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

Regulatory Impact Analysis: 

Final Rulemaking for 2017-2025 

Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate 

Average Fuel Economy Standards

EPA-420-R-12-016

August 2012
MY 2017 and Later Regulatory Impact Analysis 

I 

Table of Contents 

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
Table of Contents 

II 

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
MY 2017 and Later Regulatory Impact Analysis 

III 

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
Table of Contents 

IV 

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
MY 2017 and Later Regulatory Impact Analysis 

V 

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

MY 2017 and Later Regulatory Impact Analysis 

i 

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 CO2 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 CO2

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/endangerment.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"). 
Executive Summary 

ii 

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 Valuec

 – 3% Discount Rate 

Program Costs $150 

Fuel Savings $475 

Benefits $126 

Net Benefitsd$451 

Annualized Valuee – 3% Discount Rate 

Annualized costs $6.49 

Annualized fuel savings $20.5 

Annualized benefits $5.46 

Net benefits $19.5 

Lifetime Present Valuec - 7% Discount Rate 

Program Costs $144 

Fuel Savings $364 

Benefits $106 

Net Benefitsd$326 

Annualized Valuee – 7% Discount Rate 

Annualized costs $10.8 

Annualized fuel savings $27.3 

Annualized benefits $7.96 

Net benefits $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. 

b

 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. NHTSA has prepared its own RIA in support of its CAFE standards (see 
MY 2017 and Later Regulatory Impact Analysis 

iii 

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 CO2 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.
Executive Summary 

iv 

Chapter3: 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 (CO2), methane (CH4), nitrous oxide 

(N2O) and hydrofluorocarbons (HFC-134a); program impacts on “criteria” air pollutants, 

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

the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOx); and impacts on 

several air toxics including benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. 

CO2 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 CO2 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 
MY 2017 and Later Regulatory Impact Analysis 

v 

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 CO2 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 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 

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 οur 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 PM2.5 is a decrease of 0.007 μg/m3. 

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).
Executive Summary 

vi 

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$) 

2017 2020 2030 2040 2050 NPV, 3%a NPV, 7%a

Technology Costs $2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247,000 

Fuel Savings $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000 

3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000 

2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000 

3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000 

Monetized Net Benefits at each assumed SCC value c

5% (avg SCC) -$1,690 -$316 $68,000 $146,000 $201,000 $1,290,000 $478,000 

3% (avg SCC) -$1,650 $153 $73,900 $158,000 $217,000 $1,430,000 $616,000 

2.5% (avg SCC) -$1,610 $524 $78,300 $166,000 $228,000 $1,550,000 $736,000 

3% (95th %ile) -$1,530 $1,460 $91,200 $192,000 $267,000 $1,780,000 $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. 

b

 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

 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 
MY 2017 and Later Regulatory Impact Analysis 

vii 

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. 

MY 2017 and Later Regulatory Impact Analysis 

1-1 

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. 

CO2 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 CO2 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 
Chapter 1 

1-2 

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 
MY 2017 and Later Regulatory Impact Analysis 

1-3 

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 CO2, 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. 
Chapter 1 

1-4 

1.2 Technology Cost and Effectiveness 

EPA collected information on the cost and effectiveness of CO2 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 CO2 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-5 

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. 
Chapter 1 

1-6 

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—inline 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 

I4 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 I4 would carry EFR costs 

for an I4. 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 
MY 2017 and Later Regulatory Impact Analysis 

1-7 

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 CO2 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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Conversion to Atkinson $0 $0 $0 $0 $0 $0 $0 $0 $0

CCP-OHC-I $46 $46 $43 $42 $42 $41 $40 $40 $39

CCP-OHC-V $93 $91 $86 $84 $83 $82 $80 $79 $78

CCP-OHV-V $46 $46 $43 $42 $42 $41 $40 $40 $39

CVVL-OHC-I4 $244 $241 $220 $216 $213 $209 $206 $203 $200

CVVL-OHC-V6 $448 $441 $403 $396 $390 $384 $378 $372 $367

CVVL-OHC-V8 $489 $482 $439 $432 $426 $419 $412 $406 $400

DCP-OHC-I $95 $94 $86 $84 $83 $82 $80 $79 $78

DCP-OHC-V $205 $202 $184 $181 $178 $176 $173 $170 $168

DCP-OHV-V $104 $102 $93 $92 $90 $89 $88 $86 $85

Deac-V6 $196 $193 $176 $173 $170 $168 $165 $162 $160

Deac-V8 $220 $217 $198 $195 $191 $189 $186 $183 $180

DVVL-OHC-I4 $163 $161 $146 $144 $142 $140 $137 $135 $133

DVVL-OHC-V6 $236 $233 $212 $209 $206 $202 $199 $196 $193
Chapter 1 

1-8 

DVVL-OHC-V8 $338 $333 $303 $298 $294 $289 $285 $280 $276

EFR1-I3 $44 $44 $43 $43 $43 $43 $43 $43 $43

EFR1-I4 $59 $59 $57 $57 $57 $57 $57 $57 $57

EFR1-V6 $89 $89 $85 $85 $85 $85 $85 $85 $85

EFR1-V8 $118 $118 $113 $113 $113 $113 $113 $113 $113

EFR2-I3 $97 $97 $97 $97 $97 $97 $97 $97 $93

EFR2-I4 $126 $126 $126 $126 $126 $126 $126 $126 $121

EFR2-V6 $185 $185 $185 $185 $185 $185 $185 $185 $178

EFR2-V8 $244 $244 $244 $244 $244 $244 $244 $244 $234

EGR-I $305 $301 $296 $292 $288 $284 $280 $276 $249

EGR-V $305 $301 $296 $292 $288 $284 $280 $276 $249

LUB $4 $4 $4 $4 $4 $4 $4 $4 $4

Stoich GDI-I4 $277 $273 $248 $244 $240 $236 $233 $229 $226

Stoich GDI-I4>I3 $277 $273 $248 $244 $240 $236 $233 $229 $226

Stoich GDI-V6 $417 $411 $373 $367 $362 $356 $351 $346 $340

Stoich GDI-V8 $501 $494 $449 $442 $435 $429 $422 $416 $409

V6 OHV to V6 DOHC $682 $666 $604 $590 $576 $562 $554 $545 $537

V6 SOHC to V6 

DOHC $214 $211 $192 $189 $186 $183 $180 $178 $175

V8 OHV to V8 DOHC $747 $730 $661 $646 $631 $616 $606 $597 $588

V8 SOHC 3V to V8 

DOHC $154 $152 $138 $136 $134 $132 $130 $128 $126

V8 SOHC to V8 

DOHC $247 $243 $221 $218 $214 $211 $208 $205 $202

VVTI-OHC-I $46 $46 $43 $42 $42 $41 $40 $40 $39

VVTI-OHC-V $93 $91 $86 $84 $83 $82 $80 $79 $78

VVTI-OHV-V $46 $46 $43 $42 $42 $41 $40 $40 $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; EFR1=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 BMEP 2017 2018 2019 2020 2021 2022 2023 2024 2025

I4 to I3 wT 18 bar $427 $423 $359 $356 $352 $348 $344 $340 $337

I4 to I3 wT 24 bar $690 $681 $654 $647 $639 $632 $624 $617 $551

I4 to I3 wT 27 bar $1,214 $1,199 $1,164 $1,149 $1,134 $1,120 $1,106 $1,092 $979

I4 DOHC to I4 

DOHC wT 18 bar $482 $476 $421 $415 $410 $404 $399 $393 $388

I4 DOHC to I4 

DOHC wT 24 bar $744 $734 $716 $707 $697 $688 $679 $670 $602

I4 DOHC to I4 

DOHC wT 27 bar $1,269 $1,251 $1,226 $1,209 $1,192 $1,176 $1,160 $1,145 $1,031

V6 DOHC to 

I4 wT 18 bar $248 $250 $157 $159 $161 $163 $165 $167 $169

V6 DOHC to 

I4 wT 24 bar $510 $508 $452 $450 $449 $447 $445 $444 $383

V6 DOHC to 

I4 wT 27 bar $1,035 $1,026 $962 $953 $944 $935 $927 $918 $811

V6 SOHC to 18 bar $331 $330 $251 $251 $250 $249 $248 $248 $247
MY 2017 and Later Regulatory Impact Analysis 

1-9 

I4 wT 

V6 SOHC to 

I4 wT 24 bar $594 $589 $546 $542 $537 $533 $529 $524 $461

V6 SOHC to 

I4 wT 27 bar $1,119 $1,106 $1,056 $1,044 $1,032 $1,021 $1,010 $999 $890

V6 OHV to I4 

DOHC wT 18 bar $914 $898 $815 $799 $784 $770 $758 $746 $735

V6 OHV to I4 

DOHC wT 24 bar $1,177 $1,156 $1,110 $1,090 $1,072 $1,053 $1,038 $1,023 $949

V6 OHV to I4 

DOHC wT 27 bar $1,701 $1,674 $1,619 $1,593 $1,567 $1,542 $1,519 $1,498 $1,378

V8 DOHC to 

V6 DOHC wT 18 bar $746 $738 $635 $628 $620 $613 $606 $599 $592

V8 DOHC to 

V6 DOHC wT 24 bar $1,188 $1,174 $1,132 $1,118 $1,105 $1,092 $1,078 $1,066 $953

V8 DOHC to 

I4 DOHC wT 27 bar $789 $794 $716 $722 $726 $731 $728 $725 $623

V8 SOHC to 

V6 DOHC wT 18 bar $842 $831 $744 $733 $723 $712 $702 $692 $682

V8 SOHC to 

V6 DOHC wT 24 bar $1,284 $1,267 $1,241 $1,224 $1,207 $1,191 $1,175 $1,159 $1,043

V8 SOHC to 

I4 DOHC wT 27 bar $910 $910 $846 $845 $845 $844 $838 $832 $727

V8 SOHC 3V 

to V6 DOHC 

wT 

18 bar $806 $796 $703 $693 $684 $675 $666 $657 $648

V8 SOHC 3V 

to V6 DOHC 

wT 

24 bar $1,248 $1,232 $1,200 $1,184 $1,169 $1,153 $1,138 $1,124 $1,010

V8 SOHC 3V 

to I4 DOHC 

wT 

27 bar $864 $866 $797 $799 $800 $801 $796 $791 $688

V8 OHV to 

V6 DOHC wT 18 bar $1,339 $1,316 $1,194 $1,172 $1,151 $1,131 $1,113 $1,096 $1,080

V8 OHV to 

V6 DOHC wT 24 bar $1,781 $1,752 $1,691 $1,663 $1,636 $1,609 $1,586 $1,563 $1,441

V8 OHV to I4 

DOHC wT 27 bar $1,164 $1,152 $1,116 $1,105 $1,093 $1,082 $1,069 $1,056 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025

ASL $33 $32 $30 $30 $29 $29 $28 $28 $27

ASL2 $34 $33 $32 $32 $31 $30 $30 $29 $27

5sp AT $104 $103 $97 $95 $94 $92 $91 $89 $88

6sp AT -$9 -$9 -$10 -$9 -$9 -$9 -$9 -$8 -$8

6sp DCT-dry -$116 -$112 -$131 -$127 -$123 -$119 -$116 -$112 -$109

6sp DCT-wet -$82 -$79 -$92 -$89 -$87 -$84 -$82 -$79 -$77

6sp MT -$169 -$165 -$172 -$167 -$163 -$159 -$155 -$151 -$147

8sp AT $62 $61 $55 $54 $54 $53 $52 $51 $50

8sp DCT-dry -$16 -$15 -$15 -$14 -$14 -$13 -$13 -$12 -$15
Chapter 1 

1-10 

8sp DCT-wet $47 $47 $46 $45 $45 $44 $44 $43 $39

HEG $251 $245 $239 $233 $227 $222 $218 $215 $202

TORQ $30 $29 $27 $27 $27 $26 $26 $25 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025

EPS/EHPS $109 $108 $101 $100 $98 $96 $95 $93 $92

IACC $89 $88 $82 $81 $80 $78 $77 $76 $75

IACC2 $143 $141 $133 $131 $128 $126 $124 $122 $120

Stop-start (12V) 

for Small car, 

Standard car 

$401 $392 $354 $346 $338 $330 $322 $315 $308

Stop-start (12V) 

for Large car, 

Small MPV, 

Large MPV 

$454 $444 $402 $392 $383 $374 $366 $357 $349

Stop-start (12V) 

for Truck $498 $487 $441 $430 $420 $410 $401 $392 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aero1 $49 $48 $45 $45 $44 $43 $42 $42 $41

Aero2 $213 $210 $205 $202 $199 $196 $193 $190 $176

LDB $74 $74 $71 $71 $71 $71 $71 $71 $71

LRRT1 $7 $7 $6 $6 $6 $6 $6 $6 $6

LRRT2 $73 $73 $60 $60 $50 $49 $48 $47 $44

SAX $98 $96 $91 $89 $88 $86 $85 $83 $82

Aero1=aerodynamic treatments level 1; Aero2=aero level 2; LDB=low drag brakes; LRRT1=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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car $2,965 $2,922 $2,653 $2,612 $2,572 $2,533 $2,495 $2,457 $2,420

Standard car $2,965 $2,922 $2,653 $2,612 $2,572 $2,533 $2,495 $2,457 $2,420

Large car $3,631 $3,578 $3,249 $3,200 $3,151 $3,103 $3,056 $3,010 $2,964

Small MPV $2,971 $2,928 $2,659 $2,618 $2,578 $2,539 $2,501 $2,463 $2,426

Large MPV $2,996 $2,953 $2,682 $2,641 $2,600 $2,561 $2,522 $2,484 $2,446

Truck $4,154 $4,094 $3,718 $3,661 $3,605 $3,550 $3,496 $3,443 $3,392

All costs are incremental to the baseline case. 
MY 2017 and Later Regulatory Impact Analysis 

1-11 

Table 1.2-7 Costs for P2-Hybird Technology for the 2008 Baseline (2010$) 

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 5% $3,484 $3,431 $3,025 $2,975 $2,926 $2,878 $2,832 $2,786 $2,591 

Small car 15% 10% $3,452 $3,398 $2,996 $2,946 $2,898 $2,851 $2,805 $2,760 $2,567 

Small car 20% 15% $3,419 $3,366 $2,967 $2,918 $2,870 $2,823 $2,778 $2,733 $2,542 

Standard car 10% 5% $3,847 $3,788 $3,339 $3,284 $3,230 $3,177 $3,126 $3,076 $2,861 

Standard car 15% 10% $3,800 $3,742 $3,298 $3,244 $3,191 $3,139 $3,088 $3,038 $2,826 

Standard car 20% 15% $3,754 $3,696 $3,257 $3,204 $3,151 $3,100 $3,050 $3,001 $2,792 

Large car 10% 5% $4,481 $4,412 $3,889 $3,825 $3,762 $3,701 $3,641 $3,583 $3,332 

Large car 15% 10% $4,402 $4,334 $3,821 $3,757 $3,696 $3,635 $3,577 $3,519 $3,273 

Large car 20% 15% $4,324 $4,257 $3,752 $3,690 $3,629 $3,570 $3,513 $3,456 $3,215 

Small MPV 10% 5% $3,705 $3,648 $3,218 $3,165 $3,113 $3,062 $3,012 $2,964 $2,755 

Small MPV 15% 10% $3,664 $3,608 $3,182 $3,129 $3,078 $3,027 $2,978 $2,931 $2,724 

Small MPV 20% 15% $3,623 $3,567 $3,146 $3,093 $3,043 $2,993 $2,945 $2,897 $2,694 

Large MPV 10% 5% $4,229 $4,164 $3,670 $3,609 $3,550 $3,492 $3,436 $3,381 $3,145 

Large MPV 15% 10% $4,170 $4,106 $3,617 $3,558 $3,499 $3,442 $3,387 $3,332 $3,101 

Large MPV 20% 15% $4,110 $4,047 $3,565 $3,506 $3,449 $3,393 $3,338 $3,284 $3,057 

Truck 10% 6% $4,575 $4,504 $3,982 $3,916 $3,851 $3,788 $3,726 $3,666 $3,399 

Truck 15% 11% $4,500 $4,431 $3,916 $3,851 $3,788 $3,726 $3,665 $3,606 $3,344 

Truck 20% 16% $4,426 $4,357 $3,851 $3,787 $3,724 $3,663 $3,604 $3,546 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 5% $3,505 $3,451 $3,043 $2,993 $2,943 $2,895 $2,849 $2,803 $2,606 

Small car 15% 10% $3,470 $3,417 $3,013 $2,963 $2,914 $2,867 $2,820 $2,775 $2,580 

Small car 20% 15% $3,435 $3,383 $2,982 $2,933 $2,885 $2,838 $2,792 $2,747 $2,555 

Standard car 10% 5% $3,888 $3,828 $3,375 $3,319 $3,264 $3,211 $3,159 $3,108 $2,891 

Standard car 15% 10% $3,838 $3,779 $3,331 $3,276 $3,222 $3,170 $3,119 $3,069 $2,854 

Standard car 20% 15% $3,789 $3,731 $3,288 $3,234 $3,181 $3,129 $3,078 $3,029 $2,818 

Large car 10% 5% $4,567 $4,497 $3,963 $3,897 $3,833 $3,771 $3,710 $3,650 $3,396 

Large car 15% 10% $4,484 $4,415 $3,890 $3,826 $3,763 $3,702 $3,642 $3,584 $3,334 

Large car 20% 15% $4,401 $4,333 $3,818 $3,755 $3,693 $3,633 $3,574 $3,517 $3,273 

Small MPV 10% 5% $3,765 $3,707 $3,269 $3,215 $3,162 $3,111 $3,060 $3,011 $2,799 

Small MPV 15% 10% $3,721 $3,664 $3,230 $3,177 $3,125 $3,074 $3,024 $2,976 $2,767 

Small MPV 20% 15% $3,677 $3,620 $3,192 $3,139 $3,087 $3,037 $2,988 $2,940 $2,734 

Large MPV 10% 5% $4,261 $4,196 $3,696 $3,635 $3,576 $3,517 $3,461 $3,405 $3,169 

Large MPV 15% 10% $4,200 $4,135 $3,643 $3,582 $3,524 $3,466 $3,410 $3,356 $3,124 

Large MPV 20% 15% $4,138 $4,075 $3,589 $3,529 $3,472 $3,415 $3,360 $3,306 $3,078 

Truck 10% 6% $4,615 $4,543 $4,016 $3,950 $3,884 $3,821 $3,758 $3,698 $3,428 

Truck 15% 11% $4,538 $4,468 $3,950 $3,884 $3,820 $3,757 $3,696 $3,636 $3,372 

Truck 20% 16% $4,462 $4,393 $3,883 $3,818 $3,755 $3,693 $3,633 $3,575 $3,315 

WR=weight reduction. 

All costs are incremental to the baseline case. 

 
Chapter 1 

1-12 

Table 1.2-9 Costs for Plug-in Hybrid Technology with 20 Mile EV Range, or PHEV20, 

for the 2008 Baseline (2010$) 

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 3% $11,041 $9,938 $9,282 $8,392 $8,345 $8,298 $8,252 $8,207 $6,804 

Small car 15% 8% $10,828 $9,743 $9,103 $8,229 $8,182 $8,136 $8,091 $8,047 $6,669 

Small car 20% 13% $10,614 $9,549 $8,924 $8,065 $8,019 $7,975 $7,931 $7,888 $6,534 

Standard car 10% 3% $13,148 $11,860 $11,048 $10,009 $9,950 $9,892 $9,835 $9,779 $8,145 

Standard car 15% 8% $12,793 $11,540 $10,751 $9,739 $9,682 $9,625 $9,570 $9,516 $7,924 

Standard car 20% 13% $12,439 $11,219 $10,453 $9,469 $9,413 $9,358 $9,304 $9,252 $7,704 

Large car 10% 2% $17,521 $15,878 $14,710 $13,383 $13,298 $13,214 $13,132 $13,052 $10,971 

Large car 15% 7% $17,010 $15,409 $14,282 $12,989 $12,907 $12,826 $12,747 $12,670 $10,642 

Large car 20% 12% $16,499 $14,940 $13,853 $12,596 $12,516 $12,438 $12,362 $12,287 $10,314 

Small MPV 10% 3% $12,394 $11,159 $10,418 $9,423 $9,369 $9,316 $9,264 $9,213 $7,644 

Small MPV 15% 8% $12,126 $10,915 $10,194 $9,218 $9,165 $9,114 $9,063 $9,013 $7,475 

Small MPV 20% 13% $11,859 $10,672 $9,970 $9,013 $8,962 $8,911 $8,862 $8,814 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 3% $11,251 $10,129 $9,458 $8,554 $8,505 $8,457 $8,410 $8,363 $6,939 

Small car 15% 8% $11,031 $9,929 $9,273 $8,385 $8,337 $8,290 $8,244 $8,199 $6,799 

Small car 20% 13% $10,810 $9,728 $9,088 $8,216 $8,169 $8,123 $8,078 $8,034 $6,659 

Standard car 10% 3% $13,507 $12,191 $11,349 $10,287 $10,226 $10,166 $10,107 $10,049 $8,379 

Standard car 15% 8% $13,138 $11,857 $11,039 $10,006 $9,946 $9,887 $9,830 $9,774 $8,148 

Standard car 20% 13% $12,769 $11,523 $10,729 $9,724 $9,666 $9,609 $9,554 $9,499 $7,918 

Large car 10% 2% $18,043 $16,363 $15,146 $13,789 $13,700 $13,613 $13,528 $13,444 $11,317 

Large car 15% 7% $17,506 $15,870 $14,696 $13,375 $13,290 $13,206 $13,123 $13,043 $10,972 

Large car 20% 12% $16,969 $15,378 $14,247 $12,962 $12,879 $12,798 $12,719 $12,641 $10,626 

Small MPV 10% 3% $12,857 $11,583 $10,806 $9,779 $9,723 $9,667 $9,613 $9,559 $7,942 

Small MPV 15% 8% $12,516 $11,276 $10,520 $9,520 $9,465 $9,411 $9,358 $9,306 $7,731 

Small MPV 20% 13% $12,175 $10,968 $10,234 $9,260 $9,207 $9,154 $9,103 $9,052 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 15% 2% $14,158 $12,589 $11,931 $10,669 $10,620 $10,573 $10,527 $10,482 $8,478 

Small car 20% 7% $13,853 $12,317 $11,673 $10,438 $10,391 $10,345 $10,300 $10,256 $8,294 

Standard car 15% 3% $17,077 $15,199 $14,388 $12,877 $12,818 $12,760 $12,703 $12,647 $10,250 

Standard car 20% 8% $16,632 $14,802 $14,013 $12,540 $12,483 $12,426 $12,371 $12,317 $9,981 

Large car 15% 1% $23,903 $21,308 $20,132 $18,044 $17,958 $17,874 $17,792 $17,711 $14,401 

Large car 20% 6% $22,998 $20,505 $19,369 $17,363 $17,280 $17,199 $17,119 $17,041 $13,861 

Small MPV 15% 3% $16,263 $14,447 $13,706 $12,246 $12,192 $12,139 $12,087 $12,036 $9,717 

Small MPV 20% 8% $15,872 $14,099 $13,377 $11,951 $11,898 $11,847 $11,796 $11,747 $9,482 

WR=weight reduction. 

All costs are incremental to the baseline case. 
MY 2017 and Later Regulatory Impact Analysis 

1-13 

Table 1.2-12 Costs for Plug-in Hybrid Technology with 40 Mile EV Range, or PHEV40, 

for the 2010 Baseline (2010$) 

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 15% 3% $14,401 $12,806 $12,135 $10,852 $10,803 $10,755 $10,708 $10,662 $8,626 

Small car 20% 8% $14,076 $12,517 $11,862 $10,607 $10,559 $10,512 $10,467 $10,422 $8,431 

Standard car 15% 3% $17,551 $15,628 $14,785 $13,238 $13,177 $13,116 $13,057 $13,000 $10,545 

Standard car 20% 8% $17,082 $15,208 $14,390 $12,883 $12,824 $12,765 $12,708 $12,652 $10,261 

Large car 15% 2% $24,466 $21,821 $20,604 $18,476 $18,387 $18,300 $18,215 $18,131 $14,758 

Large car 20% 7% $23,613 $21,061 $19,886 $17,832 $17,746 $17,662 $17,580 $17,499 $14,244 

Small MPV 15% 3% $16,769 $14,908 $14,131 $12,634 $12,578 $12,522 $12,467 $12,414 $10,038 

Small MPV 20% 8% $16,358 $14,540 $13,785 $12,323 $12,268 $12,214 $12,161 $12,109 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 10% $14,165 $12,084 $12,078 $10,413 $10,407 $10,402 $10,398 $10,394 $7,658 

Small car 15% 15% $13,771 $11,732 $11,729 $10,097 $10,093 $10,090 $10,088 $10,085 $7,421 

Small car 20% 20% $13,378 $11,381 $11,379 $9,781 $9,780 $9,778 $9,777 $9,776 $7,184 

Standard car 10% 10% $17,684 $15,244 $15,216 $13,259 $13,232 $13,206 $13,189 $13,172 $9,795 

Standard car 15% 15% $17,101 $14,723 $14,697 $12,791 $12,767 $12,744 $12,729 $12,714 $9,443 

Standard car 20% 20% $16,518 $14,201 $14,179 $12,322 $12,302 $12,282 $12,269 $12,256 $9,092 

Large car 10% 10% $23,296 $20,186 $20,134 $17,638 $17,589 $17,542 $17,512 $17,482 $13,057 

Large car 15% 15% $22,333 $19,320 $19,275 $16,858 $16,815 $16,774 $16,747 $16,720 $12,471 

Large car 20% 20% $21,369 $18,454 $18,415 $16,078 $16,041 $16,005 $15,982 $15,959 $11,886 

Small MPV 10% 9% $15,909 $13,478 $13,483 $11,539 $11,545 $11,550 $11,553 $11,556 $8,460 

Small MPV 15% 14% $15,453 $13,068 $13,077 $11,170 $11,178 $11,186 $11,191 $11,196 $8,183 

Small MPV 20% 19% $14,997 $12,658 $12,670 $10,801 $10,812 $10,822 $10,829 $10,836 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 10% $14,581 $12,450 $12,442 $10,737 $10,729 $10,722 $10,718 $10,713 $7,899 

Small car 15% 15% $14,203 $12,110 $12,105 $10,430 $10,425 $10,421 $10,417 $10,414 $7,669 

Small car 20% 20% $13,824 $11,771 $11,768 $10,124 $10,122 $10,119 $10,117 $10,115 $7,439 

Standard car 10% 9% $18,311 $15,806 $15,773 $13,764 $13,733 $13,703 $13,684 $13,665 $10,173 

Standard car 15% 14% $17,700 $15,259 $15,230 $13,273 $13,245 $13,219 $13,202 $13,185 $9,805 

Standard car 20% 19% $17,089 $14,712 $14,687 $12,781 $12,758 $12,735 $12,720 $12,705 $9,436 

Large car 10% 10% $24,054 $20,863 $20,807 $18,245 $18,193 $18,141 $18,108 $18,076 $13,512 

Large car 15% 15% $23,052 $19,963 $19,913 $17,435 $17,388 $17,343 $17,313 $17,284 $12,904 

Large car 20% 20% $22,051 $19,063 $19,020 $16,624 $16,583 $16,544 $16,518 $16,493 $12,295 

Small MPV 10% 9% $16,315 $13,854 $13,855 $11,886 $11,888 $11,889 $11,890 $11,891 $8,724 

Small MPV 15% 14% $15,834 $13,421 $13,426 $11,496 $11,501 $11,505 $11,508 $11,511 $8,431 

Small MPV 20% 19% $15,353 $12,989 $12,997 $11,107 $11,114 $11,121 $11,126 $11,131 $8,138 

WR=weight reduction. 

All costs are incremental to the baseline case. 
Chapter 1 

1-14 

Table 1.2-15 Costs for Full Electric Vehicle Technology with 100 Mile Range, or EV100, 

for the 2008 Baseline (2010$) 

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 4% $17,352 $14,815 $14,806 $12,774 $12,766 $12,758 $12,752 $12,747 $9,398 

Small car 15% 9% $16,916 $14,426 $14,420 $12,427 $12,421 $12,414 $12,411 $12,407 $9,138 

Small car 20% 14% $16,480 $14,038 $14,033 $12,079 $12,075 $12,071 $12,069 $12,066 $8,878 

Standard car 10% 4% $21,247 $18,304 $18,271 $15,911 $15,880 $15,850 $15,831 $15,812 $11,750 

Standard car 15% 9% $20,636 $17,758 $17,728 $15,422 $15,394 $15,367 $15,350 $15,333 $11,384 

Standard car 20% 14% $20,024 $17,212 $17,186 $14,932 $14,908 $14,884 $14,869 $14,854 $11,017 

Large car 10% 5% $26,749 $23,167 $23,109 $20,235 $20,181 $20,128 $20,093 $20,060 $14,977 

Large car 15% 10% $25,745 $22,267 $22,215 $19,426 $19,377 $19,329 $19,299 $19,269 $14,370 

Large car 20% 15% $24,741 $21,367 $21,322 $18,616 $18,573 $18,531 $18,504 $18,478 $13,762 

Small MPV 10% 3% $20,028 $17,005 $17,007 $14,589 $14,591 $14,593 $14,594 $14,596 $10,707 

Small MPV 15% 8% $19,490 $16,526 $16,531 $14,160 $14,165 $14,169 $14,172 $14,175 $10,385 

Small MPV 20% 13% $18,952 $16,046 $16,054 $13,731 $13,738 $13,746 $13,750 $13,755 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 10% 4% $17,837 $15,239 $15,229 $13,149 $13,139 $13,129 $13,123 $13,116 $9,676 

Small car 15% 9% $17,390 $14,841 $14,833 $12,793 $12,785 $12,777 $12,772 $12,768 $9,409 

Small car 20% 14% $16,943 $14,443 $14,437 $12,436 $12,431 $12,426 $12,422 $12,419 $9,143 

Standard car 10% 3% $21,905 $18,893 $18,856 $16,440 $16,406 $16,372 $16,350 $16,329 $12,147 

Standard car 15% 8% $21,294 $18,346 $18,313 $15,950 $15,918 $15,888 $15,868 $15,849 $11,779 

Standard car 20% 13% $20,684 $17,800 $17,770 $15,459 $15,431 $15,404 $15,386 $15,369 $11,411 

Large car 10% 4% $27,850 $24,147 $24,083 $21,111 $21,051 $20,993 $20,955 $20,918 $15,632 

Large car 15% 9% $26,820 $23,223 $23,166 $20,280 $20,226 $20,173 $20,140 $20,106 $15,009 

Large car 20% 14% $25,790 $22,300 $22,249 $19,449 $19,401 $19,354 $19,324 $19,295 $14,385 

Small MPV 10% 3% $20,501 $17,439 $17,437 $14,988 $14,986 $14,984 $14,982 $14,981 $11,009 

Small MPV 15% 8% $19,943 $16,942 $16,943 $14,542 $14,543 $14,543 $14,544 $14,545 $10,674 

Small MPV 20% 13% $19,385 $16,444 $16,448 $14,096 $14,100 $14,103 $14,106 $14,108 $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 Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 20% 2% $23,024 $19,643 $19,633 $16,926 $16,916 $16,907 $16,901 $16,895 $12,448 

Standard car 20% 2% $29,050 $24,946 $24,911 $21,623 $21,591 $21,559 $21,539 $21,519 $15,947 

Large car 20% 3% $34,259 $29,569 $29,508 $25,747 $25,690 $25,635 $25,599 $25,564 $19,029 

Small MPV 20% 1% $28,183 $23,945 $23,946 $20,555 $20,556 $20,557 $20,557 $20,558 $15,090 

WR=weight reduction. 

 All costs are incremental to the baseline case. 

 
MY 2017 and Later Regulatory Impact Analysis 

1-15 

Table 1.2-18 Costs for Full Electric Vehicle Technology with 150 Mile Range, or EV150, 

for the 2010 Baseline (2010$) 

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Small car 20% 1% $23,795 $20,314 $20,302 $17,515 $17,504 $17,492 $17,485 $17,478 $12,885 

Standard car 20% 1% $29,822 $25,632 $25,594 $22,236 $22,200 $22,165 $22,142 $22,120 $16,406 

Large car 20% 3% $35,277 $30,469 $30,403 $26,547 $26,486 $26,426 $26,388 $26,350 $19,626 

Small MPV 20% 1% $28,767 $24,474 $24,471 $21,036 $21,033 $21,029 $21,027 $21,025 $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 Vehicle 

Class 2017 2018 2019 2020 2021 2022 2023 2024 2025 

PHEV20 

Charger All $79 $66 $66 $56 $56 $56 $56 $56 $41 

PHEV40 

Charger 

Small car $414 $347 $347 $294 $294 $294 $294 $294 $216 

Standard 

car 

$481 $404 $404 $342 $342 $342 $342 $342 $251 

Large car 

Small 

MPV 

$526 $441 $441 $373 $373 $373 $373 $373 $274 

EV Charger All $526 $441 $441 $373 $373 $373 $373 $373 $274 

Charger labor All $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $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. 

 
Chapter 1 

1-16 

Table 1.2-20 Costs for 10% and 20% Weight Reduction for the 19 Vehicle Typesa

 for the 

2008 Baseline (2010$) 

Vehicle

Type

Base

Weight

Applied

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

1 2633 10% $143 $139 $130 $127 $124 $121 $119 $117 $115 

20% $639 $624 $610 $597 $584 $571 $563 $555 $503 

2 3094 10% $168 $164 $153 $149 $146 $142 $140 $138 $135 

20% $751 $734 $717 $701 $686 $671 $661 $652 $591 

3 3554 10% $193 $188 $176 $172 $167 $163 $161 $158 $155 

20% $863 $843 $824 $806 $788 $771 $760 $749 $679 

4 3558 10% $193 $189 $176 $172 $168 $163 $161 $158 $156 

20% $863 $844 $825 $807 $789 $772 $760 $749 $680 

5 3971 10% $216 $210 $197 $192 $187 $182 $179 $176 $174 

20% $964 $942 $921 $900 $880 $861 $849 $836 $758 

6 3651 10% $198 $193 $181 $176 $172 $168 $165 $162 $160 

20% $886 $866 $847 $828 $809 $792 $780 $769 $697 

7 3450 10% $187 $183 $171 $167 $163 $159 $156 $153 $151 

20% $837 $818 $800 $782 $765 $748 $737 $727 $659 

8 4326 10% $235 $229 $214 $209 $204 $199 $195 $192 $189 

20% $1,050 $1,026 $1,003 $981 $959 $938 $924 $911 $826 

9 4334 10% $235 $230 $215 $209 $204 $199 $196 $193 $189 

20% $1,052 $1,028 $1,005 $983 $961 $940 $926 $913 $828 

10 4671 10% $254 $248 $231 $226 $220 $215 $211 $208 $204 

20% $1,134 $1,108 $1,083 $1,059 $1,036 $1,013 $998 $984 $892 

11 5174 10% $281 $274 $256 $250 $244 $238 $234 $230 $226 

20% $1,255 $1,227 $1,200 $1,173 $1,147 $1,122 $1,106 $1,090 $988 

12 5251 10% $285 $278 $260 $254 $247 $241 $237 $233 $230 

20% $1,274 $1,245 $1,218 $1,190 $1,164 $1,139 $1,122 $1,106 $1,003 

13 3904 10% $212 $207 $193 $189 $184 $179 $176 $174 $171 

20% $947 $926 $905 $885 $866 $847 $834 $822 $746 

14 4157 10% $226 $220 $206 $201 $196 $191 $188 $185 $182 

20% $1,009 $986 $964 $943 $922 $902 $888 $876 $794 

15 4397 10% $239 $233 $218 $212 $207 $202 $199 $195 $192 

20% $1,067 $1,043 $1,019 $997 $975 $953 $940 $926 $840 

16 5270 10% $286 $279 $261 $255 $248 $242 $238 $234 $230 

20% $1,279 $1,250 $1,222 $1,195 $1,168 $1,143 $1,126 $1,110 $1,007 

17 4967 10% $270 $263 $246 $240 $234 $228 $224 $221 $217 

20% $1,205 $1,178 $1,152 $1,126 $1,101 $1,077 $1,062 $1,046 $949 

18 4959 10% $269 $263 $246 $240 $234 $228 $224 $220 $217 

20% $1,203 $1,176 $1,150 $1,124 $1,100 $1,075 $1,060 $1,045 $947 

19 5026 10% $273 $266 $249 $243 $237 $231 $227 $223 $220 

20% $1,220 $1,192 $1,165 $1,140 $1,114 $1,090 $1,074 $1,059 $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 Typesa for the 

2010 Baseline (2010$) 

Vehicle

Type

Base

Weight

Applied

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

1 2753 10% $149 $146 $136 $133 $130 $126 $124 $122 $120 
MY 2017 and Later Regulatory Impact Analysis 

1-17 

20% $668 $653 $638 $624 $610 $597 $588 $580 $526 

2 3204 10% $174 $170 $159 $155 $151 $147 $145 $142 $140 

20% $778 $760 $743 $726 $710 $695 $685 $675 $612 

3 3651 10% $198 $193 $181 $176 $172 $168 $165 $162 $160 

20% $886 $866 $847 $828 $810 $792 $780 $769 $697 

4 3608 10% $196 $191 $179 $174 $170 $166 $163 $160 $158 

20% $876 $856 $837 $818 $800 $782 $771 $760 $689 

5 4144 10% $225 $220 $205 $200 $195 $190 $187 $184 $181 

20% $1,006 $983 $961 $939 $919 $899 $886 $873 $792 

6 3842 10% $209 $204 $190 $186 $181 $177 $174 $171 $168 

20% $932 $911 $891 $871 $852 $833 $821 $809 $734 

7 3517 10% $191 $186 $174 $170 $166 $162 $159 $156 $154 

20% $853 $834 $815 $797 $780 $763 $752 $741 $672 

8 4316 10% $234 $229 $214 $209 $203 $198 $195 $192 $189 

20% $1,047 $1,024 $1,001 $979 $957 $936 $922 $909 $824 

9 4352 10% $236 $231 $216 $210 $205 $200 $197 $193 $190 

20% $1,056 $1,032 $1,009 $987 $965 $944 $930 $917 $831 

10 4355 10% $237 $231 $216 $210 $205 $200 $197 $194 $190 

20% $1,057 $1,033 $1,010 $987 $965 $944 $931 $917 $832 

11 5381 10% $292 $285 $267 $260 $254 $247 $243 $239 $235 

20% $1,306 $1,276 $1,248 $1,220 $1,193 $1,167 $1,150 $1,134 $1,028 

12 5716 10% $310 $303 $283 $276 $269 $263 $258 $254 $250 

20% $1,387 $1,356 $1,325 $1,296 $1,267 $1,240 $1,222 $1,204 $1,092 

13 3667 10% $199 $194 $182 $177 $173 $168 $166 $163 $160 

20% $890 $870 $850 $831 $813 $795 $784 $772 $700 

14 4151 10% $225 $220 $206 $201 $196 $191 $188 $184 $181 

20% $1,007 $984 $962 $941 $920 $900 $887 $874 $793 

15 4591 10% $249 $243 $228 $222 $216 $211 $207 $204 $201 

20% $1,114 $1,089 $1,065 $1,041 $1,018 $996 $981 $967 $877 

16 5382 10% $292 $285 $267 $260 $254 $247 $243 $239 $235 

20% $1,306 $1,277 $1,248 $1,220 $1,193 $1,167 $1,150 $1,134 $1,028 

17 5025 10% $273 $266 $249 $243 $237 $231 $227 $223 $220 

20% $1,219 $1,192 $1,165 $1,139 $1,114 $1,090 $1,074 $1,059 $960 

18 5252 10% $285 $278 $260 $254 $247 $241 $237 $233 $230 

20% $1,274 $1,246 $1,218 $1,191 $1,164 $1,139 $1,122 $1,106 $1,003 

19 5224 10% $284 $277 $259 $252 $246 $240 $236 $232 $228 

20% $1,268 $1,239 $1,211 $1,184 $1,158 $1,133 $1,117 $1,100 $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 CO2 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 

Absolute CO2 Reduction (% from baseline vehicle) 

Small Car Large Car Minivan Small 

Truck 

Large 

Truck 

Low friction lubricants 0.6 0.8 0.7 0.6 0.7 

Engine friction reduction level 1 2.0 2.7 2.6 2.0 2.4 

Engine friction reduction level 2 3.5 4.8 4.5 3.4 4.2 

Cylinder deactivation (includes imp. oil pump, if n.a. 6.5 6.0 4.7 5.7 
Chapter 1 

1-18 

applicable) 

VVT – intake cam phasing 2.1 2.7 2.5 2.1 2.4 

VVT – coupled cam phasing 4.1 5.5 5.1 4.1 4.9 

VVT – dual cam phasing 4.1 5.5 5.1 4.1 4.9 

Discrete VVLT 4.1 5.6 5.2 4.0 4.9 

Continuous VVLT 5.1 7.0 6.5 5.1 6.1 

Stoichiometric Gasoline Direct Injection 1.5 1.5 1.5 1.5 1.5 

Turbo+downsize (incremental to GDI-S) (18-27 bar)* 10.8-16.6 13.6-20.6 12.9-19.6 10.7-16.4 12.3-18.8 

Cooled Exhaust Gas Recirculation (incremental to 24 

bar TRBDS+SGDI) 3.6 3.6 3.6 3.5 3.6 

Advanced diesel engine (T2B2 emissions level) 19.5 22.1 21.5 19.1 21.3 

* Note: turbo downsize engine effectiveness does not include effectiveness of valvetrain improvements 

Table 1.2-23 Transmission Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

 5-speed automatic (from 4-speed auto) 1.1 1.6 1.4 1.1 1.4 

 Aggressive shift logic 1 2.0 2.7 2.5 1.9 2.4 

 Aggressive shift logic 2 5.2 7.0 6.6 5.1 6.2 

 Early torque converter lockup 0.4 0.4 0.4 0.5 0.5 

 High Efficiency Gearbox 4.8 5.3 5.1 5.4 4.3 

 6-speed automatic (from 4-speed auto) 1.8 2.3 2.2 1.7 2.1 

 6-speed dry DCT (from 4-speed auto) 6.4 7.6 7.2 7.1 8.1 

Table 1.2-24 Hybrid Technology Effectiveness 

Technology 

Absolute CO2

 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

 12V Start-Stop 1.8 2.4 2.2 1.8 2.2 

 HV Mild Hybrid* 7.4 7.2 6.9 6.8 8.0 

 P2 Hybrid drivetrain** 15.5 15.4 14.6 13.4 15.7 

 Plug-in hybrid electric vehicle – 20 mile range*** 40 40 40 40 n.a. 

 Plug-in hybrid electric vehicle – 40 mile range*** 63 63 63 63 n.a. 

 Full electric vehicle (EV) 100 100 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 

Absolute CO2

 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

Improved high efficiency alternator & electrification of 

accessories (12 volt) 1.7 1.3 1.2 1.3 1.8 

Electric power steering 1.5 1.1 1.0 1.2 0.8 

Improved high efficiency alternator & electrification of 

accessories (42 volt) 3.3 2.5 2.4 2.6 3.5 
MY 2017 and Later Regulatory Impact Analysis 

1-19 

Table 1.2-26 Other Vehicle Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

 Aero drag reduction (20% on cars, 10% on trucks) 4.7 4.7 4.7 4.7 2.3 

 Low rolling resistance tires (20% on cars, 10% on 

trucks) 3.9 3.9 3.9 3.9 1.9 

 Low drag brakes 0.8 0.8 0.8 0.8 0.8 

 Secondary axle disconnect (unibody only) 1.3 1.3 1.3 1.3 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 

CO2 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 CO2 (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, I4, 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 
Chapter 1 

1-20 

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 consumerlevel 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.B

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 technologyspecific 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-21 

Table 1.3-1 List of 19 Vehicle Types used to Model the light-duty Fleet 

Vehicle 

Type # Base Engine Base Trans 

Vehicle 

Class Description Example Models Towing? 

1 I4 DOHC 4v 4sp AT Small car Subcompact car I4 Ford Focus, Chevy 

Aveo, Honda Fit No 

2 I4 DOHC 4v 4sp AT Standard car Compact car I4 Ford Fusion, Chevy 

Cobalt, Honda Civic No 

3 V6 DOHC 4v 4sp AT Standard car Midsize car V6 Ford Fusion, Chevy 

Malibu, Honda Accord No 

4 V6 SOHC 2v 4sp AT Standard car Midsize car V6 

Ford Mustang, Buick 

Lacrosse, Chevy 

Impala 

No 

5 V8 DOHC 4v 4sp AT Large car Large car V8 Ford Crown Vic, Ford 

Mustang, Cadillac STS No 

6 V8 OHV 2v 4sp AT Large car Large car V8 

Chrysler 300, Ford 

Mustang, Chevy 

Corvette 

No 

7 I4 DOHC 4v 4sp AT Small MPV Small MPV I4 

Ford Escape, Honda 

Element, Toyota 

RAV4 

No 

8 V6 DOHC 4v 4sp AT Large MPV Midsize MPV V6 Ford Edge, Chevy 

Equinox, Kia Sorento Yes 

9 V6 SOHC 2v 4sp AT Large MPV Midsize MPV V6 

Dodge Durango, Jeep 

Grand Cherokee, Ford 

Explorer 

Yes 

10 V6 OHV 2v 4sp AT Large MPV Midsize MPV V6 

Dodge Caravan, Jeep 

Wrangler, Chevy 

Equinox 

Yes 

11 V8 DOHC 4v 4sp AT Truck Large MPV V8 

Jeep Grand Cherokee, 

Toyota 4Runner, VW 

Touareg 

Yes 

12 V8 OHV 2v 4sp AT Truck Large MPV V8 

Chrylser Aspen, Ford 

Expedition, Chevy 

Tahoe, 

Yes 

13 I4 DOHC 4v 4sp AT Small MPV Small truck I4 

Chevy Colorado, 

Nissan Frontier, 

Toyota Tacoma 

No 

14 V6 DOHC 4v 4sp AT Large MPV Full-sized Pickup truck 

V6 

Ford F150, Honda 

Ridgeline, Toyota 

Tacoma 

Yes 

15 V6 OHV 2v 4sp AT Large MPV Full-sized Pickup truck 

V6 

Dodge Dakota, Ford 

Ranger, Chevy 

Silverado 

Yes 

16 V8 DOHC 4v 4sp AT Truck Full-sized Pickup truck 

V8 

Nissan Titan, Toyota 

Tundra Yes 

17 V8 SOHC 2v 4sp AT Truck Full-sized Pickup truck 

V8 

Dodge Ram, Ford 

F150 Yes 

18 V8 SOHC 3v 4sp AT Truck Full-sized Pickup truck 

V8 

Ford F150 Yes 

19 V8 OHV 2v 4sp AT Truck Full-sized Pickup truck 

V8 

Dodge Ram, Chevy 

Silverado, GMC Sierra 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 
Chapter 1 

1-22 

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 lbs. 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-23 

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 nontowing 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 vehiclesD

, and would put 

turbocharged/downsized HEVs on some vehicle models that are more properly treated as nontowing 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 I4 (non-HEV) Ford Escape. 
Chapter 1 

1-24 

Table 1.3-2 Potential Inconsistencies in our Treatment of Towing & Non-towing 

Vehicles in our Proposala

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 

a

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. 
MY 2017 and Later Regulatory Impact Analysis 

1-25 

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 CO2 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 CO2 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 CO2 reduction is calculated as the incremental 

CO2/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. 
Chapter 1 

1-26 

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.G

 

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”.H

 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) 

 

G

 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 TARF 

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 CO2 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-27 

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 wetDCT) 

12. 6 and 8 speed dual clutch transmission with dry clutch (6sp dry-DCT/8sp wetDCT) 

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 I4 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 
Chapter 1 

1-28 

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 nontowing vehicle types. 

Table 1.3-3 Application of Transmission Technologies in Building OMEGA Packages 

Vehicle

Type

Vehicle

class

Base

engine

Base

weight

Mass Reduction

0% 5% 10% 15% 20% 

1 Small car I4 2,633 6/8 speed dry-DCT 

2 Standard car I4 3,094 6/8 speed dry-DCT 

3 Standard car V6 3,554 6/8 speed wet-DCT 

4 Standard car V6 3,558 6/8 speed wet-DCT 

5 Large car V8 3,971 6/8 speed wet-DCT 

6 Large car V8 3,651 6/8 speed wet-DCT 

7 Small MPV I4 3,450 6/8 speed dry-DCT 

8 Large MPV V6 4,326 6/8 speed AT 

9 Large MPV V6 4,334 6/8 speed AT 

10 Large MPV V6 4,671 6/8 speed AT 

11 Truck V8 5,174 6/8 speed AT 

12 Truck V8 5,251 6/8 speed AT 

13 Small MPV I4 3,904 6/8 speed dry-DCT 

14 Large MPV V6 4,157 6/8 speed AT 

15 Large MPV V6 4,397 6/8 speed AT 

16 Truck V8 5,270 6/8 speed AT 

17 Truck V8 4,967 6/8 speed AT 

18 Truck V8 4,959 6/8 speed AT 

19 Truck V8 5,026 6/8 speed AT 

We start building a “master-set” of packages for a given model year by building nonelectrified (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 
MY 2017 and Later Regulatory Impact Analysis 

1-29 

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# MR Description Trans 2025 CO2% 

3.0000 base Auto 4VDV6 $0 0.0% 

3.0129 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +WR5% +6sp 

6sp DCTwet $733 26.4% 

3.0130 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +WR5% +6sp 

6sp DCTwet $950 31.2% 

3.0131 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+WR5% +6sp 

6sp DCTwet $822 29.8% 

3.0132 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+WR5% +6sp 

6sp DCTwet $1,039 34.3% 

3.0133 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +WR5% +6sp 

6sp DCTwet $926 28.4% 

3.0134 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +WR5% +6sp 

6sp DCTwet $1,143 32.8% 

3.0135 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +WR5% +6sp 

6sp DCTwet $1,015 31.7% 

3.0136 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +WR5% +6sp 

6sp DCTwet $1,232 35.9% 

3.0137 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +WR5% +6sp 

6sp DCTwet $1,073 27.5% 

3.0138 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +WR5% +6sp 

6sp DCTwet $1,290 32.3% 

3.0139 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +WR5% +6sp 

6sp DCTwet $1,162 30.9% 

3.0140 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +WR5% +6sp 

6sp DCTwet $1,379 35.3% 

3.0141 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +WR5% +6sp 

6sp DCTwet $1,266 29.5% 

3.0142 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +WR5% +6sp 

6sp DCTwet $1,483 33.8% 

3.0143 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +WR5% +6sp 

6sp DCTwet $1,355 32.7% 

3.0144 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +WR5% +6sp 

6sp DCTwet $1,572 36.8% 

3.0145 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +SS +WR5% +6sp 

6sp DCTwet $1,041 27.6% 

3.0146 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +SS +WR5% +6sp 

6sp DCTwet $1,258 32.3% 

3.0147 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +SS 

+WR5% +6sp 

6sp DCTwet $1,129 30.8% 
Chapter 1 

1-30 

3.0148 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +SS 

+WR5% +6sp 

6sp DCTwet $1,347 35.2% 

3.0149 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +SS +WR5% +6sp 

6sp DCTwet $1,234 29.5% 

3.0150 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +SS +WR5% +6sp 

6sp DCTwet $1,451 33.8% 

3.0151 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +SS +WR5% +6sp 

6sp DCTwet $1,323 32.6% 

3.0152 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +SS +WR5% +6sp 

6sp DCTwet $1,540 36.7% 

3.0153 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +SS +WR5% +6sp 

6sp DCTwet $1,381 28.7% 

3.0154 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +SS +WR5% +6sp 

6sp DCTwet $1,598 33.3% 

3.0155 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +WR5% +6sp 

6sp DCTwet $1,470 31.8% 

3.0156 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +WR5% +6sp 

6sp DCTwet $1,687 36.2% 

3.0157 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +SS +WR5% +6sp 

6sp DCTwet $1,574 30.5% 

3.0158 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +SS +WR5% +6sp 

6sp DCTwet $1,791 34.8% 

3.0159 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +WR5% +6sp 

6sp DCTwet $1,663 33.6% 

3.0160 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +WR5% +6sp 

6sp DCTwet $1,880 37.6% 

3.0161 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +SAX +WR5% +6sp 

6sp DCTwet $815 27.0% 

3.0162 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +SAX +WR5% +6sp 

6sp DCTwet $1,032 31.8% 

3.0163 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+SAX +WR5% +6sp 

6sp DCTwet $904 30.4% 

3.0164 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+SAX +WR5% +6sp 

6sp DCTwet $1,121 34.8% 

3.0165 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +SAX +WR5% +6sp 

6sp DCTwet $1,008 29.0% 

3.0166 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +SAX +WR5% +6sp 

6sp DCTwet $1,225 33.3% 

3.0167 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +SAX +WR5% +6sp 

6sp DCTwet $1,097 32.2% 

3.0168 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +SAX +WR5% +6sp 

6sp DCTwet $1,314 36.4% 

3.0169 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,155 28.1% 

3.0170 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,372 32.8% 

3.0171 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SAX +WR5% +6sp 

6sp DCTwet $1,244 31.4% 

3.0172 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SAX +WR5% +6sp 

6sp DCTwet $1,461 35.8% 

3.0173 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,348 30.0% 

3.0174 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,565 34.3% 

3.0175 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,437 33.3% 
MY 2017 and Later Regulatory Impact Analysis 

1-31 

3.0176 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +WR5% +6sp 

6sp DCTwet $1,654 37.3% 

3.0177 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +SS +SAX +WR5% +6sp 

6sp DCTwet $1,123 28.1% 

3.0178 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +SS +SAX +WR5% +6sp 

6sp DCTwet $1,340 32.8% 

3.0179 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +SS 

+SAX +WR5% +6sp 

6sp DCTwet $1,211 31.3% 

3.0180 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +SS 

+SAX +WR5% +6sp 

6sp DCTwet $1,428 35.7% 

3.0181 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +SS +SAX +WR5% +6sp 

6sp DCTwet $1,316 30.0% 

3.0182 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +SS +SAX +WR5% +6sp 

6sp DCTwet $1,533 34.3% 

3.0183 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +SS +SAX +WR5% +6sp 

6sp DCTwet $1,405 33.1% 

3.0184 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +SS +SAX +WR5% +6sp 

6sp DCTwet $1,622 37.1% 

3.0185 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,463 29.2% 

3.0186 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,680 33.8% 

3.0187 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,552 32.3% 

3.0188 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,769 36.7% 

3.0189 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,656 31.0% 

3.0190 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,873 35.3% 

3.0191 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,745 34.1% 

3.0192 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +WR5% +6sp 

6sp DCTwet $1,962 38.1% 

3.0193 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,009 33.9% 

3.0194 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,226 37.8% 

3.0195 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+TDS18 +WR5% +6sp 

6sp DCTwet $1,070 36.8% 

3.0196 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+TDS18 +WR5% +6sp 

6sp DCTwet $1,287 40.4% 

3.0197 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,142 34.6% 

3.0198 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,359 38.3% 

3.0199 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,203 37.4% 

3.0200 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS18 +WR5% +6sp 

6sp DCTwet $1,420 41.0% 

3.0201 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,317 34.8% 

3.0202 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,534 38.6% 

3.0203 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,378 37.5% 
Chapter 1 

1-32 

3.0204 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,595 41.1% 

3.0205 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,450 35.4% 

3.0206 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,667 39.1% 

3.0207 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,511 38.1% 

3.0208 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS18 +WR5% +6sp 

6sp DCTwet $1,728 41.6% 

3.0209 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,091 34.4% 

3.0210 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,308 38.3% 

3.0211 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,152 37.2% 

3.0212 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,369 40.9% 

3.0213 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,224 35.1% 

3.0214 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,441 38.8% 

3.0215 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,285 37.9% 

3.0216 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,502 41.4% 

3.0217 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,399 35.3% 

3.0218 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,616 39.0% 

3.0219 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,460 38.0% 

3.0220 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,677 41.6% 

3.0221 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,532 35.9% 

3.0222 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,749 39.5% 

3.0223 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,593 38.6% 

3.0224 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $1,810 42.1% 

3.0225 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,223 36.4% 

3.0226 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,440 40.0% 

3.0227 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+TDS24 +WR5% +6sp 

6sp DCTwet $1,284 39.1% 

3.0228 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+TDS24 +WR5% +6sp 

6sp DCTwet $1,501 42.5% 

3.0229 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,357 36.6% 

3.0230 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,574 40.0% 

3.0231 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,417 39.3% 
MY 2017 and Later Regulatory Impact Analysis 

1-33 

3.0232 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS24 +WR5% +6sp 

6sp DCTwet $1,634 42.6% 

3.0233 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,531 37.2% 

3.0234 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,748 40.7% 

3.0235 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,592 39.7% 

3.0236 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,809 43.1% 

3.0237 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,665 37.3% 

3.0238 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,882 40.7% 

3.0239 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,725 39.9% 

3.0240 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +WR5% +6sp 

6sp DCTwet $1,942 43.2% 

3.0241 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,305 36.9% 

3.0242 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,522 40.4% 

3.0243 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,366 39.6% 

3.0244 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,583 42.9% 

3.0245 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,439 37.0% 

3.0246 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,656 40.5% 

3.0247 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,499 39.7% 

3.0248 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,716 43.0% 

3.0249 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,613 37.7% 

3.0250 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,830 41.1% 

3.0251 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,674 40.2% 

3.0252 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,891 43.5% 

3.0253 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,747 37.8% 

3.0254 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,964 41.2% 

3.0255 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $1,807 40.4% 

3.0256 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,024 43.6% 

3.0257 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,472 38.7% 

3.0258 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,690 42.1% 

3.0259 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,533 41.3% 
Chapter 1 

1-34 

3.0260 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,750 44.5% 

3.0261 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,606 38.8% 

3.0262 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,823 42.2% 

3.0263 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,666 41.4% 

3.0264 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,883 44.6% 

3.0265 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,780 39.4% 

3.0266 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,997 42.8% 

3.0267 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,841 41.9% 

3.0268 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,058 45.1% 

3.0269 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,914 39.5% 

3.0270 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,131 42.8% 

3.0271 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,974 42.0% 

3.0272 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,191 45.2% 

3.0273 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,554 39.2% 

3.0274 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,771 42.6% 

3.0275 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,615 41.7% 

3.0276 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,832 45.0% 

3.0277 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,688 39.3% 

3.0278 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,905 42.6% 

3.0279 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,748 41.9% 

3.0280 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,965 45.1% 

3.0281 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,862 39.9% 

3.0282 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,079 43.2% 

3.0283 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,923 42.3% 

3.0284 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,140 45.5% 

3.0285 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $1,996 40.0% 

3.0286 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,213 43.3% 

3.0287 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,056 42.5% 
MY 2017 and Later Regulatory Impact Analysis 

1-35 

3.0288 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,273 45.6% 

3.0289 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $1,901 39.4% 

3.0290 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,118 42.7% 

3.0291 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+TDS27 +EGR +WR5% +6sp 

6sp DCTwet $1,961 41.9% 

3.0292 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,178 45.1% 

3.0293 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,034 39.4% 

3.0294 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,251 42.6% 

3.0295 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,095 41.9% 

3.0296 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,312 45.0% 

3.0297 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,209 40.1% 

3.0298 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,426 43.4% 

3.0299 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,269 42.5% 

3.0300 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,486 45.6% 

3.0301 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,342 40.1% 

3.0302 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,559 43.2% 

3.0303 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,403 42.5% 

3.0304 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,620 45.5% 

3.0305 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $1,983 39.9% 

3.0306 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,200 43.2% 

3.0307 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,043 42.4% 

3.0308 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,260 45.5% 

3.0309 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,116 39.8% 

3.0310 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,333 43.0% 

3.0311 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,177 42.4% 

3.0312 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,394 45.4% 

3.0313 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,291 40.5% 

3.0314 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,508 43.8% 

3.0315 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,351 42.9% 
Chapter 1 

1-36 

3.0316 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,568 46.0% 

3.0317 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,424 40.5% 

3.0318 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,641 43.6% 

3.0319 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,485 43.0% 

3.0320 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +SS +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $2,702 45.9% 

3.1681 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +MHEV +WR5% +6sp 

6sp DCTwet $1,981 34.9% 

3.1682 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +MHEV +WR5% +6sp 

6sp DCTwet $2,153 38.8% 

3.1683 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+MHEV +WR5% +6sp 

6sp DCTwet $2,070 37.9% 

3.1684 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+MHEV +WR5% +6sp 

6sp DCTwet $2,242 41.5% 

3.1685 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +MHEV +WR5% +6sp 

6sp DCTwet $2,175 36.6% 

3.1686 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +MHEV +WR5% +6sp 

6sp DCTwet $2,346 40.4% 

3.1687 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +MHEV +WR5% +6sp 

6sp DCTwet $2,263 39.5% 

3.1688 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +MHEV +WR5% +6sp 

6sp DCTwet $2,435 43.1% 

3.1689 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,322 35.9% 

3.1690 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,493 39.7% 

3.1691 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,411 38.8% 

3.1692 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,582 42.4% 

3.1693 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,515 37.6% 

3.1694 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,687 41.3% 

3.1695 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,604 40.4% 

3.1696 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +WR5% +6sp 

6sp DCTwet $2,775 43.9% 

3.1697 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,063 35.5% 

3.1698 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,235 39.3% 

3.1699 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,152 38.4% 

3.1700 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,324 42.0% 

3.1701 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,256 37.2% 

3.1702 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,428 40.9% 

3.1703 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,345 40.0% 
MY 2017 and Later Regulatory Impact Analysis 

1-37 

3.1704 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,517 43.6% 

3.1705 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,404 36.5% 

3.1706 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,575 40.2% 

3.1707 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,492 39.3% 

3.1708 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,664 42.9% 

3.1709 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG 

+DCP +DVVL +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,597 38.1% 

3.1710 5% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,768 41.8% 

3.1711 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,686 40.9% 

3.1712 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +WR5% +6sp 

6sp DCTwet $2,857 44.4% 

3.1713 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,258 41.4% 

3.1714 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,429 44.9% 

3.1715 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,318 43.9% 

3.1716 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,490 47.2% 

3.1717 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,391 42.0% 

3.1718 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,563 45.5% 

3.1719 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,452 44.5% 

3.1720 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR5% +6sp 

6sp DCTwet $2,623 47.8% 

3.1721 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,340 42.0% 

3.1722 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,511 45.4% 

3.1723 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,400 44.4% 

3.1724 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,572 47.7% 

3.1725 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,473 42.5% 

3.1726 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,645 45.9% 

3.1727 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,533 45.0% 

3.1728 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR5% +6sp 

6sp DCTwet $2,705 48.2% 

3.1729 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,472 43.7% 

3.1730 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,644 47.0% 

3.1731 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,532 46.0% 
Chapter 1 

1-38 

3.1732 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,704 49.2% 

3.1733 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,605 43.8% 

3.1734 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,777 47.1% 

3.1735 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,666 46.1% 

3.1736 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR5% +6sp 

6sp DCTwet $2,837 49.3% 

3.1737 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,554 44.1% 

3.1738 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,725 47.4% 

3.1739 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,614 46.4% 

3.1740 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,786 49.6% 

3.1741 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,687 44.2% 

3.1742 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,859 47.5% 

3.1743 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,748 46.6% 

3.1744 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR5% +6sp 

6sp DCTwet $2,919 49.7% 

3.1745 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,721 45.7% 

3.1746 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,893 48.9% 

3.1747 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,782 47.9% 

3.1748 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,953 51.0% 

3.1749 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,854 45.8% 

3.1750 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $3,026 49.0% 

3.1751 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,915 48.0% 

3.1752 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $3,086 51.1% 

3.1753 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,803 46.1% 

3.1754 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,975 49.3% 

3.1755 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,864 48.3% 

3.1756 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $3,035 51.4% 

3.1757 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,936 46.2% 

3.1758 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $3,108 49.4% 

3.1759 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $2,997 48.5% 
MY 2017 and Later Regulatory Impact Analysis 

1-39 

3.1760 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR5% +6sp 

6sp DCTwet $3,168 51.5% 

3.1761 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,149 46.3% 

3.1762 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,321 49.4% 

3.1763 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,210 48.5% 

3.1764 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,382 51.5% 

3.1765 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,283 46.2% 

3.1766 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,454 49.4% 

3.1767 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,343 48.5% 

3.1768 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,515 51.5% 

3.1769 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,231 46.7% 

3.1770 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,403 49.9% 

3.1771 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,292 48.9% 

3.1772 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,464 51.9% 

3.1773 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,365 46.7% 

3.1774 5% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,536 49.9% 

3.1775 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,425 48.9% 

3.1776 5% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR5% +6sp 

6sp DCTwet $3,597 51.9% 

3.2449 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DSL-Adv +WR5% +6sp 

6sp DCTwet $3,242 39.1% 

3.2450 5% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DSL-Adv +WR5% +6sp 

6sp DCTwet $3,459 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. 
Chapter 1 

1-40 

The next packages built are the strong HEVs (P2 HEV) and, new for this final rule, 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 run, 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# MR Description Trans 2025 CO2% 

3.0000 base Auto 4VDV6 $0 0.0% 

3.1665 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +HEV +ATKCS +WR20% +6sp 

6sp DCTwet $4,698 50.0% 

3.1666 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +HEV +ATKCS +WR20% +6sp 

6sp DCTwet $4,870 53.2% 

3.1667 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +HEV +ATKCS +WR20% +6sp 

6sp DCTwet $4,787 52.4% 

3.1668 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +HEV +ATKCS +WR20% +6sp 

6sp DCTwet $4,959 55.3% 

3.1669 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +HEV +SAX +ATKCS +WR20% +6sp 

6sp DCTwet $4,780 50.5% 

3.1670 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +HEV +SAX +ATKCS +WR20% +6sp 

6sp DCTwet $4,952 53.6% 

3.1671 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +HEV +SAX +ATKCS +WR20% +6sp 

6sp DCTwet $4,869 52.8% 

3.1672 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +HEV +SAX +ATKCS +WR20% +6sp 

6sp DCTwet $5,041 55.7% 

3.2257 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+MHEV +WR20% +6sp 

6sp DCTwet $2,621 39.4% 
MY 2017 and Later Regulatory Impact Analysis 

1-41 

3.2258 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+MHEV +WR20% +6sp 

6sp DCTwet $2,793 43.1% 

3.2259 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +MHEV 

+WR20% +6sp 

6sp DCTwet $2,710 42.1% 

3.2260 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +MHEV 

+WR20% +6sp 

6sp DCTwet $2,882 45.6% 

3.2261 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +MHEV +WR20% +6sp 

6sp DCTwet $2,815 41.0% 

3.2262 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +MHEV +WR20% +6sp 

6sp DCTwet $2,986 44.6% 

3.2263 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+MHEV +WR20% +6sp 

6sp DCTwet $2,903 43.7% 

3.2264 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+MHEV +WR20% +6sp 

6sp DCTwet $3,075 47.1% 

3.2265 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +WR20% +6sp 

6sp DCTwet $2,962 40.3% 

3.2266 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +WR20% +6sp 

6sp DCTwet $3,133 43.9% 

3.2267 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +WR20% +6sp 

6sp DCTwet $3,051 43.0% 

3.2268 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +WR20% +6sp 

6sp DCTwet $3,222 46.5% 

3.2269 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +WR20% +6sp 

6sp DCTwet $3,155 41.9% 

3.2270 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +WR20% +6sp 

6sp DCTwet $3,327 45.4% 

3.2271 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +WR20% +6sp 

6sp DCTwet $3,244 44.5% 

3.2272 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +WR20% +6sp 

6sp DCTwet $3,415 47.9% 

3.2273 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $2,703 39.9% 

3.2274 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $2,875 43.6% 

3.2275 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +MHEV 

+SAX +WR20% +6sp 

6sp DCTwet $2,792 42.6% 

3.2276 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +MHEV 

+SAX +WR20% +6sp 

6sp DCTwet $2,964 46.1% 

3.2277 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +MHEV +SAX +WR20% +6sp 

6sp DCTwet $2,897 41.5% 

3.2278 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,068 45.1% 

3.2279 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $2,985 44.1% 

3.2280 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,157 47.5% 

3.2281 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,044 40.8% 

3.2282 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,215 44.4% 

3.2283 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,133 43.5% 

3.2284 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,304 46.9% 

3.2285 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,237 42.4% 
Chapter 1 

1-42 

3.2286 20% Auto 4VDV6 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,409 45.9% 

3.2287 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,326 45.0% 

3.2288 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +WR20% +6sp 

6sp DCTwet $3,497 48.3% 

3.2289 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $2,898 45.5% 

3.2290 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,069 48.8% 

3.2291 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $2,958 47.8% 

3.2292 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,130 50.9% 

3.2293 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,031 46.0% 

3.2294 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,203 49.3% 

3.2295 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,092 48.3% 

3.2296 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +TDS18 +WR20% +6sp 

6sp DCTwet $3,263 51.5% 

3.2297 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $2,980 46.0% 

3.2298 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,151 49.2% 

3.2299 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,040 48.2% 

3.2300 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,212 51.4% 

3.2301 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,113 46.5% 

3.2302 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,285 49.7% 

3.2303 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,174 48.8% 

3.2304 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS18 +WR20% +6sp 

6sp DCTwet $3,345 51.9% 

3.2305 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,112 47.5% 

3.2306 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,284 50.7% 

3.2307 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,173 49.7% 

3.2308 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,344 52.7% 

3.2309 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,245 47.6% 

3.2310 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,417 50.8% 

3.2311 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,306 49.8% 

3.2312 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +TDS24 +WR20% +6sp 

6sp DCTwet $3,477 52.9% 

3.2313 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,194 48.0% 
MY 2017 and Later Regulatory Impact Analysis 

1-43 

3.2314 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,366 51.1% 

3.2315 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,255 50.1% 

3.2316 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,426 53.1% 

3.2317 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,327 48.1% 

3.2318 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,499 51.2% 

3.2319 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,388 50.3% 

3.2320 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS24 +WR20% +6sp 

6sp DCTwet $3,559 53.3% 

3.2321 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,361 49.4% 

3.2322 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,533 52.5% 

3.2323 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,422 51.5% 

3.2324 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,593 54.4% 

3.2325 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,494 49.5% 

3.2326 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,666 52.5% 

3.2327 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,555 51.6% 

3.2328 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,727 54.5% 

3.2329 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,443 49.8% 

3.2330 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,615 52.9% 

3.2331 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,504 51.9% 

3.2332 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,675 54.8% 

3.2333 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,576 49.9% 

3.2334 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,748 53.0% 

3.2335 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,637 52.0% 

3.2336 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS24 +EGR +WR20% +6sp 

6sp DCTwet $3,809 54.9% 

3.2337 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,790 50.0% 

3.2338 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,961 53.0% 

3.2339 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,850 52.0% 

3.2340 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,022 54.9% 

3.2341 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,923 49.9% 
Chapter 1 

1-44 

3.2342 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,094 53.0% 

3.2343 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,983 52.0% 

3.2344 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,155 54.9% 

3.2345 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,872 50.4% 

3.2346 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,043 53.4% 

3.2347 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $3,932 52.4% 

3.2348 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +GDI 

+MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,104 55.3% 

3.2349 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,005 50.4% 

3.2350 20% Auto 4VDI4 +LUB +EFR1 +ASL1 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,176 53.4% 

3.2351 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 +LRRT1 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,065 52.4% 

3.2352 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP +DVVL 

+GDI +MHEV +SAX +TDS27 +EGR +WR20% +6sp 

6sp DCTwet $4,237 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 
MY 2017 and Later Regulatory Impact Analysis 

1-45 

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.I

 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 TP# MR Description Trans 2025 CO2% 

1 1.2465 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTdry $9,327 73.7% 

1 1.2466 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTdry $11,262 83.3% 

1 1.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $9,367 100.0% 

1 1.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $11,061 100.0% 

1 1.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $14,630 100.0% 

2 2.2465 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTdry $10,585 74.4% 

2 2.2466 20% Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTdry $13,072 83.9% 

2 2.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $11,363 100.0% 

2 2.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $13,288 100.0% 

2 2.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $18,218 100.0% 

3 3.2465 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTwet $11,047 74.3% 

3 3.2466 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTwet $13,534 83.8% 

3 3.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $11,451 100.0% 

3 3.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $13,376 100.0% 

3 3.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $18,306 100.0% 

4 4.2465 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV20 +CCC +WR20% +8sp 

8sp DCTwet $11,223 74.7% 

4 4.2466 20% Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV40 +CCC +WR20% +8sp 

8sp DCTwet $13,710 84.0% 

4 4.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $11,452 100.0% 

4 4.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $13,377 100.0% 

4 4.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $18,306 100.0% 

5 5.2465 20% Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTwet $13,945 73.9% 

5 5.2466 20% Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTwet $17,726 83.4% 

5 5.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $14,324 100.0% 

5 5.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $16,200 100.0% 

 

I

 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. 
Chapter 1 

1-46 

5 5.2469 [20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $21,467 100.0% 

6 6.2465 20% Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV20 +CCC +WR20% +8sp 

8sp DCTwet $14,472 74.4% 

6 6.2466 20% Auto 4VDV8 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV40 +CCC +WR20% +8sp 

8sp DCTwet $18,253 83.7% 

6 6.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $14,263 100.0% 

6 6.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $16,139 100.0% 

6 6.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $21,406 100.0% 

7 7.2465 20% MPVnt 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTdry $10,255 73.0% 

7 7.2466 20% MPVnt 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG 

+DCP +DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTdry $12,665 82.9% 

7 7.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $10,245 100.0% 

7 7.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $12,403 100.0% 

7 7.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $17,429 100.0% 

13 13.2465 20% SmT 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV20 +WR20% +8sp 

8sp DCTdry $10,342 73.0% 

13 13.2466 20% SmT 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 +LRRT2 +HEG +DCP 

+DVVL +GDI +ATKCS +REEV40 +WR20% +8sp 

8sp DCTdry $12,751 82.9% 

13 13.2467 20% +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp $10,332 100.0% 

13 13.2468 20% +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp $12,490 100.0% 

13 13.2469 20% +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp $17,515 100.0% 

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.

