climate variables such as temperature are a meaningful proxy for changes in the risk of most
potential impacts-including those that can be monetized, and those that have not been monetized
but can be quantified in physical terms (e.g., water availability), as well as those that have not yet
been quantified or are extremely difficult to quantify (e.g., forest disturbance and catastrophic
events such as collapse of large ice sheets and subsequent sea level rise).
Table 6.4-2 Impact of MY 2017-2025 GHG Standards On Projected Changes in Global
Climate (based on a range of climate sensitivities from 1.5-6°C)
VARIABLE
Atmospheric COi Concentration
Global Mean Surface Temperature
Sea Level Rise
Ocean pH
UNITS
ppmv
°C
cm
pH units
YEAR
2100
2100
2100
2100
PROJECTED CHANGE
-3.21 to -3.58
-0.0074 to -0.0176
-0.071 to -0.159
+0.0017a
' The value for projected change in ocean pH is based on a climate sensitivity of 3.0.
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Chapter 6
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104Koren, H. S., Devlin, R. B., Becker, S., Perez, R., McDonnell, W. F. (1991). Time-dependent changes of markers
associated with inflammation in the lungs of humans exposed to ambient levels of ozone. Toxicol. Pathol., 19, 406-
411. Docket EPA-HQ-OAR-2010-0799.
105Koren, H. S., Devlin, R. B., Graham, D. E., Mann, R., McGee, M. P., Horstman, D. H., Kozumbo, W. J., Becker,
S., House, D. E., McDonnell, W. F., Bromberg, P. A. (1989). Ozone-induced inflammation in the lower airways of
human subjects. Am. Rev. Respir. Dis., 39, 407-415. Docket EPA-HQ-OAR-2010-0799.
106 Schelegle, E.S., Siefkin, A.D., McDonald, R.J. (1991). Time course of ozone-induced neutrophilia in normal
humans. Am. Rev. Respir. Dis., 743,1353-1358. Docket EPA-HQ-OAR-2010-0799.
107U.S. EPA. (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants. EPA600-P-93-004aF.
Washington. D.C.: U.S. EPA. Docket EPA-HQ-OAR-2010-0799.
108Hodgkin, I.E., Abbey, D.E., Euler, G.L., Magie, A.R. (1984). COPD prevalence in nonsmokers in high and low
photochemical air pollution areas. Chest, 86, 830-838. Docket EPA-HQ-OAR-2010-0799.
109 Euler, G.L., Abbey, D.E., Hodgkin, I.E., Magie, A.R. (1988). Chronic obstructive pulmonary disease symptom
effects of long-term cumulative exposure to ambient levels of total oxidants and nitrogen dioxide in California
Seventh-day Adventist residents. Arch. Environ. Health, 43, 279-285. Docket EPA-HQ-OAR-2010-0799.
110 Abbey, D.E., Petersen, F., Mills, P.K., Beeson, W.L. (1993). Long-term ambient concentrations of total
suspended particulates, ozone, and sulfur dioxide and respiratory symptoms in a nonsmoking population. Arch.
Environ. Health, 48, 33-46. Docket EPA-HQ-OAR-2010-0799.
111 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R-
05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799.
112U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R-
05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799.
113 Avol, E.L., Trim, S. C., Little, D.E., Spier, C.E., Smith, M. N., Peng, R.-C., Linn, W.S., Hackney, J.D., Gross,
K.B., D'Arcy, J.B., Gibbons, D., Higgins, I.T.T. (1990 June). Ozone exposure and lung function in children
attending a southern California summer camp. Paper no. 90-150.3. Paper presented at the 83rd annual meeting and
exhibition of the Air & Waste Management Association, Pittsburgh, PA. Docket EPA-HQ-OAR-2010-0799.
114Higgins, I. T.T., D'Arcy, J. B., Gibbons, D. I., Avol, E. L., Gross, K.B. (1990). Effect of exposures to ambient
ozone on ventilatory lung function in children. Am. Rev. Respir. Dis., 141, 1136-1146. Docket EPA-HQ-OAR-2010-
0799.
115Raizenne, M.E., Burnett, R.T., Stern, B., Franklin, C.A., Spengler, J.D. (1989) Acute lung function responses to
ambient acid aerosol exposures in children. Environ. Health Perspect., 79,179-185. Docket EPA-HQ-OAR-2010-
0799.
116Raizenne, M.; Stern, B.; Burnett, R.; Spengler, J. (1987 June) Acute respiratory function and transported air
pollutants: observational studies. Paper no. 87-32.6. Paper presented at the 80th annual meeting of the Air Pollution
Control Association, New York, NY. Docket EPA-HQ-OAR-2010-0799.
117 Spektor, D. M., Lippmann, M. (1991). Health effects of ambient ozone on healthy children at a summer camp. In:
Berglund, R. L.; Lawson, D. R.; McKee, D. J., eds. Tropospheric ozone and the environment: papers from an
international conference; March 1990; Los Angeles, CA. Pittsburgh, PA: Air & Waste Management Association; pp.
83-89. (A&WMA transaction series no. TR-19). Docket EPA-HQ-OAR-2010-0799.
118 Spektor, D. M., Thurston, G.D., Mao, J., He, D., Hayes, C., Lippmann, M. (1991). Effects of single- and
multiday ozone exposures on respiratory function in active normal children. Environ. Res, 55,107-122. Docket EPA-
HQ-OAR-2010-0799.
119 Spektor, D. M., Lippman, M., Lioy, P. J., Thurston, G. D., Citak, K., James, D. J., Bock, N., Speizer, F. E., Hayes,
C. (1988). Effects of ambient ozone on respiratory function in active, normal children. Am. Rev. Respir. Dis., 137,
313-320. Docket EPA-HQ-OAR-2010-0799.
120U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R-
05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799.
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121 Hazucha, M. J., Folinsbee, L. J., Seal, E., Jr. (1992). Effects of steady-state and variable ozone concentration
profiles on pulmonary function. Am. Rev. Respir. Dis., 146, 1487-1493. Docket EPA-HQ-OAR-2010-0799.
122Horstman, D.H., Ball, B.A., Folinsbee, L.J., Brown, J., Gerrity, T. (1995) Comparison of pulmonary responses of
asthmatic and nonasthmatic subjects performing light exercise while exposed to a low level of ozone. Toxicol. Ind.
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123Horstman, D.H.,; Folinsbee, L.J., Ives, P.J., Abdul-Salaam, S., McDonnell, W.F. (1990). Ozone concentration and
pulmonary response relationships for 6.6-hour exposures with five hours of moderate exercise to 0.08, 0.10, and 0.12
ppm. Am. Rev. Respir. Dis., 142, 1158-1163. Docket EPA-HQ-OAR-2010-0799.
124 U.S. EPA (2008). Integrated Science Assessment (ISA) for Sulfur Oxides - Health Criteria (Final Report).
EPA/600/R-08/047F. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 from
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=198843. Docket EPA-HQ-OAR-2010-0799.
125 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report).
EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 from
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=194645. Docket EPA-HQ-OAR-2010-0799.
126 U.S. EPA, 2010. Integrated Science Assessment for Carbon Monoxide (Final Report). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-09/019F, 2010.
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=218686. Docket EPA-HQ-OAR-2010-0799.
127 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment.
www.epa.gov/ttn/atw/nata2005/05pdf/sum_results.pdf. Docket EPA-HQ-OAR-2010-0799.
128 U.S. EPA (2010). Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards: Regulatory Impact Analysis. Chapter 7, section 7.2.2.3.2. This
material is available electronically at http://www.epa.gov/oms/climate/regulations/420rl0009.pdf. Docket EPA-HQ-
OAR-2010-0799.
129 U.S. EPA (2011) 2005 National-Scale Air Toxics Assessment, http://www.epa.gov/ttn/atw/nata2005. Docket
EPA-HQ-OAR-2010-0799.
130U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is available electronically
at: http://www.epa.gov/iris/subst/0276.htm. Docket EPA-HQ-OAR-2010-0799.
131 International Agency for Research on Cancer, I ARC monographs on the evaluation of carcinogenic risk of
chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, International Agency for Research on
Cancer, World Health Organization, Lyon, France 1982. Docket EPA-HQ-OAR-2010-0799.
132 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the benzene metabolite
hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony-stimulating factor in vitro,
Proc. Natl. Acad. Sci. 89:3691-3695. Docket EPA-HQ-OAR-2010-0799.
133 International Agency for Research on Cancer (IARC). 1987. Monographs on the evaluation of carcinogenic risk
of chemicals to humans, Volume 29, Supplement 7, Some industrial chemicals and dyestuffs, World Health
Organization, Lyon, France. Docket EPA-HQ-OAR-2010-0799.
134 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens
available at: http://ntp.niehs.nih.gov/go/16183.
135Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82:193-197.
EPA-HQ-OAR-2010-0799
136 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Re views. 3:541-554.
Docket EPA-HQ-OAR-2010-0799.
137Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T.
Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among Chinese
workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246. Docket EPA-HQ-OAR-2010-0799.
138 U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects). Environmental Protection Agency,
Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental
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Assessment, Washington DC. This material is available electronically at http://www.epa.gov/iris/subst/0276.htm.
Docket EPA-HQ-OAR-2010-0799.
139 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.;
Rupa, D.;Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.;
Zhang, X.; Li, K. (2003). HEI Report 115, Validation & Evaluation of Biomarkers in Workers Exposed to Benzene
in China. Docket EPA-HQ-OAR-2010-0799.
140Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes among Chinese
workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275-285. Docket EPA-HQ-OAR-2010-
0799.
141 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers Exposed to Low Levels
of Benzene. Science 306: 1774-1776. Docket EPA-HQ-OAR-2010-0799.
142 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to human exposure from
Urban Air. Research Reports Health Effect Inst. Report No. 113. Docket EPA-HQ-OAR-2010-0799.
143 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and Development, National Center for
Environmental Assessment, Washington Office, Washington, DC. Report No. EPA600-P-98-001F. This document
is available electronically at http://www.epa.gov/iris/supdocs/buta-sup.pdf. Docket EPA-HQ-OAR-2010-0799.
144U.S. EPA. 2002 "Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)" Environmental Protection Agency,
Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental
Assessment, Washington, DC http://www.epa.gov/iris/subst/0139.htm. Docket EPA-HQ-OAR-2010-0799.
145 International Agency for Research on Cancer (IARC) (1999) Monographs on the evaluation of carcinogenic risk
of chemicals to humans, Volume 71, Re-evaluation of some organic chemicals, hydrazine and hydrogen peroxide
and Volume 97 (in preparation), World Health Organization, Lyon, France. Docket EPA-HQ-OAR-2010-0799.
146
International Agency for Research on Cancer (IARC) (2008) Monographs on the evaluation of carcinogenic risk
of chemicals to humans, 1,3-Butadiene, Ethylene Oxide and Vinyl Halides (Vinyl Fluoride, Vinyl Chloride and
Vinyl Bromide) Volume 97, World Health Organization, Lyon, France. Docket EPA-HQ-OAR-2010-0799.
147 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens
available at: http://ntp.niehs.nih.gov/go/16183.
148Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subchronic toxicity of 4-vinylcyclohexene in rats and mice by
inhalation. Fundam. Appl. Toxicol. 32:1-10. Docket EPA-HQ-OAR-2010-0799.
149 EPA. Integrated Risk Information System. Formaldehyde (CASRN 50-00-0)
http://www.epa.gov/iris/subst/0419/htm
150 National Toxicology Program, U.S. Department of Health and Human Services (HHS), 12th Report on
Carcinogens, June 10, 2011
151 IARC Monographs on the Evaluation of Carcinogenic Risks to Humans Volume 88 (2006): Formaldehyde, 2-
Butoxyethanol and l-tert-Butoxypropan-2-ol
152 IARC Mongraphs on the Evaluation of Carcinogenic Risks to Humans Volume 100F (2012): Formaldehyde
153 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic
malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615-1623.
Docket EPA-HQ-OAR-2010-0799.
154Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among
workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130. Docket EPA-HQ-OAR-
2010-0799.
155Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009.
Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National Cancer
Institute cohort. J. National Cancer Inst. 101: 751-761. Docket EPA-HQ-OAR-2010-0799.
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156 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update.
Occup. Environ. Med. 61: 193-200. Docket EPA-HQ-OAR-2010-0799.
157 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of British chemical workers
exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. Docket EPA-HQ-OAR-2010-0799.
158 Hauptmann, M,; Stewart P. A.; Lubin J. H.; Beane Freeman, L. E.; Hornung, R. W.; Herrick, R. F.; Hoover, R.
N.; Fraumeni, J. F.; Hayes, R. B. 2009. Mortality from lymphohematopoietic malignancies and brain cancer among
embalmers exposed to formaldehyde. Journal of the National Cancer Institute 101:1696-1708.
159 ATSDR. 1999. Toxicological Profile for Formaldehyde, U.S. Department of Health and Human Services (HHS),
July 1999.
leo ATSDR. 2010. Addendum to theToxicological Profile for Formaldehyde. U.S. Department of Health and Human
Services (HHS), October 2010.
161IPCS. 2002. Concise International Chemical Assessment Document 40. Formaldehyde. World Health
Organization.
162 EPA (U.S. Environmental Protection Agency). 2010. Toxicological Review of Formaldehyde (CAS No. 50-00-0)
- Inhalation Assessment: In Support of Summary Information on the Integrated Risk Information System (IRIS).
External Review Draft. EPA/635/R-10/002A. U.S. Environmental Protection Agency, Washington DC [online].
Available: http://cfpub.epa.gov/ncea/irs_drats/recordisplay.cfm?deid=223614
163 NRC (National Research Council). 2011. Review of the Environmental Protection Agency's Draft IRIS
Assessment of Formaldehyde. Washington DC: National Academies Press.
http://books.nap.edu/openbook.php ?record_id=13142
164U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development,
National Center for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/iris/subst/0290.htm. Docket EPA-HQ-OAR-2010-0799.
165 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens
available at: http://ntp.niehs.nih.gov/go/16183.
166 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine,
and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol
71. Lyon, France.
167 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available
electronically at http://www.epa.gov/iris/subst/0290.htm. Docket EPA-HQ-OAR-2010-0799.
168 U.S. EPA. (2003). Integrated Risk Information System File of Acrolein. Research and Development, National
Center for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/iris/subst/0364.htm. Docket EPA-HQ-OAR-2010-0799.
169 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and
subacute studies. Toxicology. 23: 293-297. Docket EPA-HQ-OAR-2010-0799.
170 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acetaldehyde induces
histamine-mediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis. 148(4 Pt 1): 940-943. Docket EPA-
HQ-OAR-2010-0799.
171 U.S. EPA. (2003) Toxicological review of acrolein in support of summary information on Integrated Risk
Information System (IRIS) National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003. p.
10. Available online at: http://www.epa.gov/ncea/iris/toxreviews/0364tr.pdf. Docket EPA-HQ-OAR-2010-0799.
172U.S. EPA. (2003) Toxicological review of acrolein in support of summary information on Integrated Risk
Information System (IRIS) National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003.
Available online at: http://www.epa.gov/ncea/iris/toxreviews/0364tr.pdf. Docket EPA-HQ-OAR-2010-0799.
173 U.S. EPA. (2003) Toxicological review of acrolein in support of summary information on Integrated Risk
Information System (IRIS) National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003. p.
11. Available online at: http://www.epa.gov/ncea/iris/toxreviews/0364tr.pdf. Docket EPA-HQ-OAR-2010-0799.
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174 U.S. EPA. (2003). Integrated Risk Information System File of Acrolein. Office of Research and Development,
National Center for Environmental Assessment, Washington, DC. This material is available at
http://www.epa.gov/iris/subst/0364.htm.DocketEPA-HQ-OAR-2010-0799.
175U.S. EPA. (2003) Toxicological review of acrolein in support of summary information on Integrated Risk
Information System (IRIS) National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003. p.
15. Available online at: http://www.epa.gov/ncea/iris/toxreviews/0364tr.pdf. Docket EPA-HQ-OAR-2010-0799.
176 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Immediate sensory nerve-mediated respiratory responses to
irritants in healthy and allergic airway-diseased mice. J Appl Physiol 94(4):1563-1571. Docket EPA-HQ-OAR-
2010-0799.
177 U.S. EPA. (2003). Integrated Risk Information System File of Acrolein. Research and Development, National
Center for Environmental Assessment, Washington, DC. This material is available at
http://www.epa.gov/iris/subst/0364.htm. Docket EPA-HQ-OAR-2010-0799.
178 International Agency for Research on Cancer (IARC). 1995. Monographs on the evaluation of carcinogenic risk
of chemicals to humans, Volume 63. Dry cleaning, some chlorinated solvents and other industrial chemicals, World
Health Organization, Lyon, France. Docket EPA-HQ-OAR-2010-0799.
179 Agency for Toxic Substances and Disease Registry (ATSDR). 1995. Toxicological profile for Polycyclic
Aromatic Hydrocarbons (PAHs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health
Service. Available electronically at http://www.atsdr.cdc.gov/ToxProfiles/TP.asp?id=122&tid=25.
180 U.S. EPA (2002). Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-90/057F Office of
Research and Development, Washington DC. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. Docket
EPA-HQ-OAR-2010-0799
181 International Agency for Research on Cancer (IARC). (2012). Monographs on the Evaluation of the
Carcinogenic Risk of Chemicals for Humans, Chemical Agents and Related Occupations. Vol. 100F
benzo(a)pyrene. Lyon, France.
182U.S. EPA (1997). Integrated Risk Information System File of indeno(l,2,3-cd)pyrene. Research and Development,
National Center for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0457.htm.
183 Perera, P.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to environmental pollutants on
birth outcomes in a multiethnic population. Environ Health Perspect. Ill: 201-205.
184 Perera, P.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann,
D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on
neurodevelopment in the first 3 years of life among inner-city children. Environ Health Perspect 114: 1287-1292.
185 U. S. EPA. 1998. Toxicological Review of Naphthalene (Reassessment of the Inhalation Cancer Risk),
Environmental Protection Agency, Integrated Risk Information System, Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/iris/subst/0436.htm.
186 U. S. EPA. 1998. Toxicological Review of Naphthalene (Reassessment of the Inhalation Cancer Risk),
Environmental Protection Agency, Integrated Risk Information System, Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/iris/subst/0436.htm.
187 U. S. EPA. 1998. Toxicological Review of Naphthalene (Reassessment of the Inhalation Cancer Risk),
Environmental Protection Agency, Integrated Risk Information System, Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is available electronically at
http://www.epa.gov/iris/subst/0436.htm. Docket EPA-HQ-OAR-2010-0799.
188 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS Reassessment of the
Inhalation Carcinogenicity of Naphthalene. August 2004.
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=84403. Docket EPA-HQ-OAR-2010-0799.
189 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health Service, U.S.
Department of Health and Human Services, Research Triangle Park, NC. Available from: http://ntp-
server.niehs.nih.gov.
190 International Agency for Research on Cancer (IARC). (2002). Monographs on the Evaluation of the
Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France. Docket EPA-HQ-OAR-2010-0799.
6-121
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191U. S. EPA. 1998. lexicological Review of Naphthalene, Environmental Protection Agency, Integrated Risk
Information System, Research and Development, National Center for Environmental Assessment, Washington, DC.
This material is available electronically at http://www.epa.gov/iris/subst/0436.htm
192 Zhou, Y.; Levy, J.I. (2007) Factors influencing the spatial extent of mobile source air pollution impacts: a meta-
analysis. BMC Public Health 7: 89. doi:10.1186/1471-2458-7-89. Docket EPA-HQ-OAR-2010-0799.
193 HEI Panel on the Health Effects of Air Pollution. (2010) Traffic-related air pollution: a critical review of the
literature on emissions, exposure, and health effects. [Online at www.healtheffects.org1. Docket EPA-HQ-OAR-
2010-0799.
194 Salam, M.T.; Islam, T.; Gilliland, F.D. (2008) Recent evidence for adverse effects of residential proximity to
traffic sources on asthma. Current Opin Pulm Med 14: 3-8. Docket EPA-HQ-OAR-2010-0799.
195 Holguin, F. (2008) Traffic, outdoor air pollution, and asthma. Immunol Allergy Clinics North Am 28: 577-588.
196 Adar, S.D.; Kaufman, J.D. (2007) Cardiovascular disease and air pollutants: evaluating and improving
epidemiological data implicating traffic exposure. Inhal Toxicol 19: 135-149. Docket EPA-HQ-OAR-2010-0799.
197 Raaschou-Nielsen, O.; Reynolds, P. (2006) Air pollution and childhood cancer: a review of the epidemiological
literature. IntJ Cancer 118: 2920-2929. Docket EPA-HQ-OAR-2010-0799.
198 U.S. Census Bureau (2008) American Housing Survey for the United States in 2007. Series H-150 (National
Data), Table 1A-6. [Accessed at http://www.census.gov/hhes/www/housing/ahs/ahs07/ahs07.html on January 22,
2009]
199 Lena, T.S.; Ochieng, V.; Carter, M.; Holgum-Veras, J.; Kinney, P.L. (2002) Elemental carbon and PM2.5 levels
in an urban community heavily impacted by truck traffic. Environ Health Perspect 110: 1009-1015. Docket EPA-
HQ-OAR-2010-0799.
200 Wier, M.; Sciammas, C.; Seto, E.; Bhatia, R.; Rivard, T. (2009) Health, traffic, and environmental justice:
collaborative research and community action in San Francisco, California. Am J Public Health 99: S499-S504.
Docket EPA-HQ-OAR-2010-0799.
201 Forkenbrock, D.J. and L.A. Schweitzer, Environmental Justice and Transportation Investment Policy. Iowa City:
University of Iowa, 1997.
202 Appatova, A.S.; Ryan, P.H.; LeMasters, O.K.; Grinshpun, S.A. (2008) Proximal exposure of public schools and
students to major roadways: a nationwide US survey. J Environ Plan Mgmt. Docket EPA-HQ-OAR-2010-0799.
203 Green, R.S.; Smorodinsky, S.; Kim, J.J.; McLaughlin, R.; Ostro, B. (2004) Proximity of California public schools
to busy roads. Environ Health Perspect 112: 61-66. Docket EPA-HQ-OAR-2010-0799.
204 Houston, D.; Ong, P.; Wu, J.; Winer, A. (2006) Proximity of licensed child care facilities to near-roadway vehicle
pollution. Am J Public Health 96: 1611-1617. Docket EPA-HQ-OAR-2010-0799.
205 Wu, Y.; Batterman, S. (2006) Proximity of schools in Detroit, Michigan to automobile and truck traffic. J
Exposure Sci Environ Epidemiol 16: 457-470. Docket EPA-HQ-OAR-2010-0799.
206 National Research Council, 1993. Protecting Visibility in National Parks and Wilderness Areas. National
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211 Winner, W.E., and CJ. Atkinson. 1986. "Absorption of air pollution by plants, and consequences for growth."
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212U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,
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216 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,
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218Ollinger, S.V., Aber, J.D., Reich, P.B. (1997). Simulating ozone effects on forest productivity: interactions
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219 Winner, W.E. (1994). Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4), 651-
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221 Chappelka, A.H., Samuelson, L.J. (1998). Ambient ozone effects on forest trees of the eastern United States: a
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230U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,
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237 White, D., Kimerling, A.J., Overton, W.S. (1992). Cartographic and geometric component of a global sampling
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239 Coulston, J.W., Riitters, K.H., Smith, G.C. (2004). A preliminary assessment of the Montreal process indicators
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240 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants. EPA/600/R-05/004aF-cF.
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241 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants. EPA/600/R-05/004aF-cF.
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242 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring program—results from
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243 U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental
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244 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate Matter: Policy Assessment
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245 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria (Final).
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246 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria (Final).
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247 Environmental Protection Agency (2003). Response Of Surface Water Chemistry to the Clean Air Act
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248 Fenn, M.E. and Blubaugh, TJ. (2005) Winter Deposition of Nitrogen and Sulfur in the Eastern Columbia River
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249 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of change. Ambio 31: 64-71.
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250Bricker, Suzanne B., et al., National Estuarine Eutrophication Assessment, Effects of Nutrient Enrichment in the
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251 Smith, W.H. 1991. "Air pollution and Forest Damage." Chemical Engineering News, 69(45): 30-43. Docket
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252 Gawel, I.E.; Ahner, B.A.; Friedland, A.J.; and Morel, F.M.M. 1996. "Role for heavy metals in forest decline
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253 Cotrufo, M.F.; DeSanto, A.V.; Alfani, A.; et al. 1995. "Effects of urban heavy metal pollution on organic matter
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254Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. "Effect of heavy metals and storage time on two types of
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255 U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental
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256 Mason, R.P. and Sullivan, K.A. 1997. "Mercury in Lake Michigan." Environmental Science & Technology, 31:
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257Landis, M.S. and Keeler, G.J. 2002. "Atmospheric mercury deposition to Lake Michigan during the Lake
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258 U.S. EPA. 2000. EPA453/R-00-005, "Deposition of Air Pollutants to the Great Waters: Third Report to
Congress," Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina. Docket EPA-HQ-
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259 National Science and Technology Council (NSTC) 1999. "The Role of Monitoring Networks in the Management
of the Nation's Air Quality." Docket EPA-HQ-OAR-2010-0799.
260 Callender, E. and Rice, K.C. 2000. "The Urban Environmental Gradient: Anthropogenic Influences on the Spatial
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261 Rice, K.C. 1999. "Trace Element Concentrations in Streambed Sediment Across the Conterminous United
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262Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. "Implications of Platinum-Group Element Accumulation along U.S.
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263U.S. EPA. 1998. EPA454/R-98-014, "Locating and Estimating Air Emissions from Sources of Polycyclic Organic
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264U.S. EPA. 1998. EPA454/R-98-014, "Locating and Estimating Air Emissions from Sources of Polycyclic Organic
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265Simcik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. "Atmospheric Loading of Polycyclic Aromatic
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266 Simcik, M.F.; Eisenreich, S.J.; and Lioy, PJ. 1999. "Source apportionment and source/sink relationship of PAHs
in the coastal atmosphere of Chicago and Lake Michigan." Atmospheric Environment, 33: 5071-5079. Docket EPA-
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267 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. "Fate of Atmospherically Deposited Polycyclic Aromatic
Hydrocarbons (PAHs) in Chesapeake Bay." Environmental Science & Technology, 35, 2178-2183. Docket EPA-
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268 Park, J.S.; Wade, T.L.; and Sweet, S. 2001. "Atmospheric distribution of polycyclic aromatic hydrocarbons and
deposition to Galveston Bay, Texas, USA." Atmospheric Environment, 35: 3241-3249. Docket EPA-HQ-OAR-
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269 Poor, N.; Tremblay, R.; Kay, H.; et al. 2002. "Atmospheric concentrations and dry deposition rates of polycyclic
aromatic hydrocarbons (PAHs) for Tampa Bay, Florida, USA." Atmospheric Environment 38: 6005-6015. Docket
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270 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. "Fate of Atmospherically Deposited Polycyclic Aromatic
Hydrocarbons (PAHs) in Chesapeake Bay." Environmental Science & Technology, 35, 2178-2183. Docket EPA-
HQ-OAR-2010-0799.
271 U.S. EPA. 2000. EPA453/R-00-005, "Deposition of Air Pollutants to the Great Waters: Third Report to
Congress," Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina. Docket EPA-
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272 Van Metre, P.C.; Mahler, B.J.; and Furlong, E.T. 2000. "Urban Sprawl Leaves its PAH Signature."
Environmental Science & Technology, 34: 4064-4070. Docket EPA-HQ-OAR-2010-0799.
273 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. "A review of the processes involved in the exchange of semi-
volatile organic compounds across the air-soil interface." The Science of the Total Environment, 228: 5-24. Docket
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274 Tuhackova, J. et al. (2001) Hydrocarbon deposition and soil microflora as affected by highway traffic.
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275U.S. EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants. EPA/600/3-91/001. Docket
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276 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of
VOCs on herbaceous plants in an open-top chamber experiment. Environ. Pollut. 124:341-343. Docket EPA-HQ-
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277 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of
VOCs on herbaceous plants in an open-top chamber experiment. Environ. Pollut. 124:341-343. Docket EPA-HQ-
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278 Viskari E-L. 2000. Epicuticular wax of Norway spruce needles as indicator of traffic pollutant deposition. Water,
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279 Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and transformation of benzene and toluene by plant leaves.
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280 Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D Knoppik, B Hock. 1987. Toxic components of motor
vehicle emissions for the spruce Picea abies. Environ. Pollut. 48:235-243. Docket EPA-HQ-OAR-2010-0799.
281 Byun, D.W., Ching, J. K.S. (1999). Science algorithms of the EPA models-3 community multiscale air quality
(CMAQ) modeling system. Washington, DC: U.S. Environmental Protection Agency, Office of Research and
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282 Byun, D.W., Schere, K.L. (2006). Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Journal of Applied
Mechanics Reviews, 59(2), 51-77. Docket EPA-HQ-OAR-2010-0162.
283 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A. (1996). The next generation
of integrated air quality modeling: EPA's Models-3, Atmospheric Environment, 30, 1925-1938. Docket EPA-HQ-
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284 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009). Report on the Peer Review of
the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC.,
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285 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). Simulating regional-scale
ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17), 2627-
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286 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long-range transport of
acidifying substances in East Asia-Part I: Model evaluation and sensitivity studies. Atmospheric Environment,
42(24), 5939-5955. EPA-HQ-OAR-2010-0162.
287 U.S. Environmental Protection Agency. (2008). Technical support document for the final locomotive/marine rule:
Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental Protection Agency, Office of Air
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288 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale
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289 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale
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290 Byun, D.W., Ching, J. K.S. (1999). Science algorithms of EPA Models-3 Community Multiscale Air Quality
(CMAQ) modeling system, EPA/600/R-99/030, Office of Research and Development). Please also see:
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291 Le Sager, P. Yantosca, B., Carouge, C. (2008). GEOS-CHEMv8-01-02 User's Guide, Atmospheric Chemistry
Modeling Group, Harvard University, Cambridge, MA, December 18, 2008. Docket EPA-HQ-OAR-2010-0162.
292 U.S. EPA, (2004), Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the
(Revised) Speciated Modeled Attainment Test (SMAT)- Updated 11/8/04. Docket EPA-HQ-OAR-2010-0162.
293 U.S. EPA, (2011), Final Transport Rule Air Quality Modeling TSD. Docket EPA-HQ-OAR-2010-0162.
294 U.S. EPA (2007) Guidance on the Use of Models and Other Analyses For Demonstrating Attainment of Air
Quality Goals for Ozone, PM2.5, and Regional Haze; EPA-454/B-07-002; Research Triangle Park, NC; April 2007.
Docket EPA-HQ-OAR-2010-0162.
295 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05.
Final Report to the US EPA, RT-0400675, December 8, 2005.
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296 Dodge, M.C., 2000. Chemical oxidant mechanisms for air quality modeling: critical review. Atmospheric
Environment 34, 2103-2130. Docket EPA-HQ-OAR-2011-0135.
297 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A.,
Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC
Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version.
http://www.iupac-kinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2011-0135.
298 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A.,
Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC
Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version.
http://www.iupac-kinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2011-0135.
299 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A.,
Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC
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Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version.
http://www.iupac-kinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2011-0135.
300 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A.,
Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC
Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version.
http://www.iupac-kinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2011-0135.
301 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, O.K., Ravishankara,
A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical Kinetics and Photochemical Data for use in
Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory
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302 J.G. Calvert, A. Mellouki, J.J. Orlando, M.J. Pilling, and T.J. Wallington, 2011. The mechanisms of atmospheric
oxidation of the oxygenates. Oxford University Press, New York/Oxford.
303 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, O.K., Ravishankara,
A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical Kinetics and Photochemical Data for use in
Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory
http://jpldataeval.jpl.nasa.gov/index.html. Docket EPA-HQ-OAR-2011-0135.
304 Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond Mechanism: CB05. Final
Report to the U.S. EPA, RT-0400675. Yocke and Company, Novato, CA. Docket EPA-HQ-OAR-2011-0135.
305 Luecken, D.J., Phillips, S., Sarwar, G., Jang, C., 2008b. Effects of using the CB05 vs. SAPRC99 vs. CB4
chemical mechanism on model predictions: Ozone and gas-phase photochemical precursor concentrations.
Atmospheric Environment 42, 5805-5820. Docket EPA-HQ-OAR-2011-0135.
306 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, O.K., Ravishankara,
A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical Kinetics and Photochemical Data for use in
Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory. Docket EPA-HQ-OAR-2011-0135.
307 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, O.K., Ravishankara,
A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical Kinetics and Photochemical Data for use in
Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory. Docket EPA-HQ-OAR-2011-0135.
308 Atkinson R, Arey J (2003) Atmospheric Degradation of Volatile Organic Compounds. Chem Rev 103: 4605-
4638. Docket EPA-HQ-OAR-2011-0135.
309 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A.,
Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC
Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version.
http://www.iupac-kinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2011-0135.
310 Edney, E. O., T. E. Kleindienst, M. Lewandowski, and J. H. Offenberg, 2007. Updated SOA chemical mechanism
for the Community Multi-Scale Air Quality model, EPA 600/X-07/025, U.S. EPA, Research Triangle Park, NC.
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311 Carlton, A.G., B. J. Turpin, K. Altieri, S. Seitzinger, R. Mathur, S. Roselle, R. J. Weber, (2008), CMAQ model
performance enhanced when in-cloud SOA is included: comparisons of OC predictions with measurements, Environ.
Sci. Technol. 42 (23), 8798-8802. Docket EPA-HQ-OAR-2011-0135.
312 Lewandowski M, M Jaoui, JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary
and secondary contributions to ambient PM in the midwestern United States, Environ Sci Technol 42(9):3303-3309.
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313 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of
biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location, Atmos
Environ 41(37):8288-8300. Docket EPA-HQ-OAR-2011-0135.
314 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of
Toluene and D-pinene to SOA Formed in an Irradiated Toluene/D-pinene,NOx/Air Mixture: Comparison of Results
Using 14C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR-
2011-0135.
315 Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. (1992) Secondary organic aerosol formation and transport.
Atmos Environ 26, 2269-2282. Docket EPA-HQ-OAR-2011-0135.
316 Takekawa, H. Minoura, H. Yamazaki, S. (2003) Temperature dependence of secondary organic aerosol formation
by photo-oxidation of hydrocarbons. Atmos Environ 37: 3413-3424. Docket EPA-HQ-OAR-2011-0135.
317 Kleeman, M.J., Ying, Q., Lu, J., Mysliwiec, M.J., Griffin, R.J., Chen, J., Clegg, S. (2007) Source apportionment
of secondary organic aerosol during a severe photochemical smog episode. Atmos Environ 41: 576-591. Docket
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318 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.;
Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging.
Science 315: 1259-1262. Docket EPA-HQ-OAR-2011-0135.
319 Griffin, R. J.; Cocker, D. R.; Seinfeld, J. H.; Dabdub, D. (1999) Estimate of global atmospheric organic aerosol
from oxidation of biogenic hydrocarbons. Geophys Res Lett 26 (17) 2721- 2724
320 Lewis, C. W.; Klouda, G. A.; Ellenson, W. D. (2004) Radiocarbon measurement of the biogenic contribution to
summertime PM-2.5 ambient aerosol in Nashville, TN. Atmos Environ 38 ( 35) 6053- 6061.
321 Byun DW, Schere, KL (2006) Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J Applied Mechanics
Reviews 59: 51-76. Docket EPA-HQ-OAR-2011-0135.
322 U. S. EPA (2010) Our Nations Air, Status and Trends through 2008. EPA 454/R-09-002, February 2010.
http://www.epa.gov/airtrends/2010. Docket.EPA-HQ-OAR-2011-0135.
323 U. S. EPA. (2011) 2005 National-Scale Air Toxics Assessment.
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324 U. S. EPA (2007) Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile Sources
Rule, Chapter 3, Air Quality and Resulting Health and Welfare Effects of Air Pollution from Mobile Sources. 72 FR
8428, February 26, 2007. http://www.epa.gov/otaq/regs/toxics/420r07002.pdf. Docket EPA-HQ-OAR-2011-0135.
326 Lim, Y.B., Ziemann, PJ. (2009) Effects of Molecular Structure on Aerosol Yields from OH Radical-Initiated
Reactions of Linear, Branched, and Cyclic Alkanes in the Presence of NOx- Environ Sci Technol 43 (7): 2328-2334.
327 Kleindienst, T.E. (2008) Hypothetical SOA Production from Ethanol Photooxidation. Memo to the Docket EPA-
HQ-OAR-2005-0161. Docket EPA-HQ-OAR-2011-0135.
328 Turpin, B.J., Huntzicker, J.J., Larson, S.M., Cass, G.R. (1991) Los Angeles Summer Midday Particulate Carbon:
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329 Turpin, B.J., Huntzicker, JJ. (1995) Identification of Secondary Organic Aerosol Episodes and Quantitation of
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330 Bae M-S, Schauer JJ, Turner JR (2006) Estimation of the Monthly Average Ratios of Organic Mass to Organic
Carbon for Fine Particulate Matter at an Urban Site, Aerosol Sci Technol 40(12): 1123-1139.
http://dx.doi.org/10.1080/02786820601004085. Docket EPA-HQ-OAR-2011-0135.
331 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of
biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos
Environ 41(37):8288-8300. Docket EPA-HQ-OAR-2011-0135.
332 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of
Toluene and D-pinene to SOA Formed in an Irradiated Toluene/D-pinene,NOx/Air Mixture: Comparison of Results
Using 14C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR-
2011-0135.
333 Claeys M, R Szmigielski, I Kourtchev, P Van der Veken, R Vermeylen, W Maenhaut, M Jaoui, TE Kleindienst,
M Lewandowski, JH Offenberg, EO Edney (2007) Hydroxydicarboxylic acids: Markers for secondary organic
aerosol from the photooxidation of D -pinene. Environ Sci Technol 41(5): 1628-1634. Docket EPA-HQ-OAR-2011-
0135.