J

 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-47 

Table 1.3-7 Lifetime VMT & Baseline CO2 used for TARF Ranking Process 

Vehicle

Type Description Baseengine

Car/

Truck

a

2016MY 

Lifetime

VMT

2021MY 

Lifetime

VMT 

2025MY 

Lifetime

VMT 

Base 

CO2

(g/mi)b

1 Subcompact car I4 I4 DOHC 

4v C 

198,065 203,913 208,775 

239.8

2 Compact car I4 I4 DOHC 

4v C 254.3

3 Midsize car V6 V6 DOHC 

4v C 321.2

4 Midsize car V6 V6 SOHC 

2v C 332.7

5 Large car V8 V8 DOHC 

4v C 385.9

6 Large car V8 V8 OHV 

2v C 390.0

7 Small MPV I4 I4 DOHC 

4v C 296.6

8 Midsize MPV V6 V6 DOHC 

4v T 

211,964 218,399 223,688 

372.3

9 Midsize MPV V6 V6 SOHC 

2v T 412.2

10 Midsize MPV V6 V6 OHV 

2v T 372.0

11 Large MPV V8 V8 DOHC 

4v T 461.4

12 Large MPV V8 V8 OHV 

2v T 477.4

13 Small truck I4 I4 DOHC 

4v T 330.8

14 Full-sized Pickup 

truck V6 

V6 DOHC 

4v T 403.1

15 Full-sized Pickup 

truck V6 

V6 OHV 

2v T 420.9

16 Full-sized Pickup 

truck V8 

V8 DOHC 

4v T 477.3

17 Full-sized Pickup 

truck V8 

V8 SOHC 

2v T 455.5

18 Full-sized Pickup 

truck V8 

V8 SOHC 

3v T 480.0

19 Full-sized Pickup 

truck V8 

V8 OHV 

2v T 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 # 

To 

Tech 

Pkg # 

From 

Step 

# 

 To 

Step 

# 

Engine Trans Weight 

Red 

Cost CO2 % 

Reduction 

3.0000 3.0000 0 Auto 4VDV6 base $0 0.0% 

3.0000 3.0131 0 1 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 

+LRRT1 +HEG +DCP +WR5% +6sp 

6sp 

DCTwet 

5% $822 29.8% 

3.0131 3.0195 1 2 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero1 

+LRRT1 +HEG +DCP +GDI +TDS18 +WR5% +6sp 

6sp 

DCTwet 

5% $1,070 36.8% 

3.0195 3.0196 2 3 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS18 +WR5% +6sp 

6sp 

DCTwet 

5% $1,287 40.4% 
Chapter 1 

1-48 

3.0196 3.0388 3 4 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS18 +WR5% +8sp 

8sp 

DCTwet 

5% $1,402 42.3% 

3.0388 3.0772 4 5 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS18 +WR10% +8sp 

8sp 

DCTwet 

10% $1,519 43.9% 

3.0772 3.0804 5 6 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS24 +WR10% +8sp 

8sp 

DCTwet 

10% $1,733 45.7% 

3.0804 3.0836 6 7 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR10% +8sp 

8sp 

DCTwet 

10% $1,982 47.7% 

3.0772 3.1156 5 8 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS18 +WR15% +8sp 

8sp 

DCTwet 

15% $1,745 45.5% 

3.0836 3.1220 7 9 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR15% +8sp 

8sp 

DCTwet 

15% $2,209 49.2% 

3.1156 3.2004 8 10 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS18 +WR10% +8sp 

8sp 

DCTwet 

10% $2,722 50.2% 

3.1220 3.2036 9 11 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10% 

+8sp 

8sp 

DCTwet 

10% $3,185 53.6% 

3.2004 3.2196 10 12 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS18 +WR15% +8sp 

8sp 

DCTwet 

15% $2,948 51.4% 

3.1220 3.1604 9 13 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +TDS24 +EGR +WR20% +8sp 

8sp 

DCTwet 

20% $2,506 50.7% 

3.2036 3.2228 11 14 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,412 54.7% 

3.2196 3.2204 12 15 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS18 +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,030 51.8% 

3.2204 3.2467 15 16 +IACC1 +EPS +Aero2 +LRRT2 +EV75 mile +WR20% +0sp 20% $11,451 100.0% 

3.1604 3.2036 13 17 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10% 

+8sp 

8sp 

DCTwet 

10% $3,185 53.6% 

3.2036 3.2228 17 18 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,412 54.7% 

3.1604 3.1612 13 19 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +SS +TDS24 +EGR +WR20% 

+8sp 

8sp 

DCTwet 

20% $2,814 51.2% 

3.2228 3.2236 14 20 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR15% +8sp 

8sp 

DCTwet 

15% $3,494 55.1% 

3.2228 3.2236 18 21 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR15% +8sp 

8sp 

DCTwet 

15% $3,494 55.1% 

3.2204 3.2396 15 22 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS18 +WR20% 

+8sp 

8sp 

DCTwet 

20% $3,327 53.0% 

3.1612 3.1628 19 23 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC2 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +SS +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $2,896 51.5% 

3.2236 3.2428 20 24 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,791 56.2% 

3.2236 3.2428 21 25 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,791 56.2% 

3.2396 3.2468 22 26 +IACC1 +EPS +Aero2 +LRRT2 +EV100 mile +WR20% +0sp 20% $13,376 100.0% 

3.1628 3.2020 23 27 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +WR10% +8sp 

8sp 

DCTwet 

10% $2,936 51.9% 
MY 2017 and Later Regulatory Impact Analysis 

1-49 

3.2020 3.2036 27 28 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10% 

+8sp 

8sp 

DCTwet 

10% $3,185 53.6% 

3.2036 3.2228 28 29 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,412 54.7% 

3.2228 3.2236 29 30 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR15% +8sp 

8sp 

DCTwet 

15% $3,494 55.1% 

3.2236 3.2428 30 31 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,791 56.2% 

3.2396 3.2400 22 32 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +MHEV +SAX +TDS18 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,461 53.4% 

3.1628 3.2469 23 33 +IACC1 +EPS +Aero2 +LRRT2 +EV150 mile +WR20% +0sp 20% $18,306 100.0% 

3.2400 3.2220 32 34 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,245 53.4% 

3.2220 3.2036 34 35 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10% 

+8sp 

8sp 

DCTwet 

10% $3,185 53.6% 

3.2036 3.2228 35 36 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,412 54.7% 

3.2228 3.2236 36 37 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR15% +8sp 

8sp 

DCTwet 

15% $3,494 55.1% 

3.2236 3.2428 37 38 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,791 56.2% 

3.1628 3.2466 23 39 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +ATKCS +REEV40 

+WR20% +8sp 

8sp 

DCTwet 

20% $13,534 83.8% 

3.2400 3.2220 32 40 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,245 53.4% 

3.2220 3.2036 40 41 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR10% 

+8sp 

8sp 

DCTwet 

10% $3,185 53.6% 

3.2036 3.2228 41 42 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +TDS24 +EGR +WR15% 

+8sp 

8sp 

DCTwet 

15% $3,412 54.7% 

3.2228 3.2236 42 43 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR15% +8sp 

8sp 

DCTwet 

15% $3,494 55.1% 

3.2236 3.2428 43 44 Auto 4VDI4 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +GDI +MHEV +SAX +TDS24 +EGR 

+WR20% +8sp 

8sp 

DCTwet 

20% $3,791 56.2% 

3.1628 3.2465 23 45 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +ATKCS +REEV20 

+WR20% +8sp 

8sp 

DCTwet 

20% $11,047 74.3% 

3.2428 3.1680 24 46 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS 

+WR20% +8sp 

8sp 

DCTwet 

20% $5,156 57.3% 

3.2428 3.1680 25 47 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS 

+WR20% +8sp 

8sp 

DCTwet 

20% $5,156 57.3% 

3.2428 3.1680 31 48 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS 

+WR20% +8sp 

8sp 

DCTwet 

20% $5,156 57.3% 

3.2428 3.1680 38 49 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS 

+WR20% +8sp 

8sp 

DCTwet 

20% $5,156 57.3% 

3.2428 3.1680 44 50 Auto 4VDV6 +EFR2 +ASL2 +LDB +IACC1 +EPS +Aero2 

+LRRT2 +HEG +DCP +DVVL +GDI +HEV +SAX +ATKCS 

+WR20% +8sp 

8sp 

DCTwet 

20% $5,156 57.3% 
Chapter 1 

1-50 

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. 
MY 2017 and Later Regulatory Impact Analysis 

1-51 

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, 
Chapter 1 

1-52 

• 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 technologiesK

. 

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. 
MY 2017 and Later Regulatory Impact Analysis 

1-53 

Figure 1.4-1 Sample lumped parameter model spreadsheet 

EPA Staff Deliberative Materials--Do Not Quote or Cite

Vehicle Type Package Notes

Standard car 158 hp 161 ft-lb 3625 lb 11.3 hp 12V Stop-Start

0 0 0 0.0 Stoich GDI Turbo

Heat 

Lost To Irreversibilities,

Gearbox, Exhaust & etc.

Mass Drag Tires T.C. Coolant

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law Check

23% 37% 40%

4.0% 6.4% 6.9% 4.2% 1.3% 7.9% 5.3% 34.0% 30.0% 100.0%

0% 8% 7% 22.3% 41.7% 15.4% 81.2% n/a OK

4.0% 5.9% 6.5% 4.4% 0.8% 7.1% 1.0% 32.0% 30%

Road load kWh 0.47 0.71 0.77

Indicated Mech Brake Drivetrain Cycle Fuel Road includes some techs

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads 32.0 mpg (combined)

2008 Baseline 36.0% 59.6% 21.5% 80.6% 100.0% 17.3% 100.0% 0.031 gal/mi

New 38.0% 76.5% 29.0% 84.9% 100.0% 24.7% 94.2% GHG emissions 284 g/mi CO2E

assumes no techs

Tractive 11.95 kWh

1.95 PMEP Brake 30.4 mpg (unadj)

66.1% Fuel Consumption (GGE/mile) Original friction/brake ratio Losses Efficiency 0.033 gal/mi

33.9% FC Reduction vs no-techs Based on PMEP/IMEP >>>> 11% 25% GHG emissions 299 g/mi CO2E

51.2% FE Improvement (mpgge) (GM study) =71.1% mech efficiency

51.2% FE Improvement (mpg) fuel economy 46.03 mpg (unadj)

30.5% GHG reduction vs 2008 Ricardo baseline fuel consumption 0.022 gal/mi

33.9% GHG reduction vs no-techs GHG emissions 197 g/mi CO2E

Independent % or User Picklist

Technology FC Estimate* Loss Category Implementation into estimator Level Include? (0/1) Dev status

Vehicle mass reduction 5-6% per 10% Braking/stopped, inertia, rolling resistance 0% 0

Aero Drag Reduction 2.1% per 10% Aero 14.4% aero (cars), 9.5% aero (trucks)10% 1

Rolling Resistance Reduction 1.5% Rolling 9.5% rolling 10% 1

Low Fric Lubes 0.5% Friction 2% friction 0

EF Reduction Friction variable% friction 1 1

4V on 2V Baseline 3.0% Pumping, friction 20.5% pumping, -2.5% fric 0

ICP 2.0% Pumping 13.5% pumping, +0.2% IE, -3.5% fric 0

DCP 4.0% total VVT Pumping 23.5% pumping, +0.2% IE, -2.5% fric 1

CCP 4.0% total VVT Pumping 23.5% pumping, +0.2% IE, -2.5% fric 0

Deac 6.0% Pumping, friction 30% pumping, -2.5% frict 0

DVVL 4.0% Pumping 27% pumping, -3% friction 0% 1

CVVL 5.0% Pumping 33% pumping, -3% friction 0

Turbo/Downsize (gas engines only) Pumping variable IE ratio, P, F 35% 1

5-spd gearbox 2.5% Pumping 6% pumping 0

6-spd gearbox 5.5% Pumping 8% pumping, +0.1% IE 0

8-spd gearbox Pumping 15% pumping, 13% trans, +0.5% IE 1

CVT 6.0% Trans, pumping 41% pumping, -5% trans 0

DCT Wet 6.7% Trans 21% trans (increment) 0

DCT Dry 10.0% Trans 25% trans (increment) 0

Early upshift (formerly ASL) 2.0% Pumping 10.5% pumping 0

Optimized shift strategy 5.5% Pumping, IE, friction 11% pumping, 11% frict, +0.1% IE 1

Agg TC Lockup 0.5% Trans 2% trans 1

High efficiency gearbox (auto) Trans variable % Trans 7% 1

12V SS (idle off only) 2.0% P,F,trans 3% pumping, 3% friction, 2% trans 1

High voltage SS, with launch (BAS) 7.5% B/I, P, F, trans 11% B/I, 3% P, 3% F, 2% trans 0

Alternator regen on braking 2.0% Access 10% pumping 1 included in 12V SS

EPS 2.0% Access 22% access 100% 1 included in BAS, hybrids

Electric access (12V) 1.5% Access 12% access 1

Electric access (high V) 3.0% Access 42% access 0

High efficiency alternator (70%) Access 15% access 1 included in BAS

GDI (stoich) 1.5% Ind Eff + 0.55% IE 1

GDI (stoich) w/ cooled EGR +1.9% IE, 41% pumping 0

GDI (lean) Ind. Eff, pumping +1.3% IE, 41% pumping 0

Diesel - LNT (2008) 30.0% Ind Eff, P, F, trans see comment 0

Diesel - SCR (2008) 35.0% Ind Eff, P, F, trans see comment Motor kW 0

Hybrid drivetrain (need to select transmission style!) Inertia, trans, acc IE, F, P 0 0

Secondary axle disconnect 1.3% Trans 6% trans 0

Low drag brakes 0.8% Braking/inertia 3.5% B/I 0

Atkinson cycle engine Ind. Eff, - pumping +6% IE, -30% pumping 0

Advanced Diesel (2020) Ind Eff, P, F, trans see comment 0

Plug-In %EV = 50% 0

fuel economy

fuel consumption

Current package values

Current Results

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel

req'd fuel energy

2008 Ricardo baseline values

Regressed baseline values

Fuel Economy

Fuel Consumption

Pick one max

Pick one max

Pick one max

Pick one max

Pick one max

Pick one max

Pick one

Additive to trans;

Included in P2

Vehicle Energy Effects Estimator

Gross Indicated Energy

Brake Energy Total Engine Friction

Road Loads

Rated Power Rated Torque ETW 50mph RL

Evaluate New 

Package

Reset LP Model
Chapter 1 

1-54 

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 Atkinsoncycle 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. 
MY 2017 and Later Regulatory Impact Analysis 

1-55 

• 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. 
Chapter 1 

1-56 

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 

vehiclesM

 

• 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. 

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. 

Regression data used - net engine brake efficiency

Vehicle Power Torque ETW 50mph RL Alt regen Net BE% predicted % error Coefficients

Camry 154 160 3625 11.33 0 21.5% 21.5% 0.1% Intercept 0.207831

Vue 169 161 4000 15.08 0 24.0% 23.7% 1.3% Torque -0.00028

Caravan 205 240 4500 15.84 0 21.2% 21.7% 2.3% ETW -6.2E-06

300 250 250 4000 14.78 0 21.3% 21.0% 1.3% 50mph RL 0.006531

F-150 300 365 6000 22.86 0 21.8% 21.9% 0.5% Alt regen 0.019809

Yaris 106 103 2625 10.82 1 25.0% 25.3% 1.3%

Camry 158 161 3625 11.33 1 23.8% 23.5% 1.3%

Vue 169 161 4000 15.08 1 25.8% 25.7% 0.5%

Caravan 205 240 4500 15.84 1 23.1% 23.7% 2.3%

300 250 250 4000 14.78 1 23.2% 23.0% 0.9%

F-150 300 365 6000 22.86 1 24.0% 23.9% 0.8%

avg error 1.1%

2008 baselines 2010 baselines
MY 2017 and Later Regulatory Impact Analysis 

1-57 

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 

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, Efuel, 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: 

 



	

 
 	/

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 
Chapter 1 

1-58 

1) Ewheel: required wheel (or tractive) energy over the city/HW test cycle = 

f(ETW, RL) 

2) ηengine: net engine brake efficiency = f(torque, ETW, RL, alternator regenO ) 

3) ηD/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

 

Efuel (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. 

Figure 1.5-2 Comparison of LP model to Ricardo simulation results for 2008 and 2010 

baseline vehicles 

 

O

 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. 

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 4 spd auto 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 4 spd auto Y DCP 28.8 29.1 1.1% 30.5 31.1 2.0%

Large MPV 4 spd auto N fixed 23.1 23.7 2.4% 25.2 25.9 2.6%

Truck 4 spd auto 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
MY 2017 and Later Regulatory Impact Analysis 

1-59 

1.5.5 Identification and calibration of individual technologies 

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. 
Chapter 1 

1-60 

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 

Technology Braking / Aero Rolling Trans Access Friction Pumping Ind

Inertia Load Load Losses Losses Losses Losses Efficiency

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

DCT Wet

DCT Dry

Early upshift (formerly ASL)

Optimized shift strategy

Agg TC 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)

Code: Major

Minor

Negative
MY 2017 and Later Regulatory Impact Analysis 

1-61 

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 I4 +EFR2 +LDB +ASL2 +IACC2 +EPS +Aero2 +LRRT2 +HEG +DCP 

+GDI +TDS18 

8sp DCTwet 

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 

 

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. 

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

4% 0% 0% 0% 42% 22% 20% n/a

3.8% 6.4% 6.9% 4.5% 0.6% 6.5% 4.5% 33.9% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 85.1% Fuel Consumption

New 36.1% 67.9% 24.5% 81.6% 100.0% 20.0% 99.2% 14.9% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel
Chapter 1 

1-62 

• 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 

• 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 

• 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. 

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

8% 17% 18% 0% 42% 22% 20% n/a

3.6% 5.3% 5.6% 4.3% 0.6% 6.2% 4.3% 35.3% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 75.5% Fuel Consumption

New 34.7% 67.9% 23.6% 81.6% 100.0% 19.2% 84.8% 24.5% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

8% 17% 18% 25% 42% 22% 20% n/a

3.6% 5.3% 5.6% 3.3% 0.6% 6.2% 4.3% 35.3% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 71.5% Fuel Consumption

New 34.7% 67.9% 23.6% 86.2% 100.0% 20.3% 84.8% 28.5% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel
MY 2017 and Later Regulatory Impact Analysis 

1-63 

Table 1.5-4 

• 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 

• 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. 

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

8% 17% 18% 25% 42% 20% 39% n/a

3.6% 5.3% 5.6% 3.4% 0.6% 6.4% 3.3% 35.1% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 68.6% Fuel Consumption

New 34.9% 70.4% 24.6% 86.2% 100.0% 21.2% 84.8% 31.4% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

8% 17% 18% 25% 42% 20% 67% n/a

3.6% 5.3% 5.6% 3.8% 0.6% 6.7% 1.9% 33.4% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 61.7% Fuel Consumption

New 36.6% 74.7% 27.3% 86.2% 100.0% 23.6% 84.8% 38.3% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel
Chapter 1 

1-64 

Table 1.5-6 

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 

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 Ricardomodeled 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 

Braking / Aero Rolling Trans Access Friction Pumping Ind Eff Second

Inertia Load Load Losses Losses Losses Losses Losses Law

23% 37% 40%

3.9% 6.4% 6.9% 3.9% 1.1% 8.3% 5.6% 34.0% 30.0%

8% 17% 18% 48% 42% 20% 72% n/a

3.6% 5.3% 5.6% 2.7% 0.6% 6.8% 1.6% 32.9% 30%

Indicated Mech Brake Drivetrain Cycle Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Efficiency Loads

2008 Baseline 36.0% 58.4% 21.0% 81.6% 100.0% 17.1% 100.0% 57.4% Fuel Consumption

New 37.1% 75.5% 28.0% 90.5% 100.0% 25.3% 84.8% 42.6% GHG reduction

% of tractive energy

Baseline % of fuel

Reduction

% of NEW fuel

Technologies Individual Combined Gross 

Added Effectiveness Effectiveness Effectiveness

(for step) LP total total

EFR2, LDB, ASL2, IACC2, EPS 16.4% 14.9% 16.4%

Aero2, LRRT2, MR5 10.8% 24.5% 25.5%

HEG 5.3% 28.5% 29.4%

DCP 5.5% 31.4% 33.3%

GDI, TDS18 14.9% 38.3% 43.2%

8spDCT-wet 11.9% 42.6% 50.0%
MY 2017 and Later Regulatory Impact Analysis 

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simulation package results (for conventional stop-start and P2 hybrid packagesR

) compared to 

the lumped parameter estimates. 

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. 

0

10

20

30

40

50

60

70

80

mpg

Small Car Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid
Chapter 1 

1-66 

Figure 1.5-5 Comparison of LP to simulation results for Standard Car class 

Figure 1.5-6 Comparison of LP to simulation results for Large Car class 

0

10

20

30

40

50

60

70

mpg

Standard Car Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid

0

10

20

30

40

50

60

mpg

Large Car Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid
MY 2017 and Later Regulatory Impact Analysis 

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Figure 1.5-7 Comparison of LP to simulation results for Small MPV class 

Figure 1.5-8 Comparison of LP to simulation results for Large MPV class 

0

10

20

30

40

50

60

mpg

Small MPV Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid

0

5

10

15

20

25

30

35

40

45

50

mpg

Large MPV Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid
Chapter 1 

1-68 

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 

 Compact Midsize Small SUV Midsize SUV Pickup 

FC reduction 11.6% 11.6% 10.2% 10.5% 8.5% 

Table 1.5-2 Lumped Parameter Model Effectiveness for Mild Hybrid 

Small Car Std Car Small MPV Large MPV Truck 

FC reduction 14.1% 11.8% 10.1% 10.1% 6.9% 

0

5

10

15

20

25

30

35

mpg

Truck Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid
MY 2017 and Later Regulatory Impact Analysis 

1-69 

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. 
Chapter 1 

1-70 

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 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

0 10 20 30 40 50 60 70

RLHP

mph

RLHP comparison

F150

Caravan

Vue

300

Camry

Yaris

Yaris (alt)
MY 2017 and Later Regulatory Impact Analysis 

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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 2011 Chevy Cruze ECO 2011 Sonata Hybrid 2011 Escape Hybrid 2011 F-150 Ecoboost

Vehicle class Small Car Standard Car Small MPV Truck

Engine 

1.4L I4

turbo GDI

2.4L I4

Atkinson

2.5L I4

Atkinson

3.5L V6

turbo GDI

Transmission 6 speed auto 6 speed DCT CVT 6 speed auto

HEV motor (kW) n/a 30 67 n/a

ETW (lbs) 3375 3750 4000 6000

City/HW FE (mpg) 40.3 52.2 43.9 22.6

LP estimate (mpg) 40.2 51.7 44.0 21.9

GDI (stoich.) P2 hybrid Powersplit hybrid GDI (stoich)

turbo (30% downsize) aero improvements turbo (37% downsize)

ultra low R tires

active grill shutters

Key technologies applied 

in LP model
Chapter 1 

1-72 

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, G.K., Prevedel, K., Fuerhapter, A., 2007, “GDI Turbo – The Next 

Steps.” JSAE Technical Paper No. 20075355; Hancock, D., Fraser, N., Jeremy, M., Sykes, R., 

Blaxill, H., 2008, “A New 3 Cylinder 1.2l Advanced Downsizing Technology Demonstrator 

Engine.” SAE Technical Paper Series No. 2008-01-0611; Lumsden, G., OudeNijeweme, D., 

Fraser, 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., Fraser, 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-OAR2010-0799. 

4

 See “LDGHG 2017-2025 Cost Development Files,” CD in Docket No. EPA-HQ-OAR2010-0799. 

5

 See “LDGHG 2017-2025 Cost Development Files,” CD in Docket No. EPA-HQ-OAR2010-0799. 

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. 
MY 2017 and Later Regulatory Impact Analysis 

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11 See FRM Joint Technical Support Document 3.2.1.3 Docket No. EPA-HQ-OAR-2010-

0799 

MY 2017 and Later - Regulatory Impact Analysis 

2-1 

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 
Chapter 2 

2-2 

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, GTDrive, 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 Component Models 

Ambient N/A 

Driver N/A 

Electric Accessory (electrical) 

Engine Accessory (mechanical), Cylinder 

Transmission Clutch, Gear 

Vehicle 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 
MY 2017 and Later - Regulatory Impact Analysis 

2-3 

and transmission technologies, and driving cycles. In the future, it will undergo upgrades and 

improvements to include more technology choices and more simulation flexibilities. 
Chapter 2 

2-4 

Figure 2.2-1 LD Vehicle Simulation Tool 

GUI Inputs 
MY 2017 and Later - Regulatory Impact Analysis 

2-5 

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 proportionalintegral-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 offcycle 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. 
Chapter 2 

2-6 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

2-7 

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 
Chapter 2 

2-8 

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.14,15 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. 

Figure 2.3-1 Representative A/C Compressor Load Curves 
MY 2017 and Later - Regulatory Impact Analysis 

2-9 

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 SparkIgnition (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 Small Car Medium Car Large Car Truck 

CO2 with A/C off [g/mi] 196.4 235.7 293.7 472.4 

CO2 Increase with A/C on [g/mi] 11.7 12.0 13.8 17.2 

Total CO2 with A/C [g/mi] 208.1 247.7 307.5 489.6 

Indirect A/C Fuel Use [%] 5.6 4.8 4.5 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 CO2 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 CO2 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 CO2 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. 
Chapter 2 

2-10 

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 CO2

emissions over the SC03 driving cycle when baseline engines are used, as opposed to the 

advanced EGR boost engines. By comparing the values of CO2 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 CO2 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 Small Car Medium Car Large Car Truck 

CO2 with A/C off [g/mi] 259.3 348.0 425.4 628.1 

CO2 Increase with A/C on [g/mi] 11.3 11.1 12.5 16.2 

Total CO2 with A/C [g/mi] 270.6 359.1 437.9 644.3 

Indirect A/C Fuel Use [%] 4.2 3.1 2.9 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
MY 2017 and Later - Regulatory Impact Analysis 

2-11 

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.S

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. 
Chapter 2 

2-12 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

2-13 

Table 2.3-3 Simulated GHG Reduction Benefits of Active Aerodynamic Improvements 

Reduction in Aerodynamic Drag 

(Cd) 

GHG Reduction in Cars 

[g/mile] 

GHG Reduction in Trucks 

[g/mile] 

1% 0.2 0.3 

2% 0.4 0.6 

3% 0.6 1.0 

4% 0.8 1.3 

5% 0.9 1.6 

10% 1.9 3.2 

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, 
Chapter 2 

2-14 

reduces CO2 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 III.C.5.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 Small-Size Car 

[g/mile] 

Mid-Size Car 

[g/mile] 

Large-Size Car 

[g/mile] 

Pick-up Truck 

[g/mile] 

Vehicle Simulation 211.7 273.8 350.2 532.7 

Lumped Parameter Model 220 280 359 520 

Percent Difference 3.8% 2.2% 2.5% 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, 
MY 2017 and Later - Regulatory Impact Analysis 

2-15 

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 CO2 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 (nonhybrid) 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. 
Chapter 2 

2-16 

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 ComfortBased 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. 

Chapter 3 

3-2 

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.T

 

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 CO2 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-3 

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 CO2 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 NOI. 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 CO2 emission level, the level of CO2 reducing 

technology already present, and the vehicle’s “type,” which indicates the technology 

available for addition to that vehicle to reduce CO2 emissions. Information 

determining the applicable CO2 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.W 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. 
Chapter 3 

3-4 

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 TARF). 

OMEGA offers several different options for calculating TARF values. One TARF 

equation considers only the cost of the technology and the value of any reduced fuel 

consumption considered by the vehicle purchaser. The other two TARF 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-5 

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.Z

 

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,AA creating and ranking technology 

packages,BB 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. 

AA Joint TSD Chapter 1 

BB RIA Chapter 1 
Chapter 3 

technology penetration of the new vehicle mix and the scenario impacts (fuel savings, 

emission impacts, and other monetized benefits) are calculated 

and post- processors are Microsoft Excel spreadsheets and 

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

model utilizes five input files: Market, Technology, Fuels, Scenario, and Reference. 

3.3-1 shows the (simplified) information flow throu

Figure 3.3-1

OMEGA utilizes four basic sets of input data. The first, the market file, is a 

description of the vehicle fleet. 

manufacturer, CO2 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

discussion on how EPA defined the 19 vehicle types. In addition, the degree to wh

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 

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. 

3-6 

technology penetration of the new vehicle mix and the scenario impacts (fuel savings, 

sion impacts, and other monetized benefits) are calculated via post-processors. The pre

processors are Microsoft Excel spreadsheets and scripted programs (written in 

, while the OMEGA core model is an executable program written 

OMEGA is designed to be flexible in a number of ways. Very few numerical values 

coded in the model, and consequently, the model relies heavily on its input files

model utilizes five input files: Market, Technology, Fuels, Scenario, and Reference. 

shows the (simplified) information flow through OMEGA, and how these files interact. 

1 Information Flow in the OMEGA Model 

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 

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 

echnologies can be applied to that vehicle. Chapter 1 of the Joint TSD contains 

market forecasts were created for modeling purposes, and includes a 

discussion on how EPA defined the 19 vehicle types. In addition, the degree to wh

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 

atory Impact Analysis (RIA) contains a detailed discussion of how EPA 

accounts for technology present in the baseline fleet in OMEGA. 

technology penetration of the new vehicle mix and the scenario impacts (fuel savings, 

processors. The pre- 

(written in 

, while the OMEGA core model is an executable program written 

OMEGA is designed to be flexible in a number of ways. Very few numerical values 

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 

gh OMEGA, and how these files interact. 

OMEGA utilizes four basic sets of input data. The first, the market file, is a 

The key pieces of data required for each vehicle are its 

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 

echnologies can be applied to that vehicle. Chapter 1 of the Joint TSD contains 

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 

the situation, for example, where the 

model might try to add a basic engine improvement to a current hybrid vehicle. Section 

atory Impact Analysis (RIA) contains a detailed discussion of how EPA 
MY 2017 and Later - Regulatory Impact Analysis 

3-7 

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 CO2 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 

Joint.TSD 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 Vehicle 

Type 

Vehicle 

Class 

Auto Subcompact I3 DOHC 4v 

 Auto Subcompact I4 SOHC/DOHC 2v/4v 1 Small car 
Chapter 3 

3-8 

Auto Subcompact Electric 

Auto Compact SOHC 2v 

Auto Compact SOHC/DOHC 4v 

Auto Midsize SOHC/DOHC 4v 

Pickup Small DOHC 4v 

2 Standard 

car 

Auto Subcompact I5 SOHC 4v 

Auto Subcompact V6 SOHC/DOHC 4v 

Auto Subcompact I4 SOHC/DOHC 4v turbo/supercharged 

Auto Compact Rotary 

Auto Compact I5 DOHC 4v 

Auto Compact V6 SOHC/DOHC 4v 

Auto Compact I4 SOHC/DOHC 4v turbo/supercharged 

Auto Midsize V6 SOHC/DOHC 4v 

Auto Midsize I4 SOHC/DOHC 4v tubo/supercharged 

Auto Large V6 SOHC/DOHC 4v 

Auto Midsize I4 SOHC 4v tubo/supercharged 

3 Standard 

car 

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 

4 Standard 

car 

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 

5 Large car 

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 

6 Large car 

SUV Small I4 DOHC 4v 

SUV Midsize SOHC/DOHC 4v 

SUV Large DOHC 4v 

Minivan I4 DOHC 4v 

7 Small 

MPV 

SUV Small I4 DOHC 4v turbo/supercharged 

SUV Midsize V6 SOHC/DOHC 4v 8 Large 

MPV 
MY 2017 and Later - Regulatory Impact Analysis 

3-9 

SUV Midsize I4 SOHC/DOHC 4v turbo/supercharged 

SUV Large V6 SOHC/DOHC 4v 

SUV Large I5 DOHC 2v 

SUV Large I4 DOHC 4v turbo/supercharged 

SUV Midsize V6 SOHC 2v 

SUV Large V6 SOHC 2v 9 Large MPV 

SUV Small V6 OHV 2v 

SUV Midsize V6 OHV 2v 

SUV Large V6 OHV 2v 

Minivan V6 OHV 2v 

Cargo Van V6 OHV 2v 

10 Large 

MPV 

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 

11 Truck 

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 

12 Truck 

Pickup Large DOHC 4v 13 Small 

MPV 

Pickup Small V6 SOHC 4v 

Pickup Small I5 DOHC 2v 

Pickup Large V6 DOHC 2v/4v 

Pickup Large I5 DOHC 2v 

14 Large 

MPV 

Pickup Small V6 SOHC 2v 

Pickup Small V6 OHV 2v 

Pickup Large V6 SOHC 2v 

Pickup Large V6 OHV 2v 

15 Large 

MPV 

Pickup Large V8 DOHC 4v 16 Truck 

Pickup Large V8 SOHC 2v 17 Truck 

Pickup Large V8 SOHC/DOHC 3v turbo/supercharged 

Pickup Large V8 SOHC 3v 

18 Truck 

Pickup Large V8 OHV 2v 19 Truck 

a

I4 = 4 cylinder engine, I5 = 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 # 

Utility Class Vehicle Use 1Footprint Criteria Structure Criteria 

1 Subcompact Auto Car Footprint <43 -- 

2 Compact Auto Car 43<=Footprint<46 -- 

3 Mid Size Auto Car 46<=Footprint<53 -- 
Chapter 3 

3-10 

4 Large Auto Car 56<=Footprint -- 

5 Small SUV SUV 43<=Footprint<46 -- 

6 Large SUV SUV 46<=Footprint -- 

7 Small Pickup Pickup Footprint < 50 -- 

8 Large Pickup Pickup 50<=Footprint -- 

9 Cargo Van Van -- Ladder Frame 

10 Minivan Van -- 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 CO2 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 
MY 2017 and Later - Regulatory Impact Analysis 

3-11 

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: 

















−

−

−

















−

−

×

















−

−

=

−

−

−

1,

,

1,

,

,

1,

1

1

1

1

1

1

1

ip

ip

ip

ip

iv

iv

i

tTotalEffec

tTotalEffec

tTotalEffec

tTotalEffec

tTotalEffec

tTotalEffec

TEB

Where 

TotalEffectv,i = Total effectiveness of all of the technologies present on the baseline vehicle after 

application of technology package i 

TotalEffectv,i-1 = 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 

TotalEffectp,i-1 = 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: 

CEBi

 = 1 – (TotalCostv,i – TotalCostv,i-1) / (TotalCostp,i – TotalCostp,i-1) 

Where 

TotalCostv = 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 
Chapter 3 

3-12 

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 CO2 emission 

level. This appropriately weights vehicle models with either higher sales or CO2 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 
MY 2017 and Later - Regulatory Impact Analysis 

3-13 

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 truckbased 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

Kahane

Coefficients 1

Base 

fatalities 

per billion 

miles

adjustment for 

new FMVSS

Change in Fatalities 

per pound per mile2

PC below 3106 1.56% 11.091 0.904 1.6E-12 

PC above 3106 0.51% 9.313 0.904 4.3E-13 

LT below 4594 0.52% 13.241 0.904 6.2E-13 

LT above 4594 -0.34% 13.032 0.904 -4.0E-13 

Minivan -0.37% 7.499 0.904 -2.5E-13 

1Expressed as percent change in base fatalities per 100 pound change in vehicle weight 

2Calculated 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 

 

CC 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. 
Chapter 3 

3-14 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-15 

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 

CO2 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 CO2

( )

AIE TEB

CO AIE CO t

t

×−

× −

=

−

1

12

2

1

 

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. 
Chapter 3 

3-16 

The tailpipe CO2 is adjusted for the usage of these credits in order to calculate 

compliance CO2. 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 CO2

to produce the CO2 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 

)1(* t =CostCost

t−1 +TechCost −CEB

Equation 3.4-5 – Calculation of Average Cost for a manufacturer

MFR

MFR TotalFleetSales

TechCost ModelSales AvgVehicleCost 











=

*

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 NOI, 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, 
MY 2017 and Later - Regulatory Impact Analysis 

3-17 

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 = 

  

 ! "#$ % & '( )*+, -

.. /*+,0 -1( & 

' )+2234 5672

-+*834 934:74: 35 5672 & 

;.<= 8:6 >7* ?@A

B47*)C 934:74: 35 )+032D47 8:6EE 

 
Chapter 3 

3-18 

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 actual price in 

the U.S. for the first four months of 2012 for regular gasoline was $3.68 per gallonFF with 

prices approaching $4.00 in March and April.GG 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.HH 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. 

GG 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-19 

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.II 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,JJ

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 (EPA420-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)” 

 

II Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 through 

2010, November 2010, available at www.epa.gov/otaq/fetrends.htm. 

JJ There are no EPA LD GHG emissions regulations prior to MY 2012. 
Chapter 3 

Table 3.4-4 Fuel Economy Data for Selected Manufacturers, 1986-2003—Cars

Year Standard GM Ford Chrysler 

SalesWeighted 

average 

Delta Vehicle Weight Toyota Honda Nissan Sales- Weighted average Delta 

1986 27.5 27.0 26.7 28.6 27.1 -0.4 3145 32.3 33.6 29.9 32.0 4.5 1987 27.5 27.2 26.5 27.7 27.1 -0.4 3149 32.9 32.8 29.3 31.5 4.0 1988 27.5 28.1 27.0 28.5 27.8 0.3 3157 32.7 31.8 30.6 31.8 4.3 1989 27.5 27.4 26.9 28.0 27.3 -0.2 3207 31.8 31.3 30.2 31.2 3.7 1990 27.5 27.3 26.3 27.4 27.0 -0.5 3298 30.4 30.4 28.4 29.9 2.4 1991 27.5 27.2 27.2 27.5 27.2 -0.3 3252 30.6 30.3 29.0 30.1 2.6 1992 27.5 26.7 26.7 27.7 26.8 -0.7 3329 28.9 30.9 29.9 29.9 2.4 1993 27.5 27.3 27.8 27.9 27.6 0.1 3269 29.0 32.2 29.1 30.1 2.6 1994 27.5 27.5 27.1 26.2 27.2 -0.3 3334 29.1 32.1 29.8 30.3 2.8 1995 27.5 27.3 27.6 28.2 27.6 0.1 3330 30.0 32.8 29.2 30.8 3.3 1996 27.5 27.9 26.3 27.2 27.3 -0.2 3388 29.5 31.8 30.2 30.5 3.0 1997 27.5 28.2 26.9 27.2 27.6 0.1 3353 29.8 32.1 29.6 30.6 3.1 1998 27.5 27.6 27.3 28.3 27.6 0.1 3347 30.2 32.0 30.2 30.9 3.4 1999 27.5 27.4 27.2 27.0 27.3 -0.2 3429 30.4 30.9 29.6 30.4 2.9 2000 27.5 27.6 27.1 27.6 27.4 -0.1 3448 30.5 31.0 28.0 30.2 2.7 2001 27.5 28.1 26.8 27.6 27.6 0.1 3463 31.3 32.2 28.3 31.0 3.5 2002 27.5 28.5 27.1 27.0 27.8 0.3 3442 30.7 32.0 28.9 30.8 3.3 2003 27.5 28.6 26.7 28.5 27.9 0.4 3506 32.4 32.7 27.9 31.5 4.0 

Average 1986-2003 -0.1 3.3 

Table 3.4-5 Fuel Economy Data for Selected Manufacturers, 1986-2003—Trucks 
MY 2017 and Later - Regula

Year Standard GM Ford Chrysler 

SalesWeighted 

average 

Delta Vehicle Weight Toyota Honda Nissan Sales- Weighted average Delta 

1986 20.0 20.2 20.3 20.7 20.3 0.3 3917 26.1 24.7 25.5 5.5 1987 20.5 20.5 20.5 21.3 20.7 0.2 3876 25.9 23.5 24.9 4.4 1988 20.5 20.2 20.6 21.4 20.6 0.1 3961 24.4 22.7 23.8 3.3 1989 20.5 20.4 20.1 21.0 20.5 0.0 4016 23.2 23.7 23.3 2.8 1990 20.0 19.8 20.2 21.4 20.3 0.3 4102 21.8 25.3 23.2 3.2 1991 20.2 21.2 20.5 21.1 20.9 0.7 4026 22.4 24.8 23.1 2.9 1992 20.2 20.3 20.2 21.3 20.5 0.3 4132 21.9 24.0 22.5 2.3 1993 20.4 20.3 20.8 21.2 20.7 0.3 4141 22.1 23.7 22.7 2.3 1994 20.5 20.2 20.8 20.5 20.5 0.0 4204 22.0 20.2 22.9 22.3 1.8 1995 20.6 20.1 20.6 20.1 20.3 -0.3 4248 21.2 25.5 22.4 22.0 1.4 1996 20.7 20.8 20.8 20.2 20.6 -0.1 4295 23.1 22.2 22.9 23.0 2.3 1997 20.7 20.4 20.2 20.2 20.3 -0.4 4445 22.6 24.7 22.3 22.8 2.1 1998 20.7 21.2 20.2 20.0 20.5 -0.2 4376 23.4 25.5 22.3 23.5 2.8 1999 20.7 20.3 19.8 19.9 20.0 -0.7 4508 23.0 25.2 21.2 23.1 2.4 2000 20.7 20.7 20.0 20.4 20.4 -0.3 4456 22.0 25.0 20.8 22.2 1.5 2001 20.7 20.4 20.1 19.5 20.0 -0.7 4591 22.3 24.7 20.7 22.3 1.6 2002 20.7 19.8 20.2 20.0 20.0 -0.7 4686 22.2 25.3 20.7 22.5 1.8 2003 20.7 20.2 20.0 20.9 20.3 -0.4 4738 22.0 24.8 21.9 22.9 2.2 

Average 1986-2003 -0.1 2.6 
Chapter 3 

3-22 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-23 

improvement in their primary analyses, but we note that NHTSA chose to analyze an 

alternative market-driven baseline as a sensitivity case in their RIA.

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) 

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 

-40%

-20%

0%

20%

40%

60%

80%

12.0

13.0

14.0

15.0

16.0

17.0

18.0

19.0

20.0

21.0

22.0

23.0

24.0

1975 1980 1985 1990 1995 2000 2005 2010 2015

Weight / Horsepower changes relative to 1975

Vehicle miles per gallon (adjusted)

MPG

weight

horsepower

22.0 mpg

22.5 mpg

102 hp 

3203 lbs

220 hp

4009 lb
Chapter 3 

3-24 

technology choices were made during MY 2016, OMEGA was run with MY 2016 costs, 

which were then post-processed to the proper cost-year. 

Consistent with the proposal and 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 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-25 

Table 3.4-6 – MY 2016 EV+PHEV Penetrations, and additional potential additional 

costs in MY 20161,2 

 Manufacturer MY 2016 

Shortfall 

without 

EV/PHEV

(g/mile) 

MY 2016 

Shortfall 

with 

EV/PHEV

(g/mile) 

Reference 

Cost 

Delta 

added by 

including 

EVs 

($) 

EV+PHEV 

(% of MY 

2016 Sales 

if added) 

BMW 3 - -$89 3% 

Daimler 19 - $1,447 7% 

Geely-Volvo 20 - $1,846 8% 

PorscheOO 46 18 $2,195 11% 

Tata/ JLR 25 - $2,215 9% 

Volkswagen 14 - $803 6% 

1

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.QQ 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. 

 

OO 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. 
Chapter 3 

3-26 

With respect to car-truck trading, the OMEGA model facilitates the trading of cartruck credits on a total lifetime CO2 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 

CO2 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 CO2 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 CO2 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.1 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-27 

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-truck 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 trucks. 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 CO2 emission standard applicable for each manufacturer and its 

vehicle class (i.e., car or truck). As the reference case, the final rule, and all alternatives allow 

for averaging across a manufacturer’s car and truck fleets, the model determines the CO2

emission standard applicable to each manufacturer’s car and truck 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.SS 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).TT 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. 
Chapter 3 

3-28 

The CO2 emission reduction is the change in CO2 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 

FGHIJJKLMNJO 

∆QRSTFGHI U ∆VW

∆FX$Y ZKQ[\O][^

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 

TARF 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 TARF 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 TARF 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-29 

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.WW

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 CO2 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 

CO2 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 Car 

Target 

Truck 

Target 

Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved Shortfall 

Aston Martin 222 -- 222 222 346 -- 123 

BMW 228 285 243 245 237 287 6 

Chrysler/Fiat 230 295 259 261 227 297 0 

Daimler 234 301 250 252 253 324 21 

Ferrari 235 0 235 235 399 0 165 

Ford 230 305 256 258 232 302 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. 
Chapter 3 

3-30 

Geely 232 280 247 248 247 306 19 

General Motors 226 308 267 270 225 309 0 

Honda 223 283 241 243 222 285 0 

Hyundai 223 280 234 236 223 279 0 

Kia 218 291 235 237 223 279 0 

Lotus 206 -- 206 206 240 -- 34 

Mazda 220 276 230 231 224 262 0 

Mitsubishi 219 270 237 238 223 261 0 

Nissan 226 294 247 249 222 302 0 

PorscheXX 206 287 225 227 250 335 45 

Spyker 219 280 227 229 248 319 31 

Subaru 211 258 222 224 221 231 0 

Suzuki 208 272 219 221 209 265 0 

Tata 250 273 261 262 248 330 30 

Tesla 206 -- 206 206 0 -- 0 

Toyota 221 294 250 252 216 300 0 

Volkswagen 217 296 233 235 225 329 14 

Fleet 224 296 250 252 224 300 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-31 

Table 3.5-2 Reference Case Targets and Projected Shortfall in MY 2025 

Manufacturer Car 

Target 

Truck 

Target 

Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved Shortfall 

Aston Martin 222 - 222 222 346 - 123 

BMW 228 286 243 245 237 289 7 

Chrysler/Fiat 229 294 257 259 227 296 - 

Daimler 234 302 249 251 254 324 21 

Ferrari 235 - 235 235 399 - 165 

Ford 230 303 253 255 232 299 - 

Geely 232 280 246 248 247 306 19 

General 

Motors 226 307 264 267 225 307 - 

Honda 223 283 240 242 221 285 - 

Hyundai 223 280 234 235 223 279 - 

Kia 218 292 234 236 222 278 - 

Lotus 206 - 206 206 240 - 34 

Mazda 220 277 230 231 223 263 - 

Mitsubishi 219 270 236 238 223 261 - 

Nissan 227 292 246 248 222 301 - 

Porsche 206 287 224 226 250 335 45 

Spyker 219 280 227 228 248 319 30 

Subaru 211 258 222 223 220 230 - 

Suzuki 208 272 219 220 209 265 - 

Tata 250 273 261 261 248 330 28 

Tesla 206 - 206 206 - - - 

Toyota 221 293 247 250 215 302 - 

Volkswagen 217 296 233 235 225 329 13 

Fleet 224 295 248 250 224 299 1 

 
Chapter 3 

3-32 

3.5.1.1 Final rule and Alternatives 

Table 3.5-3 Final rule Targets and Projected Shortfall in MY 2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 171 - 171 171 192 - 21 

BMW 175 236 191 193 180 225 - 

Chrysler/Fiat 176 246 208 211 183 239 - 

Daimler 180 253 198 200 176 262 - 

Ferrari 181 - 181 181 227 - 46 

Ford 177 261 205 208 189 240 - 

Geely 178 231 195 196 174 237 - 

General Motors 174 262 217 221 187 249 - 

Honda 171 234 190 192 177 221 - 

Hyundai 171 231 183 184 175 215 - 

Kia 167 243 184 186 177 214 - 

Lotus 157 - 157 157 156 - - 

Mazda 169 227 179 180 176 198 - 

Mitsubishi 168 220 186 188 182 197 - 

Nissan 174 248 197 199 179 238 - 

Porsche 157 238 176 178 148 263 - 

Spyker 168 230 177 178 163 257 - 

Subaru 161 207 172 174 175 167 - 

Suzuki 158 222 170 171 164 199 - 

Tata 193 223 208 209 153 256 - 

Tesla 157 - 157 157 - - - 

Toyota 170 247 200 202 172 242 - 

Volkswagen 166 248 183 185 163 259 - 

Fleet 172 250 199 202 178 239 - 

 
MY 2017 and Later - Regulatory Impact Analysis 

3-33 

Table 3.5-4 Final rule Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 142 - 142 142 142 - - 

BMW 146 194 159 160 144 199 - 

Chrysler/Fiat 146 201 170 172 154 191 - 

Daimler 150 208 163 165 140 233 - 

Ferrari 150 - 150 150 168 - 17 

Ford 147 212 167 169 157 192 - 

Geely 148 189 160 162 138 207 - 

General Motors 144 213 177 180 156 202 - 

Honda 142 191 156 158 145 183 - 

Hyundai 142 188 151 152 146 172 - 

Kia 139 199 152 153 145 177 - 

Lotus 131 - 131 131 130 - - 

Mazda 140 186 148 149 145 163 - 

Mitsubishi 139 180 153 154 146 166 - 

Nissan 145 202 162 163 149 191 - 

Porsche 131 195 144 146 118 231 - 

Spyker 139 188 146 147 132 231 - 

Subaru 134 169 142 143 145 138 - 

Suzuki 132 181 140 141 133 174 - 

Tata 161 182 171 171 114 228 - 

Tesla 131 - 131 131 - - - 

Toyota 141 201 163 165 146 193 - 

Volkswagen 138 203 151 152 131 228 - 

Fleet 143 203 163 165 147 194 - 

 
Chapter 3 

3-34 

Table 3.5-5 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY 

2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 171 - 171 171 192 - 21 

BMW 175 256 197 199 188 225 - 

Chrysler/Fiat 176 267 217 221 195 248 - 

Daimler 180 273 203 206 184 263 - 

Ferrari 181 - 181 181 227 - 46 

Ford 177 282 213 216 196 249 - 

Geely 178 250 201 203 184 237 - 

General Motors 174 283 228 232 198 260 - 

Honda 171 253 196 199 182 231 - 

Hyundai 171 250 187 189 181 215 - 

Kia 167 263 189 191 181 222 - 

Lotus 157 - 157 157 156 - - 

Mazda 169 245 182 184 179 204 - 

Mitsubishi 168 238 192 195 185 209 - 

Nissan 174 267 203 206 186 244 - 

Porsche 157 258 181 184 155 263 - 

Spyker 168 249 179 181 166 257 - 

Subaru 161 225 176 178 177 180 - 

Suzuki 158 241 173 175 169 199 - 

Tata 193 242 217 219 171 260 - 

Tesla 157 - 157 157 - - - 

Toyota 170 266 207 211 180 251 - 

Volkswagen 166 268 187 189 169 259 - 

Fleet 172 270 206 210 185 247 - 

 
MY 2017 and Later - Regulatory Impact Analysis 

3-35 

Table 3.5-6 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY 

2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 171 - 171 171 192 - 21 

BMW 175 217 186 188 172 224 - 

Chrysler/Fiat 176 227 199 201 178 225 - 

Daimler 180 232 193 194 168 262 - 

Ferrari 181 - 181 181 227 - 46 

Ford 177 240 198 200 183 229 - 

Geely 178 212 189 190 165 236 - 

General Motors 174 241 207 209 177 237 - 

Honda 171 215 184 186 171 216 - 

Hyundai 171 212 179 180 170 210 - 

Kia 167 223 180 181 170 214 - 

Lotus 157 - 157 157 156 - - 

Mazda 169 208 176 177 172 193 - 

Mitsubishi 168 202 180 181 172 195 - 

Nissan 174 228 191 192 171 232 - 

Porsche 157 219 172 173 142 262 - 

Spyker 168 212 174 175 159 257 - 

Subaru 161 190 168 169 170 167 - 

Suzuki 158 204 167 168 160 199 - 

Tata 193 205 199 199 134 256 - 

Tesla 157 - 157 157 - - - 

Toyota 170 227 192 194 170 226 - 

Volkswagen 166 228 179 180 157 259 - 

Fleet 172 229 192 194 172 229 - 

 
Chapter 3 

3-36 

Table 3.5-7 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY 

2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved Shortfall 

Aston Martin 190 - 190 190 192 - 1 

BMW 196 236 206 208 200 226 - 

Chrysler/Fiat 197 246 219 221 195 248 - 

Daimler 201 253 214 215 196 263 - 

Ferrari 202 - 202 202 227 - 25 

Ford 197 261 219 221 201 255 - 

Geely 199 231 209 210 194 237 - 

General Motors 194 262 227 230 198 258 - 

Honda 190 234 204 205 187 240 - 

Hyundai 190 231 199 200 191 228 - 

Kia 187 243 199 201 188 240 - 

Lotus 176 - 176 176 175 - - 

Mazda 188 227 195 196 191 216 - 

Mitsubishi 187 220 199 200 190 216 - 

Nissan 194 248 211 212 191 254 - 

Porsche 176 238 190 192 166 264 - 

Spyker 187 230 193 194 181 257 - 

Subaru 180 207 187 187 187 187 - 

Suzuki 177 222 185 186 179 215 - 

Tata 215 223 219 219 171 260 - 

Tesla 176 - 176 176 - - - 

Toyota 189 247 212 214 180 258 - 

Volkswagen 185 248 198 200 182 260 - 

Fleet 192 250 212 214 190 251 - 

 
MY 2017 and Later - Regulatory Impact Analysis 

3-37 

Table 3.5-8 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car Achieved Truck Achieved 

Shortfall 

Aston Martin 151 - 151 151 192 - 41 

BMW 155 236 177 179 160 224 - 

Chrysler/Fiat 156 246 197 200 178 223 - 

Daimler 159 253 182 185 154 262 - 

Ferrari 160 - 160 160 227 - 67 

Ford 157 261 192 195 177 227 - 

Geely 158 231 180 183 155 236 - 

General Motors 154 262 207 211 178 239 - 

Honda 151 234 177 179 163 211 - 

Hyundai 151 231 167 169 162 193 - 

Kia 148 243 169 172 161 204 - 

Lotus 139 - 139 139 139 - - 

Mazda 149 227 163 165 160 183 - 

Mitsubishi 148 220 173 176 165 192 - 

Nissan 154 248 183 186 166 224 - 

Porsche 139 238 162 165 139 258 5 

Spyker 148 230 160 161 143 255 - 

Subaru 142 207 158 160 159 159 - 

Suzuki 140 222 155 156 146 198 - 

Tata 171 223 197 199 132 256 - 

Tesla 139 - 139 139 - - - 

Toyota 150 247 188 191 166 225 - 

Volkswagen 147 248 167 170 144 257 - 

Fleet 152 250 186 190 166 227 - 

 
Chapter 3 

3-38 

Table 3.5-9 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 142 - 142 142 142 - - 

BMW 146 213 164 166 152 199 - 

Chrysler/Fiat 146 221 179 181 163 202 - 

Daimler 150 228 168 170 148 233 - 

Ferrari 150 - 150 150 168 - 17 

Ford 147 232 173 176 162 202 - 

Geely 148 207 166 168 149 207 - 

General Motors 144 234 187 190 163 216 - 

Honda 142 210 162 164 149 194 - 

Hyundai 142 207 155 156 149 177 - 

Kia 139 218 156 158 148 187 - 

Lotus 131 - 131 131 130 - - 

Mazda 140 204 151 152 149 163 - 

Mitsubishi 139 198 159 161 154 171 - 

Nissan 145 221 167 170 153 204 - 

Porsche 131 214 149 151 125 231 - 

Spyker 139 207 148 149 135 231 - 

Subaru 134 186 146 147 149 142 - 

Suzuki 132 200 143 145 138 174 - 

Tata 161 200 179 181 128 231 - 

Tesla 131 - 131 131 - - - 

Toyota 141 221 170 173 152 204 - 

Volkswagen 138 223 155 157 137 228 - 

Fleet 143 223 170 172 153 205 - 
MY 2017 and Later - Regulatory Impact Analysis 

3-39 

Table 3.5-10 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 142 - 142 142 142 - - 