334 Edney EO, TE Kleindienst, M Jaoui, M Lewandowski, JH Offenberg, W Wang, M Claeys (2005) Formation of 2-
methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated
isoprene/NOx/SO2/air mixtures and their detection in ambient PM2.5 samples collected in the Eastern United States.
Atmos Environ 39: 5281-5289. Docket EPA-HQ-OAR-2011-0135.
335 Jaoui M, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2005) Identification and quantification of
aerosol polar oxygenated compounds bearing carboxylic or hydroxyl groups. 2. Organic tracer compounds from
monoterpenes. Environ Sci Technol 39: 5661-5673. Docket EPA-HQ-OAR-2011-0135.
336 Kleindienst TE, TS Conver, CD Mclver, EO Edney (2004) Determination of secondary organic aerosol products
from the photooxidation of toluene and their implications in ambient PM2.5. J Atmos Chem 47: 70-100. Docket
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337 Kleindienst TE, TS Conver, CD Mclver, EO Edney (2004) Determination of secondary organic aerosol products
from the photooxidation of toluene and their implication in ambient PM2.5, J Atmos Chem 47: 70-100. Docket
EPA-HQ-OAR-2011-0135.
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338 Lewandowski M, M Jaoui, JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary
and secondary contributions to ambient PM in the midwestern United States, Environ Sci Technol 42(9):3303-3309.
http://pubs.acs.org/cgi-bin/article.cgi/esthag/2008/42/i09/html/es0720412.html. Docket EPA-HQ-OAR-2011-0135.
339 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of
biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos
Environ 41(37):8288-8300. Docket EPA-HQ-OAR-2011-0135.
340 Henze DK, JH Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. Geophys Res Lett 33:
L09812. doi:10.1029/2006GL025976. Docket EPA-HQ-OAR-2011-0135.
341 Hildebrandt, L., Donahue 1, N. M, Pandisl, S. N. (2009) High formation of secondary organic aerosol from the
photo-oxidation of toluene. Atmos Chem Phys 9: 2973-2986. Docket EPA-HQ-OAR-2011-0135 9
342 Ng, N. L., Kroll, J. H., Chan, A. W. H., Chabra, P. S., Flagan, R. C., Seinfield, J. H., Secondary organic aerosol
formation from m-xylene, toluene, and benzene, Atmospheric Chemistry and Physics Discussion, 7, 3909-3922,
2007. Docket EPA-HQ-OAR-2011-0135
343 Henze, D. K., Seinfeld, J. H., Ng, N. L., Kroll, J. H., Fu, T.-M., Jacob, D. J., and Heald, C. L. (2008) Global
modeling of secondary organic aerosol formation from aromatic hydrocarbons: high- vs. low-yield pathways, Atmos.
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344 Lane, T. E., Donahue, N.M. and Pandis, S.N. (2008) Simulating secondary organic aerosol formation using the
volatility basis-set approach in a chemical transport model, Atmos. Environ., 42, 7439-7451, doi:
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345 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.G., Sarwar, g., Finder, R.W., Pouliot, G.A., Houyoux, M.,
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346 Parikh, H.M., Carlton, A.G., Vizuete, W., and Kamen, R.M. (2011) Modeling secondary organic aerosol using a
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349 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.;
Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging.
Science 315: 1259-1262. Docket EPA-HQ-OAR-2011-0135
350 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.G., Sarwar, g., Finder, R.W., Pouliot, G.A., Houyoux, M.,
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352 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, O.K., Ravishankara,
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355 CMAS 2008. Release Notes for CMAQ v4.7.
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364 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007)
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409 Moolgavkar, S.H. (2003). "Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook
Counties." In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, MA:
Health Effects Institute.
410 Ito, K. (2003). "Associations of Particulate Matter Components with Daily Mortality and Morbidity in Detroit,
Michigan." In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Health Effects
Institute, Boston, MA. EPA-HQ-OAR-2009-0472-1674
411 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the Circulatory System in
Three U.S. Metropolitan Areas. Journal of the Air and Waste Management Association 50:1199-1206. EPA-HQ-
OAR-2009-0472-1664
412 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle,
Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report.
Boston, MA: Health Effects Institute. EPA-HQ-OAR-2009-0472-0318
413 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory emergency department
visits. Epidemiology. Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472-1663
414 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory emergency room visits
in two northern New England cities: an ecological time-series study. Environ Res. Vol. 97 (3): 312-21. EPA-HQ-
OAR-2009-0472-0246
415 Morris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. (1999). An Association
between Fine Particles and Asthma Emergency Department Visits for Children in Seattle. Environmental Health
Perspectives 107(6):489-493. EPA-HQ-OAR-2009-0472-0318
416 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M.
Raizenne, and F.E. Speizer. (1996). Health Effects of Acid Aerosols On North American Children-Respiratory
Symptoms. Environmental Health Perspectives 104(5):500-505. EPA-HQ-OAR-2009-0472-0225
417 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health and PM10
Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases 144:668-674. EPA-HQ-OAR-
2009-0472-1672
418 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than Coarse Particles with
Acute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6-10.
419 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. (2001). Air Pollution and Exacerbation of
Asthma in African-American Children in Los Angeles. Epidemiology 12(2):200-208.
420 Vedal, S., J. Petkau, R. White, and J. Blair. (1998). Acute Effects of Ambient Inhalable Particles in Asthmatic
and Nonasthmatic Children. American Journal of Respiratory and Critical Care Medicine 157(4): 1034-1043. EPA-
HQ-OAR-2009-0472-1671
421 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental
Economics Management 14:87-98. EPA-HQ-OAR-2009-0472-1670
422 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). The effects of
ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology 12(l):43-54. EPA-HQ-OAR-
2009-0472-1675
423 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhal
Toxicol 12(11):997-1016. EPA-HQ-OAR-2009-0472-0224
424 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An Observational Study
of Multiple Pollutants. Environmental Research 50:238-247. EPA-HQ-OAR-2009-0472-0364
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425 U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the Analytical Plan for
EPA's Second Prospective Analysis -Benefits and Costs of the Clean Air Act, 1990—2020. EP A-SAB -COUNCIL-
AD V-04-004. EPA-HQ-OAR-2009-0472-4664
426 National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed Air Pollution
Regulations. Washington, DC: The National Academies Press.
427 Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate Projections. Memorandum to
Bryan Hubbell and Zachary Pekar, U.S. EPA.
428 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from
years 1996-1998), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human
Services, Available on the Internet at .
429
Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project.
430 American Lung Association. 1999. Chronic Bronchitis. Available on the Internet at
.
431 American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American Lung Association,
Best Practices and Program Services, Epidemiology and Statistics Unit. Available on the Internet at
.
432 Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health Interview Survey,
1996. Vital Health Stat 10(200): 1-212.
433 U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race, Hispanic Origin and
Nativity: 1999 to 2100. Population Projections Program, Population Division, U.S. Census Bureau, Washington, DC.
Available on the Internet at .
434 National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, Indicator 42: Student
Absenteeism and Tardiness. U.S. Department of Education. Washington, DC.
435 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from
years 1996-1998), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human
Services, Available on the Internet at .
436 Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods.
Washington, DC: Resources for the Future.
437 Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health and Safety Regulation. Journal of Urban
Economics 22:101-112.
438 Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing Changes in Health Risks: A
Comparison of Alternative Measures. The Southern Economic Journal 53:977-984.
439 U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient
Air Quality Standards for Particulate Matter, Chapter 5. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. October. Available on the Internet at .
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441 U.S. EPA, (2001), "Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze",
http://www.epa.gov/ttn/scram/guidance_sip.htm, Modeling Guidance, DRAFT-PM
442 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution
Regulations. Washington, DC: The National Academies Press.
443 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from
Controlling Ozone Air Pollution. National Academies Press. Washington, DC.
444 Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell,
and Patrick L. Kinney. 2008. Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine
Particulate Matter in the U.S. Environ. Sci. Technol., 42(7):2268-2274.
445 The issue is discussed in more detail in the PM NAAQS RIA from 2006. See U.S. Environmental Protection
Agency. 2006. Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards
for Particulate Matter. Prepared by: Office of Air and Radiation. October 2006. Available at
http://www.epa.gov/ttn/ecas/ria.html.
446 U.S. Environmental Protection Agency (U.S. EPA), 2010. Regulatory Impact Analysis, Final Rulemaking to
Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards.
Office of Transportation and Air Quality. April. Available at
http://www.epa.gov/otaq/climate/regulations/420rl0009.pdf. EPA-420-R-10-009
447 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient
Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. March. Available at . EPA-
HQ-OAR-2009-0472-0238
448 U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: National Emission
Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. Augues. Available on the Internet at <
http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf>. EPA-HQ-OAR-2009-0472-0241
449 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis
(RIA). Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet
at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 15, 2010. EPA-HQ-
OAR-2009-0472-0237
450 U.S. Environmental Protection Agency (U.S. EPA). 2008. Technical Support Document: Calculating Benefit
Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2007-0225-0284. Office of Air Quality Planning and
Standards, Research Triangle Park, NC. March. Available on the Internet at . EPA-
HQ-OAR-2009-0472-0228
451 Fann, N. et al. (2009). The influence of location, source, and emission type in estimates of the human health
benefits of reducing a ton of air pollution. Air Qual Atmos Health. Published online: 09 June, 2009. EPA-HQ-OAR-
2009-0472-0229
452 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. "Lung Cancer,
Cardiopulmonary Mortality, and Long-term Exposure to Fine Paniculate Air Pollution." Journal of the American
Medical Association 287:1132-1141. EPA-HQ-OAR-2009-0472-0263
453 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. "Reduction in Fine Paniculate Air Pollution and
Mortality." American Journal of Respiratory and Critical Care Medicine 173:667-672. Estimating the Public Health
Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. EPA-HQ-OAR-
2009-0472-1661
454Mrozek, J.R., and L.O. Taylor. 2002. "What Determines the Value of Life? A Meta-Analysis." Journal of
Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-0472-1677
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455 Viscusi, V.K., and I.E. Aldy. 2003. "The Value of a Statistical Life: A Critical Review of Market Estimates
throughout the World." Journal of Risk and Uncertainty 27(l):5-76. EPA-HQ-OAR-2009-0472-0245
456 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining Estimates of the Value
of Statistical Life for Environmental Policy Analysis. Environmental and Resource Economics. 34: 385-406. EPA-
HQ-OAR-2009-0472-0235
457 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA
240-R-00-003. National Center for Environmental Economics, Office of Policy Economics and Innovation.
Washington, DC. September. Available on the Internet at
. EPA-HQ-OAR-2009-0472-
0226
458 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
459 For a complete list of core references from IPCC, USGCRP/CCSP, NRC and others relied upon for development
of the TSD for EPA's Endangerment and Cause or Contribute Findings see section l(b), specifically, Table 1.1 of
theTSD. Docket: EPA-HQ-OAR-2009-0171-11645.
460 National Research Council (NRC) (2010). Advancing the Science of Climate Change. National Academy Press.
Washington, DC. Docket EPA-HQ-OAR-2010-0799.
461 Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model Documentation for the MiniCAM. PNNL-14337,
Pacific Northwest National Laboratory, Richland, Washington. Docket EPA-HQ-OAR-2010-0799.
462 Wigley, T.M.L. and Raper, S.C.B. 1992. Implications for Climate And Sea-Level of Revised IPCC Emissions
Scenarios Nature 357, 293-300. Raper, S.C.B., Wigley T.M.L. and Warrick R.A. 1996. in Sea-Level Rise and
Coastal Subsidence: Causes, Consequences and Strategies J.D. Milliman, B.U. Haq, Eds., Kluwer Academic
Publishers, Dordrecht, The Netherlands, pp. 11-45. Docket EPA-HQ-OAR-2010-0799.
463 Wigley, T.M.L. and Raper, S.C.B. 2002. Reasons for larger warming projections in the IPCC Third Assessment
Report J. Climate 15, 2945-2952. Docket EPA-HQ-OAR-2010-0799.
464 Thompson AM, KV Calvin, SJ Smith, GP Kyle, A Volke, P Patel, S Delgado-Arias, B Bond-Lamberty, MA
Wise, LE Clarke and JA Edmonds. 2010. "RCP4.5: A Pathway for Stabilization of Radiative Forcing by 2100."
Climatic Change (in review)
465 Clarke, L., J. Edmonds, H. Jacoby, H. Pitcher, J. Reilly, R. Richels, (2007) Scenarios of Greenhouse Gas
Emissions and Atmospheric Concentrations. Sub-report 2.1A of Synthesis and Assessment Product 2.1 by the U.S.
Climate Change Science Program and the Subcommittee on Global Change Research (Department of Energy, Office
of Biological & Environmental Research, Washington, DC., USA, 154 pp.). Docket EPA-HQ-OAR-2010-0799.
466 Wigley, T.M.L. 2008. MAGICC 5.3.v2 User Manual. UCAR - Climate and Global Dynamics Division, Boulder,
Colorado, http://www.cgd.ucar.edu/cas/wigley/magicc/. Docket EPA-HQ-OAR-2010-0799.
467 Meehl, G.A. et al. (2007) Global Climate Projections. In: Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)].
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Docket EPA-HQ-OAR-2010-
0799.
468 See http://epa.gov/climatechange/endangerment/comments/volume9.htmlttl-6-l or Docket EPA-HQ-OAR-
2009-0171-11676
469 See http://epa.gov/blackcarbon
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470 National Research Council, 2011. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over
Decades to Millenia. Washington, DC: National Academies Press. Docket EPA-HQ-OAR-2010-0799.
471 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC-105.
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak
Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.
472 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC-105.
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak
Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.
473 Mehrbach, C., C. H. Culberson, J. E. Hawley, and R. N. Pytkowicz. 1973. Measurement of the apparent
dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnology and Oceanography 18:897-
907. Docket EPA-HQ-OAR-2010-0799.
474 Dickson, A. G. and F. J. Millero. 1987. A comparison of the equilibrium constants for the dissociation of
carbonic acid in seawater media. Deep-Sea Res. 34, 1733-1743. (Corrigenda. Deep-Sea Res. 36, 983). Docket EPA-
HQ-OAR-2010-0799.
475 A. G. Dickson. 1990. Thermodynamics of the dissociation of boric acid in synthetic sea water from 273.15 to
318.15 K. Deep-Sea Res. 37, 755-766. Docket EPA-HQ-OAR-2010-0799.
476 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC-105.
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak
Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.
477 Khoo, K.H., R.W. Ramette, C.H. Culberson, and R. G. Bates. 1977. Determination of hydrogen ion
concentrations in seawater from 5 to 40°C: Standard potentials at salinities from 20 to 45%o. Analytical Chemistry
49(1): 29-34. Docket EPA-HQ-OAR-2010-0799.
478 Dickson, A. G. 2003. Certificate of Analysis - Reference material for oceanic CO2 measurements (Batch #62,
bottled on August 21, 2003). Certified by Andrew Dickson, Scripps Institution of Oceanography. November 21,
2003. Docket EPA-HQ-OAR-2010-0799.
Dickson, A. G. 2005. Certificate of Analysis - Reference material for oceanic CO2 measurements (Batch #69,
bottled on January 4, 2005). Certified by Andrew Dickson, Scripps Institution of Oceanography. July 12, 2005.
Docket EPA-HQ-OAR-2010-0799.
Dickson, A. G. 2009. Certificate of Analysis - Reference material for oceanic CO2 measurements (Batch #100,
bottled on November 13, 2009). Certified by Andrew Dickson, Scripps Institution of Oceanography. February 10,
2010. Docket EPA-HQ-OAR-2010-0799.
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7 Other Economic and Social Impacts
This Chapter presents a summary of the total costs and benefits of EPA's final GHG
standards.
For several reasons, the estimates for costs and benefits presented by NHTSA and
EPA, while consistent, are not directly comparable, and thus should not be expected to be
identical. Most important, NHTSA and EPA's standards will require different fuel efficiency
improvements. EPA's final GHG standard is more stringent in part reflecting our projections
regarding manufacturers' use of air conditioning leakage credits, which result from reductions
in air conditioning-related emissions of HFCs. NHTSA is finalizing standards at levels of
stringency that assume improvements in the efficiency of air conditioning systems, but that do
not account for reductions in HFCs, which are not related to fuel economy or energy
conservation. In addition, the CAFE and GHG standards offer somewhat different program
flexibilities and provisions, and the agencies' analyses differ in their accounting for these
flexibilities (examples include the treatment of EVs, dual-fueled vehicles, and transfer of
credits between car and truck fleets), primarily because NHTSA is statutorily prohibited from
considering some flexibilities when establishing CAFE standards,vvvvvvv while EPA is not
limited in establishing standards under the Clean Air Act. Also, manufacturers may opt to
pay a civil penalty in lieu of actually meeting CAFE standards, but they cannot pay civil
penalties to avoid complying with EPA's GHG standards. Some manufacturers have
traditionally paid CAFE penalties instead of complying with the CAFE standards. These
differences contribute to differences in the agencies' respective estimates of costs and benefits
resulting from the new standards. Nevertheless, it is important to note that NHTSA and EPA
have reasonably harmonized the programs, and the continuation of the National Program will
result in significant cost and other advantages for the automobile industry by allowing them to
manufacture one fleet of vehicles across the U.S., rather than comply with potentially multiple
state standards that may occur in the absence of the National Program. We also note that this
summary of costs and benefits of EPA's GHG standards does not change the fact that both the
CAFE and GHG standards, jointly, will be the source of the benefits and costs of the National
Program. These costs and benefits are appropriately analyzed separately by each agency and
should not be added together.
For the reader's reference, Table 7.1-1 below summarizes the values of a number of
joint economic and other values that the agencies used to estimate the overall costs and
benefits associated with each agency's standard. Note, however, that the values presented in
this table are summaries of the inputs used for the agencies' respective models. See Joint
TSD Chapter 4 for expanded discussion and details on each of these joint economic and other
values.
This Chapter also includes an expanded description of the agency's approach to the
monetization of GHG emission reductions and benefits from less frequent refueling. Though
the underlying monetary unit values for COi reductions are consistent with those used in
vwwwSee49U.S.C.32902(h).
7-1
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Chapter 7
NHTSA's analysis of the final CAFE standards, the specific stream of COi-related benefits
are unique to each program and EPA's benefits are therefore presented in section 7.1. While
EPA's methodology for estimating benefits due to reduced refueling time are similar to
NHTSA's, the agencies' assumptions for fuel tank sizing are unique, as described in section
7.2, to ensure internal consistency in the respective technology penetration models.
Table 7.1-1 Joint Economic and other Values for Benefits Computations (2010$)
VMT Rebound Effect
"Gap" between test and on-road MPG for liquid-
fueled vehicles
"Gap" between test and on-road wall electricity
consumption for electric and plug-in hybrid
electric vehicles
Annual growth in average vehicle use
Fuel Prices (2017-50 average, $/gallon)
Retail gasoline price
Pre-tax gasoline price
Economic Benefits from Reducing Oil Imports
($/gallon)
"Monopsony" Component
Macroeconomic Disruption Component
Military/SPR Component
Total Economic Costs ($/gallon)
Emission Damage Costs (2020, $/short ton, 3%
discount rate)
Carbon monoxide
Nitrogen oxides (NOx) - vehicle use
Nitrogen oxides (NOx) - fuel production and
distribution
Particulate matter (PM2.5) - vehicle use
Particulate matter (PlVb.s) - fuel production and
distribution
Sulfur dioxide (SO2)
Annual CC>2 Damage Cost (per metric ton)
External Costs from Additional Automobile Use
($/vehicle-mile)
Congestion
Accidents
Noise
Total External Costs
External Costs from Additional Light Truck Use
10%
20%
30%
0.6%
$4.13
$3.78
$0.00
$0.197 in 2025
$0.00
$0.197 in 2025
$0
$ 5,600
$ 5,400
$310,000
$ 250,000
$ 33,000
Variable, depending
on discount rate and
year (see RIA Chapter
7.1 below)
$ 0.056
$ 0.024
$0.001
$0.081
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MY 2017 and Later - Regulatory Impact Analysis
($/vehicle-mile)
Congestion
Accidents
Noise
Total External Costs
Discount Rates Applied to Future Benefits
$0.050
$0.027
$0.001
$ 0.078
3%, 7%
7.1 Monetized GHG Estimates
We assigned a dollar value to reductions in carbon dioxide (COi) emissions using
recent estimates of the "social cost of carbon" (SCC) in the primary benefits analysis for this
rule. The SCC is an estimate of the monetized damages associated with an incremental
increase in carbon emissions in a given year. It is intended to include (but is not limited to)
changes in net agricultural productivity, human health, property damages from increased
flood risk, and the value of ecosystem services due to climate change. The SCC estimates
used in this analysis were developed through an interagency process that included EPA,
DOT/NHTSA, and other executive branch entities, and concluded in February 2010. The
SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods
used to develop these SCC estimates.479
The interagency group selected four SCC values for use in regulatory analyses,
which we have applied in this analysis: $5, $22, $37, and $68 per metric ton of CC>2
emissions******* in the year 2010, and in 2010 dollars. The first three values are based on
the average SCC from three integrated assessment models, at discount rates of 5, 3, and 2.5
percent, respectively. SCCs at several discount rates are included 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. The fourth
value is the 95th percentile of the SCC 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 SCC distribution. Low probability, high impact events are incorporated into all of
the SCC values through explicit consideration of their effects in two of the three models as
well as the use of a probability density function for equilibrium climate sensitivity in all three
models. Treating climate sensitivity probabilistically allows the estimation of SCC at higher
temperature outcomes, which lead to higher projections of damages.
The SCC increases over time because future emissions are expected to produce larger
incremental damages as physical and economic systems become more stressed in response to
greater climatic change. Note that the interagency group estimated the growth rate of the
wwwwwww The scc estimates were converted from 2008 dollars to 2010 dollars using a GDP price deflator
(1.02). (EPA originally updated the interagency SCC estimates from 2007 to 2008 dollars in the 2012-2016
light-duty GHG rulemaking using a GDP price deflator of 1.021). All price deflators were obtained from the
Bureau of Economic Analysis, National Income and Product Accounts Table 1.1.4, Price Indexes for Gross
Domestic Product.
7-3
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Chapter 7
SCC directly using the three integrated assessment models rather than assuming a constant
annual growth rate. This helps to ensure that the estimates are internally consistent with other
modeling assumptions. Table 7.1-2 presents the SCC estimates used in this analysis.
When attempting to assess the incremental economic impacts of carbon dioxide
emissions, the analyst faces a number of serious challenges. A recent 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.480 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 interagency group noted a number of limitations to the SCC 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. The limited amount of research linking climate impacts to economic damages
makes the interagency modeling exercise even more difficult.
Commenters generally expressed support for using SCC to value reductions in COi
emissions, while also discussing its limitations and offering recommendations directed at
improving estimates. One commenter, though, disagreed with the use of SCC. However, as
discussed in III.H.6 and IV.X of the preamble, the SCC estimates were developed using a
defensible set of input assumptions that are grounded in the existing literature. As noted in
the SCC TSD, the U.S. government intends to revise these estimates over time, taking into
account new research findings that were not available in 2010. See the preamble (III.H.6) and
EPA's Response to Comments document (section 18.4.1) for a summary of the public
comments and EPA's detailed response.
Applying the global SCC estimates, shown in Table 7.1-2, to the estimated reductions in CO2
emissions under the final standards, we estimate the dollar value of the COi-related benefits
for each analysis year in our primary benefits analysis. For internal consistency, the annual
benefits are discounted back to net present value terms using the same discount rate as each
SCC estimate (i.e. 5%, 3%, and 2.5%) rather than 3% and v^c.^00000^ The SCC estimates
and the associated CCh benefit estimates for each calendar year are shown in Tables 7.1-3.
xxxxxxx jt js pOSSjbie (hat other benefits or costs of this rule unrelated to CO2 emissions will be discounted at
rates that differ from those used to develop the SCC estimates.
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MY 2017 and Later - Regulatory Impact Analysis
Table 7.1-2 Social Cost of CO2 2017-20503 (2010 dollars)
YEAR
2017
2020
2025
2030
2035
2040
2045
2050
DISCOUNT RATE AND STATISTIC
5% AVERAGE
$6
$7
$9
$10
$12
$13
$15
$16
3% AVERAGE
$26
$27
$31
$34
$37
$41
$44
$47
2.5% AVERAGE
$41
$43
$48
$52
$56
$61
$64
$68
3%95m
PERCENTILE
$79
$84
$94
$104
$114
$124
$133
$142
aThe SCC values are dollar-year and emissions-year specific.
Table 7.1-3 Undiscounted Annual Upstream and Downstream COi Benefits for the
Given SCC Value, and CO2 Benefits Discounted back to 2012, Calendar Year Analysis3
(Millions of 2010 dollars)
YEAR
2017
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
5%
(AVERAGE SCC =
$6 IN 2017)
$13.6
$44.3
$93.3
$164
$273
$419
$600
$819
$1,080
$1,350
$1,620
$1,910
$2,200
$2,500
$2,810
$3,110
$3,420
$3,720
$4,030
$4,330
$4,630
$4,930
$5,220
$5,510
$5,810
$6,100
$6,390
3%
(AVERAGE SCC =
$26 IN 2017)
$54.6
$176
$365
$633
$1,040
$1,560
$2,200
$2,960
$3,840
$4,740
$5,640
$6,560
$7,480
$8,410
$9,340
$10,200
$11,100
$12,000
$12,900
$13,800
$14,600
$15,400
$16,200
$17,000
$17,800
$18,500
$19,200
2.5%
(AVERAGE SCC =
$41 IN 2017)
$87.3
$280
$581
$1,000
$1,640
$2,460
$3,450
$4,620
$5,970
$7,330
$8,710
$10,100
$11,500
$12,900
$14,200
$15,600
$16,900
$18,200
$19,400
$20,700
$21,900
$23,100
$24,200
$25,400
$26,400
$27,400
$28,400
3%
(95™ PERCENTILE =
$79 IN 2017)
$167
$538
$1,120
$1,940
$3,180
$4,790
$6,750
$9,070
$11,800
$14,500
$17,300
$20,100
$22,900
$25,700
$28,500
$31,300
$34,000
$36,700
$39,300
$41,900
$44,500
$47,000
$49,400
$51,800
$54,100
$56,300
$58,500
7-5
-------
Chapter 7
2044
2045
2046
2047
2048
2049
2050
NPVb
$6,690
$6,980
$7,280
$7,590
$7,900
$8,220
$8,540
$32,400
$20,000
$20,700
$21,400
$22,200
$22,900
$23,700
$24,400
$170,000
$29,400
$30,300
$31,300
$32,300
$33,300
$34,300
$35,400
$290,000
$60,700
$62,800
$65,000
$67,300
$69,500
$71,800
$74,100
$519,000
aExcept for the last row (net present value), the SCC values are dollar-year and emissions-year specific.
b Net present value of reduced GHG 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, 2.5 percent) is used to calculate net
present value of SCC for internal consistency. Refer to SCC TSD for more detail.
One limitation relevant to the primary benefits analysis is that it does not include the
valuation of non-CCh GHG impacts (i.e., CH4, N2O, and HFCs). The interagency group did
not directly estimate the social costs of non-CC>2 GHG emissions when it developed the
current social cost of CO2 values. Moreover, the group determined that it would not transform
the COi estimates into estimates for non-COi GHGs using global warming potentials
(GWPs), which measure the ability of different gases to trap heat in the atmosphere (i.e.,
radiative forcing per unit of mass) over a particular timeframe relative to COi- Recognizing
that non-COi GHG impacts associated with this rulemaking (net reductions in CH4, NiO, and
HFCs) would provide economic benefits to society, however, EPA requested comment on a
methodology to value such impacts. Several commenters strongly recommended that EPA
value non-COi GHG impacts associated with this final rule. See the preamble (III.H.6) and
EPA's Response to Comments document (section 18.4.1) for a summary of the public
comments and EPA's detailed response.
One way to approximate the value of marginal non-COi GHG emission reductions in
the absence of direct model estimates is to convert the reductions to CO2-equivalents which
may then be valued using the SCC. Conversion to CO2-e is typically done using the GWP for
the non-CO2 gas; we refer to this method as the "GWP approach." The GWP is an aggregate
measure that approximates the additional energy trapped in the atmosphere over a given
timeframe from a perturbation of a non-CO2 gas relative to CO2. The time horizon most
commonly used is 100 years. One potential problem with utilizing temporally aggregated
statistics, such as the GWPs, is that the additional radiative forcing from the GHG
perturbation is not constant over time and any differences in temporal dynamics between
gases will be lost.
While the GWP approach provides an approximation of the monetized value of the
non-CO2 GHG reductions anticipated from this rule, it produces estimates that are less
accurate than those obtained from direct model computations for a variety of reasons,
including the differences in atmospheric lifetime of non-CO2 gases relative to CO2. This is a
potentially confounding issue given that the social cost of GHGs is based on a discounted
stream of damages—i.e., they are not constant over time—and that are non-linear in
temperature. For example, CH4 has an expected adjusted atmospheric lifetime of about 12
years and associated GWP of 25 (IPCC Fourth Assessment Report (AR4) 100-year GWP
estimate). Gases with a relatively shorter lifetime, such as methane, have impacts that occur
primarily in the near term and thus are not discounted as heavily as those caused by longer-
7-6
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MY 2017 and Later - Regulatory Impact Analysis
lived gases, such as CO2, while the GWP treats additional forcing the same independent of
when it occurs in time. Furthermore, the baseline temperature change is lower in the near
term and therefore the additional warming from relatively short-lived gases will have a lower
marginal impact relative to longer-lived gases that have an impact further out in the future
when baseline warming is higher. The GWP also relies on an arbitrary time horizon and
constant concentration scenario, both of which are inconsistent with the assumptions used by
the SCC interagency workgroup. Finally, impacts other than temperature change also vary
across gases in ways that are not captured by GWP. For instance, CC>2 emissions, unlike CH4,
N2O, or HFCs, will result in CO2 passive fertilization to plants.
A limited number of studies in the published literature explore the differences in the
social benefit estimates from the GWP approach and direct modeling. One recent working
paper (Marten and Newbold, 2011) found that the GWP-weighted benefit estimates for CH4
and N2O are likely to be lower than those that would be derived using a directly modeled
social cost of these gases for a variety of reasons.481 This conclusion is reached using the 100
year GWP coefficients as put forth in the IPCC Fourth Assessment Report (CH4 is 25, N2O is
298). The GWP reflects only the integrated radiative forcing of a gas over 100 years. In
contrast, the directly modeled social cost differs from the GWP because the differences in
timing of the warming between gases are explicitly modeled, the non-linear effects of
temperature change on economic damages are included, and rather than treating all impacts
over a hundred years equally, the modeled social cost applies a discount rate but calculates
impacts through the year 2300.
EPA also undertook a literature search for estimates of the marginal social cost of non-
CO2 GHGs. A range of these estimates are available in published literature (Fankhauser
(1994)482, Kandlikar (1995)483, Hammitt et al. (1996), Tol et al. (2003)485, Tol (2004)486,
Hope (2005)487 and Hope and Newbery (2008)488. Most of these estimates are based upon
modeling assumptions that are dated and inconsistent with the current SCC estimates. Some
of these studies focused on, for example, marginal methane reductions in the 1990s and early
2000s and report estimates for only the single year of interest specific to the study. The
assumptions underlying the social cost of non-CO2 GHG estimates available in the literature
differ from those agreed upon by the SCC interagency group and in many cases use older
versions of the integrated assessment models. Without additional analysis, the non-CO2 GHG
benefit estimates available in the current literature are not acceptable to use to value the non-
CO2 GHG reductions finalized in this rulemaking.
In the absence of direct model estimates from the interagency analysis, EPA has
conducted a sensitivity analysis using the GWP approach to estimate the benefits associated
with reductions of three non-CO2 GHGs in each calendar year. These estimates are presented
for illustrative purposes and therefore not included in the total benefits estimate for the
rulemaking. EPA recently used this approach to estimate the CH4 benefits in the New Source
Performance Standards final rule for oil and gas exploration (77 FR at 49535) and views the
GWP approach as an interim method for analysis until we develop values for non-CO2
7-7
-------
Chapter 7
GHGs.YYYYYYY Estimates for this ralemaking are given below for illustrative purposes and
represent the COi-e estimate of CH4, NiO, and HFC reductions multiplied by the SCC
estimates. CO2-e is calculated using the AR4 100-year GWP of each gas: CH4 (25), N2O
(298), and HFC-134a (l,430).zz z The total net present value of the annual 2017 through
2050 GHG benefits for this rulemaking would increase by about $3 billion to $50 billion,
depending on discount rate, or roughly 10 percent if these non-COi estimates were included.
Given the magnitude of this increase in the context of the total costs and benefits considered
in this rule and other critical decision factors related to technical issues, inclusion of these
estimates in the primary analysis would not affect any of the decisions regarding the
appropriateness of the standards EPA is adopting here. The estimates are provided in the
tables below.
Table 7.1-4 Undiscounted Annual Upstream and Downstream Non-COi GHG Benefits
for the Given SCC Value, and Non-CO2 GHG Benefits Discounted back to 2012,
Calendar Year Analysis3 (Millions of 2010 dollars)
YEAR
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
EMISSION REDUCTIONS (MMT CO2-E)
CH4
0.0
0.2
0.3
0.5
0.9
1.3
1.8
2.3
3.0
3.5
4.1
4.7
5.2
5.8
N2O
0.00
0.00
0.01
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
HFC-134A
0.2
0.9
2.0
3.4
5.1
6.8
8.4
10.0
11.6
13.1
14.6
16.0
17.4
18.7
TOTAL NON-CO2 GHG BENEFITS ($ MILLIONS)
5% (Ave)
$2
$7
$16
$28
$44
$62
$82
$103
$126
$150
$174
$199
$224
$250
3% (Ave)
$7
$29
$62
$107
$168
$233
$301
$374
$450
$527
$604
$682
$760
$838
2.5%
(Ave)
$12
$46
$99
$170
$265
$366
$472
$583
$699
$816
$933
$1,050
$1,170
$1,280
3% (95th)
$22
$88
$191
$330
$514
$713
$923
$1,140
$1,380
$1,610
$1,850
$2,090
$2,320
$2,560
YYYYYYY ^&& http://www.epa.gov/airqualitv/oilandgas/actions.html for details about the final oil and gas NSPS
rule.
zzzzzzz As in the My 2012-2016 LD rules and in the MY 2014-2018 MD and HD rule, the global warming
potentials (GWP) used in this rulemaking are consistent with the 100-year time frame values in the 2007
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At this time, the 100-
year GWP values from the 1995 IPCC Second Assessment Report (SAR) are used in the official U.S. GHG
inventory submission to the United Nations Framework Convention on Climate Change (UNFCCC) (per the
reporting requirements under that international convention). The UNFCCC recently agreed on revisions to the
national GHG inventory reporting requirements, and will begin using the 100-year GWP values from AR4 for
inventory submissions in the future. According to the AR4, CH4 has a 100-year GWP of 25, N2O has a 100-year
GWP of 298, and HFC-134a has a 100-year GWP of 1430.
7-8
-------
MY 2017 and Later - Regulatory Impact Analysis
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
NPVb
6.3
6.7
7.2
7.6
8.0
8.4
8.7
9.1
9.4
9.7
9.9
10.2
10.5
10.7
10.9
11.2
11.4
11.6
11.9
12.1
0.12
0.13
0.14
0.15
0.15
0.16
0.16
0.17
0.17
0.18
0.18
0.19
0.19
0.19
0.20
0.20
0.21
0.21
0.21
0.22
19.9
21.1
22.2
23.2
24.2
25.1
25.9
26.7
27.4
28.2
28.8
29.5
30.1
30.8
31.4
32.0
32.6
33.3
33.9
34.6
$275
$301
$327
$353
$378
$403
$429
$453
$478
$503
$527
$552
$578
$603
$629
$655
$682
$710
$738
$767
$3,120
$916
$992
$1,070
$1,140
$1,210
$1,280
$1,350
$1,420
$1,490
$1,550
$1,610
$1,680
$1,740
$1,800
$1,860
$1,930
$1,990
$2,060
$2,120
$2,190
$16,300
$1,400
$1,510
$1,620
$1,720
$1,830
$1,930
$2,030
$2,120
$2,220
$2,310
$2,400
$2,480
$2,560
$2,650
$2,730
$2,820
$2,910
$2,990
$3,080
$3,170
$27,700
$2,800
$3,030
$3,250
$3,480
$3,690
$3,910
$4,120
$4,320
$4,520
$4,720
$4,910
$5,090
$5,280
$5,470
$5,660
$5,850
$6,050
$6,240
$6,450
$6,650
$49,600
aExcept for the last row (net present value), the SCC values are dollar-year and emissions-year specific.
b Net present value of reduced GHG 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, 2.5 percent) is used to calculate
net present value of SCC for internal consistency. Refer to SCC TSD for more detail.
In addition to the primary benefits analysis of COi impacts in each calendar year, we
conducted a separate analysis of the CC>2 benefits over the model year lifetimes of the 2017
through 2025 model year vehicles. In contrast to the calendar year analysis, the model year
lifetime analysis shows the impacts of the final standards on each of these MY fleets over the
course of its lifetime. Full details of the inputs to this analysis can be found in Chapter 4 of
this RIA. The CC>2 benefits of the full life of each of the nine model years from 2017 through
2025 are shown in Table 7.1-4 through Table 7.1-7 for each of the four different SCC values.
The CC>2 benefits are shown for each year in the model year life and in net present value. The
same discount rate used to discount the value of damages from future emissions (SCC at 5, 3,
2.5 percent) is used to calculate net present value of SCC for internal consistency.
Table 7.1-5 Undiscounted Annual Upstream and Downstream COi Benefits for the 5%
(Average SCC) Value, COi Benefits Discounted back to the 1st Year of each MY, and
Sum of Values Across MYs, Model Year Analysis3 (Millions of 2010 dollars)
YEAR
2017
2018
2019
2020
2021
2022
2023
2024
MY
2017
$14
$14
$14
$13
$14
$14
$14
$13
MY
2018
$0
$31
$31
$31
$31
$31
$31
$31
MY
2019
$0
$0
$49
$49
$49
$49
$50
$50
MY
2020
$0
$0
$0
$71
$71
$72
$71
$72
MY
2021
$0
$0
$0
$0
$109
$108
$109
$109
MY
2022
$0
$0
$0
$0
$0
$145
$144
$145
MY
2023
$0
$0
$0
$0
$0
$0
$181
$180
MY
2024
$0
$0
$0
$0
$0
$0
$0
$220
MY
2025
$0
$0
$0
$0
$0
$0
$0
$0
SUM
7-9
-------
Chapter 7
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
NPV,
5%
$12
$12
$12
$11
$11
$10
$9
$8
$7
$6
$5
$4
$3
$3
$2
$2
$1
$1
$1
$1
$1
$1
$0
$0
$0
$0
$152
$28
$28
$27
$26
$25
$23
$22
$20
$18
$16
$14
$12
$9
$8
$6
$5
$4
$3
$3
$3
$2
$2
$2
$1
$1
$1
$344
$49
$46
$45
$44
$42
$40
$37
$35
$32
$29
$25
$22
$18
$15
$12
$10
$8
$7
$6
$5
$5
$4
$3
$3
$2
$2
$551
$72
$71
$66
$64
$63
$60
$57
$53
$49
$45
$41
$36
$31
$26
$22
$17
$14
$12
$10
$8
$7
$7
$6
$5
$5
$3
$794
$109
$109
$106
$100
$98
$94
$90
$85
$80
$74
$68
$61
$54
$47
$40
$33
$27
$22
$18
$15
$13
$12
$11
$10
$8
$8
$1,210
$144
$144
$144
$140
$131
$128
$124
$118
$111
$104
$96
$89
$80
$71
$62
$52
$44
$35
$29
$24
$20
$17
$16
$14
$13
$11
$1,590
$180
$179
$179
$179
$174
$162
$159
$153
$145
$137
$128
$119
$109
$99
$87
$76
$64
$54
$44
$36
$30
$25
$22
$19
$18
$16
$1,970
$219
$219
$217
$217
$217
$210
$196
$192
$185
$175
$165
$154
$142
$131
$118
$105
$91
$77
$64
$53
$43
$36
$30
$26
$23
$21
$2,380
$262
$260
$260
$258
$257
$256
$248
$232
$226
$218
$206
$194
$182
$168
$154
$139
$123
$107
$90
$75
$62
$50
$42
$35
$30
$27
$2,820
$11,800
aThe SCC values are dollar-year and emissions-year specific. The full vehicle lifetimes for vehicles extend
beyond 2050, see TSD Chapter 4 for details. As a result, annual data extend beyond calendar year 2050 (i.e.,
estimates go to year 2053 for the 2017MY and to 2061 for the 2025MY). These data are not shown but are
included in the NPV values. In the absence of SCC estimates for years beyond 2050, EPA has used the SCC for
year 2050 to calculate CO2 benefits in years 2051 through 2061. As discussed above, the SCC increases over
time, meaning that the year 2050 SCC value is lower than the directly modeled estimates of SCC for years after
2050.
7-10
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MY 2017 and Later - Regulatory Impact Analysis
Table 7.1-6 Undiscounted Annual Upstream and Downstream COi Benefits for the 3%
(Average SCC) SCC Value, CO2 Benefits Discounted back tothe 1st Year of each MY,
and Sum of Values Across MYs, Model Year Analysis3 (Millions of 2010 dollars)
YEAR
2017
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
NPV,
3%
MY
2017
$55
$54
$53
$52
$52
$51
$50
$45
$44
$43
$40
$38
$36
$33
$30
$27
$23
$20
$16
$13
$10
$8
$6
$5
$4
$4
$3
$3
$2
$2
$1
$1
$1
$1
$642
MY
2018
$0
$122
$119
$118
$116
$115
$114
$111
$101
$99
$95
$90
$84
$78
$72
$66
$59
$52
$44
$37
$30
$24
$19
$16
$13
$11
$9
$8
$7
$6
$6
$3
$3
$3
$1,440
MY
2019
$0
$0
$193
$188
$187
$184
$182
$181
$175
$160
$156
$149
$141
$133
$123
$113
$103
$93
$81
$70
$58
$47
$38
$30
$25
$20
$17
$15
$13
$12
$10
$9
$6
$5
$2,270
MY
2020
$0
$0
$0
$274
$269
$267
$262
$260
$257
$248
$228
$221
$212
$200
$188
$175
$161
$147
$132
$115
$99
$82
$67
$54
$43
$35
$29
$24
$21
$19
$17
$15
$13
$8
$3,230
MY
2021
$0
$0
$0
$0
$413
$405
$401
$394
$390
$385
$370
$342
$331
$317
$298
$279
$260
$238
$217
$195
$172
$148
$124
$102
$83
$67
$55
$46
$39
$34
$32
$28
$24
$22
$4,850
MY
2022
$0
$0
$0
$0
$0
$541
$530
$525
$514
$509
$502
$482
$445
$431
$412
$388
$363
$337
$309
$282
$253
$223
$192
$161
$133
$108
$87
$72
$60
$51
$45
$42
$36
$32
$6,330
MY
2023
$0
$0
$0
$0
$0
$0
$665
$650
$643
$631
$623
$615
$590
$545
$528
$504
$474
$443
$411
$377
$343
$308
$272
$234
$196
$162
$131
$106
$89
$73
$63
$56
$51
$45
$7,740
MY
2024
$0
$0
$0
$0
$0
$0
$0
$798
$780
$771
$755
$746
$736
$705
$652
$631
$602
$566
$529
$491
$449
$409
$367
$324
$279
$233
$193
$157
$127
$106
$88
$75
$67
$61
$9,260
MY
2025
$0
$0
$0
$0
$0
$0
$0
$0
$936
$915
$904
$885
$874
$862
$825
$762
$738
$703
$661
$618
$573
$524
$478
$428
$377
$324
$272
$225
$182
$148
$123
$102
$88
$78
$10,800
SUM
$46,600
aThe SCC values are dollar-year and emissions-year specific. The full vehicle lifetimes for vehicles extend
beyond 2050, see TSD 4 for details. As a result, annual data extend beyond calendar year 2050 (i.e., estimates
go to year 2053 for the 2017MY and to 2061 for the 2025MY). These data are not shown but are included in the
NPV values. In the absence of SCC estimates for years beyond 2050, EPA has used the SCC for year 2050 to
calculate CO2 benefits in years 2051 through 2061. As discussed above, the SCC increases over time, meaning
that the year 2050 SCC value is lower than the directly modeled estimates of SCC for years after 2050.
7-11
-------
Chapter 7
Table 7.1-7 Undiscounted Annual Upstream and Downstream COi Benefits for the from
2.5% (Average SCC) SCC Value, CO2 Benefits Discounted back tothe 1st Year of each
MY, and Sum of Values Across MYs, Model Year Analysis3 (Millions of 2010 dollars)
YEAR
2017
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
NPV,
2.5%
MY
2017
$87
$85
$84
$82
$81
$80
$78
$71
$69
$66
$62
$59
$55
$50
$46
$41
$36
$30
$25
$20
$15
$12
$10
$8
$6
$5
$5
$4
$3
$3
$1
$1
$1
$1
$1,040
MY
2018
$0
$195
$190
$187
$183
$181
$179
$173
$157
$152
$146
$138
$129
$120
$110
$100
$89
$78
$67
$55
$45
$36
$28
$23
$19
$16
$14
$12
$11
$9
$8
$5
$4
$4
$2,320
MY
2019
$0
$0
$307
$299
$296
$289
$286
$282
$271
$248
$240
$230
$216
$203
$188
$172
$157
$140
$123
$105
$87
$71
$57
$45
$37
$30
$25
$22
$20
$17
$15
$13
$8
$8
$3,660
MY
2020
$0
$0
$0
$434
$425
$420
$411
$405
$399
$385
$352
$340
$325
$306
$287
$266
$244
$222
$198
$173
$148
$123
$100
$80
$64
$52
$43
$36
$31
$28
$24
$21
$19
$12
$5,190
MY
2021
$0
$0
$0
$0
$653
$637
$629
$614
$606
$596
$571
$525
$508
$484
$454
$425
$393
$360
$327
$293
$257
$221
$185
$152
$123
$99
$82
$67
$57
$50
$46
$40
$35
$32
$7,760
MY
2022
$0
$0
$0
$0
$0
$851
$829
$818
$799
$788
$775
$741
$683
$659
$628
$589
$550
$510
$466
$424
$379
$333
$286
$240
$197
$159
$128
$106
$88
$75
$66
$60
$53
$47
$10,100
MY
2023
$0
$0
$0
$0
$0
$0
$1,040
$1,010
$1,000
$976
$962
$946
$904
$833
$804
$765
$718
$670
$620
$567
$515
$461
$405
$349
$291
$240
$194
$156
$130
$107
$92
$81
$74
$65
$12,300
MY
2024
$0
$0
$0
$0
$0
$0
$0
$1,240
$1,210
$1,190
$1,170
$1,150
$1,130
$1,080
$993
$958
$912
$855
$798
$738
$674
$613
$548
$482
$414
$346
$285
$230
$186
$155
$128
$109
$97
$89
$14,700
MY
2025
$0
$0
$0
$0
$0
$0
$0
$0
$1,450
$1,420
$1,390
$1,360
$1,340
$1,320
$1,260
$1,160
$1,120
$1,060
$996
$929
$860
$785
$713
$638
$560
$480
$401
$331
$268
$216
$180
$149
$127
$113
$17,100
SUM
$74,100
aThe SCC values are dollar-year and emissions-year specific. The full vehicle lifetimes for vehicles extend
beyond 2050, see TSD 4 for details. As a result, annual data extend beyond calendar year 2050 (i.e., estimates
go to year 2053 for the 2017MY and to 2061 for the 2025MY). These data are not shown but are included in the
NPV values. In the absence of SCC estimates for years beyond 2050, EPA has used the SCC for year 2050 to
calculate CO2 benefits in years 2051 through 2061. As discussed above, the SCC increases over time, meaning
that the year 2050 SCC value is lower than the directly modeled estimates of SCC for years after 2050.
7-12
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 7.1-8 Undiscounted Annual Upstream and Downstream COi Benefits for the 3%
(95th Percentile) SCC Value, CO2 Benefits Discounted back tothe 1st Year of each MY,
and Sum of Values Across MYs, Model Year Analysis3 (Millions of 2010 dollars)
YEAR
2017
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
NPV,
3%
MY
2017
$167
$164
$163
$159
$158
$157
$153
$139
$135
$130
$124
$116
$109
$100
$91
$82
$71
$61
$50
$40
$31
$25
$20
$16
$13
$11
$10
$8
$7
$6
$3
$3
$2
$2
$1,970
MY
2018
$0
$374
$366
$363
$356
$353
$349
$339
$310
$301
$289
$273
$257
$239
$220
$201
$180
$158
$135
$112
$91
$73
$58
$47
$39
$32
$28
$25
$22
$19
$17
$10
$9
$8
$4,390
MY
2019
$0
$0
$592
$578
$575
$564
$559
$553
$535
$490
$476
$456
$431
$405
$377
$346
$316
$283
$248
$212
$176
$144
$115
$92
$75
$62
$52
$45
$41
$35
$31
$28
$17
$16
$6,950
MY
2020
$0
$0
$0
$841
$825
$819
$804
$795
$786
$760
$697
$676
$648
$612
$574
$534
$490
$447
$401
$352
$301
$250
$205
$164
$131
$107
$88
$74
$65
$58
$51
$45
$40
$25
$9,880
MY
2021
$0
$0
$0
$0
$1,270
$1,240
$1,230
$1,210
$1,190
$1,180
$1,130
$1,040
$1,010
$967
$910
$853
$792
$726
$661
$594
$522
$450
$376
$311
$251
$202
$168
$139
$118
$105
$96
$84
$74
$67
$14,800
MY
2022
$0
$0
$0
$0
$0
$1,660
$1,620
$1,610
$1,570
$1,560
$1,540
$1,470
$1,360
$1,320
$1,260
$1,180
$1,110
$1,030
$942
$858
$770
$678
$584
$490
$404
$327
$264
$220
$182
$155
$137
$126
$110
$97
$19,300
MY
2023
$0
$0
$0
$0
$0
$0
$2,040
$1,990
$1,970
$1,930
$1,910
$1,880
$1,800
$1,660
$1,610
$1,540
$1,450
$1,350
$1,250
$1,150
$1,050
$939
$827
$712
$597
$493
$399
$323
$269
$223
$191
$169
$155
$136
$23,600
MY
2024
$0
$0
$0
$0
$0
$0
$0
$2,440
$2,390
$2,360
$2,310
$2,280
$2,250
$2,150
$1,990
$1,920
$1,840
$1,720
$1,610
$1,500
$1,370
$1,250
$1,120
$985
$847
$710
$587
$476
$385
$321
$266
$228
$202
$186
$28,300
MY
2025
$0
$0
$0
$0
$0
$0
$0
$0
$2,860
$2,800
$2,760
$2,700
$2,670
$2,630
$2,520
$2,330
$2,250
$2,140
$2,010
$1,880
$1,750
$1,600
$1,450
$1,300
$1,150
$986
$826
$684
$554
$448
$374
$310
$266
$236
$33,000
SUM
$142,000
aThe SCC values are dollar-year and emissions-year specific. The full vehicle lifetimes for vehicles extend
beyond 2050, see TSD 4 for details. As a result, annual data extend beyond calendar year 2050 (i.e., estimates
go to year 2053 for the 2017MY and to 2061 for the 2025MY). These data are not shown but are included in the
NPV values. In the absence of SCC estimates for years beyond 2050, EPA has used the SCC for year 2050 to
calculate CO2 benefits in years 2051 through 2061. As discussed above, the SCC increases over time, meaning
that the year 2050 SCC value is lower than the directly modeled estimates of SCC for years after 2050.
7-13
-------
Chapter 7
7.2 The Benefits Due to Reduced Refueling Time
The total time spent pumping and paying for fuel, and driving to and from fueling
stations, represents an economic cost to drivers and other vehicle occupants. Increased
driving range provides a benefit to individuals arising from the value of the time saved when
refueling cycles are eliminated. As described in this section, the EPA calculates this benefit
by applying DOT-recommended values of travel time savings to estimates of how much time
is saved.
NHTSA submitted the refueling benefits section of Chapter 4 of the NPRM Joint TSD
to peer review. The three reviewers were generally supportive of the analysis methodology,
while one reviewer made several suggestions for potentially improving the quality of the
results. EPA believed that one of these suggestions, if implemented, would have the potential
to substantially influence the results. Therefore EPA conducted a supplemental analysis to
evaluate the feasibility of forecasting the range of future vehicles by performing a regression
on the historical data for fuel economy and tank size. Based on the results of this
supplemental analysis, which is described in this section, and considering recent trends in the
range and fuel tank size of newly released vehicles, EPA has judged that there is not sufficient
justification for modifying the NPRM methodology.
7.2.1 Relationship between tank size, fuel economy, and range
The increases in fuel economy resulting from this rule are expected to lead to some
increase in vehicle driving range. The extent of this increase depends on manufacturers'
decisions to apply reduced fuel consumption requirements towards increasing range, rather
than reducing tank size while maintaining range. In MY 2010, fuel tanks were sized such that
the average driving range was 537 miles for passenger cars and 511 miles for light trucks, as
shown in Figure 7.2-1 below. Nearly all MY 2010 vehicles have a driving range of at least
350 miles, and many vehicles, in particular cars with high fuel economy, have ranges much
greater than this.
40%-
30%'
20%'
io%-
40%'
30%'
20%'
mean = 537 miles
std dev = 93 miles
N = 731
Trucks
mean = 5'H miles
std dev= 57 miles
N = 433
<= 350
400 - 450
500 - 550
600 - 650
700 - 750
800 - 850
900+
350-400 450-500 550-600 650-700
Range (miles)
750-800 850-900
Figure 7.2-1 Distribution of driving range for MY 2010 vehicles (sales-weighted)
7-14
-------
MY 2017 and Later - Regulatory Impact Analysis
For the final rale, EPA investigated the relationship between range and fuel economy,
using data for MY 2010 vehicles summarized above. The goal of this analysis was to forecast
manufacturer decisions regarding tank size and range, given the fuel economy improvements
that will occur as a result of this rule. At vehicle redesign, manufacturers typically size fuel
tanks considering the available packaging space, driving range, cargo and passenger space
(utility), mass targets, and other factors. As fuel economy improves, manufacturers may opt
at the time of vehicle redesign to reduce tank size in order to achieve moderate mass reduction
at a small cost savings, while sacrificing some customer utility from the foregone
improvements in range.
EPA performed a regression of range vs. fuel economy using several strategies of
categorizing vehicles, including vehicle type (car or track), market class, and footprint. Of
these categorizing strategies, the analysis showed that a clear range vs. fuel economy
relationship is most evident when vehicles with similar footprint values are grouped. The
apparent relationship between vehicle footprint and manufacturer tank-sizing decisions is
consistent with the limitation imposed on manufacturers for fuel tank packaging, which
depends the under-floor space available. Fuel tanks are often designed by manufacturers to be
used across multiple vehicle configurations sharing a platform. EPA assumes that
manufacturers make tank-sizing decisions considering the least efficient vehicle on a shared
tank platform, since that vehicle configuration will have the lowest range. Therefore, only
these vehicles were included in the regression analysis, the results of which are presented in
Figure 7.2-2 and Table 7.2-1 below. Note that within each footprint category, the difference
between car and truck groups was not found to be statistically significant, so both vehicle
types were considered together.
7-15
-------
Chapter 7
to
a:
700-
600-
500-
400-
300'
Footprint 35 to 40 sq. ft.
95% Cl (dash lines)
700-
500-
CD
a:
400-
300'
Footprint40 to,45 sq. ft.,
.Q
O
95% Cl (dash lines)
10 20 30 40 50 60
Fuel Economy (mpg)
70
10 20 30 40 50 60
Fuel Economy (mpg)
70
en
CE
700-
600-
500-
400-
300
12.645 */. + 146,315
Footprint 45/to 50 sq. ft.
95% Cl (dash lines)
|E_
CD
CD
C
CD
CL
10 20 30 40 50 60
Fuel Economy (mpg)
70
700-
600-
500-
400-
300'
/ 13.440 yt + 180.397
Fpotprinf&O to 60 sq. ft.
95% Cl (dash line)
10 20 30 40 50 60
Fuel Economy (mpg)
70
CD
D)
CD
QL
700-
600-
500-
400-
300
24.075 */ +116.746
Footprint 60+ sq.ft.
10
20
70
30 40 50 60
Fuel Economy (mpg)
Figure 7.2-2 MY 2010 range vs. fuel economy, by footprint category
Table 7.2-1 Range vs. Fuel Economy Regression Coefficients
Footprint
Category
35-40
40-45
45-50
50-60
60+
Total
Fuel Economy (mpg)
(sales-weighted)
Average
39.2
36.4
22.8
28.7
17.0
29.6
Std. Dev.
3.1
11.0
5.4
3.6
3.0
9.8
Regression Coefficients
y = m * x + b
m
5.8
7.9
12.6
13.4
24.1
-
b
221.6
213.2
146.3
180.4
116.7
-
R2
0.57
0.76
0.76
0.51
0.40
p (F-test)
0.000
0.000
0.000
0.000
0.028
7-16
-------
MY 2017 and Later - Regulatory Impact Analysis
The proportion of an increase in fuel economy that is applied towards increasing range
can be expressed by the equation below. For MY 2010 vehicles, manufacture range and tank
sizing decisions were estimated using regression coefficients from Table 7.2-1, centered about
the sales-weighted average for each footprint category. The results, summarized in Table 7.2-
2 below, forecast that over the entire fleet of new vehicles, 65 percent of fuel economy
improvements will be applied towards increasing range.
Proportion of fuel economy increase used for range increase =
(range2 -
(fuel economy^ — fuel economy-^ /
/fuel economy^
Table 7.2-2 Proportion of Range to Fuel Economy Increases,
Based on Regression of MY 2010 Vehicles
Footprint Category
35-40
40-45
45-50
50-60
60+
Average (sales-weighted)
% Range Increase /
%Fuel Economy Increase
0.51
0.58
0.71
0.63
0.78
0.65
The method of forecasting manufacturing tank sizing and range decisions for future
vehicles based on historical data from MY 2010 has several limitations. First, many of the
MY 2010 vehicle platforms were designed years earlier. More recent evidence shows that
manufacturers are beginning to market vehicle range as an important vehicle attribute.
Second, performing a regression across vehicle platforms does not account for all the factors a
manufacturer considers when redesigning a vehicle. For example, maintaining the current
fuel tank size for a new platform designed by a particular manufacturer, which may be similar
in layout to the previous generation, is simplified since the under-floor packaging space is
already available.
The EPA investigated the most recently redesigned platforms for some of the highest
volume vehicles, and comparing the first model year of the previous generation vehicle,
calculated the proportion of fuel economy increase used to increase vehicle range. Changes in
fuel economy and range between generations for the least efficient vehicle configuration in
each platform are shown Figure 7.2-3 below. The results of this analysis are summarized in
Table 7.2-3. A value of one indicates that tank size was maintained between generations,
while values less than one and greater than one indicate tank size reductions, and increases,
respectively. Among these recently redesigned platforms, manufacturers have in some cases
reduced tank size (Toyota Camry, Ford Focus), while in other cases have maintained (Honda
Civic, Toyota Sienna), or even increased tank size (Jeep Grand Cherokee, Chevrolet Craze.)
7-17
-------
Chapter 7
-20%
40%
30%
20%
M
C
TO
OJ
l/»
ro
10%
l0200?
u
c
2011 Chevrolet Cruze
2011 Jeep Grand
Cherokee
2010 ChevroletEquinox
ChryslerTownand^l0A1Volkswagen Jetta
Country
-10%
iHondaCRV
• 2012 Ford Focus
2012 Volkswagen Passat
Oil Honda Odyssey"
J012 Toyota Camry
2008HondaAi
• 0
2010 Toyota Prius
007Cj/yslerSebring
0 2011 Dodge Durango
2012 Nissan Altima
2011 Ford Explorer
10%
2011 Toyota Sienna
20% 30%
Increase in Fuel Economy
40%
2009 Toyota C
Drolla
'2010 Ford Taurus
2012 Hyundai Elantra
2012 Honda Civic
-20%
Figure 7.2-3 Increases in range and fuel economy from previous generation for
recent platform redesigns (least efficient vehicle configurations only)
7-18
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 7.2-3 Proportion of Range to Fuel Economy Increases,
Based on Recent Platform Redesigns
Footprint Category
2008 Chrysler Town and Country
20 11 Ford Explorer
20 12 Ford Focus
2012 Toyota Camry
2012 Nissan Altima
2011 Dodge Durango
2011 Jeep Grand Cherokee
2011 Hyundai Sonata
2012 Honda CRV
2011 Chevrolet Cruze
201 1 Volkswagen Jetta
2012 Volkswagen Passat
20 10 Toyota Prius
20 11 Honda Odyssey
2010 Chevrolet Equinox
Average
%Range Increase /
%Fuel Economy Increase
1.40
0.29a
0.67
0.47
0.37
0.19"
4.49b
1.33
1.00
2.23
1.00
1.00
1.00
1.00
4.36
1.39
a 2011 Ford Explorer redesign shares platform with 2010 Ford Taurus
b:2011 Dodge Durango redesign shares platform with 2011 Jeep Grand Cherokee
Both the regression performed on MY 2010 vehicles, and the investigation of recent
platform changes show a clear correlation between increasing range and increasing fuel
economy. While the regression analysis indicates that range does not increase in the same
proportion as fuel economy increases, the recent evidence of within-manufacturer tank sizing
decisions indicates that in some cases, range increases are at least proportional, to fuel
economy increases. As a result of this conflicting evidence, and the lack of evidence
supporting a quantitative method of forecasting manufacturer decisions to reduce tank size,
EPA maintains the NPRM assumption of constant tank size for the final rule.
7.2.2 Calculation of benefits value
EPA calculates the economic value of those time savings by applying DOT-
recommended values of travel time savings to our estimates of how much time is saved.489
The value of travel time depends on average hourly valuations of personal and business time,
which are functions of total hourly compensation costs to employers. The total hourly
compensation cost to employers, inclusive of benefits, in 2010$ is $29.68.
AAAAAAAA
Table
7.2-4 demonstrates the EPA and NHTSA approach to estimating the value of travel time
($/hour) for both urban and rural (intercity) driving. This approach relies on the use of DOT-
AAAAAAAA Total hourly employer compensation costs for 2009 (average of quarterly observations). See
http://www.bls.gov/ect/. NHTSA previously used a value of $25.50 for the total hourly compensation cost (see,
e.g., 75 FR at 25588, fn. 619) during 2008 expressed in 2007$. This earlier figure is deprecated by the
availability of more current economic data.
7-19
-------
Chapter 7
recommended weights that assign a lesser valuation to personal travel time than to business
travel time, as well as weights that adjust for the distribution between personal and business
travel.
Table 7.2-4 Estimating the Value of Travel Time For Urban and Rural (Intercity)
Travel ($/hour)
Urban Travel
Wage Rate ($/hour)
DOT-Recommended Value of Travel Time
Savings, as % of Wage Rate
Hourly Valuation (=Wage Rate * DOT-
Recommended Value)
% of Total Urban Travel
Hourly Valuation (Adjusted for % of Total
Urban Travel)
Personal travel
$29.68
50%
$14.84
94.4%
$14.01
Business Travel
$29.68
100%
$29.68
5.6%
$1.66
Total
_
_
100%
15.67
Rural (Intercity) Travel
Wage Rate ($/hour)
DOT-Recommended Value of Travel Time
Savings, as % of Wage Rate
Hourly Valuation (=Wage Rate * DOT-
Recommended Value)
% of Total Rural Travel
Hourly Valuation (Adjusted for % of Total
Rural Travel)
Personal travel
$29.68
70%
$20.77
87.0%
$18.07
Business Travel
$29.68
100%
$29.68
13.0%
$3.86
Total
_
_
100%
21.93
The estimates of the hourly value of urban and rural travel time ($15.67 and $21.93,
respectively) shown in Table 7.2-4 must be adjusted to account for the nationwide ratio of
urban to rural driving. By applying this adjustment (as shown in Table 7.2-5), an overall
estimate of the hourly value of travel time - independent of urban or rural status - may be
produced. Note that up to this point, all calculations discussed assume only one adult
occupant per vehicle. To fully estimate the average value of vehicle travel time, the presence
of additional adult passengers during refueling trips must be accounted for. EPA applies such
an adjustment as shown in Table 7.2-5; this adjustment is performed separately for passenger
7-20
-------
MY 2017 and Later - Regulatory Impact Analysis
cars and for light tracks, yielding occupancy-adjusted valuations of vehicle travel time during
refueling trips for each fleet.
Table 7.2-5 Estimating the Value of Travel Time for Light-Duty Vehicles ($/hour)
Urban Travel
Rural Travel
Total
Average Vehicle Occupancy
During Refueling Trips
(persons)c^cccccc
Weighted Value of Travel
Time ($/hour)
Occupancy- Adjusted Value
of Vehicle Travel Time During
Refueling Trips ($/hour)
Unweighted
Value of Travel
Time ($/hour)
$15.67
$21.93
—
Passenger Cars
1.21
$17.73
$21.45
Weight (% of
Total Miles
Driven)BBBBBBBB
67.1%
32.9%
100.0%
Light Trucks
1.23
$17.73
$21.81
Weighted Value
of Travel Time
($/hour)
$10.51
$7.22
$17.73
EPA is using NHTSA's estimates of the amount of refueling time saved using
(preliminary) survey data gathered as part of the 2010-2011 National Automotive Sampling
System's Tire Pressure Monitoring System (TPMS) study.DDDDDDDD The relevant TPMS
survey data on average refueling trip characteristics are presented below in Table 7.2-6, and a
more complete description of the study is available in Chapter 4 of the NPRM Joint TSD.
Weights used for urban vs. rural travel are computed using cumulative 2011 estimates of urban vs. rural
miles driven provided by the Federal Highway Administration. Available at
http://www.fhwa.dot.gov/policvinformation/traveLmonitoring/tvt.cfm (last accessed 04/27/2012).
cccccccc Source. National Automotive Sampling System 2010-2011 Tire Pressure Monitoring System (TPMS)
study. See next page for further background on the TPMS study. TPMS data are preliminary at this time and
rates are subject to change pending availability of finalized TPMS data. Average occupancy rates shown here
are specific to refueling trips, and do not include children under 16 years of age.
DDDDDDDD TpMS fa^ ^g preliminary and not yet published. Estimates derived from TPMS data are therefore
preliminary and subject to change. Observational and interview data are from distinct subsamples, each
consisting of approximately 7,000 vehicles. For more information on the National Automotive Sampling System
and to access TPMS data when they are made available, see http://www.nhtsa. gov/NASS.
7-21
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Chapter 7
Table 7.2-6 Average Refueling Trip Characteristics for Passenger Cars and Light
Trucks
Passenger Cars
Light Trucks
Gallons of
Fuel
Purchased
9.8
13.0
Round-Trip
Distance
to/from
Fueling
Station
(miles)
0.97
1.08
Round-Trip
Time to/from
Fueling
Station
(minutes)
2.28
2.53
Time to
Fill and
Pay
(minutes)
4.10
4.30
Total
Time
(minutes)
6.38
6.83
As an illustration of how we estimate the value of extended refueling range, assume a
small light truck model has an average fuel tank size of approximately 20 gallons, and a
baseline actual on-road fuel economy of 24 mpg. TPMS survey data indicate that drivers who
indicated the primary reason for their refueling trips was a low reading on the gas gauge
typically refuel when their tanks are 35 percent full (i.e., 13.0 gallons as shown in Table 7.2-6,
with 7.0 gallons in reserve). By this measure, a typical driver would have an effective driving
range of 312 miles (= 13.0 gallons x 24 mpg) before he or she is likely to refuel. Increasing
this model's actual on-road fuel economy from 24 to 25 mpg would therefore extend its
effective driving range to 325 miles (= 13.0 gallons x 25 mpg). Assuming that the truck is
driven 12,000 miles/year,EEEEEEEE this 1 mpg improvement in actual on-road fuel economy
reduces the expected number of refueling trips per year from 38.5 (= 12,000 miles per year /
312 miles per refueling) to 36.9 (= 12,000 miles per year / 325 miles per refueling), or 1.6
refuelings per year. If a typical fueling cycle for a light truck requires a total of 6.83 minutes,
then the annual value of time saved due to that 1 mpg improvement would amount to $3.94 (=
(6.83/60) x $21.62 x 1.6).
In the analysis, we repeat this calculation for each future calendar year that light-duty
vehicles of each model year affected by the alternative standards considered in this rule would
remain in service. The resulting cumulative lifetime valuations of time savings account for
both the reduction over time in the number of vehicles of a given model year that remain in
service and the reduction in the number of miles (VMT) driven by those that stay in service.
We also adjust the value of time savings that will occur in future years both to account for
expected annual growth in real wages and to apply a discount rate to determine the net present
value of time saved.™1111111111 A final adjustment is made to account for evidence from the
EEEEEEEE §ource of annual vehicle mileage: U.S. Department of Transportation, Federal Highway Administration,
2009 National Household Travel Survey (NHTS). See http://nhts.ornl.gov/2009/pub/stt.pdf (table 22, p.48).
12,000 miles/year is an approximation of a light duty vehicle's annual mileage during its initial decade of use
(the period in which the bulk of benefits are realized).
FFFFFFFF ^ 1 ^ percent annual rate of growth in real wages is used to adjust the value of travel time per vehicle
($/hour) for future years for which a given model is expected to remain in service. This rate is supported by a
BLS analysis of growth in real wages from 2000 - 2009. See
http://www.bls.gov/opub/ted/2011/ted 20110224.htm.
7-22
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MY 2017 and Later - Regulatory Impact Analysis
TPMS study which suggests that 40 percent of refueling trips are for reasons other than a low
reading on the gas gauge. It is therefore assumed that only 60 percent of the theoretical
refueling time savings will be realized, as we assume that owners who refuel on a fixed
schedule will continue to do. The assumption that the 40 percent of refueling trips that occur
for reasons other than a low reading on the gas gauge will not realize a refueling time savings
may be a conservative assumption. Results are calculated separately for a given model year's
fleet of passenger cars and that year's fleet of light trucks. Valuations of both fleets' benefits
are then summed to determine the benefit across all light-duty vehicles.
Since a reduction in the expected number of annual refueling trips leads to a decrease
in miles driven to and from fueling stations, we can also calculate the value of consumers'
fuel savings associated with this decrease. As shown in Table 7.2-6, the typical incremental
round-trip mileage per refueling cycle is 1.08 miles for light trucks and 0.97 miles for
passenger cars. Going back to the earlier example of a light truck model, a decrease of 1.6 in
the number of refuelings per year leads to a reduction of 1.73 miles driven per year (= 1.6
refuelings x 1.08 miles driven per refueling). Again, if this model's actual on-road fuel
economy was 24 mpg, the reduction in miles driven yields an annual savings of
approximately 0.07 gallons of fuel (= 1.73 miles / 24 mpg), which at $3.77/gallonGGGGGGGG
results in a savings of $0.27 per year to the owner. Note that this example is illustrative only
of the approach used to quantify this benefit; in practice, the value of this benefit is modeled
using fuel price forecasts for each year the given fleet will remain in service, and unlike the
above example excludes fuel taxes from the computation of the total social benefit, as taxes
are transfer payments.
The annual savings to each consumer shown in the above example may seem like a
small amount, but the reader should recognize that the valuation of the cumulative lifetime
benefit of this savings to owners is determined separately for passenger car and light truck
fleets and then aggregated to show the net benefit across all light-duty vehicles - which is
much more significant at the macro level. Calculations of benefits realized in future years are
adjusted for expected real growth in the price of gasoline, for the decline in the number of
vehicles of a given model year that remain in service as they age, for the decrease in the
number of miles (VMT) driven by those that stay in service, and for the percentage of
refueling trips that occur for reasons other than a low reading on the gas gauge; a discount rate
is also applied in the valuation of future benefits. EPA considered using this direct estimation
approach to quantify the value of this benefit by model year, however the value of this benefit
is implicitly captured in the separate measure of overall valuation of fuel savings, and
therefore direct estimates of this benefit are not added to net benefits calculations.
The reduction in miles driven to and from fueling stations results in other benefits,
such as a reduction in greenhouse gas emissions - CCh in particular, reductions in evaporative
emissions from refuelings, and reduced wear on vehicles. However, estimates of the values of
GGGGGGGG Estimate of $3.77/gallon is in 2010$. This figure is an average of forecasted cost per gallon (including
taxes, as individual consumers consider reduced tax expenditures to be savings) for motor gasoline for years
2017 to 2027. Source of price forecasts: U.S. Energy Information Administration, Annual Energy Outlook Early
Release 2012.
7-23
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Chapter 7
these benefits indicate that both are extremely minor in the context of the overall valuation of
benefits associated with gains in vehicle driving range, so quantitative valuations of these
additional benefits are not included within this analysis.
7.3 Summary of Costs and Benefits of the MYs 2017-2025 Final Rule
In this section, EPA presents a summary of costs, benefits, and net benefits of the final
program. Table 7.3-1 shows the estimated annual monetized costs of the final program for the
indicated calendar years. The table also shows the net present values of those costs for the
calendar years 2012-2050 using both 3 percent and 7 percent discount rates.HHHHHHHH Table
7.3-2 shows the estimated annual monetized fuel savings of the final program. The table also
shows the net present values of those fuel savings for the same calendar years using both 3
percent and 7 percent discount rates. In this table, the aggregate value of fuel savings is
calculated using pre-tax fuel prices since savings in fuel taxes do not represent a reduction in
the value of economic resources utilized in producing and consuming fuel. Note that fuel
savings shown here result from reductions in fleet-wide fuel use. Thus, they grow over time
as an increasing fraction of the fleet meets the 2025 standards. Table 7.3-3 shows the annual
reductions in petroleum-based imports and the monetized energy security benefits of the final
program for the indicated calendar years. The table also shows the net present values of
monetized energy security benefits for the calendar years 2012-2050 using both 3 percent and
7 percent discount rates.
Table 7.3-1 Undiscounted Annual Costs & Costs of the Final Program Discounted Back
to 2012 at 3% and 7% Discount Rates (Millions, 2010$)a
Technology
Costs
Maintenance
Costs
Vehicle
Program
Costs
2017
$2,440
$37
$2,470
2020
$8,860
$330
$9,190
2030
$33,700
$2,260
$35,900
2040
$37,400
$3,630
$41,000
2050
$42,000
$4,540
$46,500
NPV, Years
2012-2050, 3%
Discount Rate
$521,000
$39,500
$561,000
NPV, Years
2012-2050, 7%
Discount Rate
$231,000
$15,600
$247,000
Note:
" Technology costs for separate light-duty vehicle segments can be found in Chapter 5 of this RIA. Annual costs
shown are undiscounted values.
HHHHHHHH-
For the estimation of the stream of costs and benefits, we assume that after implementation of the
proposed MY 2017-2025 standards, the 2025 standards apply to each year out to 2050.
7-24
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MY 2017 and Later - Regulatory Impact Analysis
Table 7.3-2 Undiscounted Annual Fuel Savings & Final Program Fuel Savings
Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)a
Fuel Savings
(pre-tax)
2017
$651
2020
$7,430
2030
$86,400
2040
$155,000
2050
$212,000
NPV, Years
2012-2050, 3%
Discount Rate
$1,600,000
NPV, Years
2012-2050, 7%
Discount Rate
$607,000
Note:
" Fuel savings for separate light-duty vehicle segments can be found in Chapter 5 of this RIA. Annual costs
shown are undiscounted values.