BMW 146 174 153 154 136 199 - 

Chrysler/Fiat 146 181 161 163 143 185 - 

Daimler 150 187 158 159 134 233 - 

Ferrari 150 - 150 150 168 - 17 

Ford 147 191 161 162 148 189 - 

Geely 148 170 155 155 130 207 - 

General Motors 144 192 167 169 142 193 - 

Honda 142 172 151 152 140 176 - 

Hyundai 142 170 147 148 142 168 - 

Kia 139 179 147 148 141 171 - 

Lotus 131 - 131 131 130 - - 

Mazda 140 167 145 145 142 161 - 

Mitsubishi 139 162 147 148 137 166 - 

Nissan 145 182 156 157 141 190 - 

Porsche 131 175 140 141 113 231 - 

Spyker 139 170 143 144 129 231 - 

Subaru 134 152 138 139 139 138 - 

Suzuki 132 163 137 138 129 172 - 

Tata 161 164 162 162 97 226 - 

Tesla 131 - 131 131 - - - 

Toyota 141 181 156 157 139 184 - 

Volkswagen 138 183 147 148 125 228 - 

Fleet 143 183 156 158 139 188 - 

 
Chapter 3 

3-40 

Table 3.5-11 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 162 - 162 162 162 - - 

BMW 166 194 173 174 163 199 - 

Chrysler/Fiat 166 201 181 183 163 206 - 

Daimler 171 208 179 180 161 233 - 

Ferrari 171 - 171 171 171 - - 

Ford 168 212 181 183 168 209 - 

Geely 169 189 175 175 159 207 - 

General Motors 164 213 188 189 163 214 - 

Honda 162 191 170 171 158 196 - 

Hyundai 162 188 167 168 159 194 - 

Kia 158 199 167 168 160 193 - 

Lotus 149 - 149 149 149 - - 

Mazda 160 186 164 165 161 180 - 

Mitsubishi 159 180 166 166 162 175 - 

Nissan 165 202 176 177 159 211 - 

Porsche 149 195 159 160 137 231 - 

Spyker 159 188 163 163 151 231 - 

Subaru 153 169 156 157 157 156 - 

Suzuki 150 181 155 156 152 175 - 

Tata 183 182 183 183 134 231 - 

Tesla 149 - 149 149 - - - 

Toyota 161 201 175 177 156 207 - 

Volkswagen 157 203 166 168 150 228 - 

Fleet 163 203 176 178 160 207 - 
MY 2017 and Later - Regulatory Impact Analysis 

3-41 

Table 3.5-12 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 122 - 122 122 139 - 17 

BMW 126 194 144 146 123 199 - 

Chrysler/Fiat 126 201 158 161 141 184 - 

Daimler 129 208 147 149 120 233 - 

Ferrari 130 - 130 130 168 - 38 

Ford 127 212 153 156 139 188 - 

Geely 128 189 146 148 119 207 - 

General Motors 124 213 167 170 146 193 - 

Honda 122 191 143 145 133 168 - 

Hyundai 122 188 135 137 129 165 - 

Kia 120 199 136 138 129 167 - 

Lotus 112 - 112 112 111 - - 

Mazda 121 186 131 133 127 156 - 

Mitsubishi 120 180 140 142 128 166 - 

Nissan 125 202 147 150 132 186 - 

Porsche 112 195 130 132 103 224 - 

Spyker 120 188 129 130 112 231 - 

Subaru 115 169 127 129 130 126 - 

Suzuki 113 181 125 126 114 172 - 

Tata 139 182 159 160 97 223 - 

Tesla 112 - 112 112 - - - 

Toyota 121 201 150 153 137 178 - 

Volkswagen 119 203 135 137 112 228 - 

Fleet 123 203 150 152 133 185 - 

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 
Chapter 3 

3-42 

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 Meaning

Mass Tech Applied Mass Technology Applied, expressed as a negative 

number 

True Mass Net Mass Reduced 

Mass Penalty Mass increase due to technology 

TDS18/24/27 turbocharged & downsized at 18/24/27 bar BMEP 

AT6/8 Automatic transmission 

DCT6/8 Dual Clutch Transmission 

MT Manual transmission 

HEG High Efficiency Gearbox 

EGR Cooled exhaust gas recirculation 

HEV Hybrid electric vehicle 

EV Full electric vehicle 

PHEV Plug-in HEV 

SS 12V stop-start 

LRRT2 Lower rolling resistance tires level 2 

IACC2 Improved Accesssories level 2 

EFR2 Engine friction reduction level 2 

DI Stoichiometric gasoline direct injection 

DSL Advanced diesel 

25 Oates, 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 See 75 FR at 25457. 

27 See OMEGA documentation at http://www.epa.gov/otaq/climate/models.htm. 
MY 2017 and Later - Regula

3-43 

3.5.3 Projected Technology Penetrations in Reference Case 

Table 3.5-14 Reference Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -8% 1% 40% 15% 0% 0% 0% 60% 24% 16% 0% 15% 15% 0% 0% 55% 0% 30% 0% BMW -6% -6% 1% 45% 15% 0% 12% 0% 48% 26% 13% 0% 15% 15% 0% 0% 55% 0% 30% 0% Chrysler/Fiat -5% -5% 0% 56% 14% 0% 5% 1% 52% 28% 3% 0% 2% 0% 0% 0% 0% 0% 30% 0% Daimler -7% -6% 1% 40% 15% 0% 0% 38% 28% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0% Ferrari -4% -3% 1% 40% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 55% 0% 30% 0% Ford -5% -5% 0% 64% 15% 0% 22% 9% 36% 19% 7% 0% 10% 2% 0% 0% 0% 0% 30% 0% Geely -6% -6% 1% 52% 15% 0% 13% 4% 46% 25% 3% 0% 15% 15% 0% 0% 57% 0% 30% 0% General Motors -5% -5% 0% 47% 11% 0% 6% 2% 52% 26% 6% 0% 2% 0% 0% 0% 0% 0% 30% 0% Honda -1% -1% 0% 0% 0% 0% 0% 0% 50% 22% 12% 0% 0% 3% 0% 0% 0% 0% 2% 0% Hyundai -2% -2% 0% 28% 0% 0% 14% 7% 37% 20% 7% 0% 0% 0% 0% 0% 0% 0% 8% 0% Kia -1% -1% 0% 7% 0% 0% 5% 2% 46% 25% 9% 0% 0% 0% 0% 0% 0% 0% 2% 0% Lotus -1% 0% 1% 52% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0% Mazda -3% -3% 0% 47% 12% 0% 13% 5% 37% 20% 17% 0% 3% 0% 0% 0% 0% 0% 30% 0% Mitsubishi -5% -4% 0% 71% 15% 0% 14% 5% 42% 22% 8% 0% 15% 0% 0% 0% 12% 0% 30% 0% Nissan -2% -2% 0% 22% 8% 0% 3% 1% 49% 27% 5% 0% 1% 1% 0% 0% 0% 0% 30% 0% Porsche -2% -2% 1% 43% 15% 0% 0% 0% 28% 10% 56% 0% 15% 15% 0% 0% 55% 0% 30% 0% Spyker -8% -8% 1% 55% 15% 0% 2% 0% 49% 26% 13% 0% 15% 15% 0% 0% 55% 0% 30% 0% Subaru -3% -3% 0% 72% 15% 0% 2% 0% 42% 22% 25% 0% 15% 0% 0% 0% 0% 0% 30% 0% Suzuki 0% 0% 0% 70% 15% 0% 4% 2% 45% 25% 12% 0% 15% 0% 0% 0% 0% 0% 30% 0% Tata -8% -8% 1% 40% 15% 0% 14% 0% 55% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0% Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -1% -1% 0% 3% 0% 0% 5% 3% 50% 11% 7% 0% 0% 15% 0% 0% 0% 0% 1% 0% Volkswagen -4% -4% 1% 46% 15% 0% 9% 0% 51% 25% 14% 0% 15% 15% 0% 0% 55% 0% 30% 0% Fleet -3% -3% 0% 32% 8% 0% 8% 4% 46% 21% 8% 0% 4% 6% 0% 0% 8% 0% 19% 0% 
Chapter 3 

3-44 

Table 3.5-15 Reference Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -8% -7% 1% 67% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 65% 0% 30% 0Chrysler/Fiat -6% -6% 0% 22% 15% 0% 65% 28% 2% 1% 3% 0% 15% 0% 0% 0% 0% 0% 30% 0Daimler -9% -8% 1% 56% 13% 0% 0% 100% 0% 0% 0% 0% 13% 15% 0% 0% 62% 0% 30% 0Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -6% -6% 0% 66% 15% 0% 59% 26% 4% 2% 3% 0% 15% 2% 0% 0% 2% 0% 29% 0Geely -9% -8% 1% 68% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 68% 0% 30% 0General Motors -7% -7% 0% 33% 15% 0% 66% 29% 1% 0% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Honda -3% -3% 0% 62% 0% 0% 39% 22% 15% 8% 0% 0% 0% 0% 0% 0% 0% 0% 11% 0Hyundai -4% -4% 0% 85% 0% 0% 59% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 15% 0Kia -4% -4% 0% 84% 0% 0% 54% 30% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 15% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -8% -8% 0% 64% 15% 0% 47% 20% 17% 9% 2% 0% 11% 0% 0% 0% 0% 0% 30% 0Mitsubishi -9% -8% 0% 70% 15% 0% 51% 26% 7% 4% 0% 0% 15% 0% 0% 0% 61% 0% 30% 0Nissan -4% -4% 0% 65% 12% 0% 44% 24% 11% 6% 2% 0% 12% 0% 0% 0% 0% 0% 30% 0Porsche -8% -8% 1% 64% 15% 0% 69% 30% 0% 0% 1% 0% 15% 15% 0% 0% 62% 0% 30% 0Spyker -3% -2% 1% 70% 15% 0% 61% 30% 0% 0% 0% 0% 15% 9% 0% 0% 70% 0% 30% 0Subaru -9% -8% 0% 70% 15% 0% 17% 9% 33% 18% 8% 0% 15% 0% 0% 0% 21% 0% 30% 0Suzuki -7% -7% 0% 70% 15% 0% 55% 30% 0% 0% 0% 0% 15% 0% 0% 0% 82% 0% 30% 0Tata -6% -5% 1% 63% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 60% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -2% -2% 0% 47% 0% 0% 47% 25% 5% 3% 3% 0% 0% 5% 0% 0% 0% 0% 12% 0Volkswagen -8% -8% 1% 67% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 67% 0% 30% 0Fleet -5% -5% 0% 50% 9% 0% 55% 28% 5% 3% 2% 0% 9% 3% 0% 0% 7% 0% 23% 0
MY 2017 and Later - Regula

3-45 

Table 3.5-16 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -8% 1% 40% 15% 0% 0% 0% 60% 24% 16% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -7% -6% 1% 51% 15% 0% 27% 8% 35% 19% 9% 0% 15% 15% 0% 0% 57% 0% 30% 0Chrysler/Fiat -6% -6% 0% 41% 15% 0% 32% 13% 29% 16% 3% 0% 8% 0% 0% 0% 0% 0% 30% 0Daimler -8% -7% 1% 44% 14% 0% 0% 54% 21% 22% 0% 0% 14% 15% 0% 0% 57% 0% 30% 0Ferrari -4% -3% 1% 40% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -6% -6% 0% 65% 15% 0% 35% 15% 25% 13% 6% 0% 11% 2% 0% 0% 1% 0% 29% 0Geely -7% -6% 1% 57% 15% 0% 31% 12% 32% 17% 2% 0% 15% 15% 0% 0% 61% 0% 30% 0General Motors -6% -6% 0% 40% 13% 0% 36% 16% 27% 14% 3% 0% 8% 0% 0% 0% 0% 0% 30% 0Honda -2% -2% 0% 19% 0% 0% 12% 7% 39% 18% 8% 0% 0% 2% 0% 0% 0% 0% 5% 0Hyundai -2% -2% 0% 40% 0% 0% 23% 12% 30% 16% 6% 0% 0% 0% 0% 0% 0% 0% 9% 0Kia -2% -2% 0% 24% 0% 0% 16% 9% 35% 19% 7% 0% 0% 0% 0% 0% 0% 0% 5% 0Lotus -1% 0% 1% 52% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -4% -4% 0% 50% 13% 0% 19% 7% 33% 18% 14% 0% 4% 0% 0% 0% 0% 0% 30% 0Mitsubishi -6% -6% 0% 70% 15% 0% 27% 12% 30% 16% 5% 0% 15% 0% 0% 0% 29% 0% 30% 0Nissan -3% -3% 0% 35% 9% 0% 15% 8% 37% 20% 4% 0% 4% 1% 0% 0% 0% 0% 30% 0Porsche -4% -3% 1% 48% 15% 0% 16% 7% 22% 8% 43% 0% 15% 15% 0% 0% 57% 0% 30% 0Spyker -8% -7% 1% 57% 15% 0% 10% 4% 42% 22% 11% 0% 15% 14% 0% 0% 57% 0% 30% 0Subaru -5% -5% 0% 72% 15% 0% 6% 2% 40% 21% 21% 0% 15% 0% 0% 0% 5% 0% 30% 0Suzuki -1% -1% 0% 70% 15% 0% 13% 7% 37% 20% 10% 0% 15% 0% 0% 0% 15% 0% 30% 0Tata -7% -6% 1% 51% 15% 0% 42% 15% 28% 15% 0% 0% 15% 15% 0% 0% 58% 0% 30% 0Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -2% -2% 0% 20% 0% 0% 21% 12% 32% 8% 5% 0% 0% 12% 0% 0% 0% 0% 5% 0Volkswagen -5% -4% 1% 50% 15% 0% 22% 6% 40% 20% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0Fleet -4% -4% 0% 39% 8% 0% 24% 12% 32% 15% 6% 0% 6% 5% 0% 0% 7% 0% 21% 0
Chapter 3 

3-46 

Table 3.5-17 Reference Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -8% 1% 40% 15% 0% 0% 0% 60% 24% 16% 0% 15% 15% 0% 0% 55% 0% 30% 0% BMW -6% -6% 1% 45% 15% 0% 12% 0% 48% 26% 13% 0% 15% 15% 0% 0% 55% 0% 30% 0% Chrysler/Fiat -5% -5% 0% 51% 14% 0% 4% 1% 52% 28% 3% 0% 1% 0% 0% 0% 0% 0% 30% 0% Daimler -7% -7% 1% 40% 15% 0% 0% 39% 28% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0% Ferrari -4% -3% 1% 40% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 55% 0% 30% 0% Ford -5% -5% 0% 64% 15% 0% 23% 9% 35% 19% 7% 0% 11% 1% 0% 0% 0% 0% 30% 0% Geely -6% -6% 1% 51% 15% 0% 13% 4% 46% 25% 3% 0% 15% 15% 0% 0% 57% 0% 30% 0% General Motors -5% -5% 0% 47% 11% 0% 5% 2% 52% 26% 6% 0% 2% 0% 0% 0% 0% 0% 30% 0% Honda -1% -1% 0% 0% 0% 0% 0% 0% 50% 22% 12% 0% 0% 3% 0% 0% 0% 0% 2% 0% Hyundai -2% -2% 0% 28% 0% 0% 13% 7% 38% 21% 7% 0% 0% 0% 0% 0% 0% 0% 6% 0% Kia -1% -1% 0% 6% 0% 0% 4% 2% 52% 19% 9% 0% 0% 0% 0% 0% 0% 0% 2% 0% Lotus -1% 0% 1% 52% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0% Mazda -3% -3% 0% 46% 12% 0% 13% 4% 37% 20% 18% 0% 3% 0% 0% 0% 0% 0% 30% 0% Mitsubishi -4% -4% 0% 71% 15% 0% 13% 5% 42% 22% 8% 0% 15% 0% 0% 0% 11% 0% 30% 0% Nissan -2% -2% 0% 22% 8% 0% 3% 1% 49% 27% 5% 0% 1% 1% 0% 0% 0% 0% 30% 0% Porsche -2% -2% 1% 43% 15% 0% 0% 0% 28% 10% 56% 0% 15% 15% 0% 0% 55% 0% 30% 0% Spyker -8% -8% 1% 55% 15% 0% 2% 0% 49% 26% 13% 0% 15% 15% 0% 0% 55% 0% 30% 0% Subaru -4% -3% 0% 72% 15% 0% 2% 0% 42% 22% 25% 0% 15% 0% 0% 0% 0% 0% 30% 0% Suzuki 0% 0% 0% 70% 15% 0% 3% 2% 45% 25% 12% 0% 15% 0% 0% 0% 0% 0% 30% 0% Tata -9% -8% 1% 40% 15% 0% 14% 0% 55% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0% Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -1% -1% 0% 3% 0% 0% 5% 3% 50% 11% 7% 0% 0% 16% 0% 0% 0% 0% 1% 0% Volkswagen -4% -4% 1% 46% 15% 0% 9% 0% 51% 25% 14% 0% 15% 15% 0% 0% 55% 0% 30% 0% Fleet -3% -3% 0% 32% 8% 0% 8% 4% 46% 21% 8% 0% 5% 6% 0% 0% 8% 0% 19% 0% 
MY 2017 and Later - Regula

3-47 

Table 3.5-18 Reference Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -8% -7% 1% 67% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 64% 0% 30% 0Chrysler/Fiat -6% -6% 0% 23% 15% 0% 65% 28% 2% 1% 3% 0% 15% 0% 0% 0% 0% 0% 30% 0Daimler -9% -8% 1% 56% 13% 0% 0% 100% 0% 0% 0% 0% 13% 15% 0% 0% 62% 0% 30% 0Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -6% -6% 0% 66% 15% 0% 59% 26% 4% 2% 3% 0% 15% 3% 0% 0% 4% 0% 29% 0Geely -8% -8% 1% 68% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 68% 0% 30% 0General Motors -7% -7% 0% 36% 15% 0% 66% 29% 1% 0% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Honda -3% -3% 0% 61% 0% 0% 39% 21% 16% 8% 0% 0% 0% 0% 0% 0% 0% 0% 11% 0Hyundai -4% -4% 0% 85% 0% 0% 59% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 15% 0Kia -4% -4% 0% 84% 0% 0% 54% 30% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 15% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -8% -8% 0% 65% 15% 0% 48% 21% 15% 8% 2% 0% 11% 0% 0% 0% 0% 0% 30% 0Mitsubishi -9% -8% 0% 70% 15% 0% 51% 26% 7% 4% 0% 0% 15% 0% 0% 0% 61% 0% 30% 0Nissan -4% -4% 0% 65% 12% 0% 44% 24% 10% 6% 2% 0% 12% 0% 0% 0% 0% 0% 30% 0Porsche -8% -8% 1% 64% 15% 0% 69% 30% 0% 0% 1% 0% 15% 15% 0% 0% 62% 0% 30% 0Spyker -3% -2% 1% 70% 15% 0% 61% 30% 0% 0% 0% 0% 15% 9% 0% 0% 70% 0% 30% 0Subaru -9% -8% 0% 70% 15% 0% 17% 10% 33% 18% 8% 0% 15% 0% 0% 0% 23% 0% 30% 0Suzuki -7% -7% 0% 70% 15% 0% 55% 30% 0% 0% 0% 0% 15% 0% 0% 0% 82% 0% 30% 0Tata -6% -5% 1% 63% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 60% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -2% -2% 0% 38% 0% 0% 47% 25% 5% 3% 3% 0% 0% 6% 0% 0% 0% 0% 11% 0Volkswagen -8% -8% 1% 67% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 67% 0% 30% 0Fleet -5% -5% 0% 50% 9% 0% 55% 28% 5% 3% 1% 0% 9% 3% 0% 0% 7% 0% 23% 0
Chapter 3 

3-48 

Table 3.5-19 Reference Fleet (Sales-Weighted) Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -8% 1% 40% 15% 0% 0% 0% 60% 24% 16% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -7% -6% 1% 51% 15% 0% 28% 8% 35% 19% 9% 0% 15% 15% 0% 0% 57% 0% 30% 0Chrysler/Fiat -5% -5% 0% 39% 14% 0% 31% 13% 30% 16% 3% 0% 7% 0% 0% 0% 0% 0% 30% 0Daimler -8% -7% 1% 43% 14% 0% 0% 53% 22% 23% 0% 0% 14% 15% 0% 0% 57% 0% 30% 0Ferrari -4% -3% 1% 40% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -6% -6% 0% 65% 15% 0% 34% 14% 26% 13% 6% 0% 12% 2% 0% 0% 1% 0% 29% 0Geely -7% -6% 1% 56% 15% 0% 30% 12% 33% 18% 2% 0% 15% 15% 0% 0% 60% 0% 30% 0General Motors -6% -6% 0% 42% 13% 0% 34% 15% 28% 14% 3% 0% 8% 0% 0% 0% 0% 0% 30% 0Honda -2% -2% 0% 18% 0% 0% 12% 6% 40% 18% 8% 0% 0% 2% 0% 0% 0% 0% 5% 0Hyundai -2% -2% 0% 39% 0% 0% 22% 12% 30% 16% 6% 0% 0% 0% 0% 0% 0% 0% 8% 0Kia -2% -2% 0% 23% 0% 0% 15% 8% 41% 15% 7% 0% 0% 0% 0% 0% 0% 0% 5% 0Lotus -1% 0% 1% 52% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -4% -4% 0% 49% 13% 0% 19% 7% 34% 18% 15% 0% 4% 0% 0% 0% 0% 0% 30% 0Mitsubishi -6% -5% 0% 70% 15% 0% 25% 12% 31% 16% 5% 0% 15% 0% 0% 0% 28% 0% 30% 0Nissan -3% -3% 0% 35% 9% 0% 15% 8% 38% 21% 4% 0% 4% 1% 0% 0% 0% 0% 30% 0Porsche -4% -3% 1% 47% 15% 0% 15% 6% 22% 8% 44% 0% 15% 15% 0% 0% 56% 0% 30% 0Spyker -8% -7% 1% 57% 15% 0% 10% 4% 43% 22% 11% 0% 15% 14% 0% 0% 57% 0% 30% 0Subaru -5% -5% 0% 72% 15% 0% 5% 2% 40% 21% 21% 0% 15% 0% 0% 0% 5% 0% 30% 0Suzuki -1% -1% 0% 70% 15% 0% 12% 7% 37% 20% 10% 0% 15% 0% 0% 0% 14% 0% 30% 0Tata -7% -7% 1% 50% 15% 0% 40% 14% 29% 16% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -2% -2% 0% 15% 0% 0% 20% 11% 34% 8% 5% 0% 0% 12% 0% 0% 0% 0% 5% 0Volkswagen -5% -4% 1% 50% 15% 0% 21% 6% 41% 20% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0Fleet -4% -4% 0% 38% 8% 0% 23% 12% 33% 15% 6% 0% 6% 5% 0% 0% 8% 0% 20% 0
MY 2017 and Later - Regula

3-49 

3.5.4 Projected Technology Penetrations in Final rule case Table 3.5-20 Final rule Car Technology Penetrations in MY 2021

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 60BMW -10% -9% 1% 52% 28% 6% 0% 0% 14% 72% 10% 60% 30% 9% 4% 0% 36% 75% 57% 60Chrysler/Fiat -6% -6% 0% 67% 21% 1% 1% 4% 21% 72% 2% 55% 9% 0% 0% 0% 0% 75% 79% 54Daimler -12% -11% 1% 41% 29% 12% 0% 7% 6% 78% 0% 58% 30% 7% 9% 0% 41% 75% 54% 60Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 60Ford -6% -6% 0% 74% 17% 1% 6% 23% 14% 49% 7% 45% 14% 2% 0% 0% 4% 74% 72% 47Geely -11% -10% 1% 36% 30% 13% 3% 11% 9% 66% 2% 59% 30% 13% 9% 0% 46% 75% 49% 60General Motors -6% -6% 0% 48% 15% 1% 1% 5% 22% 66% 6% 48% 9% 0% 0% 0% 0% 75% 79% 28Honda -2% -2% 0% 15% 5% 0% 0% 0% 21% 64% 12% 10% 0% 3% 0% 0% 0% 73% 77% 5% Hyundai -3% -3% 0% 41% 14% 0% 5% 20% 17% 51% 7% 44% 2% 0% 0% 0% 0% 75% 75% 2Kia -3% -3% 0% 17% 5% 0% 2% 7% 21% 62% 9% 17% 0% 0% 0% 0% 0% 75% 78% 9% Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 57Mazda -4% -4% 0% 72% 28% 0% 3% 13% 14% 54% 16% 55% 28% 0% 0% 0% 3% 75% 76% 55Mitsubishi -6% -6% 0% 71% 29% 0% 3% 14% 16% 59% 8% 58% 29% 0% 0% 0% 7% 75% 75% 58Nissan -3% -3% 0% 42% 19% 0% 1% 4% 21% 69% 5% 48% 9% 1% 0% 0% 0% 74% 78% 32Porsche -6% -2% 4% 4% 28% 15% 0% 0% 3% 56% 29% 59% 30% 25% 12% 15% 34% 75% 36% 59Spyker -14% -12% 2% 20% 30% 15% 0% 0% 8% 72% 8% 58% 30% 22% 12% 2% 46% 75% 49% 59Subaru -6% -5% 1% 71% 29% 0% 0% 0% 15% 64% 20% 60% 29% 0% 0% 0% 5% 75% 74% 58Suzuki -1% 0% 1% 70% 30% 0% 1% 5% 16% 68% 9% 60% 30% 0% 0% 0% 9% 75% 73% 60Tata -16% -13% 3% 13% 30% 15% 0% 0% 10% 77% 0% 57% 30% 25% 13% 4% 35% 75% 40% 57Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 23% 0% 0% 1% 4% 18% 55% 7% 4% 0% 15% 0% 0% 0% 63% 63% 0% Volkswagen -7% -5% 1% 49% 30% 12% 0% 0% 11% 71% 10% 59% 30% 1% 8% 0% 49% 75% 56% 60Fleet -5% -5% 0% 43% 14% 2% 2% 7% 17% 61% 8% 36% 11% 4% 1% 0% 7% 72% 71% 29
Chapter 3 

3-50 

Table 3.5-21 Final rule Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -13% -12% 1% 65% 30% 5% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 57% 6Chrysler/Fiat -7% -6% 0% 24% 19% 3% 19% 75% 1% 3% 3% 60% 21% 0% 0% 0% 5% 75% 72% 3Daimler -15% -14% 1% 51% 28% 10% 11% 89% 0% 0% 0% 60% 29% 0% 0% 0% 65% 75% 61% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -8% -7% 1% 54% 21% 6% 18% 71% 1% 5% 2% 59% 28% 2% 0% 0% 7% 73% 58% 4Geely -15% -14% 1% 64% 30% 6% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 52% 6General Motors -8% -8% 0% 35% 15% 5% 20% 78% 0% 1% 0% 59% 20% 0% 0% 0% 8% 75% 66% 2Honda -7% -7% 0% 61% 18% 0% 14% 57% 7% 21% 0% 60% 3% 0% 0% 0% 0% 75% 66% 2Hyundai -9% -9% 0% 75% 25% 0% 20% 80% 0% 0% 0% 60% 5% 0% 0% 0% 0% 75% 60% 2Kia -8% -8% 0% 75% 25% 0% 20% 79% 0% 0% 1% 60% 5% 0% 0% 0% 0% 75% 60% 2Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -13% -12% 0% 72% 28% 0% 14% 54% 8% 23% 2% 60% 28% 0% 0% 0% 26% 75% 66% 5Mitsubishi -14% -13% 1% 67% 30% 0% 17% 69% 3% 11% 0% 60% 30% 3% 0% 0% 56% 75% 53% 6Nissan -5% -5% 0% 79% 17% 3% 16% 64% 5% 14% 1% 60% 16% 0% 0% 0% 7% 75% 68% 2Porsche -15% -14% 1% 59% 30% 11% 20% 79% 0% 0% 1% 60% 30% 2% 0% 0% 65% 75% 61% 6Spyker -3% -2% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 2% 0% 0% 61% 75% 56% 6Subaru -15% -13% 1% 50% 30% 0% 6% 25% 12% 51% 6% 60% 30% 20% 0% 0% 24% 75% 60% 6Suzuki -11% -11% 1% 70% 30% 0% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 50% 6Tata -10% -9% 1% 58% 30% 12% 20% 80% 0% 0% 0% 60% 30% 22% 0% 0% 65% 75% 63% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -4% -4% 0% 61% 2% 3% 17% 66% 2% 7% 3% 50% 3% 5% 0% 0% 0% 71% 63% 2Volkswagen -14% -13% 1% 63% 30% 7% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 54% 6Fleet -7% -7% 0% 53% 16% 4% 18% 71% 2% 7% 1% 57% 16% 2% 0% 0% 11% 74% 64% 2
MY 2017 and Later - Regula

3-51 

Table 3.5-22 Final rule Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 6BMW -11% -10% 1% 55% 29% 6% 5% 21% 10% 53% 7% 60% 30% 7% 3% 0% 43% 75% 57% 6Chrysler/Fiat -6% -6% 0% 48% 20% 2% 9% 36% 12% 41% 3% 57% 15% 0% 0% 0% 2% 75% 76% 4Daimler -13% -12% 1% 44% 29% 11% 3% 27% 4% 58% 0% 58% 29% 5% 7% 0% 47% 75% 55% 6Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 6Ford -7% -7% 0% 67% 19% 3% 10% 39% 10% 34% 5% 50% 19% 2% 0% 0% 5% 74% 67% 4Geely -12% -11% 1% 45% 30% 11% 8% 32% 6% 46% 1% 59% 30% 9% 6% 0% 52% 75% 50% 6General Motors -7% -7% 0% 41% 15% 3% 10% 41% 11% 34% 3% 53% 14% 0% 0% 0% 4% 75% 72% 2Honda -4% -4% 0% 29% 9% 0% 4% 18% 17% 51% 8% 25% 1% 2% 0% 0% 0% 73% 74% 1Hyundai -5% -4% 0% 48% 17% 0% 8% 32% 13% 41% 6% 48% 2% 0% 0% 0% 0% 75% 72% 2Kia -4% -4% 0% 30% 10% 0% 6% 23% 16% 48% 7% 26% 1% 0% 0% 0% 0% 75% 74% 1Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 5Mazda -5% -5% 0% 72% 28% 0% 5% 20% 13% 49% 13% 56% 28% 0% 0% 0% 7% 75% 74% 5Mitsubishi -9% -9% 0% 70% 29% 0% 8% 33% 11% 42% 5% 59% 29% 1% 0% 0% 24% 75% 68% 5Nissan -3% -3% 0% 54% 18% 1% 6% 22% 16% 52% 4% 52% 11% 1% 0% 0% 2% 75% 75% 3Porsche -8% -5% 3% 17% 29% 14% 5% 19% 2% 43% 22% 59% 30% 20% 9% 11% 42% 75% 42% 5Spyker -13% -10% 2% 26% 30% 14% 3% 11% 7% 62% 7% 59% 30% 19% 10% 2% 48% 75% 50% 6Subaru -8% -7% 1% 66% 30% 0% 2% 6% 14% 61% 17% 60% 30% 5% 0% 0% 10% 75% 71% 5Suzuki -3% -2% 1% 70% 30% 0% 5% 18% 13% 56% 7% 60% 30% 0% 0% 0% 19% 75% 69% 6Tata -13% -11% 2% 36% 30% 13% 10% 40% 5% 39% 0% 58% 30% 23% 7% 2% 50% 75% 52% 5Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -3% -3% 0% 38% 1% 1% 7% 28% 12% 36% 5% 22% 1% 12% 0% 0% 0% 66% 63% Volkswagen -8% -7% 1% 52% 30% 11% 4% 16% 8% 57% 8% 59% 30% 1% 6% 0% 52% 75% 56% 6Fleet -6% -5% 0% 46% 15% 3% 7% 30% 12% 42% 5% 44% 12% 4% 1% 0% 8% 73% 68% 2
Chapter 3 

3-52 

Table 3.5-23 Final rule Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 77BMW -11% -10% 2% 6% 60% 20% 0% 0% 0% 82% 6% 88% 75% 1% 12% 0% 33% 100% 38% 88Chrysler/Fiat -8% -7% 1% 24% 72% 3% 0% 4% 0% 94% 2% 100% 75% 0% 0% 0% 2% 100% 73% 10Daimler -15% -13% 2% 6% 60% 12% 0% 0% 0% 83% 0% 83% 72% 4% 17% 0% 33% 100% 33% 83Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 77Ford -10% -9% 1% 21% 70% 4% 0% 29% 0% 63% 4% 97% 73% 1% 2% 0% 15% 99% 61% 97Geely -14% -11% 3% 5% 46% 26% 0% 13% 0% 71% 1% 85% 72% 5% 15% 4% 32% 100% 32% 85General Motors -8% -7% 1% 23% 72% 3% 0% 6% 0% 89% 5% 100% 74% 0% 0% 0% 0% 100% 78% 10Honda -3% -3% 0% 24% 73% 0% 0% 0% 0% 85% 12% 97% 73% 3% 0% 0% 0% 97% 97% 97Hyundai -5% -4% 0% 25% 75% 0% 0% 24% 0% 69% 7% 100% 75% 0% 0% 0% 0% 100% 90% 10Kia -3% -3% 0% 43% 57% 0% 0% 7% 0% 84% 9% 100% 57% 0% 0% 0% 0% 100% 98% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 80Mazda -6% -5% 1% 20% 75% 0% 0% 15% 0% 74% 10% 98% 75% 3% 2% 0% 7% 100% 56% 98Mitsubishi -9% -7% 2% 19% 74% 0% 0% 16% 0% 76% 4% 96% 74% 3% 4% 0% 11% 100% 46% 96Nissan -4% -3% 1% 25% 74% 0% 0% 5% 0% 91% 3% 99% 74% 1% 0% 0% 2% 99% 77% 99Porsche -7% -2% 4% 2% 56% 9% 0% 0% 0% 65% 12% 77% 65% 2% 23% 9% 18% 100% 18% 77Spyker -16% -13% 3% 8% 60% 8% 0% 0% 0% 74% 4% 79% 69% 2% 21% 0% 29% 100% 29% 79Subaru -9% -8% 1% 10% 75% 0% 0% 0% 0% 78% 16% 95% 75% 10% 5% 0% 0% 100% 68% 95Suzuki -1% 0% 1% 2% 75% 0% 0% 6% 0% 79% 7% 93% 75% 16% 7% 0% 3% 100% 62% 93Tata -19% -14% 5% 0% 21% 37% 0% 0% 0% 77% 0% 77% 58% 13% 23% 6% 21% 100% 21% 77Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 48% 34% 1% 0% 4% 0% 74% 7% 84% 31% 16% 0% 0% 0% 84% 84% 84Volkswagen -8% -6% 2% 9% 73% 2% 0% 0% 0% 79% 6% 85% 75% 0% 15% 0% 35% 100% 35% 85Fleet -6% -6% 1% 25% 63% 3% 0% 8% 0% 79% 6% 93% 65% 4% 3% 0% 7% 96% 73% 93
MY 2017 and Later - Regula

3-53 

Table 3.5-24 Final rule Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -17% -16% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -10% -8% 1% 20% 69% 8% 0% 95% 0% 4% 1% 100% 75% 2% 0% 0% 48% 100% 51% 10Daimler -20% -18% 2% 12% 58% 23% 0% 100% 0% 0% 0% 100% 67% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -11% -9% 2% 14% 64% 20% 0% 88% 0% 9% 1% 99% 74% 28% 0% 0% 45% 99% 49% 9Geely -20% -19% 2% 22% 72% 6% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -11% -10% 1% 17% 61% 15% 0% 98% 0% 2% 0% 100% 75% 0% 0% 0% 49% 100% 50% 10Honda -11% -10% 1% 25% 75% 0% 0% 72% 0% 28% 0% 100% 75% 0% 0% 0% 3% 100% 64% 10Hyundai -14% -12% 2% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Kia -12% -10% 1% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 51% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -19% -17% 1% 18% 75% 0% 0% 70% 0% 26% 1% 98% 75% 5% 2% 0% 35% 100% 56% 9Mitsubishi -20% -18% 2% 22% 70% 0% 0% 86% 0% 11% 0% 98% 70% 7% 2% 0% 43% 100% 48% 9Nissan -9% -7% 2% 15% 70% 9% 0% 80% 0% 18% 1% 98% 75% 13% 2% 0% 41% 100% 48% 9Porsche -20% -18% 1% 11% 61% 28% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 50% 100% 50% 10Spyker -4% -2% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Subaru -19% -17% 2% 8% 75% 0% 0% 32% 0% 60% 4% 95% 75% 12% 5% 0% 16% 100% 45% 9Suzuki -15% -14% 1% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tata -13% -11% 2% 9% 59% 33% 0% 100% 0% 0% 0% 100% 75% 16% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -8% -7% 1% 21% 68% 8% 0% 86% 0% 10% 1% 97% 72% 3% 0% 0% 0% 97% 68% 9Volkswagen -18% -17% 2% 19% 69% 11% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Fleet -11% -10% 1% 19% 67% 11% 0% 89% 0% 9% 1% 99% 74% 5% 0% 0% 32% 99% 55% 9
Chapter 3 

3-54 

Table 3.5-25 Final rule Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 7BMW -13% -11% 2% 8% 62% 20% 0% 26% 0% 60% 4% 91% 75% 1% 9% 0% 37% 100% 41% 9Chrysler/Fiat -9% -8% 1% 22% 71% 5% 0% 43% 0% 55% 1% 100% 75% 1% 0% 0% 22% 100% 63% 10Daimler -16% -14% 2% 7% 60% 14% 0% 23% 0% 64% 0% 87% 71% 3% 13% 0% 37% 100% 37% 8Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Ford -10% -9% 1% 19% 68% 9% 0% 48% 0% 46% 4% 97% 73% 10% 1% 0% 24% 99% 57% 9Geely -16% -13% 3% 10% 54% 20% 0% 39% 0% 50% 1% 90% 73% 3% 10% 3% 37% 100% 37% 9General Motors -9% -8% 1% 20% 66% 9% 0% 50% 0% 47% 3% 100% 74% 0% 0% 0% 23% 100% 65% 10Honda -5% -5% 0% 24% 73% 0% 0% 21% 0% 68% 8% 98% 73% 2% 0% 0% 1% 98% 87% 9Hyundai -7% -6% 1% 25% 75% 0% 0% 39% 0% 55% 6% 100% 75% 0% 0% 0% 10% 100% 82% 10Kia -5% -5% 0% 39% 61% 0% 0% 27% 0% 66% 7% 100% 61% 0% 0% 0% 0% 100% 88% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 8Mazda -8% -7% 1% 20% 75% 0% 0% 24% 0% 66% 8% 98% 75% 3% 2% 0% 12% 100% 56% 9Mitsubishi -13% -11% 2% 20% 73% 0% 0% 39% 0% 55% 3% 97% 73% 4% 3% 0% 22% 100% 47% 9Nissan -5% -5% 1% 22% 73% 3% 0% 27% 0% 69% 3% 99% 75% 4% 0% 0% 14% 100% 69% 9Porsche -10% -6% 4% 4% 57% 13% 0% 21% 0% 51% 9% 82% 67% 1% 18% 7% 25% 100% 25% 8Spyker -14% -11% 3% 9% 61% 10% 0% 13% 0% 65% 4% 81% 70% 1% 19% 0% 31% 100% 31% 8Subaru -11% -10% 1% 9% 75% 0% 0% 7% 0% 74% 13% 95% 75% 10% 5% 0% 4% 100% 63% 9Suzuki -4% -2% 1% 6% 75% 0% 0% 22% 0% 66% 6% 94% 75% 13% 6% 0% 11% 100% 60% 9Tata -16% -13% 3% 4% 38% 35% 0% 46% 0% 41% 0% 88% 66% 15% 12% 3% 34% 100% 34% 8Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -5% -5% 0% 38% 46% 4% 0% 34% 0% 51% 5% 89% 46% 11% 0% 0% 0% 89% 78% 8Volkswagen -10% -8% 2% 11% 72% 4% 0% 20% 0% 63% 5% 88% 75% 0% 12% 0% 38% 100% 38% 8Fleet -8% -7% 1% 23% 64% 6% 0% 35% 0% 56% 4% 95% 68% 5% 2% 0% 15% 97% 67% 9
MY 2017 and Later - Regula

3-55 

3.5.5 Projected Technology Penetrations in Alternative Cases 

Table 3.5-26 Alternative 1- (Trucks +20) Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 60BMW -10% -9% 1% 58% 30% 6% 0% 0% 17% 71% 10% 58% 30% 4% 2% 0% 33% 75% 60% 60Chrysler/Fiat -6% -6% 0% 36% 11% 1% 1% 4% 23% 70% 3% 38% 1% 0% 0% 0% 0% 75% 79% 17Daimler -12% -11% 1% 52% 28% 5% 0% 8% 7% 79% 0% 59% 29% 7% 6% 0% 38% 75% 57% 60Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 60Ford -6% -6% 0% 52% 16% 1% 6% 23% 15% 48% 7% 45% 9% 2% 0% 0% 0% 74% 73% 29Geely -11% -10% 1% 49% 27% 11% 3% 11% 13% 66% 2% 60% 30% 8% 6% 0% 43% 75% 52% 59General Motors -5% -5% 0% 37% 1% 0% 1% 5% 22% 66% 6% 12% 0% 0% 0% 0% 0% 75% 79% 1% Honda -2% -2% 0% 15% 0% 0% 0% 0% 21% 64% 12% 0% 0% 3% 0% 0% 0% 73% 43% 0% Hyundai -3% -3% 0% 27% 8% 0% 5% 20% 17% 51% 7% 23% 0% 0% 0% 0% 0% 75% 75% 10Kia -2% -2% 0% 19% 0% 0% 2% 7% 21% 62% 9% 5% 0% 0% 0% 0% 0% 75% 63% 0% Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 57Mazda -4% -4% 0% 82% 18% 0% 3% 13% 16% 51% 17% 53% 5% 0% 0% 0% 3% 75% 77% 54Mitsubishi -5% -5% 0% 74% 26% 0% 3% 14% 16% 58% 9% 58% 26% 0% 0% 0% 3% 75% 77% 5Nissan -3% -3% 0% 36% 6% 0% 1% 4% 22% 67% 5% 33% 0% 1% 0% 0% 0% 74% 78% 7% Porsche -5% -2% 3% 10% 30% 15% 0% 0% 3% 55% 30% 58% 30% 21% 12% 12% 36% 75% 39% 59Spyker -14% -12% 2% 30% 30% 15% 0% 0% 9% 72% 8% 59% 30% 12% 11% 2% 45% 75% 50% 59Subaru -6% -5% 1% 73% 27% 0% 0% 0% 17% 61% 21% 57% 27% 0% 0% 0% 0% 75% 78% 58Suzuki 0% 0% 0% 75% 25% 0% 1% 5% 21% 63% 10% 47% 25% 0% 0% 0% 4% 75% 78% 60Tata -15% -14% 1% 34% 24% 15% 0% 0% 14% 78% 0% 60% 30% 19% 8% 0% 38% 75% 49% 58Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -2% -2% 0% 23% 0% 0% 1% 4% 18% 55% 7% 0% 0% 15% 0% 0% 0% 63% 2% 0% Volkswagen -6% -5% 1% 61% 27% 4% 0% 0% 12% 72% 11% 60% 30% 1% 6% 0% 43% 75% 59% 60Fleet -4% -4% 0% 38% 9% 1% 2% 7% 18% 61% 8% 25% 7% 4% 1% 0% 6% 72% 56% 18
Chapter 3 

3-56 

Table 3.5-27 Alternative 1- (Trucks +20) Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -13% -12% 1% 65% 30% 5% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 57% 6Chrysler/Fiat -7% -7% 0% 22% 9% 3% 19% 75% 1% 3% 3% 60% 6% 0% 0% 0% 0% 75% 74% 3Daimler -15% -14% 1% 51% 28% 10% 11% 89% 0% 0% 0% 60% 29% 0% 0% 0% 65% 75% 61% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -7% -7% 0% 37% 16% 6% 18% 71% 1% 5% 2% 59% 18% 2% 0% 0% 3% 73% 60% 2Geely -15% -14% 1% 64% 30% 6% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 52% 6General Motors -7% -7% 0% 34% 11% 1% 20% 78% 0% 1% 0% 59% 3% 0% 0% 0% 0% 75% 71% 2Honda -6% -6% 0% 61% 8% 0% 14% 57% 7% 21% 0% 43% 0% 0% 0% 0% 0% 75% 67% 8Hyundai -9% -9% 0% 75% 25% 0% 20% 80% 0% 0% 0% 60% 5% 0% 0% 0% 0% 75% 60% 2Kia -7% -7% 0% 94% 0% 0% 20% 79% 0% 0% 1% 60% 0% 0% 0% 0% 0% 75% 60% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -11% -10% 0% 82% 18% 0% 14% 54% 8% 23% 2% 60% 18% 0% 0% 0% 13% 75% 66% 3Mitsubishi -11% -10% 1% 75% 25% 0% 17% 69% 3% 10% 0% 60% 25% 0% 0% 0% 17% 75% 63% 3Nissan -5% -5% 0% 66% 17% 3% 16% 64% 5% 14% 1% 60% 9% 0% 0% 0% 0% 75% 68% 2Porsche -15% -14% 1% 59% 30% 11% 20% 79% 0% 0% 1% 60% 30% 2% 0% 0% 65% 75% 61% 6Spyker -3% -2% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 2% 0% 0% 61% 75% 56% 6Subaru -12% -12% 0% 73% 27% 0% 6% 25% 15% 46% 8% 60% 27% 0% 0% 0% 21% 75% 70% 5Suzuki -11% -11% 1% 70% 30% 0% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 50% 6Tata -10% -9% 1% 58% 30% 12% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 63% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 63% 0% 3% 17% 66% 2% 7% 3% 11% 3% 5% 0% 0% 0% 71% 24% 0Volkswagen -14% -13% 1% 63% 30% 7% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 54% 6Fleet -7% -6% 0% 50% 12% 3% 18% 71% 2% 6% 1% 47% 8% 1% 0% 0% 7% 74% 57% 2
MY 2017 and Later - Regula

3-57 

Table 3.5-28 Alternative 1- (Trucks +20) Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 6BMW -11% -10% 1% 60% 30% 6% 5% 21% 13% 52% 8% 59% 30% 3% 1% 0% 41% 75% 59% 6Chrysler/Fiat -6% -6% 0% 30% 10% 1% 9% 36% 13% 39% 3% 47% 4% 0% 0% 0% 0% 75% 77% 2Daimler -13% -12% 1% 52% 28% 6% 3% 28% 5% 60% 0% 60% 29% 5% 5% 0% 44% 75% 58% 6Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 6Ford -6% -6% 0% 47% 16% 3% 10% 39% 10% 34% 6% 49% 12% 2% 0% 0% 1% 74% 69% 2Geely -12% -11% 1% 53% 28% 9% 8% 32% 9% 45% 2% 60% 30% 5% 4% 0% 49% 75% 52% 5General Motors -6% -6% 0% 36% 6% 1% 10% 41% 11% 34% 3% 35% 2% 0% 0% 0% 0% 75% 75% 1Honda -3% -3% 0% 29% 2% 0% 4% 18% 17% 51% 8% 13% 0% 2% 0% 0% 0% 73% 50% 2Hyundai -4% -4% 0% 36% 11% 0% 8% 32% 13% 41% 6% 30% 1% 0% 0% 0% 0% 75% 72% 1Kia -3% -3% 0% 36% 0% 0% 6% 23% 16% 48% 7% 17% 0% 0% 0% 0% 0% 75% 63% 0Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 5Mazda -5% -5% 0% 82% 18% 0% 5% 20% 15% 46% 14% 54% 8% 0% 0% 0% 5% 75% 75% 5Mitsubishi -7% -7% 0% 74% 26% 0% 8% 33% 11% 41% 6% 59% 26% 0% 0% 0% 8% 75% 72% 4Nissan -3% -3% 0% 46% 9% 1% 6% 22% 17% 51% 4% 42% 3% 1% 0% 0% 0% 75% 75% 1Porsche -8% -5% 3% 22% 30% 14% 5% 19% 3% 42% 23% 59% 30% 16% 9% 9% 43% 75% 44% 6Spyker -13% -11% 2% 35% 30% 14% 3% 11% 7% 62% 7% 59% 30% 10% 9% 2% 47% 75% 51% 5Subaru -7% -7% 0% 73% 27% 0% 2% 6% 17% 58% 18% 58% 27% 0% 0% 0% 5% 75% 76% 5Suzuki -2% -2% 0% 74% 26% 0% 5% 18% 17% 52% 9% 49% 26% 0% 0% 0% 15% 75% 73% 6Tata -13% -12% 1% 46% 27% 13% 10% 40% 7% 39% 0% 60% 30% 10% 4% 0% 52% 75% 56% 5Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -2% -2% 0% 39% 0% 1% 7% 28% 12% 36% 5% 4% 1% 12% 0% 0% 0% 66% 11% 0Volkswagen -8% -7% 1% 62% 28% 5% 4% 16% 10% 57% 8% 60% 30% 1% 5% 0% 47% 75% 58% 6Fleet -5% -5% 0% 42% 10% 2% 7% 30% 13% 42% 6% 33% 7% 3% 1% 0% 6% 73% 56% 1
Chapter 3 

3-58 

Table 3.5-29 Alternative 2- (Trucks -20) Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 60BMW -10% -8% 1% 47% 28% 6% 0% 0% 12% 71% 9% 60% 30% 12% 7% 0% 40% 75% 54% 59Chrysler/Fiat -6% -6% 0% 68% 28% 1% 1% 4% 19% 74% 2% 59% 29% 0% 0% 0% 1% 75% 78% 58Daimler -12% -10% 2% 27% 30% 13% 0% 6% 5% 79% 0% 59% 30% 16% 10% 3% 44% 75% 49% 59Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 60Ford -8% -7% 0% 65% 26% 1% 6% 23% 14% 50% 6% 53% 27% 2% 0% 0% 10% 74% 70% 56Geely -12% -9% 2% 22% 30% 14% 3% 11% 8% 66% 2% 59% 30% 21% 10% 4% 42% 75% 44% 59General Motors -7% -7% 0% 68% 25% 1% 1% 5% 19% 70% 5% 57% 26% 0% 0% 0% 1% 75% 79% 55Honda -2% -2% 0% 36% 10% 0% 0% 0% 21% 64% 12% 28% 5% 3% 0% 0% 0% 73% 77% 32Hyundai -3% -3% 0% 62% 17% 0% 5% 20% 17% 51% 7% 45% 4% 0% 0% 0% 0% 75% 75% 37Kia -3% -3% 0% 36% 12% 0% 2% 7% 21% 62% 9% 43% 0% 0% 0% 0% 0% 75% 78% 15Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 57Mazda -5% -4% 0% 70% 30% 0% 3% 13% 14% 55% 15% 60% 30% 0% 0% 0% 10% 75% 74% 59Mitsubishi -7% -6% 1% 66% 30% 0% 3% 14% 14% 61% 6% 60% 30% 3% 0% 0% 21% 75% 57% 60Nissan -3% -3% 0% 78% 21% 0% 1% 4% 20% 70% 5% 49% 10% 1% 0% 0% 1% 74% 78% 56Porsche -6% -2% 4% 1% 28% 15% 0% 0% 3% 55% 26% 56% 30% 25% 15% 15% 31% 75% 33% 56Spyker -14% -11% 3% 17% 30% 15% 0% 0% 8% 72% 8% 58% 30% 22% 12% 5% 44% 75% 47% 59Subaru -6% -5% 1% 57% 29% 0% 0% 0% 15% 66% 19% 60% 29% 14% 0% 0% 8% 75% 68% 58Suzuki -1% 0% 1% 51% 30% 0% 1% 5% 16% 68% 9% 60% 30% 19% 0% 0% 9% 75% 73% 60Tata -16% -11% 5% 5% 25% 15% 0% 0% 5% 79% 0% 58% 27% 26% 16% 14% 35% 75% 28% 59Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 22% 4% 0% 1% 4% 18% 55% 7% 15% 1% 15% 0% 0% 0% 63% 67% 3% Volkswagen -7% -5% 1% 43% 30% 15% 0% 0% 8% 71% 10% 59% 30% 1% 11% 0% 49% 75% 54% 59Fleet -5% -5% 0% 50% 19% 2% 2% 7% 16% 62% 7% 44% 17% 5% 2% 0% 8% 72% 70% 42
MY 2017 and Later - Regula

3-59 

Table 3.5-30 Alternative 2- (Trucks -20) Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -13% -13% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 57% 6Chrysler/Fiat -9% -8% 1% 36% 27% 3% 19% 75% 1% 3% 2% 60% 29% 0% 0% 0% 45% 75% 63% 4Daimler -15% -14% 1% 51% 28% 10% 11% 89% 0% 0% 0% 60% 29% 0% 0% 0% 65% 75% 61% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -9% -8% 1% 61% 29% 6% 18% 71% 1% 5% 2% 59% 29% 2% 0% 0% 48% 73% 54% 5Geely -15% -14% 1% 64% 30% 6% 20% 80% 0% 0% 0% 60% 30% 4% 0% 0% 65% 75% 52% 6General Motors -9% -9% 1% 39% 24% 5% 20% 78% 0% 1% 0% 59% 29% 0% 0% 0% 24% 75% 60% 4Honda -8% -8% 0% 82% 18% 0% 14% 57% 7% 21% 0% 60% 10% 0% 0% 0% 6% 75% 66% 2Hyundai -10% -10% 0% 75% 25% 0% 20% 80% 0% 0% 0% 60% 23% 0% 0% 0% 18% 75% 60% 2Kia -8% -8% 0% 75% 25% 0% 20% 79% 0% 0% 1% 60% 5% 0% 0% 0% 0% 75% 60% 2Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -13% -12% 1% 71% 29% 0% 14% 54% 7% 23% 2% 60% 29% 1% 0% 0% 44% 75% 59% 5Mitsubishi -14% -13% 1% 65% 30% 0% 17% 69% 3% 10% 0% 59% 30% 4% 1% 0% 60% 75% 51% 6Nissan -6% -5% 0% 72% 25% 3% 16% 64% 5% 14% 1% 60% 28% 0% 0% 0% 17% 75% 66% 5Porsche -15% -14% 1% 59% 30% 11% 20% 79% 0% 0% 1% 60% 30% 13% 0% 0% 65% 75% 61% 6Spyker -3% -2% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 2% 0% 0% 61% 75% 56% 6Subaru -15% -13% 1% 50% 30% 0% 6% 25% 12% 51% 6% 60% 30% 20% 0% 0% 24% 75% 60% 6Suzuki -11% -11% 1% 70% 30% 0% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 50% 6Tata -10% -9% 1% 58% 30% 12% 20% 80% 0% 0% 0% 60% 30% 22% 0% 0% 65% 75% 63% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -5% -5% 0% 68% 19% 3% 17% 67% 2% 7% 3% 57% 8% 5% 0% 0% 0% 71% 62% 2Volkswagen -14% -13% 1% 63% 30% 7% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 54% 6Fleet -8% -8% 0% 58% 24% 4% 18% 71% 2% 7% 1% 59% 22% 2% 0% 0% 24% 74% 61% 4
Chapter 3 

3-60 

Table 3.5-31 Alternative 2- (Trucks -20) Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 6BMW -11% -10% 1% 51% 29% 7% 5% 21% 9% 53% 7% 60% 30% 9% 5% 0% 47% 75% 55% 5Chrysler/Fiat -7% -7% 0% 54% 28% 2% 9% 36% 11% 41% 2% 59% 29% 0% 0% 0% 21% 75% 71% 5Daimler -13% -11% 2% 33% 29% 13% 3% 26% 4% 59% 0% 60% 29% 12% 8% 2% 49% 75% 52% 6Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 6Ford -8% -7% 0% 64% 27% 3% 10% 39% 10% 35% 5% 55% 28% 2% 0% 0% 23% 74% 65% 5Geely -13% -11% 2% 35% 30% 11% 8% 32% 6% 45% 1% 59% 30% 16% 7% 3% 49% 75% 47% 5General Motors -8% -8% 0% 54% 25% 3% 10% 41% 10% 36% 3% 58% 27% 0% 0% 0% 12% 75% 69% 5Honda -4% -4% 0% 50% 12% 0% 4% 18% 16% 51% 8% 38% 6% 2% 0% 0% 2% 73% 74% 2Hyundai -5% -5% 0% 64% 18% 0% 8% 32% 13% 41% 6% 48% 8% 0% 0% 0% 4% 75% 72% 3Kia -4% -4% 0% 45% 15% 0% 6% 23% 16% 48% 7% 47% 1% 0% 0% 0% 0% 75% 74% 1Lotus -3% 0% 3% 15% 29% 13% 0% 0% 0% 39% 49% 58% 30% 22% 12% 9% 38% 75% 45% 5Mazda -6% -6% 0% 70% 30% 0% 5% 20% 13% 49% 13% 60% 30% 0% 0% 0% 16% 75% 71% 5Mitsubishi -10% -9% 1% 66% 30% 0% 8% 33% 10% 44% 4% 60% 30% 4% 1% 0% 35% 75% 55% 6Nissan -4% -4% 0% 76% 22% 1% 6% 22% 15% 53% 4% 52% 16% 1% 0% 0% 6% 75% 75% 5Porsche -8% -5% 4% 15% 29% 14% 5% 19% 2% 42% 20% 57% 30% 23% 12% 11% 39% 75% 40% 5Spyker -13% -10% 3% 23% 30% 14% 3% 11% 7% 62% 7% 59% 30% 19% 10% 4% 46% 75% 48% 6Subaru -8% -7% 1% 55% 30% 0% 2% 6% 14% 62% 16% 60% 30% 15% 0% 0% 12% 75% 66% 5Suzuki -3% -2% 1% 55% 30% 0% 5% 18% 13% 56% 7% 60% 30% 15% 0% 0% 19% 75% 69% 6Tata -13% -10% 3% 32% 27% 13% 10% 40% 3% 40% 0% 59% 29% 24% 8% 7% 50% 75% 45% 5Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -4% -4% 0% 40% 10% 1% 7% 29% 12% 36% 5% 32% 3% 11% 0% 0% 0% 66% 65% 1Volkswagen -8% -7% 1% 47% 30% 13% 4% 16% 7% 57% 8% 59% 30% 1% 9% 0% 52% 75% 54% 5Fleet -6% -6% 0% 53% 21% 3% 7% 30% 11% 43% 5% 49% 18% 4% 1% 0% 14% 73% 67% 4
MY 2017 and Later - Regula

3-61 

Table 3.5-32 Alternative 3- (Cars +20) Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 60BMW -9% -9% 0% 70% 24% 6% 0% 0% 17% 69% 13% 58% 30% 0% 0% 0% 0% 75% 77% 60Chrysler/Fiat -6% -6% 0% 36% 11% 1% 1% 4% 23% 70% 3% 38% 1% 0% 0% 0% 0% 75% 79% 17Daimler -12% -11% 1% 58% 28% 4% 0% 11% 8% 79% 0% 57% 29% 7% 2% 0% 31% 75% 61% 60Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 60Ford -6% -6% 0% 42% 10% 1% 6% 23% 16% 47% 7% 35% 3% 2% 0% 0% 0% 74% 73% 10Geely -11% -10% 1% 60% 30% 8% 3% 11% 16% 66% 2% 59% 30% 0% 2% 0% 40% 75% 56% 60General Motors -5% -5% 0% 37% 1% 0% 1% 5% 22% 66% 6% 14% 1% 0% 0% 0% 0% 75% 79% 1% Honda -1% -1% 0% 15% 0% 0% 0% 0% 21% 64% 12% 0% 0% 3% 0% 0% 0% 73% 6% 0% Hyundai -2% -2% 0% 32% 0% 0% 5% 20% 17% 51% 7% 0% 0% 0% 0% 0% 0% 75% 21% 0% Kia -2% -2% 0% 19% 0% 0% 2% 7% 21% 62% 9% 0% 0% 0% 0% 0% 0% 75% 0% 0% Lotus -1% 0% 1% 40% 30% 11% 0% 0% 0% 32% 58% 59% 30% 7% 11% 2% 46% 75% 54% 58Mazda -3% -3% 0% 31% 17% 0% 3% 13% 17% 50% 17% 46% 2% 0% 0% 0% 0% 75% 77% 23Mitsubishi -5% -5% 0% 70% 22% 0% 3% 14% 16% 57% 9% 52% 4% 0% 0% 0% 0% 75% 77% 46Nissan -3% -3% 0% 30% 1% 0% 1% 4% 22% 67% 5% 3% 0% 1% 0% 0% 0% 74% 78% 1% Porsche -5% -2% 2% 18% 30% 15% 0% 0% 3% 49% 36% 58% 30% 21% 12% 4% 44% 75% 47% 59Spyker -13% -12% 1% 55% 30% 4% 0% 0% 12% 72% 9% 59% 30% 4% 7% 0% 41% 75% 56% 60Subaru -5% -5% 0% 88% 10% 0% 0% 0% 17% 56% 27% 46% 5% 0% 0% 0% 0% 75% 80% 55Suzuki 0% 0% 0% 98% 2% 0% 1% 5% 21% 62% 12% 11% 0% 0% 0% 0% 0% 75% 79% 57Tata -15% -14% 1% 34% 24% 15% 0% 0% 14% 78% 0% 60% 30% 19% 8% 0% 38% 75% 49% 58Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -2% -2% 0% 23% 0% 0% 1% 4% 20% 53% 7% 0% 0% 15% 0% 0% 0% 63% 2% 0% Volkswagen -6% -5% 1% 68% 30% 1% 0% 0% 16% 72% 11% 60% 30% 1% 0% 0% 33% 75% 64% 60Fleet -4% -4% 0% 37% 7% 1% 2% 7% 19% 60% 8% 19% 5% 4% 0% 0% 4% 72% 48% 13
Chapter 3 

3-62 

Table 3.5-33 Alternative 3- (Cars +20) Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -13% -12% 1% 65% 30% 5% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 61% 6Chrysler/Fiat -7% -7% 0% 22% 9% 3% 19% 75% 1% 3% 3% 60% 6% 0% 0% 0% 0% 75% 74% 3Daimler -15% -14% 1% 51% 28% 10% 11% 89% 0% 0% 0% 60% 29% 0% 0% 0% 65% 75% 61% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -7% -7% 0% 30% 15% 6% 18% 71% 1% 5% 2% 59% 14% 2% 0% 0% 1% 73% 68% 2Geely -15% -14% 1% 64% 30% 6% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 52% 6General Motors -7% -7% 0% 35% 12% 5% 20% 78% 0% 1% 0% 59% 7% 0% 0% 0% 0% 75% 71% 2Honda -4% -4% 0% 68% 0% 0% 14% 57% 7% 21% 0% 23% 0% 0% 0% 0% 0% 75% 31% 0Hyundai -5% -5% 0% 95% 0% 0% 20% 80% 0% 0% 0% 54% 0% 0% 0% 0% 0% 75% 54% 0Kia -5% -5% 0% 94% 0% 0% 20% 79% 0% 0% 1% 0% 0% 0% 0% 0% 0% 75% 1% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -9% -9% 0% 59% 17% 0% 14% 54% 8% 23% 2% 60% 4% 0% 0% 0% 0% 75% 66% 2Mitsubishi -10% -10% 0% 75% 25% 0% 17% 69% 3% 10% 0% 60% 21% 0% 0% 0% 13% 75% 63% 2Nissan -4% -4% 0% 47% 15% 3% 16% 64% 5% 14% 1% 53% 6% 0% 0% 0% 0% 75% 70% 1Porsche -15% -14% 1% 59% 30% 11% 20% 79% 0% 0% 1% 60% 30% 0% 0% 0% 65% 75% 61% 6Spyker -3% -2% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 61% 75% 56% 6Subaru -11% -11% 0% 92% 8% 0% 6% 25% 15% 46% 8% 60% 8% 0% 0% 0% 6% 75% 74% 2Suzuki -8% -8% 0% 75% 25% 0% 20% 80% 0% 0% 0% 60% 25% 0% 0% 0% 20% 75% 60% 2Tata -10% -9% 1% 58% 30% 12% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 63% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 63% 0% 0% 17% 66% 2% 7% 3% 0% 0% 5% 0% 0% 0% 71% 13% 0Volkswagen -14% -13% 1% 63% 30% 7% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 54% 6Fleet -6% -6% 0% 50% 10% 3% 18% 71% 2% 6% 1% 41% 8% 1% 0% 0% 6% 74% 52% 1
MY 2017 and Later - Regula