Table 7.3-3 Undiscounted Annual Energy Security Benefits, & Final Program Benefits
Discounted back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)a
Petroleum-
based
imports
reduced
(mmb)
Monetized
benefits
2017
4.5
$33
2020
48.6
$371
2030
520
$4,560
2040
880
$8,320
2050
1,103
$10,400
NPV, Years
2012-2050, 3%
Discount Rate
$84,500
NPV, Years
2012-2050, 7%
Discount Rate
$32,200
Note:
a When conducting its analysis, Oak Ridge National Laboratory (ORNL) estimated energy security premiums by
quantifying two components of the economic cost of importing petroleum into the U.S. (in addition to the
purchase price of petroleum itself): monopsony and macroeconomic disruption costs. For this rule, EPA worked
with ORNL to update the energy security premiums by incorporating the AEO 2012 Early Release oil price
forecasts and market trends. The components of ORNL's energy security premiums and their values are
discussed in detail in the Joint TSD Chapter 4.2.8. EPA did not include the monopsony cost component in our
cost-benefit analysis (see discussion in Section III.H.S.c). The ORNL analysis did not include military or SPR
costs nor did EPA quantify them for this rule (see discussion in Section III.H.S.e). Based upon the ORNL
analysis, EPA has developed estimates of energy security premiums (i.e., $/barrel of imported crude oil and
finished petroleum products) for 2020, 2025, 2030 and 2035. The method and estimated premiums are discussed
in detail in Chapter 4 of the Joint TSD along with our approach for estimating the reductions in petroleum-based
imports. The energy security benefit (macroeconomic disruption component only) is estimated to be
$8.26/barrel or about $0.197/gallon in 2025. EPA linearly interpolated the premium values for the years 2017
through 2035, using the 2015 and 2035 values as endpoints and the 2020, 2025, and 2030 values as midpoints.
Since ORNL uses AEO 2012 forecasts that end in 2035, EPA assumes that the post-2035 energy security
premium do not change through 2050. Annual costs shown are undiscounted values.
Table 7.3-4 presents estimated annual monetized benefits for the indicated calendar
years. The table also shows the net present values of those benefits for the calendar years
2012-2050 using both 3 percent and 7 percent discount rates. The table shows the benefits of
reduced CC>2 emissions—and consequently the annual quantified benefits (i.e., total
benefits)—for each of four SCC values estimated by the interagency working group. As
discussed above in section 7.1 of this RIA, there are some limitations to the SCC analysis,
including the incomplete way in which the integrated assessment models capture catastrophic
and non-catastrophic impacts, their incomplete treatment of adaptation and technological
7-25
-------
Chapter 7
change, uncertainty in the extrapolation of damages to high temperatures, and assumptions
regarding risk aversion.
In addition, these monetized CO2 benefits exclude the value of net reductions in non-
CC>2 GHG emissions (CH4, N2O, HFC) expected under this action. As discussed in Chapter
7.1 of this RIA, EPA applied the GWP approach to estimate the benefits associated with
reductions of CH4, N2O, HFC in each calendar year in a sensitivity analysis. In sum, the
sensitivity analysis suggests that the total net present value of the annual 2017 through 2050
GHG benefits for this rulemaking would increase by about $3 billion to $50 billion,
depending on discount rate, or roughly 10 percent if these non-CO2 estimates were included.
Given the magnitude of this increase in the context of the total costs and benefits considered
in this rule and other critical decision factors related to technical issues, inclusion of these
estimates in the primary analysis would not affect any of the decisions regarding the
appropriateness of the standards EPA is adopting here. EPA, however, presented these
estimates for illustrative purposes and chose not to include them in the primary benefits
analysis because of the uncertainties discussed in Chapter 7.1.
Table 7.3-4 Monetized Undiscounted Annual Benefits & Benefits of the Final Program
Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)
2017
2020
2030
2040
2050
NPV, Years
2012-2050,
3% Discount
Rate3
NPV, Years
2012-2050,
7% Discount
Rate2
Benefits of Reduced CO2 Emissions at each assumed SCC value b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
Energy Security
Benefits
Accidents,
Congestion, Noise
Costs'1
Increased Travel
Benefits8
Refueling Time
Savings
Non-GHG Related
Health Impacts c'd>e
Non-CO2 GHG
Impactsf
$14
$55
$87
$167
$33
-$54
$79
$25
B
n/a
$164
$633
$1,000
$1,940
$371
-$564
$865
$282
B
n/a
$2,500
$8,410
$12,900
$25,700
$4,560
-$5,710
$9,560
$3,360
$920-
$1000
n/a
$5,510
$17,000
$25,400
$51,800
$8,320
-$9,650
$17,000
$6,350
$920-
$1000
n/a
$8,540
$24,400
$35,400
$74,100
$10,400
-$12,100
$14,500
$8,870
$920-
$1000
n/a
$32,400
$170,000
$290,000
$519,000
$84,500
-$101,000
$167,000
$64,900
$9,190
n/a
$32,400
$170,000
$290,000
$519,000
$32,200
-$39,200
$64,800
$24,500
$3,050
n/a
Total Annual Benefits at each assumed SCC value b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$97
$138
$171
$250
$1,120
$1,590
$1,960
$2,890
$15,300
$21,200
$25,600
$38,500
$28,500
$40,000
$48,400
$74,800
$31,300
$47,200
$58,100
$96,900
$257,000
$395,000
$515,000
$743,000
$118,000
$256,000
$376,000
$604,000
Notes:
" Net present value of reduced CO2 emissions is
rate used to discount the value of damages from
calculated differently than other benefits. The same discount
future emissions (SCC at 5, 3, 2.5 percent) is used to calculate
7-26
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MY 2017 and Later - Regulatory Impact Analysis
net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown
are undiscounted values.
* RIA Chapter 7.1 notes that SCC increases over time. For the years 2017-2050, the SCC estimates range as
follows: for Average SCC at 5%: $6-$16; for Average SCC at 3%: $26-$47; for Average SCC at 2.5%: $41-
$68; and for 95th percentile SCC at 3%: $79-$142.
c Note that "B" indicates unquantified criteria pollutant benefits in years prior to 2030 (2017-2029). For the final
rule, EPA only conducted full-scale photochemical air quality modeling to estimate the rule's PM2.s- and ozone-
related impacts in the calendar year 2030. For the purposes of estimating a stream of future-year criteria
pollutant benefits associated with the final standards, we assume that the annual benefits out to 2050 are equal to,
and no less than, those modeled in 2030 as reflected by the stream of estimated future emission reductions. The
NPV of criteria pollutant-related benefits should therefore be considered a conservative estimate of the potential
benefits associated with the final rule.
d The PM2.5-related portion of the health benefits presented in this table are based on an estimate of premature
mortality derived from the ACS study (Pope et al., 2002). However, EPA's primary method of characterizing
PM-related premature mortality is to use both the ACS and the Six Cities study (Laden et al., 2006) to generate a
co-equal range of benefits estimates. The decision to present only the ACS-based estimate in this table does not
convey any preference for one study over the other. We note that this is also the more conservative of the two
estimates - PM-related benefits would be approximately 245 percent (or nearly two-and-a-half times) larger had
we used the per-ton benefit values based on the Six Cities study instead. Refer to Chapter 6.3.1 to see the full
range of non-GHG related health benefits in Calendar Year 2030.
e The range of calendar year non-GHG benefits presented in this table assume either a 3% discount rate in the
valuation of PM-related premature mortality ($1,000 million) or a 7% discount rate ($920 million) to account for
a twenty-year segmented cessation lag. Note that the benefits estimated using a 3% discount rate were used to
calculate the NPV using a 3% discount rate and the benefits estimated using a 7% discount rate were used to
calculate the NPV using a 7% discount rate.
^EPA applied the GWP approach to estimate the benefits associated with reductions of CH4, N2O, HFC in each
calendar year. EPA presented these estimates for illustrative purposes but chose not to include them in the
primary benefits analysis. See RIA Chapter 7.1.
8 Refer to Chapter 4.2.6 of the joint TSD for a description of how increased travel benefits are derived.
''Note that accidents, congestion and noise are costs, so the negative values shown represent increased costs
which we treat as negative benefits.
Table 7.3-5 presents estimated annual net benefits for the indicated calendar years.
The table also shows the net present values of those net benefits for the calendar years 2017-
2050 using both 3 percent and 7 percent discount rates. The table includes the benefits of
reduced COi emissions (and consequently the annual net benefits) for each of four SCC
values considered by EPA.
Table 7.3-5 Undiscounted Annual Monetized Net Benefits & Net Benefits of the Final
Program Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)
Vehicle Program
Costs
Fuel Savings
2017
$2,470
$651
2020
$9,190
$7,430
2030
$35,900
$86,400
2040
$41,000
$155,000
2050
$46,500
$212,000
NPV, 3%a
$561,000
$1,600,000
NPV, 7%a
$247,000
$607,000
Total Annual Benefits at each assumed SCC value b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$97
$138
$171
$250
$1,120
$1,590
$1,960
$2,890
$15,300
$21,200
$25,600
$38,500
$28,500
$40,000
$48,400
$74,800
$31,300
$47,200
$58,100
$96,900
$257,000
$395,000
$515,000
$743,000
$118,000
$256,000
$376,000
$604,000
Monetized Net Benefits at each assumed SCC value0
7-27
-------
Chapter 7
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
-$1,690
-$1,650
-$1,610
-$1,530
-$316
$153
$524
$1,460
$68,000
$73,900
$78,300
$91,200
$146,000
$158,000
$166,000
$192,000
$201,000
$217,000
$228,000
$267,000
$1,290,000
$1,430,000
$1,550,000
$1,780,000
$478,000
$616,000
$736,000
$964,000
Notes:
" 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, 2.5 percent) is used to calculate
net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown
are undiscounted values.
6 RIA Chapter 7.1 notes that SCC increases over time. For the years 2017-2050, the SCC estimates range as
follows: for Average SCC at 5%: $6-$16; for Average SCC at 3%: $26-$47; for Average SCC at 2.5%: $41-
$68; and for 95* percentile SCC at 3%: $79-$142. RIA Chapter 7.1 also presents these SCC estimates.
c Net Benefits equal Fuel Savings minus Technology Costs plus Benefits.
EPA also conducted a separate analysis of the total benefits over the model year
lifetimes of the 2017 through 2025 model year vehicles. In contrast to the calendar year
analysis presented above in Table 7.3-1 through Table 7.3-5, the model year lifetime analysis
below shows the impacts of the program on vehicles produced during each of the model years
2017 through 2025 over the course of their expected lifetimes. The net societal benefits over
the full lifetimes of vehicles produced during each of the nine model years from 2017 through
2025 are shown in Table 7.3-6 and Tables 7.3-7 at both 3 percent and 7 percent discount rates,
respectively.
Table 7.3-6 Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with
the Lifetimes of 2017-2025 Model Year Light-Duty Vehicles (Millions, 2010$; 3%
Discount Rate)h
Vehicle Program Costs
Fuel Savings (pre-tax)
Energy Security
Benefits
Accidents, Congestion,
Noise Costs f
Increased Travel
Benefits'
Refueling Time
Savings
PM2.5 Related Health
Impacts0*6
Non-CO2 GHG
Impacts8
2017
MY
$2,770
$7,040
$365
-$548
$1,000
$273
$74
n/a
2018
MY
$5,460
$15,500
$807
-$1,150
$2,180
$604
$171
n/a
2019
MY
$7,720
$24,300
$1,260
-$1,770
$3,390
$945
$271
n/a
2020
MY
$10,100
$34,100
$1,780
-$2,440
$4,700
$1,330
$385
n/a
2021
MY
$14,000
$50,400
$2,650
-$3,480
$6,840
$1,970
$606
n/a
2022
MY
$19,900
$64,900
$3,430
-$4,420
$8,650
$2,550
$768
n/a
2023
MY
$25,400
$78,500
$4,170
-$5,270
$10,200
$3,100
$912
n/a
2024
MY
$30,900
$92,900
$4,950
-$6,160
$11,900
$3,680
$1,060
n/a
2025
MY
$33,600
$107,000
$5,750
-$7,040
$13,600
$4,280
$1,210
n/a
Sum
$150,000
$475,000
$25,200
-$32,300
$62,500
$18,700
$5,460
n/a
Benefits of Reduced CO2 Emissions at each assumed SCC value a'b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$152
$642
$1,040
$1,970
$344
$1,440
$2,320
$4,390
$551
$2,270
$3,660
$6,950
$794
$3,230
$5,190
$9,880
$1,210
$4,850
$7,760
$14,800
$1,590
$6,330
$10,100
$19,300
$1,970
$7,740
$12,300
$23,600
$2,380
$9,260
$14,700
$28,300
$2,820
$10,800
$17,100
$33,000
$11,800
$46,600
$74,100
$142,000
Monetized Net Benefits at each assumed SCC value a'b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$5,590
$6,080
$6,480
$7,400
$13,000
$14,100
$15,000
$17,100
$21,200
$22,900
$24,300
$27,600
$30,500
$33,000
$34,900
$39,600
$46,200
$49,900
$52,800
$59,800
$57,500
$62,200
$66,000
$75,200
$68,100
$73,900
$78,500
$89,800
$79,700
$86,600
$92,000
$106,000
$94,400
$102,000
$109,000
$125,000
$416,000
$451,000
$479,000
$547,000
7-28
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MY 2017 and Later - Regulatory Impact Analysis
Notes:
" 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, 2.5 percent) is used to calculate
net present value of SCC for internal consistency. Refer to the SCC TSD for more detail.
* RIA Chapter 7.1 notes that SCC increases over time. For the years 2017-2050, the SCC estimates range as
follows: for Average SCC at 5%: $6-$16; for Average SCC at 3%: $26-$47; for Average SCC at 2.5%: $41-
$68; and for 95th percentile SCC at 3%: $79-$142. RIA Chapter 7.1 also presents these SCC estimates.
c Note that the PM2.5-related co-pollutant impacts associated with Model Year analysis presented here do not
include the full complement of endpoints that, if quantified and monetized, would change the total monetized
estimate non-GHG impacts. Instead, the PM2.5-related benefits are based on benefit-per-ton values that reflect
only human health impacts associated with reductions in PM2 5 exposure. Ideally, human health and
environmental benefits would be based on changes in ambient PM2 5 and ozone as determined by full-scale air
quality modeling. However, EPA was unable to conduct a full-scale air quality modeling for the Model Year
analysis. Full scale air quality modeling was conducted for the Calendar Year analysis. See Chapter 6 for a
discussion of that analysis.
d The PM2 s-related health benefits (derived from benefit-per-ton values) presented in this table are based on an
estimate of premature mortality derived from the ACS study (Pope et al., 2002). However, EPA's primary
method of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Laden
et al., 2006) to generate a co-equal range of benefits estimates. The decision to present only the ACS-based
estimate in this table does not convey any preference for one study over the other. We note that this is also the
more conservative of the two estimates - PM-related benefits would be approximately 245 percent (or nearly
two-and-a-half times) larger had we used the per-ton benefit values based on the Six Cities study instead. See
Chapter 6.3.1.
e The PM25-related health benefits (derived from benefit-per-ton values) presented in this table assume a 3%
discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation lag. If a
7% discount rate had been used, the values would be approximately 9% lower.
/ Negative values shown for Accidents, Congestion, and Noise represent disbenefits.
8 EPA applied the GWP approach to estimate the benefits associated with reductions of CH4, N2O, HFC in each
calendar year. EPA presented these estimates for illustrative purposes but chose not to include them in the
primary benefits analysis. See RIA Chapter 7.1.
h Model year values are discounted to the first year of each model year; the "Sum" represents those discounted
values summed across model years.
' Refer to Chapter 4.2.6 of the joint TSD for a description of how increased travel benefits are derived.
Table 7.3-7 Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with
the Lifetimes of 2017-2025 Model Year Light-Duty Vehicles (Millions, 2010$; 7%
Discount Rate)h
Vehicle Program Costs
Fuel Savings (pre-tax)
Energy Security
Benefits
Accidents, Congestion,
Noise Costs f
Increased Travel
Benefits'
Refueling Time
Savings
PM2.5 Related Health
ImpactscAe
Non-CO2 GHG
Impacts8
2017
MY
$2,650
$5,410
$279
-$425
$761
$209
$59
n/a
2018
MY
$5,220
$11,90
0
$615
-$893
$1,650
$461
$136
n/a
2019
MY
$7,370
$18,600
$964
-$1,370
$2,550
$721
$215
n/a
2020
MY
$9,610
$26,10
0
$1,360
-$1,890
$3,530
$1,020
$305
n/a
2021
MY
$13,300
$38,600
$2,020
-$2,690
$5,120
$1,500
$478
n/a
2022
MY
$19,200
$49,700
$2,620
-$3,410
$6,470
$1,940
$607
n/a
2023
MY
$24,600
$60,100
$3,180
-$4,070
$7,640
$2,360
$721
n/a
2024
MY
$29,900
$71,100
$3,780
-$4,760
$8,870
$2,800
$840
n/a
2025
MY
$32,500
$82,300
$4,400
-$5,440
$10,100
$3,260
$959
n/a
Sum
$144,000
$364,000
$19,200
-$24,900
$46,700
$14,300
$4,320
n/a
Benefits of Reduced CO2 Emissions at each assumed SCC value a'b
5% (avg SCC)
3% (avg SCC)
$152
$642
$344
$1,440
$551
$2,270
$794
$3,230
$1,210
$4,850
$1,590
$6,330
$1,970
$7,740
$2,380
$9,260
$2,820
$10,800
$11,800
$46,600
7-29
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Chapter 7
2.5% (avg SCC)
3% (95th %ile)
$1,040
$1,970
$2,320
$4,390
$3,660
$6,950
$5,190
$9,880
$7,760
$14,800
$10,100
$19,300
$12,300
$23,600
$14,700
$28,300
$17,100
$33,000
$74,100
$142,000
Monetized Net Benefits at each assumed SCC value a'b
5% (avg SCC)
3% (avg SCC)
2.5% (avg SCC)
3% (95th %ile)
$3,800
$4,290
$4,690
$5,610
$9,010
$10,10
0
$11,00
0
$13,10
0
$14,900
$16,600
$18,000
$21,300
$21,60
0
$24,10
0
$26,00
0
$30,70
0
$32,900
$36,500
$39,400
$46,500
$40,300
$45,000
$48,800
$58,000
$47,300
$53,100
$57,600
$69,000
$55,100
$62,000
$67,400
$81,000
$65,800
$73,800
$80,100
$96,100
$291,000
$326,000
$353,000
$421,000
Notes:
" 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, 2.5 percent) is used to calculate
net present value of SCC for internal consistency. Refer to the SCC TSD for more detail.
6 RIA Chapter 7.1 notes that SCC increases over time. For the years 2017-2050, the SCC estimates range as
follows: for Average SCC at 5%: $6-$16; for Average SCC at 3%: $26-$47; for Average SCC at 2.5%: $41-
$68; and for 95th percentile SCC at 3%: $79-$142. RIA Chapter 7.1 also presents these SCC estimates.
c Note that the PM2.5-related co-pollutant impacts associated with Model Year analysis presented here do not
include the full complement of endpoints that, if quantified and monetized, would change the total monetized
estimate non-GHG impacts. Instead, the PM2.5-related benefits are based on benefit-per-ton values that reflect
only human health impacts associated with reductions in PM2.5 exposure. Ideally, human health and
environmental benefits would be based on changes in ambient PM2 5 and ozone as determined by full-scale air
quality modeling. However, EPA was unable to conduct a full-scale air quality modeling for the Model Year
analysis. Full scale air quality modeling was conducted for the Calendar Year analysis. See Chapter 6 for a
discussion of that analysis.
d The PM2 s-related health benefits (derived from benefit-per-ton values) presented in this table are based on an
estimate of premature mortality derived from the ACS study (Pope et al., 2002). However, EPA's primary
method of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Laden
et al., 2006) to generate a co-equal range of benefits estimates. The decision to present only the ACS-based
estimate in this table does not convey any preference for one study over the other. We note that this is also the
more conservative of the two estimates - PM-related benefits would be approximately 245 percent (or nearly
two-and-a-half times) larger had we used the per-ton benefit values based on the Six Cities study instead. See
Chapter 6.3.1
e The PM25-related health benefits (derived from benefit-per-ton values) presented in this table assume a 3%
discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation lag. If a
7% discount rate had been used, the values would be approximately 9% lower.
/ Negative values shown for Accidents, Congestion, and Noise represent disbenefits.
8 EPA applied the GWP approach to estimate the benefits associated with reductions of CH4, N2O, HFC in each
calendar year. EPA presented these estimates for illustrative purposes but chose not to include them in the
primary benefits analysis. See RIA Chapter 7.1.
h Model year values are discounted to the first year of each model year; the "Sum" represents those discounted
values summed across model years.
1 Refer to Chapter 4.2.6 of the joint TSD for a description of how increased travel benefits are derived.
7.4 Summary of Costs and Benefits of the MYs 2012-2016 & 2017-2025 Final Rules
In this section, EPA presents a summary of costs, benefits, and net benefits, along with
other impacts such as oil reductions and consumer savings, associated with the combined
MYs 2012-2016 and MYs 2017-2025 GHG emission standards. Here we focus on the
primary impacts of most interest for the MYs 2012-2016 and MYs 2017-2025 programs. As
a reference case, we use the 2011 standards.
7-30
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MY 2017 and Later - Regulatory Impact Analysis
It is important to understand that the results presented here are not a simple addition of
the results presented in each of the two individual rulemaking analyses, for several reasons.
First, the MYs 2012-2016 rule showed MY 2016 costs of $948 while the MYs 2017-2025 rule
shows MY 2025 costs of $1836. One cannot add these two costs to arrive at a total control
case cost for MY 2025 of $2784. Instead, one must add a MY 2025 cost of meeting the 2016
standard ($719, see RIA Table 3.6-1) to the MY 2025 cost of meeting the 2025 MY standard
($1,836, see RIA Table 3.6-3) to arrive at a total control case cost of $2555. (We describe
this in more detail in section 18.2.1 of the Response to Comments document).11111111
Similarly, the full MY lifetime benefits from the two rulemakings cannot be added due
to the change in reference case for the two rules. While the MYs 2012-2016 rule used the
MY 2011 CAFE standards as a reference case, this rulemaking uses the MY 2016 rulemaking
as a reference case. Thus, the MY lifetime benefits attributable to bringing the MY 2017 to
MY 2025 vehicles to compliance with the MY 2016 standards were not reported in either this
rulemaking, or the previous rulemaking. By contrast, as the calendar year analysis inherently
includes benefits accruing to additional MYs, the future calendar year (CY) benefits are
approximately additive between the two rules - although differences in the analyses (such as
changes to fleet projections, fuel prices, and VMT schedules) preclude direct addition of
benefits. We present two sets of tables: the first set (section 7.4.1) shows results from our
model year lifetime analysis; the second set (section 7.4.2) shows results from our calendar
year analysis. Finally, we show results of our consumer cost of ownership analysis.
7.4.1 Model Year Lifetime Results
The results presented here are impacts associated with the lifetime operation of the
new vehicles sold in the 14 model years 2012-2025. It is important to note that while the
incremental vehicle technology costs associated with any given model year will in fact occur
in that same calendar year, the benefits, fuel savings and maintenance costs associated with
the given model year of vehicles will be split among all the subsequent calendar years until
the last vehicle is retired.
Table 7.4-1 shows the lifetime total fuel reductions for the lifetimes of MYs 2012
through 2025 vehicles. Table 7.4-2 shows the lifetime total CC^e reductions for the lifetimes
of MYs 2012 through 2025 vehicles. Table 7.4-3 shows the lifetime present value monetized
fuel savings for the lifetimes of MYs 2012 through 2025 vehicles. In 7.4-3, the present values
of the lifetime fuel savings are discounted to the first year of each model year; the sums are
those discounted lifetime values summed across model years. Table 7.4-4 shows the lifetime
nnnn T^s $2,555 cost is conservative and overstated, as we did not subtract the cost of bringing the MY 2008
baseline to compliance with the MY 2011 standards, but rather used the direct estimate of bringing the MY 2008
vehicles to the MY 2016 technology. In the MYs 2012-2016 rule, we estimated this cost at $89 (See Page 4-18
in EPA-420-R-10-009) per vehicle on average, in 2007 dollars and using MY 2016 technology costs. This cost
would be lower in later MYs, and higher in earlier MYs due to the effects of cost learning. We did not repeat the
analysis of MY 2011 compliance costs for this rulemaking.
7-31
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Chapter 7
present value monetized fuel savings that vehicles in each model year 2012-2025 will
provide, relative to the vehicles were they to meet the MY 2011 CAFE standards rather than
the new GHG standards. Note that the savings shown in Table 7.4-4 use retail fuel prices.
Table 7.4-5 shows the estimated technology costs per vehicle for each model year 2012-2025
and do not include maintenance costs.
Table 7.4-1 MY Lifetime Fuel Reductions Associated with the MYs 2012-2016 &
2017-2025 Final Rules
Model Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
Fuel
(Million gallons)
6,740
10,300
13,500
19,100
25,700
28,700
31,400
34,600
38,800
45,200
50,700
55,800
61,100
66,600
488,000
Oil
(Million barrels)
160
246
321
455
611
683
749
824
925
1,080
1,210
1,330
1,450
1,580
11,600
Table 7.4-2 MY Lifetime CO2e Reductions Associated with the MYs 2012-2016
& 2017-2025 Final Rules
Model Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
CO2e
(Million metric tons)
81
130
160
230
310
340
380
420
470
550
610
670
730
790
5,900
7-32
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MY 2017 and Later - Regulatory Impact Analysis
Table 7.4-3 MY Lifetime Present Value
2012-2016 & 2017-2025 Final
Fuel Savings Associated with the MYs
Rules (2010 dollars)
Model
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
Untaxed, 0%
Discount Rate
(Billions)
$23
$36
$47
$68
$92
$100
$110
$130
$140
$170
$190
$210
$230
$250
$1,800
Untaxed, 3%
Discount Rate
(Billions)
$18
$28
$37
$53
$72
$81
$90
$100
$110
$130
$150
$160
$180
$200
$1,400
Untaxed, 7%
Discount Rate
(Billions)
$14
$21
$28
$40
$55
$62
$69
$76
$86
$100
$110
$130
$140
$150
$1,100
Retail, 0%
Discount Rate
(Billions)
$26
$40
$53
$76
$100
$120
$130
$140
$160
$190
$210
$230
$260
$280
$2,000
Retail, 3%
Discount Rate
(Billions)
$20
$31
$41
$59
$80
$90
$100
$110
$120
$150
$160
$180
$200
$220
$1,600
Retail, 7%
Discount Rate
(Billions)
$15
$24
$32
$45
$61
$69
$76
$85
$96
$110
$130
$140
$150
$170
$1,200
Table 7.4-4 MY Lifetime Present Value Fuel Savings per Vehicle Associated with
the MYs 2012-2016 & 2017-2025 Final Rules (2010 dollars)3
Model Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Fuel Savings per
Vehicle,
3% Discount Rate
($/vehicle)
$1,400
$1,900
$2,600
$3,700
$5,000
$5,700
$6,400
$7,100
$7,900
$9,000
$10,000
$10,900
$11,800
$12,700
Fuel Savings per
Vehicle,
7% Discount Rate
($/vehicle)
$1,000
$1,500
$2,000
$2,800
$3,800
$4,400
$4,900
$5,400
$6,000
$6,900
$7,700
$8,400
$9,100
$9,800
Using retail fuel prices and rebound miles in the control case.
7-33
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Chapter 7
Table 7.4-5 Industry Average Technology Costs per Vehicle Associated with the
MYs 2012-2016 & 2017-2025 Final Standards (2010$)a
Model Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Cars
$342
$507
$631
$749
$869
$1,044
$1,179
$1,284
$1,377
$1,478
$1,776
$2,040
$2,291
$2,381
Trucks
$314
$496
$652
$820
$1,098
$1,119
$1,222
$1,293
$1,367
$1,680
$2,086
$2,445
$2,780
$2,909
Combined
$331
$503
$639
$774
$948
$1,069
$1,193
$1,287
$1,373
$1,549
$1,881
$2,176
$2,454
$2,555
a This $2,555 cost is conservative and overstated, as we did not subtract
the cost of bringing the MY 2008 baseline to compliance with the MY
2011 standards, but rather used the direct estimate of bringing the MY
2008 vehicles to the MY 2016 technology. In the MYs 2012-2016 rule,
we estimated this cost at $89 (See Page 4-18 in EPA-420-R-10-009) per
vehicle on average, in 2007 dollars and using MY 2016 technology
costs. This cost would be lower in later MYs, and higher in earlier MYs
due to the effects of cost learning. We did not repeat the analysis of MY
2011 compliance costs for this rulemaking.
7.4.2 Calendar Year Results
The results presented here project the environmental and economic impacts associated
with the tailpipe COi standards during specific calendar years out to 2050. This calendar year
approach reflects the timeframe when the benefits would be achieved and the costs incurred.
Because the EPA CO2 emissions standards will remain in effect unless and until they are
changed, the projected impacts in this calendar year analysis beyond calendar year 2025
reflect vehicles sold in model years after 2025 (e.g., most of the benefits in calendar year 2040
would be due to vehicles sold after MY 2025).
Table 7.4-7 shows the annual fuel and oil reductions for the years 2012 through 2050.
Table 7.4-8 shows the annual CO2e reductions for the years 2012 through 2050. Table 7.4-9
shows the annual monetized fuel savings for the years 2012 through 2050.
7-34
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MY 2017 and Later - Regulatory Impact Analysis
Table 7.4-7 Annual Fuel Reductions Associated with the MYs 2012-2016 & 2017-
2025 Final Rules
Calendar
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
Total
Fuel
(Million gallons)
529
1,320
2,320
3,730
5,590
7,620
9,780
12,100
14,600
17,500
20,700
24,100
27,700
31,600
49,000
72,900
89,800
1,850,000
Oil
(Million barrels)
13
31
55
89
133
182
233
288
348
417
493
574
660
752
1,170
1,740
2,140
44,000
Oil
(Million
Barrels/day)
0.0
0.1
0.2
0.2
0.4
0.5
0.6
0.8
1.0
1.1
1.4
1.6
1.8
2.1
3.2
4.8
5.9
Table 7.4-8 Annual CO2e Reductions Associated with the MYs 2012-2016 &
2017-2025 Final Rules
Calendar
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
Total
CO2e
(Million metric tons)
6
16
28
45
68
92
120
150
180
210
250
290
330
380
580
860
1,100
22,000
7-35
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Chapter 7
Table 7.4-9 Annual Fuel Savings and Net Present Values in 2012 Associated with
the MYs 2012-2016 & 2017-2025 Final Rules (2010 dollars)
Calendar
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Untaxed
(Billions)
$1.6
$3.9
$7.3
$12
$18
$26
$33
$42
$51
$62
$73
$85
$99
$110
$190
$290
$390
$3,400
$1,400
Retail
(Billions)
$1.8
$4.5
$8.3
$14
$21
$29
$37
$47
$57
$69
$82
$95
$110
$130
$210
$320
$420
$3,700
$1,600
7.4.3 Consumer Cost of Ownership Results
Table 7.4-10 summarizes the consumer cost of ownership for cash purchases of
2025MY vehicles.
Table 7.4-10 Consumer Cost of Ownership Metrics, Cash Purchase of 2025MY
Vehicle (2010$)a
Metric
Increased Lifetime Costs
Lifetime Fuel Savings"
Lifetime Net Savings
"Breakeven" payback period
3% discount rate
$3,200
$13,500
$10,300
2.3 years
7% discount rate
$3,100
$10,400
$7,200
2.4 years
a The costs here are slightly conservative and overstated, as we did not
subtract the cost of bringing the MY 2008 baseline to compliance with
the MY 2011 standards, but rather used the direct estimate of bringing
the MY 2008 vehicles to the MY 2016 technology. In the MYs 2012-
2016 rule, we estimated this cost at $89 (See Page 4-18 in EPA-420-R-
10-009) per vehicle on average, in 2007 dollars and using MY 2016
technology costs. This cost would be lower in later MYs, and higher in
earlier MYs due to the effects of cost learning. We did not repeat the
analysis of MY 2011 compliance costs for this rulemaking.
b Rebound miles are excluded in the control case.
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MY 2017 and Later - Regulatory Impact Analysis
References
479 Docket ID EPA-HQ-OAR-2010-0799-0737, Technical Support Document: Social Cost of
Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working
Group on Social Cost of Carbon, with participation by Council of Economic Advisers,
Council on Environmental Quality, Department of Agriculture, Department of Commerce,
Department of Energy, Department of Transportation, Environmental Protection Agency,
National Economic Council, Office of Energy and Climate Change, Office of Management
and Budget, Office of Science and Technology Policy, and Department of Treasury (February
2010). Also available at http://www.epa.gov/oms/climate/regulations/scc-tsd.pdf
480 National Research Council (2009). Hidden Costs of Energy: Unpriced Consequences of
Energy Production and Use. National Academies Press. See docket ID EPA-HQ-OAR-2010-
0799-0738.
481 Marten, A. and S. Newbold. 2011. "Estimating the Social Cost of Non-CO2 GHG
Emissions: Methane and Nitrous Oxide." NCEE Working Paper Series #11-01.
http://yosemite.epa.gov/ee/epa/eed.nsf/WPNumber/2011-017opendocument. Accessed May
24, 2012. Docket ID EPA-HQ-OAR-2010-0799.
482 Fankhauser, S., 1994. The social costs of greenhouse gas emissions: an expected value
approach. The Energy Journal 15 (2), 157-184.
483 Kandlikar, M., 1995. The relative role of trace gas emissions in greenhouse abatement
policies. Energy Policy 23 (10), 879-883.
484 Hammitt, J., Jain, A., Adams, J., Wuebbles, D., 1996. A welfare-based index for assessing
environmental effects of greenhouse-gas emissions. Nature 381 (6580), 301-303.
485 Tol, R.S.J., R.J. Heintz and P.E.M. Lammers (2003), Methane Emission Reduction: An
Application of FUND, Climatic Change, 57 (1-2), 71-98.
486 Tol, R.S.J. (2004), Multi-Gas Emission Reduction for Climate Change Policy: An
Application of FUND, Energy Journal (Multi-Greenhouse Gas Mitigation and Climate Policy
Special Issue), 235-250. http://emf.stanford.edu/files/pubs/22519/SpecialIssueEMF21.pdf
(last accessed 08/08/12).
487 Hope, C., 2005. The climate change benefits of reducing methane emissions. Climatic
Change 68(1), 21-39.
488 Hope, C. and Newbery, D. (2008) "Calculating the social cost of carbon." In Grubb, M.,
Jamasb, T. and Pollitt, M.G. (eds.): Delivering a low carbon electricity system. Cambridge:
Cambridge University Press, pp.31-63.
http://www.jbs.cam.ac.uk/research/faculty/hopecpub.html (last accessed 08/08/12).
489 See http://ostpxweb.dot.gov/policv/Data/VOT97guid.pdf and
http://ostpxweb.dot.gov/policv/Data/VOTrevisionl 2-1 l-03.pdf (last accessed 07/18/2011).
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MY 2017 and Later - Regulatory Impact Analysis
8 Vehicle Sales and Employment Impacts
8.1 Vehicle Sales Impacts
8.1.1 How Vehicle Sales Impacts were Estimated for this Rule
Predicting the effects of this rule on vehicle sales entails comparing two competing
effects. On the one hand, as a result of this rule, the vehicles will become more expensive,
which would, by itself, discourage sales. On the other hand, the vehicles will have improved
fuel economy and thus lower operating costs, which makes them more attractive to
consumers. As discussed in Preamble III.H.l.a, there are many competing hypotheses for
why private markets are not providing what appear to be cost-effective energy-saving
technologies, for vehicles as well as for other energy-conservation technologies. There are
few empirical studies testing these hypotheses, though. The empirical literature does not
provide clear evidence on how much of the value of fuel savings consumers consider at the
time of vehicle purchase. It also generally does not speak to the efficiency of manufacturing
and dealer pricing decisions. Thus, we do not provide quantified estimates of potential sales
impacts.
In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by
comparing the up-front costs of the vehicles with the present value of five years' worth of fuel
savings. We assumed that the costs for the fuel-saving technologies would be passed along
fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take
into account several factors that would affect consumer costs: the increased sales tax that
consumers would pay, the increase in insurance premiums, the increase in loan payments that
buyers would face, and a higher resale value, with all of these factors due to the higher up-
front cost of the vehicle. Those calculations resulted in an adjusted increase in costs to
consumers. We then assumed that consumers considered the present value of five years of
fuel savings in their vehicle purchase, which is consistent with the length of a typical new
light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds
onto the vehicle.JJJJJJJJ The present value of fuel savings was subtracted from technology costs
to get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity
of -1 to convert a change in price into a change in quantity demanded of vehicles.KKKKKKKK
An elasticity of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold.
We do not here present a vehicle sales analysis using this approach. This rule takes
effect for MY 2017-2025. In the intervening years, it is possible that the assumptions
underlying this analysis, as well as market conditions, might change. Instead, Chapter 5.5
jjjjjjjj jn ^ mje^ jjje 5_year payback assumption corresponds to an assumption that vehicle buyers take into
account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation
depending on discount rate and model year).
KKKKKKKK por & ^j-^jg gOOCj sucjj as gjj auto> jjjg elasticity may be smaller in the long run: though people may
be able to change the timing of their purchase when price changes in the short run, they must eventually make
the investment. We request comment on whether or when a long-run elasticity should be used for a rule that
phases in over time, as well as how to find good estimates for the long -run elasticity.
8-1
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includes a payback period analysis to estimate the number of years of fuel savings needed to
recover the up-front costs of the new technologies. In other words, the payback period
identifies the break-even point for new vehicle buyers. As discussed there, the payback
period is 3.2 - 3.4 years for new vehicles, and even shorter for used vehicles (just over 1 year
for a 5-year-old MY 2025 vehicle). That chapter also includes an assessment of the lifetime
costs and benefits that accrue to a vehicle owner.
8.1.2 Consumer Vehicle Choice ModelingLLLLLLLL
An alternative to the vehicle sales analysis approach discussed above is the use of
consumer vehicle choice models. In this section we describe some of the consumer vehicle
choice models EPA has reviewed in the literature, and we describe the models' results and
limitations that we have identified. The evidence from consumer vehicle choice models
indicates a huge range of estimates for consumers' willingness to pay for additional fuel
economy. Because consumer surplus estimates from consumer vehicle choice models depend
critically on this value, we would consider any consumer surplus estimates of the effect of our
rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle
choice models has not been tested. While vehicle choice models are based on sales of
existing vehicles, vehicle models are likely to change, both independently and in response to
this rule. The models may not predict well in response to these changes. Instead, we compare
the value of the fuel savings associated with this rule with the increase in technology costs.
EPA will continue its efforts to review the literature and (as described below) to explore the
use of consumer choice modeling but, given the known limitations and uncertainties of
vehicle choice models, EPA has not conducted an analysis using these models for this rule.
This rule will lead automakers to change characteristics - in particular, the fuel
economy - of the vehicles they produce. These changes will affect the cost of manufacturing
the vehicle; as a result, the prices of the vehicles will also change.
In response to these changes, the number and types of vehicles sold is likely to change.
When consumers buy vehicles, they consider both their personal characteristics (such as age,
family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g.,
vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics,
consumers will reconsider their purchases. Increases in fuel economy are likely to be
attractive to consumers, but increases in price, as well as any detrimental changes in other
vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a
different vehicle than they would have purchased in the absence of the rule. The changes in
prices and vehicle characteristics are likely to influence consumers on multiple market scales:
the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the
sales on the used vehicle market.
LLLLLLLL -pj^ se(,jjon js drawn heavily from Helfand, Gloria, and Ann Wolverton, "Evaluating the Consumer
Response to Fuel Economy: A Review of the Literature." International Review of Environmental and Resource
Economics 5 (2011): 103-146.
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MY 2017 and Later - Regulatory Impact Analysis
Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles
consumers will purchase based on vehicle characteristics and prices. In principle, it should
produce more accurate estimates of total compliance costs compared to models that hold fleet
mix constant, since it predicts changes in the fleet mix that can affect total compliance costs.
It can also be used to measure changes in consumer surplus, the benefit that consumers
perceive from a good over and above the purchase price. (Consumer surplus is the difference
between what consumers would be willing to pay for a good, represented by the demand
curve, and the amount they actually pay. For instance, if a consumer were willing to pay
$30,000 for a new vehicle, but ended up paying $25,000, the $5000 difference would be
called consumer surplus.)
A number of consumer vehicle choice models have been developed. They vary in the
methods used, the data sources, the factors included in the models, the research questions they
are designed to answer, and the results of the models related to the effects of fuel economy on
consumer decisions. This section will give some background on these differences among the
models.
8.1.2.1 Methods
Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete
choice modeling. Discrete choice models seek to explain discrete rather than continuous
decisions. An example of a continuous decision is how many pounds of food a farm might
grow: the pounds of food can take any numerical value. Discrete decisions can take only a
limited set of values. The decision to purchase a vehicle, for instance, can only take two
values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the
choice is whether to purchase a specified vehicle. The result of these models is a prediction
of the probability that a consumer will purchase a specified vehicle. A minor variant on
discrete choice models estimates the market share (a continuous variable between 0 and 1) for
each vehicle. Because the market share is, essentially, the probability that consumers will
purchase a specific vehicle, these approaches are similar in process; they differ mostly in the
kinds of data that they use.
The primary methods used to model vehicle choices are nested logit and mixed
logit MMMMNtMMM ^ & ^^ ^^ ^ modd ^ strucmred in layers por instance, me first
layer may be the choice of whether to buy a new or used vehicle. Given that the person
chooses a new vehicle, the second layer may be whether to buy a car or a truck. Given that
the person chooses a car, the third layer may be the choice among an economy, midsize, or
luxury car. Examples of nested logit models include Goldberg,490 Greene et al.,491 and
McManus.492
In a mixed logit, personal characteristics of consumers play a larger role than in nested
logit. While nested logit can look at the effects of a change in average consumer
characteristics, mixed logit allows consideration of the effects of the distribution of consumer
MMMMMMMM Logjt refers to a statistical analysis method used for analyzing the factors that affect discrete choices
(i.e., yes/no decisions or the choice among a countable number of options).
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Chapter 8
characteristics. As a result, mixed logit can be used to examine the distributional effects on
various socioeconomic groups, which nested logit is not designed to do. Examples of mixed
logit models include Berry, Levinsohn, and Pakes,493 Bento et al.,494 and Train and
Winston.495
While discrete choice modeling appears to be the primary method for consumer choice
modeling, others (such as Kleit496 and Austin and Dinan4 7) have used a matrix of demand
elasticities to estimate the effects of changes in cost. The discrete choice models can produce
such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM
vehicle choice model.
8.1.2.2 Data Sources
The predictions of vehicle purchases from CCMs are based on consumer and vehicle
characteristics. The CCMs identify the effects of changing the characteristics on the purchase
decisions. These effects are typically called the parameters or coefficients of the models. For
instance, the model parameters might predict that an increase in a person's income of 10%
would increase the probability of her purchasing vehicle A by 5%, and decrease the
probability of her purchasing vehicle B by 10%.
The parameters in CCMs can be developed either from original data sources
(estimated models), or using values taken from other studies (calibrated models).
Estimated models use datasets on consumer purchase patterns, consumer
characteristics, and vehicle characteristics to develop their original sets of parameters. The
datasets used in these studies sometimes come from surveys of individuals' behaviors.498
Because they draw on the behavior of individuals, they provide what is sometimes called
micro-level data. Other studies, that estimate market shares instead of discrete purchase
decisions, use aggregated data that can cover long time periods.499
Calibrated models rely on existing studies for their parameters. Researchers may draw
on results from a number of estimated models, or even from research other than CCM, to
choose the parameters of the models. The Fuel Economy Regulatory Analysis Model
developed for the Energy Information Administration500 and the New Vehicle Market Model
developed by NERA Economic Consulting501 are examples of calibrated models.
8.1.2.3 Factors Included in the Models
Consumer choice models vary in their complexity and levels of analysis. Some focus
only on the new vehicle market;502 others consider the choice between new vehicles and an
outside good (possibly including a used vehicle);503 others explicitly consider the relationship
between the new and used vehicle markets.504 Some models include consideration of vehicle
miles traveled,505 though most do not.
The models vary in their inclusion of both consumer and vehicle information. One
model includes only vehicle price and the distribution of income in the population influencing
choice;506 others include varying numbers and kinds of vehicle and consumer attributes.
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MY 2017 and Later - Regulatory Impact Analysis
Some models include only the consumer side of the vehicle market;507 others seek to
represent both consumer and producer decisions.508 Models that include only the consumer
side are suitable for reflecting consumer choices, but they do not allow for revisions of
vehicle characteristics in response to consumer preferences. Including producer behavior
allows for vehicle characteristics such as price and fuel economy to be the result of market
forces rather than characteristics of the existing fleet. For instance, in the context of
"feebates" (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient
vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to
addition of technology rather than changes in vehicles sold.509 Including auto maker response
is a complex exercise. Auto makers are commonly considered to have market power; they
can influence the prices that consumers pay to increase their profits. As a result, the price
increases that consumers face may reflect strategic factors that could make them higher or
lower than the technology costs. In addition, auto makers may seek to influence consumer
preferences through marketing and advertising.510 Even those vehicle choice models that
include a producer model may not include much detail, due to computational limits: it is
unusual for models to allow both buyers and producers to choose one vehicle characteristic,
much less multiple characteristics.511
8.1.2.4 Research Questions for the Models
Consumer choice models have been developed to analyze many different research and
policy questions. In part, these models have been developed to advance the state of economic
modeling. The work of Berry, Levinsohn, and Pakes,512 for instance, is often cited outside the
motor vehicle context for its incorporation of multiple new modeling issues into its
framework. In addition, because the vehicle sector is a major part of the U.S. economy and
involved in many public policy discussions, research questions cover a wide gamut. These
topics have included the effects of voluntary export restraints on Japanese vehicles compared
to tariffs and quotas,513 the market acceptability of alternative-fuel vehicles,514 the effects of
introduction and exit of vehicles from markets,515 causes of the decline in market shares of
U.S. automakers,516 and the effects of gasoline taxes517 and "feebates"518 (subsidizing fuel-
efficient cars with revenue collected by taxing fuel-inefficient vehicles).
8.1.2.5 The Effect of Fuel Economy on Consumer Decisions
Consumer vehicle choice models typically consider the effect of fuel economy on
vehicle purchase decisions. Fuel economy can appear in various forms in these models.
Some models519 incorporate fuel economy through its effects on the cost of owning a
vehicle. With assumptions on the number of miles traveled per year and the cost of fuel, it is
possible to estimate the fuel savings (and perhaps other operating costs) associated with a
more fuel-efficient vehicle. Those savings are considered to reduce the cost of owning a
vehicle: effectively, they reduce the purchase price. This approach relies on the assumption
that, when purchasing vehicles, consumers can estimate the fuel savings that they expect to
receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction
in purchase price. The vehicle sales method described in Chapter 8.1.1 uses a variant on this
approach, in which it is assumed that consumers consider some fraction of future fuel savings.
Turrentine and Kurani520 question this assumption; they find, in fact, that consumers do not
8-5
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Chapter 8
make this calculation when they purchase a vehicle. The question remains, then, how or
whether consumers take fuel economy into account when they purchase their vehicles.
Most estimated consumer choice models, instead of making assumptions about how
consumers incorporate fuel economy into their decisions, use data on consumer behavior to
identify that effect. In some models, miles per gallon is one of the vehicle characteristics
included to explain purchase decisions. Other models use fuel consumption per mile, the
inverse of miles per gallon, as a measure.521 Since consumers pay for gallons of fuel, then
this measure can assess fuel savings relatively directly.522 Yet other models multiply fuel
consumption per mile by the cost of fuel to get the cost of driving a mile,523 or they divide
fuel economy by fuel cost to get miles per dollar.524 It is worth noting that these last two
measures assume that consumers respond the same way to an increase in fuel economy as
they do to a decrease in the price of fuel when each has the same effect on cost per mile
driven.NNNNNNNN On the one hand, while this assumption does not rely on as complex a
calculation as the present value of fuel savings that Turrentine and Kurani examined, it
suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to
recognize the role of fuel prices in consumers' purchase of fuel economy: recent research525
presents results that higher fuel prices play a major role in that decision.
Greene and Liu,526 in a paper published in 1988, reviewed 10 papers using consumer
vehicle choice models and estimated for each one how much consumers would be willing to
pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that
people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating
costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that
people are not willing to pay $1 once to get $1 per year reduced costs of operating their
vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the
present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7%
discount rate produces a present value of $8.78. While this study is quite old, it suggests that,
at least as of that time, consumer vehicle choice models produced widely varying estimates of
the value of reduced vehicle operating costs.
A newer literature review from David Greene527 suggests continued lack of
convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness
to pay for fuel economy as a percent of the expected value of fuel savings varied from highly
positive to highly negative. Significant numbers of studies found that consumers overvalued
fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative
to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be
correlated (either positively or negatively) with other vehicle attributes, such as size, power,
or quality, not all of which may be included in the analyses; as a result, "fuel economy" may
in fact represent several characteristics at the same time. Indeed, Gramlich528 includes both
NNNNNNNN Ljjcewjsg; these measures assume consumers respond the same way to increases and decreases in cost
per mile of driving, as well as if those increases and decreases are large shocks rather than small, gradual
changes. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other
types of changes to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see
Section III.H.4 of the Preamble and Chapter 4.2.5 of the TSD).
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fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles
per gallon measures other undesirable quality attributes, while fuel cost picks up the
consumer's demand for improved fuel economy. Greene finds that, while some of the
variation may be explainable due to issues in some of the studies, the variation shows up in
studies that appear to be well conducted. As a result, further work needs to be conducted
before it is possible to identify the role of fuel economy in consumer purchase decisions.
Some studies529 argue that automakers could increase profits by increasing fuel
economy because the amount that consumers are willing to pay for increased fuel economy
outweighs the costs of that improvement. Other studies530 have found that increasing fuel
economy standards imposes welfare losses on consumers and producers, because consumers
should already be buying as much fuel economy as they want. In the course of reaching this
result, though, at least one of these studies531 notes that its baseline model implies that
consumers are willing to buy more fuel economy than producers have provided; they have to
adjust their model to eliminate these "negative-cost" fuel economy improvements.
The models do not appear to yield very consistent results on the role of fuel economy
in consumer and producer decisions.
8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciation for
Fuel Economy that Pays for Itself
A detailed and wide ranging literature attempts to explain why market outcomes for
energy-using products appear to reflect under-investment in energy saving technologies that -
at least using a present value calculation based on engineering estimates - appear to pay for
themselves. Existing research does not provide a definitive answer to this question. Potential
explanations are bounded by two scenarios. On the one hand, purely private benefits of fuel
economy (fuel savings, time savings, increases in driving time) must be accompanied by
private losses of the same magnitude. However, if there is no such private loss, or if it is small
or insignificant, then there is a market or behavioral failure.
This disconnect between net present value estimates of energy-conserving cost savings
and what consumers actually spend on energy conservation is often referred to as the Energy
Paradox,532 since consumers appear to undervalue a wide range of investments in energy
conservation. There are many possible explanations for the paradox discussed in the
literature.533 Some explanations point to costs or aspects of consumer decision-making
unaccounted for in a simple present value calculation, while others point to potential
behavioral or market failures. There is little empirical literature to help the analyst determine
which combination of hypothesis offers the most credible explanation. Some possibilities
include:
• Consumers might be "myopic" and hence undervalue future fuel savings in their
purchasing decisions.
• Consumers might lack the information necessary to estimate the value of future fuel
savings, or not have a full understanding of this information even when it is presented.
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Chapter 8
• Consumers may be accounting for uncertainty in future fuel savings when comparing
upfront cost to future returns.
• Consumers may consider fuel economy after other vehicle attributes and, as such, not
optimize the level of this attribute (instead "satisficing" - that is, selecting a vehicle that
is acceptable rather than optimal -- or selecting vehicles that have some sufficient
amount of fuel economy).
• Consumers might be especially averse to the short-term losses associated with the
higher prices of energy efficient products relative to the future fuel savings (the
behavioral phenomenon of "loss aversion").
• Consumers might associate higher fuel economy with inexpensive, less well designed
vehicles.
• When buying vehicles, consumers may focus on visible attributes that convey status,
such as size, and pay less attention to attributes such as fuel economy that do not visibly
convey status.
• Even if consumers have relevant knowledge, selecting a vehicle is a highly complex
undertaking, involving many vehicle characteristics. In the face of such a complicated
choice, consumers may use simplified decision rules.
• In the case of vehicle fuel efficiency, and perhaps as a result of one or more of the
foregoing factors, consumers may have relatively few choices to purchase vehicles with
greater fuel economy once other characteristics, such as vehicle class, are
chosen.00000000
The extent to which fuel economy is optimized relative to other, potentially more
salient vehicle attributes (such as engine horsepower and seating capacity) in market
outcomes for new vehicles remains an important area of uncertainty. There are significant
challenges involved in effectively interpreting and anticipating consumer preferences for
various vehicle attributes and amenities. There are significant lead times to market, potential
return to scale limits on the range of options provided for a given attribute or amenity, market
transaction frictional factors, and other factors inherent to the nature of these costly durable
goods which may contribute to imperfect satisfaction of market demand for fuel economy
among a highly heterogeneous customer base. Both sides of the market would be expected to
attempt to maximize the utility they gain from these transactions; they presumably rely
heavily in their calculations on the uncertain benefits of savings from fuel economy
improvements, and yet market outcomes may still appear to reflect potential foregone
opportunities to increase utility. We remain interested in these market dynamics, their
oooooooo por jns(ancg; jn MY 2010, the range of fuel economy (combined city and highway) available among
all listed 6-cylinder minivans was 18 to 20 miles per gallon. With a manual-transmission 4-cylinder minivan, it
is possible to get 24 mpg. See http://www.fueleconomy.gov, which is jointly maintained by the U.S. Department
of Energy and the EPA.
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MY 2017 and Later - Regulatory Impact Analysis
underlying causes, and their potential significance for assessing the potential incremental
effects of pollution control standards.
8.1.2.7 Electric Vehicles and Other New Vehicles
Modeling the introduction of new vehicles can be a greater challenge than modeling
the existing vehicle market, because the modeler does not have data on how many of the new
vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new
vehicle introduction by identifying characteristics for the new vehicles and using those in a
vehicle choice model. For instance, as discussed above, the models can estimate effects on
the vehicle market when vehicles change their fuel economy or price. If the model
incorporates other vehicle attributes important to the new vehicles, such as size, performance,
or range, then the effect of the introduction can be modeled by applying the parameters for
those features to the new vehicle characteristics.
As discussed above, some models rely on vehicle price as the primary or only
explanatory variable. Even in these models, it is possible, with some additional information,
to consider the effects of new vehicle introduction. The first step is to find a vehicle similar
on as many dimensions as possible to the new vehicle. For instance, if the change is to create
an electric vehicle (EV) version of an existing model, then the existing model serves as the
base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to
potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may
be two such attributes. The next requirement is information on the value to consumers of the
attributes that change between the new and the base vehicle. Multiplying the value for that
attribute by the change in the attribute provides an estimate of the benefit or cost associated
with changing that characteristic. That amount can then be added to or subtracted from the
vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic.
This procedure is just an extension of the approach, discussed above, used to incorporate fuel
economy improvements into vehicle choice models, by calculating future fuel savings and
subtracting them (either in whole or a fraction) from vehicle purchase price.
Incorporating new vehicles into a vehicle choice model, then, requires estimates of the
changes in key attributes from conventional vehicles, and estimates of the value, also called
the willingness to pay (WTP), that consumers put on those attributes.
Electric vehicles (EVs) will have a number of changes in vehicle characteristics from
any baseline model. EVs are likely to have a smaller driving range between refuelings than
conventional vehicles, due to the large battery capacity needed to increase range. The ability
to recharge at home may be a convenient, desirable feature for people who have garages with
electric hookups, but not for people who park on the street. If an infrastructure develops for
recharging vehicles with the convenience approaching that of buying gasoline, range or home
recharging may become less of a barrier to purchase. The reduced tailpipe emissions and
reduced noise may be attractive features to some consumers.pppppppp They may have different
PPPPPPPP por jnstance> Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P.
Gardner. "Willingness to Pay for Electric Vehicles and their Attributes." Resource and Energy Economics
8-9
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Chapter 8
performance or storage capacity. If sufficient data were available, the changes in these
attributes, combined with WTP for each of the attributes, could be used to adjust the purchase
price of the baseline vehicle to estimate consumers' WTP for the electric version of a vehicle.
Greene (2001), for instance, used this approach for a model that simulates choice, not only for
EVs, but also for other alternative-fuel vehicles.534 In that model, he considers only one base
vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range,
acceleration, and several other vehicle attributes.
Vehicle driving range has received attention because of the current paucity of
recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a
place to recharge. Because range appears to be a major factor in EV acceptability -- indeed, a
factor in the development of plug-in hybrid-electric vehicles is responding to this concern --it
is starting to draw attention in the research community.
In several studies, researchers have used stated preference conjoint analysis to
estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis,
consumers are given a choice between several vehicles with different attributes. One choice
might be, for instance, between a baseline car and another car with higher range and a higher
purchase price. The choices that consumers make (e.g., how much higher does the purchase
price have to be for the consumer not to choose more range?) provide data that can be used to
estimate the role of vehicle attributes in the consumer's choice. Stated preference analysis is
sometimes considered less reliable than actual market behavior, because what people say they
will do in hypothetical situations may not match what they would do in actual situations. On
the other hand, stated preference methods can be used to study goods where market data do
not exist, such as future market products undergoing development (marketing studies often
use stated preference methods), or environmental goods. Because electric vehicles are not in
widespread enough use for market studies, stated preference studies are, at this point, one of
the few options to examine consumer behavior relating to these vehicles.
Table 8.1-1 summarizes results from several conjoint studies that include the effects of
extending range (in the table, from 150 to 300 miles, to present standardized results).
Variation of results in the table is from income or other demographic factors, not from
confidence intervals. The results suggest that the value of additional range varies among
consumers, and the amount of that variation is changing (perhaps shrinking) in more recent
studies.
33(3) (2011): 686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower
emissions than the vehicles they otherwise aim to purchase.
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Table 8.1-1 Willingness to Pay for Increasing Range Calculated from Various Studies
Study (Date)
Bunch etal. (1993)b
Kavelek (1996) for California
Energy Commission0
Resource Systems Group (2009) for
California Energy Commission11
Hess et al. (2009), using the same
data as Resource Systems Group
(2009)e
Hidrueetal. (20 II)1
Value of extending range
from 150 to 300 miles (dollar
year)
$7,600 (1991$)
$2600 - $41,900 (1993$)
$2900 - $7500 (2009$)
$2400 - $8500 (2009$)
$3800 - $10,400 (2009$)
Value of additional
range in 2010$a
$11,300
$3700 - $59,400
$2900 - $7600
$2400 - $8600
$3800 -$10,500
aValues adjusted to 2010$ using the Bureau of Economic Analysis GDP deflator.
bBunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. "Demand for Clean-Fuel Vehicles
in California: A Discrete-Choice Stated Preference Pilot Project." Transportation Research Part A 27A(3)
(1993): 237-253. The value of range was, in their model, assumed to be the same for all people.
cKavelek, Chris. "CALCARS: The California Conventional and Alternative Fuel Response Simulator."
Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to
willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on
range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was
for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across
both ownership and income categories.
dResource Systems Groups, Inc. "Transportation Fuel Demand Forecast Household and Commercial Fleet
Survey Task 8 Report: Logistic Regression Analysis and Results." Prepared for California Energy Commission,
June 2009.
eHess, S., T. Adler, M. Fowler and A. Bahreinian "The Use of Cross-nested Logit Models for Multi-Dimensional
Choice Processes: The Case of the Demand for Alternative Fuel Vehicles," Proceedings of the 2009 European
Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems
Group study. The coefficient on range was not statistically significantly different from zero in these regressions:
t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by
income levels and statistical specification. The minima and maxima presented here represent the values across
both income categories and specifications.
fHidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. "Willingness to Pay for
Electric Vehicles and their Attributes." Resource and Energy Economics 33(3) (2011): 686-705. The range of
values is due to the model separating consumers into "gasoline vehicle-oriented" and "electric vehicle-oriented"
groups. The EV-oriented group has higher WTP for additional range.
Driving range may be a major factor in consumers' decisions on EVs, but it is not the
only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that
some consumers appear willing to pay substantially for reduced tailpipe emissions). A model
that does not incorporate the other factors important to consumers' decisions may not perform
well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table
8.1-1, it is likely that the WTP values for attributes of EVs will change over time, particularly
if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible,
and consumers develop more familiarity and understanding of the vehicles. Thus, challenges
associated with predicting market shares for EVs are even more serious than those already
serious challenges associated with predicting market shares for conventional vehicles.
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8.1.2.8 EPA Exploration of Vehicle Choice Modeling
In order to develop greater understanding of these models, EPA is developing a
vehicle choice model, although not for this rulemaking. In its current form, the model
assumes that the vehicle fleet and all characteristics of each vehicle, except vehicle prices and
fuel economy, stay the same. The model will predict changes in the vehicle fleet, at the
individual-configuration level and at more aggregated levels, in response to changes in
vehicle fuel economy and price.
The EPA model uses a nested logit structure common in the vehicle choice modeling
literature, as discussed above in Chapter 8.1.2.1. "Nesting" refers to the decision-tree
structure of the model, and "logit" refers to the fact that the choices are discrete (i.e., yes/no
decisions about which vehicles to purchase, instead of continuous values).
The nesting involves a hierarchy of choices. In its current form, at the initial decision
node, consumers choose between buying a new vehicle or not. Conditional on choosing a
new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultra-
luxury vehicles. The next set of choices subdivides each of these categories into vehicle type
(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes
(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual
vehicle configurations.
At this bottom level, vehicles that are similar to each other (such as standard
subcompacts, or prestige large vehicles) end up in the same "nest." Substitution within a nest
is considered much more likely than substitution across nests, because the vehicles within a
nest are more similar to each other than vehicles in different nests. For instance, a person is
more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo
and a pickup truck. In addition, substitution is greater at low decision nodes (such as
individual configurations) than at higher decision nodes (such as the buy/no buy decision),
because there are more choices at lower levels than at higher levels.
Parameters for the model (including demand elasticities and the value of fuel economy
in purchase decisions) were selected based on a review of values found in the literature on
vehicle choice modeling. As discussed above, a number of studies have estimated these
parameters. Those estimates, combined with some theoretical requirements,QQQQQQQQ assist
in assigning values for the parameters. The model will allow individual users to change those
parameters.
The fuel economy of a vehicle is used to adjust the price of the vehicle, using a
version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on
fuel economy is multiplied by the change in fuel economy and incorporated into the "effective
QQQQQQQQ -pjjg theorv of nested logit requires that the price slopes (the change in utility as vehicle full price
changes, a measure of consumer responsiveness to price changes) must be higher in absolute value for lower
nests. This condition reflects the point, discussed above, that substitution is greater at lower decision notes than
at higher ones.
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price" of the vehicle. In practice, implementing this calculation involves calculating the
change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices
in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no
consensus value for consumers' willingness to pay for improved fuel economy: estimates
vary tremendously. The model assumes that consumers will use some years of discounted
fuel savings, with the modeler able to input both the number of years and the discount rate to
be used in the analysis.
The vehicle choice model takes as inputs an initial fleet of vehicles (including the
initial sales and fuel economy) in the absence of standards, the cost of technologies added to
each vehicle to comply with standards, and the change in fuel economy. With the initial sales
mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the
value of all attributes of the vehicle other than price and fuel economy. This constant ensures
that the model will predict changes in consumer response that would result only from changes
in price and fuel economy. This constant substitutes for estimating the effects of changes in
all other vehicle characteristics; the underlying assumption is that these other vehicle
characteristics do not change.RRRRRRRR For instance, it assumes that a Ford Escape will not
change in size, power, or accessories; the only changes will be to its cost and its fuel
economy.
The model assumes that the increase in vehicle cost associated with increased
technology is fully passed through as an increase in vehicle price, and some years of fuel
savings (which the modeler may select) offset this price increase. It then calculates changes
in total fleet size and in sales mix, at the individual-configuration level and at the level of
vehicle class, due to the changes in fuel economy and vehicle prices. It also calculates
changes in consumer surplus associated with the changes in fuel economy and vehicle prices.
The model has undergone peer review.535 The reviewers were generally supportive of
the model structure and parameters, with two major qualifications.
First, peer reviewers recommended that the model should interact closely with
OMEGA, EPA's technology cost and effectiveness model, and its appropriate use may
depend on that interaction. For instance, it is possible that the predicted changes in fleet mix
will lead to predictions of vehicle sales for auto makers that do not meet the standards,
because the mix and volume of vehicles sold changed from the initial levels. To correct this
problem, it is necessary to feed the new fleet mix back into OMEGA (which calculates costs
and compliance) and get a new set of output, which is then fed back into the vehicle choice
model. OMEGA increases technologies, and thus costs, to improve compliance; those
adjustments would then again affect vehicle demand. We expect that this iterative process
would converge to a fleet mix that would meet standards. Performing this iteration requires
development of an interface between the vehicle choice model and OMEGA to ensure
accurate transmission of data between the models. At this time, the vehicle choice model
jn section III.D of the preamble, as part of the technology cost analysis for the rule, the
agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.
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takes output from OMEGA, but the results of the modeling do not feed easily back into
OMEGA. Building this interface is an expected part of our future modeling work.
Second, the peer review raised the issue of the uncertainties surrounding the model
parameters and suggested the development of capacity to conduct uncertainty analysis. As
discussed in Section 8.1.2.5, the role of fuel economy in consumer decisions is one source of
uncertainty; the price slopes used for the different nests are also not known with certainty.
EPA agrees that use of the model should involve, at the least, sensitivity analysis over key
parameters, and we plan to investigate greater incorporation of uncertainty analysis in the
model.
We note as well that the current model does not take EVs or other alternatively fueled
vehicles into account. As discussed in Section 8.1.2.7, the values for willingness to pay for
features such as range and different refueling infrastructures appear to be subject to great
uncertainty. EPA's current model does not include these vehicles, because we are seeking to
gain experience and confidence with the modeling where it is likely to work best before we
investigate modeling where more uncertainty is involved. The incorporation of new vehicles
of any kind in the modeling is another area for future work.
As discussed in Preamble Section III.H.l, EPA is not using its preliminary consumer
choice model in this rulemaking because we believe it needs further development and testing
before we have confidence in its use. As the peer reviewers noted, it has not yet been
integrated with OMEGA, an important step for ensuring that changes in the vehicle fleet
estimated by the model will result in a fleet compliant with the standards. In addition,
concerns remain that vehicle choice models have rarely been validated against real-world
data. In response to these concerns, we would expect any use of the model to involve, at the
least, a number of sensitivity analyses to examine the robustness of results to key parameters.
We will continue model development and testing to understand better the results and
limitations of using the model.
8.1.2.9 Summary and Additional Considerations
Consumer vehicle choice modeling in principle could provide a great deal of useful
information for regulatory analysis, helping to answer some of the central questions about
relevant effects on consumer welfare. In practice, the advantages depend on the success of
models in predicting changes in fleet size and mix.
First, consumer vehicle choice modeling has the potential to describe more accurately
the impact of a policy, by identifying market shifts. More accurate description of the market
resulting from a policy can improve other estimates of policy impacts, such as the change in
total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is
not proven.
Vehicle choice models can incorporate the effects on consumer decisions of changes
in vehicle characteristics, if there are estimates of the value that consumers put on changes in
those characteristics. These willingness-to-pay values may, however, be sensitive to the ways
they are estimated, as indicated in the discussion of the value that consumers place on fuel
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economy in their purchase decisions. Especially for characteristics associated with advanced
technology vehicles, such as EVs, the willingness-to-pay values may change over time as
consumers develop more experience with the vehicles and these characteristics. Models
based on current estimates may not predict well for the future.
Consumer choice modeling has the potential to improve estimates of the compliance
costs of a rule. Consumers can either accept the new costs and vehicle characteristics, or they
can change which vehicles they buy. Using a vehicle choice model is likely to reduce
estimated compliance costs: because the model allows consumers to choose among accepting
the new vehicle, buying a different vehicle, or not buying a vehicle, the model assumes that
consumers have additional options, which improves their welfare relative to the assumption
that consumers will not change their buying behavior.
An additional complication associated with consumer choice modeling is accurate
prediction of producers' responses to the rule. While it is possible to include auto makers'
decisions (for instance, on setting prices) into vehicle choices, computational limits affect the
richness of these models. In addition, the pricing paths predicted by these models have not, to
our knowledge, been tested against actual behavior; and auto makers may not pass along
vehicle costs in the same way in the future as they have in the past. Technology costs, while
an accurate measure of the opportunity cost of resources to society, may overestimate or
underestimate the effect on the prices that consumers face.
Consumer choice models can be used to calculate consumer surplus impacts on
vehicle purchase decisions. Because these values are based on the estimates of changes in
vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in
vehicle sales and fleet mix are not well estimated.
Principles of welfare analysis can be useful for understanding the role of consumer
vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology
cost estimates developed in this rule take into account the costs to hold other vehicle
attributes, such as size and performance, constant. In addition, the analysis assumes that the
full technology costs are passed along to vehicle buyers. With these assumptions, because
welfare losses are monetary estimates of how much buyers would have to be compensated to
be made as well off as in the absence of the change,888 888S the price increase measures the
loss to the buyer.TTTTTTTT Assuming that the full technology cost gets passed along to the
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 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.
TTTTTTTT Intjee(^ .^ js jj^jy to ke gjj overestimate of the loss to the buyer, 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
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buyer as an increase in price, the technology cost thus measures the welfare loss to the buyer.
Increasing fuel economy would have to lead to other changes in the vehicles that buyers find
undesirable for there to be additional losses not included in the technology costs.
Given the current limitations in modeling the role of fuel economy in vehicle purchase
decisions, and limitations in modeling market responses to the new regulations, in this rule
EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of this
rule, and compares the fuel and other savings that consumers will receive with the technology
costs of the vehicles. EPA continues to explore options for including consumer and producer
choice in modeling the impacts of fuel economy-related regulations. This effort includes
further review of existing consumer vehicle choice models, the estimates of consumers'
willingness to pay for increased fuel economy, and overall effects on consumer welfare, as
well as EPA's further development of its vehicle choice model for use in the future.
8.1.3 Impact of the Rule on Affordability of Vehicles and Low-Income Households
Because this rule is expected to increase the up-front costs of vehicles, with the fuel
savings that recover those costs coming in over time, questions have arisen about the effects
of this rule on whether access to credit may limit consumers' ability to purchase new vehicles,
on low-income households, and on the availability of low-priced vehicles. Section III.H. 1 l.b.
of the Preamble discusses these issues in the context of public comments received on the rule;
here we provide some background and information on sources of data in that discussion.
When a lender is deciding whether to issue a loan to a prospective vehicle buyer, the
amount of the vehicle loan and the person's income are two major factors in the loan
application. If lenders in fact restrict themselves to consideration of only those two factors,
then the higher up-front costs of the new vehicles subject to this rule would reduce buyers'
abilities to get loans. The fuel savings would not come into play to counter-balance this cost,
even though, as shown in the payback period analysis (RIA Chapter 5.5), the fuel savings
exceed the increased loan payments from the first month of the loan. Thus, if lenders do not
take fuel savings into account in providing loans, people who are borrowing near the limit of
their abilities to borrow will either have to change what vehicles they buy, or not buy vehicles
at all.
On the other hand, some evidence suggests that the loan market may evolve to take
fuel savings into greater account in the lending decision. Some lenders currently give
discounts for loans to purchase more fuel-efficient vehicles.536 An internet search on the term
"green auto loan" produced more than 50 lending institutions that provide reduced loan rates
for more fuel-efficient vehicles.537 Indeed, it is possible (though unknown at this time) that
the auto loan market may evolve to include further consideration of fuel savings, as those
savings play a significant factor in offsetting the increase in up-front costs of vehicles.
welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the
vehicle less desirable to buyers.
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It is possible that future trends in the auto loan market may affect future vehicle sales.
It is also possible that some people who have significant debt loads may not be able to get
financing for some of these new vehicles; they may have to buy different vehicles (including
used vehicles) or delay purchase. For others who borrow on credit, though, as discussed in
RIA Chapter 5.5, the fuel savings are expected to outweigh the increased loan costs from the
time of vehicle purchase. The rule thus may make vehicles more affordable to the public, by
reducing consumers' vulnerability to fuel price jumps.
The effects of this rule on low-income households depends on its impacts, not only in
the new vehicle market, but also in the used vehicle market. Two sources of information on
vehicle ownership by income are the 2010 Consumer Expenditure Survey (CES) conducted
by the Bureau of Labor Statistics,538 and the 2007 Survey of Consumer Finances (SCF)
conducted by the Federal Reserve System.539 The Consumer Expenditure Survey data
indicate that, though the average household spent more on vehicle purchases ($2,588) than on
gasoline and oil ($2,132), households in the bottom 40 percent of income spent more on fuel
($1,304) than on vehicles ($1,106); in addition, they spent more on used vehicles ($756) than
on new vehicles ($330). Households in the lowest 20 percent of income spent only $127 on
new vehicles, $497 on used vehicles, and $1,009 on fuel. These data suggest that the used-
vehicle market is more important for low-income households than the new-vehicle market,
and that they are more vulnerable to changes in fuel prices than they are to changes in vehicle
prices. The Survey of Consumer Finances asks households about purchase information in a
number of categories, including vehicles. For the 2007 survey, we identified the households
in the survey who had bought MY 2007 or 2008 vehicles, and further looked at the income
categories for those consumers. Those with income less than $35,200 (the maximum income
for those in the bottom 40 percent of income in the CES) bought about 17 percent of new
vehicles; those with income below $18,400 (the bottom quintile) bought fewer than 2 percent
of new vehicles. These data further support the idea that low-income households are more
affected by the impact of the rule on the used-vehicle market than on the new-vehicle market.
The effect of this rule on the used vehicle market will be related to its effects on new
vehicle prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles,
and the total sales of new vehicles. If the value of fuel savings resulting from improved fuel
efficiency to the typical potential buyer of a new vehicle outweighs the average increase in
new models' prices, sales of new vehicles could rise, and the used vehicle market may
increase in volume as new vehicle buyers sell their older vehicles. In this case, low-income
households are likely to benefit from the increased availability of used vehicles. However, if
potential buyers value future fuel savings resulting from the increased fuel efficiency of new
models at less than the increase in their average selling price, sales of new vehicles will
decline, and the used vehicle market may decrease in volume as people hold onto their
vehicles longer. In this case, low-income households are likely to face increased costs due to
reduced availability of used vehicles. Because, as discussed in 8.1.1, we have not estimated
the effects of the rule on the new vehicle market, and because we do not have a good model of
the relationship between the new and used vehicle markets, we do not have estimates of the
impact of this rule on the used vehicle market. However, due to the significant effect of the
rule on fuel savings, especially for used vehicles (see RIA Chapter 5.5), we expect low-
income households to benefit from the more rapid payback period for used vehicles, though
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some of this benefit may be affected by the net effect of this rule on the prices and availability
of used vehicles.
The low-priced vehicle segment of the market may also deserve consideration,
because it may be an entry point for first-car buyers. Vehicles in the low-priced (economy-
class) segment will bear technology costs needed to meet the new standards, but it is not
known how manufacturers will decide to pass on these costs across their vehicle fleets,
including in the low-priced vehicle segment. If manufacturers decide to pass on the full cost
of compliance in this segment, then it is possible that consumers who might barely afford new
vehicles may be priced out of the new-vehicle market or may not have access to loans. As
discussed above, the rule's impacts on availability of loans is unclear, because some lenders
do factor fuel economy into their loans, and it is possible that this trend may expand. In
addition, auto makers have some flexibility in how both technologies and price changes are
applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced
vehicle segment if they so choose. Though the rule is expected to increase the prices of these
vehicles, the degrees of price increase and the impacts of the price increases, especially when
combined with the fuel savings that will accompany these changes, are much less clear.