3-63 

Table 3.5-34 Alternative 3- (Cars +20) Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 6BMW -10% -10% 0% 69% 25% 6% 5% 21% 13% 51% 10% 59% 30% 0% 0% 0% 17% 75% 73% 6Chrysler/Fiat -6% -6% 0% 30% 10% 1% 9% 36% 13% 39% 3% 47% 4% 0% 0% 0% 0% 75% 77% 2Daimler -12% -12% 1% 56% 28% 6% 3% 31% 6% 59% 0% 58% 29% 5% 2% 0% 39% 75% 61% 6Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 6Ford -6% -6% 0% 38% 12% 3% 10% 39% 11% 33% 6% 43% 7% 2% 0% 0% 0% 74% 71% 1Geely -12% -11% 1% 61% 30% 8% 8% 32% 11% 46% 2% 59% 30% 0% 1% 0% 48% 75% 55% 6General Motors -6% -6% 0% 36% 6% 3% 10% 41% 11% 34% 3% 36% 4% 0% 0% 0% 0% 75% 75% 1Honda -2% -2% 0% 32% 0% 0% 4% 18% 17% 51% 8% 7% 0% 2% 0% 0% 0% 73% 14% 0Hyundai -3% -3% 0% 45% 0% 0% 8% 32% 14% 41% 6% 11% 0% 0% 0% 0% 0% 75% 28% 0Kia -2% -2% 0% 36% 0% 0% 6% 23% 16% 48% 7% 0% 0% 0% 0% 0% 0% 75% 0% 0Lotus -1% 0% 1% 40% 30% 11% 0% 0% 0% 32% 58% 59% 30% 7% 11% 2% 46% 75% 54% 5Mazda -4% -4% 0% 36% 17% 0% 5% 20% 15% 45% 14% 49% 2% 0% 0% 0% 0% 75% 75% 2Mitsubishi -7% -7% 0% 72% 23% 0% 8% 33% 12% 41% 6% 55% 10% 0% 0% 0% 5% 75% 72% 3Nissan -3% -3% 0% 36% 5% 1% 6% 22% 17% 51% 4% 18% 2% 1% 0% 0% 0% 75% 76% 5Porsche -7% -5% 2% 28% 30% 14% 5% 19% 3% 38% 28% 59% 30% 16% 9% 3% 49% 75% 50% 6Spyker -12% -11% 1% 56% 30% 5% 3% 11% 10% 61% 8% 59% 30% 4% 6% 0% 44% 75% 56% 6Subaru -6% -6% 0% 89% 10% 0% 2% 6% 17% 53% 22% 49% 6% 0% 0% 0% 2% 75% 78% 4Suzuki -1% -1% 0% 94% 6% 0% 5% 18% 17% 51% 10% 19% 5% 0% 0% 0% 4% 75% 75% 5Tata -13% -12% 1% 46% 27% 13% 10% 40% 7% 39% 0% 60% 30% 10% 4% 0% 52% 75% 56% 5Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -2% -2% 0% 39% 0% 0% 7% 28% 13% 35% 5% 0% 0% 12% 0% 0% 0% 66% 6% 0Volkswagen -8% -7% 1% 67% 30% 2% 4% 16% 13% 57% 9% 60% 30% 1% 0% 0% 40% 75% 62% 6Fleet -5% -5% 0% 41% 8% 2% 7% 30% 13% 41% 6% 27% 6% 3% 0% 0% 4% 73% 49% 1
Chapter 3 

3-64 

Table 3.5-35 Alternative 4- (Cars -20) Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 60BMW -10% -8% 2% 27% 30% 14% 0% 0% 9% 72% 9% 59% 30% 16% 11% 2% 43% 75% 49% 59Chrysler/Fiat -7% -7% 0% 68% 28% 1% 1% 4% 19% 74% 2% 59% 29% 0% 0% 0% 1% 75% 78% 58Daimler -13% -9% 4% 12% 29% 13% 0% 4% 5% 79% 0% 58% 30% 23% 12% 10% 39% 75% 41% 60Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 60Ford -8% -8% 1% 63% 28% 1% 6% 23% 13% 52% 5% 59% 29% 3% 0% 0% 24% 74% 61% 59Geely -12% -9% 3% 16% 30% 14% 3% 11% 6% 65% 2% 55% 30% 22% 13% 5% 43% 75% 39% 58General Motors -7% -7% 0% 71% 23% 1% 1% 5% 19% 69% 5% 57% 21% 0% 0% 0% 1% 75% 79% 55Honda -2% -2% 0% 61% 18% 0% 0% 0% 19% 66% 12% 47% 6% 3% 0% 0% 0% 73% 77% 42Hyundai -4% -4% 0% 73% 27% 0% 5% 20% 15% 53% 7% 55% 27% 0% 0% 0% 5% 75% 75% 53Kia -3% -3% 0% 79% 16% 0% 2% 7% 19% 63% 9% 43% 5% 0% 0% 0% 2% 75% 78% 53Lotus -4% 0% 4% 0% 27% 14% 0% 0% 0% 45% 40% 56% 30% 26% 15% 15% 30% 75% 36% 56Mazda -5% -4% 1% 62% 30% 1% 3% 13% 13% 58% 11% 60% 30% 6% 2% 0% 52% 75% 56% 60Mitsubishi -7% -6% 1% 61% 30% 1% 3% 14% 14% 60% 6% 59% 30% 5% 3% 0% 57% 75% 55% 59Nissan -3% -3% 0% 70% 29% 0% 1% 4% 19% 72% 4% 56% 29% 1% 0% 0% 3% 74% 74% 59Porsche -7% -2% 5% 1% 24% 15% 0% 0% 3% 55% 26% 56% 30% 29% 16% 15% 30% 75% 32% 60Spyker -15% -10% 5% 4% 28% 15% 0% 0% 8% 74% 6% 59% 30% 25% 12% 15% 34% 75% 36% 59Subaru -7% -5% 1% 49% 30% 0% 0% 0% 14% 64% 18% 60% 30% 17% 4% 0% 25% 75% 62% 60Suzuki -1% 0% 1% 37% 30% 0% 1% 5% 16% 65% 8% 60% 30% 28% 5% 0% 56% 75% 64% 55Tata -16% -11% 6% 5% 23% 15% 0% 0% 5% 79% 0% 58% 27% 26% 16% 15% 34% 75% 27% 59Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 44% 4% 0% 1% 4% 18% 55% 7% 24% 1% 15% 0% 0% 0% 63% 67% 14Volkswagen -7% -5% 3% 17% 30% 15% 0% 0% 8% 72% 9% 59% 30% 22% 11% 6% 43% 75% 48% 59Fleet -5% -5% 1% 55% 22% 2% 2% 7% 16% 63% 7% 50% 19% 7% 2% 1% 12% 72% 67% 48
MY 2017 and Later - Regula

3-65 

Table 3.5-36 Alternative 4- (Cars -20) Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -13% -13% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 57% 6Chrysler/Fiat -8% -8% 1% 42% 27% 3% 19% 75% 1% 3% 2% 60% 29% 0% 0% 0% 39% 75% 56% 5Daimler -15% -14% 1% 51% 28% 10% 11% 89% 0% 0% 0% 60% 29% 0% 0% 0% 65% 75% 61% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -9% -8% 1% 61% 29% 6% 18% 71% 1% 6% 2% 59% 29% 3% 0% 0% 51% 74% 53% 5Geely -15% -14% 1% 64% 30% 6% 20% 80% 0% 0% 0% 60% 30% 8% 0% 0% 65% 75% 52% 6General Motors -9% -9% 1% 36% 24% 5% 20% 78% 0% 1% 0% 59% 29% 0% 0% 0% 17% 75% 62% 4Honda -8% -8% 0% 75% 25% 0% 14% 57% 7% 21% 0% 60% 25% 0% 0% 0% 13% 75% 66% 3Hyundai -14% -13% 1% 70% 30% 0% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 63% 75% 51% 6Kia -10% -9% 1% 75% 25% 0% 20% 79% 0% 0% 1% 60% 25% 0% 0% 0% 20% 75% 60% 3Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -15% -13% 1% 55% 30% 3% 14% 54% 6% 23% 1% 58% 30% 10% 2% 0% 54% 75% 53% 6Mitsubishi -15% -14% 1% 60% 30% 4% 17% 69% 1% 11% 0% 60% 30% 4% 1% 0% 62% 75% 51% 6Nissan -7% -6% 1% 64% 29% 3% 16% 64% 5% 15% 1% 60% 29% 4% 0% 0% 51% 75% 61% 5Porsche -15% -14% 1% 53% 30% 15% 20% 79% 0% 0% 1% 60% 30% 30% 0% 0% 65% 75% 61% 6Spyker -3% -2% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 9% 0% 0% 61% 75% 56% 6Subaru -15% -13% 1% 46% 30% 0% 6% 25% 12% 47% 5% 56% 30% 20% 4% 0% 38% 75% 57% 6Suzuki -12% -11% 1% 65% 30% 5% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 65% 75% 50% 6Tata -10% -9% 1% 58% 30% 12% 20% 80% 0% 0% 0% 60% 30% 22% 0% 0% 65% 75% 63% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -5% -5% 0% 71% 20% 3% 17% 67% 2% 7% 2% 57% 9% 5% 0% 0% 0% 71% 62% 2Volkswagen -14% -13% 1% 63% 30% 7% 20% 80% 0% 0% 0% 60% 30% 10% 0% 0% 65% 75% 54% 6Fleet -9% -8% 1% 57% 25% 4% 18% 71% 2% 7% 1% 59% 24% 3% 0% 0% 28% 74% 60% 4
Chapter 3 

3-66 

Table 3.5-37 Alternative 4- (Cars -20) Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 7% 22% 15% 0% 0% 4% 73% 7% 59% 30% 26% 16% 15% 35% 75% 24% 6BMW -11% -9% 2% 36% 30% 12% 5% 21% 6% 53% 7% 59% 30% 12% 8% 1% 49% 75% 51% 5Chrysler/Fiat -7% -7% 0% 56% 28% 2% 9% 36% 11% 41% 2% 59% 29% 0% 0% 0% 18% 75% 68% 5Daimler -13% -11% 3% 22% 29% 13% 3% 25% 4% 59% 0% 59% 29% 17% 9% 7% 46% 75% 46% 6Ferrari -8% -3% 5% 6% 22% 15% 0% 0% 4% 78% 2% 59% 30% 26% 16% 15% 35% 75% 24% 6Ford -9% -8% 1% 62% 29% 3% 10% 39% 9% 37% 4% 59% 29% 3% 0% 0% 33% 74% 58% 5Geely -13% -11% 3% 31% 30% 11% 8% 32% 4% 45% 1% 57% 30% 18% 9% 4% 50% 75% 43% 5General Motors -8% -8% 0% 54% 23% 3% 10% 41% 10% 36% 3% 58% 25% 0% 0% 0% 9% 75% 71% 5Honda -4% -4% 0% 65% 20% 0% 4% 18% 16% 52% 8% 51% 12% 2% 0% 0% 4% 73% 74% 4Hyundai -6% -6% 0% 72% 28% 0% 8% 32% 12% 42% 6% 56% 28% 0% 0% 0% 17% 75% 70% 5Kia -4% -4% 0% 78% 18% 0% 6% 23% 15% 49% 7% 47% 10% 0% 0% 0% 6% 75% 74% 4Lotus -4% 0% 4% 0% 27% 14% 0% 0% 0% 45% 40% 56% 30% 26% 15% 15% 30% 75% 36% 5Mazda -7% -6% 1% 61% 30% 1% 5% 20% 12% 52% 10% 59% 30% 6% 2% 0% 53% 75% 56% 6Mitsubishi -10% -9% 1% 61% 30% 2% 8% 33% 10% 43% 4% 59% 30% 5% 2% 0% 59% 75% 53% 6Nissan -4% -4% 0% 68% 29% 1% 6% 22% 15% 54% 3% 57% 29% 2% 0% 0% 18% 75% 70% 5Porsche -9% -5% 4% 13% 25% 15% 5% 19% 2% 42% 20% 57% 30% 29% 12% 11% 38% 75% 39% 6Spyker -13% -9% 4% 12% 29% 14% 3% 11% 7% 63% 5% 59% 30% 23% 10% 13% 38% 75% 39% 5Subaru -9% -7% 1% 48% 30% 0% 2% 6% 14% 60% 15% 59% 30% 18% 4% 0% 28% 75% 61% 6Suzuki -3% -2% 1% 42% 30% 1% 5% 18% 13% 53% 6% 60% 30% 23% 4% 0% 57% 75% 61% 5Tata -13% -10% 3% 32% 27% 13% 10% 40% 3% 40% 0% 59% 29% 24% 8% 8% 49% 75% 45% 5Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -4% -4% 0% 55% 10% 1% 7% 29% 12% 36% 5% 37% 4% 11% 0% 0% 0% 66% 65% 1Volkswagen -9% -6% 2% 26% 30% 13% 4% 16% 6% 57% 7% 59% 30% 20% 9% 5% 48% 75% 49% 5Fleet -7% -6% 1% 56% 23% 3% 7% 30% 11% 43% 5% 53% 21% 6% 1% 1% 18% 73% 65% 4
MY 2017 and Later - Regula

3-67 

Table 3.5-38 Alternative 1- (Trucks +20) Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 77BMW -11% -10% 2% 8% 62% 20% 0% 0% 0% 85% 6% 91% 75% 1% 9% 0% 21% 100% 41% 9Chrysler/Fiat -7% -7% 0% 24% 73% 3% 0% 4% 0% 94% 2% 100% 74% 0% 0% 0% 0% 100% 98% 10Daimler -15% -13% 2% 8% 61% 14% 0% 0% 0% 86% 0% 86% 74% 3% 14% 0% 31% 100% 39% 86Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 77Ford -9% -8% 1% 23% 68% 4% 0% 29% 0% 64% 5% 98% 72% 1% 0% 0% 12% 99% 69% 98Geely -14% -12% 2% 7% 55% 26% 0% 13% 0% 73% 1% 87% 75% 0% 13% 0% 38% 100% 38% 87General Motors -7% -7% 0% 29% 65% 3% 0% 6% 0% 88% 6% 100% 68% 0% 0% 0% 0% 100% 100% 10Honda -2% -2% 0% 28% 60% 0% 0% 0% 0% 85% 12% 97% 36% 3% 0% 0% 0% 97% 97% 97Hyundai -4% -4% 0% 25% 75% 0% 0% 24% 0% 69% 7% 100% 75% 0% 0% 0% 0% 100% 100% 10Kia -3% -3% 0% 43% 57% 0% 0% 7% 0% 84% 9% 100% 21% 0% 0% 0% 0% 100% 100% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 80Mazda -6% -5% 1% 23% 75% 0% 0% 15% 0% 72% 12% 98% 75% 0% 2% 0% 7% 100% 67% 98Mitsubishi -8% -7% 1% 24% 75% 0% 0% 16% 0% 78% 4% 99% 75% 0% 1% 0% 8% 100% 58% 99Nissan -3% -3% 0% 25% 74% 0% 0% 5% 0% 90% 4% 99% 74% 1% 0% 0% 0% 99% 89% 99Porsche -6% -2% 4% 2% 60% 9% 0% 0% 0% 62% 15% 77% 69% 2% 23% 5% 22% 100% 22% 77Spyker -16% -13% 3% 8% 62% 8% 0% 0% 0% 76% 4% 80% 71% 2% 20% 0% 30% 100% 30% 80Subaru -8% -8% 1% 20% 75% 0% 0% 0% 0% 76% 18% 95% 75% 0% 5% 0% 0% 100% 75% 95Suzuki -1% 0% 1% 2% 75% 0% 0% 6% 0% 78% 9% 93% 75% 16% 7% 0% 3% 100% 73% 93Tata -19% -16% 3% 0% 32% 38% 0% 0% 0% 83% 0% 83% 70% 7% 17% 5% 28% 100% 28% 83Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 26% 27% 1% 0% 4% 0% 74% 7% 84% 19% 16% 0% 0% 0% 84% 84% 84Volkswagen -8% -6% 2% 11% 73% 2% 0% 0% 0% 80% 7% 87% 75% 0% 13% 0% 39% 100% 40% 87Fleet -6% -5% 0% 24% 60% 3% 0% 8% 0% 79% 7% 94% 57% 4% 2% 0% 6% 96% 81% 94
Chapter 3 

3-68 

Table 3.5-39 Alternative 1- (Trucks +20) Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -17% -16% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -9% -8% 1% 9% 65% 8% 0% 94% 0% 4% 2% 100% 73% 0% 0% 0% 7% 100% 63% 10Daimler -20% -18% 2% 12% 58% 23% 0% 100% 0% 0% 0% 100% 67% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -10% -9% 1% 15% 63% 20% 0% 88% 0% 8% 1% 98% 73% 6% 0% 0% 45% 98% 49% 9Geely -20% -19% 2% 22% 72% 6% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -9% -8% 1% 14% 53% 15% 0% 98% 0% 2% 0% 100% 68% 0% 0% 0% 1% 100% 70% 10Honda -10% -10% 0% 25% 75% 0% 0% 72% 0% 28% 0% 100% 75% 0% 0% 0% 0% 100% 97% 10Hyundai -13% -11% 2% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 0% 100% 50% 10Kia -14% -14% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -19% -17% 1% 18% 75% 0% 0% 70% 0% 26% 1% 98% 75% 5% 2% 0% 35% 100% 56% 9Mitsubishi -18% -16% 2% 22% 75% 0% 0% 86% 0% 12% 0% 99% 75% 2% 1% 0% 43% 100% 49% 9Nissan -7% -6% 1% 20% 70% 9% 0% 80% 0% 19% 1% 100% 75% 0% 0% 0% 35% 100% 59% 10Porsche -20% -18% 1% 11% 61% 28% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 50% 100% 50% 10Spyker -4% -2% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Subaru -20% -18% 1% 8% 75% 0% 0% 32% 0% 57% 6% 95% 75% 12% 5% 0% 16% 100% 67% 9Suzuki -15% -14% 1% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tata -13% -11% 1% 9% 59% 33% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -6% -6% 0% 20% 67% 8% 0% 82% 0% 9% 3% 94% 71% 6% 0% 0% 0% 94% 92% 9Volkswagen -18% -17% 2% 19% 69% 11% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Fleet -10% -9% 1% 17% 64% 11% 0% 88% 0% 9% 1% 98% 72% 2% 0% 0% 14% 99% 71% 9
MY 2017 and Later - Regula

3-69 

Table 3.5-40 Alternative 1- (Trucks +20) Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 7BMW -13% -11% 1% 10% 63% 20% 0% 26% 0% 62% 5% 93% 75% 1% 7% 0% 29% 100% 44% 9Chrysler/Fiat -8% -7% 0% 17% 69% 5% 0% 43% 0% 55% 2% 100% 74% 0% 0% 0% 3% 100% 83% 10Daimler -16% -14% 2% 9% 60% 16% 0% 23% 0% 66% 0% 89% 73% 2% 11% 0% 35% 100% 42% 8Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Ford -9% -8% 1% 20% 67% 9% 0% 48% 0% 47% 4% 98% 72% 3% 0% 0% 22% 98% 63% 9Geely -16% -14% 2% 11% 60% 20% 0% 39% 0% 51% 1% 91% 75% 0% 9% 0% 42% 100% 42% 9General Motors -8% -8% 0% 22% 59% 9% 0% 50% 0% 47% 3% 100% 68% 0% 0% 0% 0% 100% 85% 10Honda -5% -5% 0% 27% 65% 0% 0% 21% 0% 68% 8% 98% 48% 2% 0% 0% 0% 98% 97% 9Hyundai -6% -5% 0% 25% 75% 0% 0% 39% 0% 55% 6% 100% 75% 0% 0% 0% 0% 100% 90% 10Kia -5% -5% 0% 39% 61% 0% 0% 27% 0% 66% 7% 100% 32% 0% 0% 0% 0% 100% 100% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 8Mazda -8% -7% 1% 22% 75% 0% 0% 24% 0% 64% 10% 98% 75% 1% 2% 0% 12% 100% 65% 9Mitsubishi -11% -10% 1% 23% 75% 0% 0% 39% 0% 56% 3% 99% 75% 1% 1% 0% 20% 100% 55% 9Nissan -4% -4% 1% 23% 73% 3% 0% 27% 0% 69% 3% 99% 75% 1% 0% 0% 10% 99% 80% 9Porsche -9% -6% 3% 4% 60% 13% 0% 21% 0% 49% 12% 82% 70% 1% 18% 4% 28% 100% 28% 8Spyker -14% -12% 2% 9% 63% 10% 0% 13% 0% 66% 4% 83% 71% 1% 17% 0% 33% 100% 33% 8Subaru -11% -10% 1% 17% 75% 0% 0% 7% 0% 72% 15% 95% 75% 3% 5% 0% 4% 100% 73% 9Suzuki -3% -2% 1% 6% 75% 0% 0% 22% 0% 65% 7% 94% 75% 13% 6% 0% 11% 100% 69% 9Tata -16% -14% 2% 4% 45% 36% 0% 46% 0% 44% 0% 91% 72% 4% 9% 3% 38% 100% 38% 9Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -4% -4% 0% 24% 42% 4% 0% 33% 0% 50% 5% 88% 38% 12% 0% 0% 0% 88% 87% 8Volkswagen -10% -8% 2% 13% 72% 4% 0% 20% 0% 64% 5% 89% 75% 0% 11% 0% 41% 100% 42% 8Fleet -7% -7% 1% 21% 61% 6% 0% 35% 0% 56% 5% 96% 62% 3% 2% 0% 9% 97% 78% 9
Chapter 3 

3-70 

Table 3.5-41 Alternative 2- (Trucks -20) Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 77BMW -12% -9% 2% 5% 51% 20% 0% 0% 0% 79% 5% 84% 72% 7% 16% 0% 34% 100% 34% 84Chrysler/Fiat -9% -7% 1% 18% 73% 3% 0% 4% 0% 91% 1% 96% 75% 3% 4% 0% 3% 100% 49% 96Daimler -15% -13% 3% 6% 56% 14% 0% 0% 0% 80% 0% 80% 70% 4% 20% 1% 29% 100% 29% 80Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 77Ford -11% -9% 1% 14% 70% 4% 0% 29% 0% 62% 4% 95% 74% 6% 5% 0% 23% 99% 52% 95Geely -15% -11% 4% 3% 44% 24% 0% 13% 0% 68% 1% 82% 68% 5% 18% 6% 26% 100% 26% 82General Motors -9% -8% 1% 19% 72% 3% 0% 6% 0% 87% 3% 96% 75% 1% 4% 0% 3% 100% 54% 96Honda -4% -3% 0% 24% 73% 0% 0% 0% 0% 85% 11% 97% 73% 3% 0% 0% 0% 97% 83% 97Hyundai -5% -4% 1% 25% 75% 0% 0% 24% 0% 69% 7% 100% 75% 0% 0% 0% 12% 100% 77% 10Kia -3% -3% 0% 25% 75% 0% 0% 7% 0% 84% 9% 100% 75% 0% 0% 0% 0% 100% 91% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 80Mazda -6% -5% 2% 19% 75% 0% 0% 15% 0% 75% 8% 98% 75% 4% 2% 0% 9% 100% 49% 98Mitsubishi -9% -7% 2% 15% 74% 0% 0% 16% 0% 72% 4% 92% 74% 3% 8% 0% 36% 100% 43% 92Nissan -5% -4% 1% 23% 74% 0% 0% 5% 0% 90% 3% 98% 75% 0% 2% 0% 2% 100% 57% 98Porsche -7% -2% 5% 0% 52% 9% 0% 0% 0% 69% 8% 77% 61% 2% 23% 14% 13% 100% 13% 77Spyker -16% -13% 3% 3% 65% 8% 0% 0% 0% 74% 4% 78% 73% 2% 22% 0% 28% 100% 28% 78Subaru -9% -7% 2% 7% 75% 0% 0% 0% 0% 81% 12% 94% 75% 11% 6% 0% 2% 100% 47% 94Suzuki -2% 0% 2% 2% 75% 0% 0% 6% 0% 82% 5% 93% 75% 16% 7% 0% 43% 100% 43% 93Tata -19% -12% 8% 0% 13% 22% 0% 0% 0% 77% 0% 77% 35% 20% 23% 22% 5% 100% 5% 77Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -4% -3% 0% 20% 63% 1% 0% 4% 0% 74% 7% 85% 63% 15% 0% 0% 1% 85% 72% 85Volkswagen -8% -6% 2% 9% 70% 2% 0% 0% 0% 77% 5% 82% 72% 1% 18% 0% 32% 100% 32% 82Fleet -7% -6% 1% 18% 69% 3% 0% 8% 0% 78% 6% 92% 71% 5% 5% 0% 10% 96% 59% 92
MY 2017 and Later - Regula

3-71 

Table 3.5-42 Alternative 2- (Trucks -20) Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -17% -16% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -12% -10% 2% 20% 70% 8% 0% 95% 0% 3% 1% 99% 75% 9% 1% 0% 48% 100% 49% 9Daimler -20% -18% 2% 12% 58% 23% 0% 100% 0% 0% 0% 100% 67% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -12% -10% 2% 13% 64% 20% 0% 88% 0% 8% 1% 98% 74% 28% 1% 0% 47% 99% 48% 9Geely -20% -19% 2% 22% 72% 6% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -13% -11% 2% 17% 67% 15% 0% 98% 0% 2% 0% 100% 75% 15% 0% 0% 50% 100% 50% 10Honda -14% -13% 1% 25% 75% 0% 0% 72% 0% 28% 0% 100% 75% 2% 0% 0% 36% 100% 57% 10Hyundai -18% -16% 2% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Kia -14% -13% 1% 25% 75% 0% 0% 99% 0% 1% 0% 100% 75% 0% 0% 0% 49% 100% 50% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -19% -18% 2% 18% 75% 0% 0% 70% 0% 27% 1% 98% 75% 5% 2% 0% 36% 100% 48% 9Mitsubishi -20% -18% 2% 22% 70% 0% 0% 86% 0% 11% 0% 98% 70% 7% 2% 0% 43% 100% 48% 9Nissan -9% -7% 2% 15% 70% 9% 0% 80% 0% 18% 1% 98% 74% 17% 2% 0% 41% 100% 48% 9Porsche -20% -18% 1% 11% 61% 28% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 50% 100% 50% 10Spyker -4% -2% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Subaru -19% -17% 2% 8% 75% 0% 0% 32% 0% 60% 4% 95% 75% 12% 5% 0% 16% 100% 45% 9Suzuki -16% -15% 1% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tata -13% -11% 2% 9% 59% 33% 0% 100% 0% 0% 0% 100% 75% 33% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -9% -8% 1% 20% 68% 8% 0% 86% 0% 10% 1% 97% 72% 6% 0% 0% 43% 97% 50% 9Volkswagen -18% -17% 2% 19% 69% 11% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Fleet -13% -11% 2% 18% 69% 11% 0% 89% 0% 9% 1% 99% 74% 11% 1% 0% 45% 99% 50% 9
Chapter 3 

3-72 

Table 3.5-43 Alternative 2- (Trucks -20) Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -12% 8% 0% 0% 29% 0% 0% 0% 76% 1% 77% 29% 27% 23% 22% 5% 100% 5% 7BMW -13% -11% 2% 8% 55% 20% 0% 26% 0% 58% 4% 88% 73% 5% 12% 0% 38% 100% 38% 8Chrysler/Fiat -10% -9% 2% 19% 72% 5% 0% 43% 0% 53% 1% 98% 75% 5% 2% 0% 22% 100% 49% 9Daimler -16% -14% 3% 7% 56% 16% 0% 23% 0% 61% 0% 84% 69% 3% 16% 0% 34% 100% 34% 8Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Ford -11% -9% 2% 14% 68% 9% 0% 48% 0% 45% 3% 96% 74% 13% 4% 0% 30% 99% 51% 9Geely -16% -13% 3% 9% 53% 18% 0% 39% 0% 48% 1% 87% 70% 3% 13% 4% 33% 100% 33% 8General Motors -11% -9% 1% 18% 70% 9% 0% 50% 0% 46% 2% 98% 75% 8% 2% 0% 25% 100% 52% 9Honda -7% -6% 1% 24% 73% 0% 0% 21% 0% 69% 8% 98% 73% 3% 0% 0% 11% 98% 75% 9Hyundai -8% -7% 1% 25% 75% 0% 0% 39% 0% 55% 6% 100% 75% 0% 0% 0% 20% 100% 71% 10Kia -6% -5% 0% 25% 75% 0% 0% 27% 0% 66% 7% 100% 75% 0% 0% 0% 10% 100% 83% 10Lotus -3% 0% 3% 7% 56% 0% 0% 0% 0% 55% 25% 80% 56% 11% 20% 5% 25% 100% 25% 8Mazda -9% -7% 2% 19% 75% 0% 0% 24% 0% 67% 7% 98% 75% 4% 2% 0% 13% 100% 49% 9Mitsubishi -13% -11% 2% 17% 73% 0% 0% 39% 0% 52% 2% 94% 73% 4% 6% 0% 38% 100% 44% 9Nissan -6% -5% 1% 21% 73% 3% 0% 27% 0% 69% 2% 98% 75% 5% 2% 0% 14% 100% 54% 9Porsche -10% -6% 4% 2% 54% 13% 0% 21% 0% 54% 6% 82% 64% 1% 18% 11% 21% 100% 21% 8Spyker -14% -11% 3% 4% 65% 10% 0% 13% 0% 64% 4% 81% 74% 1% 19% 0% 31% 100% 31% 8Subaru -11% -9% 2% 7% 75% 0% 0% 7% 0% 76% 10% 94% 75% 12% 6% 0% 5% 100% 47% 9Suzuki -4% -3% 2% 6% 75% 0% 0% 22% 0% 68% 4% 94% 75% 13% 6% 0% 44% 100% 44% 9Tata -16% -11% 5% 4% 34% 27% 0% 46% 0% 41% 0% 88% 54% 26% 12% 12% 26% 100% 26% 8Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -6% -5% 1% 20% 65% 4% 0% 34% 0% 51% 5% 89% 67% 12% 0% 0% 17% 89% 64% 8Volkswagen -10% -8% 2% 11% 70% 4% 0% 20% 0% 62% 4% 86% 72% 1% 14% 0% 36% 100% 36% 8Fleet -9% -8% 1% 18% 69% 6% 0% 35% 0% 55% 4% 94% 72% 7% 3% 0% 22% 97% 56% 9
MY 2017 and Later - Regula

3-73 

Table 3.5-44 Alternative 3- (Cars +20) Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -15% 5% 0% 0% 43% 0% 0% 0% 73% 4% 77% 43% 27% 23% 7% 20% 100% 20% 77BMW -11% -9% 1% 11% 63% 20% 0% 0% 0% 87% 8% 95% 75% 0% 5% 0% 0% 100% 50% 95Chrysler/Fiat -7% -7% 0% 24% 73% 3% 0% 4% 0% 94% 2% 100% 74% 0% 0% 0% 0% 100% 98% 10Daimler -15% -13% 1% 11% 65% 14% 0% 0% 0% 91% 0% 91% 74% 0% 9% 0% 27% 100% 46% 9Ferrari -10% -3% 7% 0% 0% 28% 0% 0% 0% 77% 0% 77% 28% 27% 23% 22% 5% 100% 5% 77Ford -8% -8% 0% 23% 68% 4% 0% 29% 0% 64% 5% 99% 72% 1% 0% 0% 0% 99% 86% 99Geely -13% -12% 2% 8% 58% 26% 0% 13% 0% 77% 2% 92% 75% 0% 8% 0% 12% 100% 44% 92General Motors -7% -7% 0% 29% 65% 3% 0% 6% 0% 88% 6% 100% 68% 0% 0% 0% 0% 100% 99% 10Honda -2% -2% 0% 18% 30% 0% 0% 0% 0% 85% 12% 97% 11% 3% 0% 0% 0% 97% 97% 97Hyundai -3% -3% 0% 38% 34% 0% 0% 24% 0% 68% 7% 100% 24% 0% 0% 0% 0% 100% 100% 10Kia -3% -3% 0% 5% 31% 0% 0% 7% 0% 84% 9% 100% 5% 0% 0% 0% 0% 100% 100% 10Lotus -2% 0% 2% 7% 75% 0% 0% 0% 0% 49% 36% 85% 75% 3% 15% 0% 36% 100% 36% 85Mazda -4% -4% 0% 25% 75% 0% 0% 15% 0% 70% 15% 100% 75% 0% 0% 0% 0% 100% 98% 10Mitsubishi -7% -7% 1% 25% 75% 0% 0% 16% 0% 78% 6% 100% 75% 0% 0% 0% 0% 100% 75% 10Nissan -3% -3% 0% 36% 63% 0% 0% 5% 0% 90% 5% 99% 27% 1% 0% 0% 0% 99% 99% 99Porsche -5% -2% 3% 8% 60% 9% 0% 0% 0% 60% 19% 79% 69% 2% 21% 0% 29% 100% 29% 79Spyker -15% -14% 2% 9% 69% 8% 0% 0% 0% 80% 6% 87% 75% 0% 13% 0% 34% 100% 41% 87Subaru -8% -7% 0% 22% 75% 0% 0% 0% 0% 77% 20% 97% 75% 0% 3% 0% 0% 100% 85% 97Suzuki 0% 0% 0% 21% 75% 0% 0% 6% 0% 79% 11% 96% 75% 0% 4% 0% 3% 100% 93% 96Tata -19% -16% 2% 0% 37% 38% 0% 0% 0% 83% 0% 83% 75% 7% 17% 0% 33% 100% 33% 83Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -3% -3% 0% 6% 27% 1% 0% 4% 0% 74% 7% 84% 4% 16% 0% 0% 0% 84% 84% 84Volkswagen -7% -6% 1% 16% 73% 2% 0% 0% 0% 85% 8% 92% 75% 0% 8% 0% 0% 100% 46% 92Fleet -6% -5% 0% 20% 52% 3% 0% 8% 0% 79% 7% 95% 44% 4% 2% 0% 1% 96% 86% 95
Chapter 3 

3-74 

Table 3.5-45 Alternative 3- (Cars +20) Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -17% -15% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -8% -7% 1% 9% 65% 8% 0% 94% 0% 4% 2% 100% 73% 0% 0% 0% 7% 100% 72% 10Daimler -20% -18% 2% 12% 58% 23% 0% 100% 0% 0% 0% 100% 67% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -9% -8% 1% 14% 63% 20% 0% 88% 0% 8% 1% 97% 73% 3% 0% 0% 16% 97% 62% 9Geely -20% -19% 2% 22% 72% 6% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -9% -8% 1% 14% 53% 15% 0% 98% 0% 2% 0% 100% 68% 0% 0% 0% 15% 100% 66% 10Honda -9% -9% 0% 25% 75% 0% 0% 72% 0% 28% 0% 100% 75% 0% 0% 0% 0% 100% 98% 10Hyundai -10% -10% 0% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 0% 100% 100% 10Kia -9% -9% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -13% -12% 1% 25% 75% 0% 0% 70% 0% 28% 2% 100% 75% 0% 0% 0% 0% 100% 67% 10Mitsubishi -18% -17% 1% 25% 75% 0% 0% 86% 0% 14% 0% 100% 75% 0% 0% 0% 43% 100% 57% 10Nissan -6% -5% 1% 20% 70% 9% 0% 80% 0% 19% 1% 100% 75% 0% 0% 0% 0% 100% 74% 10Porsche -20% -18% 1% 11% 61% 28% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 50% 100% 50% 10Spyker -4% -2% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Subaru -19% -19% 0% 25% 75% 0% 0% 32% 0% 61% 8% 100% 75% 0% 0% 0% 16% 100% 84% 10Suzuki -14% -13% 1% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tata -13% -11% 1% 9% 59% 33% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -5% -5% 0% 29% 57% 8% 0% 82% 0% 9% 3% 94% 66% 6% 0% 0% 0% 94% 94% 9Volkswagen -18% -17% 2% 19% 69% 11% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Fleet -9% -8% 1% 19% 62% 11% 0% 88% 0% 9% 1% 98% 71% 2% 0% 0% 11% 98% 76% 9
MY 2017 and Later - Regula

3-75 

Table 3.5-46 Alternative 3- (Cars +20) Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -15% 5% 0% 0% 43% 0% 0% 0% 73% 4% 77% 43% 27% 23% 7% 20% 100% 20% 7BMW -12% -11% 1% 12% 64% 20% 0% 26% 0% 64% 6% 96% 75% 0% 4% 0% 13% 100% 50% 9Chrysler/Fiat -7% -7% 0% 17% 69% 5% 0% 43% 0% 55% 2% 100% 74% 0% 0% 0% 3% 100% 87% 10Daimler -16% -15% 1% 11% 64% 16% 0% 23% 0% 70% 0% 93% 73% 0% 7% 0% 32% 100% 47% 9Ferrari -10% -3% 7% 0% 0% 28% 0% 0% 0% 77% 0% 77% 28% 27% 23% 22% 5% 100% 5% 7Ford -8% -8% 1% 20% 67% 9% 0% 48% 0% 47% 4% 98% 72% 2% 0% 0% 5% 98% 78% 9Geely -15% -14% 2% 12% 62% 20% 0% 39% 0% 54% 1% 94% 75% 0% 6% 0% 24% 100% 46% 9General Motors -8% -8% 0% 22% 59% 9% 0% 50% 0% 47% 3% 100% 68% 0% 0% 0% 7% 100% 83% 10Honda -4% -4% 0% 20% 43% 0% 0% 21% 0% 68% 8% 98% 30% 2% 0% 0% 0% 98% 97% 9Hyundai -5% -5% 0% 36% 42% 0% 0% 39% 0% 55% 6% 100% 35% 0% 0% 0% 0% 100% 100% 10Kia -4% -4% 0% 9% 41% 0% 0% 27% 0% 66% 7% 100% 20% 0% 0% 0% 0% 100% 100% 10Lotus -2% 0% 2% 7% 75% 0% 0% 0% 0% 49% 36% 85% 75% 3% 15% 0% 36% 100% 36% 8Mazda -6% -6% 0% 25% 75% 0% 0% 24% 0% 63% 13% 100% 75% 0% 0% 0% 0% 100% 92% 10Mitsubishi -11% -10% 1% 25% 75% 0% 0% 39% 0% 57% 4% 100% 75% 0% 0% 0% 14% 100% 69% 10Nissan -4% -4% 0% 31% 65% 3% 0% 27% 0% 69% 4% 99% 41% 1% 0% 0% 0% 99% 92% 9Porsche -8% -6% 3% 9% 61% 13% 0% 21% 0% 47% 15% 83% 70% 1% 17% 0% 33% 100% 33% 8Spyker -14% -12% 2% 10% 69% 10% 0% 13% 0% 70% 5% 88% 75% 0% 12% 0% 36% 100% 42% 8Subaru -10% -10% 0% 23% 75% 0% 0% 7% 0% 73% 17% 98% 75% 0% 2% 0% 4% 100% 85% 9Suzuki -2% -2% 0% 22% 75% 0% 0% 22% 0% 65% 9% 97% 75% 0% 3% 0% 11% 100% 86% 9Tata -16% -14% 2% 4% 47% 36% 0% 46% 0% 44% 0% 91% 75% 4% 9% 0% 41% 100% 41% 9Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -4% -4% 0% 14% 38% 4% 0% 33% 0% 50% 5% 88% 26% 12% 0% 0% 0% 88% 88% 8Volkswagen -9% -8% 1% 17% 73% 4% 0% 20% 0% 68% 6% 94% 75% 0% 6% 0% 10% 100% 47% 9Fleet -7% -6% 0% 20% 56% 6% 0% 35% 0% 56% 5% 96% 53% 3% 1% 0% 5% 97% 83% 9
Chapter 3 

3-76 

Table 3.5-47 Alternative 4- (Cars -20) Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -11% 9% 0% 0% 5% 0% 0% 0% 76% 1% 77% 5% 50% 23% 22% 5% 100% 5% 77BMW -12% -9% 3% 3% 48% 20% 0% 0% 0% 74% 4% 78% 68% 7% 22% 0% 28% 100% 28% 78Chrysler/Fiat -9% -7% 2% 18% 72% 3% 0% 4% 0% 91% 1% 96% 75% 3% 4% 0% 11% 100% 46% 96Daimler -16% -11% 4% 0% 52% 14% 0% 0% 0% 78% 0% 78% 66% 4% 22% 7% 21% 100% 21% 78Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 77Ford -11% -10% 2% 12% 70% 4% 0% 29% 0% 59% 3% 92% 74% 6% 8% 0% 30% 99% 43% 92Geely -15% -10% 5% 3% 30% 23% 0% 13% 0% 66% 1% 80% 53% 14% 20% 10% 20% 100% 20% 80General Motors -9% -8% 1% 21% 72% 3% 0% 6% 0% 88% 4% 98% 75% 1% 2% 0% 3% 100% 57% 98Honda -4% -3% 1% 22% 73% 0% 0% 0% 0% 86% 9% 96% 73% 4% 2% 0% 0% 98% 65% 96Hyundai -7% -6% 2% 18% 75% 0% 0% 24% 0% 69% 3% 97% 75% 5% 3% 0% 16% 100% 47% 97Kia -4% -3% 1% 20% 75% 0% 0% 7% 0% 83% 6% 97% 75% 2% 3% 0% 4% 100% 59% 97Lotus -5% 0% 5% 2% 52% 0% 0% 0% 0% 62% 15% 77% 52% 11% 23% 12% 15% 100% 15% 77Mazda -7% -5% 2% 14% 66% 0% 0% 15% 0% 70% 6% 91% 66% 11% 9% 1% 39% 100% 41% 9Mitsubishi -10% -7% 2% 6% 71% 0% 0% 16% 0% 68% 3% 88% 71% 10% 12% 0% 34% 100% 38% 88Nissan -5% -4% 2% 17% 74% 0% 0% 5% 0% 88% 2% 95% 74% 4% 5% 0% 14% 100% 45% 95Porsche -8% -2% 6% 0% 46% 5% 0% 0% 0% 73% 4% 77% 51% 5% 23% 22% 5% 100% 5% 77Spyker -16% -11% 5% 0% 56% 8% 0% 0% 0% 74% 2% 77% 64% 2% 23% 11% 16% 100% 16% 77Subaru -9% -7% 2% 5% 74% 0% 0% 0% 0% 79% 11% 90% 74% 11% 10% 0% 37% 100% 40% 90Suzuki -3% 0% 3% 2% 44% 0% 0% 6% 0% 75% 4% 86% 44% 40% 14% 0% 36% 100% 36% 86Tata -19% -12% 8% 0% 13% 22% 0% 0% 0% 77% 0% 77% 35% 20% 23% 22% 5% 100% 5% 77Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% Toyota -4% -4% 1% 21% 63% 1% 0% 4% 0% 75% 6% 86% 64% 14% 0% 0% 2% 86% 65% 86Volkswagen -9% -5% 4% 6% 65% 2% 0% 0% 0% 73% 4% 77% 66% 1% 23% 4% 24% 100% 24% 77Fleet -8% -6% 2% 16% 67% 3% 0% 8% 0% 77% 5% 91% 70% 6% 6% 1% 13% 97% 49% 9
MY 2017 and Later - Regula

3-77 

Table 3.5-48 Alternative 4- (Cars -20) Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -17% -16% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -12% -11% 2% 20% 69% 8% 0% 95% 0% 3% 1% 99% 73% 10% 1% 0% 48% 100% 49% 9Daimler -20% -18% 2% 12% 58% 23% 0% 100% 0% 0% 0% 100% 67% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -12% -10% 2% 12% 62% 20% 0% 88% 0% 8% 1% 97% 72% 30% 2% 0% 46% 99% 47% 9Geely -20% -19% 2% 22% 72% 6% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -13% -11% 2% 17% 67% 15% 0% 98% 0% 2% 0% 100% 75% 15% 0% 0% 50% 100% 50% 10Honda -15% -14% 2% 18% 75% 0% 0% 72% 0% 26% 0% 98% 75% 8% 2% 0% 36% 100% 48% 9Hyundai -20% -19% 2% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Kia -17% -16% 1% 25% 75% 0% 0% 99% 0% 1% 0% 100% 75% 0% 0% 0% 49% 100% 50% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -20% -18% 2% 18% 63% 0% 0% 70% 0% 25% 1% 95% 63% 15% 5% 0% 37% 100% 45% 9Mitsubishi -20% -18% 2% 22% 70% 0% 0% 86% 0% 11% 0% 98% 70% 7% 2% 0% 48% 100% 48% 9Nissan -9% -8% 2% 15% 62% 9% 0% 80% 0% 16% 1% 97% 67% 23% 3% 0% 41% 100% 47% 9Porsche -20% -18% 2% 0% 50% 50% 0% 99% 0% 0% 1% 100% 75% 39% 0% 0% 50% 100% 50% 10Spyker -4% -2% 1% 15% 65% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Subaru -20% -17% 3% 8% 48% 0% 0% 32% 0% 55% 3% 90% 48% 34% 10% 0% 16% 100% 40% 9Suzuki -16% -15% 1% 25% 75% 0% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Tata -13% -11% 2% 0% 59% 41% 0% 100% 0% 0% 0% 100% 75% 50% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -10% -8% 2% 18% 68% 8% 0% 86% 0% 9% 1% 96% 72% 16% 1% 0% 43% 97% 46% 9Volkswagen -18% -17% 2% 19% 69% 11% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Fleet -13% -11% 2% 17% 67% 11% 0% 89% 0% 8% 1% 98% 73% 15% 1% 0% 45% 99% 48% 9
Chapter 3 

3-78 

Table 3.5-49 Alternative 4- (Cars -20) Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -20% -11% 9% 0% 0% 5% 0% 0% 0% 76% 1% 77% 5% 50% 23% 22% 5% 100% 5% 7BMW -13% -11% 3% 6% 52% 20% 0% 26% 0% 54% 3% 84% 70% 5% 16% 0% 34% 100% 34% 8Chrysler/Fiat -10% -9% 2% 19% 71% 5% 0% 43% 0% 53% 1% 98% 74% 6% 2% 0% 27% 100% 48% 9Daimler -17% -13% 4% 3% 54% 16% 0% 23% 0% 60% 0% 83% 66% 3% 17% 6% 27% 100% 27% 8Ferrari -10% -3% 8% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Ford -12% -10% 2% 12% 68% 9% 0% 48% 0% 44% 2% 93% 73% 13% 6% 0% 35% 99% 44% 9Geely -17% -13% 4% 9% 42% 18% 0% 39% 0% 47% 0% 86% 59% 10% 14% 7% 29% 100% 29% 8General Motors -11% -9% 1% 19% 70% 9% 0% 50% 0% 47% 2% 99% 75% 8% 1% 0% 25% 100% 53% 9Honda -7% -6% 1% 21% 73% 0% 0% 21% 0% 69% 7% 96% 73% 5% 2% 0% 11% 98% 60% 9Hyundai -10% -8% 2% 19% 75% 0% 0% 39% 0% 56% 3% 97% 75% 4% 3% 0% 22% 100% 47% 9Kia -7% -6% 1% 21% 75% 0% 0% 27% 0% 66% 5% 97% 75% 1% 3% 0% 13% 100% 57% 9Lotus -5% 0% 5% 2% 52% 0% 0% 0% 0% 62% 15% 77% 52% 11% 23% 12% 15% 100% 15% 7Mazda -9% -7% 2% 15% 65% 0% 0% 24% 0% 63% 5% 92% 65% 11% 8% 0% 39% 100% 41% 9Mitsubishi -13% -11% 2% 11% 70% 0% 0% 39% 0% 49% 2% 91% 70% 9% 9% 0% 39% 100% 41% 9Nissan -7% -5% 2% 17% 71% 3% 0% 27% 0% 67% 2% 95% 72% 9% 4% 0% 22% 100% 46% 9Porsche -10% -5% 5% 0% 47% 15% 0% 21% 0% 57% 3% 82% 56% 12% 18% 17% 15% 100% 15% 8Spyker -15% -10% 5% 2% 57% 10% 0% 13% 0% 65% 2% 80% 66% 1% 20% 10% 20% 100% 20% 8Subaru -12% -9% 2% 5% 68% 0% 0% 7% 0% 74% 9% 90% 68% 16% 10% 0% 32% 100% 40% 9Suzuki -5% -3% 3% 6% 50% 0% 0% 22% 0% 62% 3% 88% 50% 33% 12% 0% 38% 100% 38% 8Tata -17% -11% 5% 0% 34% 31% 0% 46% 0% 41% 0% 88% 54% 34% 12% 12% 26% 100% 26% 8Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0Toyota -6% -5% 1% 20% 65% 4% 0% 34% 0% 51% 5% 90% 67% 15% 0% 0% 17% 90% 58% 9Volkswagen -11% -8% 3% 9% 66% 3% 0% 20% 0% 59% 3% 82% 68% 1% 18% 3% 29% 100% 29% 8Fleet -9% -8% 2% 16% 67% 6% 0% 35% 0% 55% 3% 93% 71% 9% 5% 0% 24% 98% 49% 9
MY 2017 and Later - Regulatory Impact Analysis 

3-79 

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 

the 19 vehicle types into 9 narrower vehicle classes.YY

 

Table 3.5-50 Aggregation of Vehicle types for Mass Reduction Presentation 

VehType 2025 sales Vehicle Class 

SubAuto_4_4_4_DOHC 1 2,343,764 Small car 

Auto_4_4_4_DOHC 2 3,717,990 Standard car 

Auto_6_6_4_DOHC 3 2,684,824 Standard car 

Auto_6_6_2_SOHC 4 486,136 Standard car 

Auto_8_8_4_DOHC 5 566,356 Large car 

Auto_8_8_2_OHV 6 168,301 Large car 

MPVnt_4_4_4_DOHC 7 1,098,943 Small MPV 

MPVt_6_6_4_DOHC 8 3,910,859 Large MPV 

MPVt_6_6_2_SOHC 9 90,504 Large MPV 

MPVt_6_6_2_OHV 10 442,375 Large MPV 

MPVt_8_8_4_DOHC 11 263,513 Truck 

MPVt_8_8_2_OHV 12 123,898 Truck 

Truck_4_4_4_DOHC 13 61,359 Small MPV 

Truck_6_6_4_DOHC 14 258,882 Large MPV 

Truck_6_6_2_OHV 15 162,502 Large MPV 

Truck_8_8_4_DOHC 16 217,954 Truck 

Truck_8_8_2_SOHC 17 103,184 Truck 

Truck_8_8_3_SOHC 18 161,734 Truck 

Truck_8_8_2_OHV 19 387,383 Truck 

 

 

YY Just to limit the size of this table. 
Chapter 3 

3-80 

After aggregations here are the weight reductions by vehicle class. 

Reference Control Sales 

2021 2025 2021 2025 2025 

Subcompact car I4 -0.2% -0.2% -0.3% -0.5% 2,343,764

Compact car I4 -1.3% -1.3% -1.6% -1.7% 3,717,990

Midsize car V6 -5.3% -5.2% -7.2% -8.3% 3,170,959

Large car V8 -7.2% -7.2% -10.6% -13.2% 734,656

Small MPV I4 -5.1% -5.1% -8.2% -13.3% 1,160,302

Midsize MPV V6 -5.6% -5.7% -7.6% -11.2% 4,443,738

Large MPV V8 -7.1% -7.0% -9.7% -12.3% 387,411

Full pickup V6 -1.8% -1.7% -2.0% -1.9% 421,385

Full pickup V8 -7.0% -7.0% -7.6% -8.8% 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 Case 2021 2025 

Car 

Reference $68 $64 

Control $79 $69 

Total $147 $133 

Truck 

Reference $52 $49 

Control $95 $84 

Total $147 $133 

Fleet Total $147 $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 
MY 2017 and Later - Regulatory Impact Analysis 

3-81 

help ensure a conservative cost analysis for the rule (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 

New 

technology 

Stranded capital cost per vehicle 

when replaced technology’s production is 

ended after: 

3 years 5 years 8 years 

6-speed AT 6-speed DCT $55 $39 $16 

6-speed AT 8-speed AT $48 $34 $14 

6-speed DCT 8-speed DCT $28 $20 $8 

Conventional V6 DSTGDI I4 $56 $40 $16 

Conventional V8 DSTGDI V6 $60 $43 $17 

Conventional V6 Power-split HEV $111 $79 $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 
Chapter 3 

3-82 

and truck. 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 CapitalZZ (2010$) 

MY2016 MY 2021 MY 2025 

Manufacturer Engine Transmission Total Engine Transmission Total Engine Transmission Total 

Aston Martin $51 $16 $67 $27 $8 $35 $14 $3 $17

BMW $9 $3 $13 $15 $16 $31 $7 $4 $11

Chrysler/Fiat $60 $0 $60 $10 $14 $24 $11 $5 $16

Daimler $11 $6 $17 $13 $9 $22 $6 $4 $10

Ferrari $0 $1 $1 $28 $12 $40 $19 $3 $22

Ford $15 $0 $15 $9 $12 $21 $4 $5 $9

Geely $13 $0 $13 $14 $16 $30 $7 $5 $12

General 

Motors $18 $0 $18 $8 $10 $18 $9 $5 $14

Honda $12 $0 $12 $3 $10 $13 $17 $4 $21

Hyundai $7 $0 $7 $3 $6 $9 $11 $4 $15

Kia $7 $0 $7 $26 $17 $43 $21 $4 $25

Lotus $30 $0 $30 $13 $0 $13 $8 $2 $10

Mazda $12 $0 $12 $23 $15 $38 $10 $4 $13

Mitsubishi $9 $0 $9 $23 $15 $39 $9 $4 $13

Nissan $11 $0 $11 $4 $9 $13 $10 $4 $14

Porsche $16 $1 $17 $17 $10 $27 $12 $3 $15

Spyker $41 $0 $41 $6 $8 $14 $11 $4 $15

Subaru $7 $0 $7 $8 $7 $15 $2 $3 $5

Suzuki $32 $0 $32 $6 $8 $14 $7 $4 $11

Tata $12 $4 $16 $9 $12 $21 $16 $4 $20

Tesla $1 $0 $1 $0 $0 $0 $0 $0 $0

Toyota $2 $0 $2 $13 $6 $19 $16 $9 $25

Volkswagen $9 $2 $12 $15 $10 $25 $1 $4 $5

Fleet $13 $0 $14 $10 $10 $20 $10 $5 $16

 

ZZ 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-83 

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 2021MY, 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 2021 2025

Cars Trucks Fleet Cars Trucks Fleet 

Aston Martin $2,632 $0 $2,632 $2,417 $0 $2,417 

BMW $1,989 $2,126 $2,025 $1,820 $1,955 $1,855 

Chrysler/Fiat $811 $978 $887 $718 $909 $801 

Daimler $2,212 $2,238 $2,219 $2,044 $2,065 $2,049 

Ferrari $2,455 $0 $2,455 $2,248 $0 $2,248 

Ford $911 $1,334 $1,054 $859 $1,255 $981 

Geely-Volvo $2,038 $1,959 $2,014 $1,865 $1,804 $1,847 

GM $769 $924 $846 $712 $853 $780 

Honda $110 $416 $205 $94 $397 $183 

Hyundai $401 $670 $456 $376 $647 $430 

Kia $303 $712 $395 $281 $688 $367 

Lotus $1,867 $0 $1,867 $1,715 $0 $1,715 

Mazda $726 $890 $755 $673 $835 $700 

Mitsubishi $1,182 $1,566 $1,316 $1,076 $1,443 $1,198 

Nissan $410 $890 $559 $373 $818 $505 

Porsche $1,884 $1,910 $1,890 $1,728 $1,739 $1,730 

Spyker-Saab $1,913 $2,112 $1,941 $1,751 $1,946 $1,776 

Subaru $1,044 $1,191 $1,080 $1,023 $1,147 $1,051 

Suzuki $1,016 $1,330 $1,072 $951 $1,244 $1,001 

Tata-JLR $2,518 $2,548 $2,533 $2,313 $2,345 $2,328 

TeslaAAA $68 $0 $68 $63 $0 $63 

Toyota $160 $417 $260 $149 $375 $231 

VW $1,743 $1,735 $1,742 $1,589 $1,574 $1,586 

Fleet $710 $917 $783 $655 $849 $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. 
Chapter 3 

3-84 

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 pervehicle 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 2021 Costs 2021 Sales 

Cars Trucks Fleet Cars Truck Fleet 

Aston 

Martin $6,724 $0 $6,724 1,058 0 1,058

BMW $967 $529 $852 359,098 128,724 487,822

Chrysler/Fiat $681 $796 $733 421,013 348,613 769,626

Daimler $1,985 $659 $1,655 300,378 99,449 399,827

Ferrari $6,712 $0 $6,712 7,059 0 7,059

Ford $680 $875 $746 1,401,617 714,181 2,115,798

Geely-Volvo $2,132 $734 $1,698 92,726 41,768 134,494

GM $519 $720 $619 1,564,277 1,530,020 3,094,297

Honda $532 $829 $624 1,198,880 535,916 1,734,796

Hyundai $773 $875 $794 613,355 156,466 769,821

Kia $625 $908 $689 331,319 95,432 426,751

Lotus $3,739 $0 $3,739 278 0 278

Mazda $959 $1,246 $1,010 274,740 59,227 333,967

Mitsubishi $611 $1,127 $791 65,851 35,309 101,160

Nissan $644 $904 $725 912,629 408,029 1,320,658

Porsche $4,878 $604 $3,871 36,475 11,242 47,716

Spyker-Saab $3,019 $607 $2,674 21,294 3,560 24,854

Subaru $982 $1,594 $1,128 230,780 72,773 303,553

Suzuki $1,032 $1,210 $1,064 95,725 20,767 116,492

Tata-JLR $3,916 $1,061 $2,495 58,677 58,153 116,830

Tesla $79 $0 $79 28,623 0 28,623

Toyota $488 $600 $532 1,903,706 1,215,539 3,119,245

VW $1,492 $508 $1,293 585,607 148,734 734,341

Fleet $767 $763 $766 10,505,165 5,683,902 16,189,066
MY 2017 and Later - Regulatory Impact Analysis 

3-85 

Table 3.6-3 Control Case Costs for the Standards MY 2025 (2010$) 

Company 2025 2025 Sales 

Cars Trucks Fleet Cars Truck Fleet 

Aston 

Martin $7,480 $0 $7,480 1,182 0 1,182

BMW $2,147 $1,250 $1,910 405,256 145,409 550,665

Chrysler/Fiat $1,617 $2,388 $1,950 436,479 331,762 768,241

Daimler $3,011 $1,284 $2,616 340,719 101,067 441,786

Ferrari $7,864 $0 $7,864 7,658 0 7,658

Ford $1,811 $2,505 $2,025 1,540,109 684,476 2,224,586

Geely-Volvo $3,177 $1,504 $2,681 101,107 42,588 143,696

GM $1,518 $2,237 $1,861 1,673,936 1,524,008 3,197,943

Honda $1,525 $1,923 $1,642 1,340,321 557,697 1,898,018

Hyundai $1,673 $2,268 $1,792 677,250 168,136 845,386

Kia $1,572 $1,977 $1,658 362,783 97,653 460,436

Lotus $3,566 $0 $3,566 316 0 316

Mazda $1,979 $2,449 $2,057 306,804 61,368 368,172

Mitsubishi $1,939 $2,169 $2,015 73,305 36,387 109,692

Nissan $1,618 $2,391 $1,847 1,014,775 426,454 1,441,229

Porsche $4,807 $1,274 $4,044 40,696 11,219 51,915

Spyker-Saab $3,580 $964 $3,238 23,130 3,475 26,605

Subaru $1,926 $2,495 $2,054 256,970 74,722 331,692

Suzuki $2,112 $1,848 $2,066 103,154 21,374 124,528

Tata-JLR $5,077 $1,447 $3,390 65,418 56,805 122,223

Tesla $69 $0 $69 31,974 0 31,974

Toyota $1,239 $1,700 $1,407 2,108,053 1,210,016 3,318,069

VW $2,412 $1,237 $2,181 630,163 154,284 784,447

Fleet $1,726 $2,059 $1,836 11,541,560 5,708,899 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 $1,836/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. 
Chapter 3 

3-86 

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 ICE Only 

Sales

MHEV +HEV 

Sales 

EV+PHEV 

Sales Total Sales

2017 14,779,343 975,369 51,609 15,806,322 

2018 14,364,044 1,137,524 74,842 15,576,410 

2019 14,165,763 1,314,056 98,839 15,578,658 

2020 14,249,833 1,520,778 125,327 15,895,939 

2021 14,304,401 1,732,100 152,565 16,189,066 

2022 13,707,594 2,526,963 205,216 16,439,772 

2023 12,976,088 3,420,545 258,856 16,655,489 

2024 12,266,523 4,352,578 314,986 16,934,087 

2025 11,551,765 5,325,056 373,638 17,250,459 

Total 122,365,357 22,304,969 1,655,878 146,326,204 

Fraction 84% 15% 1% 100% 

3.7 Alternative Program Stringencies 

Table 3.7-1 Control Case Costs for the Alternative 1 (Trucks +20) Standards (2010$) 

Company 2021 2025 

Cars Trucks Fleet Cars Trucks Fleet 

Aston Martin $6,724 $0 $6,724 $7,480 $0 $7,480

BMW $444 $529 $467 $1,679 $1,250 $1,566

Chrysler/Fiat $296 $474 $377 $1,236 $1,832 $1,494

Daimler $1,428 $616 $1,226 $2,441 $1,284 $2,176

Ferrari $6,712 $0 $6,712 $7,864 $0 $7,864

Ford $404 $505 $438 $1,537 $1,906 $1,650

Geely-Volvo $1,373 $722 $1,171 $2,410 $1,504 $2,141

GM $176 $367 $271 $1,196 $1,513 $1,347

Honda $398 $567 $450 $1,344 $1,452 $1,376

Hyundai $556 $875 $620 $1,520 $2,007 $1,617

Kia $514 $674 $550 $1,442 $1,477 $1,449

Lotus $3,739 $0 $3,739 $3,566 $0 $3,566

Mazda $834 $967 $858 $1,803 $2,449 $1,911

Mitsubishi $445 $510 $468 $1,495 $1,838 $1,609

Nissan $417 $670 $495 $1,439 $1,748 $1,530

Porsche $4,258 $604 $3,397 $4,341 $1,274 $3,678

Spyker-Saab $2,671 $607 $2,375 $3,272 $964 $2,971

Subaru $878 $822 $865 $1,722 $2,252 $1,842

Suzuki $760 $1,210 $840 $1,967 $1,848 $1,946

Tata-JLR $2,161 $562 $1,365 $3,801 $1,075 $2,534

Tesla $79 $0 $79 $69 $0 $69

Toyota $328 $406 $359 $1,020 $1,411 $1,163

VW $1,056 $508 $945 $2,129 $1,237 $1,953

Fleet $497 $492 $496 $1,460 $1,582 $1,500
MY 2017 and Later - Regulatory Impact Analysis 

3-87 

Table 3.7-2 Control Case Costs for the Alternative 2 (Trucks -20) Standards (2010$) 

Company 2021 2025 

Cars Trucks Fleet Cars Trucks Fleet 

Aston Martin $6,724 $0 $6,724 $7,480 $0 $7,480

BMW $1,569 $576 $1,307 $2,676 $1,250 $2,300

Chrysler/Fiat $904 $1,460 $1,156 $2,233 $2,792 $2,474

Daimler $2,704 $662 $2,196 $3,503 $1,284 $2,995

Ferrari $6,712 $0 $6,712 $7,864 $0 $7,864

Ford $962 $1,418 $1,116 $2,283 $2,631 $2,390

Geely-Volvo $3,065 $846 $2,376 $3,986 $1,504 $3,250

GM $916 $1,262 $1,087 $2,234 $2,828 $2,517

Honda $754 $1,035 $841 $1,718 $2,363 $1,907

Hyundai $937 $1,067 $963 $1,906 $2,504 $2,025

Kia $861 $910 $872 $1,744 $2,328 $1,868

Lotus $3,739 $0 $3,739 $3,566 $0 $3,566

Mazda $1,139 $1,467 $1,198 $2,155 $2,624 $2,233

Mitsubishi $1,153 $1,263 $1,192 $2,468 $2,169 $2,369

Nissan $929 $1,126 $990 $2,027 $2,503 $2,168

Porsche $5,579 $861 $4,468 $5,305 $1,274 $4,434

Spyker-Saab $3,410 $607 $3,009 $3,719 $964 $3,360

Subaru $1,311 $1,594 $1,379 $2,262 $2,495 $2,314

Suzuki $1,277 $1,210 $1,265 $2,470 $1,953 $2,381

Tata-JLR $6,220 $1,061 $3,652 $7,074 $1,809 $4,627

Tesla $79 $0 $79 $69 $0 $69

Toyota $556 $1,044 $746 $1,546 $2,210 $1,788

VW $1,975 $508 $1,678 $2,857 $1,237 $2,538

Fleet $1,062 $1,159 $1,096 $2,146 $2,434 $2,241
Chapter 3 

3-88 

Table 3.7-3 Control Case Costs for the Alternative 3 (Cars +20) Standards (2010$) 

Company 2021 2025 

Cars Trucks Fleet Cars Trucks Fleet 

Aston Martin $6,724 $0 $6,724 $5,723 $0 $5,723

BMW -$258 $474 -$65 $1,068 $1,195 $1,102

Chrysler/Fiat $296 $474 $377 $1,236 $1,659 $1,419

Daimler $692 $616 $673 $1,723 $1,284 $1,622

Ferrari $6,712 $0 $6,712 $7,416 $0 $7,416

Ford $238 $287 $254 $1,212 $1,505 $1,302

Geely-Volvo $579 $722 $623 $1,708 $1,504 $1,647

GM $187 $442 $313 $1,201 $1,619 $1,400

Honda $305 $377 $327 $990 $1,382 $1,105

Hyundai $304 $535 $351 $1,118 $1,218 $1,138

Kia $364 $313 $353 $989 $1,230 $1,040

Lotus $2,125 $0 $2,125 $2,346 $0 $2,346

Mazda $395 $491 $412 $1,223 $1,587 $1,284

Mitsubishi $275 $241 $263 $1,133 $1,658 $1,307

Nissan $259 $335 $282 $1,176 $1,407 $1,244

Porsche $3,315 $548 $2,663 $3,472 $1,274 $2,997

Spyker-Saab $1,437 $532 $1,308 $2,223 $964 $2,059

Subaru $462 $511 $474 $1,369 $1,526 $1,405

Suzuki $342 $419 $356 $1,297 $1,776 $1,379

Tata-JLR $2,161 $562 $1,365 $3,297 $1,075 $2,264

Tesla $79 $0 $79 $69 $0 $69

Toyota $324 $294 $312 $857 $1,303 $1,020

VW $147 $484 $215 $1,292 $1,237 $1,281

Fleet $298 $388 $330 $1,151 $1,448 $1,249

 
MY 2017 and Later - Regulatory Impact Analysis 

3-89 

Table 3.7-4 Control Case Costs for the Alternative 4 (Cars -20) Standards (2010$) 

Company 2021 2025 

Cars Trucks Fleet Cars Trucks Fleet 

Aston Martin $6,724 $0 $6,724 $7,885 $0 $7,885

BMW $2,597 $616 $2,075 $3,684 $1,250 $3,041

Chrysler/Fiat $912 $1,562 $1,206 $2,332 $2,851 $2,556

Daimler $4,014 $664 $3,181 $4,580 $1,284 $3,826

Ferrari $6,712 $0 $6,712 $7,864 $0 $7,864

Ford $1,343 $1,521 $1,403 $2,832 $2,728 $2,800

Geely-Volvo $4,153 $925 $3,151 $5,049 $1,504 $3,998

GM $879 $1,155 $1,015 $2,043 $2,828 $2,417

Honda $1,019 $1,227 $1,083 $2,086 $2,790 $2,293

Hyundai $1,311 $1,879 $1,426 $2,654 $2,718 $2,666

Kia $1,217 $1,359 $1,249 $2,425 $2,552 $2,452

Lotus $5,282 $0 $5,282 $4,908 $0 $4,908

Mazda $1,881 $2,100 $1,920 $3,086 $2,958 $3,064

Mitsubishi $1,625 $1,444 $1,562 $3,078 $2,186 $2,782

Nissan $1,182 $1,539 $1,292 $2,524 $2,722 $2,583

Porsche $5,849 $1,347 $4,788 $6,148 $2,205 $5,296

Spyker-Saab $4,917 $773 $4,324 $5,040 $964 $4,507

Subaru $1,915 $2,061 $1,950 $2,793 $3,238 $2,893

Suzuki $2,204 $1,276 $2,039 $3,301 $1,953 $3,070

Tata-JLR $6,360 $1,061 $3,723 $7,074 $2,214 $4,815

Tesla $79 $0 $79 $69 $0 $69

Toyota $708 $1,091 $857 $1,631 $2,565 $1,971

VW $3,234 $766 $2,734 $4,018 $1,237 $3,471

Fleet $1,422 $1,261 $1,365 $2,556 $2,612 $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 

 

Reference 

Case CO2 

MY 2021 

CO2 

Delta 

g/mile 

Delta 

Cost^ 

$ per 

g/mile 

OMEGA projection of 224 178 $46 $767 $ 17 
Chapter 3 

3-90 

average 2021 Car in 

control Case 

EV100 (45 sqft, VT 3, 

no multiplier) 263 - 263 $16,877 $64 

EV100 (45 sqft, VT 3, 

1.5 multiplier) 263 - 395 $16,877 $43 

 

OMEGA projection of 

average 2021 Truck in 

control Case 

296 239 57 $763 $13 

HEV (65 sqft, VT 16, 

no credit) 334 251 93 $6,054 $65 

HEV (65 sqft, VT 16, 

20 g credit) 334 231 113 $6,054 $54 

^Note 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 CO2 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 CO2 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 costeffectiveness basis. Further in MY 2025, when the average cost per gram/mile is higher, 

these technologies are relatively more cost effective. 