In summary, we recognize that this rule may have impacts on consumers' access to
loans for new vehicles, on low-income households, and on the market for low-priced vehicles;
less clear are the directions of these effects. Lenders who only consider consumers' debt-to-
income ratios may reduce consumers' abilities to purchase more expensive vehicles, but some
lenders already take the fuel efficiency of vehicles into account. Low-income households will
benefit from reduced fuel costs; we do not estimate the direction of effects of this rule on used
vehicle prices, which are more relevant for low-income households than effects on new
vehicles. The effects of this rule on low-priced vehicles depends on how manufacturers add
technologies and price vehicles in this segment; they have flexibility to keep some vehicles in
this segment if they so wish.
8.2 Employment Impacts
8.2.1 Introduction
Although analysis of employment impacts is not part of a cost-benefit analysis (except
to the extent that labor costs contribute to costs), employment impacts of federal rules are of
particular concern in the current economic climate of sizeable unemployment. When
President Obama requested that the agencies develop this program, he sought a program that
would "strengthen the [auto] industry and enhance job creation in the United
States."540' uuuuu The recently issued Executive Order 13563, "Improving Regulation and
Regulatory Review" (January 18, 2011), states, "Our regulatory system must protect public
health, welfare, safety, and our environment while promoting economic growth, innovation,
uuuuuuuu The May 2^ 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the
technical assessment to inform the rulemaking process (which was issued by the agencies and CARB on
September 30, 2010), include, among other things, the "impacts on jobs and the automotive manufacturing base
in the United States."
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MY 2017 and Later - Regulatory Impact Analysis
competitiveness, and job creation" (emphasis added). EPA is accordingly providing partial
estimates of the effects of this rule on domestic employment in the auto manufacturing and
parts sectors, while qualitatively discussing how it may affect employment in other sectors
more generally.
This rule is expected to affect employment in the United States through the regulated
sector - the auto manufacturing industry - and through several related sectors, specifically,
industries that supply the auto manufacturing industry (e.g., vehicle parts), auto dealers, the
fuel refining and supply sectors, and the general retail sector. According to the U.S. Bureau
of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in Motor
Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000
people in the U.S. were employed in the Automobile and Light Truck Manufacturing Sector
(NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses
the auto manufacturers that are responsible for complying with the standards.541 Changes in
light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers.
The employment effects of this rule are expected to expand beyond the regulated sector.
Though some of the parts used to achieve the standards are likely to be built by auto
manufacturers themselves, the auto parts manufacturing sector also plays a significant role in
providing those parts, and will also be affected by changes in vehicle sales. As discussed in
Chapter 5.4 of this RIA, this rule is expected to reduce the amount of fuel these vehicles use,
and thus affect the petroleum refinery and supply industries. Finally, since the net reduction
in cost associated with this rule is expected to lead to lower household expenditures on fuel
net of vehicle costs, consumers then will have additional discretionary income that can be
spent on other goods and services.
When the economy is at full employment, an environmental regulation is unlikely to
have much impact on net overall U.S. employment; instead, labor would primarily be shifted
from one sector to another. These shifts in employment impose an opportunity cost on
society, approximated by the wages of the employees, as regulation diverts workers from
other activities in the economy. In this situation, any effects on net employment are likely to
be transitory as workers change jobs (e.g., some workers may need to be retrained or require
time to search for new jobs, while shortages in some sectors or regions could bid up wages to
attract workers).
On the other hand, if a regulation comes into effect during a period of high
unemployment, a change in labor demand due to regulation may affect net overall U.S.
employment because the labor market is not in equilibrium. In such a period, both positive
and negative employment effects are possible.vvvvvvvv Schmalansee and Stavins point out
that net positive employment effects are possible in the near term when the economy is at less
than full employment due to the potential hiring of idle labor resources by the regulated sector
to meet new requirements (e.g., to install new equipment) and new economic activity in
sectors related to the regulated sector.542 In the longer run, the net effect on employment is
more difficult to predict and will depend on the way in which the related industries respond to
vwwwv Masur ancj posner> available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441
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Chapter 8
the regulatory requirements. As Schmalansee and Stavins note, it is possible that the
magnitude of the effect on employment could vary over time, region, and sector, and positive
effects on employment in some regions or sectors could be offset by negative effects in other
regions or sectors. For this reason, they urge caution in reporting partial employment effects
since it can "paint an inaccurate picture of net employment impacts if not placed in the
broader economic context."
It is assumed that the official unemployment rate will have declined to 5.3 percent by
the time by the time this rule takes effect and so the effect of the regulation on labor will be to
shift workers from one sector to another.wvwwwwww Those shifts in employment impose an
opportunity cost on society, approximated by the wages of the employees, as regulation
diverts workers from other activities in the economy. In this situation, as discussed above, any
effects on net employment are likely to be transitory. It is also possible that the state of the
economy will be such that positive or negative employment effects will occur.
A number of different approaches have been used in published literature to conduct
employment analysis. This section describes some of the common methods, as well as some
of their limitations.
8.2.2 Approaches to Quantitative Employment Analysis
Measuring the employment impacts of a policy depend on a number of inputs and
assumptions. For instance, as discussed, assumptions about the overall state of
unemployment in the economy play a major role in measured job impacts. The inputs to the
models commonly are the changes in quantities or expenditures in the affected sectors; model
results may vary in different studies depending on the assumptions about the levels of those
inputs, and which sectors receive those changes. Which sectors are included in the study can
also affect the results. For instance, a study of this program that looks only at employment
impacts in the refinery sector may find negative effects, because consumers will purchase less
gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive
impacts, because the program will require redesigned or additional parts for vehicles. In both
instances, these would only be partial perspectives on the overall change in national
employment due to Federal regulation.
8.2.2.1 Conceptual Framework for Employment Impacts in the
Regulated Sector
One study by Morgenstern, Pizer, and Shih543 provides a retrospective look at the
impacts of regulation in employment in the regulated sectors by estimating the effects on
employment of spending on pollution abatement for four highly polluting/regulated U.S.
industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years
between 1979 and 1991. The paper provides a theoretical framework that can be useful for
examining the impacts of a regulatory change on the regulated sector in the medium to longer
wwwwwwww Office of Management and Budget, "Fiscal Year 2012 Mid-Session Review: Budget of the U.S.
Government." http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf, p. 10.
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MY 2017 and Later - Regulatory Impact Analysis
term. In particular, it identifies three separate ways that employment levels may change in the
regulated industry in response to a new (or more stringent) regulation.
• Demand effect: higher production costs due to the regulation will lead to higher
market prices; higher prices in turn reduce demand for the good, reducing the
demand for labor to make that good. In the authors' words, the "extent of this
effect depends on the cost increase passed on to consumers as well as the demand
elasticity of industry output."
• Cost effect: as costs go up, plants add more capital and labor (holding other
factors constant), with potentially positive effects on employment. In the authors'
words, as "production costs rise, more inputs, including labor, are used to produce
the same amount of output."
• Factor shift effect: post-regulation production technologies may be more or less
labor-intensive (i.e., more/less labor is required per dollar of output). In the
authors' words, "environmental activities may be more labor intensive than
conventional production," meaning that "the amount of labor per dollar of output
will rise," though it is also possible that "cleaner operations could involve
automation and less employment, for example."
According to the authors, the "demand effect" is expected to have a negative effect on
employment,xxxxxxxx the "cost effect" to have a positive effect on employment, and the
"factor shift effect" to have an ambiguous effect on employment. Without more information
with respect to the magnitudes of these competing effects, it is not possible to predict the total
effect environmental regulation will have on employment levels in a regulated sector.
The authors conclude that increased abatement expenditures generally have not caused
a significant change in employment in those sectors. More specifically, their results show
that, on average across the industries studied, each additional $1 million spent on pollution
abatement results in a (statistically insignificant) net increase of 1.5 jobs.
This approach to employment analysis has the advantage of carefully controlling for
many possibly confounding effects in order to separate the effect of changes in regulatory
costs on employment. It was, however, conducted for only four sectors. It could also be very
difficult to update the study for other sectors, because one of the databases on which it relies,
the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since
1994, with the last survey conducted in 2005. The empirical estimates provided by
Morgenstern et al. are not relevant to the case of fuel economy standards, which are very
different from the pollution control standards on industrial facilities that were considered in
xxxxxxxx ^s wjjj ke discussed below, the demand effect in this rule is potentially an exception to this rule.
While the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs
provide a countervailing impact on vehicle sales. As discussed in Preamble Section III.H. 1, this possibility that
vehicles may become more attractive to consumers after the program poses a conundrum: why have interactions
between vehicle buyers and producers not provided these benefits without government intervention?
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Chapter 8
that study. In addition, it does not examine the effects of regulation on employment in sectors
related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al.
developed continues to be useful in this context.
The following discussion of additional methodologies draws from Berck and
Hoffmann's review of employment models.544
8.2.2.2 Computable General Equilibrium (CGE) Models
Computable general equilibrium (CGE) models are often used to assess the impacts of
policy. These models include a stylized representation of supply and demand curves for all
major markets in the economy. The labor market is commonly included. CGE models are
very useful for looking at interaction effects of markets: "they allow for substitution among
inputs in production and goods in consumption." Thus, if one market experiences a change,
such as a new regulation, then the effects can be observed in all other markets. As a result,
they can measure the employment changes in the economy due to a regulation. Because they
usually assume equilibrium in all markets, though, they typically lack involuntary
unemployment. If the total amount of labor changes, it is due to people voluntarily entering
or leaving the workforce. As a result, these models may not be appropriate for measuring
effects of a policy on unemployment, because of the assumption that there is no involuntary
unemployment. In addition, because of the assumptions of equilibrium in all markets and
forward-looking consumers and firms, they are designed for examining the long-run effects of
a policy but may offer little insight into its short-run effects.
8.2.2.3 Input-Output (IO) Models
Input-output models represent the economy through a matrix of coefficients that
describe the connections between supplying and consuming sectors. In that sense, like CGE
models, they describe the interconnections of the economy. These interconnections look at
how changes in one sector ripple through the rest of the economy. For instance, a
requirement for additional technology for vehicles requires additional steel, which requires
more workers in both the auto and steel sectors; the additional workers in those sectors then
have more money to spend, which leads to more employment in retail sectors. These are
known as "multiplier" effects, because an initial impact in one sector gets multiplied through
the economy. Unlike CGE models, input-output models have fixed, linear relationships
among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity
supplied need not equal quantity demanded. In particular, these models do not allow for price
changes - an increase in the demand for labor or capital does not result in a change in its price
to help reallocate it to its best use. As a result, these models cannot capture opportunity costs
from using resources in one area of the economy over another. The multipliers take an initial
impact and can increase it substantially.
IO models are commonly used for regional analysis of projects. In a regional analysis,
the markets are commonly considered small enough that wages and prices are determined
outside the region, and any excess supply or demand is due to exports and imports (or, in the
case of labor, emigration or immigration). For national-level employment analysis, the use of
input-output models requires the assumption that workers flow into or out of the labor market
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perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool
as production requires them. For other markets as well, there is no substitution of less
expensive inputs for more expensive ones. As a result, IO models provide an upper bound on
employment impacts. As Berck and Hoffmann note, "For the same reason, they can be
thought of as simulating very short-run adjustment," in contrast to the CGE's implicit
assumption of long-run adjustment. Changes in production processes, introductions of new
technologies, or learning over time due to new regulatory requirements are also generally not
captured by IO models, as they are calibrated to already established relationships between
inputs and outputs.
8.2.2.4 Hybrid Models
As Berck and Hoffmann note, input-output models and CGE models "represent a
continuum of closely related models." Though not separately discussed by Berck and
Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices
that can change) with input-output relationships. For instance, a hybrid model may include
the ability to examine disequilibrium phenomena, such as labor being at less than full
employment. Hybrid models depend on assumptions about how adjustments in the economy
occur. CGE models characterize equilibria but say little about the pathway between them,
while IO models assume that adjustments are largely constrained by previously defined
relationships; the effectiveness of hybrid models depends on their success in overcoming the
limitations of each of these approaches. Hybrid models could potentially be used to model
labor market impacts of various vehicle policy options although a number of judgments need
to be made about the appropriate assumptions underlying the model as well the empirical
basis for the modeling results.
8.2.2.5 Single Sectors
It is possible to conduct a bottom-up analysis of the partial effect of regulation on
employment in a single sector by estimating the change in output or expenditures in a sector
and multiplying it by an estimate of the number of workers per unit of output or expenditures,
under the assumption that labor demand is proportional to output or expenditures. As Berck
and Hoffmann note, though, "Compliance with regulations may create additional jobs that are
not accounted for." While such an analysis can approximate the effects in that one sector in a
simple way, it also may miss important connections to related sectors.
8.2.2.6 Ex-Post Econometric Studies
A number of ex-post econometric analyses examine the net effect of regulation on
employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and
Berman and Bui (2001) are two notable examples that rely on highly disaggregated
establishment-level time series data to estimate longer-run employment effects.545 While
often a sophisticated treatment of the issues analyzed, these studies commonly analyze
specific scenarios or sectors in the past; care needs to be taken in extrapolating their results to
other scenarios and to the future. For instance, neither of these two studies examines the auto
industry and are therefore of limited applicability in this context.
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8.2.2.7 Summary
All methods of estimating employment impacts of a regulation have advantages and
limitations. CGE models may be most appropriate for long-term impacts, but the usual
assumption of equilibrium in the employment market means that it is not useful for looking at
changes in overall employment: overall levels are likely to be premised on full employment.
IO models, on the other hand, may be most appropriate for small-scale, short-term effects,
because they assume fixed relationships across sectors and do not require market equilibria.
Hybrid models, which combine some features of CGEs with IO models, depend upon key
assumptions and economic relationships that are built into them. Single-sector models are
simple and straightforward, but they are often based on the assumptions that labor demand is
proportional to output, and that other sectors are not affected. Finally, econometric models
have been developed to evaluate the longer-run net effects of regulation on sector
employment, though these are ex-post analyses commonly of specific sectors or situations,
and the results may not have direct bearing for the regulation being reviewed.
8.2.3 Employment Analysis of This Rule
As mentioned above, this program is expected to affect employment in the regulated
sector (auto manufacturing) and in other sectors directly affected by the rule: auto parts
suppliers, auto dealers, and the fuel supply market (which will face reduced petroleum
production due to reduced fuel demand but which may see additional demand for electricity
or other fuels). Changes in consumer expenditures due to higher vehicle costs and lower fuel
expenses will also affect employment. In addition, as the discussion above suggests, each of
these sectors could potentially have ripple effects in the rest of the economy. These ripple
effects depend much more heavily on the state of the macroeconomy than do the direct
effects. At the national level, employment may increase in one industry or region and
decrease in another, with the net effect being smaller than either individual-sector effect.
EPA does not attempt to quantify the net effects of the regulation on overall national
employment.
The discussion that follows provides a partial, bottom-up quantitative estimate of the
effects of this rule on the regulated sector (i.e., the auto industry; for reasons discussed below,
we include some quantitative assessment of effects on suppliers to the auto industry although
suppliers are not regulated directly). It also includes qualitative discussion of the effects of
the rule on other sectors. Focusing quantification of employment impacts on the regulated
sector has some advantages over quantifying all impacts. First, the analysis relies on data
generated as part of the rulemaking process, which focuses on the regulated sector; as a result,
what is presented here is based on internally consistent assumptions and estimates made in
this rule. Second, as discussed above, net effects on employment in the economy as a whole
depend heavily on the overall state of the economy when this rule has its effects. Focusing on
the regulated sector provides insight into employment effects in that sector without having to
make assumptions about the state of the economy when this rule has its impacts. We include
a qualitative discussion of employment effects on other sectors to provide a broader
perspective on the impacts of this rule.
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As noted above, in a full-employment economy, any changes in employment will
result from people changing jobs or voluntarily entering or exiting the workforce. In a full-
employment economy, employment impacts of this rule will change employment in specific
sectors, but it will have small, if any, effect on aggregate employment. This rule would take
effect in model years 2017 through 2025; by then, the current high unemployment may be
moderated or ended. For that reason, this analysis does not include multiplier effects, but
instead focuses on employment impacts in the most directly affected industries. Those sectors
are likely to face the most concentrated employment impacts.
8.2.3.1 Employment Impacts in the Auto Industry
Following the Morgenstern et al. conceptual framework for the impacts of regulation
on employment in the regulated sector, we consider three effects for the auto sector: the
demand effect, the cost effect, and the factor shift effect. However, we are only able to offer
quantitative estimates for the cost effect. We note that these estimates, based on
extrapolations from current data, become more uncertain as time goes on.
8.2.3.1.1 The Demand Effect
The demand effect depends on the effects of this rule on vehicle sales. If vehicle sales
increase, then more people will be required to assemble vehicles and their components. If
vehicle sales decrease, employment associated with these activities will unambiguously
decrease. Unlike in Morgenstern et al.'s study, where the demand effect decreased
employment, there are countervailing effects in the vehicle market due to the fuel savings
resulting from this program. On one hand, this rule will increase vehicle costs; by itself, this
effect would reduce vehicle sales. On the other hand, this rule will reduce the fuel costs of
operating the vehicle; by itself, this effect would increase vehicle sales, especially if potential
buyers have an expectation of higher fuel prices. The sign of demand effect will depend on
which of these effects dominates. Because, as described in Chapter 8.1, we have not
quantified the impact on sales for this rule, we do not quantify the demand effect.
8.2.3.2 The Cost Effect
The demand effect, discussed above, measures employment changes due to new
vehicle sales only. The cost effect measures employment impacts due to the development,
manufacturing, and installation by auto suppliers and manufacturers of the new or additional
technologies needed for vehicles to comply with the standards.
One way to estimate the cost effect, given the cost estimates for complying with the
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 light-duty vehicle 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
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Chapter 8
expenditures are required on specific activities, as the factor shift effect (discussed below)
indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the
ratio for all vehicle manufacturing, not just for fuel efficiency improvements. 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
auto 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.
Some of the costs of this rule will be spent directly in the auto manufacturing sector,
but some of the costs will be spent in the auto parts manufacturing sector. Because we do not
have information on the proportion of expenditures in each sector, we separately present the
ratios for both the auto manufacturing sector and the auto parts manufacturing sector. These
are not additive, but should instead be considered as a range of estimates for the cost effect,
depending on which sector adds technologies to the vehicles to comply with the regulation.
We use several sources for estimates of employment per $1 million expenditures. The
U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix
(ERM),546 which provides direct estimates of the employment per $1 million in sales of goods
in 202 sectors. The estimates used here, updated from the NPRM, are from 2010 (adjusted to
2010$). Not all expenditures are for domestically produced vehicles, however. To estimate
the proportion of domestic expenditures affected by the rule, we use data from Ward's
Automotive Group for total car and truck production in the U.S. compared to total car and
truck sales in the U.S.547 For the period 2001-2010, the proportion is 66.7%. We thus weight
sales by this factor to get an estimate of the effect on employment in the motor vehicle
manufacturing sector due to this rule.
The Annual Survey of Manufactures548 (ASM) provides another source of estimates
based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing
establishments. It includes more sectoral detail than the BLS ERM: for instance, while the
ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit
NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle
manufacturing). While the ERM provides direct estimates of employees/$ 1 million in
expenditures, the ASM separately provides number of employees and value of shipments; the
direct employment estimates here are the ratio of those values. The data in the ASM are
updated annually, except for years when the full Economic Census occurs. The tables
presented here use data from 2010 (also updated from the NPRM). As with the ERM, we
adjust for the ratio of domestic production to domestic sales. The values reported are for
Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle
Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).
The Economic Census includes all large companies and a sample of smaller ones. The
ASM is a subset of the Economic Census; though the Census itself is more complete, it is
conducted only every 5 years, while the ASM is annual. The values presented here use data
from 2007 (adjusted to 2010$), with the domestic production-to-sales adjustment. The values
reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty
Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing
(NAICS 3363).
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Table 8.2-2 provides the values, either given (BLS) or calculated (ASM, Economic
Census) for employment per $1 million of expenditures, all based on 2010 dollars, though the
underlying data come from different years (which may account for some of the differences).
The different data sources provide similar magnitudes for the estimates for the sectors. Parts
manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty
vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle
manufacturing as a whole.
Table 8.2-2 Employment per $1 Million Expenditures (2010$) in the Motor Vehicle
Manufacturing Sector*
Source
BLS ERM
ASM
ASM
Economic
Census
Economic
Census
BLS ERM
ASM
Economic
Census
Sector
Motor Vehicle Mfg
Motor Vehicle Mfg
Light Duty Vehicle
Mfg
Motor Vehicle Mfg
Light Duty Vehicle
Mfg
Motor Vehicle Parts
Mfg
Motor Vehicle Parts
Mfg
Motor Vehicle Parts
Mfg
Ratio of
workers per
$1 million
expenditures
0.770
0.655
0.609
0.665
0.602
2.614
2.309
2.712
Ratio of workers per $1
million expenditures,
adjusted for domestic vs.
foreign production
0.514
0.437
0.406
0.443
0.402
1.743
1.540
1.809
BLS ERM refers to the U.S. Bureau of Labor Statistics' Employment Requirement Matrix. ASM refers to the
U.S. Census Bureau's Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau's
Economic Census.
Over time, the amount of labor needed in the auto industry has changed: automation
and improved production methods have led to significant productivity increases. The BLS
ERM, for instance, provided estimates that, in 1993, 1.64 workers in the Motor Vehicle
Manufacturing sector were needed per $1 million of 2005$, but only 0.86 workers by 2010 (in
2005$). Because the ERM is available annually for 1993-2010, we used these data to
estimate productivity improvements over time. We regressed logged ERM values on year for
both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. We
used this approach because the coefficient describing the relationship between time and
productivity is a direct measure of the percent change in productivity per year. The results
suggest a 3.9 percent per year productivity improvement in the Motor Vehicle Manufacturing
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Chapter 8
Sector, and a 3.8 percent per year improvement in the Motor Vehicle Parts Manufacturing
Sector. We then used the equation resulting from the regression to project the ERM through
2025. In the results presented below, these projected values (adjusted to 2010$) were used
directly for the BLS ERM estimates. For the ASM, we used the ratio of the projected value in
the future to the projected value in 2010 (the base year for the ASM); for the Economic
Census estimates, we used the ratio of the projected value in the future to the projected value
in 2007 (the base year for that estimate). This is a simple way to examine the relationship
between labor required and expenditure.
Table 8.2-3 shows the cost estimates developed for this rule, discussed in Chapter 5.
The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures
(after accounting for the share of domestic production) is 1.809 in 2010 if all the additional
costs are in the parts sector; the minimum value is 0.402 in 2010, if all the additional costs are
in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is
between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2010. The
results in Table 8.2-3 include the productivity adjustment described above.
While we estimate employment impacts, measured in job-years, beginning with the
first year of the standard (2017), some of these employment gains may occur earlier as auto
manufacturers and parts suppliers hire staff in anticipation of compliance with the standard.
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 8.2-3 Employment per $1 Million in the Motor Vehicle Manufacturing Sector, in
job-years
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
Total
Costs (before
adjustment for
domestic proportion
of production)
($Millions)
$ 2,435
$ 4,848
$ 6,818
$ 8,858
$ 12,400
$ 18,323
$ 23,734
$ 29,101
$ 31,678
Minimum
employment effect
(if all expenditures
are in the parts
sector)
700
1,300
1,700
2,100
2,900
4,100
5,100
6,000
6,300
30,300
Maximum
employment effect (if
all expenditures are
in the light duty
vehicle mfg sector)
3,200
6,200
8,400
10,500
14,200
20,200
25,200
29,700
31,100
148,800
We note that the cost effect depends only on technology costs, not vehicle sales. It is
therefore not sensitive to assumptions about how consumers consider fuel savings at the time
of vehicle purchase.
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8.2.3.2.1 The Factor Shift Effect
The factor shift effect looks at the effects on employment due to changes in labor
intensity associated with a regulation. As noted above, the estimates of the cost effect assume
constant labor per $1 million in expenditures, though the new technologies may be either
more or less labor-intensive than the existing ones. An estimate of the factor shift effect
would either increase or decrease the estimate used for the cost effect.
We are not quantifying the factor shift effect here, for lack of data on the labor
intensity of all the possible technologies that manufacturers could use to comply with the
standards. For a subset of the technologies, though, EPA-sponsored research (discussed in
Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing ones at the
level of individual components provides some insights into the factor shift effect.
The comparison involved tearing down the selected technologies to their individual
components and looking at the differences in materials and labor needs in moving from the
conventional to the new technologies. For instance, the analysis compared all the parts and
labor associated with an 8-speed automatic transmission to those needed for a 6-speed
automatic transmission.
Because labor cost was one of the sources of differences between the technologies, it
is possible, for those technologies, to see whether labor needs increase or decrease with the
switch to technologies that might contribute to compliance with the standards. An increase in
labor cost for the new technology indicates an increase in the labor needed for the new
technology compared to the baseline technology. For instance, an 8-speed transmission
requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA's
cost estimates for the rule). Dividing the labor cost by a wage per hour estimate provides an
estimate of the additional hours (and thus the additional labor) needed for the new technology
compared to the baseline technology. As with labor cost, an increase in labor hours per
technology indicates greater employment needs for the new technologies. For this
conversion, a weighted average wage rate (90 percent of the average wage in the Motor
Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle
Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the
FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an
additional 0.33 hours of labor per transmission.
Table 8.2-4 shows the changes in labor hours in moving from baseline to new fuel-
saving technologies for technologies in the FEV study. It indicates that, in switching from the
baseline to the new technologies, labor use per technology increased: the fuel-saving
technologies use more labor than the baseline technologies. For a subset of the technologies
likely to be used to meet the standards in this rule, then, the factor shift effect increases labor
demand, at least in the short run; in the long run, as with all technologies, the cost structure is
likely to change due to learning, economies of scale, etc. The technologies examined in this
research are, however, only a subset of the technologies that auto makers may use to comply
with the standards in this program. As a result, these results cannot be considered definitive
evidence that the factor shift effect increases employment for this rule. We therefore do not
quantify the factor shift effect for this rule.
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Chapter 8
Table 8.2-4 Estimated Change in Labor for Selected Compliance Technologies
Technology
Downsized
Turbo GDI 4
Downsized
Turbo GDI V6
Downsized
Turbo GDI V6
Electric A/C
compressor
Power split
hybrid
6- to 8-speed
transmission
FEV
Case
Study
0101
0102
0104
0602
0502
0803
Vehicle Class
Compact C
Mid/Large C
SUV/Trucks
Mid/Large C
Mid/Large C
Labor
Costs
$72.58
$25.76
$84.19
$4.68
$395.85
$15.11
Total
Costs
$537.70
$87.38
$789.53
$167.54
$3,435.01
$61.84
Hours/
Technology
1.57
0.56
1.82
0.10
8.54
0.33
8.2.3.2.2 Summary of Employment Effects in the Auto Sector
While we are not able to quantify the demand or factor shift effects, the cost effect
results show that the employment effects of the increased spending in the regulated sector
(and, possibly, the parts sector) are expected to be positive and on the order of a few thousand
in the initial years of the program. As noted above, motor vehicle and parts manufacturing
sectors employed about 677,000 people in 2010, with automobile and light truck
manufacturing accounting for about 129,000 of that total.
8.2.4 Effects on Employment for Auto Dealers
The effects of the standards on employment for auto dealers depend principally on the
effects of the standards on light duty vehicle sales: increases in sales are likely to contribute
to employment at dealerships, while reductions in sales are likely to have the opposite effect.
In addition, auto dealers may be affected by changes in maintenance and service costs.
Increases in those costs are likely to increase labor demand in dealerships, and reductions are
likely to decrease labor demand.
Although this rule predicts very small penetration of plug-in hybrid and electric
vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater.
As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles
and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than
gasoline, attractive; they may find other characteristics, such as the limited range for electric
vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs,
but other buyers will choose more conventional vehicles. Auto dealers may play a major role
in explaining the merits and disadvantages of these new technologies to vehicle buyers. There
may be a temporary need for increased employment to train sales staff in the new
technologies as the new technologies become available.
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8.2.5 Effects on Employment in the Auto Parts Sector
As discussed in the context of employment in the auto industry, some vehicle parts are
made in-house by auto manufacturers; others are made by independent suppliers who are not
directly regulated, but who will be affected by the standards as well. The additional
expenditures on technologies are expected to have a positive effect on employment in the
parts sector as well as the manufacturing sector; the breakdown in employment between the
two sectors is difficult to predict. The effects on the parts sector also depend on the effects of
the standards on vehicle sales and on the labor intensity of the new technologies, qualitatively
in the same ways as for the auto manufacturing sector.
8.2.6 Effects on Employment for Fuel Suppliers
In addition to the effects on the auto manufacturing and parts sectors, these rules will
result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in
liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry
sectors throughout the supply chain, from refineries to gasoline stations. To the extent that
the standards result in increased use of electricity or other new fuels, employment effects will
result from providing these fuels and developing the infrastructure to supply them to
consumers.
Expected petroleum fuel consumption reductions can be found in RIA Chapter 5.4.
While this reduced consumption represents fuel savings for purchasers of fuel, it represents a
loss in value of output for the petroleum refinery industry, fuel distributors, and gasoline
stations. The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel
supply chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced
employment in these sectors.
This rule is also expected to lead to increases in electricity consumption by vehicles,
as discussed in RIA Chapter 5.4. This new fuel may require additional infrastructure, such as
electricity charging locations. Providing this infrastructure, as well as infrastructure for other
alternative fuels (such as CNG), will require some increased employment. In addition, the
generation of electricity is likely to require some additional labor. We have insufficient
information at this time to predict whether the increases in labor associated with increased
infrastructure provision and generation for electricity will be greater or less than the
employment reductions associated with reduced demand for petroleum fuels.
8.2.7 Effects on Employment due to Impacts on Consumer Expenditures
As a result of these standards, consumers will pay a higher up-front cost for the
vehicles, but they will recover those costs in a fairly short payback period (see Preamble
Section III.H.5 and Chapter 5.5 of this RIA); indeed, people who finance their vehicles are
expected to find that their fuel savings per month exceed the increase in the loan cost (though
this depends on the particular loan rate a consumer receives). As a result, consumers will
have additional money to spend on other goods and services, though, for those who do not
finance their vehicles, it will occur after the initial payback period. These increased
expenditures will support employment in those sectors where consumers spend their savings.
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Chapter 8
These increased expenditures will occur in 2017 and beyond. If the economy returns
to full employment by that time, any change in consumer expenditures would primarily
represent a shift in employment among sectors. If, on the other hand, the economy still has
substantial unemployment, these expenditures would contribute to employment through
increased consumer demand.
8.2.8 Summary
The primary employment effects of this rule are expected to be found throughout
several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel
production and supply, and consumers. These standards initially take effect in model year
2017, a time period sufficiently far in the future that the current sustained high unemployment
at the national level may be moderated or ended. 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, the cost effect is expected to increase employment by 700 -
3,200 job-years in 2017, depending on the share of that employment that is in the auto
manufacturing sector compared to the auto parts manufacturing sector. 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. The demand effect depends on changes in vehicle
sales, which are not quantified for this rule. Though we do not have estimates of the factor
shift effect for all potential compliance technologies, the evidence which we do have for some
technologies suggests that many of the technologies will have increased labor needs.
Effects in other sectors that are predicated on vehicle sales are also ambiguous.
Changes in vehicle sales are expected to affect labor needs in auto dealerships and in parts
manufacturing. Increased expenditures for auto parts are expected to require increased labor
to build parts, though this effect also depends on any changes in the labor intensity of
production; as noted, the subset of potential compliance technologies for which data are
available show increased labor requirements. Reduced petroleum fuel production implies less
employment in the petroleum sectors, although there could be increases in employment
related to providing infrastructure for alternative fuels such as electricity and CNG. Finally,
consumer spending is expected to affect employment through changes in expenditures in
general retail sectors; net fuel savings by consumers are expected to increase demand (and
therefore employment) in other sectors.
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References
490 Goldberg, Pinelopi Koujianou, "Product Differentiation and Oligopoly in International Markets:
The Case of the U.S. Automobile Industry," Econometrica 63(4) (July 1995): 891-951 (Docket EPA-
HQ-OAR-2010-0799-0665); Goldberg, Pinelopi Koujianou, "The Effects of the Corporate Average
Fuel Efficiency Standards in the US," Journal of Industrial Economics 46(1) (March 1998): 1-33
(Docket EPA-HQ-OAR-2010-0799-0666).
491 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, "A Fuel Economy Regulatory
Analysis Model (FERAM) For the Energy Information Administration," prepared by the Oak Ridge
National Laboratory for the U.S. Department of Energy under contract No. DE-AC0500OR22725,
2005 (Docket EPA-HQ-OAR-2010-0799-0715); Greene, David L., Philip D. Patterson, Margaret
Singh, and Jia Li, "Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased
Fuel Economy." Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686).
492 McManus, Walter M., "Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-
Price Risks?" University of Michigan Transportation Research Institute, September 14, 2006 (Docket
EPA-HQ-OAR-2010-0799-0687).
493 Berry, Steven, James Levinsohn, and Ariel Pakes, "Automobile Prices in Market Equilibrium,"
Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688); Berry, Steven,
James Levinsohn, and Ariel Pakes, "Differentiated Products Demand Systems from a Combination of
Micro and Macro Data: The New Car Market," Journal of Political Economy 112(1) (2004): 68-105
(Docket EPA-HQ-OAR-2010-0799-0689).
494 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von
Haefen, "Distributional and Efficiency Impacts of Gasoline Taxes" American Economic Review 99(3)
2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690).
495 Train, Kenneth E., and Clifford Winston, "Vehicle Choice Behavior and the Declining Market
Share of U.S. Automakers," International Economic Review 48(4) (November 2007): 1469-1496
(Docket EPA-HQ-OAR-2010-0799-0691).
496 Kleit, Andrew N., "Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,"
Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-0799-0826).
497 Austin, David, and Terry Dinan, "Clearing the Air: The Costs and Consequences of Higher CAFE
Standards and Increased Gasoline Taxes," Journal of Environmental Economics and Management 50
(2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).
498 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H.
von Haefen, "Distributional and Efficiency Impacts of Gasoline Taxes," American Economic Review
99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Train, Kenneth E., and Clifford
Winston, "Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,"
International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-
0799-0691).
499 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, "Automobile Prices in Market
Equilibrium." Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688).
8-33
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500 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, "A Fuel Economy Regulatory
Analysis Model (FERAM) For the Energy Information Administration," prepared by the Oak Ridge
National Laboratory for the U.S. Department of Energy under contract No. DE-AC0500OR22725,
2005 (Docket EPA-HQ-OAR-2010-0799-0715).
501 NERA Economic Consulting, "Evaluation of NHTSA's Benefit-Cost Analysis of 2011-2015 CAFE
Standards," 2008, available at
http://www.hearttand.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_Analysis_Of
_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799-0693).
502 E.g., Train, Kenneth E., and Clifford Winston, "Vehicle Choice Behavior and the Declining Market
Share of U.S. Automakers," International Economic Review 48(4) (November 2007): 1469-1496
(Docket EPA-HQ-O AR-2010-0799-0691).
503 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, "Automobile Prices in Market
Equilibrium." Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688).
504 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H.
von Haefen, "Distributional and Efficiency Impacts of Gasoline Taxes," American Economic Review
99(3)2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690).
505 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von
Haefen, "Distributional and Efficiency Impacts of Gasoline Taxes," American Economic Review
99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don Fullerton, and Li
Gan, "Vehicle Choices, Miles Driven and Pollution Policies," National Bureau of Economic Analysis
Working Paper 11553, available at http://econweb.tamu.edu/gan/wll553.pdf, accessed 5/12/09
(Docket EPA-HQ-OAR-2010-0799-0694).
506 NERA Economic Consulting, "Appendix B: New Vehicle Market Model," "Impacts of the
California Greenhouse Gas Emission Standards on Motor Vehicle Sales," comments submitted to the
U.S. Environmental Protection Agency at Regulations.gov, document number EPA-HQ-OAR-2006-
0173-9053.1 (Docket EPA-HQ-O AR-2010-0799-0716).
507 E.g., Train, Kenneth E., and Clifford Winston, "Vehicle Choice Behavior and the Declining Market
Share of U.S. Automakers," International Economic Review 48(4) (November 2007): 1469-1496
(Docket EPA-HQ-O AR-2010-0799-0691)
508 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, "Automobile Prices in Market
Equilibrium." Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688).
509 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, "Feebates, Rebates, and Gas-
Guzzler Taxes: A Study of Incentives for Increased Fuel Economy," Energy Policy 33 (2005): 757-
775 (Docket EPA-HQ-OAR-2010-0799-0686).
510 Rex, Emma, and Henrikke Baumann (2007). "Beyond ecolabels: what green marketing can learn
from conventional marketing." Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-
2010-0799-0695).
511 Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, "Product Design Responses to Industrial
Policy: Evaluating Fuel Economy Standards Using an Engineering Model of Endogenous Product
8-34
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MY 2017 and Later - Regulatory Impact Analysis
Design," Energy Institute at Haas Working Paper 214, University of California Energy Institute,
February 2011 (Docket EPA-HQ-OAR-2010-0799).
512 Berry, Steven, James Levinsohn, and Ariel Fakes, "Automobile Prices in Market Equilibrium,"
Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688).
513 Goldberg, Pinelopi Koujianou, "Product Differentiation and Oligopoly in International Markets:
The Case of the U.S. Automobile Industry," Econometrica 63(4) (July 1995): 891-951 (Docket EPA-
HQ-OAR-2010-0799-0665).
514 Brownstone, David, and Kenneth Train, "Forecasting New Product Penetration with Flexible
Substitution Patterns," Journal of Econometrics 89 (1999): 109-129 (Docket EPA-HQ-OAR-2010-
0799-0697); Brownstone, David, David S. Bunch, and Kenneth Train, "Joint Mixed Logit Models of
Stated and Revealed Preferences for Alternative-Fuel Vehicles," Transportation Research Part B 34
(2000): 315-338 (Docket EPA-HQ-OAR-2010-0799-0698); Greene David L., "TAFV Alternative
Fuels and Vehicles Choice Model Documentation," prepared by the Oak Ridge National Laboratory
for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799-0717); Greene,
David L., K. G. Duleep, and Walter McManus, "Future Potential of Hybrid and Diesel Powertrains in
the U.S. Light-Duty Vehicle Market," prepared by the Oak Ridge National Laboratory for the U.S.
Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799-0718).
515 Petrin, Amil, "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of
Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799-0699); Berry, Steven,
James Levinsohn, and Ariel Pakes, "Differentiated Products Demand Systems from a Combination of
Micro and Macro Data: The New Car Market," Journal of Political Economy 112 (2004): 68-105
(Docket EPA-HQ-OAR-2010-0799-0689).
516 Train, Kenneth E., and Clifford Winston, "Vehicle Choice Behavior and the Declining Market
Share of U.S. Automakers," International Economic Review 48 (November 2007): 1469-1496
(Docket EPA-HQ-OAR-2010-0799-0691).
517 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von
Haefen, "Distributional and Efficiency Impacts of Gasoline Taxes," American Economic Review
99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don Fullerton, and Li
Gan, "Vehicle Choices, Miles Driven and Pollution Policies," National Bureau of Economic Analysis
Working Paper 11553, available at http://econweb.tamu.edu/gan/wll553.pdf, accessed 5/12/09
(Docket EPA-HQ-OAR-2010-0799-0694).
518 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, "Feebates, Rebates, and Gas-
Guzzler Taxes: A Study of Incentives for Increased Fuel Economy," Energy Policy 33 (2005): 757-
775 (Docket EPA-HQ-OAR-2010-0799-0686); Feng, Yi, Don Fullerton, and Li Gan, "Vehicle
Choices, Miles Driven and Pollution Policies," National Bureau of Economic Analysis Working Paper
11553, available at http://econweb.tamu.edu/gan/wl 1553.pdf, accessed 5/12/09 (Docket EPA-HQ-
OAR-2010-0799-0694); Greene, David L., "Feebates, Footprints and Highway Safety," Transportation
Research Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR-2010-0799-0685).
519 E.g., Austin, David, and Terry Dinan, "Clearing the Air: The Costs and Consequences of Higher
CAFE Standards and Increased Gasoline Taxes," Journal of Environmental Economics and
Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).
8-35
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Chapter 8
520 Turrentine, Thomas S., and Kenneth S. Kurani, "Car Buyers and Fuel Economy?" Energy Policy
35(2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799-0661).
521 E.g., Espey, Molly, and Santosh Nair, 2005. "Automobile Fuel Economy: What Is It Worth?"
Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799-0700).
522 Larrick, Richard P., and Jack B. Soil, 2008. "The MPG Illusion," Science 320(5883): 1593-1594
(Docket EPA-HQ-OAR-2010-0799-0662).
523 E.g., Goldberg, Pinelopi Koujianou, "Product Differentiation and Oligopoly in International
Markets: The Case of the U.S. Automobile Industry," Econometrica 63(4) (July 1995): 891-951
(Docket EPA-HQ-OAR-2010-0799-0665).
524 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, "Automobile Prices in Market
Equilibrium." Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688).
525 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. "Pain at the Pump: How
Gasoline Prices Affect Automobile Purchasing in New and Used Markets," working paper,
http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-
OAR-2010-0799-0667); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 2009. "How
Do Gasoline Prices Affect Fleet Fuel Economy?" American Economic Journal: Economic Policy
1(2): 113-137 (Docket EPA-HQ-OAR-2010-0799-0701); Congressional Budget Office (2008)
(Docket EPA-HQ-OAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices
on Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883 (Docket
EPA-HQ-OAR-2010-0799-0719); West. Sarah (2007). "The Effect of Gasoline Prices on the Demand
for Sport Utility Vehicles." Working Paper. Macalester College,
http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPA-HQ-
OAR-2010-0799-0702).
526 Greene, David L., and Jin-Tan Liu, 1988. "Automotive Fuel Economy Improvements and
Consumers' Surplus," Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQ-OAR-
2010-0799-0703).
527 Greene, David L. (2010). "How Consumers Value Fuel Economy: A Literature Review." EPA
Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799).
528 Gramlich, Jacob, "Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S.
Automobile Industry," http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf, accessed 11/1/11
(Docket EPA-HQ-OAR-2010-0799-0711).
529 Gramlich, Jacob, "Gas Prices and Endogenous Product Selection in the U.S. Automobile Industry,"
http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf, accessed 11/1/11 (Docket EPA-HQ-OAR-
2010-0799-0704); McManus, Walter, 2007. "The Impact of Attribute-Based Corporate Average Fuel
Economy (CAFE) Standards: Preliminary Results." Ann Arbor, MI: University of Michigan
Transportation Research Institute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799-
0705).
530 For instance, Kleit, Andrew N., "Impacts of Long-Range Increases in the Fuel Economy (CAFE)
Standard," Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-0799-0672);
Austin, David, and Terry Dinan, "Clearing the Air: The Costs and Consequences of Higher CAFE
Standards and Increased Gasoline Taxes," Journal of Environmental Economics and Management 50
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MY 2017 and Later - Regulatory Impact Analysis
(2005): 562- (Docket EPA-HQ-OAR-2010-0799-0692); Klier, Thomas, and Joshua Linn, 2008.
"New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,"
Resources for the Future Paper RFF DP 10-50 (December 2010),
http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPA-HQ-OAR-2010-0799-0706);
Jacobsen, Mark, 2010. "Evaluating U.S. Fuel Economy Standards in a Model with Producer and
Household Heterogeneity," working paper, athttp://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf
(accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799-0829).
531 Austin, David, and Terry Dinan, "Clearing the Air: The Costs and Consequences of Higher CAFE
Standards and Increased Gasoline Taxes," Journal of Environmental Economics and Management 50
(2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).
532IEA. 2007. "Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency." Paris,
France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799-0720); Jaffe, Adam B.,
Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and Climate
Change Policies: Issues and Evidence." In Climate Change Economics and Policy, Toman, Michael
A., ed., Washington, D.C.: Resources for the Future, p.171-181 (Docket EPA-HQ-OAR-2010-0799-
0828); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the Energy Savings From Home
Improvement Investments: Evidence From Monthly Billing Data." The Review of Economics and
Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-0799-0659); Tietenberg, T. (2009).
"Reflections - Energy Efficiency Policy: Pipe Dream or Pipeline to the Future?" Review of
Environmental Economics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799-0707).
533 Helfand, Gloria, and Ann Wolverton (2011). "Evaluating the Consumer Response to Fuel
Economy: A Review of the Literature." International Review of Environmental and Resource
Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799-0652).
534 Greene David L., "TAFV Alternative Fuels and Vehicles Choice Model Documentation," prepared
by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-
HQ-OAR-2010-0799-0717).
535 U.S. Environmental Protection Agency. "Peer Review for the Consumer Vehicle Choice Model
and Documentation." Office of Transportation and Air Quality, Assessment and Standards Division,
EPA-420-R-12-013, April 2012 (Docket EPA-HQ-OAR-2010-0799).
536 See, for instance, Ladika, Susan (2009). "'Green' auto loans offer lower rates," Bankrate.com,
http://www.bankrate.com/finance/auto/green-auto-loans-offer-lower-rates-l.aspx, accessed 2/28/12
(Docket EPA-HQ-OAR-2010-0799).
537 Helfand, Gloria (2012). "Memorandum: Lending institutions that provide discounts for more fuel-
efficient vehicles." Assessment and Standards Division, Office of Transportation and Air Quality,
U.S. Environmental Protection Agency, Docket EPA-HQ-OAR-0799 (Docket EPA-HQ-OAR-2010-
0799).
538 U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey 2010.
http://www.bls.gov/cex/tttables. This analysis uses the table Quintiles of Income before Taxes,
accessed 6/14/12 (Docket EPA-HQ-OAR-2010-0799).
539
Federal Reserve System, Survey of Consumer Finances 2007.
http://www.federalreserve.gov/econresdata/scf/scfindex.htm. We applied the weighting scheme for
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Chapter 8
the data, to make the data representative of the population as a whole, provided in the documentation
for the results presented here (Docket EPA-HQ-OAR-2010-0799).
540 President Barack Obama. "Presidential Memorandum Regarding Fuel Efficiency Standards. The
White House, Office of the Press Secretary, May 21, 2010. http://www.whitehouse.gov/the-press-
office/presidential-memorandum-regar ding-fuel-efficiency-standards
541 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on
August 9, 2011.
542 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 (Docket
EPA-HQ-O AR-2010-0799-0676).
543 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-2010-0799-0677).
544 Berck, Peter, and Sandra Hoffmann. "Assessing the Employment Impacts of Environmental and
Natural Resource Policy." Environmental and Resource Economics 22 (2002): 133-156 (Docket
EPA-HQ-OAR-2010-0799-0678).
545 Berman, Eli, and Linda T. Bui, (2001) "Environmental Regulation and Labor Demand: Evidence
from the South Coast Air Basin," Journal of Public Economics, 79, 265 - 295 (Docket EPA-HQ-
OAR-2010-0799-0679).
546 http://www.bls.gov/emp/ep_data_emp_requirements.htm; see the "Cost Effect Employment
Impacts Final Rulemaking" spreadsheet, Docket EPA-HQ-OAR-2010-0799.
547 U.S. Vehicle Production by Manufacturer, UsaPr05.xls, and U.S. Vehicle Sales, UsaSa01.xls,
accessed July 25, 2011 at http://subscribers.wardsauto.com/refcenter/, ©Copyright 2011, Ward's
Automotive Group, a division of Penton Media Inc. Redistribution prohibited. Docket EPA-HQ-
OAR-2010-0799.
548 http://www.census.gov/manufacturing/asm/index.html; see the "Cost Effect Employment Impacts
Final Rulemaking" spreadsheet, Docket EPA-HQ-OAR-2010-0799.
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MY 2017 and Later - Regulatory Impact Analysis
9 Small Business Flexibility Analysis
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. As a part of this
analysis, an agency is directed to convene a Small Business Advocacy Review Panel (SBAR
Panel or 'the Panel'), unless the agency certifies that the rule will not have a significant
economic impact on a substantial number of small entities. During such a Panel process, the
agency would gather information and recommendations from Small Entity Representatives
(SERs) on how to reduce the impact of the rule on small entities. As discussed below, EPA is
certifying that this rule will not have a significant economic impact on a substantial number of
small entities, and thus we have not conducted an SBAR Panel for this rulemaking.
The following discussion provides an overview of small entities in the vehicle market.
Small entities include small businesses, small organizations, and small governmental
jurisdictions. For the purposes of assessing the impacts of the rule on small entities, a small
entity is defined as: (1) a small business that meets the definition for business based on the
Small Business Administration's (SBA) size standards (see Table 9.1-1); (2) a small
governmental jurisdiction that is a government of a city, county, town, school district or
special district with a population of less than 50,000; and (3) a small organization that is any
not-for-profit enterprise which is independently owned and operated and is not dominant in its
field. Table 9.1-1 provides an overview of the primary SBA small business categories
potentially affected by this regulation.
Table 9.1-1 Primary Vehicle SBA Small Business Categories
Industry a
Vehicle manufacturers (including
small volume manufacturers)
Independent commercial
importers
Alternative Fuel Vehicle
Converters
Defined as Small Entity
by SBA if Less Than or
Equal to:
1,000 employees
$7 million annual sales
$23 million annual sales
100 employees
750 employees
1,000 employees
$7 million annual sales
NAICS Codes b
336111,336112
811111,811112,811198
441120
423110
336312,336322,336399
335312
811198
' Light-duty vehicle entities that qualify as small businesses are not be subject to this rule. We are exempting
small business entities from the GHG standards.
b North American Industrial Classification System
We compiled a list of vehicle manufacturers, independent commercial importers
(ICIs), and alternative fuel converters that would be potentially affected by the rule from our
2011 and 2012 model year certification databases. These companies are already certifying
their vehicles for compliance with applicable EPA emissions standards (e.g., Tier 2). We then
identified companies that appear to meet the definition of small business provided in the table
9-1
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Chapter 9
above. We were able to identify companies based on certification information and previous
rulemakings where we conducted Regulatory Flexibility Analyses.
Based on this assessment, EPA identified a total of about 24 entities that appear to fit
the Small Business Administration (SBA) criterion of a small business. EPA estimates there
are about 5 small vehicle manufacturers, including three electric vehicle manufacturers, 8
independent commercial importers (ICIs), and 11 alternative fuel vehicle converters in the
light-duty vehicle market which may qualify as small businesses.549 Independent commercial
importers (ICIs) are companies that hold a Certificate (or Certificates) of Conformity
permitting them to import nonconforming vehicles and to modify these vehicles to meet U.S.
emission standards. ICIs are not required to meet the emission standards in effect when the
vehicle is modified, but instead they must meet the emission standards in effect when the
vehicle was originally produced (with an annual production cap of a total of 50 light-duty
vehicles and trucks). Alternative fuel vehicle converters are businesses that convert gasoline
or diesel vehicles to operate on alternative fuel (e.g., compressed natural gas), and converters
must seek a certificate for all of their vehicle models. Model year 1993 and newer vehicles
that are converted are required to meet the standards applicable at the time the vehicle was
originally certified. Converters serve a niche market, and these businesses primarily convert
vehicles to operate on compressed natural gas (CNG) and liquefied petroleum gas (LPG), on a
dedicated or dual fuel basis.
EPA is exempting from the GHG standards any manufacturer, domestic or foreign,
meeting SBA's size definitions of small business as described in 13 CFR 121.201. EPA
adopted the same type of exemption for small businesses in the MY 2012-2016 rulemaking.550
Together, we estimate that small entities comprise less than 0.1 percent of total annual vehicle
sales and exempting them will have a negligible impact on the GHG emissions reductions
from the standards. Because we are exempting small businesses from the GHG standards, we
are certifying that the rule will not have a significant economic impact on a substantial
number of small entities. Therefore, EPA has not conducted a Regulatory Flexibility Analysis
or a SBREFA SBAR Panel for the rule.
EPA is finalizing provisions to allow small businesses to voluntarily waive their small
business exemption and optionally certify to the GHG standards. This will allow small entity
manufacturers to earn CO2 credits under the GHG program, if their actual fleetwide COi
performance is better than their fleetwide CC>2 target standard. Manufacturers may choose to
opt-in as early as MY 2013. Once the small business manufacturer opting into the GHG
program in MY 2013 completes certification for MY 2013, the company will also be eligible
to generate GHG credits for their MY 2012 production. Manufacturers waiving their small
business exemption are required to meet all aspects of the GHG standards and program
requirements across their entire product line. However, the exemption waiver is optional for
small entities and thus we believe that manufacturers would only opt into the GHG program if
it is economically advantageous for them to do so, for example in order to generate and sell
COi credits. Therefore, EPA believes adding this voluntary option does not affect EPA's
determination that the standards will impose no significant adverse impact on small entities.
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MY 2017 and Later - Regulatory Impact Analysis
References
549 "Updated List of Potential Small Businesses in the Light-duty Vehicle Market,"
Memorandum from Chris Lieske to Docket EPA-HQ-OAR-2010-0799.
550 75 FR 25424, May 7, 2010.
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MY 2017 and Later - Regulatory Impact Analysis
10 Alternate Analysis Using 2010 MY Baseline
10.1 Why an Alternate Analysis?
For this final ralemaking, the agencies have analyzed the costs and benefits of the
standards using two different forecasts of the light vehicle fleet through MY 2025. The
agencies have concluded that the significant uncertainty associated with forecasting sales
volumes, vehicle technologies, fuel prices, consumer demand, and so forth out to MY 2025,
make it reasonable and appropriate to evaluate the impacts of the final CAFE and GHG
standards using two baselines.YYYYYYYY One market forecast (or fleet projection), very
similar to the one used for the NPRM, uses (corrected) MY 2008 CAFE certification data,
information from AEO 2011, and information purchased from CSM in December of 2009.
See Joint TSD Chapter 1.3. The agencies received comments regarding the market forecast
used in the NPRM suggesting that updates in several respects could be helpful to the
agencies' analysis of final standards; given those comments and since the agencies were
already considering producing an updated fleet projection, the final rulemakings also utilize
a second market forecast using MY 2010 CAFE certification data, information from AEO
2012, and information purchased from LMC Automotive (formerly J.D. Powers Forecasting).
See Joint TSD Chapter 1.4.
These two market forecasts contain certain differences, although as discussed in TSD
Chapter 1, the differences are not significant enough to change the agencies' decision as to the
structure and stringency of the final standards, and indeed corroborate the reasonableness of
the final standards. See Joint TSD Chapter 1.5. For example, the predicted fleet penetrations
of advanced technologies for the final rule are identical or virtually identical under either
market forecast. See RIA Tables 10-27 and 10-30 and preamble tables 111-47 and 111-52 (fleet
penetration values for TDS 24, TDS-27, HEV, and EV/PHEV in MYs 2021 and 2025). For
this reason, EPA did not model alternative standards 1-4 in this sensitivity analysis since the
analysis and conclusions would mirror those set forth in section III.D.6.
The 2008 based fleet forecast uses the MY 2008 "baseline" fleet, which represents the
most recent model year for which the industry had sales data that was not affected by the
subsequent economic recession. On the other hand, the 2010 based fleet projection employs a
market forecast (provided by LMC Automotive) which is more current than the projection
provided by CSM (utilized for the MY 2008 based fleet projection). The CSM forecast
appears to have been particularly influenced by the recession and shows major declines in
market share for some manufacturers (e.g., Chrysler) which the agencies do not believe are
reasonably reflective of future trends.
However, the MY 2010 based fleet projection also is highly influenced by the
economic recession. The MY 2010 CAFE certification data has become available since the
proposal (see section 1.2.1 of the Joint TSD for the proposed rule, which noted the possibility
YYYYYYYY ^g K^r lo jj^g baselines as "fleet projections" or "market forecasts" in Section II.B of the preamble
and Chapter 1 of the TSD and elsewhere in the administrative record. The term "baseline" has a specific
definition and is described in Chapter 1 of the TSD.
10-1
-------
Chapter 10
of these data becoming available), and continues to show the effects of the recession. For
example, industry-wide sales were skewed down 20% compared to pre-recession MY 2008
levels. For some companies like Chrysler, Mitsubishi, and Subaru, sales were down 30-40%
from MY 2008 levels. For BMW, General Motors, Jaguar/Land Rover, Porsche, and Suzuki,
77777777
sales were down more than 40% from 2008 levels. Using the MY 2008 vehicle data
avoids projecting these abnormalities in predicting the future fleet, although it also
perpetuates vehicle brands and models (and thus, their outdated fuel economy levels and
engineering characteristics) that have since been discontinued. The MY 2010 CAFE
certification data accounts for the phase-out of some brands (e.g., Saab) and the introduction
of some technologies (e.g., Ford's Ecoboost engine), which may be more reflective of the
future fleet in this respect.
Thus, given the volume of information that goes into creating a baseline forecast and
given the significant uncertainty in any projection out to MY 2025, the agencies think that the
best way to illustrate the possible impacts of that uncertainty for purposes of this rulemaking
is the approach taken here of analyzing the effects of the final standards under both the MY
2008-based and the MY 2010-based fleet projections. EPA is presenting its primary analysis
of the standards using essentially the same baseline/future fleet projection that was used in the
NPRM (i.e., corrected MY 2008 CAFE certification data, information from AEO 2011, and a
future fleet forecast purchased from CSM). EPA also conducted an alternative analysis of
the standards based on MY2010 CAFE certification data, updated AEO 2012 (early release)
projections of the future fleet sales volumes, and a forecast of the future fleet mix projections
to MY 2025 purchased from LMC Automotive. We have concluded that the final standards
are likewise appropriate using this alternative baseline/fleet projection.
This chapter presents the analysis of the alternative baseline. For details on how the
numbers presented here were generated, please see all the relevant portions of this
rulemaking, in particular, RIA chapters 1, 3, and 4, and the technical support document. In
general, the same methodology was used here as used for the 2008 baseline, except in those
specific sections where those documents describe differences.
10.2 Level of the standard
Table 10-1 - Projected Level of Targets - Cars
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
MY
2017
220
217
221
224
221
216
212
217
2018
209
206
211
213
210
205
202
206
2019
199
195
200
203
200
195
192
196
2020
189
186
190
192
189
185
182
186
2021
179
176
180
182
179
175
173
176
2022
171
168
172
174
172
167
165
168
2023
163
161
164
167
164
160
158
161
2024
156
153
156
159
156
152
151
154
2025
149
147
149
152
149
146
144
147
' See TSD chapter 1.
10-2
-------
MY 2017 and Later - Regulatory Impact Analysis
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
215
214
209
203
212
203
215
218
207
202
228
214
187
214
204
203
198
193
202
193
205
208
197
192
217
204
178
203
194
193
188
183
192
183
195
197
187
182
205
194
168
193
184
183
178
174
182
174
185
187
178
173
195
184
159
184
175
174
169
165
173
165
175
178
168
164
185
174
151
174
167
166
162
157
165
157
167
170
161
157
176
166
144
166
159
158
154
150
158
150
160
162
154
150
169
159
137
159
152
151
147
143
150
143
153
155
147
143
161
152
131
152
145
144
141
137
144
137
146
148
140
136
153
145
125
145
Table 10-2 - Projected Level of Targets - Trucks
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
MY
2017
NA
278
293
288
NA
316
276
313
285
270
286
NA
274
254
293
287
248
253
274
295
281
299
2018
NA
267
283
278
NA
308
264
305
275
258
276
NA
263
242
284
274
236
241
262
285
270
289
2019
NA
259
276
269
NA
304
256
298
267
250
267
NA
255
235
278
266
229
234
254
278
261
283
2020
NA
250
267
261
NA
297
247
289
258
242
258
NA
247
227
270
258
221
226
246
270
253
274
2021
NA
231
248
241
NA
277
228
267
239
223
238
NA
228
209
252
238
204
208
227
253
234
255
2022
NA
220
235
229
NA
263
217
254
227
212
226
NA
217
199
240
227
194
198
216
241
222
243
2023
NA
209
224
218
NA
251
207
242
216
202
215
NA
206
189
228
216
184
188
206
229
211
231
2024
NA
199
213
208
NA
239
196
230
205
192
204
NA
196
180
217
205
175
179
195
218
201
219
2025
NA
189
203
197
NA
227
187
218
195
182
194
NA
186
171
207
195
166
170
186
207
191
209
Table 10-3 - Projected Level of Targets - Fleet (sales weighted)
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
MY
2017
220
232
258
242
221
259
236
258
238
2018
209
221
247
233
210
250
224
248
226
2019
199
211
238
223
200
242
215
240
217
2020
189
201
227
213
189
232
205
230
207
2021
179
189
212
200
179
218
192
215
194
2022
171
180
202
191
172
207
183
205
185
2023
163
171
192
183
164
198
174
195
176
2024
156
163
182
174
156
188
166
186
167
2025
149
156
173
166
149
179
158
177
159
10-3
-------
Chapter 10
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
222
217
203
224
213
235
254
220
206
259
246
203
244
211
207
193
213
203
225
242
209
195
247
235
194
234
200
197
183
203
193
216
234
200
186
237
226
185
225
190
187
174
194
184
207
224
191
177
228
217
176
215
180
176
165
182
173
195
209
179
167
212
204
166
202
171
168
157
174
165
186
199
171
160
202
194
158
192
163
160
150
166
157
177
190
163
153
192
185
151
183
156
153
143
158
150
169
181
155
146
182
176
143
174
149
146
137
151
143
161
172
148
139
174
168
137
166
10.3 Targets and Achieved Levels
10.3.1.1 Reference Case
Table 10-4 Reference Case Targets and Projected Shortfall in MY 2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
Car Target
243
239
244
247
244
238
236
240
238
236
231
225
235
225
238
241
230
224
250
237
233
238
Truck Target
-
292
307
301
-
331
289
324
299
285
299
-
289
271
309
299
266
270
288
308
294
312
Fleet Target
(Sales Weighted)
243
252
274
264
244
277
254
276
256
242
238
225
245
234
256
271
241
228
274
264
244
264
Fleet Target
(VMT
and Sales
weighted)
243
253
276
265
244
280
256
279
258
243
239
225
246
235
258
273
242
229
276
267
245
266
Car
Achieved
325
244
229
267
371
230
230
230
221
227
219
255
226
221
226
268
235
215
274
216
220
227
Truck
Achieved
-
286
301
324
-
317
291
312
297
267
301
-
274
239
303
323
226
253
354
312
294
306
Shortfall
82
2
-
21
128
-
-
-
-
-
-
30
-
-
-
25
-
-
52
-
-
-
10-4
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-5 Reference Case Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General
Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
Car
Target
243
239
243
247
244
238
236
240
238
236
231
225
235
225
238
242
230
225
249
237
233
238
Truck
Target
-
292
308
301
-
331
289
323
299
285
298
-
289
271
310
299
266
270
288
308
294
312
Fleet Target
(Sales Weighted)
243
251
272
264
244
276
253
275
255
242
237
225
244
234
256
271
241
228
274
263
244
263
Fleet
Target
(VMT
and Sales
weighted)
243
252
274
266
244
279
255
278
257
242
238
225
246
235
258
273
242
229
275
265
245
265
Car
Achieved
326
246
228
267
373
230
231
230
221
227
219
256
226
220
226
269
234
215
274
217
220
227
Truck
Achieved
-
286
302
323
-
317
291
311
301
267
301
-
275
239
304
323
226
253
354
312
293
306
Shortfall
83
3
-
21
129
-
-
-
-
-
-
31
-
-
-
26
-
-
52
-
-
-
10-5
-------
Chapter 10
10.3.1.2
Final rule
Table 10-6 Final rule Targets and Projected Shortfall in MY 2021
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
Car Target
179
176
180
182
179
175
173
176
175
174
169
165
173
165
175
178
168
164
185
174
171
175
Truck Target
-
231
248
241
-
277
228
267
239
223
238
-
228
209
252
238
204
208
227
253
234
255
Fleet Target
(Sales Weighted)
179
189
212
200
179
218
192
215
194
180
176
165
182
173
195
209
179
167
212
204
182
203
Fleet Target
(VMT
and Sales
weighted)
179
190
215
202
179
222
194
219
196
180
177
165
184
174
197
212
180
168
213
207
184
205
Car
Achieved
179
179
184
172
212
191
174
188
179
177
172
163
178
173
184
161
186
165
149
180
172
182
Truck
Achieved
-
223
242
261
-
258
226
253
230
197
217
-
206
179
230
252
169
186
275
245
229
243
Shortfall
0
-
-
-
33
-
-
-
-
-
-
-
-
-
-
-
-
-
20
-
-
-
10-6
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-7 Final rule Targets and Projected Shortfall in MY 2025
Manufacturer
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata
Toyota
Volkswagen
Fleet
Car Target
149
147
149
152
149
146
144
147
145
144
141
137
144
137
146
148
140
136
153
145
142
145
Truck Target
-
189
203
197
-
227
187
218
195
182
194
-
186
171
207
195
166
170
186
207
191
209
Fleet Target
(Sales Weighted)
149
156
173
166
149
179
158
177
159
149
146
137
151
143
161
172
148
139
174
168
151
167
Fleet Target
(VMT
and Sales
weighted)
149
157
175
168
149
182
159
180
161
149
147
137
152
144
163
174
149
139
175
170
152
169
Car
Achieved
148
143
154
134
157
158
138
158
149
147
142
137
147
144
151
115
151
138
108
148
138
150
Truck
Achieved
-
197
196
231
-
211
197
203
186
161
182
-
170
141
193
220
145
145
239
202
203
200
Shortfall
-
-
-
-
8
-
-
-
-
-
-
-
-
-
-
-
-
-
19
-
-
-
10-7
-------
Chapter 10
10.4 Manufacturer Compliance Costs
Interpolated costs by manufacturer by model year, inclusive of AC-related costs and
stranded capital (Note that AC and stranded capital costs are identical to those used for the
2008 baseline), are shown in Table 10-8 through Table 10-10.
Table 10-8 - Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs - Cars (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata-JLR
Toyota
Volkswagen
Fleet
2017
$1,824
$432
$229
$852
$1,740
$146
$276
$169
$138
$171
$151
$1,050
$229
$262
$161
$44
$235
$54
$43
$90
$292
$195
2018
$3,385
$786
$359
$1,590
$3,287
$257
$496
$295
$241
$307
$273
$1,952
$415
$484
$283
$61
$438
$65
$58
$161
$531
$346
2019
$4,559
$1,057
$471
$2,163
$4,454
$344
$668
$390
$321
$413
$370
$2,633
$553
$660
$380
$2,233
$588
$522
$2,871
$219
$713
$464
2020
$5,741
$1,314
$576
$2,710
$5,629
$425
$835
$482
$396
$513
$461
$3,314
$692
$832
$471
$3,149
$736
$707
$4,129
$271
$891
$577
2021
$6,909
$1,582
$684
$3,268
$6,789
$510
$1,006
$578
$475
$617
$555
$3,990
$834
$1,005
$566
$4,884
$885
$1,078
$6,398
$327
$1,073
$695
2022
$7,009
$2,012
$1,012
$3,704
$7,450
$824
$1,514
$883
$755
$907
$826
$4,134
$1,149
$1,333
$893
$5,524
$1,273
$1,409
$7,034
$599
$1,481
$1,011
2023
$7,055
$2,397
$1,309
$4,086
$8,019
$1,107
$1,981
$1,158
$1,007
$1,168
$1,070
$4,240
$1,432
$1,632
$1,189
$6,076
$1,622
$1,705
$7,579
$844
$1,849
$1,295
2024
$7,056
$2,763
$1,597
$4,434
$8,511
$1,375
$2,420
$1,419
$1,246
$1,416
$1,301
$4,316
$1,699
$1,908
$1,469
$6,575
$1,951
$1,981
$8,062
$1,079
$2,194
$1,565
2025
$6,478
$2,851
$1,715
$4,371
$8,244
$1,500
$2,609
$1,534
$1,357
$1,520
$1,402
$4,031
$1,795
$1,998
$1,598
$6,465
$2,081
$2,064
$7,810
$1,200
$2,317
$1,675
Note: Results correspond to the 2010 baseline fleet.
Table 10-9 - Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs - Trucks (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
2017
$0
$53
$76
$34
$0
$58
$48
$30
$40
$73
$69
$0
2018
$0
$198
$217
$160
$0
$153
$205
$128
$182
$266
$218
$0
2019
$0
$299
$313
$258
$0
$198
$298
$223
$290
$377
$360
$0
2020
$0
$408
$455
$358
$0
$307
$401
$342
$408
$508
$508
$0
2021
$0
$680
$809
$615
$0
$688
$657
$673
$710
$846
$863
$0
2022
$0
$1,588
$1,268
$2,817
$0
$990
$1,217
$1,142
$960
$1,041
$904
$0
2023
$0
$1,565
$1,773
$2,362
$0
$1,406
$1,386
$1,643
$1,385
$1,411
$1,198
$0
2024
$0
$1,536
$2,230
$1,914
$0
$1,809
$1,540
$2,113
$1,785
$1,754
$1,477
$0
2025
$0
$1,391
$2,465
$1,377
$0
$2,008
$1,555
$2,351
$1,993
$1,918
$1,603
$0
10-8
-------
MY 2017 and Later - Regulatory Impact Analysis
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata-JLR
Toyota
Volkswagen
Fleet
$100
$216
$92
$21
$202
$31
$20
$40
$57
$50
$366
$474
$259
$67
$413
$71
$65
$177
$274
$178
$513
$617
$379
$197
$527
$572
$363
$271
$425
$272
$709
$804
$574
$276
$674
$735
$516
$379
$603
$398
$1,220
$1,290
$1,100
$636
$1,062
$1,570
$1,243
$665
$1,053
$751
$1,345
$1,573
$1,100
$4,026
$1,248
$1,699
$5,477
$746
$1,299
$1,144
$1,788
$2,076
$1,566
$3,181
$1,561
$2,250
$4,569
$1,115
$1,479
$1,535
$2,206
$2,540
$2,016
$2,366
$1,849
$2,758
$3,679
$1,465
$1,646
$1,902
$2,389
$2,743
$2,246
$1,464
$1,956
$2,983
$2,609
$1,655
$1,660
$2,071
Note: Results correspond to the 2010 baseline fleet.
Table 10-10 - Control Case Costs by Manufacturer by MY including AC & Stranded
Capital Costs - Trucks (2010$)
Company
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely-Volvo
GM
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Subaru
Suzuki
Tata-JLR
Toyota
Volkswagen
Fleet
2017
$1,824
$344
$156
$601
$1,740
$109
$198
$110
$108
$159
$142
$1,050
$206
$253
$144
$32
$225
$52
$28
$71
$249
$145
2018
$3,385
$650
$291
$1,152
$3,287
$213
$396
$223
$223
$302
$267
$1,952
$406
$482
$277
$64
$430
$65
$62
$167
$485
$288
2019
$4,559
$881
$396
$1,579
$4,454
$282
$542
$318
$312
$409
$369
$2,633
$546
$652
$379
$1,170
$569
$526
$1,272
$239
$661
$398
2020
$5,741
$1,104
$518
$1,989
$5,629
$376
$686
$422
$400
$513
$466
$3,314
$695
$827
$497
$1,650
$717
$709
$1,825
$312
$839
$515
2021
$6,909
$1,373
$744
$2,454
$6,789
$585
$887
$619
$546
$644
$588
$3,990
$902
$1,059
$703
$2,668
$940
$1,116
$3,111
$455
$1,069
$714
2022
$7,009
$1,920
$1,126
$3,426
$7,450
$892
$1,417
$993
$812
$922
$833
$4,134
$1,182
$1,377
$945
$4,744
$1,266
$1,432
$6,069
$652
$1,448
$1,055
2023
$7,055
$2,216
$1,517
$3,546
$8,019
$1,230
$1,788
$1,365
$1,112
$1,195
$1,083
$4,240
$1,492
$1,714
$1,285
$4,570
$1,604
$1,748
$5,714
$943
$1,782
$1,375
2024
$7,056
$2,496
$1,879
$3,645
$8,511
$1,553
$2,135
$1,715
$1,397
$1,454
$1,318
$4,316
$1,784
$2,025
$1,608
$4,385
$1,920
$2,043
$5,347
$1,219
$2,095
$1,677
2025
$6,478
$2,534
$2,050
$3,433
$8,244
$1,708
$2,268
$1,883
$1,536
$1,564
$1,421
$4,031
$1,895
$2,136
$1,762
$3,863
$2,043
$2,136
$4,589
$1,365
$2,199
$1,807
Note: Results correspond to the 2010 baseline fleet.
These costs per vehicle are then carried forward for future MYs to arrive at the costs
presented in Table 10-11, including costs associated with the air conditioning program and
estimates of stranded capital.
Table 10-11 - Industry Average Vehicle Costs Associated with the Proposed Standards
(2010$)
Model Year
$/car
$/truck
Combined
2017
$195
$50
$145
2018
$346
$178
$288
2019
$464
$272
$398
2020
$577
$398
$515
2021
$695
$751
$714
2022
$1,011
$1,144
$1,055
2023
$1,295
$1,535
$1,375
2024
$1,565
$1,902
$1,677
2025
$1,675
$2,071
$1,807
2030
$1,660
$2,055
$1,788
2040
$1,660
$2,055
$1,785
2050
$1,660
$2,055
$1,785
10-9
-------
Chapter 10
Note: Results correspond to the 2010 baseline fleet.
The costs presented here represent the costs for newly added technology to comply
with the program incremental to the costs of the 2012-2016 standards. Together with the
projected increases in car and track sales, the increases in per-car and per-truck average costs
shown in Table 10-11 above result in the total annual technology costs presented in Table
10-12 below. Note that the costs presented in Table 10-12 do not include the fuel savings that
consumers would realize as a result of driving a vehicle with improved fuel economy.
Table 10-12 - Undiscounted Annual Technology Costs & Costs Discounted back
2012 at 3% and 7% Discount Rates (2010 dollars)
to
Calendar
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Sales
Cars
10,213,312
10,088,966
10,139,761
10,194,353
10,310,594
10,455,061
10,593,727
10,811,530
10,981,082
11,467,094
12,264,435
13,122,182
Tracks
5,598,788
5,516,434
5,522,339
5,435,847
5,419,506
5,432,139
5,413,473
5,435,470
5,473,718
5,591,140
5,910,536
6,323,905
$/unit
$/car
$195
$346
$464
$577
$695
$1,011
$1,295
$1,565
$1,675
$1,660
$1,660
$1,660
$/truck
$50
$178
$272
$398
$751
$1,144
$1,535
$1,902
$2,071
$2,055
$2,055
$2,055
$Million/year
Cars
$1,990
$3,490
$4,700
$5,880
$7,160
$10,600
$13,700
$16,900
$18,400
$19,000
$20,400
$21,800
$292,000
$132,000
Tracks
$280
$980
$1,500
$2,160
$4,070
$6,220
$8,310
$10,300
$11,300
$11,500
$12,100
$13,000
$172,000
$76,200
Combined
$2,300
$4,490
$6,230
$8,050
$11,200
$16,800
$22,000
$27,200
$29,700
$30,500
$32,400
$34,700
$463,000
$208,000
Note: Results correspond to the 2010 baseline fleet.
Note that costs are estimated to decrease slightly in years beyond 2025. This
represents the elimination of stranded capital that is included in the costs for 2017 through
2025. These costs are described in detail in Chapter 3 of the Joint TSD.
Looking at these costs by model year gives us the technology costs as shown in Table
10-13.