 
MY 2017 and Later - Regulatory Impact Analysis 

3-91 

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 CO2 footprint-based 

standard curves to determine which of these vehicles would meet or be lower than the final 

MY 2017 – MY 2025 footprint-based CO2 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 CO2 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.1. No adjustments were made to vehicle CO2 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 CO2 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 CO2 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 CO2 targets with current powertrain designs, assuming air 

conditioning credit generation consistent with this final rule. The table highlights the vehicles 

with CO2 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) 
Chapter 3 

3-92 

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 

optionsDDD

. 

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 2017 2018 2019 2020 2025 

2011 7.0% 6.2% 5.9% 5.2% 1.8% 

2012 16.8% 13.6% 8.4% 6.6% 3.1% 

Table 3.9-2 Percentage of Projected Sales Within 5% of Final Targets 

Model Year 2017 2018 2019 2020 2025 

2011 7.6% 2.5% 1.5% 1.7% 0.8% 

2012 12.2% 10.9% 7.1% 2.6% 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 

 

DDD 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. 
MY 2017 and Later - Regulatory Impact Analysis 

3-93 

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 Gasoline Diesel CNG HEV PHEV EV FCV Total 

2011/2012 27 1 1 27 1 3 0 60 

2012/2013 65 3 1 29 1 8 1 108 

Table 3.9-4 Number of Vehicle Models Within 5% of 2017 Targets by Technology 

Model 

Year Gasoline Diesel CNG HEV PHEV EV FCV Total 

2011/2012 38 6 0 3 0 0 0 47 

2012/2013 58 6 0 2 0 0 0 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 CO2 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 
Chapter 3 

3-94 

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 CO2 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 CO2 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. 
MY 2017 and Later - Regula

3-95 

Table 3.9-5 Vehicles that Meet or Exceed Final Targets With Current Powertrain Desig

2017 2018 2019 2020 

2012 Azure Dynamics Transit Connect Electric Van 89.0 0 47.9 EV A1 n/a Van T 100% 100% 100% 100% 

2012 Azure Dynamics Transit Connect Electric Wagon 89.0 0 47.9 EV A1 n/a Van T 100% 100% 100% 100% 

2012 CODA CODA 103.9 0 41.4 EV A1 n/a Subcompact Cars C 100% 100% 100% 100% 

2012 Ford Focus FWD BEV 150.0 0 44.2 EV A1 n/a Compact Cars C 100% 100% 100% 100% 

2012 Mercedes-Benz F-Cell 75.5 0 49.4 Fuel Cell A1 n/a Small Station Wagons C 100% 100% 100% 100% 

2011 Mercedes-Benz Smart fortwo (cabriolet) 123.9 0 26.8 EV A1 n/a Two Seaters C 100% 100% 100% 100% 

2011 Mercedes-Benz Smart fortwo (coupe) 123.9 0 26.8 EV A1 n/a Two Seaters C 100% 100% 100% 100% 

2012 Mitsubishi i-MiEV 160.3 0 38.4 EV A1 n/a Subcompact Cars C 100% 100% 100% 100% 

2012 Nissan Leaf 141.7 0 44.7 EV A1 n/a Midsize Cars C 100% 100% 100% 100% 2012 Chevrolet Volt 122.0 56 45.3 PHEV CVT 1.4 Compact Cars C 80% 79% 79% 79% 

2012 Toyota Prius 70.7 126 44.2 HEV CVT 1.8 Midsize Cars C 46% 44% 42% 40% 

2012 Toyota Prius c 70.7 126 42.3 HEV CVT 1.5 Compact Cars C 44% 42% 39% 37% 

2012 Honda Civic Hybrid 63.1 141 43.5 HEV CVT 1.5 Compact Cars C 38% 35% 33% 30% 

2012 Toyota Prius v 58.7 151 46.1 HEV CVT 1.8 Midsize Station Wagons C 36% 34% 31% 28% 2012 Toyota Camry Hybrid LE 57.4 155 47.2 HEV CVT 2.5 Midsize Cars C 36% 34% 31% 28% 

2012 Toyota Camry Hybrid XLE 54.8 162 46.9 HEV CVT 2.5 Midsize Cars C 33% 30% 27% 24% 

2012 Hyundai Sonata Hybrid 52.2 170 47.8 HEV A6 2.4 Midsize Cars C 30% 27% 24% 21% 2012 Lexus CT 200h 57.5 155 42.7 HEV CVT 1.8 Compact Cars C 30% 27% 24% 21% 2012 Ford Fusion Hybrid FWD 54.2 164 45.6 HEV CVT 2.5 Midsize Cars C 30% 27% 24% 21% 

2012 Lincoln MKZ Hybrid FWD 54.2 164 45.6 HEV CVT 2.5 Midsize Cars C 30% 27% 24% 21% 2012 Honda Insight 58.9 151 40.5 HEV A7 1.3 Compact Cars C 29% 26% 23% 20% 2012 Honda Insight 58.8 151 40.5 HEV CVT 1.3 Compact Cars C 29% 26% 23% 19% 

2012 Kia Optima Hybrid 50.6 175 48.2 HEV A6 2.4 Midsize Cars C 28% 25% 22% 19% 

2012 Honda Civic CNG 41.3 163 43.4 CNG A5 1.8 Subcompact Cars C 27% 24% 20% 17% 

2013 Lexus RX 450h 40.4 220 48.0 HEV A6 3.5 Sport Utility Vehicle T 23% 22% 21% 19% 2012 Toyota Highlander Hybrid 4WD 38.5 231 48.8 HEV CVT 3.5 Sport Utility Vehicle T 21% 19% 18% 16% 2013 Lexus RX 450h AWD 38.6 230 48.0 HEV A6 3.5 Sport Utility Vehicle T 20% 18% 17% 15% 

2012 Ford Focus FWD 41.1 216 44.2 Gasoline A6 2.0 Compact Cars T 19% 18% 17% 15% 2012 Chevrolet C15 Silverado 2WD Hybrid 28.5 311 68.0 HEV CVT 6.0 Standard Pick-up Truck T 14% 14% 14% 14% 2012 GMC C15 Sierra 2WD Hybrid 28.5 311 68.0 HEV CVT 6.0 Standard Pick-up Truck T 14% 14% 14% 14% 

2012 Chevrolet K15 Silverado 4WD Hybrid 28.4 313 68.0 HEV CVT 6.0 Standard Pick-up Truck T 13% 13% 14% 13% 2012 GMC K15 Sierra 4WD Hybrid 28.4 313 68.0 HEV CVT 6.0 Standard Pick-up Truck T 13% 13% 14% 13% 2012 Scion iQ 52.3 170 31.6 Gasoline CVT 1.3 Minicompact Cars C 19% 16% 12% 8% 2012 Lexus HS 250h 47.3 188 44.5 HEV CVT 2.4 Compact Cars C 17% 13% 9% 5% 

2013 Mazda CX-5 4WD 36.8 241 46.1 Gasoline A6 2.0 Sport Utility Vehicle T 13% 11% 9% 7% 

2013 Mercedes-Benz Smart fortwo (Convertible) 50.3 177 26.8 Gasoline A5 1.0 Two Seaters C 16% 12% 8% 4% 

2013 Mercedes-Benz Smart fortwo (Coupe) 50.3 177 26.8 Gasoline A5 1.0 Two Seaters C 16% 12% 8% 4% 2012 Honda CR-Z 50.1 177 39.5 HEV A7 1.5 Two Seaters C 15% 12% 8% 4% 2013 Hyundai Elantra Blue 45.2 197 45.2 Gasoline A6 1.8 Midsize Cars C 14% 10% 6% 2% 

2013 Hyundai Elantra 44.7 199 45.2 Gasoline M6 1.8 Midsize Cars C 13% 9% 5% 1% 2013 Hyundai Elantra Coupe 44.6 199 45.2 Gasoline M6 1.8 Midsize Cars C 13% 9% 5% 1% 

2013 Hyundai Elantra 44.4 200 45.2 Gasoline A6 1.8 Midsize Cars C 12% 9% 4% 0% 

2013 Lexus GS 450h 41.6 214 48.5 HEV A6 3.5 Midsize Cars C 12% 8% 4% 0% 2012 Toyota Sienna 29.4 302 56.1 Gasoline A6 2.7 Minivan 2WD T 7% 5% 3% 1%2012 Dodge Ram C/V 25.9 343 65.9 Gasoline A6 3.6 Minivan 2WD T 5% 5% 4% 2%

2013 Lincoln MKT Livery FWD 30.5 292 53.5 Gasoline A6 2.0 Sport Utility Vehicle T 6% 4% 3% 0%2012 Chevrolet Cruze ECO 44.4 200 44.8 Gasoline M6 1.4 Midsize Cars C 11% 8% 3% -1%2013 Kia Rio ECO 46.7 190 42.1 Gasoline A6 1.6 Compact Cars C 11% 7% 3% -1%

2012 Hyundai Veloster 43.8 203 44.6 Gasoline A6 1.6 Compact Cars C 10% 6% 2% -3%

2013 Kia Rio 45.8 194 42.1 Gasoline M6 1.6 Compact Cars C 9% 5% 1% -4%

Powertrain 

Type Transmission

Engine 

Displacement

(L)

Vehicle Class Car/ Truck Com

Model Year Manufacturer Vehicle Unadjusted Fuel Economy (mpg) Tailpipe CO2 (g/mile) Footprint (ft

2

)
Chapter 3 

3-96 

2017 2018 2019 2020 

2013 Hyundai Elantra Coupe 42.8 208 45.2 Gasoline A6 1.8 Midsize Cars C 9% 5% 0% -4%

2012 Ford Focus SFE FWD 43.6 204 44.2 Gasoline A6 2.0 Compact Cars C 9% 5% 0% -4%

2012 Ford Focus SFE FWD FFV 43.6 204 44.2 Gasoline A6 2.0 Compact Cars C 9% 5% 0% -4%2012 Honda Civic HF 44.3 201 43.4 Gasoline A5 1.8 Compact Cars C 9% 5% 0% -4%

2012 Toyota Tacoma 2WD - Access cab 29.9 297 54.0 Gasoline M5 2.7 Small Pick-up Truck T 5% 3% 1% -1%

2012 Honda Odyssey 2WD 29.0 307 55.9 Gasoline A6 3.5 Minivan 2WD T 5% 3% 1% -1%2012 Hyundai Veloster 43.1 206 44.6 Gasoline M6 1.6 Compact Cars C 8% 4% 0% -5%

2013 Dodge Dart 42.3 210 45.6 Gasoline M6 1.4 Midsize Cars C 8% 4% 0% -5%2013 Kia Rio 45.1 197 42.1 Gasoline A6 1.6 Compact Cars C 8% 4% -1%2013 Hyundai Accent 45.3 196 41.7 Gasoline M6 1.6 Compact Cars C 7% 3% -1%

2012 Volkswagen Passat 46.4 220 47.2 Diesel M6 2.0 Midsize Cars C 7% 3% -1%2013 Hyundai Accent 45.1 197 41.7 Gasoline A6 1.6 Compact Cars C 7% 3% -2%

2012 Mazda Mazda3 DI 4-Door 43.8 203 43.1 Gasoline A6 2.0 Compact Cars C 7% 3% -2%

2012 Nissan Versa 45.1 197 41.5 Gasoline CVT 1.6 Compact Cars C 7% 2% -2%

2012 Infiniti M35h 38.8 229 49.1 Gasoline A7 3.5 Midsize Cars C 6% 2% -2%2012 BMW 528i 36.8 241 51.6 Gasoline A8 2.0 Midsize Cars C 6% 2% -3%2012 Honda Civic 43.0 207 43.4 Gasoline A5 1.8 Compact Cars C 6% 2% -3%2012 Ford Focus FWD 42.1 211 44.2 Gasoline A6 2.0 Compact Cars C 5% 1% -4%

2012 Ford Focus FWD FFV 42.1 211 44.2 Gasoline A6 2.0 Compact Cars C 5% 1% -4%

2012 Kia Soul ECO 42.8 208 43.3 Gasoline A6 1.6 Small Station Wagons C 5% 1% -4%

2012 Honda CR-Z 44.9 198 39.5 Gasoline M6 1.5 Two Seaters C 5% 1% -4%

2012 Toyota Yaris 44.9 198 39.9 Gasoline M5 1.5 Compact Cars C 5% 1% -4%

2012 Mazda Mazda3 DI 5-Door 42.9 207 43.1 Gasoline A6 2.0 Midsize Cars C 5% 1% -4%

2012 Mercedes-Benz S 350 BLUETEC 4MATIC 32.3 315 56.6 Diesel A7 3.0 Large Cars T 3% 1% -1% -3%

2013 BMW X3 xDrive28i 31.5 283 48.8 Gasoline A8 2.0 Sport Utility Vehicle T 2% 0% -2% -5%

2012 Toyota Tacoma 2WD - Access Cab 28.1 317 55.9 Gasoline A4 2.7 Small Pick-up Truck T 2% 0% -2%2012 Toyota Tacoma 2WD - Double Cab 28.1 317 55.9 Gasoline A4 2.7 Small Pick-up Truck T 2% 0% -2%2012 Cadillac Escalade 2WD Hybrid 28.5 311 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2%2012 Chevrolet C1500 Tahoe 2WD Hybrid 28.5 311 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2%2012 GMC C1500 Yukon 2WD Hybrid 28.5 311 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2%

2012 Ford Fiesta SFE FWD 44.6 199 39.3 Gasoline A6 1.6 Subcompact Cars C 4% 0% -5%

2012 Fiat 500 44.5 200 34.7 Gasoline M5 1.4 Minicompact Cars C 4% 0% -5%

2013 Buick Lacrosse 38.7 230 48.0 Gasoline A6 2.40 Midsize Cars C 4% 0% -5%2012 Chevrolet K1500 Tahoe 4WD Hybrid 28.4 313 54.8 HEV CVT 6.00 Sport Utility Vehicle T 1% -1% -3%2012 GMC K1500 Yukon 4WD Hybrid 28.4 313 54.8 HEV CVT 6.0 Sport Utility Vehicle T 1% -1% -3%2013 Ford Escape AWD 33.2 268 45.3 Gasoline A6 1.6 Sport Utility Vehicle T 1% -1% -3%

2012 Chevrolet Cruze ECO 40.9 217 44.8 Gasoline A6 1.4 Midsize Cars C 4% -1%

2012 Ford Fiesta FWD 44.2 201 39.3 Gasoline A6 1.6 Subcompact Cars C 3% -1%2012 Volkswagen Passat 44.6 228 47.2 Diesel A6 2.0 Midsize Cars C 3% -1%2012 Honda Civic 41.8 212 43.4 Gasoline M5 1.8 Compact Cars C 3% -1%2012 Chevrolet Sonic 44.0 202 41.0 Gasoline M6 1.4 Compact Cars C 3% -2%2012 Chevrolet Sonic 5 44.0 202 41.0 Gasoline M6 1.4 Subcompact Cars C 3% -2%

2012 Ford Fiesta FWD 43.9 202 39.3 Gasoline M5 1.6 Subcompact Cars C 3% -2%

2012 Chevrolet Cruze 40.4 220 44.8 Gasoline M6 1.4 Midsize Cars C 2% -2%2012 Kia Forte ECO 40.7 218 44.4 Gasoline A6 2.0 Midsize Cars C 2% -2%

2012 Mini Mini Cooper 43.6 204 36.7 Gasoline M6 1.6 Minicompact Cars C 2% -2%

2012 Mini Mini Cooper Coupe 43.6 204 38.8 Gasoline M6 1.6 Two Seaters C 2% -2%2013 Mazda CX-5 2WD 39.2 227 46.1 Gasoline M6 2.0 Sport Utility Vehicle C 2% -3%2013 Buick Regal 38.7 230 46.8 Gasoline A6 2.4 Midsize Cars C 2% -3%2013 Chevrolet Malibu 38.7 230 46.6 Gasoline A6 2.4 Midsize Cars C 1% -3%

Powertrain 

Type Transmission

Engine 

Displacement

(L)

Vehicle Class Car/ Truck Com

Model Year Manufacturer Vehicle Unadjusted Fuel Economy (mpg) Tailpipe CO2 (g/mile) Footprint (ft2)
MY 2017 and Later - Regula

3-97 

2017 2018 2019 2020 

2012 BMW 528i xDrive 35.3 252 51.6 Gasoline A8 2.0 Midsize Cars C 1% -3%2012 Chevrolet Cruze 40.1 222 44.8 Gasoline A6 1.4 Midsize Cars C 1% -3%2012 Mazda Mazda3 DI 4-Door 41.3 215 43.1 Gasoline M6 2.0 Compact Cars C 1% -4%

2012 Toyota Yaris 43.1 206 39.9 Gasoline A4 1.5 Compact Cars C 1% -4%

2012 Audi A6 35.4 251 50.9 Gasoline CVT 2.0 Midsize Cars C 1% -4%

2012 Mazda Mazda3 DI 5-Door 41.1 216 43.1 Gasoline M6 2.0 Midsize Cars C 0% -4%

2012 BMW 328i 39.4 225 45.0 Gasoline A8 2.0 Compact Cars C 0% -4%

2012 Ford F150 Pickup 2WD (157 in) 24.0 371 73.0 Gasoline A6 3.5 Standard Pick-up Truck T -3% -3% -3% -4% 2012 Ford F150 Pickup 2WD (163 in) 24.0 371 75.8 Gasoline A6 3.5 Standard Pick-up Truck T -3% -3% -3% -4% 2012 Ford F150 Pickup 2WD FFV (145 in) 24.5 363 67.5 Gasoline A6 3.7 Standard Pick-up Truck T -1% -1% -1% -2%

2012 Ford F150 Pickup 2WD (145 in) 24.0 371 67.5 Gasoline A6 3.5 Standard Pick-up Truck T -3% -3% -3% -5%

2012 Cadillac Escalade 4WD Hybrid 28.0 317 54.8 Gasoline CVT 6.0 Sport Utility Vehicle T 0% -3% -4%2012 GMC K1500 Yukin Denali Hybrid 4WD 28.0 317 54.8 Gasoline CVT 6.0 Sport Utility Vehicle T 0% -3% -4%2012 Chevrolet Equinox AWD 30.8 289 48.8 Gasoline A6 2.4 Sport Utility Vehicle T 0% -3% -5%2012 Chevrolet Equinox AWD 30.8 289 48.8 Gasoline A6 2.4 Sport Utility Vehicle T 0% -3% -5%2012 GMC Terrain AWD 30.8 289 48.8 Gasoline A6 2.4 Sport Utility Vehicle T 0% -3% -5%

2012 GMC Terrain AWD 30.8 289 48.8 Gasoline A6 2.4 Sport Utility Vehicle T 0% -3% -5%

2012 Honda Odyssey 2WD 27.5 324 55.9 Gasoline A5 3.5 Minivan T 0% -3% -5%2012 Toyota Tacoma 2WD - Access Cab 28.1 317 54.0 Gasoline A4 2.7 Small Pick-up Truck T -1% -4%2012 Toyota Tacoma 2WD - Double Cab 28.1 317 54.0 Gasoline A4 2.7 Small Pick-up Truck T -1% -4%2012 Nissan Quest 27.2 326 55.9 Gasoline CVT 3.5 Minivan T -1% -4%

2012 Ford Transit Connect Wagon FWD 31.1 286 47.9 Gasoline A4 2.0 Van T -1% -3%2012 Subaru Outback Wagon AWD 31.9 279 45.7 Gasoline CVT 2.5 Sport Utility Vehicle T -2% -5%2012 Toyota Venza AWD 30.2 294 48.8 Gasoline A6 2.70 Sport Utility Vehicle T -2% -5%2012 Toyota Sienna 26.9 331 56.1 Gasoline A6 3.5 Minivan T -2% -5%2012 Suzuki Equator 2WD 27.3 325 55.0 Gasoline M5 2.5 Small Pick-up Truck T -2% -5%2012 Honda CR-V 4WD 33.0 269 44.1 Gasoline A5 2.4 Sport Utility Vehicle T -2% -4%2012 Volkswagen Jetta 46.1 221 43.9 Diesel A6 2.0 Compact Cars C 0% -5%2013 Mazda CX-5 2WD 38.5 231 46.1 Gasoline A6 2.0 Sport Utility Vehicle C 0% -5%

2012 Toyota Camry 37.7 236 47.1 Gasoline A6 2.5 Midsize Cars C 0% -5%2012 Volkswagen Jetta 46.1 221 43.9 Diesel M6 2.0 Compact Cars C 0% -5%2012 Porsche Panamera S Hybrid 34.4 259 52.1 Gasoline A8 3.0 Large Cars C 0% -5%

Powertrain 

Type Transmission

Engine 

Displacement

(L)

Vehicle Class Car/ Truck Com

Model Year Manufacturer Vehicle Unadjusted Fuel Economy (mpg) Tailpipe CO2 (g/mile) Footprint (ft

2

)
Chapter 3 

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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 sideanalysis, 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 CO2 curves, under the MY 2025 OMEGA 

projections, Ferrari falls short of its 2025 target (150 grams/mile CO2) by seventeen grams.

EEE 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) CO2 in 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 Low side sensitivity High side sensitivity 

Mass reduction direct 

manufacturing costs 

40% lower 40% higher 

Battery pack direct 

manufacturing costs 

10% lower for P2 HEVs 

20% lower for PHEV/EV 

10% higher for P2 HEVs 

20% higher for PHEV/EV 

Indirect cost multipliers Low side of 95% confidence 

interval of modified Delphi 

survey results 

High side of 95% 

confidence interval of 

modified Delphi survey 

results 

Learning ratesaP-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 

P-value of 10% on steep 

portion of the curve; cost 

reductions of 2%/1%/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 Mass reduction DMC equation used

Low side DMC=$2.60x, where x=% mass reduction

Primary case DMC=$4.33x, where x=% mass reduction

High side 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 
Chapter 3 

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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 P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$163x+$653 -$688x+$2,026 -$1,214x+$2,917 -$1,488x+$4,105 -$1,734x+$4,892 -$1,636x+$6,464

Standard 

car

-$216x+$721 -$1,235x+$2,370 -$1,756x+$3,511 -$2,203x+$4,818 -$2,367x+$5,645 -$2,042x+$7,803

Large car -$332x+$843 -$1,505x+$2,987 -$3,760x+$4,808 -$3,485x+$6,180 -$3,718x+$6,904 -$2,272x+$8,896

Small MPV -$202x+$701 -$858x+$2,268 -$1,565x+$3,397 -$1,649x+$4,797 -$2,119x+$5,834 -$15x+$8,087

Large MPV -$272x+$788

Truck -$330x+$909 

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 P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$181x+$726 -$861x+$2,533 -$1,517x+$3,646 -$1,859x+$5,131 -$2,168x+$6,115 -$2,045x+$8,080

Standard 

car -$240x+$801 -$1,543x+$2,962 -$2,195x+$4,389 -$2,754x+$6,023 -$2,958x+$7,056 -$2,552x+$9,753

Large car -$369x+$937 -$1,881x+$3,734 -$4,700x+$6,010 -$4,356x+$7,725 -$4,647x+$8,630 -$2,840x+$11,120

Small MPV -$224x+$779 -$1,073x+$2,835 -$1,957x+$4,247 -$2,061x+$5,997 -$2,649x+$7,293 -$19x+$10,109

Large MPV -$303x+$876

Truck -$367x+$1,010 

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 (-

$181)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 P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$200x+$798 -$1,033x+$3,039 -$1,821x+$4,376 -$2,231x+$6,157 -$2,601x+$7,338 -$2,455x+$9,696

Standard 

car -$264x+$881 -$1,852x+$3,555 -$2,633x+$5,266 -$3,305x+$7,227 -$3,550x+$8,467 -$3,063x+$11,704

Large car -$406x+$1,031 -$2,257x+$4,480 -$5,639x+$7,212 -$5,227x+$9,269 -$5,577x+$10,357 -$3,407x+$13,344

Small MPV -$247x+$857 -$1,287x+$3,402 -$2,348x+$5,096 -$2,473x+$7,196 -$3,179x+$8,752 -$23x+$12,131

Large MPV -$333x+$963

Truck -$404x+$1,111 

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 rulemaking 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 nonbattery 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 High1 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 

Near term Long term Summed 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.004 0.113 0.001 0.090 1.118 1.091 

Medium 0.037 0.225 0.025 0.148 1.262 1.174 

High1 0.043 0.361 0.027 0.217 1.404 1.243 

High2 0.048 0.479 0.041 0.272 1.528 1.313 

Table 3.11-7 ICMs used for the Primary Analysis 

Near term Long term Summed 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.012 0.230 0.005 0.187 1.242 1.193 

Medium 0.045 0.343 0.031 0.259 1.387 1.290 

High1 0.065 0.499 0.032 0.314 1.564 1.345 

High2 0.074 0.696 0.049 0.448 1.770 1.497 

 

FFF “Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,” 

Helfand, G., and Sherwood, T., Memorandum dated August 2009. 
Chapter 3 

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Table 3.11-8 ICMs used for the High Side Sensitivity 

Near term Long term Summed 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.019 0.347 0.010 0.284 1.366 1.294 

Medium 0.052 0.461 0.037 0.369 1.513 1.406 

High1 0.087 0.637 0.037 0.411 1.723 1.447 

High2 0.099 0.914 0.057 0.623 2.012 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 Steep learning rate Flat learning rate 

Low side 30% 4%, 3%, 2% 

Primary case 20% 3%, 2%, 1% 

High side 10% 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 Reference 

Case Change 

Control Case 

 Change 

Impact 

Primary Case -- -- -- 

 Mass Cost High $21 $65 $44 
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Mass Cost Low -$24 -$87 -$63 

Battery Cost High $2 $72 $69 

Battery Cost Low -$2 -$74 -$72 

ICM High $110 $316 $206 

ICM Low -$114 -$317 -$203 

Learning Rate High $48 $177 $129 

Learning Rate Low -$44 -$159 -$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 LightDuty 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-1 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, 
Chapter 3 

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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 CO2/fuel 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 
MY 2017 and Later Regulatory Impact Analysis 

3-105 

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-HQOAR-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 EPAHQ-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 
Chapter 3 

3-106 

- “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. 
MY 2017 and Later Regulatory Impact Analysis 

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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. 
Chapter 3 

3-108 

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-HQOAR-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, Cruff, 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-specfirs.pdf” in Docket No. EPA-HQ-OAR-2010-0799. 
MY 2017 and Later Regulatory Impact Analysis 

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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 lightduty 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 (CO2) is the end product of fossil fuel combustion. 

During combustion, the carbon stored in the fuels is oxidized and emitted as CO2 and smaller 

amounts of other carbon compounds. Methane (CH4) 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 N2O (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 N2O. 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 
Chapter 4 

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impacts on the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4), nitrous oxide 

(N2O) and hydrofluorocarbons (HFC-134a); program impacts on “criteria” air pollutants, 

including carbon monoxide (CO), fine particulate matter (PM2.5) and sulfur dioxide (SO2) 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. 

CO2 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,GGG 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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 CO2 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).III 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 miles * Emission rate grams/mile

 Equation 9 - Emissions 

 

HHH 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. 

IIIThe 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.
Chapter 4 

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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 SO2 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 SO2 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 CO2 equivalent (CO2 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 Global Warming potential 

(CO2 Equivalent) 

CO2 1 

CH4 25 

N2O 298 

HFC (R134a) 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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 DefinitionsLLL

CAR DEFINITION TRUCK DEFINITION 

Passenger Car – Vehicles defined pre-MY 2011as Cars + 2 

wheel drive SUVs below 6,000 GVW 

Light Duty Truck – Remaining light duty 

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. 

LLL While the formal definitions are lengthy, brief summaries of the classifications are shown here. 

MMM A lengthier discussion of both survival and mileage schedules are provides in Joint TSD Chapter 4. 
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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 preMY 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.OOO

 

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. 

NHTSA 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://wwwnrd.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. 

OOO 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 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-3 Survival Rates 

VEHICLE AGE

ESTIMATED 

SURVIVAL 

FRACTION

CARS

ESTIMATED

SURVIVAL 

FRACTION

LIGHT TRUCKS

1 1.0000 1.0000

2 0.9878 0.9776

3 0.9766 0.9630

4 0.9614 0.9428

5 0.9450 0.9311

6 0.9298 0.9152

7 0.9113 0.8933

8 0.8912 0.8700

9 0.8689 0.8411

10 0.8397 0.7963

11 0.7999 0.7423

12 0.7556 0.6916

13 0.7055 0.6410

14 0.6527 0.5833

15 0.5946 0.5350

16 0.5311 0.4861

17 0.4585 0.4422

18 0.3832 0.3976

19 0.3077 0.3520

20 0.2414 0.3092

21 0.1833 0.2666

22 0.1388 0.2278

23 0.1066 0.2019

24 0.0820 0.1750

25 0.0629 0.1584

26 0.0514 0.1452

27 0.0420 0.1390

28 0.0337 0.1250

29 0.0281 0.1112

30 0.0235 0.1028

31 0.0000 0.0933

32 0.0000 0.0835

33 0.0000 0.0731

34 0.0000 0.0619

35 0.0000 0.0502

36 0.0000 0.0384

37 0.0000 0.0273 
Chapter 4 

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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.] 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-4CY 2009 Mileage Schedules based on NHTS Data 

VEHICLE AGE

ESTIMATED 

VEHICLE MILES 

TRAVELED

CARS

ESTIMATED

VEHICLE MILES 

TRAVELED 

LIGHT TRUCKS

1 14,700 15,974

2 14,252 15,404

3 14,025 14,841

4 13,593 14,435

5 13,324 14,038

6 13,064 13,650

7 12,809 12,590

8 11,378 12,192

9 11,087 11,810

10 10,806 11,443

11 10,535 11,091

12 10,273 10,755

13 10,021 10,434

14 9,779 10,129

15 9,547 9,839

16 9,324 9,564

17 9,111 9,305

18 8,908 9,061

19 8,714 8,833

20 8,530 8,620

21 8,356 8,423

22 8,192 8,241

23 8,037 8,075

24 7,892 7,923

25 7,757 7,788

26 7,632 7,668

27 7,516 7,563

28 7,410 7,473

29 7,314 7,399

30 7,227 7,341

31 7,151 7,298

32 7,083 7,270

33 7,026 7,258

34 6,979 7,246

35 6,941 7,233

36 6,912 7,221

37 6,894 7,209 
Chapter 4 

4-118 

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 

use.RRR 

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 2011.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) 
MY 2017 and Later Regulatory Impact Analysis 

4-119 

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.WWW 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 permile 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. 

VVV 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. 

WWW 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.

YYY This 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.
Chapter 4 

4-120 

Percent difference in VMT = (elasticity of VMT with respect to FCPM * (FCPMreference case – FCPMpolicy 

case)/FCPMreference 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. 

ZKQ_\
`\[ ^\[ a ,\ ^  cZ ^d & 1 f ghE

ij & 1 U h & VFkKO,^ U VFkK a,^E/VFkKO,^

Where: 

Vy = Average miles driven in CY 2009 (from NHTSA 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

FCPMx,y = 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: 

 

ZZZ 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. NHTSA 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. 
MY 2017 and Later Regulatory Impact Analysis 

4-121 

VFkKl\
`\[ ^\[ a  F^ & ka f gF^ & gka f mF^ & mka

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

DCy = 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’ 

AnalysesBBBB

MY 2021 MY 2025 

Cars Light 

Trucks 

Cars Light 

Trucks 

FRM 204,161 218,399 209,037 223,688 

NPRM 204,688 242,576 210,898 249,713 

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 CO2 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 

 

BBBB 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. 
Chapter 4 

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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. 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-6 Comparison of NPRM and FRM Gasoline Production Emission Rates. 

 g/mmbtu 

 NPRM FRM 

CO 4.3 2.7 

NOx 13.3 6.5 

PM2.5 1.8 1.0 

SOx 8.2 4.4 

VOC 44.6 44.2 

1,3-Butadiene 0.001 0.001 

Acetaldehyde 0.005 0.005 

Acrolein 0.001 0.001 

Benzene 0.096 0.090 

Formaldehyde 0.036 0.038 

Naphthalene 0.003 0.011 

CH4 106.6 106.6 

N2O 0.3 0.3 

CO2 18792.2 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 CH4.

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 
Chapter 4 

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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 IPM (g/kwh) Feedstock (g/kwh)

Total 

(g/kwh) 

VOC 8.28E-03 4.69E-02 5.52E-02

CO 2.89E-01 5.01E-02 3.39E-01

NOx 1.13E-01 1.27E-01 2.41E-01

PM2.5 5.81E-03 6.51E-02 7.09E-02

SO2 1.90E-01 4.69E-02 2.37E-01

CO2 4.45E+02 3.55E+01 4.80E+02

N2O 6.76E-03 6.81E-04 7.44E-03

CH4 8.60E-03 3.31E+00 3.32E+00

1,3-butadiene 0.0E+00 0.00E+00 0.00E+00

Acetaldehyde 5.5E-05 9.47E-06 6.40E-05

Acrolein 2.8E-05 3.15E-05 5.95E-05

Benzene 1.3E-04 1.41E-03 1.54E-03

Formaldehyde 3.0E-05 7.51E-06 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. 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-8 – A/C Credits 

Reference Control

Car Truck Car Truck

 

Indirect Direct Total Indirect Direct Total Indirect Direct Total Indirect Direct Total 

MY 2017 4.8 5.4 10.2 4.8 6.6 11.5 5.0 7.8 12.8 5 7 12

MY 2018 4.8 5.4 10.2 4.8 6.6 11.5 5.0 9.3 14.3 6.5 11 17.5

MY2019 4.8 5.4 10.2 4.8 6.6 11.5 5.0 10.8 15.8 7.2 13.4 20.6

MY2020 4.8 5.4 10.2 4.8 6.6 11.5 5.0 12.3 17.3 7.2 15.3 22.5

MY2021 4.8 5.4 10.2 4.8 6.6 11.5 5.0 13.8 18.8 7.2 17.2 24.4

MY2022 4.8 5.4 10.2 4.8 6.6 11.5 5.0 13.8 18.8 7.2 17.2 24.4

MY2023 4.8 5.4 10.2 4.8 6.6 11.5 5.0 13.8 18.8 7.2 17.2 24.4

MY2024 4.8 5.4 10.2 4.8 6.6 11.5 5.0 13.8 18.8 7.2 17.2 24.4

MY2025 4.8 5.4 10.2 4.8 6.6 11.5 5.0 13.8 18.8 7.2 17.2 24.4

Indirect air conditioning emissions, or the additional load put on the engine by the 

operation of the air conditioning unit, were modeled similarly to the modeling in the MY 

2012-2016 rulemaking, although with slightly different values. The credits for air 

conditioning efficiency improvements from the tables above (i.e. “indirect”) were applied 

directly to the two cycle emissions projected by OMEGA. 

Air conditioning credits are modeled similarly to the MYs 2012-2016 rule and identically 

to the approach in the proposal, and their derivation is more fully described in TSD 5. In the 

impacts modeling, both credits are modeled as environmentally neutral, or that the impacts of 

the credits are larger than their 2 cycle credit values by the on-road gap. See TSD 5 for more 

details. 

4.3.4.2 Reference Case 

As described in RIA chapter 3 and Preamble III.D, we assume a flat reference case of 

MY 2016 standards. No additional compliance flexibilities which lead to environmental 

disbenefits were explicitly modeled for the MY 2016 standards. Compliance flexibilities such 

as the A/C credit and fleet averaging were included in the modeling. The EPA flexible 

fueled vehicle (FFV) credit expires before MY 2016.DDDD The Temporary Leadtime 

Allowance Alternative Standards (TLAAS), as analyzed in RIA chapter 5 of the MY 2012-

2016 rule, is projected have an impact of approximately 0.1 g/mile in MY 2016, and (by rule) 

will expire afterwards. Therefore, no credits which lead to environmental disbenefits are 

projected to be available to the reference case. Off-cycle credits, which are designed to be 

environmentally neutral, would only lower costs. In a change from the proposal, as in the 

 

DDDD The credit available for producing FFVs will have expired, although the real world usage credits will be 

available. 
Chapter 4 

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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 CO2 emission rates for MY 2016, 2021 and 2025 were taken 

from fleet projections against the curves. Two cycle CO2 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 Car Truck Fleet 

2017 234 308 262 

2018 234 308 261 

2019 234 308 261 

2020 234 308 260 

2021 234 308 260 

2022 234 308 260 

2023 234 307 259 

2024 234 307 259 

2025 234 307 258 

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 There is 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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 CO2 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.GGGG

In each of these tables, the column “Projected CO2 Compliance Target” represents our 

projected fleetwide average CO2 compliance target value based on the CO2-footprint 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 

advanced technology multiplier incentiveHHHH, 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 CO2 emissions levels slightly higher than 

otherwise required , but do not reflect actual real-world CO2 emissions reductions. As such 

they reduce the emissions reductions that the main CO2 standards would otherwise be 

expected to achieve. 

The column “Projected Achieved CO2” is the sum of the CO2 Compliance Target and 

the values in the “Incentive” columns. This Achieved CO2 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 The advanced 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. 
Chapter 4 

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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 CO2 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 CO2 values, but they do allow manufacturers to meet their compliance targets with 

2-cycle test CO2 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 CO2 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 CO2” is the projected fleetwide 2-cycle CO2 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 CO2 Standards – Passenger CarsIIII

(grams per mile)

IncentivesJJJJ Projected

Achieved

CO2

Credits 

Model 

Year 

Projected 

CO2 

Compliance 

Target 

Advanced 

Technology 

Multiplier 

Intermediate 

Volume 

Provisions 

Off 

Cycle 

Credit 

A/C 

Refrigerant 

A/C 

Efficiency 

Projected 

2-cycle 

CO2

2017 212 0.6 0.1 213 0.5 7.8 5.0 226 

2018 202 1.1 0.3 203 0.6 9.3 5.0 218 

2019 191 1.6 0.1 193 0.7 10.8 5.0 210 

2020 182 1.5 0.1 183 0.8 12.3 5.0 201 

2021 172 1.2 0.0 173 0.8 13.8 5.0 193 

2022 164 0.0 0.0 164 0.9 13.8 5.0 184 

2023 157 0.0 0.0 157 1.0 13.8 5.0 177 

2024 150 0.0 0.0 150 1.1 13.8 5.0 170 

2025 143 0.0 0.0 143 1.4 13.8 5.0 163 

 

IIII 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. 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-11 EPA Projections for Fleetwide Tailpipe Emissions Compliance with CO2

Standards – Light TrucksKKKK

(grams per mile)

Table 4.3-12 EPA Projections for Fleetwide Tailpipe Emissions Compliance with CO2 

Standards – 

Combined Passenger Cars and Light TrucksMMMM (grams per mile) 

IncentivesNNNN Projected

Achieved

CO2

Credits

Model 

Year 

Projected 

CO2 

Compliance 

Target 

Advanced 

Technology 

Multiplier 

Pickup 

Mild 

HEV + 

Strong 

HEV 

Intermediate 

Volume 

Provision 

Off 

Cycle 

Credit 

A/C 

Refrigerant 

A/C 

Efficiency 

Projected 

2-cycle 

CO2

2017 243 0.4 0.0 0.1 243 0.6 7.5 5.0 256 

2018 232 0.7 0.1 0.3 234 0.8 9.9 5.0 249 

2019 222 1.0 0.1 0.1 223 0.9 11.7 5.8 242 

2020 213 1.0 0.1 0.1 214 1.0 13.4 5.8 234 

2021 199 0.8 0.2 - 200 1.1 15.0 5.8 222 

2022 190 0.0 0.2 - 190 1.4 15.0 5.8 212 

2023 180 0.0 0.2 - 181 1.7 15.0 5.8 203 

2024 171 0.0 0.2 - 172 1.9 14.9 5.7 194 

2025 163 0.0 0.3 - 163 2.3 14.9 5.7 186 

 

KKKK 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. 

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 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. 

NNNN The one incentive not reflected in this table is the 0 gram per mile compliance value for EV/PHEV/FCVs. 

See text for explanation. 

IncentivesLLLL Projected

Achieved

CO2

Credits

Model 

Year 

Projected 

CO2 

Compliance 

Target 

Pickup 

Mild HEV + 

Strong HEV 

Intermediate 

Volume 

Provisions 

Off Cycle 

Credit 

A/C 

Refrigerant 

A/C 

Efficiency 

Projected 

2-cycle 

CO2

2017 295 0.1 0.2 295 0.9 7 5 308 

2018 286 0.2 0.3 287 1.0 11 5 304 

2019 277 0.3 0.2 278 1.2 13.4 7.2 299 

2020 269 0.4 0.2 270 1.4 15.3 7.2 294 

2021 249 0.5 0.0 250 1.5 17.2 7.2 276 

2022 237 0.6 0.0 238 2.2 17.2 7.2 264 

2023 225 0.6 0.0 226 2.9 17.2 7.2 253 

2024 214 0.7 0.0 214 3.6 17.2 7.2 242 

2025 203 0.8 0.0 204 4.3 17.2 7.2 233 
Chapter 4 

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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 Projected Sales of 

Full Size Pickup Trucks 

Pickup Trucks 

(of Trucks) 

Pickup Trucks 

(of Fleet) 

Trucks 

(of fleet) 

2017 1,218,829 21% 8% 37% 

2018 1,163,965 21% 8% 36% 

2019 1,110,802 20% 7% 36% 

2020 1,134,230 20% 7% 35% 

2021 1,100,818 19% 7% 35% 

2022 1,082,815 19% 7% 35% 

2023 1,026,579 18% 6% 34% 

2024 993,161 17% 6% 34% 

2025 983,954 17% 6% 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

Mild 

HEV 

Credit

Mild 

HEV 

Maximum 

Potential 

Impact 

(Trucks)

Strong 

HEV 

Credit

Strong HEV 

Maximum 

Potential 

Impact 

(Trucks)

2017 10.0 2.1 20.0 4.2

2018 10.0 2.1 20.0 4.1

2019 10.0 2.0 20.0 4.0

2020 10.0 2.0 20.0 4.0

2021 10.0 1.9 20.0 3.9

2022 0.0 0.0 20.0 3.8

2023 0.0 0.0 20.0 3.6

2024 0.0 0.0 20.0 3.5

2025 0.0 0.0 20.0 3.4

Not every pickup truck will get these credits. Unlike in the proposal, where we postprocessed 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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. OOOO 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 

Average 2 cycle 

Electricity 

Consumption for the 

fleet (kwh/mile)

 Model 

Year 

Cars Trucks 

2017 0.001 0.000

2018 0.001 0.000

2019 0.002 0.000

2020 0.002 0.000

2021 0.003 0.000

2022 0.004 0.000

2023 0.004 0.000

2024 0.005 0.000

2025 0.006 0.001

 

OOOO By 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. 
Chapter 4 

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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.RRRR 

PHEV sales, as projected by OMEGA, are not significant.SSSS

Table 4.3-16 – EV Fraction of the MY Fleets 

Model Year Cars Truck EV 

multiplier 

2017 0.3% 0.0% 2 

2018 0.5% 0.0% 2 

2019 0.8% 0.0% 2 

2020 1.1% 0.0% 1.75 

2021 1.4% 0.0% 1.5 

2022 1.8% 0.1% 0 

2023 2.2% 0.2% 0 

2024 2.6% 0.2% 0 

2025 3.0% 0.3% 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 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. 

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 Please note that the 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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: 2020 2030 2040 2050

Net Delta*

-27 -271 -455 -569 

Net CO2

-23 -247 -417 -522 

Net other GHG 

-4 -25 -38 -47 

Downstream

-22 -223 -374 -467 

CO2 (excluding A/C) 

-18 -201 -341 -428 

A/C – indirect CO2

-1 -3 -4 -5 

A/C – direct HFCs

-3 -19 -28 -35 

CH4 (rebound effect) 

0 0 0 0 

N2O (rebound effect) 

0 0 0 0 

Gasoline Upstream

-5 -57 -96 -121 

CO2

-5 -50 -84 -105 

CH4

-1 -7 -12 -15 

N2O 

0 0 0 -1 

Electricity Upstream 

1 9 15 19 

CO2

1 7 13 16 

CH4

0 1 2 3 

N2O 

0 0 0 0 
Chapter 4 

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Table 4.3-18 Impacts of Program on GHG Emissions in all CYs 

CY 

CO2 

(MMT) 

HFC134a 

 (MMT CO2eq) 

CH4 

(MMT CO2eq) 

N2O 

(MMT CO2eq) Total (MMT CO2eq) 

2017 -2 0 0 0.0 -2 

2018 -7 -1 0 0.0 -8 

2019 -14 -2 0 0.0 -16 

2020 -23 -3 -1 0.0 -27 

2021 -37 -5 -1 0.0 -43 

2022 -54 -7 -1 0.0 -63 

2023 -75 -8 -2 0.0 -85 

2024 -99 -10 -2 0.0 -111 

2025 -125 -12 -3 -0.1 -140 

2026 -151 -13 -4 -0.1 -167 

2027 -176 -15 -4 -0.1 -195 

2028 -200 -16 -5 -0.1 -221 

2029 -224 -17 -5 -0.1 -247 

2030 -247 -19 -6 -0.1 -271 

2031 -268 -20 -6 -0.1 -295 

2032 -289 -21 -7 -0.1 -317 

2033 -309 -22 -7 -0.1 -338 

2034 -327 -23 -8 -0.1 -358 

2035 -344 -24 -8 -0.2 -377 

2036 -361 -25 -8 -0.2 -394 

2037 -376 -26 -9 -0.2 -411 

2038 -391 -27 -9 -0.2 -427 

2039 -404 -27 -9 -0.2 -441 

2040 -417 -28 -10 -0.2 -455 

2041 -429 -29 -10 -0.2 -468 

2042 -440 -29 -10 -0.2 -480 

2043 -451 -30 -10 -0.2 -492 

2044 -462 -31 -11 -0.2 -504 

2045 -472 -31 -11 -0.2 -515 

2046 -482 -32 -11 -0.2 -526 

2047 -492 -33 -11 -0.2 -537 

2048 -502 -33 -12 -0.2 -548 

2049 -512 -34 -12 -0.2 -558 

2050 -522 -35 -12 -0.2 -569 

 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-19 Annual Criteria Pollutant Emission Impacts of Program (short tons) 

 CY 2020 CY 2030 

 Pollutant Impacts 

(Short Tons) 

% of Total 

US Inventory 

 Impacts 

(Short Tons) 

% of Total 

US Inventory 

Total VOC -11,712 -0.1% -123,070 -1.0%

CO 14,164 0.0% 224,875 0.4%

NOX -904 0.0% -6,509 -0.1%

PM2.5 -136 0.0% -1,254 0.0%

SOX -1,270 0.0% -13,377 -0.2%

Downstream VOC 249 0.0% 4,835 0.0%

CO 14,414 0.0% 227,250 0.4%

NOX 498 0.0% 8,281 0.1%

PM2.5 40 0.0% 568 0.0%

SOX -420 0.0% -4,498 -0.1%

 Fuel Production 

 and Distribution 

VOC -12,043 -0.1% -128,823 -1.0%

CO -749 0.0% -8,009 0.0%

NOX -1,757 0.0% -18,795 -0.2%

PM2.5 -280 0.0% -3,000 -0.1%

SOX -1,198 0.0% -12,813 -0.2%

Electricity VOC 81 0.0% 917 0.0%

CO 499 0.0% 5,634 0.0%

NOX 355 0.0% 4,005 0.0%

PM2.5 104 0.0% 1,179 0.0%

SOX 348 0.0% 3,933 0.0%

 
Chapter 4 

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Table 4.3-20 Annual Air Toxic Emission Impacts of Program (short tons) 

 CY 2020 CY 2030 

 Pollutant Impacts 

(Short Tons) 

% of Total 

US Inventory 

 Impacts 

(Short Tons) 

% of Total 

US Inventory 

Total 1,3- Butadiene 1 0.0% 25 0.2%

Acetaldehyde 3 0.0% 57 0.1%

Acrolein 0 0.0% 2 0.0%

Benzene -16 0.0% -101 0.0%

Formaldehyde -7 0.0% -43 0.0%

Downstream 1,3- Butadiene 1 0.0% 28 0.2%

Acetaldehyde 4 0.0% 70 0.1%

Acrolein 0 0.0% 3 0.0%

Benzene 8 0.0% 160 0.1%

Formaldehyde 3 0.0% 66 0.0%

 Fuel Production 

and Distribution 

1,3- Butadiene 0 0.0% -2 0.0%

Acetaldehyde -1 0.0% -14 0.0%

Acrolein 0 0.0% -2 0.0%

Benzene -24 0.0% -261 -0.1%

Formaldehyde -10 0.0% -110 -0.1%

Electricity 1,3- Butadiene 0 0.0% 0 0.0%

Acetaldehyde 0 0.0% 1 0.0%

Acrolein 0 0.0% 1 0.0%

Benzene 0 0.0% 0 0.0%

Formaldehyde 0 0.0% 1 0.0%

4.3.5.2 Model Year Lifetime Analyses 

Table 4.3-21 Projected Net GHG Deltas (MMTCO2eq per model year lifetime) 

MY Downstream Upstream

(Gasoline) Electricity Total CO2e

2017 -25 -6 1 -30 

2018 -58 -14 2 -70 

2019 -89 -21 3 -108 

2020 -124 -29 4 -149 

2021 -178 -43 5 -216 

2022 -222 -55 7 -270 

2023 -262 -66 9 -320 

2024 -304 -78 11 -371 

2025 -347 -90 14 -423 

Total -1,610 -402 57 -1,956 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-22 Projected Net Non-GHG Deltas 

Criteria Emission Impacts of Program (short tons) 

MY VOC CO NOx PM2.5 SO2TTTT

2017 -12,972 36,172 -258 -102 -1,446 

2018 -28,424 78,396 -625 -245 -3,247 

2019 -44,042 120,847 -991 -384 -5,047 

2020 -61,383 167,694 -1,404 -539 -7,042 

2021 -90,206 243,828 -2,366 -884 -10,672 

2022 -115,470 311,003 -2,861 -1,075 -13,471 

2023 -138,798 372,813 -3,216 -1,219 -15,947 

2024 -163,022 436,805 -3,561 -1,358 -18,479 

2025 -187,348 500,822 -3,871 -1,484 -20,972 

Sum -841,664 2,268,380 -19,151 -7,292 -96,322 

Model Year Lifetime Air Toxic Emissions (short tons) 

MY Benzene 1,3 Butadiene Formaldehyde Acetaldehyde Acrolein

2017 7 5 2 13 1 

2018 14 12 3 27 1 

2019 21 18 5 42 2 

2020 29 25 6 58 2 

2021 38 36 8 84 3 

2022 48 46 10 107 4 

2023 58 55 11 128 5 

2024 69 64 13 150 6 

2025 79 73 14 171 7 

Sum 364 332 72 778 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 CO2 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 80ppm sulfur, and the average 

content must be 30ppm sulfur. 

UUUU EPA similarly assumes a value of 10,180 grams of CO2 per gallon of diesel fuel. 
Chapter 4 

4-138 

Table 4.3-23 Calendar Year Fuel Consumption Impacts 

CY Fuel Delta 

(Billion Gallons 

petroleum gasoline)

Fuel Delta (Billion 

Barrels petroleum 

gasoline)

Electricity Delta 

(Billion kwh) 

2017 0 0.0 0 

2018 -1 0.0 0 

2019 -1 0.0 1 

2020 -2 -0.1 1 

2021 -3 -0.1 2 

2022 -5 -0.1 3 

2023 -7 -0.2 4 

2024 -9 -0.2 6 

2025 -12 -0.3 7 

2026 -14 -0.3 9 

2027 -16 -0.4 10 

2028 -19 -0.4 12 

2029 -21 -0.5 14 

2030 -23 -0.5 15 

2031 -25 -0.6 17 

2032 -27 -0.6 18 

2033 -29 -0.7 19 

2034 -30 -0.7 20 

2035 -32 -0.8 22 

2036 -34 -0.8 23 

2037 -35 -0.8 24 

2038 -36 -0.9 25 

2039 -38 -0.9 26 

2040 -39 -0.9 27 

2041 -40 -1.0 27 

2042 -41 -1.0 28 

2043 -42 -1.0 29 

2044 -43 -1.0 29 

2045 -44 -1.0 30 

2046 -45 -1.1 31 

2047 -46 -1.1 31 

2048 -47 -1.1 32 

2049 -48 -1.1 33 

2050 -49 -1.2 33 

Sum 2017-2050 -903 -22 607 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.3-24 Model Year Fuel Consumption Impacts 

MY Fuel Delta 

( Billion 

Gallons 

petroleum 

gasoline) 

Fuel Delta 

(Billion 

Barrels 

petroleum 

gasoline) 

Electricity Delta 

(Billion kwh) 

2017 -3 -0.1 2

2018 -5 -0.1 3

2019 -9 -0.2 5

2020 -12 -0.3 7

2021 -17 -0.4 9

2022 -22 -0.5 13

2023 -27 -0.6 16

2024 -31 -0.7 20

2025 -36 -0.9 24

Sum -163 -3.9 100

4.3.7 GHG and Fuel Consumption Impacts from Alternatives 

Table 4.3-25 Calendar Year Impacts of Alternative Scenarios 

 GHG Delta 

(MMT2 CO2eq)

Fuel Savings 

(B. Gallons petroleum 

gasoline)

Scenario 2020 2030 2040 2050 2020 2030 2040 2050 

Primary -27 -271 -455 -569 -2 -23 -39 -49

A - Cars +20 

g/mile -19 -223 -382 -480 -1 -18 -32 -40

B - Cars -20 

g/mile -34 -311 -514 -641 -3 -28 -46 -58

C - Trucks +20 

g/mile -27 -249 -420 -526 -2 -21 -36 -45

D - Trucks -20 

g/mile -36 -294 -484 -604 -3 -25 -42 -53

 
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Table 4.3-26 Model Year Lifetime Impacts of Alternative Scenarios 

(Summary of MY 2017-MY2025)

 Total 

CO2e 

Fuel Delta 

 (b gal 

petroleum 

gasoline) 

Fuel Delta 

(b. barrels 

petroleum 

gasoline) 

Primary -1,956 -163 -3.9 

A - Cars +20 g/mile -1,537 -122 -2.9 

B - Cars -20 g/mile -2,314 -200 -4.8 

C - Trucks +20 g/mile -1,781 -146 -3.5 

D - Trucks -20 g/mile -2,231 -189 -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 

MY 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 Total 

Reference 

Case 

Passenger 

cars 61 61 63 66 69 71 73 76 78 618

Light 

trucks -105 -101 -99 -99 -100 -100 -100 -100 -101 -905

Total -44 -40 -36 -33 -31 -29 -27 -24 -22 -286

Control 

Case 

Passenger 

cars 65 71 78 86 95 101 108 115 123 842

Light 

trucks -110 -112 -115 -121 -128 -141 -152 -164 -178 -1,222

Total -45 -41 -38 -35 -34 -40 -45 -49 -55 -381

Delta 

Passenger 

cars 5 9 14 20 26 30 35 40 45 223

Light 

trucks -5 -11 -16 -22 -29 -40 -52 -64 -77 -317

Total -1 -1 -2 -2 -3 -10 -18 -25 -32 -94
MY 2017 and Later Regulatory Impact Analysis 

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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.WWWW 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 

 MY Lifetime 

2017-2025 

CY 2030 

Rebound 

Rate 

GHG Benefits 

(MMT CO2e) 

Fuel Savings 

(B. Gallons) 

GHG Benefits 

(MMT CO2e) 

Fuel Savings 

(B. Gallons) 

0% 2,115 176 292 25

5% 2,035 169 282 24

10% 1,956 163 271 23

15% 1,877 156 261 22

20% 1,798 149 250 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 

 

VVVV Vehicle fuel efficiency is more often measured in terms of fuel consumption (gallons per mile) rather than 

fuel economy (miles per gallon) in rebound estimates. 

WWWW 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. 
Chapter 4 

4-142 

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 Cumulative 

EV/PHEV sales 

MYs 2017-2025 

Cumulative 

EV/PHEV sales 

MYs 2022-2025 

Cumulative 

Decrease in 

GHG Emission 

Reductions MYs 

2017-2025 

Percentage 

Decrease in 

GHG 

Emission 

Reductions 

MYs 2017-

2025 

No EV/PHEVs 0 0 0 0 

EPA OMEGA 

model 

Projection 

1.5 million 1.1 million 56 MMT 2.7% 

Sensitivity 

Scenario 2.8 million 2.0 million 101 MMT 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 lightduty onroad vehicles affected directly by the regulations and the “upstream” emissions that 
MY 2017 and Later Regulatory Impact Analysis 

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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 MOVES2010b results by model year. 

The emissions were summed and the ratio of the resulting emissions was applied to the gridlevel 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 Reference Control Difference Percent 

Difference

PM2.5 58100 56794 -1306 -2.2%

PM10 121011 120504 -507 -0.4%

NOX 1609568 1618449 8881 0.6%

VOC 935118 941090 5972 0.6%

CO 18023434 18278399 254965 1.4%

SOX 22742 18625 -4117 -18.1%

NH3 95331 96909 1578 1.7%

Acetaldehyde 9677 9752 75 0.8%

Acrolein 746 750 3 0.5%

Benzene 19284 19483 199 1.0%

1,3-Butadiene 3064 3098 34 1.1%

Formaldehyde 15713 15785 72 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 The air quality modeling 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. 
MY 2017 and Later Regulatory Impact Analysis 

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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.ZZZZ

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 2011.59,60 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 

 

ZZZZ 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. 
Chapter 4 

4-146 

energy they use. Refineries rely on upstream energy from residual oil, natural gas, coal and 

electricity. 

GREET 1.8.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 

 Percent of Emission Rates from Energy Feedstocks

Pollutant Emission 

Rate 

(g/mmBTU) 

Residual Oil Natural Gas Coal Electric 

VOC 0.622 5 45 26 24 

CO 1.069 5 38 5 52 

NOx 2.960 7 40 11 42 

PM10 4.445 0 1 85 14 

PM2.5 1.158 1 2 81 16 

SOx 2.398 4 28 8 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. 
MY 2017 and Later Regulatory Impact Analysis 

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Table 4.6-3 Upstream Refinery Emission Impacts in Tons and Inventory Scalars 

Residual Oil 

Production

Natural Gas 

Production

Coal Production Electricity 

Production

Pollutant Impact Scalar Impact Scalar Impact Scalar Impact Scalar

VOC -51 0.9992 -474 0.9883 -270 0.9026 -252 0.9951

CO -93 0.9990 -676 0.9758 -97 0.9817 -932 0.9991

NOx -351 0.9943 -2010 0.9583 -551 0.9136 -2070 0.9989

PM10 -36 0.9997 -51 0.9199 -6366 0.7195 -1027 0.9966

PM2.5 -17 0.9990 -39 0.9353 -1584 0.7874 -309 0.9987

SOx -179 0.9913 -1168 0.9753 -325 0.8947 -2482 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 
Chapter 4 

4-148 

plant storage (BPS) 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 Reference Case (Impacts of 

Medium- and Heavy-Duty 

Greenhouse Gas Rule)

Control Case (Impacts of 

Medium- and Heavy-Duty 

Greenhouse Gas Rule Plus 

this Rule)

Refinery to Bulk Terminal/ 

Bulk Plant Storage 

0.9972 0.7944 

Bulk Terminal to Pump 0.9976 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 1.8.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 C1 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 

 Commercial 

Marine Vessels

Pipelines Rail Truck

Pollutant Impact Scalar Impact Scalar Impact Scalar Impact Scalar

VOC -159 0.9776 -287 0.9959 -46 0.9966 -84 0.9979

CO -532 0.9962 -1447 0.9864 -377 0.9979 -241 0.9988

NOx -1989 0.9933 -6137 0.9192 -859 0.9976 -609 0.9985

PM10 -76 0.9926 -241 0.9895 -30 0.9958 -61 0.9936

PM2.5 -63 0.9936 -130 0.9932 -22 0.9969 -31 0.9968

SOx -32 0.9907 -1292 0.9890 -1 0.9969 -172 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 Tons Percent of Total Upstream 

Inventory 

PM2.5 -3663 0.2

PM10 -8509 0.4

NOX -18391 0.3

VOC -149398 1.7

CO -13918 0.1

SOX -14748 0.3

NH3 0 0

Acetaldehyde -22 0.05

Acrolein -1 0.02

Benzene -1503 1.3

1,3-Butadiene -2 0.04

Formaldehyde -720 0.2

 
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Table 4.6-7 Percent Contribution by Source Category to Reduction in Fuel Production 

and Distribution Emissions by Source Category in 2030 

Source VOC CO NOx PM10 PM2.5 SOx NH3

Crude Oil 

Production and 

Transport 

0.9 0 0 0 0 0 0

Petroleum 

Production and 

Refining 

4.8 89.3 75.0 30.4 56.6 89.7 0

Production of 

Energy for Refinery 

Use 

0.3 4.0 8.6 68.5 41.1 10.1 0

Gasoline Transport, 

Storage and 

Distribution, NonCombustion 

93.9 0 0 0 0 0 0

Gasoline Transport, 

Storage and 

Distribution, 

Combustion from 

Locomotive and 

Marine Engines 

0.1 6.7 16.4 1.1 2.3 0.2 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 1,3-

Butadiene

Acetaldehyde Acrolein Benzene Formaldehyde

Crude Oil Production 

and Transport 

0 0.1 0 0.7 0.1

Petroleum Production 

and Refining 

90.4 10.8 24.8 26.5 93.2

Production of Energy 

for Refinery Use 

1.1 12 1.2 0.6 1.5

Gasoline Transport, 

Storage and 

Distribution, NonCombustion 

0 0 0 71.8 0

Gasoline Transport, 

Storage and 

Distribution, 

Combustion from 

Locomotive and 

Marine Engines 

Combustion 

8.5 77.1 74.0 0.3 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 multiregional, 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 
Chapter 4 

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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. 

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, 
MY 2017 and Later Regulatory Impact Analysis 

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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 nonexistent, 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. 

 
Chapter 4 

Figure 4.6-10 Time

Thirdly, a national average emissions

nature of electricity generation; the location of electric power plants 

emissions – varies with the availability of fuel sources. Coal

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 

one region of the nation will be typically charged from electric power plants within that 

region. A location-varying approach 

plants can capture the impact of such variations whereas a national aver

definition, cannot. 

Finally, a national average emissions

within the high-voltage transmission system.

U.S. electric power grid, electricity 

region in which it was generated due to the location and capacity of high

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

(“AZNM”), the Northwest Power 

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 Calif

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 deferr

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. 

4-154 

Time-varying nature of electric power generation

national average emissions approach fails to capture the location

nature of electricity generation; the location of electric power plants – and their associated 

varies with the availability of fuel sources. Coal-fired electric power plants in 

stance, will have considerable higher emissions that electricity generated in 

hydroelectric power plants in the Pacific Northwest. This is relevant because PHEV/EV 

as with fuel sources – are not uniform across the nation. As such, PHEV/EVs

one region of the nation will be typically charged from electric power plants within that 

varying approach to estimating upstream emissions from electric power 

can capture the impact of such variations whereas a national average approach, by 

national average emissions approach fails to capture regional constraints 

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 

If it does, the flow of that power is greatly limited by the location and capacity of high

transmission lines, called “regional interties”. For instance, southern California 

S” in Figure 4.7-3) is connected to southern Nevada (“SNV”), Arizona 

(“AZNM”), the Northwest Power Pool - East (“NWPE”), and northern California (CA

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 

power generation

approach fails to capture the location-varying 

and their associated 

fired electric power plants in 

stance, will have considerable higher emissions that electricity generated in 

hydroelectric power plants in the Pacific Northwest. This is relevant because PHEV/EV 

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 

upstream emissions from electric power 

age approach, by 

approach fails to capture regional constraints 

Despite the interconnected nature of much of the 

generated in one region may not necessarily leave the 

voltage transmission 

If it does, the flow of that power is greatly limited by the location and capacity of hightransmission lines, called “regional interties”. For instance, southern California 

3) is connected to southern Nevada (“SNV”), Arizona 

(“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 

ornia 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 

ed 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 
 

Figure 4.6-11 Regional constraints in high

4.6.3.3 Estimating Nationwide Power Estimates for PHEV/EVs 

To estimate upstream electric power plant emissions associated w

charging, we used OMEGA to estimate the nationwide PHEV/EV energy requirement

total of eight OMEGA vehicle types considere

vehicles or as PHEVs years 2011

from a PHEV/EV fleet size of zero vehicles 

OMEGA using the NPRM estimates (approximately 3%)

electricity demand as an input to the IPM mode

that the degree of PHEV/EV technology penetration 

vehicle fleet growth between the years 

and non-electrified portions of the vehicle fleet. 

In the NPRM analysis, e

small car (type 2 & 3), large car

minivan and and large truck (types

MY 2017 and Later Regulatory Impact Analysis

4-155 

Regional constraints in high-voltage transmission for southern California

Estimating Nationwide Power Estimates for PHEV/EVs 

To estimate upstream electric power plant emissions associated with electric vehicle 

estimate the nationwide PHEV/EV energy requirement

total of eight OMEGA vehicle types considered likely candidates for electrification as electric 

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 

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 

that the degree of PHEV/EV technology penetration stabilized at 2025 levels, so tha

vehicle fleet growth between the years 2025-2050 are attributed to the turnover

ions of the vehicle fleet. 

In the NPRM analysis, eight OMEGA NPRM vehicle types – subcompact

ar (type 5 & 6), minivan and small truck (types 4

minivan and and large truck (types 7 & 15) – were considered potential candidates 

MY 2017 and Later Regulatory Impact Analysis

voltage transmission for southern California

Estimating Nationwide Power Estimates for PHEV/EVs 

ith electric vehicle 

estimate the nationwide PHEV/EV energy requirements for a 

ification as electric 

, we interpolated 

sales volume size estimated by 

We used the NPRM modeling of 

this method assumed 

at 2025 levels, so that electric 

turnover of electrified 

ubcompact (type 1), 

4 & 8), and 

d potential candidates as electric 
Chapter 4 

4-156 

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 

Total Electricity for 

PHEV/EV Charging 

(kwh) 

2017 123,800,453

2020 1,265,251,557

2030 26,126,391,091

2040 45,558,246,553

2050 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 
MY 2017 and Later Regulatory Impact Analysis 

4-157 

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, 4/14th

of the MSA’s vehicle sales attributed to the RFCO region, and 1/14th 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 Average Annual HEV 

Sales (2006-2009) 

AZNM 9,793 

CA-N 32,401 

CA-S 42,654 

COMD 6,016 

ENTG 5,539 

ENTG 5,539 

ERCT 14,562 

FRCC 17,727 

GWAY 3,266 

LILC 1,673 

MACE 19,023 

MACS 6,637 

MACW 4,834 

MECS 7,477 

MRO 7,218 

NENG 15,652 

NWPE 2,264 

NYC 4,182 

PNW 16,515 

RFCO 10,976 

RFCP 4,813 

RMPA 7,387 

SNV 1,623 

SOU 7,393 

SPPN 2,689 

SPPS 3,267 

TVA 3,575 

TVAK 2,051 

UPNY 2,904 

VACA 12,483 

VAPW 9,740 
Chapter 4 

4.6.3.5 Generation of 

The charging of PHEV/EV varies by time of day. 

the time of day that electric vehicle owners charged their vehicles was available when this 

analysis was first started. As such, we developed a

varies by time of day assuming 

AM and 7:00 PM. We term this period the “on

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

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

charging profile is distributed as a Gaussian

both the on-peak and off-peak charging profiles were mathematically defined; shoul

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

Subsequent to our analysis, 

“EV Project” became available. This actual 

consistent (within a few percent) 

developed for our analysis.72 The

profiles to estimate associated incremental emissions and 

4.6.3.6 Results 

IPM uses these charging 

emissions and price impacts associated with electric power plant emissions. 

runs,73 natural gas is generally projected

power plants in future years. As such, the expected fuel mix for all 

plant generation is expected to change significantly over the 

4-158 

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 

ing 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, the same amount of charging is expected to occur for the 

hour period starting at, say, 6:00 AM as would be expected to occur for a one

starting at 4:00 PM. The remaining 75% of PHEV/EV charging is expected to occur between 

f 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). 

peak charging profiles were mathematically defined; shoul

charging profiles based upon historical data be found to differ from this profile, the impact of 

life deviations could be better diagnosed. 

PHEV/EV Charging Profile by Time of Day

Subsequent to our analysis, electric vehicle and charging infrastructure data for 

became available. This actual charging data was found to be were

(within a few percent) with both of the on-peak and off-peak charging profiles 

These charging profiles are input into IPM, which 

associated incremental emissions and price impacts. 

IPM uses these charging profiles and total demand to estimate associated incremen

associated with electric power plant emissions. In these IPM 

projected to offset coal as the primary fuel used in electric 

. As such, the expected fuel mix for all national electric power 

plant generation is expected to change significantly over the years analyzed. 

However, very little historic data on 

the time of day that electric vehicle owners charged their vehicles was available when this 

n electric vehicle charging profile which 

that 25% of the charging will occur between the hours of 6:00 

peak” period. Charging rate during this time 

is, the same amount of charging is expected to occur for the 

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 

peak” period and this 

. In this way, 

peak charging profiles were mathematically defined; should 

charging profiles based upon historical data be found to differ from this profile, the impact of 

electric vehicle and charging infrastructure data for DOE’s 

was found to be were largely 

peak charging profiles 

IPM, which uses the 

to estimate associated incremental 

In these IPM 

coal as the primary fuel used in electric 

electric power 
Correspondingly, the electric power plant generation resulting from electric vehicle charging 

is expected to similarly shift towards natural gas and away from coal

4.6-15). 

Figure 4.6-15 Fuel mix for electric power plants providing electricity to charging 

For instance, the use of coal to fuel electric power plants for all end

to decrease from approximately 45% in 2020

power is expected to decrease from roughly 20% in 2020 to just over 5

time, the use of natural gas to fuel electric power plants is expe

20% in 2020 to over 41% in 2050

The fuel mix for electric power plant

charging is expected to be similar to 

the use of natural gas to fuel all 

42% in 2020 to over 48% in 2050.

other sources, with similar numbers in 2040 

 

AAAAA The incremental power demand in 2020 is very small, near negligible, and should not 

be ascribed significance. 

MY 2017 and Later Regulatory Impact Analysis

4-159 

electric power plant generation resulting from electric vehicle charging 

expected to similarly shift towards natural gas and away from coal-fired generation (

Fuel mix for electric power plants providing electricity to charging 

vehicles 

For instance, the use of coal to fuel electric power plants for all end-users is expected 

o decrease from approximately 45% in 2020AAAAA to 41% in 2050 while the use of nuclear 

cted 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 

% in 2050 (See Figure 4.6-15). 

The fuel mix for electric power plants that produce electricity for electric vehicle 

be similar to the overall trend towards natural gas. During this time, 

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 

 

The incremental power demand in 2020 is very small, near negligible, and should not 

MY 2017 and Later Regulatory Impact Analysis

electric power plant generation resulting from electric vehicle charging 

fired generation (Figure 

Fuel mix for electric power plants providing electricity to charging 

users is expected 

% in 2050 while the use of nuclear 

% in 2050. During this 

cted in increase from roughly 

electricity for electric vehicle 

. During this time, 

electric power plants is expected in increase from roughly 

In 2030, it is 80% natural gas, 14% coal, and 6%wind and 

The incremental power demand in 2020 is very small, near negligible, and should not 
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 

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

4-160 

Projected fuel mix for PHEV/EV charging in 2030

Projected fuel mix for all electric power in 2030

rtion of electricity consumed by charging PHEV/EVs is 

s 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% 

2040, and just over 1% in 2050 (see Figure 1-10). 

Projected fuel mix for PHEV/EV charging in 2030

power in 2030

rtion of electricity consumed by charging PHEV/EVs is 

s 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% 
Figure 4.6-18 Electric power consumption for PHEV/EVs charging compared to total 

U.S. electric power consumption in 2030

In 2030, based on the NPRM OMEGA runs, 

charging are expected to be 12 MM

power plants for purposes of electric vehicle charging, mercury emissions are expected to 

virtually negligible, as natural gas is virtually mercury

be 3 M Tons while SO2 emissions are estimated to be 6 M

cases, the contribution of emissions from electric power plants related 

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

of 67 tons and 172 tons, respectively

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] 

MY 2017 and Later Regulatory Impact Analysis

4-161 

Electric power consumption for PHEV/EVs charging compared to total 

electric power consumption in 2030

based on the NPRM OMEGA runs, CO2 emissions related to electric vehicle 

charging are expected to be 12 MMT. As compared to electricity generated in coal

power plants for purposes of electric vehicle charging, mercury emissions are expected to 

natural gas is virtually mercury-free. NOx emissions are estimated to 

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 

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). 

Electric power plant emissions due to charging PHEV/EVs

2020 2030 2040 2050

CO2 [MM Tonnes] 0 12 17 29

-2 3 1 2

-4 6 4 6

0 0 0 0

15 172 322 846

16 67 360 837

MY 2017 and Later Regulatory Impact Analysis

Electric power consumption for PHEV/EVs charging compared to total 

emissions related to electric vehicle 

. As compared to electricity generated in coal-fired 

power plants for purposes of electric vehicle charging, mercury emissions are expected to be 

free. NOx emissions are estimated to 

Tons nationwide. In both of these 

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 

fired power plants, in this case, on the order 

Electric power plant emissions due to charging PHEV/EVs

2050

29

846

837
Chapter 4 

4-162 

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. 

Calendar year 

2030 Impacts 

% Impact relative to total IPM 

emissions 

Annual CO (Tons) 8,544 0.8% 

Annual NOx (Tons) 2,528 0.1% 

Annual VOC (Tons) 245 0.5% 

Annual SO2 (Tons) 5,612 0.3% 

Annual Primary PM10 (Tons) 67 0.0% 

Annual Primary PM25 (Tons) 172 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). 

 
MY 2017 and Later Regulatory Impact Analysis 

4-163 

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 Tons Percent of Total Upstream 

Inventory 

PM2.5 --136 -0.06

PM10 -923 -0.31

NOX 459 0.02

VOC -7 1.7

CO 7618 0.72

SOX 3131 0.15

NH3 541 1.10

Acetaldehyde 0 0

Acrolein 0 0

Benzene -12 0.24

1,3-Butadiene 0 0

Formaldehyde 112 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 

 AQ FRM 

Pollutant Reference Control Delta Delta 

CO 43,939,504 44,190,468 250,963 224,875

NOX 9,160,190 9,150,007 -10,183 -6,509

PM2_5 2,888,030 2,883,315 -4,715 -1,254

SO2 4,870,847 4,854,965 -15,882 -13,377

VOC 10,805,700 10,658,356 -147,344 -123,070

 

BBBBB The difference 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. 
Chapter 4 

4-164 

References

40Historically, 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-inautocatalysts/ 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/wg1/ar4-wg1-

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://wwwnrd.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 VM1 at http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm (last accessed Feb. 15, 2010). (EPA-HQOAR-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/420r11008.pdf 

49 EPA. The 2008 National Emissions Inventory. 

http://www.epa.gov/ttnchie1/net/2008inventory.html 

50 EPA. eGrid 2010. 

http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010V1_1_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 1.8c.0, available at 
MY 2017 and Later Regulatory Impact Analysis 

4-165 

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/cleanenergy/energy-resources/egrid/index.html (Docket No. 

(EPA-HQ-OAR-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/420r11901.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/420r11901.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

62 IPM 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/FR2012-02-16/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/epaipm/ 

66 IPM Analysis of the Proposed GHG New Source Performance Standards for Electric Generating 

Units (EGU GHG NSPS). http://www.epa.gov/airmarkt/progsregs/epaipm/proposedEGU_GHG_NSPS.html 
Chapter 4 

4-166 

67 IPM 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 

74 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”
MY 2017 and Later - Regulatory Impact Analysis 

5-1 

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. 

 

CCCCC The ICM approach was updated for the proposal and has not changed for this final rule. 
Chapter 5 

5-2 

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 2021MY 2025MY

Car Truck Combined Car Truck Combined 

Aston Martin $6,688 $0 $6,688 $7,463 $0 $7,463 

BMW $936 $499 $821 $2,137 $1,240 $1,900 

Chrysler/Fiat $657 $772 $709 $1,601 $2,372 $1,934 

Daimler $1,962 $637 $1,633 $3,002 $1,275 $2,607 

Ferrari $6,672 $0 $6,672 $7,843 $0 $7,843 

Ford $659 $854 $725 $1,803 $2,497 $2,017 

Geely $2,102 $705 $1,668 $3,166 $1,492 $2,670 

GM $501 $702 $600 $1,505 $2,223 $1,847 

Honda $519 $816 $611 $1,505 $1,903 $1,622 

Hyundai $764 $866 $785 $1,658 $2,253 $1,777 

Kia $583 $866 $646 $1,548 $1,953 $1,634 

Lotus $3,727 $0 $3,727 $3,556 $0 $3,556 

Mazda $921 $1,208 $972 $1,966 $2,436 $2,044 

Mitsubishi $572 $1,089 $752 $1,926 $2,157 $2,003 

Nissan $631 $890 $711 $1,604 $2,378 $1,833 

Porsche $4,851 $577 $3,844 $4,793 $1,259 $4,029 

Spyker $3,005 $593 $2,659 $3,566 $950 $3,224 

Subaru $967 $1,579 $1,114 $1,921 $2,491 $2,050 

Suzuki $1,018 $1,196 $1,049 $2,101 $1,837 $2,056 

Tata-JLR $3,895 $1,040 $2,474 $5,058 $1,427 $3,370 

Tesla $79 $0 $79 $70 $0 $70 

Toyota $469 $581 $512 $1,215 $1,675 $1,383 

Volkswagen $1,467 $484 $1,268 $2,408 $1,232 $2,176 

Fleet $748 $744 $746 $1,711 $2,044 $1,821 

Note: Results correspond to the 2008 baseline fleet. 
MY 2017 and Later - Regulatory Impact Analysis 

5-3 

To get the costs per vehicle for the intervening years 2017-2020 and 2022-2024, we 

have interpolated costs based on target CO2 levels for each individual company. For this final 

rule, those target CO2 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 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin 232.7 223.3 214.3 205.6 197.4 189.4 181.8 174.4 167.4 160.7 

BMW 238.5 228.9 219.7 210.8 202.3 194.2 186.4 178.8 171.6 164.7 

Chrysler/Fiat 243.0 230.6 221.0 211.9 204.0 195.3 186.9 179.4 171.8 164.9 

Daimler 243.1 234.1 224.8 215.9 207.2 198.6 190.6 183.1 175.9 168.7 

Ferrari 245.1 235.2 225.7 216.6 207.9 199.5 191.5 183.8 176.4 169.3 

Ford 239.5 230.6 221.2 212.2 204.0 195.8 187.9 180.3 173.0 166.1 

Geely 242.6 232.4 223.0 214.0 205.4 197.1 189.2 181.5 174.1 167.1 

GM 238.6 227.4 218.1 209.2 200.8 192.5 184.7 177.4 170.0 163.1 

Honda 233.1 223.6 214.5 205.8 197.5 189.5 181.9 174.7 167.6 160.8 

Hyundai 232.4 223.6 214.5 205.9 197.5 189.5 181.8 174.6 167.5 160.7 

Kia 227.8 219.8 210.8 202.3 194.0 186.1 178.5 171.4 164.4 157.7 

Lotus 216.3 207.5 199.2 191.1 183.4 176.0 169.0 162.1 155.6 149.3 

Mazda 229.0 220.6 211.8 203.3 195.3 187.5 180.1 172.9 165.8 159.0 

Mitsubishi 229.7 220.0 211.1 202.5 194.4 186.6 179.0 171.9 164.9 158.2 

Nissan 236.0 227.1 217.9 209.0 200.7 192.7 185.0 177.6 170.4 163.6 

Prosche 216.3 216.3 216.3 199.2 191.1 176.0 169.0 162.1 155.6 149.3 

Spyker 229.0 219.7 210.9 202.4 194.2 186.4 178.9 171.7 164.8 158.1 

Subaru 221.2 212.2 203.6 195.4 187.6 180.0 172.8 165.9 159.2 152.8 

Suzuki 217.8 217.8 217.8 200.7 192.5 177.3 170.1 163.3 156.7 150.4 

Tata-JLR 260.2 260.2 260.2 239.7 230.0 211.8 203.3 195.1 187.2 179.7 

Tesla 216.3 207.5 199.2 191.1 183.4 176.0 169.0 162.1 155.6 149.3 

Toyota 231.8 222.4 213.4 204.7 196.5 188.5 181.0 173.7 166.7 159.9 

Volkswagen 227.3 218.1 209.3 200.9 192.8 185.0 177.5 170.4 163.5 156.9 

Fleet 234.5 225.3 216.3 207.4 199.2 190.9 183.2 175.9 168.7 161.9 

Note: Results correspond to the 2008 baseline fleet.

 
Chapter 5 

5-4 

Table 5.1-3 Target CO2 Levels, excluding AC, by MY for Trucks (g/mi) 

Company 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

BMW 294.1 295.1 289.1 284.4 277.7 260.6 249.2 238.4 228.0 218.0 

Chrysler/Fiat 306.6 305.1 300.5 295.9 288.8 270.8 258.9 247.3 236.2 225.7 

Daimler 305.8 310.8 306.3 301.1 294.6 277.0 265.0 253.5 242.6 232.0 

Ferrari 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Ford 316.6 315.8 311.4 307.4 303.4 285.2 272.7 259.9 247.3 236.1 

Geely 291.7 290.4 283.7 278.9 272.3 255.3 244.0 233.2 223.0 213.1 

GM 324.4 321.0 316.4 311.4 305.8 286.2 273.3 260.7 248.7 237.5 

Honda 292.2 292.1 287.1 282.4 275.4 258.5 247.0 236.3 225.6 215.7 

Hyundai 289.9 288.9 283.2 278.5 271.8 254.9 243.6 233.0 222.5 212.8 

Kia 300.9 300.9 296.7 292.0 284.7 267.2 255.4 244.3 233.4 223.2 

Lotus 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Mazda 282.4 283.8 276.9 272.3 266.6 250.9 240.5 230.3 219.9 210.1 

Mitsubishi 280.7 277.9 271.5 267.0 260.6 244.3 233.5 223.3 213.4 204.0 

Nissan 306.7 305.1 300.2 295.1 288.6 272.4 260.4 248.4 236.7 226.0 

Porsche 298.4 298.4 298.4 291.7 286.7 262.4 250.8 239.8 229.2 219.1 

Spyker 291.0 289.6 283.0 278.2 271.5 254.6 243.3 232.6 222.4 212.6 

Subaru 268.9 263.5 257.4 253.2 247.0 231.6 221.4 211.7 202.3 193.4 

Suzuki 283.4 283.4 283.4 274.1 269.4 246.6 235.7 225.3 215.4 205.9 

Tata-JLR 284.0 284.0 284.0 275.2 270.4 247.4 236.4 225.9 215.9 206.4 

Tesla 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Toyota 306.2 304.5 299.2 294.3 288.8 271.0 259.0 247.2 235.9 225.3 

Volkswagen 304.2 306.8 301.4 296.2 289.7 272.2 260.4 249.2 238.0 227.5 

Fleet 308.6 306.8 302.3 297.7 292.0 273.8 261.6 249.7 238.1 227.5 

Note: Results correspond to the 2008 baseline fleet.

Interpolating the costs shown in Table 5.1-1 by CO2 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 CO2 targets do not include AC-related GHG controls, we first backed out the ACrelated 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-overyear 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.
MY 2017 and Later - Regulatory Impact Analysis 

5-5 

Table 5.1-4 Scalars Applied to Interpolated Costs to Reflect Learning Effects 

Scaler 2017 2018 2019 2020 2021 2022 2023 2024 2025

Costs as % of 2021 118% 114% 105% 102% 100% 

Costs as % of 2025 133% 129% 120% 116% 114% 113% 111% 110% 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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $60 $54 $48 $41 $35 $31 $26 $21 $17 

BMW $16 $20 $23 $27 $31 $26 $21 $16 $11 

Chrysler/Fiat $53 $45 $38 $31 $24 $22 $20 $18 $16 

Daimler $18 $19 $20 $21 $22 $19 $16 $13 $10 

Ferrari $9 $16 $24 $32 $40 $35 $31 $26 $22 

Ford $16 $17 $19 $20 $21 $18 $15 $12 $9 

Geely $16 $20 $23 $26 $30 $25 $21 $16 $12 

GM $18 $18 $18 $18 $18 $17 $16 $15 $14 

Honda $12 $12 $13 $13 $13 $15 $17 $19 $21 

Hyundai $7 $8 $8 $8 $9 $11 $12 $14 $15 

Kia $14 $21 $28 $36 $43 $38 $34 $29 $25 

Lotus $26 $23 $20 $16 $13 $12 $11 $11 $10 

Mazda $17 $22 $28 $33 $38 $32 $26 $20 $13 

Mitsubishi $15 $21 $27 $33 $39 $32 $26 $19 $13 

Nissan $12 $12 $13 $13 $13 $14 $14 $14 $14 

Porsche $19 $21 $23 $25 $27 $24 $21 $18 $15 

Spyker $36 $30 $25 $20 $14 $14 $15 $15 $15 

Subaru $8 $10 $11 $13 $15 $12 $10 $7 $5 

Suzuki $28 $25 $21 $18 $14 $14 $13 $12 $11 

Tata-JLR $17 $18 $19 $20 $21 $21 $20 $20 $20 

Tesla $1 $1 $1 $0 $0 $0 $0 $0 $0 

Toyota $5 $9 $12 $16 $19 $21 $22 $24 $25 

Volkswagen $14 $17 $19 $22 $25 $20 $15 $10 $5 

Fleet $14 $16 $17 $18 $20 $19 $18 $17 $16 

Note: Results correspond to the 2008 baseline fleet.
Chapter 5 

5-6 

The end results are presented in Table 5.1-6 for cars, Table 5.1-7 for trucks 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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,778 $3,296 $4,438 $5,588 $6,724 $7,173 $7,546 $7,854 $7,480 

BMW $261 $475 $643 $804 $967 $1,351 $1,700 $2,024 $2,147 

Chrysler/Fiat $256 $390 $491 $577 $681 $993 $1,257 $1,520 $1,617 

Daimler $495 $944 $1,288 $1,633 $1,985 $2,358 $2,687 $2,981 $3,011 

Ferrari $1,720 $3,250 $4,403 $5,565 $6,712 $7,280 $7,763 $8,174 $7,864 

Ford $180 $334 $455 $564 $680 $1,042 $1,369 $1,676 $1,811 

Geely-Volvo $577 $1,054 $1,414 $1,771 $2,132 $2,511 $2,851 $3,162 $3,177 

GM $164 $272 $358 $435 $519 $834 $1,114 $1,389 $1,518 

Honda $151 $266 $359 $443 $532 $843 $1,124 $1,396 $1,525 

Hyundai $200 $373 $510 $640 $773 $1,066 $1,329 $1,578 $1,673 

Kia $153 $295 $409 $516 $625 $928 $1,200 $1,459 $1,572 

Lotus $987 $1,831 $2,469 $3,107 $3,739 $3,811 $3,850 $3,866 $3,566 

Mazda $244 $460 $634 $795 $959 $1,288 $1,591 $1,877 $1,979 

Mitsubishi $170 $303 $409 $508 $611 $1,026 $1,407 $1,765 $1,939 

Nissan $171 $316 $430 $535 $644 $954 $1,234 $1,501 $1,618 

Porsche $44 $61 $2,210 $3,120 $4,878 $5,018 $5,114 $5,176 $4,807 

Spyker-Saab $810 $1,488 $2,000 $2,511 $3,019 $3,286 $3,513 $3,709 $3,580 

Subaru $262 $482 $652 $815 $982 $1,295 $1,578 $1,842 $1,926 

Suzuki $54 $65 $496 $679 $1,032 $1,388 $1,711 $2,009 $2,112 

Tata-JLR $43 $58 $1,777 $2,506 $3,916 $4,392 $4,809 $5,176 $5,077 

TeslaDDDDD $26 $41 $57 $66 $79 $77 $72 $71 $69 

Toyota $130 $238 $325 $404 $488 $726 $942 $1,147 $1,239 

Volkswagen $395 $729 $986 $1,238 $1,492 $1,816 $2,104 $2,369 $2,412 

Fleet $206 $374 $510 $634 $767 $1,079 $1,357 $1,622 $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. 
MY 2017 and Later - Regulatory Impact Analysis 

5-7 

Table 5.1-7 Control Case Costs by Manufacturer by MY including AC & Stranded 

Capital Costs -- Trucks (2010$) 

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $0 $0 $0 $0 $0 $0 $0 $0 $0 

BMW $5 $136 $220 $310 $529 $1,095 $1,189 $1,274 $1,250 

Chrysler/Fiat $89 $223 $324 $455 $796 $1,222 $1,713 $2,166 $2,388 

Daimler -$91 $55 $186 $316 $659 $1,851 $1,695 $1,541 $1,284 

Ferrari $0 $0 $0 $0 $0 $0 $0 $0 $0 

Ford $42 $207 $327 $426 $875 $1,235 $1,764 $2,272 $2,505 

Geely-Volvo $43 $218 $321 $438 $734 $2,024 $1,892 $1,759 $1,504 

GM $84 $209 $307 $400 $720 $1,057 $1,551 $2,008 $2,237 

Honda $18 $184 $307 $460 $829 $978 $1,365 $1,739 $1,923 

Hyundai $35 $224 $346 $497 $875 $1,259 $1,683 $2,086 $2,268 

Kia $16 $176 $315 $492 $908 $1,062 $1,443 $1,805 $1,977 

Lotus $0 $0 $0 $0 $0 $0 $0 $0 $0 

Mazda -$40 $292 $477 $683 $1,246 $1,430 $1,851 $2,270 $2,449 

Mitsubishi $106 $354 $492 $672 $1,127 $1,112 $1,552 $1,965 $2,169 

Nissan $59 $232 $367 $521 $904 $1,184 $1,689 $2,165 $2,391 

Porsche $21 $67 $191 $266 $604 $3,964 $3,067 $2,205 $1,274 

Spyker-Saab $60 $202 $283 $372 $607 $2,510 $2,001 $1,511 $964 

Subaru $263 $577 $742 $978 $1,594 $1,482 $1,919 $2,326 $2,495 

Suzuki $31 $71 $389 $526 $1,210 $1,323 $1,567 $1,792 $1,848 

Tata-JLR $20 $65 $330 $457 $1,061 $3,314 $2,708 $2,121 $1,447 

Tesla $0 $0 $0 $0 $0 $0 $0 $0 $0 

Toyota $36 $165 $258 $342 $600 $873 $1,220 $1,543 $1,700 

Volkswagen -$20 $102 $194 $283 $508 $1,476 $1,433 $1,386 $1,237 

Fleet $57 $196 $304 $415 $763 $1,186 $1,562 $1,914 $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). 
Chapter 5 

5-8 

Table 5.1-8 Control Case Costs by Manufacturer by MY including AC & Stranded 

Capital Costs – Combined Fleet (2010$) 

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,778 $3,296 $4,438 $5,588 $6,724 $7,173 $7,546 $7,854 $7,480 

BMW $193 $386 $531 $673 $852 $1,283 $1,565 $1,826 $1,910 

Chrysler/Fiat $180 $314 $416 $521 $733 $1,092 $1,454 $1,799 $1,950 

Daimler $349 $723 $1,014 $1,305 $1,655 $2,242 $2,460 $2,652 $2,616 

Ferrari $1,720 $3,250 $4,403 $5,565 $6,712 $7,280 $7,763 $8,174 $7,864 

Ford $133 $291 $412 $517 $746 $1,102 $1,491 $1,860 $2,025 

Geely-Volvo $412 $794 $1,075 $1,357 $1,698 $2,366 $2,567 $2,746 $2,681 

GM $125 $241 $333 $418 $619 $940 $1,322 $1,684 $1,861 

Honda $110 $241 $343 $448 $624 $883 $1,194 $1,497 $1,642 

Hyundai $166 $343 $477 $611 $794 $1,105 $1,400 $1,679 $1,792 

Kia $123 $269 $388 $511 $689 $957 $1,251 $1,532 $1,658 

Lotus $987 $1,831 $2,469 $3,107 $3,739 $3,811 $3,850 $3,866 $3,566 

Mazda $193 $430 $606 $775 $1,010 $1,312 $1,634 $1,942 $2,057 

Mitsubishi $148 $321 $438 $565 $791 $1,055 $1,455 $1,831 $2,015 

Nissan $136 $290 $411 $531 $725 $1,022 $1,369 $1,697 $1,847 

Prosche $39 $62 $1,734 $2,447 $3,871 $4,790 $4,672 $4,534 $4,044 

Spyker-Saab $703 $1,304 $1,754 $2,205 $2,674 $3,185 $3,315 $3,422 $3,238 

Subaru $262 $505 $673 $854 $1,128 $1,337 $1,655 $1,951 $2,054 

Suzuki $50 $66 $477 $651 $1,064 $1,377 $1,686 $1,972 $2,066 

Tata-JLR $31 $61 $1,057 $1,486 $2,495 $3,891 $3,832 $3,756 $3,390 

Tesla $26 $41 $57 $66 $79 $77 $72 $71 $69 

Toyota $94 $210 $299 $380 $532 $780 $1,043 $1,291 $1,407 

Volkswagen $311 $602 $825 $1,044 $1,293 $1,749 $1,972 $2,176 $2,181 

Fleet $154 $311 $438 $557 $766 $1,115 $1,425 $1,718 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 2040 2050

$/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 

Combined $154 $311 $438 $557 $766 $1,115 $1,425 $1,718 $1,836 $1,818 $1,816 $1,816 

Note: Results correspond to the 2008 baseline fleet.
MY 2017 and Later - Regulatory Impact Analysis 

5-9 

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 truck sales, the increases in per-car and per-truck 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 rule. 

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

Sales $/unit $Million/year

Cars Trucks $/car $/truck Cars Trucks Combined

2017 9,987,667 5,818,655 $206 $57 $2,060 $334 $2,440 

2018 9,905,364 5,671,046 $374 $196 $3,700 $1,110 $4,850 

2019 9,995,696 5,582,962 $510 $304 $5,100 $1,700 $6,820 

2020 10,291,562 5,604,377 $634 $415 $6,530 $2,320 $8,860 

2021 10,505,165 5,683,902 $767 $763 $8,060 $4,340 $12,400 

2022 10,735,777 5,703,996 $1,079 $1,186 $11,600 $6,760 $18,300 

2023 10,968,003 5,687,486 $1,357 $1,562 $14,900 $8,880 $23,700 

2024 11,258,138 5,675,949 $1,622 $1,914 $18,300 $10,900 $29,100 

2025 11,541,560 5,708,899 $1,726 $2,059 $19,900 $11,800 $31,700 

2030 12,535,870 5,986,092 $1,710 $2,044 $21,400 $12,200 $33,700 

2040 14,097,092 6,505,226 $1,710 $2,044 $24,100 $13,300 $37,400 

2050 15,822,370 7,301,371 $1,710 $2,044 $27,100 $14,900 $42,000 

NPV, 3% $336,000 $186,000 $521,000 

NPV, 7% $149,000 $81,900 $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. 

 Looking at these costs by model year gives us the technology costs as shown in Table 

5.2-2. 

 
Chapter 5 

5-10 

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) 

NPV at 2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum 

3% 

Car $2,030 $3,650 $5,020 $6,430 $7,940 $11,400 $14,700 $18,000 $19,600 $88,800 

Truck $330 $1,100 $1,670 $2,290 $4,280 $6,670 $8,750 $10,700 $11,600 $47,400 

Fleet $2,400 $4,780 $6,720 $8,730 $12,200 $18,100 $23,400 $28,700 $31,200 $136,000 

7% 

Car $1,990 $3,580 $4,930 $6,320 $7,800 $11,200 $14,400 $17,700 $19,300 $87,200 

Truck $323 $1,080 $1,640 $2,250 $4,200 $6,540 $8,590 $10,500 $11,400 $46,500 

Fleet $2,360 $4,690 $6,590 $8,570 $12,000 $17,700 $23,000 $28,100 $30,600 $134,000 

Note: Results correspond to the 2008 baseline fleet.

5.2.2 Maintenance & Repair Costs 

New for this final rule 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. 
MY 2017 and Later - Regulatory Impact Analysis 

5-11 

Table 5.2-3 Maintenance Event Costs & Intervals (2010 dollars) 

New Technology Reference Case 

Cost per 

Maintenance 

Event 

Maintenance 

Interval 

(mile) 

Low rolling resistance tires level 1 Standard tires $6.44 40,000 

Low rolling resistance tires level 2 Standard tires $43.52 40,000 

Diesel fuel filter replacement Gasoline vehicle $49.25 20,000 

EV oil change Gasoline vehicle -$38.67 7,500 

EV air filter replacement Gasoline vehicle -$28.60 30,000 

EV engine coolant replacement Gasoline vehicle -$59.00 100,000 

EV spark plug replacement Gasoline vehicle -$83.00 105,000 

EV/PHEV battery coolant replacement Gasoline vehicle $117.00 150,000 

EV/PHEV battery health check Gasoline vehicle $38.67 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. 
Chapter 5 

5-12 

Table 5.2-4 Maintenance Intervals for the Average 2017MY Car (Events/CY) 

CY MY Age 

Cumulative 

VMT (with 

rebound) 

LRRT1 LRRT2 

Diesel 

fuel 

filter 

EV oil 

change 

EV 

air 

filter 

EV 

engine 

coolant 

EV 

spark 

plugs 

EV 

battery 

coolant 

EV 

battery 

health 

2017 2017 1 15,692 0.4 0.4 0.8 2.1 0.5 0.2 0.1 0.1 1.0 

2018 2017 2 30,772 0.4 0.4 0.8 2.0 0.5 0.2 0.1 0.1 1.0 

2019 2017 3 45,473 0.4 0.4 0.7 2.0 0.5 0.1 0.1 0.1 1.0 

2020 2017 4 59,579 0.4 0.4 0.7 1.9 0.5 0.1 0.1 0.1 0.9 

2021 2017 5 73,244 0.3 0.3 0.7 1.8 0.5 0.1 0.1 0.1 0.9 

2022 2017 6 86,486 0.3 0.3 0.7 1.8 0.4 0.1 0.1 0.1 0.9 

2023 2017 7 99,160 0.3 0.3 0.6 1.7 0.4 0.1 0.1 0.1 0.8 

2024 2017 8 110,378 0.3 0.3 0.6 1.5 0.4 0.1 0.1 0.1 0.7 

2025 2017 9 121,074 0.3 0.3 0.5 1.4 0.4 0.1 0.1 0.1 0.7 

2026 2017 10 131,168 0.3 0.3 0.5 1.3 0.3 0.1 0.1 0.1 0.7 

2027 2017 11 140,558 0.2 0.2 0.5 1.3 0.3 0.1 0.1 0.1 0.6 

2028 2017 12 149,235 0.2 0.2 0.4 1.2 0.3 0.1 0.1 0.1 0.6 

2029 2017 13 157,170 0.2 0.2 0.4 1.1 0.3 0.1 0.1 0.1 0.5 

2030 2017 14 164,353 0.2 0.2 0.4 1.0 0.2 0.1 0.1 0.0 0.5 

2031 2017 15 170,782 0.2 0.2 0.3 0.9 0.2 0.1 0.1 0.0 0.4 

2032 2017 16 176,433 0.1 0.1 0.3 0.8 0.2 0.1 0.1 0.0 0.4 

2033 2017 17 181,264 0.1 0.1 0.2 0.6 0.2 0.0 0.0 0.0 0.3 

2034 2017 18 185,279 0.1 0.1 0.2 0.5 0.1 0.0 0.0 0.0 0.3 

2035 2017 19 188,503 0.1 0.1 0.2 0.4 0.1 0.0 0.0 0.0 0.2 

2036 2017 20 191,044 0.1 0.1 0.1 0.3 0.1 0.0 0.0 0.0 0.2 

2037 2017 21 192,993 0.0 0.0 0.1 0.3 0.1 0.0 0.0 0.0 0.1 

2038 2017 22 194,488 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.1 

2039 2017 23 195,661 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.1 

2040 2017 24 196,582 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 

2041 2017 25 197,316 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 

2042 2017 26 197,933 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 

2043 2017 27 198,461 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 

2044 2017 28 198,902 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 

2045 2017 29 199,277 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 

2046 2017 30 199,604 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2047 2017 31 199,736 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2048 2017 32 199,856 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2049 2017 33 199,960 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2050 2017 34 200,049 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2051 2017 35 200,122 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2052 2017 36 200,178 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2053 2017 37 200,218 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2054 2017 38 200,218 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2055 2017 39 200,218 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2056 2017 40 200,218 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 
MY 2017 and Later - Regulatory Impact Analysis 

5-13 

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 Fleet Mix LRRT1 LRRT2 Diesel EV PHEV 

Car Truck Car Truck Car Truck Car Truck Car Truck Car Truck 

2016 85.0% 85.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2017 63% 37% 0.6% 0.2% 14.4% 14.8% -0.4% -0.1% 0.2% 0.0% 0.0% 0.0% 

2018 64% 36% -13.9% -14.6% 28.9% 29.6% -0.8% -0.2% 0.4% 0.0% 0.0% 0.0% 

2019 64% 36% -28.3% -29.3% 43.3% 44.3% -1.2% -0.3% 0.7% 0.0% 0.1% 0.0% 

2020 65% 35% -42.7% -44.1% 57.7% 59.1% -1.7% -0.4% 0.9% 0.0% 0.1% 0.0% 

2021 65% 35% -57.2% -58.9% 72.2% 73.9% -2.1% -0.5% 1.1% 0.0% 0.1% 0.0% 

2022 65% 35% -63.2% -65.2% 78.2% 80.2% -2.1% -0.5% 1.5% 0.1% 0.1% 0.0% 

2023 66% 34% -69.2% -71.6% 84.2% 86.6% -2.1% -0.5% 1.9% 0.2% 0.1% 0.0% 

2024 66% 34% -75.2% -77.9% 90.2% 92.9% -2.1% -0.5% 2.3% 0.2% 0.1% 0.0% 

2025 67% 33% -81.2% -84.3% 96.2% 99.3% -2.1% -0.6% 2.7% 0.3% 0.1% 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. 

 

EEEEE 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. 
Chapter 5 

5-14 

Table 5.2-6 Sales Weighted Maintenance Costs for a 2017MY Car (2010 dollars) 

CY MY Age 

Cumulative 

VMT (with 

rebound) 

LRRT1 LRRT2 Diesel fuel 

filter 

EV 

(all items) 

PHEV 

(all items) Total 

2017 2017 1 15,692 $0.01 $2.47 -$0.16 -$0.14 $0.01 $2.19

2018 2017 2 30,772 $0.01 $2.37 -$0.15 -$0.14 $0.01 $2.10

2019 2017 3 45,473 $0.01 $2.31 -$0.15 -$0.13 $0.01 $2.05

2020 2017 4 59,579 $0.01 $2.21 -$0.14 -$0.13 $0.01 $1.96

2021 2017 5 73,244 $0.01 $2.15 -$0.14 -$0.12 $0.01 $1.91

2022 2017 6 86,486 $0.01 $2.08 -$0.14 -$0.12 $0.01 $1.85

2023 2017 7 99,160 $0.01 $1.99 -$0.13 -$0.11 $0.01 $1.77

2024 2017 8 110,378 $0.01 $1.76 -$0.11 -$0.10 $0.01 $1.56

2025 2017 9 121,074 $0.01 $1.67 -$0.11 -$0.10 $0.01 $1.48

2026 2017 10 131,168 $0.01 $1.59 -$0.10 -$0.09 $0.01 $1.41

2027 2017 11 140,558 $0.01 $1.47 -$0.10 -$0.08 $0.01 $1.30

2028 2017 12 149,235 $0.01 $1.36 -$0.09 -$0.08 $0.01 $1.21

2029 2017 13 157,170 $0.01 $1.25 -$0.08 -$0.07 $0.00 $1.11

2030 2017 14 164,353 $0.01 $1.13 -$0.07 -$0.06 $0.00 $1.00

2031 2017 15 170,782 $0.01 $1.01 -$0.07 -$0.06 $0.00 $0.90

2032 2017 16 176,433 $0.01 $0.89 -$0.06 -$0.05 $0.00 $0.79

2033 2017 17 181,264 $0.00 $0.76 -$0.05 -$0.04 $0.00 $0.67

2034 2017 18 185,279 $0.00 $0.63 -$0.04 -$0.04 $0.00 $0.56

2035 2017 19 188,503 $0.00 $0.51 -$0.03 -$0.03 $0.00 $0.45

2036 2017 20 191,044 $0.00 $0.40 -$0.03 -$0.02 $0.00 $0.35

2037 2017 21 192,993 $0.00 $0.31 -$0.02 -$0.02 $0.00 $0.27

2038 2017 22 194,488 $0.00 $0.24 -$0.02 -$0.01 $0.00 $0.21

2039 2017 23 195,661 $0.00 $0.19 -$0.01 -$0.01 $0.00 $0.16

2040 2017 24 196,582 $0.00 $0.14 -$0.01 -$0.01 $0.00 $0.13

2041 2017 25 197,316 $0.00 $0.12 -$0.01 -$0.01 $0.00 $0.10

2042 2017 26 197,933 $0.00 $0.10 -$0.01 -$0.01 $0.00 $0.09

2043 2017 27 198,461 $0.00 $0.08 -$0.01 $0.00 $0.00 $0.07

2044 2017 28 198,902 $0.00 $0.07 $0.00 $0.00 $0.00 $0.06

2045 2017 29 199,277 $0.00 $0.06 $0.00 $0.00 $0.00 $0.05

2046 2017 30 199,604 $0.00 $0.05 $0.00 $0.00 $0.00 $0.05

2047 2017 31 199,736 $0.00 $0.02 $0.00 $0.00 $0.00 $0.02

2048 2017 32 199,856 $0.00 $0.02 $0.00 $0.00 $0.00 $0.02

2049 2017 33 199,960 $0.00 $0.02 $0.00 $0.00 $0.00 $0.01

2050 2017 34 200,049 $0.00 $0.01 $0.00 $0.00 $0.00 $0.01

2051 2017 35 200,122 $0.00 $0.01 $0.00 $0.00 $0.00 $0.01

2052 2017 36 200,178 $0.00 $0.01 $0.00 $0.00 $0.00 $0.01

2053 2017 37 200,218 $0.00 $0.01 $0.00 $0.00 $0.00 $0.01

2054 2017 38 200,218 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

2055 2017 39 200,218 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

2056 2017 40 200,218 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Note: Results correspond to the 2008 baseline fleet. 

Again, similar tables could be generated for trucks 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. 
MY 2017 and Later - Regulatory Impact Analysis 

5-15 

Table 5.2-7 Sales Weighted Maintenance Costs for a 2025MY Car (2010 dollars) 

CY MY Age Cumulative VMT 

(with rebound) LRRT1 LRRT2 Diesel fuel filter 

EV 

(all items) 

PHEV 

(all items) Total 

2025 2025 1 16,906 -$2.21 $17.70 -$0.87 -$1.87 $0.07 $12.82

2026 2025 2 33,154 -$2.13 $17.00 -$0.84 -$1.80 $0.07 $12.30

2027 2025 3 48,996 -$2.07 $16.50 -$0.82 -$1.75 $0.07 $11.93

2028 2025 4 64,197 -$1.99 $15.90 -$0.79 -$1.68 $0.06 $11.51

2029 2025 5 78,924 -$1.92 $15.40 -$0.76 -$1.63 $0.06 $11.15

2030 2025 6 93,196 -$1.87 $14.90 -$0.74 -$1.58 $0.06 $10.77

2031 2025 7 106,862 -$1.79 $14.30 -$0.71 -$1.51 $0.06 $10.35

2032 2025 8 118,950 -$1.58 $12.70 -$0.63 -$1.34 $0.05 $9.20

2033 2025 9 130,479 -$1.51 $12.00 -$0.60 -$1.28 $0.05 $8.66

2034 2025 10 141,359 -$1.42 $11.30 -$0.56 -$1.20 $0.05 $8.16

2035 2025 11 151,483 -$1.32 $10.60 -$0.52 -$1.12 $0.04 $7.68

2036 2025 12 160,840 -$1.23 $9.81 -$0.48 -$1.04 $0.04 $7.10

2037 2025 13 169,397 -$1.12 $8.95 -$0.44 -$0.95 $0.04 $6.48

2038 2025 14 177,144 -$1.01 $8.11 -$0.40 -$0.86 $0.03 $5.88

2039 2025 15 184,078 -$0.91 $7.25 -$0.36 -$0.77 $0.03 $5.24

2040 2025 16 190,173 -$0.80 $6.38 -$0.32 -$0.68 $0.03 $4.62

2041 2025 17 195,381 -$0.68 $5.45 -$0.27 -$0.58 $0.02 $3.94

2042 2025 18 199,707 -$0.57 $4.53 -$0.23 -$0.48 $0.02 $3.28

2043 2025 19 203,178 -$0.45 $3.64 -$0.18 -$0.38 $0.01 $2.64

2044 2025 20 205,912 -$0.36 $2.86 -$0.14 -$0.30 $0.01 $2.07

2045 2025 21 208,008 -$0.27 $2.19 -$0.11 -$0.23 $0.01 $1.58

2046 2025 22 209,612 -$0.21 $1.68 -$0.08 -$0.18 $0.01 $1.22

2047 2025 23 210,870 -$0.16 $1.32 -$0.07 -$0.14 $0.01 $0.96

2048 2025 24 211,856 -$0.13 $1.03 -$0.05 -$0.11 $0.00 $0.75

2049 2025 25 212,640 -$0.10 $0.82 -$0.04 -$0.09 $0.00 $0.59

2050 2025 26 213,298 -$0.09 $0.69 -$0.03 -$0.07 $0.00 $0.50

2051 2025 27 213,860 -$0.07 $0.59 -$0.03 -$0.06 $0.00 $0.43

2052 2025 28 214,329 -$0.06 $0.49 -$0.02 -$0.05 $0.00 $0.35

2053 2025 29 214,727 -$0.05 $0.42 -$0.02 -$0.04 $0.00 $0.30

2054 2025 30 215,073 -$0.05 $0.36 -$0.02 -$0.04 $0.00 $0.26

2055 2025 31 215,210 -$0.02 $0.14 -$0.01 -$0.02 $0.00 $0.10

2056 2025 32 215,333 -$0.02 $0.13 -$0.01 -$0.01 $0.00 $0.09

2057 2025 33 215,440 -$0.01 $0.11 -$0.01 -$0.01 $0.00 $0.08

2058 2025 34 215,532 -$0.01 $0.10 $0.00 -$0.01 $0.00 $0.07

2059 2025 35 215,607 -$0.01 $0.08 $0.00 -$0.01 $0.00 $0.06

2060 2025 36 215,664 -$0.01 $0.06 $0.00 -$0.01 $0.00 $0.04

2061 2025 37 215,705 -$0.01 $0.04 $0.00 $0.00 $0.00 $0.03

2062 2025 38 215,705 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

2063 2025 39 215,705 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

2064 2025 40 215,705 $0.00 $0.00 $0.00 $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. 
Chapter 5 

5-16 

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 LRRT1 LRRT2 Diesel EV PHEV Total 

Car Truck Car Truck Car Truck Car Truck Car Truck Car Truck Vehicle 

2017 $0 $0 $25 $16 -$2 $0 -$1 $0 $0 $0 $22 $16 $37 

2018 -$3 -$2 $73 $45 -$5 -$1 -$4 $0 $0 $0 $61 $43 $103 

2019 -$11 -$7 $146 $88 -$10 -$1 -$8 $0 $1 $0 $118 $80 $199 

2020 -$22 -$13 $250 $146 -$16 -$2 -$14 $0 $1 $0 $199 $131 $330 

2021 -$37 -$22 $381 $221 -$25 -$3 -$22 $0 $1 $0 $298 $196 $494 

2022 -$55 -$32 $526 $299 -$34 -$4 -$32 $0 $2 $0 $408 $262 $670 

2023 -$75 -$42 $685 $379 -$42 -$5 -$46 -$1 $3 $0 $525 $331 $856 

2024 -$97 -$52 $862 $462 -$52 -$6 -$63 -$2 $3 $0 $654 $402 $1,060 

2025 -$121 -$64 $1,050 $554 -$61 -$8 -$84 -$3 $4 $0 $792 $479 $1,270 

2030 -$234 -$119 $1,940 $976 -$103 -$13 -$183 -$9 $8 $0 $1,430 $836 $2,260 

2040 -$396 -$193 $3,190 $1,540 -$160 -$20 -$331 -$17 $13 $0 $2,320 $1,310 $3,630 

2050 -$493 -$247 $3,950 $1,970 -$195 -$25 -$417 -$22 $16 $0 $2,860 $1,680 $4,540 

NPV, 3% -$4,140 -$2,070 $34,100 $17,000 -$1,770 -$223 -$3,350 -$163 $135 $0 $24,900 $14,500 $39,500 

NPV, 7% -$1,600 -$807 $13,400 $6,710 -$706 -$89 -$1,270 -$60 $53 $0 $9,830 $5,760 $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 Tires Diesel EV PHEV Total $Million per MY 

$/c $/t $/c $/t $/c $/t $/c $/t $/c $/t $/veh $/c $/t $/veh 

2017 $25 $27 -$2 $0 -$1 $0 $0 $0 $22 $26 $24 $222 $153 $375 

2018 $47 $50 -$3 -$1 -$3 $0 $0 $0 $41 $49 $44 $406 $279 $684 

2019 $69 $74 -$5 -$1 -$4 $0 $0 $0 $60 $72 $65 $600 $404 $1,000 

2020 $92 $97 -$7 -$2 -$6 $0 $0 $0 $80 $95 $85 $819 $534 $1,350 

2021 $115 $123 -$8 -$2 -$7 $0 $0 $0 $99 $121 $107 $1,040 $686 $1,730 

2022 $125 $134 -$9 -$2 -$10 -$1 $1 $0 $107 $131 $115 $1,150 $747 $1,890 

2023 $135 $146 -$9 -$2 -$13 -$1 $1 $0 $114 $142 $124 $1,250 $810 $2,060 

2024 $146 $157 -$9 -$2 -$16 -$2 $1 $0 $122 $153 $132 $1,380 $867 $2,240 

2025 $157 $169 -$9 -$2 -$19 -$2 $1 $0 $130 $164 $141 $1,490 $936 $2,430 

Sum $911 $975 -$60 -$15 -$80 -$6 $4 $0 $775 $954 $836 $8,360 $5,420 $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 Tires Diesel EV PHEV Total $Million per MY 

$/c $/t $/c $/t $/c $/t $/c $/t $/c $/t $/veh $/c $/t $/veh 

2017 $20 $21 -$1 $0 -$1 $0 $0 $0 $17 $20 $18 $172 $118 $290 

2018 $36 $38 -$3 -$1 -$2 $0 $0 $0 $32 $38 $34 $314 $214 $528 

2019 $54 $56 -$4 -$1 -$3 $0 $0 $0 $47 $56 $50 $465 $310 $775 

2020 $71 $75 -$5 -$1 -$5 $0 $0 $0 $62 $73 $66 $634 $411 $1,050 

2021 $89 $94 -$7 -$2 -$6 $0 $0 $0 $77 $92 $82 $812 $523 $1,330 

2022 $97 $102 -$7 -$2 -$8 $0 $0 $0 $83 $100 $89 $887 $570 $1,460 

2023 $106 $112 -$7 -$2 -$10 -$1 $0 $0 $89 $109 $96 $977 $620 $1,600 

2024 $113 $121 -$7 -$2 -$12 -$1 $1 $0 $94 $118 $102 $1,060 $669 $1,730 

2025 $122 $129 -$7 -$2 -$15 -$2 $1 $0 $101 $126 $109 $1,160 $718 $1,880 

Sum $707 $747 -$46 -$12 -$62 -$4 $3 $0 $601 $731 $646 $6,480 $4,150 $10,600 

Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet. 
MY 2017 and Later - Regulatory Impact Analysis 

5-17 

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 
Chapter 5 

5-18 

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 “The 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.” 
MY 2017 and Later - Regulatory Impact Analysis 

5-19 

Table 5.2-11 NADA Repair Cost Data Technology Being Compared

Vehicle Estimated 5 Year 

Repair Cost 

Repair Cost 

Difference 

Hybrid FWD to Non-Hybrid 

AWD 

2012 Ford Fusion HEV FWD $2,691 $71 

2012 Ford Fusion SEL AWD $2,620 

Hybrid to Non-Hybrid 2012 Honda Civic Hybrid $2,157 $24 

2012 Honda Civic LX $2,133 

Hybrid to Non-Hybrid 2012 Toyota Camry Hybrid LE $2,133 $0 

2012 Toyota Camry Auto LE $2,133 

Hybrid to Non-Hybrid 2012 Ford Escape XLT FWD $2,275 $0 

2012 Ford Escape Hybrid FWD $2,275 

Turbo Diesel to Standard Gas 

2012 Volkswagen Touareg TDI 

Sport $3,298 $455 

2012 Volkswagen Touareg VR6 

Sport $2,843 

6 Speed Manual Trans (Base) to 6 

Speed Auto Trans 

2012 Kia Sorento I4 Base $1,071 $0 2012 Kia Sorento I4 LX $1,071 

Hybrid to Non-Hybrid to Turbo 

Downsized Engine 

2012 Hyundai Sonata 2.0T Auto 

Limited $1,142 $71 

2012 Hyundai Sonata 2.4L Auto $0 

Hybrid $1,071 

2012 Hyundai Sonata 2.4L Auto 

Limited $1,071 

Turbo Charged Engine to 

Natually Aspirated Engine 

2012 Ford Taurus SHO (Turbo) 

AWD $2,843 $0 

2012 Ford Taurus SEL AWD $2,843 

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 (VDS) 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 VDS 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. 
Chapter 5 

5-20 

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 

Vehicle 

Model Year 

Covered by 

Survey 

Industry 

Average 

Repairs per 

100 Vehicles 

2012 2009 132 

2011 2008 151 

2010 2007 155 

2009 2006 170 

2008 2005 206 

2007 2004 216 

2006 2003 227 

2005 2002 237 

2004 2001 269 

2003 2000 273 

Figure 5-1 J. D. Power VDS Data 2000 Model Year to 2009 Model Year 
MY 2017 and Later - Regulatory Impact Analysis 

5-21 

For J. D. Power’s VDS 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 VDS 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 2000 Model Year 2009 Model Year

Powertrain 

Gasoline 99.90% 97.20%

Gasoline Hybrid 0.00% 2.30%

Diesel 0.10% 0.50%

Fuel Injection Metering Method 

Gasoline Direct Injection - 4.2%

Port Fuel Injection 99.80% 95.20%

Throttle Body Injection 0.00% -

Diesel 0.10% 0.50%

Multi-Valve 44.80% 83.60%

Variable Valve Timing 15.00% 72.00%

Cylinder Deactivation - 7.40%

Boosted (Turbocharged or Supercharged) 1.70% 3.50%

 
Chapter 5 

5-22 

Table 5.2-14 Transmission and Drive Characteristics of MY 2000 to MY 2009 Light 

Duty Vehicles 

Cars and Trucks 2000 Model Year 2009 Model Year 

Manual 9.7% 4.7% 

CVT 0.0% 9.5% 

4 Gears or Fewer 83.8% 31.5% 

5 Gears 15.8% 31.6% 

6 Gears 0.5% 24.7% 

7 Gears or More - 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 III-49 and III-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 

 

GGGGG The data is available on its website (http://www.consumerreports.org/) to subscribers. 
MY 2017 and Later - Regulatory Impact Analysis 

5-23 

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 

 

HHHHH A free web based automotive news site. http://paultan.org/ 
Chapter 5 

5-24 

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 Car Truck 

Total 

Annual 

Costs 

2017 $2,080 $350 $2,470 

2018 $3,760 $1,150 $4,950 

2019 $5,220 $1,780 $7,020 

2020 $6,730 $2,450 $9,190 

2021 $8,360 $4,530 $12,900 

2022 $12,000 $7,030 $19,000 

2023 $15,400 $9,210 $24,600 

2024 $18,900 $11,300 $30,200 

2025 $20,700 $12,200 $32,900 

2030 $22,900 $13,100 $35,900 

2040 $26,400 $14,600 $41,000 

2050 $29,900 $16,600 $46,500 

NPV, 3% $361,000 $200,000 $561,000 

NPV, 7% $159,000 $87,700 $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 MY  2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum 

3% 

Cars $2,250 $4,050 $5,620 $7,250 $8,990 $12,600 $15,900 $19,400 $21,100 $97,200 

Trucks $483 $1,370 $2,070 $2,820 $4,960 $7,410 $9,560 $11,600 $12,500 $52,800 

Combined $2,770 $5,460 $7,720 $10,100 $14,000 $19,900 $25,400 $30,900 $33,600 $150,000 

7% 

Cars $2,170 $3,890 $5,400 $6,950 $8,610 $12,100 $15,400 $18,700 $20,400 $93,600 

Trucks $441 $1,290 $1,950 $2,660 $4,720 $7,110 $9,210 $11,200 $12,100 $50,600 

Combined $2,650 $5,220 $7,370 $9,610 $13,300 $19,200 $24,600 $29,900 $32,500 $144,000 

Note: Results correspond to the 2008 baseline fleet. 
MY 2017 and Later - Regulatory Impact Analysis 

5-25 

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 CO2eq 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) 

Calendar 

Year 

Undiscounted 

Annual Costs 

($millions) 

Undiscounted Annual 

Pre-tax Fuel Savings 

($millions) 

Annual CO2eq 

Reduction 

(mmt) 

$/ton 

(w/o fuel 

savings) 

$/ton 

(w/ fuel 

savings 

Cars 

2020 $6,730 $6,000 21 $316 $34 

2030 $22,900 $56,700 179 $128 -$189 

2040 $26,400 $102,000 300 $88 -$252 

2050 $29,900 $138,000 374 $80 -$289 

Trucks 

2020 $2,450 $1,430 6 $430 $179 

2030 $13,100 $29,700 92 $142 -$180 

2040 $14,600 $53,400 155 $94 -$251 

2050 $16,600 $73,700 196 $85 -$292 

Combined 

2020 $9,190 $7,430 27 $340 $65 

2030 $35,900 $86,400 271 $132 -$186 

2040 $41,000 $155,000 455 $90 -$251 

2050 $46,500 $212,000 569 $82 -$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 CO2 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 CO2 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 
Chapter 5 

5-26 

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 

Petroleum-based 

Gasoline Reference 

(million gallons) 

Petroleum-based 

Gasoline Reduced 

(million gallons) 

Electricity Increased 

(million kWh)

2017 128,136 197 125 

2018 126,732 620 370 

2019 125,458 1,265 739 

2020 124,513 2,149 1,242 

2021 123,886 3,435 1,881 

2022 123,530 5,055 2,743 

2023 123,431 6,967 3,830 

2024 123,596 9,158 5,148 

2025 124,074 11,620 6,704 

2030 129,995 22,986 14,026 

2040 150,053 38,901 24,661 

2050 177,323 48,743 30,943 

Total 5,464,349 903,298 564,873 

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.IIIII AEO is a 

 

IIIII 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.” 
MY 2017 and Later - Regulatory Impact Analysis 

5-27 

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.JJJJJ 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 posttax 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 

Gasoline 

Savings 

(pre-tax) 

Gasoline 

Savings 

(taxed) 

Electricity 

Costs 

Total Fuel 

Savings 

(pre-tax) 

Total Fuel 

Savings 

(taxed) 

2017 $662 $747 $11.5 $651 $735 

2018 $2,110 $2,360 $34.1 $2,070 $2,330 

2019 $4,370 $4,920 $67.9 $4,310 $4,850 

2020 $7,540 $8,440 $114 $7,430 $8,320 

2021 $12,200 $13,600 $175 $12,000 $13,400 

2022 $17,900 $20,000 $258 $17,700 $19,700 

2023 $24,700 $27,600 $366 $24,400 $27,200 

2024 $32,800 $36,500 $499 $32,300 $36,000 

2025 $42,300 $47,000 $658 $41,700 $46,300 

2030 $87,900 $97,000 $1,450 $86,400 $95,500 

2040 $158,000 $172,000 $2,800 $155,000 $169,000 

2050 $216,000 $233,000 $3,800 $212,000 $229,000 

NPV, 3% $1,630,000 $1,780,000 $28,100 $1,600,000 $1,750,000 

NPV, 7% $617,000 $677,000 $10,600 $607,000 $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 While EPA did 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. 
Chapter 5 

5-28 

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 Year 

of each MY at 3% & 7% (millions of 2010 dollars) 

NPV 

at 2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum

3% 

Car $6,770 $12,800 $19,300 $26,600 $34,400 $43,000 $50,800 $59,400 $68,200 $321,000

Truck $275 $2,700 $4,950 $7,480 $16,000 $21,800 $27,600 $31,200 $39,300 $151,000

Total $7,050 $15,500 $24,300 $34,100 $50,400 $64,800 $78,400 $90,600 $108,000 $472,000

7% 

Car $5,200 $9,870 $14,900 $20,500 $26,500 $33,100 $39,100 $45,700 $52,400 $247,000

Truck $209 $2,050 $3,750 $5,670 $12,100 $16,600 $21,000 $23,900 $29,800 $115,000

Total $5,410 $11,900 $18,700 $26,200 $38,600 $49,700 $60,100 $69,600 $82,200 $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.1.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.1. 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 
MY 2017 and Later - Regulatory Impact Analysis 

5-29 

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 

Increased 

Purchase 

Costsa 

Increased 

Insurance 

Costs b

Increased 

Maintenance 

Costs 

Total 

Increased 

Costs 

Cumulative 

Discounted 

Increased Costs 

at 3% 

Cumulative 

Discounted 

Increased Costs 

at 7% 

1 -$1,937 -$34 -$14 -$1,984 -$1,984 -$1,984 

2 $0 -$33 -$13 -$46 -$2,029 -$2,027 

3 $0 -$31 -$13 -$44 -$2,070 -$2,065 

4 $0 -$29 -$12 -$41 -$2,108 -$2,099 

5 $0 -$28 -$12 -$39 -$2,143 -$2,129 

6 $0 -$26 -$11 -$38 -$2,175 -$2,156 

7 $0 -$25 -$11 -$35 -$2,205 -$2,179 

8 $0 -$23 -$10 -$33 -$2,232 -$2,200 

↓ ↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$1,937 -$313 -$139 -$2,389 -$2,389 

NPV, 7% -$1,937 -$254 -$109 -$2,300 -$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 . 
Chapter 5 

5-30 

Table 5.5-2 Increased Costs on a 2025 MY New Vehicle Purchase via Credit (2010 

dollars) 

Year of 

Ownership 

Increased 

Purchase 

Costsa 

Increased 

Insurance 

Costs b

Increased 

Maintenance 

Costs 

Total 

Increased 

Costs 

Cumulative 

Discounted 

Increased Costs 

at 3% 

Cumulative 

Discounted 

Increased Costs 

at 7%

1 -$452 -$34 -$14 -$500 -$500 -$500 

2 -$452 -$33 -$13 -$497 -$982 -$964 

3 -$452 -$31 -$13 -$495 -$1,449 -$1,397 

4 -$452 -$29 -$12 -$493 -$1,900 -$1,799 

5 -$452 -$28 -$12 -$491 -$2,337 -$2,174 

6 $0 -$26 -$11 -$38 -$2,369 -$2,201 

7 $0 -$25 -$11 -$35 -$2,399 -$2,224 

8 $0 -$23 -$10 -$33 -$2,425 -$2,245 

↓ ↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$2,131 -$313 -$139 -$2,583 -$2,583 

NPV, 7% -$1,982 -$254 -$109 -$2,345 -$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 

Fuel 

Price 

Miles 

Driven Reference Fuel Control Fuel Fuel Savings 

Cumulative 

Discounted 

Fuel Savings 

at 3% 

Cumulative 

Discounted 

Fuel Savings 

at 7% 

1 $3.87 16,779 $2,407 $1,702 $705 $695 $682 

2 $3.91 16,052 $2,325 $1,644 $681 $1,347 $1,298 

3 $3.94 15,539 $2,265 $1,601 $664 $1,964 $1,859 

4 $3.96 14,902 $2,183 $1,543 $640 $2,541 $2,365 

5 $4.00 14,424 $2,134 $1,508 $626 $3,089 $2,827 

6 $4.04 13,941 $2,082 $1,471 $611 $3,608 $3,248 

7 $3.96 13,106 $1,912 $1,350 $562 $4,072 $3,610 

8 $3.96 11,866 $1,739 $1,229 $510 $4,480 $3,917 

↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% $25,261 $17,859 $7,402 $7,402 

NPV, 7% $19,354 $13,680 $5,674 $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). 
MY 2017 and Later - Regulatory Impact Analysis 

5-31 

Table 5.5-4 Payback Period for 2025MY Cash & Credit Purchases – 3% discount rate 

(2010 dollars) 

Year of 

Ownership

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$1,984 -$500 $695 -$1,290 $195 

2 -$2,029 -$982 $1,347 -$682 $365 

3 -$2,070 -$1,449 $1,964 -$106 $515 

4 -$2,108 -$1,900 $2,541 $433 $641 

5 -$2,143 -$2,337 $3,089 $946 $752 

6 -$2,175 -$2,369 $3,608 $1,433 $1,239 

7 -$2,205 -$2,399 $4,072 $1,867 $1,673 

8 -$2,232 -$2,425 $4,480 $2,249 $2,055 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$2,389 -$2,583 $7,402 $5,013 $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 Note that payback 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. 
Chapter 5 

5-32 

Figure 5-2 Cumulative 3% Discounted Costs & Fuel Savings for a 2025MY New Vehicle 

Purchase via Cash (2010 dollars) 

Figure 5-3 Cumulative 3% Discounted Costs & Fuel Savings for a 2025MY New Vehicle 

Purchase via Credit (2010 dollars) 

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

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

Breakeven Payback

Payback of Full Lifetime Costs

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

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

Breakeven Payback

Payback of Full Lifetime Costs
MY 2017 and Later - Regulatory Impact Analysis 

5-33 

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

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$1,984 -$500 $682 -$1,302 $183 

2 -$2,027 -$964 $1,298 -$729 $334 

3 -$2,065 -$1,397 $1,859 -$206 $462 

4 -$2,099 -$1,799 $2,365 $266 $565 

5 -$2,129 -$2,174 $2,827 $697 $653 

6 -$2,156 -$2,201 $3,248 $1,092 $1,047 

7 -$2,179 -$2,224 $3,610 $1,431 $1,386 

8 -$2,200 -$2,245 $3,917 $1,717 $1,672 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 7% -$2,300 -$2,345 $5,674 $3,375 $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

 
Chapter 5 

5-34 

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

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$654 -$272 $602 -$52 $330 

2 -$673 -$535 $1,140 $467 $604 

3 -$689 -$789 $1,613 $924 $824 

4 -$704 -$804 $2,055 $1,351 $1,251 

5 -$717 -$818 $2,460 $1,743 $1,643 

6 -$729 -$830 $2,827 $2,098 $1,998 

7 -$740 -$840 $3,156 $2,417 $2,316 

8 -$749 -$849 $3,450 $2,701 $2,600 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$790 -$891 $5,000 $4,210 $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

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$654 -$272 $591 -$64 $319 

2 -$672 -$525 $1,099 $427 $574 

3 -$687 -$761 $1,529 $842 $769 

4 -$700 -$774 $1,916 $1,216 $1,142 

5 -$712 -$786 $2,258 $1,546 $1,473 

6 -$722 -$795 $2,556 $1,834 $1,760 

7 -$730 -$804 $2,813 $2,083 $2,009 

8 -$737 -$811 $3,033 $2,296 $2,223 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$764 -$838 $3,994 $3,230 $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
MY 2017 and Later - Regulatory Impact Analysis 

5-35 

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

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$218 -$93 $425 $208 $333 

2 -$227 -$182 $807 $580 $625 

3 -$234 -$267 $1,147 $913 $880 

4 -$241 -$274 $1,446 $1,205 $1,172 

5 -$247 -$279 $1,708 $1,461 $1,428 

6 -$251 -$284 $1,934 $1,683 $1,650 

7 -$256 -$288 $2,126 $1,870 $1,837 

8 -$259 -$292 $2,285 $2,026 $1,993 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$271 -$304 $2,944 $2,673 $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

Cumulative 

Discounted 

Increased Costs - 

Cash purchaseb

Cumulative 

Discounted 

Increased Costs - 

Credit purchaseb

Cumulative 

Discounted 

Fuel Savings

Cumulative 

Discounted Net 

Savings – 

Cash purchase 

Cumulative 

Discounted Net 

Savings – 

Credit purchase 

1 -$218 -$93 $418 $200 $325 

2 -$226 -$178 $778 $552 $600 

3 -$233 -$257 $1,087 $854 $830 

4 -$239 -$263 $1,349 $1,110 $1,086 

5 -$244 -$268 $1,570 $1,326 $1,302 

6 -$248 -$272 $1,753 $1,505 $1,481 

7 -$251 -$276 $1,903 $1,652 $1,627 

8 -$254 -$278 $2,023 $1,769 $1,745 

↓ ↓ ↓ ↓ ↓ ↓

NPV, 3% -$262 -$286 $2,435 $2,173 $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. 
Chapter 5 

5-36 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

5-37 

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.