Table 10-13 - Model Year Lifetime Present Value Technology Costs, Discounted back
to the 1st Year of each MY at 3% and 7% Discount Rates (millions of 2010 dollars)
NPV at
3%
7%
Car
Truck
Fleet
Car
Truck
Fleet
2017
$1,960
$276
$2,260
$1,930
$271
$2,220
2018
$3,440
$965
$4,430
$3,370
$947
$4,340
2019
$4,640
$1,480
$6,140
$4,550
$1,450
$6,030
2020
$5,800
$2,130
$7,940
$5,690
$2,090
$7,790
2021
$7,060
$4,010
$11,100
$6,930
$3,940
$10,900
2022
$10,400
$6,130
$16,500
$10,200
$6,010
$16,200
2023
$13,500
$8,190
$21,700
$13,300
$8,040
$21,300
2024
$16,700
$10,200
$26,900
$16,400
$10,000
$26,400
2025
$18,100
$11,200
$29,300
$17,800
$11,000
$28,800
Sum
$81,600
$44,500
$126,000
$80,100
$43,700
$124,000
Note: Results correspond to the 2010 baseline fleet.
Using the maintenance event costs, the maintenance intervals and the technology
penetration rates, we can estimate the maintenance cost changes resulting from the new
standards. These are shown in Table 10-14 through Table 10-16.
10-10
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-14 - Undiscounted Sales Weighted Annual Maintenance Costs & Costs
Discounted back to 2012 at 3% and 7% Discount Rates (millions of 2010 dollars)
CY
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
LRRT1
Car
$0
-$4
-$11
-$22
-$37
-$54
-$73
-$94
-$116
-$217
-$348
-$413
-$3,700
-$1,450
Truck
$0
-$2
-$7
-$13
-$21
-$30
-$40
-$51
-$62
-$111
-$175
-$214
-$1,900
-$747
LRRT2
Car
$25
$75
$150
$249
$377
$516
$669
$836
$1,010
$1,790
$2,800
$3,310
$30,400
$12,100
Truck
$15
$45
$88
$143
$213
$287
$363
$446
$533
$914
$1,400
$1,710
$15,600
$6,220
Diesel
Car
-$1
-$3
-$5
-$8
-$13
-$16
-$19
Z1
Z1
-$22
-$20
-$20
-$322
-$150
Truck
$0
-$1
-$1
-$2
-$3
-$4
-$4
-$5
-$5
-$7
-$10
-$11
-$120
-$51
EV
Car
-$1
-$3
-$5
-$9
-$14
-$21
-$29
-$41
-$54
-$117
-$203
-$244
-$2,060
-$790
Truck
$0
$0
$0
$0
$0
$0
$0
$0
-$1
-$2
-$3
-$4
-$33
-$12
PHEV
Car
$0
$1
$1
$2
$3
$4
$5
$6
$7
$11
$15
$18
$180
$75
Truck
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
Car
$24
$67
$130
$212
$317
$430
$554
$688
$826
$1,440
$2,240
$2,650
$24,500
$9,780
Truck
$15
$42
$80
$128
$189
$253
$319
$390
$465
$794
$1,220
$1,480
$13,500
$5,410
Vehicle
$39
$108
$210
$340
$505
$683
$872
$1,080
$1,290
$2,240
$3,460
$4,130
$38,000
$15,200
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
Table 10-15 - Model Year Lifetime Present Value Maintenance Costs and Savings,
Discounted to the 1st Year of each MY at 3% (millions of 2010 dollars)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Tires
$/c
$25
$47
$70
$93
$116
$126
$137
$147
$158
$919
$/t
$27
$50
$74
$98
$124
$135
$146
$158
$170
$980
Diesel
$/c
-$1
-$2
-$3
-$4
-$4
-$4
-$3
-$2
-$1
-$23
$/t
$0
-$1
-$1
-$1
-$2
-$2
-$2
-$1
-$1
-$H
EV
$/c
-$1
-$2
-$3
-$4
-$5
-$7
-$9
-$11
-$13
-$55
$/t
$0
$0
$0
$0
$0
$0
$0
$0
-$1
-$1
PHEV
$/c
$0
$0
$1
$1
$1
$1
$1
$1
$1
$8
$/t
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
$/c
$24
$44
$65
$86
$108
$116
$126
$135
$145
$849
$/t
$27
$50
$73
$96
$122
$133
$144
$156
$168
$968
$/veh
$25
$46
$68
$90
$112
$122
$132
$142
$153
$890
$Million per MY
$/c
$243
$445
$658
$879
$1,110
$1,220
$1,340
$1,460
$1,590
$8,940
$/t
$149
$274
$403
$524
$659
$721
$778
$847
$919
$5,280
$/veh
$392
$719
$1,060
$1,400
$1,770
$1,940
$2,120
$2,310
$2,510
$14,200
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
Table 10-16 - Model Year Lifetime Present Value Maintenance Costs and Savings,
Discounted to the 1st Year of each MY at 7% (millions of 2010 dollars)
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Tires
$/c
$20
$37
$54
$72
$89
$98
$106
$114
$122
$711
$/t
$21
$39
$57
$75
$94
$103
$111
$121
$129
$750
Diesel
$/c
-$1
-$1
-$2
-$3
-$3
-$3
-$2
-$2
-$1
-$18
$/t
$0
-$1
-$1
-$1
-$1
-$1
-$1
-$1
-$1
-$9
EV
$/c
-$1
-$1
-$2
-$3
-$4
-$5
-$7
-$9
-$10
-$42
$/t
$0
$0
$0
$0
$0
$0
$0
$0
$0
-$1
PHEV
$/c
$0
$0
$1
$1
$1
$1
$1
$1
$1
$6
$/t
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Total
$/c
$18
$34
$50
$67
$83
$90
$98
$105
$112
$657
$/t
$20
$38
$56
$74
$93
$102
$110
$120
$128
$741
$/veh
$19
$36
$52
$69
$86
$94
$102
$110
$117
$686
$Million per MY
$/c
$188
$345
$509
$680
$854
$945
$1,040
$1,130
$1,230
$6,920
$/t
$114
$210
$309
$403
$503
$551
$596
$650
$700
$4,040
$/veh
$302
$555
$818
$1,080
$1,360
$1,500
$1,630
$1,780
$1,930
$11,000
Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet.
10-11
-------
Chapter 10
Annual costs of the vehicle program are the annual technology costs shown in Table
10-12 and the annual maintenance costs shown in Table 10-14. Those results are shown in
Table 10-17.
Table 10-17 - Undiscounted Annual Program Costs & Costs Discounted back to 2012 at
3% and 7% Discount Rates (2010 dollars)
Calendar Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Car
$2,020
$3,550
$4,830
$6,090
$7,480
$11,000
$14,300
$17,600
$19,200
$20,500
$22,600
$24,400
$317,000
$141,000
Truck
$295
$1,020
$1,580
$2,290
$4,260
$6,470
$8,630
$10,700
$11,800
$12,300
$13,400
$14,500
$185,000
$81,600
Total
Annual
Costs
$2,330
$4,600
$6,440
$8,390
$11,700
$17,400
$22,900
$28,300
$31,000
$32,700
$35,900
$38,800
$501,000
$223,000
Note: Results correspond to the 2010 baseline fleet.
Model year lifetime costs of the vehicle program are the MY lifetime technology costs
shown in Table 10-13 and the MY lifetime maintenance costs shown in Table 10-15 and
Table 10-16. Those results are shown in Table 10-18.
Table 10-18 - Model Year Lifetime Present Value Vehicle Program Costs
Discounted to the 1st Year of each MY at 3% & 7% (millions of 2010 dollars)
NPV
at
3%
7%
MY-»
Cars
Trucks
Combined
Cars
Trucks
Combined
2017
$2,210
$425
$2,650
$2,120
$386
$2,520
2018
$3,880
$1,240
$5,150
$3,720
$1,160
$4,900
2019
$5,290
$1,880
$7,200
$5,060
$1,760
$6,840
2020
$6,680
$2,660
$9,340
$6,370
$2,500
$8,870
2021
$8,170
$4,670
$12,800
$7,780
$4,440
$12,200
2022
$11,600
$6,850
$18,500
$11,200
$6,560
$17,700
2023
$14,900
$8,970
$23,800
$14,300
$8,630
$22,900
2024
$18,100
$11,000
$29,200
$17,500
$10,700
$28,100
2025
$19,700
$12,100
$31,800
$19,000
$11,700
$30,700
Sum
$90,600
$49,800
$140,000
$87,000
$47,700
$135,000
Note: Results correspond to the 2010 baseline fleet.
10-12
-------
MY 2017 and Later - Regulatory Impact Analysis
10.5 Technology Penetrations
10.5.1 Projected Technology Penetrations in Reference Case
Table 10-19 Reference Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-5%
-8%
-8%
-3%
-4%
-6%
-1%
-2%
0%
-1%
-1%
-3%
-1%
-5%
NA
-5%
-1%
-9%
NA
-1%
-4%
-3%
g i
H S
-7%
-5%
-5%
-7%
-7%
-3%
-3%
-6%
-1%
-2%
0%
0%
-1%
-3%
-1%
-4%
NA
-5%
-1%
-8%
NA
-1%
-3%
-3%
05 -&1
M r3
•3 «
S£
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
NA
0%
0%
1%
NA
0%
0%
0%
oo
CO
e
40%
44%
68%
40%
40%
35%
47%
27%
0%
3%
0%
42%
11%
46%
28%
43%
NA
32%
48%
43%
NA
2%
68%
24%
TDS24
15%
15%
15%
15%
15%
0%
15%
5%
0%
0%
0%
15%
0%
7%
0%
15%
NA
9%
7%
15%
NA
0%
13%
4%
i^
IN
t/3
e
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
S
<
9%
13%
11%
0%
0%
18%
10%
8%
1%
6%
0%
0%
3%
11%
3%
0%
NA
1%
1%
10%
NA
10%
14%
8%
gs
<
0%
2%
4%
52%
0%
5%
3%
1%
1%
1%
0%
0%
1%
3%
10%
40%
NA
1%
1%
0%
NA
2%
2%
4%
£
u
Q
53%
49%
48%
18%
20%
43%
48%
53%
50%
53%
66%
15%
47%
46%
44%
15%
NA
47%
49%
55%
NA
52%
50%
49%
oo
u
Q
26%
27%
26%
30%
79%
23%
26%
28%
26%
24%
12%
0%
25%
25%
27%
22%
NA
25%
27%
30%
NA
16%
26%
24%
H
12%
8%
1%
0%
0%
5%
5%
5%
3%
5%
7%
85%
14%
7%
2%
24%
NA
13%
9%
0%
NA
2%
8%
4%
o
w
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
&
§
15%
15%
15%
15%
15%
0%
15%
0%
0%
0%
0%
15%
0%
2%
0%
15%
NA
1%
0%
15%
NA
10%
13%
4%
W
15%
15%
0%
15%
15%
3%
15%
0%
6%
0%
0%
15%
0%
0%
1%
15%
NA
0%
0%
15%
NA
12%
3%
4%
£
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
W
PH
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
t/3
t/3
55%
55%
0%
55%
55%
0%
56%
0%
0%
0%
0%
55%
0%
0%
0%
55%
NA
0%
0%
55%
NA
0%
56%
6%
LRRT2
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
1
30%
30%
30%
30%
30%
29%
30%
30%
0%
0%
0%
30%
30%
30%
26%
30%
NA
30%
30%
30%
NA
0%
30%
18%
w
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
Q
70%
74%
83%
70%
83%
34%
71%
41%
0%
1%
0%
70%
10%
53%
28%
83%
NA
40%
55%
78%
NA
7%
85%
31%
_)
%
15%
16%
0%
15%
15%
0%
14%
0%
0%
0%
0%
15%
0%
0%
0%
15%
NA
0%
0%
15%
NA
0%
11%
2%
MHEV
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
12%
1%
10-13
-------
Chapter 10
Table 10-20 Reference Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-8%
-8%
-9%
NA
-8%
-8%
-8%
-4%
-4%
-3%
NA
-7%
-8%
-4%
-8%
NA
-8%
0%
-8%
NA
-3%
-8%
-7%
g i
H S
NA
-8%
-8%
-8%
NA
-8%
-8%
-8%
-4%
-4%
-3%
NA
-7%
-8%
-4%
-8%
NA
-8%
0%
-7%
NA
-3%
-8%
-7%
05 -&1
M r3
•3 «
S£
NA
1%
0%
1%
NA
0%
1%
0%
0%
0%
0%
NA
0%
0%
0%
1%
NA
0%
0%
1%
NA
0%
0%
0%
oo
CO
e
NA
62%
51%
60%
NA
25%
70%
29%
47%
0%
0%
NA
58%
70%
44%
65%
NA
44%
70%
60%
NA
13%
61%
33%
TDS24
NA
13%
15%
14%
NA
2%
15%
6%
0%
0%
0%
NA
0%
15%
2%
15%
NA
2%
15%
15%
NA
0%
12%
5%
i^
IN
t/3
e
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
S
<
NA
69%
64%
1%
NA
60%
70%
64%
46%
43%
36%
NA
47%
29%
37%
70%
NA
7%
15%
70%
NA
51%
69%
55%
gs
<
NA
30%
28%
99%
NA
26%
30%
28%
24%
15%
30%
NA
20%
12%
29%
30%
NA
4%
8%
30%
NA
25%
30%
28%
£
u
Q
NA
0%
2%
0%
NA
7%
0%
3%
10%
27%
0%
NA
17%
33%
13%
0%
NA
45%
40%
0%
NA
6%
1%
7%
oo
u
Q
NA
0%
1%
0%
NA
4%
0%
2%
6%
15%
0%
NA
9%
18%
7%
0%
NA
24%
22%
0%
NA
3%
0%
4%
H
NA
1%
4%
0%
NA
0%
0%
0%
0%
0%
0%
NA
2%
0%
1%
0%
NA
5%
0%
0%
NA
3%
0%
1%
o
w
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
&
§
NA
13%
15%
14%
NA
0%
15%
6%
0%
0%
0%
NA
0%
15%
0%
15%
NA
2%
15%
15%
NA
9%
12%
6%
W
NA
15%
0%
15%
NA
1%
15%
0%
0%
0%
0%
NA
0%
0%
0%
15%
NA
0%
0%
15%
NA
3%
0%
2%
£
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
W
PH
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
t/3
t/3
NA
66%
8%
64%
NA
0%
63%
0%
0%
0%
0%
NA
0%
0%
0%
64%
NA
0%
0%
58%
NA
0%
67%
6%
LRRT2
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
1
NA
30%
30%
30%
NA
30%
30%
30%
1%
0%
0%
NA
30%
30%
30%
30%
NA
30%
30%
30%
NA
9%
30%
23%
w
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
Q
NA
75%
66%
74%
NA
27%
78%
40%
46%
0%
0%
NA
57%
85%
47%
94%
NA
46%
85%
84%
NA
13%
83%
39%
_)
%
NA
14%
0%
13%
NA
0%
7%
0%
0%
0%
0%
NA
0%
0%
0%
6%
NA
0%
0%
12%
NA
0%
17%
1%
MHEV
NA
0%
0%
0%
NA
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
15%
0%
10-14
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-21 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-6%
-8%
-8%
-5%
-6%
-7%
-2%
-2%
-1%
-1%
-2%
-4%
-2%
-7%
NA
-6%
-1%
-8%
NA
-2%
-4%
-4%
0 "
£1
-7%
-5%
-6%
-7%
-7%
-5%
-5%
-7%
-2%
-2%
-1%
0%
-2%
-4%
-2%
-6%
NA
-6%
-1%
-7%
NA
-2%
-4%
-4%
«, &
a "3
Sl
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
NA
0%
0%
1%
NA
0%
0%
0%
TDS18
40%
48%
60%
46%
40%
31%
55%
28%
15%
3%
0%
42%
19%
51%
32%
54%
NA
36%
50%
54%
NA
6%
67%
27%
TDS24
15%
14%
15%
15%
15%
1%
15%
6%
0%
0%
0%
15%
0%
9%
1%
15%
NA
7%
7%
15%
NA
0%
13%
4%
TDS27
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
1
9%
26%
36%
0%
0%
36%
31%
32%
15%
10%
4%
0%
11%
14%
11%
36%
NA
3%
2%
48%
NA
25%
24%
24%
OO
%
0%
9%
15%
66%
0%
14%
12%
12%
8%
3%
3%
0%
4%
5%
15%
35%
NA
2%
1%
19%
NA
11%
7%
12%
VO
53%
38%
26%
13%
20%
28%
31%
32%
38%
50%
59%
15%
41%
44%
36%
7%
NA
46%
48%
20%
NA
35%
41%
34%
OO
26%
21%
14%
21%
79%
15%
17%
17%
20%
23%
11%
0%
23%
24%
22%
10%
NA
25%
26%
11%
NA
11%
21%
17%
H
12%
7%
2%
0%
0%
3%
3%
3%
2%
5%
7%
85%
12%
6%
2%
11%
NA
11%
8%
0%
NA
3%
7%
3%
1
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
-------
Chapter 10
Table 10-22 Reference Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-5%
-8%
-8%
-3%
-4%
-6%
-1%
-2%
0%
-1%
-1%
-3%
-1%
-5%
NA
-5%
-1%
-9%
NA
-1%
-4%
-3%
0 "
£1
-7%
-5%
-5%
-7%
-7%
-3%
-4%
-6%
-1%
-2%
0%
0%
-1%
-3%
-1%
-4%
NA
-5%
-1%
-8%
NA
-1%
-3%
-3%
«, &
3 "3
Sl
1%
1%
0%
1%
1%
0%
1%
0%
0%
0%
0%
1%
0%
0%
0%
1%
NA
0%
0%
1%
NA
0%
0%
0%
TDS18
55%
59%
68%
55%
55%
35%
60%
27%
0%
3%
0%
57%
11%
46%
28%
58%
NA
34%
48%
58%
NA
0%
70%
25%
TDS24
15%
15%
15%
15%
15%
0%
15%
5%
0%
0%
0%
15%
0%
7%
0%
15%
NA
10%
7%
15%
NA
0%
13%
4%
TDS27
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
1
9%
13%
10%
0%
0%
18%
10%
7%
1%
6%
0%
0%
3%
11%
3%
0%
NA
1%
1%
10%
NA
10%
14%
8%
oo
%
0%
2%
4%
52%
0%
5%
3%
1%
1%
1%
0%
0%
0%
3%
10%
40%
NA
0%
1%
0%
NA
2%
2%
4%
VO
53%
49%
48%
18%
20%
43%
48%
54%
50%
53%
71%
15%
47%
46%
44%
15%
NA
47%
49%
55%
NA
52%
50%
49%
OO
26%
27%
26%
30%
79%
23%
26%
28%
26%
24%
8%
0%
25%
25%
27%
22%
NA
25%
27%
30%
NA
16%
26%
24%
H
12%
8%
1%
0%
0%
5%
4%
5%
3%
5%
7%
85%
13%
7%
2%
24%
NA
14%
9%
0%
NA
2%
8%
4%
1
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-23 Reference Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-8%
-8%
-9%
NA
-8%
-8%
-8%
-4%
-4%
-3%
NA
-7%
-9%
-4%
-8%
NA
-9%
0%
-8%
NA
-3%
-8%
-7%
-------
Chapter 10
Table 10-24 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-8%
-6%
-6%
-8%
-8%
-5%
-6%
-7%
-2%
-2%
0%
-1%
-2%
-4%
-2%
-7%
NA
-6%
-1%
-8%
NA
-2%
-4%
-4%
-------
MY 2017 and Later - Regulatory Impact Analysis
10.5.2 Projected Technology Penetrations in Final rule case
Table 10-25 Final rule Car Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-14%
-8%
-6%
-14%
-14%
-4%
-7%
-6%
-2%
-3%
0%
-3%
-2%
-4%
-3%
-10%
NA
-6%
-1%
-16%
NA
-2%
-5%
-4%
g i
H S
-9%
-7%
-6%
-11%
-8%
-4%
-6%
-6%
-2%
-3%
0%
0%
-2%
-4%
-3%
-7%
NA
-6%
-1%
-10%
NA
-2%
-5%
-4%
-S1
M ^
•3 «
S£
6%
1%
0%
3%
6%
0%
1%
0%
0%
0%
0%
3%
0%
0%
0%
3%
NA
0%
0%
6%
NA
0%
1%
0%
oo
t/3
e
9%
44%
74%
17%
9%
35%
55%
42%
16%
18%
2%
15%
22%
73%
32%
7%
NA
83%
73%
6%
NA
20%
57%
34%
TDS24
15%
28%
23%
30%
15%
5%
30%
13%
0%
0%
0%
30%
20%
27%
11%
29%
NA
17%
27%
19%
NA
0%
26%
10%
i^
IN
t/3
e
15%
8%
1%
14%
15%
1%
6%
1%
0%
0%
0%
14%
0%
0%
0%
15%
NA
0%
0%
15%
NA
0%
2%
1%
S
<
0%
0%
3%
0%
0%
3%
2%
0%
0%
1%
0%
0%
0%
2%
1%
0%
NA
0%
0%
0%
NA
1%
1%
1%
gs
<
0%
0%
11%
9%
0%
13%
8%
2%
2%
3%
0%
0%
1%
7%
8%
7%
NA
1%
1%
0%
NA
4%
3%
5%
£
U
Q
8%
13%
19%
1%
1%
19%
14%
22%
22%
23%
23%
0%
21%
19%
20%
0%
NA
20%
21%
9%
NA
20%
15%
20%
oo
u
Q
70%
73%
67%
79%
83%
57%
69%
71%
67%
68%
69%
42%
64%
65%
68%
66%
NA
65%
70%
75%
NA
61%
72%
66%
H
6%
6%
1%
0%
0%
5%
4%
5%
3%
5%
7%
47%
14%
7%
2%
11%
NA
13%
7%
0%
NA
2%
6%
4%
O
w
59%
58%
56%
59%
59%
32%
59%
40%
9%
23%
0%
59%
54%
52%
31%
55%
NA
54%
57%
59%
NA
0%
60%
30%
&
§
25%
30%
22%
30%
25%
3%
30%
8%
0%
0%
0%
30%
3%
27%
1%
30%
NA
10%
27%
26%
NA
10%
26%
9%
W
30%
11%
0%
22%
30%
3%
5%
0%
6%
0%
0%
18%
0%
0%
1%
25%
NA
0%
0%
29%
NA
12%
4%
4%
£
16%
7%
0%
10%
16%
0%
5%
0%
0%
0%
0%
11%
0%
0%
0%
16%
NA
0%
0%
16%
NA
0%
2%
1%
W
PH
15%
0%
0%
6%
15%
0%
0%
0%
0%
0%
0%
12%
0%
0%
0%
8%
NA
0%
0%
15%
NA
0%
0%
0%
t/3
t/3
30%
42%
3%
42%
30%
0%
42%
0%
0%
0%
0%
36%
0%
6%
0%
33%
NA
0%
1%
30%
NA
0%
39%
5%
LRRT2
75%
75%
75%
75%
75%
73%
75%
75%
71%
75%
75%
75%
75%
75%
74%
75%
NA
75%
75%
75%
NA
66%
75%
73%
1
24%
55%
77%
46%
24%
74%
56%
79%
75%
79%
71%
42%
80%
78%
78%
36%
NA
80%
79%
27%
NA
12%
61%
65%
w
60%
60%
53%
60%
60%
14%
60%
33%
23%
29%
0%
59%
54%
57%
31%
55%
NA
44%
58%
60%
NA
0%
60%
28%
Q
84%
92%
97%
90%
84%
41%
95%
60%
16%
19%
2%
89%
43%
100%
43%
84%
NA
100%
100%
84%
NA
22%
89%
47%
_)
%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
9%
0%
MHEV
0%
19%
1%
8%
0%
1%
25%
0%
0%
0%
0%
12%
0%
3%
0%
5%
NA
0%
14%
1%
NA
0%
26%
2%
10-19
-------
Chapter 10
Table 10-26 Final rule Truck Technology Penetrations in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-15%
-9%
-15%
NA
-8%
-15%
-9%
-9%
-9%
-5%
NA
-11%
-12%
-7%
-15%
NA
-11%
-1%
-13%
NA
-4%
-15%
-8%
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-27 Final rule Fleet Technology Penetration in MY 2021
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-14%
-10%
-7%
-14%
-14%
-6%
-9%
-7%
-4%
-4%
-1%
-3%
-3%
-5%
-4%
-13%
NA
-8%
-1%
-14%
NA
-3%
-7%
-6%
-------
Chapter 10
Table 10-28 Final rule Car Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-10%
-10%
-17%
-17%
-6%
-8%
-8%
-3%
-4%
0%
-3%
-3%
-7%
-3%
-13%
NA
-10%
-2%
-20%
NA
-3%
-7%
-6%
g i
H S
-11%
-8%
-9%
-13%
-8%
-6%
-6%
-8%
-3%
-4%
0%
0%
-3%
-6%
-3%
-7%
NA
-9%
-1%
-11%
NA
-3%
-5%
-5%
&
M ^
•3 «
S£
6%
2%
1%
4%
9%
0%
2%
0%
0%
0%
0%
3%
0%
1%
0%
6%
NA
1%
1%
9%
NA
0%
2%
1%
oo
t/3
e
0%
5%
23%
2%
0%
22%
8%
23%
27%
35%
4%
3%
24%
22%
25%
0%
NA
19%
18%
0%
NA
41%
11%
25%
TDS24
4%
57%
73%
60%
0%
67%
64%
70%
67%
64%
67%
70%
75%
75%
74%
26%
NA
75%
75%
2%
NA
41%
66%
64%
i^
IN
t/3
e
40%
17%
2%
11%
5%
5%
14%
5%
0%
1%
0%
0%
0%
0%
0%
21%
NA
0%
0%
3%
NA
1%
6%
3%
S
<
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
gs
<
0%
0%
12%
0%
0%
16%
9%
2%
2%
4%
0%
0%
2%
9%
6%
0%
NA
2%
2%
0%
NA
4%
4%
5%
£
U
Q
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
oo
u
Q
75%
81%
86%
78%
77%
78%
76%
95%
89%
91%
93%
52%
87%
85%
91%
75%
NA
88%
87%
77%
NA
82%
82%
87%
H
2%
3%
1%
0%
0%
3%
2%
3%
3%
5%
7%
26%
10%
3%
1%
2%
NA
8%
6%
0%
NA
2%
4%
3%
O
w
77%
85%
98%
78%
77%
97%
87%
100%
94%
100%
100%
78%
99%
97%
99%
77%
NA
98%
95%
77%
NA
88%
90%
95%
A
§
44%
74%
74%
70%
5%
71%
75%
74%
46%
25%
4%
70%
75%
75%
74%
46%
NA
75%
75%
5%
NA
28%
70%
57%
W
23%
5%
0%
3%
50%
3%
1%
0%
6%
0%
0%
4%
0%
0%
1%
15%
NA
4%
2%
50%
NA
12%
2%
4%
£
23%
15%
2%
22%
23%
0%
13%
0%
0%
0%
0%
22%
1%
3%
0%
23%
NA
2%
5%
23%
NA
0%
10%
2%
W
PH
10%
0%
0%
3%
22%
0%
0%
0%
0%
0%
0%
2%
0%
0%
0%
16%
NA
0%
0%
22%
NA
0%
0%
0%
t/3
t/3
17%
35%
6%
25%
5%
0%
38%
0%
0%
0%
0%
26%
1%
6%
0%
11%
NA
1%
1%
5%
NA
0%
36%
4%
LRRT2
100%
100%
100%
100%
100%
97%
100%
100%
94%
100%
100%
100%
100%
100%
99%
100%
NA
100%
100%
100%
NA
88%
100%
97%
1
17%
35%
65%
25%
5%
86%
38%
84%
94%
100%
100%
26%
86%
68%
82%
11%
NA
65%
62%
5%
NA
88%
40%
80%
w
77%
85%
98%
78%
77%
97%
87%
100%
94%
100%
100%
78%
99%
97%
99%
77%
NA
98%
95%
77%
NA
88%
90%
95%
Q
77%
84%
97%
78%
77%
93%
87%
97%
94%
100%
71%
78%
99%
97%
99%
77%
NA
98%
95%
77%
NA
83%
85%
92%
_)
%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
5%
0%
MHEV
27%
45%
33%
47%
0%
10%
49%
16%
0%
0%
0%
46%
13%
29%
17%
35%
NA
29%
30%
0%
NA
0%
48%
14%
10-22
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-29 Final rule Truck Technology Penetrations in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
NA
-20%
-12%
-20%
NA
-13%
-20%
-14%
-12%
-15%
-11%
NA
-19%
-19%
-11%
-20%
NA
-20%
-2%
-17%
NA
-8%
-20%
-13%
-------
Chapter 10
Table 10-30 Final rule Fleet Technology Penetration in MY 2025
Aston Martin
BMW
Chrysler/Fiat
Daimler
Ferrari
Ford
Geely
General Motors
Honda
Hyundai
Kia
Lotus
Mazda
Mitsubishi
Nissan
Porsche
Spyker
Subaru
Suzuki
Tata
Tesla
Toyota
Volkswagen
Fleet
Mass Tech
Applied
-16%
-12%
-11%
-18%
-17%
-9%
-12%
-11%
-5%
-5%
-1%
-3%
-6%
-9%
-5%
-17%
NA
-13%
-2%
-18%
NA
-5%
-9%
-8%
4> S»
£1
-n%
-10%
-10%
-15%
-8%
-9%
-10%
-10%
-5%
-5%
-1%
0%
-5%
-8%
-4%
-13%
NA
-12%
-1%
-14%
NA
-5%
-8%
-7%
«, &
a "3
Sl
6%
2%
1%
3%
9%
0%
2%
1%
0%
0%
0%
3%
1%
1%
1%
4%
NA
1%
1%
5%
NA
0%
2%
1%
TDS18
0%
8%
20%
6%
0%
16%
10%
18%
27%
34%
6%
3%
24%
20%
23%
8%
NA
20%
17%
0%
NA
33%
13%
21%
TDS24
4%
59%
70%
61%
0%
63%
64%
60%
69%
65%
68%
70%
75%
75%
73%
46%
NA
75%
75%
32%
NA
50%
66%
63%
TDS27
40%
15%
8%
13%
5%
16%
17%
15%
0%
1%
0%
0%
0%
0%
3%
20%
NA
0%
0%
32%
NA
5%
6%
8%
1
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
oo
%
0%
22%
48%
31%
0%
45%
38%
41%
24%
9%
10%
0%
13%
15%
24%
52%
NA
5%
4%
62%
NA
33%
21%
32%
VO
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
NA
0%
0%
0%
NA
0%
0%
0%
oo
75%
64%
49%
54%
77%
51%
51%
57%
70%
86%
84%
52%
77%
79%
74%
36%
NA
86%
85%
29%
NA
56%
68%
62%
H
2%
3%
1%
0%
0%
2%
1%
2%
2%
5%
6%
26%
9%
3%
1%
1%
NA
7%
6%
0%
NA
3%
3%
2%
1
77%
88%
99%
85%
77%
98%
91%
100%
96%
100%
100%
78%
99%
97%
99%
89%
NA
99%
94%
91%
NA
91%
92%
97%
(K
§
44%
73%
75%
70%
5%
72%
75%
74%
54%
30%
11%
70%
75%
75%
74%
61%
NA
75%
75%
49%
NA
45%
69%
62%
m
23%
4%
3%
2%
50%
2%
0%
0%
4%
0%
0%
4%
0%
2%
1%
7%
NA
3%
3%
50%
NA
9%
1%
3%
a
23%
12%
1%
15%
23%
0%
9%
0%
0%
0%
0%
22%
1%
3%
0%
11%
NA
1%
6%
9%
NA
0%
8%
1%
£
£
10%
0%
0%
2%
22%
0%
0%
0%
0%
0%
0%
2%
0%
0%
0%
8%
NA
0%
0%
8%
NA
0%
0%
0%
t/3
t/3
17%
38%
25%
33%
5%
9%
42%
25%
6%
0%
0%
26%
6%
9%
10%
31%
NA
3%
2%
33%
NA
0%
39%
14%
LRRT2
100%
100%
100%
100%
100%
98%
100%
100%
96%
100%
100%
100%
100%
100%
99%
100%
NA
100%
100%
100%
NA
91%
100%
98%
-------
MY 2017 and Later - Regulatory Impact Analysis
10.6 GHG Impacts
The GHG reductions and fuel savings are shown in this section.
Table 10-31 Calendar year GHG impacts
Calendar Year:
Net Reduction*
Net CO2
Net other GHG
Downstream Reduction
CO2 (excluding A/C)
A/C- indirect CO2
A/C - direct HFCs
CH4 (rebound effect)
N2O (rebound effect)
Gasoline Upstream Reduction
CO2
CH4
N20
Electricity Upstream Increase
CO2
CH4
N2O
2020
-28
-24
-4
-23
-18
-1
-3
0
0
-5
-5
-1
0
1
0
0
0
2030
-262
-238
-24
-213
-193
-3
-18
0
0
-55
-48
-7
0
6
5
1
0
2040
-423
-387
-35
-344
-314
-4
-26
0
0
-89
-77
-11
0
10
9
1
0
2050
-506
-464
-42
-412
-377
-5
-30
0
0
-106
-93
-13
0
12
10
2
0
Table 10-32 Model year GHG impacts
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
Sum
Downstream
-27
-59
-90
-124
-174
-216
-254
-295
-334
-1,573
Upstream
Gasoline
-7
-14
-21
-29
-42
-54
-64
-75
-86
-394
Upstream Electricity
1
1
2
3
4
5
7
8
10
41
Total CO2e
-33
-72
-110
-150
-213
-264
-312
-362
-411
-1,926
Monetized values of CO2 reductions associated with the 2010 baseline are presented in
Section 10.7 below.
10-25
-------
Chapter 10
10.7 Fuel Savings
The expected impacts on fuel consumption are shown in Table 10-33. The gallons
reduced and kilowatt hours increased (kWh) as shown in the tables reflect impacts from the
final CC>2 standards, including the A/C credit program, and include the increased fuel
consumption resulting from the rebound effect.
Table 10-33 - Fuel Consumption Impacts of the Final Standards and A/C Credit
Programs
Calendar Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
Total
Petroleum-based
Gasoline Reference
(million gallons)
125,346
124,204
123,247
122,408
121,775
121,386
121,210
121,293
121,645
125,979
139,497
157,428
5,186,805
Petroleum-based
Gasoline Reduced
(million gallons)
209
645
1,297
2,181
3,441
5,016
6,866
8,984
11,353
22,017
35,838
42,960
833,756
Electricity Increased
(million kWh)
92
273
544
904
1,357
1,972
2,751
3,698
4,809
9,911
16,748
20,036
383,605
Note: The electricity
results correspond to
increase shown is that needed to charge EVs/PHEVs, not that generated by power plants;
the 2010 baseline fleet.
Monetized fuel savings are shown in Table 10-34 and Table 10-35.
Table 10-34 - Undiscounted Annual Fuel Savings & Fuel Savings Discounted back to
2012 at 3% and 7% Discount Rates (millions of 2010 dollars)
Calendar
Year
2017
2018
2019
2020
2021
2022
2023
2024
2025
2030
2040
2050
NPV, 3%
NPV, 7%
Gasoline
Savings
(pre-tax)
$704
$2,190
$4,480
$7,650
$12,200
$17,800
$24,400
$32,200
$41,400
$84,200
$145,000
$190,000
$1,510,000
$578,000
Gasoline
Savings
(taxed)
$794
$2,460
$5,040
$8,570
$13,600
$19,900
$27,200
$35,800
$45,900
$92,900
$159,000
$205,000
$1,650,000
$634,000
Electricity
Costs
$8.5
$25.2
$49.9
$83
$126
$186
$263
$358
$472
$1,030
$1,900
$2,460
$19,200
$7,270
Total Fuel
Savings
(pre-tax)
$696
$2,160
$4,430
$7,570
$12,100
$17,600
$24,100
$31,800
$40,900
$83,100
$143,000
$188,000
$1,490,000
$571,000
Total Fuel
Savings
(taxed)
$786
$2,430
$4,990
$8,480
$13,500
$19,700
$26,900
$35,500
$45,400
$91,900
$157,000
$203,000
$1,630,000
$627,000
Note: Annual values represent undiscounted values; net present values represent annual costs discounted to
2012; results correspond to the 2010 baseline fleet.
10-26
-------
MY 2017 and Later - Regulatory Impact Analysis
Table 10-35 - Model Year Lifetime Present Value Fuel Savings Discounted to the 1st
Year of each MY at 3% & 7% (millions of 2010 dollars)
NPV
at
3%
7%
Car
Truck
Total
Car
Truck
Total
2017
$7,380
$158
$7,540
$5,670
$119
$5,790
2018
$13,700
$2,330
$16,000
$10,600
$1,770
$12,400
2019
$20,400
$4,310
$24,700
$15,700
$3,270
$19,000
2020
$27,300
$6,990
$34,300
$21,000
$5,300
$26,300
2021
$34,800
$14,900
$49,700
$26,700
$11,300
$38,000
2022
$42,700
$20,700
$63,400
$32,900
$15,700
$48,600
2023
$50,100
$26,300
$76,400
$38,500
$19,900
$58,400
2024
$58,200
$30,000
$88,200
$44,800
$22,900
$67,700
2025
$66,100
$38,000
$104,000
$50,900
$28,800
$79,700
Sum
$321,000
$144,000
$465,000
$247,000
$109,000
$356,000
Note: Results correspond to the 2010 baseline fleet.
10.8 Comparison to analysis using the MY 2008 based market forecast
As noted in the introduction to this chapter, the MY 2010 baseline supports the
reasonableness of the standards finalized here. While there are minor differences in costs and
benefits, these minor differences support the overall analytic approach and results as robust
despite a significant change in inputs. Table 10-36 presents a high level comparison of the
two analyses.
Table 10-36 - Comparison of Analyses
MY 2025 Per Vehicle Average
Costs relative to the MY 2016
standard reference case($)
MY Lifetime GHG emission
reductions (MMT CO2eq)
MY Lifetime Fuel savings (B.
Barrels)
CY 2030 GHG emission
reductions (MMT CO2eq)
CY 2030 Fuel savings (B.
Barrels)
Analysis using the MY 2008
based market forecast.
$1,836
1,956
3.9
271
0.55
Analysis using the MY 2010
based market forecast
$1,785
1,926
3.8
262
0.53
10-27
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