82 Ibid.

83 Ibid.

84 Ibid.

85 Ibid.

MY 2017 and Later - Regulatory Impact Analysis 

6-1 

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 

(μm, 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 μm). Atmospheric particles can be grouped into several classes 

according to their aerodynamic and physical sizes, including ultrafine particles (<0.1 μm), 

accumulation mode or ‘fine’ particles (< 1 to 3 μm), and coarse particles (>1 to 3 μm). For 

regulatory purposes, fine particles are measured as PM2.5 and inhalable or thoracic coarse 

particles are measured as PM10-2.5, 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 PM10.

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 Regulatory definitions of PM size fractions, and information on reference and equivalent methods for 

measuring PM in ambient air, are provided in 40 CFR Parts 50, 53, and 58. 
Chapter 6 

6-2 

6.1.1.2 Particulate 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.OOOOO The ISA characterizes the weight of evidence for different health 

effects associated with three PM size ranges: PM2.5, PM10-2.5, 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 PM2.5 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 PM10-2.5

The ISA summarizes evidence related to short-term exposure to PM10-2.5. PM10-2.5 is the 

fraction of PM10 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 PM10-2.5 and cardiovascular 

 

MMMMM Personal exposure includes contributions from many different types of particles, from many sources, and in 

many different environments. Total personal exposure to PM includes both ambient and nonambient components 

and collectively these components may contribute to adverse health effects. 

NNNNN The ISA is available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 

OOOOO The ISA evaluates the health evidence associated with different health effects, assigning one of five “weight 

of evidence” determinations: causal relationship, likely to be a causal relationship, suggestive of a causal 

relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship. For definitions of 

these levels of evidence, please refer to Section 1.5 of the ISA. 
MY 2017 and Later - Regulatory Impact Analysis 

6-3 

effects. It also concludes that the available evidence is suggestive of a causal relationship 

between short-term exposures to PM10-2.5 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 PM10-2.5 and mortality. Data are inadequate to draw 

conclusions regarding health effects associated with long-term exposure to PM10-2.5.

90

6.1.1.2.4 Effects Associated with Ultrafine Particles 

The ISA concludes that the evidence is suggestive of a causal relationship between shortterm 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 (NO2); as the air moves downwind and the cycle continues, 

the NO2 forms additional ozone. The importance of this reaction depends, in part, on the relative 
Chapter 6 

6-4 

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, 99, 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. 
MY 2017 and Later - Regulatory Impact Analysis 

6-5 

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, 110 

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, 116, 117, 118, 119 Further, children are more at risk of experiencing health effects from ozone 

exposure than adults because their respiratory systems are still developing. These individuals (as 

well as people with respiratory illnesses, such as asthma, especially asthmatic children) can 

experience reduced lung function and increased respiratory symptoms, such as chest pain and 

cough, when exposed to relatively low ozone levels during prolonged periods of moderate 

exertion.120, 121, 122, 123 

6.1.1.5 Background on Nitrogen Oxides and Sulfur Oxides 

Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed from 

burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting 

metals from ore. Nitrogen dioxide (NO2) is a member of the nitrogen oxide (NOX) family of 

gases. Most NO2 is formed in the air through the oxidation of nitric oxide (NO) emitted when 

fuel is burned at a high temperature. 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 SO2 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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ranging from 12 to 75 ppb. Important new multicity studies and several other studies have found 

an association between 24-hour average ambient SO2 concentrations and respiratory symptoms in 

children, particularly those with asthma. Generally consistent associations also have been 

observed between ambient SO2 concentrations and emergency department visits and 

hospitalizations for all respiratory causes, particularly among children and older adults (≥ 65 

years), and for asthma. A limited subset of epidemiologic studies has examined potential 

confounding by copollutants using multipollutant regression models. These analyses indicate 

that although copollutant adjustment has varying degrees of influence on the SO2 effect 

estimates, the effect of SO2 on respiratory health outcomes appears to be generally robust and 

independent of the effects of gaseous and particulate copollutants, suggesting that the observed 

effects of SO2 on respiratory endpoints occur independent of the effects of other ambient air 

pollutants. 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 SO2 on the human respiratory 

system. 

Consistent associations between short-term exposure to SO2 and mortality have been 

observed in epidemiologic studies, with larger effect estimates reported for respiratory mortality 

than for cardiovascular mortality. While this finding is consistent with the demonstrated effects 

of SO2 on respiratory morbidity, uncertainty remains with respect to the interpretation of these 

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 SO2 and mortality. Significant associations between short-term exposure to SO2 and 

emergency department visits and hospital admissions for cardiovascular diseases have also been 

reported. However, these findings have been inconsistent across studies and do not provide 

adequate evidence to infer a causal relationship between SO2 exposure and cardiovascular 

morbidity. 

6.1.1.7 Health Effects of NO2

Information on the health effects of NO2 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 

NO2 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 NO2 exposure. First, the ISA concludes that NO2 exposure may enhance the 

sensitivity to allergen-induced decrements in lung function and increase the allergen-induced 

airway inflammatory response following 30-minute exposures of asthmatics to NO2

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 NO2. 
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Enhanced airway responsiveness could have important clinical implications for asthmatics since 

transient increases in airway responsiveness following NO2 exposure have the potential to 

increase symptoms and worsen asthma control. Together, the epidemiologic and experimental 

data sets form a plausible, consistent, and coherent description of a relationship between NO2

exposures and an array of adverse health effects that range from the onset of respiratory 

symptoms to hospital admission. 

Although the weight of evidence supporting a causal relationship is somewhat less certain 

than that associated with respiratory morbidity, NO2 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 

CO.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 The ISA evaluates the health evidence associated with different health effects, assigning one of five “weight 

of evidence” determinations: causal relationship, likely to be a causal relationship, suggestive of a causal 

relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship. For definitions of 

these levels of evidence, please refer to Section 1.6 of the ISA. 

RRRRR Personal 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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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, SO2, and PM in two-pollutant models and found that CO risk estimates were 

generally robust, although this limited evidence makes it difficult to disentangle effects attributed 

to CO itself from those of the larger complex air pollution mixture. Controlled human exposure 

studies have not extensively evaluated the effect of CO on respiratory morbidity. Animal studies 

at levels of 50-100 ppm CO show preliminary evidence of altered pulmonary vascular 

remodeling and oxidative injury. The 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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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.130,131,132 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. 

 

SSSSS 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. 

TTTTT Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 

10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal 

species). 

UUUUU Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 

VVVVV Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 
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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.145,146,147 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.157 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 2010,160 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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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.174 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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carcinogenicity.177 The IARC determined in 1995 that acrolein was not classifiable as to its 

carcinogenicity in humans.178 

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[1,2,3-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 

system.185 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.WWWWW

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 

 

WWWWW U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
Chapter 6 

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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 trafficrelated 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 owneroccupied 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. 

Figure 6.1-1 Mandatory Class I Federal Areas in the U.S. 
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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 longterm 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 PM10 and PM2.5

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 

fine soil, by season.208

6.1.2.2 Plant and Ecosystem Effects of Ozone 

There are a number of environmental or public welfare effects associated with the 

presence of ozone in the ambient air.209 In this section we discuss the impact of ozone on plants, 

including trees, agronomic crops and urban ornamentals. 

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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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.215,216 

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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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/treeresponse 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 

may be higher or lower depending on the tree species.221

 

Some of the common tree species in the United States that are sensitive to ozone are black 

cherry (Prunus serotina), 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.226 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.231

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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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 MidAtlantic 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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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. 

Figure 6.1-2 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab
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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.243,244,245 

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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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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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 

developed in more than half of U.S. estuaries.250

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 reenter the soil and further cycle in the environment. Although there has been no direct evidence 
Chapter 6 

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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.256,257 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 μm in diameter, long range transport is possible. However, studies 

have shown that PAH compounds adsorbed onto diesel exhaust particulate and exposed to ozone 

have half lives of 0.5 to 1.0 hours.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.277

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 Multiscale 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 wellknown 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 

4.7.1XXXXX 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. 
Chapter 6 

6-28 

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 threedimensional 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 (latitudelongitude) 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 

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary
MY 2017 and Later - Regulatory Impact Analysis 

6-29 

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.ZZZZZ 

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 These other modeling studies represent a wide range of modeling analyses which cover various models, model 

configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. 
Chapter 6 

6-30 

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 baseyear 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 PM2.5 and its non-carbon components. This 

characterization of PM2.5 mass also reflects crustal material and other minor constituents. The 

resulting characterization provides a complete mass balance. It does not have any unknown mass 

that is sometimes presented as the difference between measured PM2.5 mass and the characterized 

chemical components derived from routine speciation measurements. However, the assumption 

that all mass difference is organic carbon has not been validated in many areas of the 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 

μg/m3). More complete details of the SMAT procedures can be found in the report "Procedures 

for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the (Revised) 

Speciated Modeled Attainment Test (SMAT)".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 
MY 2017 and Later - Regulatory Impact Analysis 

6-31 

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 formation.AAAAAA

NO2 + ·OH + M → HNO3 + M k = 1.19 x 10-11 cm3molecule-1

s

-1

 

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). 
Chapter 6 

6-32 

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

 

299

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 cm

3molecule-1

s

-1

 

300

Acetaldehyde can react with the NO3 radical, ground state oxygen atom (O3P) and 

chlorine, although these reactions are much slower. Acetaldehyde can also photolyze (hν), which 

predominantly produces ·CH3 (which reacts as shown above to form CH3OO·) and HCO (which 

rapidly forms HO2 and CO): 

CH3CHO + hν +2 O2 → CH3OO· +HO2 + CO λ = 240-380 nm 301

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 + hν + 2 O2 → 2 HO2 + CO λ = 240-360 nm 303

HO2 + NO → NO2+ OH 

Photolysis of HCHO can also proceed by a competing pathway which makes only stable 

products: H2 and CO. 

 

CCCCCC All rate coefficients are listed at 298 K and, if applicable, 1 bar of air. 
MY 2017 and Later - Regulatory Impact Analysis 

6-33 

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, CXO3). 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 (CH3CH2OH) 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 weaklybonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy 

 

DDDDDDAll rate coefficients are listed at 298 K and, if applicable, 1 bar of air. 
Chapter 6 

6-34 

radical (HO2), and the remainder of the reaction occurs at the –CH3 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 lowvolatility 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.314,315,316,317,318 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 

concentrations.321

 

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 μg/m3

) of the total average nationwide ambient concentration of toluene (2.48

μg/m3

), when the contribution of the estimated “background” is apportioned among source 

sectors. 
MY 2017 and Later - Regulatory Impact Analysis 

6-35 

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 highNOX 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 
Chapter 6 

6-36 

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 β-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 μg carbon/m3

 for Bondville, IL, East St. Louis, IL, Northbrook, IL, 

Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations occurring in the 
MY 2017 and Later - Regulatory Impact Analysis 

6-37 

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 μg/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, 

344,345,346 Studies have indicated 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 NO2 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

-1 351

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. NO2 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, NO2 further photolyzes to produce ozone (O3). 

NO2 + hν → NO + O(3

P) λ = 300-800 nm 352

O(3

P) + O2 + M → O3 + M 

 

EEEEEE Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, pxylene, and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal and biacetyl, which are also strong generators of 

acetyl radicals (Smith, D.F., T.E. Kleindienst, C.D. McIver (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 All rate coefficients are listed at 298 K and, if applicable, 1 bar of air. 
Chapter 6 

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The temperature sensitivity of PAN allows it to be stable enough at low temperatures to 

be transported long distances before decomposing to release NO2. NO2 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 NO2 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. 
MY 2017 and Later - Regulatory Impact Analysis 

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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. 

. 

 

GGGGGG 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. 
Chapter 6 

6-40 

Figure 6.2-3 1997 8-hour Ozone Nonattainment Areas 
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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 The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air 

Basin 8-hour ozone nonattainment area are designated as extreme and will have to attain before June 15, 2024. The 

Sacramento, Coachella Valley, Western Mojave, and Houston 8-hour ozone nonattainment areas are designated as 

severe and will have to attain by June 15, 2019. 
Chapter 6 

6-42 

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. IIIIII

 

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 

 

IIIIII 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. 
MY 2017 and Later - Regulatory Impact Analysis 

6-43 

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). 

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 

 

JJJJJJ 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. 
Chapter 6 

6-44 

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 Change in 8-hour 

Ozone Design 

Value (ppb)

Population in 

2030a

San Bernardino Co, California -0.20 2,784,490 

Riverside Co, California -0.23 2,614,198 

Los Angeles Co, California -0.13 10,742,722 

Kern Co, California 0.05 981,806 

Harris Co, Texas -0.31 5,268,889 

Tulare Co, California -0.02 528,663 

Orange Co, California -0.12 4,431,071 

Fresno Co, California -0.04 1,196,950 

Suffolk Co, New York -0.06 1,705,822 

Brazoria Co, Texas -0.30 364,257 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

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Table 6.2-2 Average Change in Projected 8-hour Ozone Design Value 

Averagea Number 

of US 

Counties 

2030 

Populationb

Change in 

2030 design 

value (ppb) 

All 

675 261,439,344

0.01

All, population-weighted 0.00 

Counties whose 2005 base year is above the 2008 

8-hour ozone standard 

393 194,118,748

0.02

Counties whose 2005 base year is above the 2008 

8-hour ozone standard, population-weighted 0.00 

Counties whose 2005 base year is within 10 percent 

of the 2008 8-hour ozone standard 

201 44,436,103

0.02

Counties whose 2005 base year is within 10 percent 

of the 2008 8-hour ozone standard, populationweighted 0.01 

Counties whose 2030 control case is above the 2008 

8-hour ozone standard 

10 30,618,868

-0.14

Counties whose 2030 control case is above the 2008 

8-hour ozone standard, population-weighted -0.16 

Counties whose 2030 control case is within 10% of 

the 2008 8-hour ozone standard 

40 29,661,201

-0.02

Counties whose 2030 control case is within 10% of 

the 2008 8-hour ozone standard, populationweighted 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. 
Chapter 6 

6-46 

 

6.2.2.2 Particulate Matter 

As described in Section 6.1.1.2, exposure to PM2.5 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 PM2.5 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 PM2.5

concentrations in 2010. In 2010, the highest annual average PM2.5 concentrations were in 

California, Indiana, Pennsylvania, and Hawaii and the highest 24-hour PM2.5 concentrations were 

in California and Alaska. 

Figure 6.2-6 Annual Average PM2.5 Concentrations in μg/m3

 for 2010359
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6-47 

Figure 6.2-7 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in 

μg/m3

 for 2010360

There are two NAAQS for PM2.5: an annual standard (15.0 μg/m3) and a 24-hour standard 

(35 μg/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 μg/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

 
Chapter 6 

6-48 

Figure 6.2-8 1997 PM2.5 Nonattainment Areas 
MY 2017 and Later - Regulatory Impact Analysis 

6-49 

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 PM10 NAAQS. There are 39 full or partial counties that make up the PM10

nonattainment areas. Nonattainment areas for the PM10 NAAQS are pictured in Figure 6.2-10.
Chapter 6 

6-50 

Figure 6.2-10 PM10 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, 
MY 2017 and Later - Regulatory Impact Analysis 

6-51 

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 μg/m3 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 μg/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 PM2.5 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 μg/m3

 and -0.05 μg/m3 in their 

annual PM2.5 design values due to the vehicle standards. 

Figure 6.2-11 presents the changes in annual PM2.5 design values in 2030.KKKKKK

 

 

KKKKKK An annual 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. 
Chapter 6 

6-52 

 

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 μg/m3

. These 

counties are in the Gulf Coast and in Missouri. The maximum projected decrease in an annual 

PM2.5 design value is 0.16 μg/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 PM2.5 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 (μg/m3

) for Counties Projected to be 

Above the Annual PM2.5 NAAQS in 2030 

County Name Change in 

Annual 

PM2.5

Design 

Value 

(μg/m3)

Population 

in 2030a

Riverside County, California -0.01 2,614,198

San Bernardino County, California 0 2,784,489

Kern County, California -0.02 981,806

Tulare County, California -0.01 528,662

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 μg/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 μg/m3 and 0.05 μg/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 A 24-hour PM2.5 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 PM2.5 design value are given in appendix 

N of 40 CFR part 50. 
Chapter 6 

6-54 

Figure 6.2-12 Projected Change in 2030 24-hour PM2.5 Design Values Due to the Final 

Standards 

As shown in Figure 6.2-12, design value concentrations will increase more than 0.05 

μg/m3 in six counties and design value concentrations will decrease more than 0.05 μg/m3 in 23 

counties. The decreases in 24-hour PM2.5 design values in some counties are likely due to 

emission reductions related to lower fuel production. The maximum projected decrease in a 24-

hour PM2.5 design value is 0.76 μg/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 μg/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 (μg/m3

) for Counties Projected to be 

Above the 24-hour PM2.5 NAAQS in 2030 

County Name Change in 24-

hour PM2.5

Design Value 

(μg/m3)

Population in 2030a

Kern Co, California 0.02 981,806 

Riverside Co, California -0.01 2,614,198 

Fresno Co, California -0.02 1,196,950 

San Bernardino Co, California -0.27 2,784,490 

Sacramento Co, California 0 1,856,971 

Kings Co, California -0.03 195,067 

Los Angeles Co, California 0.03 10,742,722 

Tulare Co, California -0.03 528,663 

Lane Co, Oregon 0 460,993 

Cache Co, Utah 0.04 141,446 

Allegheny Co, Pennsylvania -0.01 1,234,931 

Stanislaus Co, California -0.02 688,246 

Lake Co, Montana 0 40,126 

Orange Co, California 0.05 4,431,071 

Klamath Co, Oregon 0 77,200 

Salt Lake Co, Utah 0.02 1,431,946 

Ravalli Co, Montana 0 63,914 

Butte Co, California 0 287,236 

Missoula Co, Montana 0.01 141,264 

Pierce Co, Washington 0.03 1,082,579 

Lincoln Co, Montana 0 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 μg/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 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 24-hour PM2.5 standard, and (5) counties with 2030 

design values that did not exceed the standard, but were within 10% of it. 
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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.367 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 longrange 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. 
MY 2017 and Later - Regulatory Impact Analysis 

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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.OOOOOO,PPPPPP,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. 

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 longrange 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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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 Heavyduty 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 μg/m3 across 

much of the country with decreases as high as 0.1 μg/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 μg/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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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 μg/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 μg/m3 and due to reductions in emissions from refineries in 

that area. Increases in ambient formaldehyde concentrations range between 0.001 to 0.1 μg/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 μg/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 μg/m3 in the western half of the U.S. with decreases up to 0.01 μg/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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Figure 6.2-17 Changes in Benzene Ambient Concentrations in 2030 due to the Final 

Standards: Percent Changes (left) and Absolute Changes in μg/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 μg/m3 nationwide (Figure 6.2-18). 
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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 μg/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 μg/m3 in 2030 (Figure 6.2-19). 
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Figure 6.2-19 Changes in Acrolein Ambient Concentrations in 2030 due to the Final 

Standards: Percent Changes (left) and Absolute Changes in μg/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. 
Chapter 6 

6-64 

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 Acetaldehyde Formaldehyde Benzene 1,3-Butadiene Acrolein

≤ -100 

> -100 to ≤ -50 

> -50 to ≤ -10 

> -10 to ≤ -5 0.0% 0.0% 0.8% 0.2% 

> -5 to ≤ -2.5 1.5% 0.1% 1.8% 0.0% 2.0% 

> -2.5 to ≤ -1 15.3% 1.2% 13.0% 0.2% 10.3% 

> -1 to < 1 83.1% 98.7% 84.4% 99.2% 86.1% 

≥ 1 to < 2.5 0.0% 0.6% 0.9% 

≥ 2.5 to < 5 0.0% 0.0% 

≥ 5 to < 10 0.0% 

≥ 10 to < 50 

≥ 50 to < 100 

≥ 100 

 

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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Figure 6.2-20 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2005 -2007 
Chapter 6 

Figure 6.2-21 Total Nitrogen Deposition in the Contiguous U.S., 1989

2007 

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-1991 and 2005-
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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. 

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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6-68 

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%. 

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 PM2.5 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 

suffer from visibility impairment.372 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 multistate regions, urban areas, and remote Mandatory Class I Federal areas. 
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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) State 2005 Base 

2030 

LDGHG 

Reference 

2030 

LDGHG 

Control 

Natural 

Background 

Sipsey Wilderness AL 29.88 20.54 20.52 11.39 

Caney Creek Wilderness AR 26.69 19.84 19.84 11.33 

Upper Buffalo Wilderness AR 26.97 20.17 20.18 11.28 

Chiricahua NM AZ 12.89 12.08 12.07 6.92 

Chiricahua Wilderness AZ 12.89 12.08 12.07 6.91 

Galiuro Wilderness AZ 12.89 12.09 12.09 6.88 

Grand Canyon NP AZ 11.86 10.92 10.91 6.95 

Mazatzal Wilderness AZ 13.95 12.46 12.45 6.91 

Mount Baldy Wilderness AZ 11.32 10.74 10.74 6.95 

Petrified Forest NP AZ 13.56 12.65 12.65 6.97 

Pine Mountain Wilderness AZ 13.95 12.42 12.41 6.92 

Saguaro NM AZ 14.39 13.43 13.43 6.84 

Sierra Ancha Wilderness AZ 14.45 13.28 13.28 6.92 

Superstition Wilderness AZ 14.15 12.85 12.85 6.88 

Sycamore Canyon Wilderness AZ 15.45 14.67 14.67 6.96 

 

QQQQQQ The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless 

visibility index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a 

scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic 

conditions, the average person can generally perceive a change of one deciview. The higher the deciview value, the 

worse the visibility. Thus, an improvement in visibility is a decrease in deciview value. 
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Agua Tibia Wilderness CA 22.36 18.41 18.39 7.17 

Ansel Adams Wilderness (Minarets) CA 15.24 14.39 14.39 7.12 

Caribou Wilderness CA 13.65 12.68 12.67 7.29 

Cucamonga Wilderness CA 18.44 15.64 15.64 7.17 

Desolation Wilderness CA 12.87 12.10 12.09 7.13 

Emigrant Wilderness CA 16.87 15.94 15.94 7.14 

Hoover Wilderness CA 11.61 11.07 11.06 7.12 

John Muir Wilderness CA 15.24 14.34 14.34 7.14 

Joshua Tree NM CA 18.90 16.39 16.41 7.08 

Kaiser Wilderness CA 15.24 14.11 14.10 7.13 

Kings Canyon NP CA 23.73 22.19 22.19 7.13 

Lassen Volcanic NP CA 13.65 12.66 12.66 7.31 

Lava Beds NM CA 14.13 13.19 13.19 7.49 

Mokelumne Wilderness CA 12.87 12.08 12.07 7.14 

Pinnacles NM CA 17.90 15.42 15.42 7.34 

Point Reyes NS CA 22.40 21.00 21.00 7.39 

Redwood NP CA 18.55 17.66 17.66 7.81 

San Gabriel Wilderness CA 18.44 15.54 15.53 7.17 

San Gorgonio Wilderness CA 21.43 19.27 19.28 7.10 

San Jacinto Wilderness CA 21.43 18.10 18.11 7.12 

San Rafael Wilderness CA 19.43 17.40 17.39 7.28 

Sequoia NP CA 23.73 21.68 21.67 7.13 

South Warner Wilderness CA 14.13 13.31 13.31 7.32 

Thousand Lakes Wilderness CA 13.65 12.65 12.64 7.32 

Ventana Wilderness CA 17.90 16.37 16.37 7.32 

Yosemite NP CA 16.87 15.95 15.95 7.14 

Black Canyon of the Gunnison NM CO 10.00 9.21 9.21 7.06 

Eagles Nest Wilderness CO 8.82 8.05 8.05 7.08 

Flat Tops Wilderness CO 8.82 8.32 8.31 7.07 

Great Sand Dunes NM CO 11.82 11.20 11.20 7.10 

La Garita Wilderness CO 10.00 9.49 9.49 7.06 

Maroon Bells-Snowmass Wilderness CO 8.82 8.27 8.26 7.07 

Mesa Verde NP CO 12.14 11.31 11.31 7.09 

Mount Zirkel Wilderness CO 9.72 9.20 9.19 7.08 

Rawah Wilderness CO 9.72 9.15 9.14 7.08 

Rocky Mountain NP CO 12.85 12.15 12.15 7.05 

Weminuche Wilderness CO 10.00 9.46 9.46 7.06 

West Elk Wilderness CO 8.82 8.21 8.21 7.07 

Everglades NP FL 22.48 18.43 18.43 11.15 

Okefenokee GA 27.21 20.28 20.29 11.45 

Wolf Island GA 27.21 20.12 20.15 11.42 

Craters of the Moon NM ID 14.06 12.94 12.94 7.13 

Sawtooth Wilderness ID 14.97 14.70 14.70 7.15 

Mammoth Cave NP KY 32.00 22.29 22.29 11.53 

Acadia NP ME 22.75 18.34 18.33 11.45 

Moosehorn ME 21.19 17.58 17.58 11.36 

Roosevelt Campobello International Park ME 21.19 17.57 17.56 11.36 

Isle Royale NP MI 21.31 18.19 18.19 11.22 
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Seney MI 25.05 20.80 20.80 11.37 

Boundary Waters Canoe Area MN 20.20 16.56 16.56 11.21 

Voyageurs NP MN 19.62 16.61 16.61 11.09 

Hercules-Glades Wilderness MO 26.95 21.00 21.00 11.27 

Anaconda-Pintler Wilderness MT 17.11 16.69 16.68 7.28 

Bob Marshall Wilderness MT 16.13 15.63 15.63 7.36 

Cabinet Mountains Wilderness MT 14.31 13.65 13.65 7.43 

Gates of the Mountains Wilderness MT 11.94 11.48 11.47 7.22 

Glacier NP MT 19.62 18.73 18.73 7.56 

Medicine Lake MT 18.21 17.17 17.17 7.30 

Mission Mountains Wilderness MT 16.13 15.50 15.49 7.39 

Red Rock Lakes MT 11.19 10.62 10.62 7.14 

Scapegoat Wilderness MT 16.13 15.59 15.59 7.29 

Selway-Bitterroot Wilderness MT 17.11 16.74 16.74 7.32 

UL Bend MT 15.49 15.00 15.00 7.18 

Linville Gorge Wilderness NC 29.66 20.08 20.07 11.43 

Shining Rock Wilderness NC 28.54 19.49 19.48 11.45 

Lostwood ND 19.61 17.64 17.64 7.33 

Theodore Roosevelt NP ND 17.88 16.02 16.02 7.31 

Great Gulf Wilderness NH 21.43 16.46 16.46 11.31 

Presidential Range-Dry River Wilderness NH 21.43 16.39 16.39 11.33 

Brigantine NJ 28.68 20.96 20.95 11.28 

Bandelier NM NM 11.97 10.51 10.51 7.02 

Bosque del Apache NM 13.81 12.40 12.40 6.97 

Carlsbad Caverns NP NM 16.51 14.48 14.47 7.02 

Gila Wilderness NM 13.12 12.41 12.40 6.95 

Pecos Wilderness NM 9.60 8.85 8.85 7.04 

Salt Creek NM 18.27 16.19 16.18 6.99 

San Pedro Parks Wilderness NM 10.42 9.63 9.62 7.03 

Wheeler Peak Wilderness NM 9.60 8.66 8.65 7.07 

White Mountain Wilderness NM 13.01 12.05 12.05 6.98 

Jarbidge Wilderness NV 12.26 11.92 11.92 7.10 

Wichita Mountains OK 23.63 18.27 18.26 11.07 

Crater Lake NP OR 13.21 12.49 12.49 7.71 

Diamond Peak Wilderness OR 13.21 12.39 12.39 7.77 

Eagle Cap Wilderness OR 17.34 16.31 16.31 7.34 

Gearhart Mountain Wilderness OR 13.21 12.61 12.61 7.46 

Hells Canyon Wilderness OR 19.00 17.57 17.58 7.32 

Kalmiopsis Wilderness OR 16.38 15.36 15.36 7.71 

Mount Hood Wilderness OR 14.68 13.03 13.03 7.77 

Mount Jefferson Wilderness OR 15.80 14.78 14.78 7.81 

Mount Washington Wilderness OR 15.80 14.78 14.77 7.89 

Mountain Lakes Wilderness OR 13.21 12.42 12.42 7.57 

Strawberry Mountain Wilderness OR 17.34 16.37 16.37 7.49 

Three Sisters Wilderness OR 15.80 14.87 14.87 7.87 

Cape Romain SC 27.43 19.70 19.70 11.36 

Badlands NP SD 16.82 14.91 14.90 7.30 

Wind Cave NP SD 15.95 14.21 14.21 7.24 
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Great Smoky Mountains NP TN 30.56 21.28 21.27 11.44 

Joyce-Kilmer-Slickrock Wilderness TN 30.56 20.97 20.97 11.45 

Big Bend NP TX 17.21 15.35 15.34 6.93 

Guadalupe Mountains NP TX 16.51 14.47 14.46 7.03 

Arches NP UT 10.77 9.98 9.97 6.99 

Bryce Canyon NP UT 11.62 10.95 10.95 6.99 

Canyonlands NP UT 10.77 10.12 10.11 7.01 

Capitol Reef NP UT 10.86 10.39 10.39 7.03 

James River Face Wilderness VA 28.93 19.62 19.62 11.24 

Shenandoah NP VA 29.42 19.58 19.58 11.25 

Lye Brook Wilderness VT 24.11 16.87 16.86 11.25 

Alpine Lake Wilderness WA 16.99 15.06 15.04 7.86 

Glacier Peak Wilderness WA 13.29 12.18 12.17 7.80 

Goat Rocks Wilderness WA 12.67 11.35 11.34 7.82 

Mount Adams Wilderness WA 12.67 11.39 11.39 7.78 

Mount Rainier NP WA 17.07 15.36 15.35 7.90 

North Cascades NP WA 13.29 12.15 12.14 7.78 

Olympic NP WA 15.83 14.31 14.31 7.88 

Pasayten Wilderness WA 15.35 14.36 14.36 7.77 

Dolly Sods Wilderness WV 29.94 19.65 19.64 11.32 

Otter Creek Wilderness WV 29.94 19.73 19.72 11.33 

Bridger Wilderness WY 10.73 10.29 10.29 7.08 

Fitzpatrick Wilderness WY 10.73 10.29 10.28 7.09 

Grand Teton NP WY 11.19 10.57 10.56 7.09 

North Absaroka Wilderness WY 11.30 10.90 10.90 7.09 

Teton Wilderness WY 11.19 10.68 10.68 7.09 

Washakie Wilderness WY 11.30 10.90 10.90 7.09 

Yellowstone NP WY 11.19 10.61 10.61 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, PM2.5, 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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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 cobenefits 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 

(PM2.5) and ozone air quality resulting from reductions in precursors including NOX and SO2? 

2. What is the economic value of these effects? 

 

RRRRRR EPA 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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6-74 

For the final rule, 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 PMrelated human health impacts (the decrease in national population-weighted annual average PM2.5

is 0.0065 μg/m3 in 2030).SSSSSS We estimate that there is a very small increase in populationweighted national average ozone exposure, which results in a very small net increase in ozonerelated 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),TTTTTT,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)VVVVVV,WWWWWW 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 rule 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 PM2.5 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 μg/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 

 

SSSSSS Note that the national, population-weighted PM2.5 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 

PM2.5 and ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air 

quality monitor. 

TTTTTT Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). Lung 

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the 

American Medical Association, 287, 1132-1141. 

UUUUUU Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities, 1987-2000.Journal of 

the American Medical Association, 292(19), 2372-2378. 

VVVVVV 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 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4), 458-68. 
MY 2017 and Later - Regulatory Impact Analysis 

6-75 

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, 379 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).XXXXXX

 

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 mortalityrelated 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 Benefitsa

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Billions, 2010$, 3% 

Discount Rate)b,c 

Total Benefits 

(Billions, 2010$, 7% 

Discount Rate) b,c

Multi-city analyses Bell et al., 2004 Total: $1.0 – $2.6

PM: $1.1 - $2.6

Ozone: -$0.006 

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006 

Huang et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.006 

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006 

Schwartz, 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.009 

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.009 

Meta-analyses Bell et al., 2005 Total: $1.0 - $2.6 Total: $0.92 - $2.3

 

XXXXXX Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 
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6-76 

PM: $1.1 - $2.6

Ozone: -$0.019 

PM: $0.95 - $2.3

Ozone: -$0.019 

Ito et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.026 

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.026 

Levy et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.027 

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.027 

Notes: 

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was developed 

by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature 

mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b

 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. 

 
MY 2017 and Later - Regulatory Impact Analysis 

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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: healtha

Premature mortality based on cohort study 

estimatesb 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 impactsc

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 
Chapter 6 

6-78 

Pollutant/ 

Effect 

Quantified and monetized in primary estimate Unquantified 

Nitrate 

Deposition: 

welfare 

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 

Sulfate 

Deposition: 

welfare 

Commercial fishing and forestry from acidic 

deposition effects 

Recreation in terrestrial and aquatic ecosystems 

from acid deposition effects 

Increased mercury methylation 

HC/Toxics: 

healthd 

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) 

HC/Toxics: 

welfare 

Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 

Notes: 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

6-79 

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 SixCities 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 PM2.5-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 In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was reached 

on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials. 
Chapter 6 

6-80 

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 PM2.5-related premature 

mortality based on the C-R distributions provided by each expert, as well as that from the dataderived 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 Impactsa

Health Effect 2030 Annual Reduction in 

Incidence (5th - 95th percentile) 

Premature Mortality – Derived from epidemiology literatureb

 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) 

110 

(30 – 190) 

280 

(130 – 440) 

0 

(0 – 1) 

Chronic bronchitis (adult, age 26 and over) 76 

(1 – 150) 

Non-fatal myocardial infarction (adult, age 18 and over) 130 

(32 – 230) 

Hospital admissions - respiratory (all ages)c20 

(8 – 32) 

Hospital admissions - cardiovascular (adults, age >18)d 50 

(33 – 60) 

Emergency room visits for asthma (age 18 years and younger) 72 

(34 – 110) 

Acute bronchitis, (children, age 8-12) 160 

(-42 – 370) 
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6-81 

Lower respiratory symptoms (children, age 7-14) 2,100 

(770 – 3,400) 

Upper respiratory symptoms (asthmatic children, age 9-18) 1,600 

(260 – 2,900) 

Asthma exacerbation (asthmatic children, age 6-18) 3,500 

(-120 – 9,700) 

Work loss days 14,000 

(12,000 – 16,000) 

Minor restricted activity days (adults age 18-65) 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 Impactsa 

Health Effect 2030 Annual Reduction in Incidence 

(5th - 95th percentile) 

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 

-1 

(-4 – 3) 

-1 

(-5 – 4) 

-1 

(-6 – 4) 

-2 

(-10 – 6) 

-3 

(-11 – 6) 

-3 

(-10 – 4) 

Hospital admissions- respiratory causes (adult, 65 and older)c -6 

(-30 – 15) 

Hospital admissions -respiratory causes (children, under 2) -3 

(-12 – 6) 

Emergency room visit for asthma (all ages) -1 

(-18 – 15) 

Minor restricted activity days (adults, age 18-65) -930 

(-18,000 – 16,000) 

 

ZZZZZZ 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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6-82 

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 

2030 Incidence 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 30 110 190 

Laden et al. (2006) 130 280 440 

Expert A 6 300 590 

Expert B -23 220 530 

Expert C -4 230 530 

Expert D 23 160 280 

Expert E 150 370 60 

Expert F 120 200 290 

Expert G 0 130 260 

Expert H -38 170 430 

Expert I 19 220 430 

Expert J 19 180 440 

Expert K 0 36 190 

Expert L 8 140 330 

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 PM2.5-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 

 

AAAAAAA 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-illnessbased unit values. For these, we apply the same unit value regardless of the future year of analysis. 
MY 2017 and Later - Regulatory Impact Analysis 

6-83 

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 PM2.5-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 PMrelated 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., costof-illness). As such, the true value of these effects may be higher than that reported here. 

 

BBBBBBB Based in part on prior SAB 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 Nonfatal myocardial 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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6-84 

Table 6.3-6: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (millions of 2010$) a,b

2030 

PM2.5-Related Health Effect (5th and 95th Percentile) 

Premature Mortality – 

Derived from Epidemiology 

Studiesc,d

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

$980 

($110 - $2,600) 

$880 

($97 - $2,400) 

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

$2,500 

($340 - $6,300) 

$2,300 

($310 - $5,700) 

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

$3.8 

(-$3.9 - $15) 

Chronic bronchitis (adults, 26 and over) $42 

($0.4 - $140) 

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

$14 

($2.3 - $36) 

$12 

($1.8 - $30) 

Hospital admissions for respiratory causes $0.32 

($0.13 - $0.51) 

Hospital admissions for cardiovascular causes $0.73 

($0.07 - $1.4) 

Emergency room visits for asthma $0.03 

($0.01 - $0.05) 

Acute bronchitis (children, age 8–12) $0.08 

(-$0.02 - $0.21) 

Lower respiratory symptoms (children, 7–14) $0.04 

($0.01 - $0.09) 

Upper respiratory symptoms (asthma, 9–11) $0.05 

($0.009 - $0.12) 

Asthma exacerbations $0.20 

(-$0.007 - $0.58) 

Work loss days $2.2 

($1.9 - $2.6) 

Minor restricted-activity days (MRADs) $5.6 

($3.2 - $8.1) 

Premature Mortality, All ages – 

Derived from Multi-city analyses

Bell et al., 2004 -$5.8 

(-$45 - $27) 

Huang et al., 2005 -$6.2 

(-$60 - $41) 

Schwartz, 2005 -$8.7 

(-$71 - $44) 

Premature Mortality, All ages – 

Derived from Meta-analyses 

Bell et al., 2005 -$19 

(-$120 - $38) 
MY 2017 and Later - Regulatory Impact Analysis 

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Ito et al., 2005 -$26 

(-$140 - $58) 

Levy et al., 2005 -$27 

(-$120 - $38) 

Hospital admissions- respiratory causes (adult, 65 and older) -$0.16 

(-$0.77 - $0.39) 

Hospital admissions- respiratory causes (children, under 2) -$0.03 

(-$130 - $0.07) 

Emergency room visit for asthma (all ages) -$0.0003 

(-$0.007 - $0.006) 

Minor restricted activity days (adults, age 18-65) -$0.06 

(-$1.3 - $1.1) 

School absence days -$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 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Multi-city Bell et al., 

2004 

$1.0 - $2.6 Multi-city Bell et al., 

2004 

$0.95 - $2.3 

Huang et al., 

2005 

$1.0 - $2.6 Huang et al., 

2005 

$0.94 - $2.3 

Schwartz, 

2005 

$1.0 - $2.6 Schwartz, 

2005 

$0.94 - $2.3 

Meta-analysis Bell et al., 

2005 

$1.0 - $2.6 Meta-analysis Bell et al., 

2005 

$0.93 - $2.3 

Ito et al., 

2005 

$1.0 - $2.6 Ito et al., 

2005 

$0.92 - $2.3 

Levy et al., 

2005 

$1.0 - $2.6 Levy et al., 

2005 

$0.92 - $2.3 

Total Ozone and PM Benefits (billions, 2010$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

3% Discount Rate 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Multi-city Bell et al., 

2004 

$0.39 - $3.4 Multi-city Bell et al., 

2004 

$0.35 - $3.1 

Huang et al., 

2005 

$0.39 - $3.4 Huang et al., 

2005 

$0.35 - $3.1 
Chapter 6 

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Schwartz, 

2005 

$0.39 - $3.4 Schwartz, 

2005 

$0.35 - $3.1 

Meta-analysis Bell et al., 

2005 

$0.38 - $3.4 Meta-analysis Bell et al., 

2005 

$0.34 - $3.1 

Ito et al., 

2005 

$0.37 - $3.4 Ito et al., 

2005 

$0.33 - $3.0 

Levy et al., 

2005 

$0.37 - $3.4 Levy et al., 

2005 

$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: 

 ( 1) 0 −⋅=∆

∆⋅ x

eyy

β

, 

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), β is the effect estimate, and ∆x is the estimated change in the 

summary pollutant measure. There are other functional forms, but the basic elements remain the 

same. 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 PM2.5 and 

Ozone Reductions 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

Premature Mortality 

Premature mortality – 

daily time series 

O3 Multi-city

Bell et al (2004) (NMMAPS study)392 – Nonaccidental 

Huang et al (2005)393 - Cardiopulmonary 

Schwartz (2005)394 – Non-accidental 

Meta-analyses:

Bell et al (2005)395 – All cause 

Ito et al (2005)396 – Non-accidental 

Levy et al (2005)397 – All cause 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)398

Laden et al. (2006)399

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)400 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)401 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)402 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)403 Adults (>18 years) 
Chapter 6 

6-88 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

Hospital Admissions 

Respiratory 

O3 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)404

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)405,406

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)407

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

Burnett et al. (2001)408 <2 years 

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)409

Ito (2003)—ICD 490-496 (COPD)410

>64 years 

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)411 20–64 years 

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years 

PM2.5 Sheppard (2003)—ICD 493 (asthma)412 <65 years 

Cardiovascular PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure) 

>64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER 

visits 

O3 Pooled estimate:

Peel et al (2005)413

Wilson et al (2005)414

All ages 

All ages 

Asthma-related ER 

visits (cont’d) 

PM2.5 Norris et al. (1999)415 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)416 8–12 years 

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)417 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)418 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)419 (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998)420 (cough) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987)421 18–65 years 

School absence days 

O3 

Pooled estimate:

Gilliland et al. (2001)422

Chen et al. (2000)423

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs) 

O3 Ostro and Rothschild (1989)424 18–65 years 

PM2.5 Ostro and Rothschild (1989) 18–65 years 

Notes: 
MY 2017 and Later - Regulatory Impact Analysis 

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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-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits 

of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 

b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent 

advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school 

absences for all school-aged children based on the biological similarity between children aged 5 to 17. 

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 

U.S. To account for the potential impacts of different health care systems or underlying health 

status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due 

to the potential for confounding by co-pollutants, we give preference to effect estimates from 

models including both ozone and PM over effect estimates from 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
Chapter 6 

6-90 

Table 6.3-9: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population 

Endpoint Parameter Rates 

Value Source 

Mortality Daily or annual mortality 

rate projected to 2020 

Age-, cause-, and 

county-specific rate 

CDC Wonder (2006–2008)

428

U.S. Census bureau 

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate 

2007 HCUP data filesa,429

Asthma ER Visits Daily asthma ER visit rate Age-, region-, state-, 

county- and causespecific rate 

2007 HCUP data filesa

Chronic Bronchitis Annual prevalence rate per 

person 

Aged 18–44 

Aged 45–64 

Aged 65 and older 

0.0367 

0.0505 

0.0587 

1999 NHIS (American Lung 

Association, 2002, Table 4)430 

Annual incidence rate per 

person 

0.00378 Abbey et al. (1993, Table 3) 

Nonfatal Myocardial 

Infarction (heart 

attacks) 

Daily nonfatal myocardial 

infarction incidence rate 

per person, 18+ 

Age-, region-, state-, 

and county- specific 

rate 

2007 HCUP data filesa; adjusted by 

0.93 for probability of surviving after 

28 days (Rosamond et al., 1999) 

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily dyspnea

0.076

0.067

0.037 

Ostro et al. (2001)

Acute Bronchitis Annual bronchitis 

incidence rate, children 

0.043 American Lung Association (2002, 

Table 11)431

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

childrenb

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD incidence rate 

per person (18–65) 

Aged 18–24 

Aged 25–44 

Aged 45–64 

0.00540 

0.00678 

0.00492 

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41);432 U.S. 

Bureau of the Census (2000)433

School Loss Days Rate per person per year, 

assuming 180 school days 

per year 

9.9 National Center for Education 

Statistics (1996)434 and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity Days Daily MRAD incidence 

rate per person 

0.02137 Ostro and Rothschild (1989, p. 243) 

Notes: 

a

 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. 

b

 Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. 
MY 2017 and Later - Regulatory Impact Analysis 

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Table 6.3-10: Asthma Prevalence Rates Used for this Analysis 

Population Group Asthma Prevalence Rates 

Value Source 

All Ages 0.0780 

American Lung Association (2010, Table 7) 

< 18 0.0941 

5–17 0.1070 

18–44 0.0719 

45–64 0.0745 

65+ 0.0716 

African American, 5 to 17 0.1776 American Lung Association (2010, Table 9) 

African American, <18 0.1553 American Lung Associationb

Notes: 

a

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. 

b 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
Chapter 6 

6-92 

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) 

$8,000,000 $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 pollutionrelated 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$1

06,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548

$89,373

$100,690

$106,053

$185,785

$89,373

$88,547

$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 (IEc, 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 (IEc, 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 
Chapter 6 

6-94 

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. 

Acute Bronchitis $452 $494 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 IEc 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 $110. 

Work Loss Days 

(WLDs) 

Variable 

(U.S. 

median = 

$137) 

Variable 

(U.S. 

median = 

$137) 

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. 

Minor Restricted 

Activity Days 

(MRADs) 

$64 $69 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. 

School Absence Days $95 $95 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 

season.

DDDDDDD,EEEEEEE The predicted changes in ozone concentrations from the future-year base 

 

DDDDDDD The ozone season for this analysis is defined as the 5-month period from May to September. 

EEEEEEE Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at 
MY 2017 and Later - Regulatory Impact Analysis 

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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.FFFFFFF,GGGGGGG 

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 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

GGGGGGG This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap. 
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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

2030 

Statistica Baseline Changeb

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 (μg/m3) 

Annual Average Concentration 8.1135 -0.0065 

Notes: 

a

 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 PM2.5

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 PM2.5 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 misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; and 

• biases due to omissions or other research limitations. 

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 PM2.5 exposure. Due to analytical limitations, 

this analysis does not estimate benefits related to other criteria pollutants (such as ozone, NO2 or 

SO2) 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 PM2.5, 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 Sourcesd Upstream (Non-EGU) 

Sourcesd 

Mobile Sources 

SO2 NOX Direct PM2.5 NOX Direct PM2.5

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated 

Using a 3 Percent Discount Ratec

2015 $30,000 $4,900 $230,000 $5,100 $280,000 

2020 $33,000 $5,400 $250,000 $5,600 $310,000 

2030 $38,000 $6,400 $290,000 $6,700 $370,000 

2040 $45,000 $7,600 $340,000 $8,000 $440,000 

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated 

Using a 7 Percent Discount Ratec 

2015 $27,000 $4,500 $210,000 $4,600 $250,000 

2020 $30,000 $4,900 $230,000 $5,100 $280,000 

2030 $35,000 $5,800 $270,000 $6,100 $330,000 

2040 $41,000 $6,900 $310,000 $7,300 $400,000 

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 3 

Percent Discount Ratec 

2015 $73,000 $12,000 $560,000 $12,000 $680,000 

2020 $80,000 $13,000 $620,000 $14,000 $750,000 

2030 $94,000 $16,000 $720,000 $16,000 $900,000 

2040 $110,000 $19,000 $840,000 $20,000 $1,100,000 

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 7 

Percent Discount Ratec 

2015 $66,000 $11,000 $510,000 $11,000 $620,000 

2020 $72,000 $12,000 $560,000 $12,000 $680,000 

2030 $84,000 $14,000 $650,000 $15,000 $810,000 

2040 $99,000 $17,000 $760,000 $18,000 $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,IIIIIII

 

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 As we discuss in the 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. 

IIIIIII 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) 
Chapter 6 

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Table 6.3-15 Human Health and Welfare Effects of PM2.5 

Quantified and Monetized 

in Primary Estimates

Un-quantified Effects 

Changes in:

Adult premature mortality 

Bronchitis: chronic and acute

Hospital admissions: respiratory and 

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial 

infarction)

Lower and upper respiratory illness

Minor restricted-activity days

Work loss days

Asthma exacerbations (asthmatic 

population)

Infant mortality

Subchronic bronchitis cases

Low birth weight

Pulmonary function

Chronic respiratory diseases other than 

chronic bronchitis

Non-asthma respiratory emergency room 

visits

Visibility

Household soiling

Consistent with the NO2 NAAQS,JJJJJJJ the benefits estimates utilize concentrationresponse 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., NO2 emitted from mobile 

sources; direct PM emitted from stationary sources). Our estimate of total PM2.5 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 The 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-perton estimates: http://www.epa.gov/air/benmap/bpt.html 
MY 2017 and Later - Regulatory Impact Analysis 

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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 PMrelated 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 Based on a review of the 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). EPA600-R-08-139F. National Center for Environmental Assessment – RTP 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 concentrationresponse 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. 
Chapter 6 

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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 peerreview 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 VSL.NNNNNNN

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 PM2.5 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 the 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 This 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. 
MY 2017 and Later - Regulatory Impact Analysis 

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benefits from reducing fine particles in areas with varied initial concentrations of PM2.5,

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 CO2 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 
Chapter 6 

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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 
MY 2017 and Later - Regulatory Impact Analysis 

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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 wellmixed 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.OOOOOOO

 

6.4.2 Projected Change in Atmospheric CO2 Concentrations, Global Mean Surface 

Temperature and Sea Level Rise 

 To assess the impact of the emissions reductions from the finalrule, EPA estimated 

changes in projected atmospheric CO2 concentrations, global mean surface temperature and sealevel 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 CO2, N2O, CH4, HFC-134a, NOx, CO, and SO2 emissions reductions estimated from 

this rule were applied as net reductions to the GCAM global baseline net emissions for each 

 

OOOOOOO Due to timing 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-OAR2010-0799). 

PPPPPPP GCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use that 

considers the sources of emissions of a suite of greenhouse gases (GHG's), emitted in 14 globally disaggregated 

regions, the fate of emissions to the atmosphere, and the consequences of changing concentrations of greenhouse 

related gases for climate change. GCAM begins with a representation of demographic and economic developments 

in each region and combines these with assumptions about technology development to describe an internally 

consistent representation of energy, agriculture, land-use, and economic developments that in turn shape global 

emissions. 

QQQQQQQ MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single 

framework. The framework allows the user to determine changes in greenhouse-gas concentrations, global-mean 

surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane 

(CH4), nitrous oxide (N2O), reactive gases (CO, NOx, VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and sulfur 

dioxide (SO2). MAGICC emulates the global-mean temperature responses of more sophisticated coupled 

Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. 
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substance. RRRRRRRThe 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 CO2 concentrations rise throughout the century and reach 760 to 

820 ppmv by 2100, depending on climatic parameters, with total radiative forcing increasing 

more than 5 Watts per square meter (W/m2

) above 1990 levels by 2100. Forest land declines in 

the reference scenario to accommodate increases in land use for food and bioenergy crops. Even 

with the assumed agricultural productivity increases, the amount of land devoted to crops 

increases in the first half of the century due to increases in population and income (higher income 

drives increases in land-intensive meat consumption). After 2050 the rate of growth in food 

demand slows, in part due to declining population. As a result the amount of cropland and also 

land use change (LUC) emissions decline as agricultural crop productivity continues to increase. 

The GCAM reference scenario uses non-CO2 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 CO2 concentrations, global mean 

temperature, and sea level were projected at five-year time steps to 2100 for both the reference 

(no climate policy) scenario and the emissions reduction scenario specific to the rule. To capture 

some of the uncertainty in the climate system, the changes in projected atmospheric CO2

concentrations, global mean temperature and sea level were estimated across the most current 

Intergovernmental Panel on Climate Change (IPCC) range of climate sensitivities, 1.5°C to 

6.0°C.SSSSSSS The range as illustrated in Chapter 10, Box 10.2, Figure 2 of the IPCC’s Working 

 

RRRRRRR Due to timing 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-OAR2010-0799), and the files used as inputs for the MAGICC model are also available (see “MAGICC Input File 

(policy)” and “MAGICC Input File (reference)”). 

SSSSSSS In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global 

surface temperature following a doubling of the atmospheric equivalent carbon dioxide concentration. The 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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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 CO2, CH4, N2O, HFCs, and tropospheric ozone. It also includes the effects of 

temperature changes on stratospheric ozone and the effects of CH4 emissions on stratospheric 

water vapor. Changes in CH4, NOx, VOC, and CO emissions affect both O3 concentrations and 

CH4 concentrations. MAGICC includes the relative climate forcing effects of changes in sulfate 

concentrations due to changing SO2 emissions, including both the direct effect of sulfate particles 

and the indirect effects related to cloud interactions. However, MAGICC does not calculate the 

effect of changes in concentrations of other aerosols such as nitrates, black carbon, or organic 

carbon, making the assumption that the sulfate cooling effect is a proxy for the sum of all the 

aerosol effects. Therefore, the climate effects of changes in PM2.5 emissions and precursors 

(besides SO2) presented in Chapter 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 CO2 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 

CO2 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 cmTTTTTTT. For 

 

“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 complete results from the MAGICC modeling can be found in the docket (see " Supporting Document 

for MAGICC Analysis " in Docket EPA-HQ-OAR-2010-0799). 
MY 2017 and Later - Regulatory Impact Analysis 

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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 

CO2 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 CO2 concentrations because CO2 concentrations are only weakly 

coupled to climate sensitivity through the dependence on temperature of the rate of ocean 

absorption of CO2, whereas the magnitude of temperature change response to CO2 changes (and 

therefore sea level rise) is more tightly coupled to climate sensitivity in the MAGICC model. 

Figure 6.4-1 Projected Reductions in Atmospheric CO2 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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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) 

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 
MY 2017 and Later - Regulatory Impact Analysis 

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are small relative to the overall expected increase in temperature (1.8 – 4.8 oC) 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 (CO2) 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 (CO2) system in seawater. The program was developed by 

Ernie Lewis at Brookhaven National Laboratory and Doug Wallace at the Institut für 

Meereskunde in Germany, supported by the U.S. Department of Energy, Office of Biological and 

Environmental Research, under Contract No. DE-ACO2-76CH00016. 

The CO2SYS program uses two of the four measurable parameters of the CO2 system 

[total alkalinity (TA), total inorganic CO2 (TC), pH, and either fugacity (fCO2) or partial pressure 

of CO2 (pCO2)] to calculate the other two parameters given a specific set of input conditions 

(temperature and pressure) and output conditions chosen by the user. EPA utilized the 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 CO2SYS program required the input of a number of variables and constants for each 

scenario for calculating the result for both the reference case and the rule’s emissions reduction 

 

UUUUUUU Due to timing constraints, the modeling analysis in this section was conducted with preliminary estimates of 

the CO2 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 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 CO2 emissions 

reduction projections are available in the docket (see "Emissions for MAGICC modeling" in Docket EPA-HQ-OAR2010-0799). 
Chapter 6 

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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]. 

2) Choice of constants: Mehrbach et al. (1973)473, refit by Dickson and Millero 

(1987)474 

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 CO2 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 CO2 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 CO2 concentrations that were 

derived from the MAGICC modeling. 

Table 6.4-1 Impact of the MY 2017-2025 Standards On Ocean pH 

CLIMATE 

SENSITIVITY

DIFFERENCE 

IN CO2 IN 2100 

YEAR PROJECTED 

pH CHANGE 

3.0 -3.37 ppm 2100 +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 UNITS YEAR PROJECTED CHANGE 

Atmospheric CO2 Concentration ppmv 2100 -3.21 to -3.58 

Global Mean Surface Temperature oC 2100 -0.0074 to -0.0176 

Sea Level Rise cm 2100 -0.071 to -0.159 

Ocean pH pH units 2100 +0.0017a

a

 The value for projected change in ocean pH is based on a climate sensitivity of 3.0. 
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93 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799. 

94 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of 

Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R-07-003. Washington, DC, U.S. EPA. Docket 

EPA-HQ-OAR-2010-0799 

95 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. The National Academies Press: Washington, D.C. Docket EPA-HQ-OAR-2010-

0799. 

96 Bates, D.V., Baker-Anderson, M., Sizto, R. (1990). Asthma attack periodicity: a study of hospital emergency 

visits in Vancouver. Environ. Res., 51,51-70. Docket EPA-HQ-OAR-2010-0799. 

97 Thurston, G.D., Ito, K., Kinney, P.L., Lippmann, M. (1992). A multi-year study of air pollution and respiratory 

hospital admissions in three New York State metropolitan areas: results for 1988 and 1989 summers. J. Exposure 

Anal. Environ. Epidemiol, 2,429-450. Docket EPA-HQ-OAR-2010-0799. 

98 Thurston, G.D., Ito, K., Hayes, C.G., Bates, D.V., Lippmann, M. (1994) Respiratory hospital admissions and 

summertime haze air pollution in Toronto, Ontario: consideration of the role of acid aerosols. Environ. Res., 65, 271-

290. Docket EPA-HQ-OAR-2010-0799. 

99 Lipfert, F.W., Hammerstrom, T. (1992). Temporal patterns in air pollution and hospital admissions. Environ. Res., 

59,374-399. Docket EPA-HQ-OAR-2010-0799. 

100 Burnett, R.T., Dales, R.E., Raizenne, M.E., Krewski, D., Summers, P.W., Roberts, G.R., Raad-Young, M., 

Dann,T., Brook, J. (1994). Effects of low ambient levels of ozone and sulfates on the frequency of respiratory 

admissions to Ontario hospitals. Environ. Res., 65, 172-194. Docket EPA-HQ-OAR-2010-0799. 

101 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799 

102 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799. 

103 Devlin, R. B., McDonnell, W. F., Mann, R., Becker, S., House, D. E., Schreinemachers, D., Koren, H. S. (1991). 

Exposure of humans to ambient levels of ozone for 6.6 hours causes cellular and biochemical changes in the lung. 

Am. J. Respir. Cell Mol. Biol., 4, 72-81. 
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104 Koren, 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. 

105 Koren, H. S., Devlin, R. B., Graham, D. E., Mann, R., McGee, M. P., Horstman, D. H., Kozumbo, W. J., Becker, 

S., House, D. E., McDonnell, W. F., Bromberg, P. A. (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., 143,1353-1358. Docket EPA-HQ-OAR-2010-0799. 

107 U.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. 

108 Hodgkin, J.E., Abbey, D.E., Euler, G.L., Magie, A.R. (1984). COPD prevalence in nonsmokers in high and low 

photochemical air pollution areas. Chest, 86, 830-838. Docket EPA-HQ-OAR-2010-0799. 

109 Euler, G.L., Abbey, D.E., Hodgkin, J.E., Magie, A.R. (1988). Chronic obstructive pulmonary disease symptom 

effects of long-term cumulative exposure to ambient levels of total oxidants and nitrogen dioxide in California 

Seventh-day Adventist residents. Arch. Environ. Health, 43, 279-285. 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/R05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799. 

112 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-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. 

114 Higgins, I. T.T., D'Arcy, J. B., Gibbons, D. I., Avol, E. L., Gross, K.B. (1990). Effect of exposures to ambient 

ozone on ventilatory lung function in children. Am. Rev. Respir. Dis., 141, 1136-1146. Docket EPA-HQ-OAR-2010-

0799. 

115 Raizenne, M.E., Burnett, R.T., Stern, B., Franklin, C.A., Spengler, J.D. (1989) Acute lung function responses to 

ambient acid aerosol exposures in children. Environ. Health Perspect., 79,179-185. Docket EPA-HQ-OAR-2010-

0799. 

116 Raizenne, 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 EPAHQ-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. 

120 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Docket EPA-HQ-OAR-2010-0799. 
Chapter 6 

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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. 

122 Horstman, D.H., Ball, B.A., Folinsbee, L.J., Brown, J., Gerrity, T. (1995) Comparison of pulmonary responses of 

asthmatic and nonasthmatic subjects performing light exercise while exposed to a low level of ozone. Toxicol. Ind. 

Health., 11(4), 369-85. 

123 Horstman, D.H.,; Folinsbee, L.J., Ives, P.J., Abdul-Salaam, S., McDonnell, W.F. (1990). Ozone concentration and 

pulmonary response relationships for 6.6-hour exposures with five hours of moderate exercise to 0.08, 0.10, and 0.12 

ppm. Am. Rev. Respir. Dis., 142, 1158-1163. 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/recordisplay.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/recordisplay.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/recordisplay.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/420r10009.pdf. Docket EPA-HQOAR-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. 

130 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is available electronically 

at: http://www.epa.gov/iris/subst/0276.htm. Docket EPA-HQ-OAR-2010-0799. 

131 International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risk of 

chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, International Agency for Research on 

Cancer, World Health Organization, Lyon, France 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. 

135 Aksoy, 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 Reviews. 3: 541-554.

Docket EPA-HQ-OAR-2010-0799. 

137 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T. 

Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among Chinese 

workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246. 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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6-119 

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.

140 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes among Chinese 

workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275-285. 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. 

144 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)” Environmental Protection Agency, 

Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental 

Assessment, Washington, DC http://www.epa.gov/iris/subst/0139.htm. 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. 

148 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subchronic toxicity of 4-vinylcyclohexene in rats and mice by 

inhalation. Fundam. Appl. Toxicol. 32:1-10. 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 1-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. 

154 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among 

workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130. Docket EPA-HQ-OAR2010-0799. 

155 Beane 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. 
Chapter 6 

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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. 

160 ATSDR. 2010. Addendum to theToxicological Profile for Formaldehyde. U.S. Department of Health and Human 

Services (HHS), October 2010. 

161 IPCS. 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

164 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/iris/subst/0290.htm. 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 EPAHQ-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. 

172 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. 

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. Docket EPA-HQ-OAR-2010-0799. 

175 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. 

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-OAR2010-0799. 

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(1,2,3-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, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to environmental pollutants on 

birth outcomes in a multiethnic population. Environ Health Perspect. 111: 201-205. 

184 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, 

D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on 

neurodevelopment in the first 3 years of life among inner-city children. Environ Health Perspect 114: 1287-1292. 

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://ntpserver.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. 
Chapter 6 

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191 U. S. EPA. 1998. Toxicological Review of Naphthalene, Environmental Protection Agency, Integrated Risk 

Information System, Research and Development, National Center for Environmental Assessment, Washington, DC. 

This material is available electronically at http://www.epa.gov/iris/subst/0436.htm 

192 Zhou, Y.; Levy, J.I. (2007) Factors influencing the spatial extent of mobile source air pollution impacts: a metaanalysis. 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.org]. Docket EPA-HQ-OAR2010-0799. 

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. Int J 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 

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199 Lena, T.S.; Ochieng, V.; Carter, M.; Holguín-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 EPAHQ-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. 

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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, G.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 

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206 National Research Council, 1993. Protecting Visibility in National Parks and Wilderness Areas. National 

Academy of Sciences Committee on Haze in National Parks and Wilderness Areas. National Academy Press, 

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207 See U.S. EPA 2009 Final PM ISA, Note Error! Bookmark not defined.. 

208 U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental 

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209 U.S. EPA. 1999. The Benefits and Costs of the Clean Air Act, 1990-2010. Prepared for U.S. Congress by U.S. 

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210 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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211 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences for growth.” 

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212 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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213 Tingey, D.T., and Taylor, G.E. (1982) Variation in plant response to ozone: a conceptual model of physiological 

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214 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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215 U.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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217 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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218 Ollinger, S.V., Aber, J.D., Reich, P.B. (1997). Simulating ozone effects on forest productivity: interactions 

between leaf canopy and stand level processes. Ecological Applications, 7, 1237-1251. Docket EPA-HQ-OAR2010-0799. 

219 Winner, W.E. (1994). Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4), 651-

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220 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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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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222 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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223 Fox, S., Mickler, R. A. (Eds.). (1996). Impact of Air Pollutants on Southern Pine Forests, Ecological Studies.

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224 De Steiguer, J., Pye, J., Love, C. (1990). Air Pollution Damage to U.S. Forests. Journal of Forestry, 88(8), 17-22. 

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225 Pye, J.M. (1988). Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 

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226 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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227 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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228 McBride, J.R., Miller, P.R., Laven, R.D. (1985). Effects of oxidant air pollutants on forest succession in the 

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229 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest 

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230 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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231 Grulke, N.E. (2003). The physiological basis of ozone injury assessment attributes in Sierran conifers. In A. 

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232 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

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233 Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. (1985). Implications of environmental policy for U.S. 

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234 Adams, R. M., Hamilton, S. A., McCarl, B. A. (1986). The benefits of pollution control: the case of ozone and 

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235 Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. (1989). A reassessment of the economic effects of 

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236 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic losses. U.S. 

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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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238 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring program—results from 

field surveys of ozone sensitive plants in Northeastern forests (1994-2000). Environmental Monitoring and 

Assessment, 87, 271-291. Docket EPA-HQ-OAR-2010-0799. 

239 Coulston, J.W., Riitters, K.H., Smith, G.C. (2004). A preliminary assessment of the Montréal process indicators 

of air pollution for the United States. Environmental Monitoring and Assessment, 95, 57-74. Docket EPA-HQ-OAR2010-0799. 

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. 

Docket EPA-HQ-OAR-2010-0799. 

242 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring program—results from 

field surveys of ozone sensitive plants in Northeastern forests (1994-2000). Environmental Monitoring and 

Assessment, 87, 271-291. Docket EPA-HQ-OAR-2010-0799. 

243 U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental 

Protection Agency, Washington, DC, EPA/600/R-08/139F, 2009. Docket EPA-HQ-OAR-2010-0799. 

244 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate Matter: Policy Assessment 

of Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R-05-005. Docket EPA-HQ-OAR-2010-

0799. 

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, T.J. (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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250 Bricker, 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, J.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 

decomposition in Quercus ilix L. woods.” Environmental Pollution, 89: 81-87. Docket EPA-HQ-OAR-2010-0799. 

254 Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. “Effect of heavy metals and storage time on two types of 

forest litter: basal respiration rate and exchangeable metals.” Ecotoxicological Environmental Safety, 41: 8-18. 

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255 U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental 

Protection Agency, Washington, DC, EPA/600/R-08/139F, 2009. Section 9.4.5.2. Docket EPA-HQ-OAR-2010-

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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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257 Landis, M.S. and Keeler, G.J. 2002. “Atmospheric mercury deposition to Lake Michigan during the Lake 

Michigan Mass Balance Study.” Environmental Science & Technology, 21: 4518-24. Docket EPA-HQ-OAR-2010-

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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-HQOAR-2010-0799. 

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 

and Temporal Distributions of Lead and Zinc in Sediments.” Environmental Science & Technology, 34: 232-238. 

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261 Rice, K.C. 1999. “Trace Element Concentrations in Streambed Sediment Across the Conterminous United 

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262 Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. “Implications of Platinum-Group Element Accumulation along U.S. 

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263 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources of Polycyclic Organic 

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264 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources of Polycyclic Organic 

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265 Simcik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. “Atmospheric Loading of Polycyclic Aromatic 

Hydrocarbons to Lake Michigan as Recorded in the Sediments.” Environmental Science and Technology, 30: 3039-

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266 Simcik, M.F.; Eisenreich, S.J.; and Lioy, P.J. 1999. “Source apportionment and source/sink relationship of PAHs 

in the coastal atmosphere of Chicago and Lake Michigan.” Atmospheric Environment, 33: 5071-5079. Docket EPAHQ-OAR-2010-0799. 

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 EPAHQ-OAR-2010-0799. 

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-OAR2010-0799. 

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 EPAHQ-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 EPAHQ-OAR-2010-0799. 

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 semivolatile 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. 

Environmental Pollution, 113: 255-262. Docket EPA-HQ-OAR-2010-0799. 

275 U.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-HQOAR-2010-0799. 

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-HQOAR-2010-0799. 

278 Viskari E-L. 2000. Epicuticular wax of Norway spruce needles as indicator of traffic pollutant deposition. Water, 

Air, and Soil Pollut. 121:327-337. Docket EPA-HQ-OAR-2010-0799. 

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-HQOAR-2010-0162. 

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: 

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288 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale 

Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO. Docket EPAHQ-OAR-2010-0162. 

289 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale 

Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO. EPA-HQOAR-2010-0162. 

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-CHEM v8-01-02 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. 

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295 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05. 

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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., 

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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. 

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 
Chapter 6 

6-128 

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, G.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. 

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, G.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, G.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, G.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. 

Docket EPA-HQ-OAR-2011-0135. 

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. 

http://pubs.acs.org/cgi-bin/article.cgi/esthag/2008/42/i09/html/es0720412.html. Docket EPA-HQ-OAR-2011-0135. 

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 -pinene to SOA Formed in an Irradiated Toluene/-pinene,NOx/Air Mixture: Comparison of Results 

Using 14C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR2011-0135. 

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 

EPA-HQ-OAR-2011-0135. 
MY 2017 and Later - Regulatory Impact Analysis 

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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. 

http://www.epa.gov/ttn/atw/nata2005/risksum.html. Docket EPA-HQ-OAR-2011-0135. 

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, P.J. (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 EPAHQ-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: 

Primary and Secondary Aerosol. Environ Sci Technol 25: 1788-1793. Docket EPA-HQ-OAR-2011-0135. 

329 Turpin, B.J., Huntzicker, J.J. (1995) Identification of Secondary Organic Aerosol Episodes and Quantitation of 

Primary and Secondary Organic Aerosol Concentrations During SCAQS. Atmos Environ 29(23): 3527-3544. Docket 

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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 -pinene to SOA Formed in an Irradiated Toluene/-pinene,NOx/Air Mixture: Comparison of Results 

Using 14C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR2011-0135. 

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 -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 McIver, 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 

EPA-HQ-OAR-2011-0135. 

337 Kleindienst TE, TS Conver, CD McIver, 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. 
Chapter 6 

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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., Donahue1, N. M, Pandis1, 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 

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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. 

Chem. Phys., 8, 2405-2420, doi:10.5194/acp-8-2405-2008 

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: 

10.1016/j.atmosenv.2008.06.026 

345 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.O., Sarwar, g., Pinder, 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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347 Volkamer, R., J.L. Jimenez, F. SanMartini,K.Dzepina,Q. Zhang,D. Salcedo,L. T. Molina, D. R.Worsnop, and M. 

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expected, Geophys. Res. Lett., 33, L17811, doi:10.1029/2006GL026899. 

348 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.O., Sarwar, g., Pinder, R.W., Pouliot, G.A., Houyoux, M., 

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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.O., Sarwar, g., Pinder, R.W., Pouliot, G.A., Houyoux, M., 

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351 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., 

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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 

352 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.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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353 Finlayson-Pitts BJ, Pitts JN Jr. (1986) Atmospheric Chemistry: Fundamentals and Experimental Techniques, 

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354 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05. 

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355 CMAS 2008. Release Notes for CMAQ v4.7. 

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356 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

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357 77FR 30088 (May 21, 2012) 

358 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, 

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359 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

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360 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

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361 U.S. EPA. (2007). PM2.5 National Ambient Air Quality Standard Implementation Rule (Final). Washington, DC: 

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363 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment. 

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364 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007) 

365 US EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis. EPA 

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366 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

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367 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

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368 U.S. EPA. (2011) 2005 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005/. Docket 

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369 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment. 

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370 U.S. EPA. (2012). U.S. EPA’s Report on the Environment. Data accessed online February 15, 2012 at: 

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377 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis. Prepared by: 

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380 U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: National Emission 

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381 Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. Assessment of Deaths 

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384 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing 

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385 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

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388 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related photochemical oxidants 

(second external review draft). Research Triangle Park, NC: National Center for Environmental Assessment; report 

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392 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 U.S. urban communities, 1987-2000. JAMA, 2004. 

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393 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for summer ozone 

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394 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control for temperature? 

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395 Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of time-series studies of ozone and mortality 

with comparison to the national morbidity, mortality, and air pollution study. Epidemiology. 16(4): p. 436-45. EPAHQ-OAR-2009-0472-0222 

396 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between ozone and daily mortality: analysis and 

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397 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4): p. 458-68. EPA-HQ-OAR-2009-0472-0236 

398 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). “Lung 

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141. EPA-HQ-OAR-2009-0472-0263 

399 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. EPA-HQ-OAR-2009-0472-

1661 

400 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared for: Office of Air 

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401 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. EPA-HQ-OAR-2009-0472-0382 

402 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). Estimated Long-Term Ambient 

Concentrations of PM(10) and Development of Respiratory Symptoms in a Nonsmoking Population. Archives of 

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403 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. (2001). Increased Particulate Air Pollution and the 

Triggering of Myocardial Infarction. Circulation. 103:2810-2815. EPA-HQ-OAR-2009-0472-0239 

404 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory 

disease. Thorax. 50(5):531-538. 

405 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, 

Minnesota. Arch Environ Health. 49(5):366-374. EPA-HQ-OAR-2009-0472-1673 

406 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J Respir 

Crit Care Med. 150(3):648-655. EPA-HQ-OAR-2009-0472-1674 

407 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions for respiratory causes 

in Minneapolis St. Paul and Birmingham. Epidemiology. 8(4):364-370. EPA-HQ-OAR-2009-0472-1673 

408 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). Association between 

ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. Am J Epidemiol. 

153(5):444-452. EPA-HQ-OAR-2009-0472-0223 
Chapter 6 

6-134 

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-HQOAR-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-HQOAR-2009-0472-0246 

415 Norris, 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-OAR2009-0472-1672 

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. EPAHQ-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(1):43-54. EPA-HQ-OAR2009-0472-1675 

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 
MY 2017 and Later - Regulatory Impact Analysis 

6-135 

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. EPA-SAB-COUNCILADV-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 <http://wonder.cdc.gov>. 

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 

<http://www.lungusa.org/diseases/lungchronic.html>. 

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 

<http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>. 

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 <http://www.census.gov/population/projections/nation/summary/np-t.txt>. 

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 <http://wonder.cdc.gov>. 

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 <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205-

-Benefits.pdf>. 

440 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 <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. 
Chapter 6 

6-136 

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/420r10009.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 <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. EPAHQ-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-HQOAR-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 <http://www.regulations.gov>. EPAHQ-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-OAR2009-0472-0229 

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 Particulate 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 Particulate 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-OAR2009-0472-1661 

454 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of 

Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-0472-1677 
MY 2017 and Later - Regulatory Impact Analysis 

6-137 

455 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates 

throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 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. EPAHQ-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 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. 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/downloads12/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 1(b), specifically, Table 1.1 of 

the TSD. 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.html#1-6-1 or Docket EPA-HQ-OAR2009-0171-11676 

469 See http://epa.gov/blackcarbon 
Chapter 6 

6-138 

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 EPAHQ-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‰. 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-1 

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 CO2 reductions are consistent with those used in 

 

VVVVVVV See 49 U.S.C. 32902(h). 
Chapter 7 

7-2 

NHTSA’s analysis of the final CAFE standards, the specific stream of CO2-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 10%

“Gap” between test and on-road MPG for liquidfueled vehicles 20%

“Gap” between test and on-road wall electricity 

consumption for electric and plug-in hybrid 

electric vehicles 

30%

Annual growth in average vehicle use 0.6% 

Fuel Prices (2017-50 average, $/gallon) 

Retail gasoline price $4.13

Pre-tax gasoline price $3.78

Economic Benefits from Reducing Oil Imports 

($/gallon) 

"Monopsony" Component $ 0.00

Macroeconomic Disruption Component $ 0.197 in 2025

Military/SPR Component $ 0.00

Total Economic Costs ($/gallon) $ 0.197 in 2025

Emission Damage Costs (2020, $/short ton, 3% 

discount rate) 

Carbon monoxide $ 0

Nitrogen oxides (NOX) – vehicle use $ 5,600

Nitrogen oxides (NOX) – fuel production and 

distribution $ 5,400

Particulate matter (PM2.5) – vehicle use $ 310,000

Particulate matter (PM2.5) – fuel production and 

distribution $ 250,000

Sulfur dioxide (SO2) $ 33,000

Annual CO2 Damage Cost (per metric ton) 

Variable, depending 

on discount rate and 

year (see RIA Chapter 

7.1 below)

External Costs from Additional Automobile Use 

($/vehicle-mile) 

Congestion $ 0.056

Accidents $ 0.024

Noise $ 0.001

Total External Costs $ 0.081

External Costs from Additional Light Truck Use 
MY 2017 and Later - Regulatory Impact Analysis 

7-3 

($/vehicle-mile) 

Congestion $0.050

Accidents $0.027

Noise $0.001

Total External Costs $ 0.078

Discount Rates Applied to Future Benefits 3%, 7%

7.1 Monetized GHG Estimates 

We assigned a dollar value to reductions in carbon dioxide (CO2) 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 CO2

emissionsWWWWWWW 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. 
Chapter 7 

7-4 

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 noncatastrophic 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 CO2

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 CO2-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 7%.XXXXXXX The SCC estimates 

and the associated CO2 benefit estimates for each calendar year are shown in Tables 7.1-3.

 

XXXXXXX It is possible that 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-5 

Table 7.1-2 Social Cost of CO2 2017-2050a

 (2010 dollars) 

YEAR

DISCOUNT RATE AND STATISTIC 

5% AVERAGE 3% AVERAGE 2.5% AVERAGE 3% 95TH

PERCENTILE 

2017 $6 $26 $41 $79 

2020 $7 $27 $43 $84 

2025 $9 $31 $48 $94 

2030 $10 $34 $52 $104 

2035 $12 $37 $56 $114 

2040 $13 $41 $61 $124 

2045 $15 $44 $64 $133 

2050 $16 $47 $68 $142 

a The SCC values are dollar-year and emissions-year specific. 

Table 7.1-3 Undiscounted Annual Upstream and Downstream CO2 Benefits for the 

Given SCC Value, and CO2 Benefits Discounted back to 2012, Calendar Year Analysisa

(Millions of 2010 dollars) 

YEAR 

5% 

(AVERAGE SCC = 

$6 IN 2017) 

3% 

(AVERAGE SCC = 

$26 IN 2017) 

2.5% 

(AVERAGE SCC = 

$41 IN 2017) 

3% 

(95TH PERCENTILE = 

$79 IN 2017) 

2017 $13.6 $54.6 $87.3 $167

2018 $44.3 $176 $280 $538

2019 $93.3 $365 $581 $1,120

2020 $164 $633 $1,000 $1,940

2021 $273 $1,040 $1,640 $3,180

2022 $419 $1,560 $2,460 $4,790

2023 $600 $2,200 $3,450 $6,750

2024 $819 $2,960 $4,620 $9,070

2025 $1,080 $3,840 $5,970 $11,800

2026 $1,350 $4,740 $7,330 $14,500

2027 $1,620 $5,640 $8,710 $17,300

2028 $1,910 $6,560 $10,100 $20,100

2029 $2,200 $7,480 $11,500 $22,900

2030 $2,500 $8,410 $12,900 $25,700

2031 $2,810 $9,340 $14,200 $28,500

2032 $3,110 $10,200 $15,600 $31,300

2033 $3,420 $11,100 $16,900 $34,000

2034 $3,720 $12,000 $18,200 $36,700

2035 $4,030 $12,900 $19,400 $39,300

2036 $4,330 $13,800 $20,700 $41,900

2037 $4,630 $14,600 $21,900 $44,500

2038 $4,930 $15,400 $23,100 $47,000

2039 $5,220 $16,200 $24,200 $49,400

2040 $5,510 $17,000 $25,400 $51,800

2041 $5,810 $17,800 $26,400 $54,100

2042 $6,100 $18,500 $27,400 $56,300

2043 $6,390 $19,200 $28,400 $58,500
Chapter 7 

7-6 

2044 $6,690 $20,000 $29,400 $60,700

2045 $6,980 $20,700 $30,300 $62,800

2046 $7,280 $21,400 $31,300 $65,000

2047 $7,590 $22,200 $32,300 $67,300

2048 $7,900 $22,900 $33,300 $69,500

2049 $8,220 $23,700 $34,300 $71,800

2050 $8,540 $24,400 $35,400 $74,100

NPVb$32,400 $170,000 $290,000 $519,000

a Except 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-CO2 GHG impacts (i.e., CH4, N2O, and HFCs). The interagency group did 

not directly estimate the social costs of non-CO2 GHG emissions when it developed the 

current social cost of CO2 values. Moreover, the group determined that it would not transform 

the CO2 estimates into estimates for non-CO2 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 CO2. Recognizing 

that non-CO2 GHG impacts associated with this rulemaking (net reductions in CH4, N2O, 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-CO2 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-CO2 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-
MY 2017 and Later - Regulatory Impact Analysis 

7-7 

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, CO2 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 nonCO2 GHGs. A range of these estimates are available in published literature (Fankhauser 

(1994)482, Kandlikar (1995)483, Hammitt et al. (1996)484, 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 nonCO2 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
Chapter 7 

7-8 

GHGs.YYYYYYY Estimates for this rulemaking are given below for illustrative purposes and 

represent the CO2-e estimate of CH4, N2O, 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 (1,430).ZZZZZZZ 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. The estimates are provided in the 

tables below. 

Table 7.1-4 Undiscounted Annual Upstream and Downstream Non-CO2 GHG Benefits 

for the Given SCC Value, and Non-CO2 GHG Benefits Discounted back to 2012, 

Calendar Year Analysisa

 (Millions of 2010 dollars)

 

YYYYYYY See http://www.epa.gov/airquality/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. 

YEAR EMISSION REDUCTIONS (MMT CO2-E) TOTAL NON-CO2 GHG BENEFITS ($ MILLIONS) 

 CH4 N2O HFC-134A 5% (Ave) 3% (Ave) 2.5% 

(Ave) 

3% (95th) 

2017 0.0 0.00 0.2 $2 $7 $12 $22

2018 0.2 0.00 0.9 $7 $29 $46 $88

2019 0.3 0.01 2.0 $16 $62 $99 $191

2020 0.5 0.01 3.4 $28 $107 $170 $330

2021 0.9 0.02 5.1 $44 $168 $265 $514

2022 1.3 0.03 6.8 $62 $233 $366 $713

2023 1.8 0.04 8.4 $82 $301 $472 $923

2024 2.3 0.05 10.0 $103 $374 $583 $1,140

2025 3.0 0.06 11.6 $126 $450 $699 $1,380

2026 3.5 0.07 13.1 $150 $527 $816 $1,610

2027 4.1 0.08 14.6 $174 $604 $933 $1,850

2028 4.7 0.09 16.0 $199 $682 $1,050 $2,090

2029 5.2 0.10 17.4 $224 $760 $1,170 $2,320

2030 5.8 0.11 18.7 $250 $838 $1,280 $2,560
MY 2017 and Later - Regulatory Impact Analysis 

7-9 

a Except 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 CO2 impacts in each calendar year, we 

conducted a separate analysis of the CO2 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 CO2 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 CO2 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 CO2 Benefits for the 5% 

(Average SCC) Value, CO2 Benefits Discounted back to the 1st Year of each MY, and 

Sum of Values Across MYs, Model Year Analysisa (Millions of 2010 dollars) 

YEAR MY 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023

MY 

2024 

MY 

2025 SUM 

2017 $14 $0 $0 $0 $0 $0 $0 $0 $0

2018 $14 $31 $0 $0 $0 $0 $0 $0 $0

2019 $14 $31 $49 $0 $0 $0 $0 $0 $0

2020 $13 $31 $49 $71 $0 $0 $0 $0 $0

2021 $14 $31 $49 $71 $109 $0 $0 $0 $0

2022 $14 $31 $49 $72 $108 $145 $0 $0 $0

2023 $14 $31 $50 $71 $109 $144 $181 $0 $0

2024 $13 $31 $50 $72 $109 $145 $180 $220 $0

2031 6.3 0.12 19.9 $275 $916 $1,400 $2,800

2032 6.7 0.13 21.1 $301 $992 $1,510 $3,030

2033 7.2 0.14 22.2 $327 $1,070 $1,620 $3,250

2034 7.6 0.15 23.2 $353 $1,140 $1,720 $3,480

2035 8.0 0.15 24.2 $378 $1,210 $1,830 $3,690

2036 8.4 0.16 25.1 $403 $1,280 $1,930 $3,910

2037 8.7 0.16 25.9 $429 $1,350 $2,030 $4,120

2038 9.1 0.17 26.7 $453 $1,420 $2,120 $4,320

2039 9.4 0.17 27.4 $478 $1,490 $2,220 $4,520

2040 9.7 0.18 28.2 $503 $1,550 $2,310 $4,720

2041 9.9 0.18 28.8 $527 $1,610 $2,400 $4,910

2042 10.2 0.19 29.5 $552 $1,680 $2,480 $5,090

2043 10.5 0.19 30.1 $578 $1,740 $2,560 $5,280

2044 10.7 0.19 30.8 $603 $1,800 $2,650 $5,470

2045 10.9 0.20 31.4 $629 $1,860 $2,730 $5,660

2046 11.2 0.20 32.0 $655 $1,930 $2,820 $5,850

2047 11.4 0.21 32.6 $682 $1,990 $2,910 $6,050

2048 11.6 0.21 33.3 $710 $2,060 $2,990 $6,240

2049 11.9 0.21 33.9 $738 $2,120 $3,080 $6,450

2050 12.1 0.22 34.6 $767 $2,190 $3,170 $6,650

NPVb $3,120 $16,300 $27,700 $49,600
Chapter 7 

7-10 

2025 $12 $28 $49 $72 $109 $144 $180 $219 $262

2026 $12 $28 $46 $71 $109 $144 $179 $219 $260

2027 $12 $27 $45 $66 $106 $144 $179 $217 $260

2028 $11 $26 $44 $64 $100 $140 $179 $217 $258

2029 $11 $25 $42 $63 $98 $131 $174 $217 $257

2030 $10 $23 $40 $60 $94 $128 $162 $210 $256

2031 $9 $22 $37 $57 $90 $124 $159 $196 $248

2032 $8 $20 $35 $53 $85 $118 $153 $192 $232

2033 $7 $18 $32 $49 $80 $111 $145 $185 $226

2034 $6 $16 $29 $45 $74 $104 $137 $175 $218

2035 $5 $14 $25 $41 $68 $96 $128 $165 $206

2036 $4 $12 $22 $36 $61 $89 $119 $154 $194

2037 $3 $9 $18 $31 $54 $80 $109 $142 $182

2038 $3 $8 $15 $26 $47 $71 $99 $131 $168

2039 $2 $6 $12 $22 $40 $62 $87 $118 $154

2040 $2 $5 $10 $17 $33 $52 $76 $105 $139

2041 $1 $4 $8 $14 $27 $44 $64 $91 $123

2042 $1 $3 $7 $12 $22 $35 $54 $77 $107

2043 $1 $3 $6 $10 $18 $29 $44 $64 $90

2044 $1 $3 $5 $8 $15 $24 $36 $53 $75

2045 $1 $2 $5 $7 $13 $20 $30 $43 $62

2046 $1 $2 $4 $7 $12 $17 $25 $36 $50

2047 $0 $2 $3 $6 $11 $16 $22 $30 $42

2048 $0 $1 $3 $5 $10 $14 $19 $26 $35

2049 $0 $1 $2 $5 $8 $13 $18 $23 $30

2050 $0 $1 $2 $3 $8 $11 $16 $21 $27

NPV, 

5% $152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2,820 $11,800

a The 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.
MY 2017 and Later - Regulatory Impact Analysis 

7-11 

Table 7.1-6 Undiscounted Annual Upstream and Downstream CO2 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 Analysisa (Millions of 2010 dollars)

YEAR MY 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023

MY 

2024 

MY 

2025 SUM

2017 $55 $0 $0 $0 $0 $0 $0 $0 $0

2018 $54 $122 $0 $0 $0 $0 $0 $0 $0

2019 $53 $119 $193 $0 $0 $0 $0 $0 $0

2020 $52 $118 $188 $274 $0 $0 $0 $0 $0

2021 $52 $116 $187 $269 $413 $0 $0 $0 $0

2022 $51 $115 $184 $267 $405 $541 $0 $0 $0

2023 $50 $114 $182 $262 $401 $530 $665 $0 $0

2024 $45 $111 $181 $260 $394 $525 $650 $798 $0

2025 $44 $101 $175 $257 $390 $514 $643 $780 $936

2026 $43 $99 $160 $248 $385 $509 $631 $771 $915

2027 $40 $95 $156 $228 $370 $502 $623 $755 $904

2028 $38 $90 $149 $221 $342 $482 $615 $746 $885

2029 $36 $84 $141 $212 $331 $445 $590 $736 $874

2030 $33 $78 $133 $200 $317 $431 $545 $705 $862

2031 $30 $72 $123 $188 $298 $412 $528 $652 $825

2032 $27 $66 $113 $175 $279 $388 $504 $631 $762

2033 $23 $59 $103 $161 $260 $363 $474 $602 $738

2034 $20 $52 $93 $147 $238 $337 $443 $566 $703

2035 $16 $44 $81 $132 $217 $309 $411 $529 $661

2036 $13 $37 $70 $115 $195 $282 $377 $491 $618

2037 $10 $30 $58 $99 $172 $253 $343 $449 $573

2038 $8 $24 $47 $82 $148 $223 $308 $409 $524

2039 $6 $19 $38 $67 $124 $192 $272 $367 $478

2040 $5 $16 $30 $54 $102 $161 $234 $324 $428

2041 $4 $13 $25 $43 $83 $133 $196 $279 $377

2042 $4 $11 $20 $35 $67 $108 $162 $233 $324

2043 $3 $9 $17 $29 $55 $87 $131 $193 $272

2044 $3 $8 $15 $24 $46 $72 $106 $157 $225

2045 $2 $7 $13 $21 $39 $60 $89 $127 $182

2046 $2 $6 $12 $19 $34 $51 $73 $106 $148

2047 $1 $6 $10 $17 $32 $45 $63 $88 $123

2048 $1 $3 $9 $15 $28 $42 $56 $75 $102

2049 $1 $3 $6 $13 $24 $36 $51 $67 $88

2050 $1 $3 $5 $8 $22 $32 $45 $61 $78

NPV, 

3% $642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,260 $10,800 $46,600

a The 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.
Chapter 7 

7-12 

Table 7.1-7 Undiscounted Annual Upstream and Downstream CO2 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 Analysisa (Millions of 2010 dollars)

YEAR MY 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023

MY 

2024 

MY 

2025 SUM

2017 $87 $0 $0 $0 $0 $0 $0 $0 $0

2018 $85 $195 $0 $0 $0 $0 $0 $0 $0

2019 $84 $190 $307 $0 $0 $0 $0 $0 $0

2020 $82 $187 $299 $434 $0 $0 $0 $0 $0

2021 $81 $183 $296 $425 $653 $0 $0 $0 $0

2022 $80 $181 $289 $420 $637 $851 $0 $0 $0

2023 $78 $179 $286 $411 $629 $829 $1,040 $0 $0

2024 $71 $173 $282 $405 $614 $818 $1,010 $1,240 $0

2025 $69 $157 $271 $399 $606 $799 $1,000 $1,210 $1,450

2026 $66 $152 $248 $385 $596 $788 $976 $1,190 $1,420

2027 $62 $146 $240 $352 $571 $775 $962 $1,170 $1,390

2028 $59 $138 $230 $340 $525 $741 $946 $1,150 $1,360

2029 $55 $129 $216 $325 $508 $683 $904 $1,130 $1,340

2030 $50 $120 $203 $306 $484 $659 $833 $1,080 $1,320

2031 $46 $110 $188 $287 $454 $628 $804 $993 $1,260

2032 $41 $100 $172 $266 $425 $589 $765 $958 $1,160

2033 $36 $89 $157 $244 $393 $550 $718 $912 $1,120

2034 $30 $78 $140 $222 $360 $510 $670 $855 $1,060

2035 $25 $67 $123 $198 $327 $466 $620 $798 $996

2036 $20 $55 $105 $173 $293 $424 $567 $738 $929

2037 $15 $45 $87 $148 $257 $379 $515 $674 $860

2038 $12 $36 $71 $123 $221 $333 $461 $613 $785

2039 $10 $28 $57 $100 $185 $286 $405 $548 $713

2040 $8 $23 $45 $80 $152 $240 $349 $482 $638

2041 $6 $19 $37 $64 $123 $197 $291 $414 $560

2042 $5 $16 $30 $52 $99 $159 $240 $346 $480

2043 $5 $14 $25 $43 $82 $128 $194 $285 $401

2044 $4 $12 $22 $36 $67 $106 $156 $230 $331

2045 $3 $11 $20 $31 $57 $88 $130 $186 $268

2046 $3 $9 $17 $28 $50 $75 $107 $155 $216

2047 $1 $8 $15 $24 $46 $66 $92 $128 $180

2048 $1 $5 $13 $21 $40 $60 $81 $109 $149

2049 $1 $4 $8 $19 $35 $53 $74 $97 $127

2050 $1 $4 $8 $12 $32 $47 $65 $89 $113

NPV, 

2.5% $1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100

a The 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.
MY 2017 and Later - Regulatory Impact Analysis 

7-13 

Table 7.1-8 Undiscounted Annual Upstream and Downstream CO2 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 Analysisa (Millions of 2010 dollars)

YEAR MY 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023

MY 

2024 

MY 

2025 SUM

2017 $167 $0 $0 $0 $0 $0 $0 $0 $0

2018 $164 $374 $0 $0 $0 $0 $0 $0 $0

2019 $163 $366 $592 $0 $0 $0 $0 $0 $0

2020 $159 $363 $578 $841 $0 $0 $0 $0 $0

2021 $158 $356 $575 $825 $1,270 $0 $0 $0 $0

2022 $157 $353 $564 $819 $1,240 $1,660 $0 $0 $0

2023 $153 $349 $559 $804 $1,230 $1,620 $2,040 $0 $0

2024 $139 $339 $553 $795 $1,210 $1,610 $1,990 $2,440 $0

2025 $135 $310 $535 $786 $1,190 $1,570 $1,970 $2,390 $2,860

2026 $130 $301 $490 $760 $1,180 $1,560 $1,930 $2,360 $2,800

2027 $124 $289 $476 $697 $1,130 $1,540 $1,910 $2,310 $2,760

2028 $116 $273 $456 $676 $1,040 $1,470 $1,880 $2,280 $2,700

2029 $109 $257 $431 $648 $1,010 $1,360 $1,800 $2,250 $2,670

2030 $100 $239 $405 $612 $967 $1,320 $1,660 $2,150 $2,630

2031 $91 $220 $377 $574 $910 $1,260 $1,610 $1,990 $2,520

2032 $82 $201 $346 $534 $853 $1,180 $1,540 $1,920 $2,330

2033 $71 $180 $316 $490 $792 $1,110 $1,450 $1,840 $2,250

2034 $61 $158 $283 $447 $726 $1,030 $1,350 $1,720 $2,140

2035 $50 $135 $248 $401 $661 $942 $1,250 $1,610 $2,010

2036 $40 $112 $212 $352 $594 $858 $1,150 $1,500 $1,880

2037 $31 $91 $176 $301 $522 $770 $1,050 $1,370 $1,750

2038 $25 $73 $144 $250 $450 $678 $939 $1,250 $1,600

2039 $20 $58 $115 $205 $376 $584 $827 $1,120 $1,450

2040 $16 $47 $92 $164 $311 $490 $712 $985 $1,300

2041 $13 $39 $75 $131 $251 $404 $597 $847 $1,150

2042 $11 $32 $62 $107 $202 $327 $493 $710 $986

2043 $10 $28 $52 $88 $168 $264 $399 $587 $826

2044 $8 $25 $45 $74 $139 $220 $323 $476 $684

2045 $7 $22 $41 $65 $118 $182 $269 $385 $554

2046 $6 $19 $35 $58 $105 $155 $223 $321 $448

2047 $3 $17 $31 $51 $96 $137 $191 $266 $374

2048 $3 $10 $28 $45 $84 $126 $169 $228 $310

2049 $2 $9 $17 $40 $74 $110 $155 $202 $266

2050 $2 $8 $16 $25 $67 $97 $136 $186 $236

NPV, 

3% $1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000

a The 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.
Chapter 7 

7-14 

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. 

Figure 7.2-1 Distribution of driving range for MY 2010 vehicles (sales-weighted) 
MY 2017 and Later - Regulatory Impact Analysis 

7-15 

For the final rule, 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 truck), 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. 
Chapter 7 

7-16 

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 

Fuel Economy (mpg) 

(sales-weighted) 

Regression Coefficients 

y = m * x + b 

Average Std. Dev. m b R2 p (F-test) 

35-40 39.2 3.1 5.8 221.6 0.57 0.000 

40-45 36.4 11.0 7.9 213.2 0.76 0.000 

45-50 22.8 5.4 12.6 146.3 0.76 0.000 

50-60 28.7 3.6 13.4 180.4 0.51 0.000 

60+ 17.0 3.0 24.1 116.7 0.40 0.028 

Total 29.6 9.8 - - 
MY 2017 and Later - Regulatory Impact Analysis 

7-17 

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. 

knGoGnIpGM GJ JNRq RSGMGrs pMSnRLHR NHRt JGn nLMuR pMSnRLHR 

 nLMuR$ U nLMuRvE

nLMuRv

w

 JNRq RSGMGrs$ U JNRq RSGMGrsv

E

JNRq RSGMGrsv

x

Table 7.2-2 Proportion of Range to Fuel Economy Increases, 

Based on Regression of MY 2010 Vehicles

Footprint Category %Range Increase / 

%Fuel Economy Increase 

35-40 0.51 

40-45 0.58 

45-50 0.71 

50-60 0.63 

60+ 0.78 

Average (sales-weighted) 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 Cruze.) 
Chapter 7 

7-18 

Figure 7.2-3 Increases in range and fuel economy from previous generation for 

recent platform redesigns (least efficient vehicle configurations only) 

2008 Chrysler Town and 

Country

2007 Chrysler Sebring

2011 Ford Explorer

2012 Ford Focus

2012 Toyota Camry

2012 Nissan Altima

2008 Honda Accord

2011 Jeep Grand 

Cherokee

2011 Hyundai Sonata

2011 Toyota Sienna

2012 Honda CRV

2011 Chevrolet Cruze

2012 Hyundai Elantra

2009 Toyota Corolla

2012 Honda Civic

2011 Volkswagen Jetta

2012 Volkswagen Passat

2010 Toyota Prius

2011 Honda Odyssey

2010 Chevrolet Equinox

2011 Dodge Durango

2010 Ford Taurus

-20%

-10%

0%

10%

20%

30%

40%

-20% -10% 0% 10% 20% 30% 40% Increase in Range

Increase in Fuel Economy
MY 2017 and Later - Regulatory Impact Analysis 

7-19 

Table 7.2-3 Proportion of Range to Fuel Economy Increases, 

Based on Recent Platform Redesigns

Footprint Category %Range Increase / 

%Fuel Economy Increase 

2008 Chrysler Town and Country 1.40 

2011 Ford Explorer 0.29a

2012 Ford Focus 0.67 

2012 Toyota Camry 0.47 

2012 Nissan Altima 0.37 

2011 Dodge Durango 0.19b

2011 Jeep Grand Cherokee 4.49b

2011 Hyundai Sonata 1.33 

2012 Honda CRV 1.00 

2011 Chevrolet Cruze 2.23 

2011 Volkswagen Jetta 1.00 

2012 Volkswagen Passat 1.00 

2010 Toyota Prius 1.00 

2011 Honda Odyssey 1.00 

2010 Chevrolet Equinox 4.36 

Average 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 DOTrecommended 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. 
Chapter 7 

7-20 

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 

Personal travel Business Travel Total

Wage Rate ($/hour) $29.68 $29.68 -

-

DOT-Recommended Value of Travel Time 

Savings, as % of Wage Rate

50% 100% -

-

Hourly Valuation (=Wage Rate * DOTRecommended Value)

$14.84 $29.68 -

-

% of Total Urban Travel 94.4% 5.6% 1

100%

Hourly Valuation (Adjusted for % of Total 

Urban Travel)

$14.01 $1.66 $

15.67

Rural (Intercity) Travel 

Personal travel Business Travel Total

Wage Rate ($/hour) $29.68 $29.68 -

-

DOT-Recommended Value of Travel Time 

Savings, as % of Wage Rate

70% 100% -

-

Hourly Valuation (=Wage Rate * DOTRecommended Value)

$20.77 $29.68 -

-

% of Total Rural Travel 87.0% 13.0% 1

100%

Hourly Valuation (Adjusted for % of Total 

Rural Travel)

$18.07 $3.86 $

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 
MY 2017 and Later - Regulatory Impact Analysis 

7-21 

cars and for light trucks, 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) 

Unweighted 

Value of Travel 

Time ($/hour)

Weight (% of 

Total Miles 

Driven)BBBBBBBB

Weighted Value 

of Travel Time 

($/hour)

Urban Travel $15.67 67.1% $10.51

Rural Travel $21.93 32.9% $7.22

Total -- 100.0% $17.73 

Passenger Cars Light Trucks

Average Vehicle Occupancy 

During Refueling Trips 

(persons)CCCCCCCC 1.21 1.23

Weighted Value of Travel 

Time ($/hour) $17.73 $17.73

Occupancy-Adjusted Value 

of Vehicle Travel Time During 

Refueling Trips ($/hour) $21.45 $21.81 

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. 

 

BBBBBBBB 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/policyinformation/travel_monitoring/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 data are 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. 
Chapter 7 

7-22 

Table 7.2-6 Average Refueling Trip Characteristics for Passenger Cars and Light 

Trucks 

Gallons of 

Fuel 

Purchased 

Round-Trip 

Distance 

to/from 

Fueling 

Station 

(miles) 

Round-Trip 

Time to/from 

Fueling 

Station 

(minutes) 

Time to 

Fill and 

Pay 

(minutes) 

Total 

Time 

(minutes) 

Passenger Cars 9.8 0.97 2.28 4.10 6.38

Light Trucks 13.0 1.08 2.53 4.30 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.FFFFFFFF A final adjustment is made to account for evidence from the 

 

EEEEEEEE Source 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 A 1.1 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-23 

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 – CO2 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. 
Chapter 7 

7-24 

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

2017 2020 2030 2040 2050 

NPV, Years 

2012-2050, 3% 

Discount Rate 

NPV, Years 

2012-2050, 7% 

Discount Rate 

Technology 

Costs $2,440 $8,860 $33,700 $37,400 $42,000 $521,000 $231,000 

Maintenance 

Costs $37 $330 $2,260 $3,630 $4,540 $39,500 $15,600 

Vehicle 

Program 

Costs 

$2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247,000 

Note:

a

 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-25 

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

 

2017 2020 2030 2040 2050 

NPV, Years 

2012-2050, 3% 

Discount Rate 

NPV, Years 

2012-2050, 7% 

Discount Rate 

Fuel Savings 

(pre-tax) $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 

Note:

a

 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

2017 2020 2030 2040 2050 

NPV, Years 

2012-2050, 3% 

Discount Rate 

NPV, Years 

2012-2050, 7% 

Discount Rate 

Petroleumbased 

imports 

reduced 

(mmb) 

4.5 48.6 520 880 1,103 

Monetized 

benefits $33 $371 $4,560 $8,320 $10,400 $84,500 $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.8.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.8.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 CO2 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 
Chapter 7 

7-26 

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 nonCO2 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 

Ratea

NPV, Years 

2012-2050, 

7% Discount 

Ratea

Benefits of Reduced CO2 Emissions at each assumed SCC value b

5% (avg SCC) $14 $164 $2,500 $5,510 $8,540 $32,400 $32,400

3% (avg SCC) $55 $633 $8,410 $17,000 $24,400 $170,000 $170,000

2.5% (avg SCC) $87 $1,000 $12,900 $25,400 $35,400 $290,000 $290,000

3% (95th %ile) $167 $1,940 $25,700 $51,800 $74,100 $519,000 $519,000

Energy Security 

Benefits $33 $371 $4,560 $8,320 $10,400 $84,500 $32,200

Accidents, 

Congestion, Noise 

Costsh

-$54 -$564 -$5,710 -$9,650 -$12,100 -$101,000 -$39,200

Increased Travel 

Benefitsg $79 $865 $9,560 $17,000 $14,500 $167,000 $64,800

Refueling Time 

Savings $25 $282 $3,360 $6,350 $8,870 $64,900 $24,500

Non-GHG Related 

Health Impacts c,d,e B B $920 - $1000

$920 - 

$1000

$920 - 

$1000 $9,190 $3,050 

Non-CO2 GHG 

Impactsf n/a n/a n/a n/a n/a n/a n/a 

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000

3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000

2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000

3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000

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 
MY 2017 and Later - Regulatory Impact Analysis 

7-27 

net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown 

are undiscounted values. 

b

 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 PM2.5- and ozonerelated 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. 

f 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. 

g Refer to Chapter 4.2.6 of the joint TSD for a description of how increased travel benefits are derived. 

h 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 CO2 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$) 

2017 2020 2030 2040 2050 NPV, 3%a NPV, 7%a

Vehicle Program 

Costs $2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247,000 

Fuel Savings $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000 

3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000 

2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000 

3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000 

Monetized Net Benefits at each assumed SCC value c
Chapter 7 

7-28 

5% (avg SCC) -$1,690 -$316 $68,000 $146,000 $201,000 $1,290,000 $478,000 

3% (avg SCC) -$1,650 $153 $73,900 $158,000 $217,000 $1,430,000 $616,000 

2.5% (avg SCC) -$1,610 $524 $78,300 $166,000 $228,000 $1,550,000 $736,000 

3% (95th %ile) -$1,530 $1,460 $91,200 $192,000 $267,000 $1,780,000 $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. 

b

 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

 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

 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY Sum 

Vehicle Program Costs $2,770 $5,460 $7,720 $10,100 $14,000 $19,900 $25,400 $30,900 $33,600 $150,000 

Fuel Savings (pre-tax) $7,040 $15,500 $24,300 $34,100 $50,400 $64,900 $78,500 $92,900 $107,000 $475,000 

Energy Security 

Benefits $365 $807 $1,260 $1,780 $2,650 $3,430 $4,170 $4,950 $5,750 $25,200 

Accidents, Congestion, 

Noise Costs f -$548 -$1,150 -$1,770 -$2,440 -$3,480 -$4,420 -$5,270 -$6,160 -$7,040 -$32,300 

Increased Travel 

Benefitsi $1,000 $2,180 $3,390 $4,700 $6,840 $8,650 $10,200 $11,900 $13,600 $62,500 

Refueling Time 

Savings $273 $604 $945 $1,330 $1,970 $2,550 $3,100 $3,680 $4,280 $18,700 

PM2.5 Related Health 

Impactsc,d,e $74 $171 $271 $385 $606 $768 $912 $1,060 $1,210 $5,460 

Non-CO2 GHG 

Impactsg n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Benefits of Reduced CO2 Emissions at each assumed SCC value a, b

5% (avg SCC) $152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2,820 $11,800 

3% (avg SCC) $642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,260 $10,800 $46,600 

2.5% (avg SCC) $1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100 

3% (95th %ile) $1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000 

Monetized Net Benefits at each assumed SCC value a, b

5% (avg SCC) $5,590 $13,000 $21,200 $30,500 $46,200 $57,500 $68,100 $79,700 $94,400 $416,000 

3% (avg SCC) $6,080 $14,100 $22,900 $33,000 $49,900 $62,200 $73,900 $86,600 $102,000 $451,000 

2.5% (avg SCC) $6,480 $15,000 $24,300 $34,900 $52,800 $66,000 $78,500 $92,000 $109,000 $479,000 

3% (95th %ile) $7,400 $17,100 $27,600 $39,600 $59,800 $75,200 $89,800 $106,000 $125,000 $547,000 
MY 2017 and Later - Regulatory Impact Analysis 

7-29 

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. 

b

 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 PM2.5-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 PM2.5-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. 

.

f

 Negative values shown for Accidents, Congestion, and Noise represent disbenefits. 

g

 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. 

i 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 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY Sum 

Vehicle Program Costs $2,650 $5,220 $7,370 $9,610 $13,300 $19,200 $24,600 $29,900 $32,500 $144,000 

Fuel Savings (pre-tax) $5,410 $11,90

0 $18,600 $26,100 $38,600 $49,700 $60,100 $71,100 $82,300 $364,000 

Energy Security 

Benefits $279 $615 $964 $1,360 $2,020 $2,620 $3,180 $3,780 $4,400 $19,200 

Accidents, Congestion, 

Noise Costs f -$425 -$893 -$1,370 -$1,890 -$2,690 -$3,410 -$4,070 -$4,760 -$5,440 -$24,900 

Increased Travel 

Benefitsi $761 $1,650 $2,550 $3,530 $5,120 $6,470 $7,640 $8,870 $10,100 $46,700 

Refueling Time 

Savings $209 $461 $721 $1,020 $1,500 $1,940 $2,360 $2,800 $3,260 $14,300 

PM2.5 Related Health 

Impactsc,d,e $59 $136 $215 $305 $478 $607 $721 $840 $959 $4,320 

Non-CO2 GHG 

Impactsg n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Benefits of Reduced CO2 Emissions at each assumed SCC value a, b

5% (avg SCC) $152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2,820 $11,800 

3% (avg SCC) $642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,260 $10,800 $46,600 
Chapter 7 

7-30 

2.5% (avg SCC) $1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100 

3% (95th %ile) $1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000 

Monetized Net Benefits at each assumed SCC value a, b

5% (avg SCC) $3,800 $9,010 $14,900 $21,60

0 $32,900 $40,300 $47,300 $55,100 $65,800 $291,000 

3% (avg SCC) $4,290 $10,10

0 $16,600 $24,100 $36,500 $45,000 $53,100 $62,000 $73,800 $326,000 

2.5% (avg SCC) $4,690 $11,00

0 $18,000 $26,000 $39,400 $48,800 $57,600 $67,400 $80,100 $353,000 

3% (95th %ile) $5,610 $13,10

0 $21,300 $30,700 $46,500 $58,000 $69,000 $81,000 $96,100 $421,000 

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. 

b

 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 PM2.5-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 PM2.5-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. 

.

f

 Negative values shown for Accidents, Congestion, and Noise represent disbenefits. 

g

 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. 

i 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-31 

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).IIIIIIII

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 CO2e 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 

 

IIIIIIII 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. 
Chapter 7 

7-32 

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 Fuel 

(Million gallons) 

Oil 

(Million barrels) 

2012 6,740 160 

2013 10,300 246 

2014 13,500 321 

2015 19,100 455 

2016 25,700 611 

2017 28,700 683 

2018 31,400 749 

2019 34,600 824 

2020 38,800 925 

2021 45,200 1,080 

2022 50,700 1,210 

2023 55,800 1,330 

2024 61,100 1,450 

2025 66,600 1,580 

Total 488,000 11,600 

Table 7.4-2 MY Lifetime CO2e Reductions Associated with the MYs 2012-2016 

& 2017-2025 Final Rules

Model Year CO2e 

(Million metric tons) 

2012 81 

2013 130 

2014 160 

2015 230 

2016 310 

2017 340 

2018 380 

2019 420 

2020 470 

2021 550 

2022 610 

2023 670 

2024 730 

2025 790 

Total 5,900 
MY 2017 and Later - Regulatory Impact Analysis 

7-33 

Table 7.4-3 MY Lifetime Present Value Fuel Savings Associated with the MYs 

2012-2016 & 2017-2025 Final Rules (2010 dollars)

Model 

Year 

Untaxed, 0% 

Discount Rate 

(Billions) 

Untaxed, 3% 

Discount Rate 

(Billions) 

Untaxed, 7% 

Discount Rate 

(Billions) 

Retail, 0% 

Discount Rate 

(Billions) 

Retail, 3% 

Discount Rate 

(Billions) 

Retail, 7% 

Discount Rate 

(Billions) 

2012 $23 $18 $14 $26 $20 $15 

2013 $36 $28 $21 $40 $31 $24 

2014 $47 $37 $28 $53 $41 $32 

2015 $68 $53 $40 $76 $59 $45 

2016 $92 $72 $55 $100 $80 $61 

2017 $100 $81 $62 $120 $90 $69 

2018 $110 $90 $69 $130 $100 $76 

2019 $130 $100 $76 $140 $110 $85 

2020 $140 $110 $86 $160 $120 $96 

2021 $170 $130 $100 $190 $150 $110 

2022 $190 $150 $110 $210 $160 $130 

2023 $210 $160 $130 $230 $180 $140 

2024 $230 $180 $140 $260 $200 $150 

2025 $250 $200 $150 $280 $220 $170 

Total $1,800 $1,400 $1,100 $2,000 $1,600 $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)a

Model Year 

Fuel Savings per 

Vehicle, 

3% Discount Rate 

($/vehicle) 

Fuel Savings per 

Vehicle, 

 7% Discount Rate 

($/vehicle) 

2012 $1,400 $1,000 

2013 $1,900 $1,500 

2014 $2,600 $2,000 

2015 $3,700 $2,800 

2016 $5,000 $3,800 

2017 $5,700 $4,400 

2018 $6,400 $4,900 

2019 $7,100 $5,400 

2020 $7,900 $6,000 

2021 $9,000 $6,900 

2022 $10,000 $7,700 

2023 $10,900 $8,400 

2024 $11,800 $9,100 

2025 $12,700 $9,800 

a

Using retail fuel prices and rebound miles in the control case.

 
Chapter 7 

7-34 

Table 7.4-5 Industry Average Technology Costs per Vehicle Associated with the 

MYs 2012-2016 & 2017-2025 Final Standards (2010$) a

Model Year Cars Trucks Combined 

2012 $342 $314 $331 

2013 $507 $496 $503 

2014 $631 $652 $639 

2015 $749 $820 $774 

2016 $869 $1,098 $948 

2017 $1,044 $1,119 $1,069 

2018 $1,179 $1,222 $1,193 

2019 $1,284 $1,293 $1,287 

2020 $1,377 $1,367 $1,373 

2021 $1,478 $1,680 $1,549 

2022 $1,776 $2,086 $1,881 

2023 $2,040 $2,445 $2,176 

2024 $2,291 $2,780 $2,454 

2025 $2,381 $2,909 $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 CO2 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. 

 
MY 2017 and Later - Regulatory Impact Analysis 

7-35 

Table 7.4-7 Annual Fuel Reductions Associated with the MYs 2012-2016 & 2017-

2025 Final Rules 

Calendar 

Year 

Fuel 

(Million gallons) 

Oil 

(Million barrels) 

Oil 

(Million 

Barrels/day) 

2012 529 13 0.0 

2013 1,320 31 0.1 

2014 2,320 55 0.2 

2015 3,730 89 0.2 

2016 5,590 133 0.4 

2017 7,620 182 0.5 

2018 9,780 233 0.6 

2019 12,100 288 0.8 

2020 14,600 348 1.0 

2021 17,500 417 1.1 

2022 20,700 493 1.4 

2023 24,100 574 1.6 

2024 27,700 660 1.8 

2025 31,600 752 2.1 

2030 49,000 1,170 3.2 

2040 72,900 1,740 4.8 

2050 89,800 2,140 5.9 

Total 1,850,000 44,000 

Table 7.4-8 Annual CO2e Reductions Associated with the MYs 2012-2016 & 

2017-2025 Final Rules 

Calendar 

Year 

CO2e 

(Million metric tons) 

2012 6 

2013 16 

2014 28 

2015 45 

2016 68 

2017 92 

2018 120 

2019 150 

2020 180 

2021 210 

2022 250 

2023 290 

2024 330 

2025 380 

2030 580 

2040 860 

2050 1,100 

Total 22,000 

 
Chapter 7 

7-36 

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 

Untaxed 

(Billions) 

Retail 

(Billions) 

2012 $1.6 $1.8 

2013 $3.9 $4.5 

2014 $7.3 $8.3 

2015 $12 $14 

2016 $18 $21 

2017 $26 $29 

2018 $33 $37 

2019 $42 $47 

2020 $51 $57 

2021 $62 $69 

2022 $73 $82 

2023 $85 $95 

2024 $99 $110 

2025 $110 $130 

2030 $190 $210 

2040 $290 $320 

2050 $390 $420 

NPV, 3% $3,400 $3,700 

NPV, 7% $1,400 $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 3% discount rate 7% discount rate 

Increased Lifetime Costs $3,200 $3,100 

Lifetime Fuel Savingsb $13,500 $10,400 

Lifetime Net Savings $10,300 $7,200 

“Breakeven” payback period 2.3 years 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-R10-009) 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. 
MY 2017 and Later - Regulatory Impact Analysis 

7-37 

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/2011-01?opendocument. 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/policy/Data/VOT97guid.pdf and 

http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf (last accessed 07/18/2011).

MY 2017 and Later - Regulatory Impact Analysis 

8-1 

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.1.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 upfront 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 In this rule, the 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 For a durable good such as an auto, the 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. 
Chapter 8 

8-2 

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 This section is 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-3 

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.MMMMMMMM In a nested logit, the model is structured in layers. For instance, the 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 Logit 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). 
Chapter 8 

8-4 

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 Dinan497) 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-5 

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 fuelefficient 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 
Chapter 8 

8-6 

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 Likewise, 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). 
MY 2017 and Later - Regulatory Impact Analysis 

8-7 

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. 
Chapter 8 

8-8 

• 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.OOOOOOOO

 

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 For instance, in 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-9 

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 For instance, 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
Chapter 8 

8-10 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

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Table 8.1-1 Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2010$a 

Bunch et al. (1993)b $7,600 (1991$) $11,300

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $59,400

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7600

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8600

Hidrue et al. (2011)f$3800 - $10,400 (2009$) $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. 
Chapter 8 

8-12 

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 ultraluxury 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 The theory 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-13 

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 

 

RRRRRRRR As explained in 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. 
Chapter 8 

8-14 

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.1, 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 
MY 2017 and Later - Regulatory Impact Analysis 

8-15 

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,SSSSSSSS the price increase measures the 

loss to the buyer.TTTTTTTT Assuming that the full technology cost gets passed along to the 

 

SSSSSSSS This approach describes the economic concept of compensating variation, a payment of money after a 

change that would make a consumer as well off after the change as before it. A related concept, equivalent 

variation, estimates 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 Indeed, it is likely to be an 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 
Chapter 8 

8-16 

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.11.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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-17 

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 usedvehicle 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 lowincome households to benefit from the more rapid payback period for used vehicles, though 
Chapter 8 

8-18 

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 (economyclass) 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-toincome 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,UUUUUUUU 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 21, 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.” 
MY 2017 and Later - Regulatory Impact Analysis 

8-19 

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 

 

VVVVVVVV Masur and Posner, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441 
Chapter 8 

8-20 

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.WWWWWWWW 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-21 

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 As will be 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? 
Chapter 8 

8-22 

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 
MY 2017 and Later - Regulatory Impact Analysis 

8-23 

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. 
Chapter 8 

8-24 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-25 

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 fullemployment 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 
Chapter 8 

8-26 

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). 
MY 2017 and Later - Regulatory Impact Analysis 

8-27 

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 Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.770 0.514

ASM Motor Vehicle Mfg 0.655 0.437

ASM Light Duty Vehicle 

Mfg

0.609 0.406

Economic 

Census 

Motor Vehicle Mfg 0.665 0.443

Economic 

Census 

Light Duty Vehicle 

Mfg

0.602 0.402

BLS ERM Motor Vehicle Parts 

Mfg

2.614 1.743

ASM Motor Vehicle Parts 

Mfg

2.309 1.540

Economic 

Census 

Motor Vehicle Parts 

Mfg

2.712 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 
Chapter 8 

8-28 

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 Costs (before 

adjustment for 

domestic proportion 

of production) 

($Millions) 

Minimum 

employment effect 

(if all expenditures 

are in the parts 

sector) 

Maximum 

employment effect (if 

all expenditures are 

in the light duty 

vehicle mfg sector) 

2017 $ 2,435 700 3,200 

2018 $ 4,848 1,300 6,200 

2019 $ 6,818 1,700 8,400 

2020 $ 8,858 2,100 10,500 

2021 $ 12,400 2,900 14,200 

2022 $ 18,323 4,100 20,200 

2023 $ 23,734 5,100 25,200 

2024 $ 29,101 6,000 29,700 

2025 $ 31,678 6,300 31,100 

Total 30,300 148,800

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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-29 

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 fuelsaving 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. 
Chapter 8 

8-30 

Table 8.2-4 Estimated Change in Labor for Selected Compliance Technologies 

Technology FEV 

Case 

Study 

Vehicle Class Labor 

Costs 

 Total 

Costs 

 Hours/ 

Technology 

Downsized 

Turbo GDI 4 

0101 Compact C $72.58 $537.70 1.57 

Downsized 

Turbo GDI V6 

0102 Mid/Large C $25.76 $87.38 0.56

Downsized 

Turbo GDI V6 

0104 SUV/Trucks $84.19 $789.53 1.82 

Electric A/C 

compressor 

0602 $4.68 $167.54 0.10

Power split 

hybrid 

0502 Mid/Large C $395.85 $3,435.01 8.54 

6- to 8-speed 

transmission 

0803 Mid/Large C $15.11 $61.84 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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-31 

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. 
Chapter 8 

8-32 

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.
MY 2017 and Later - Regulatory Impact Analysis 

8-33 

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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/#tables. 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 
Chapter 8 

8-38 

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-pressoffice/presidential-memorandum-regarding-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-OAR-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-HQOAR-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-HQOAR-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.
MY 2017 and Later - Regulatory Impact Analysis 

9-1 

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 Defined as Small Entity 

by SBA if Less Than or 

Equal to: 

NAICS Codes b

Vehicle manufacturers (including 

small volume manufacturers) 

1,000 employees 336111, 336112 

Independent commercial 

importers 

$7 million annual sales 

$23 million annual sales 

100 employees 

811111, 811112, 811198 

441120 

423110 

Alternative Fuel Vehicle 

Converters 

750 employees 

1,000 employees 

$7 million annual sales 

336312, 336322, 336399 

335312 

811198 

a. 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 
Chapter 9 

9-2 

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 CO2 

performance is better than their fleetwide CO2 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 

CO2 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. 
MY 2017 and Later - Regulatory Impact Analysis 

9-3 

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. 

MY 2017 and Later - Regulatory Impact Analysis 

10-1 

10 Alternate Analysis Using 2010 MY Baseline 

10.1 Why an Alternate Analysis? 

For this final rulemaking, 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 III-47 and III-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 We refer to these 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. 
Chapter 10 

10-2 

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, 

sales were down more than 40% from 2008 levels.ZZZZZZZZ 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 

MY 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin 220 209 199 189 179 171 163 156 149 

BMW 217 206 195 186 176 168 161 153 147 

Chrysler/Fiat 221 211 200 190 180 172 164 156 149 

Daimler 224 213 203 192 182 174 167 159 152 

Ferrari 221 210 200 189 179 172 164 156 149 

Ford 216 205 195 185 175 167 160 152 146 

Geely 212 202 192 182 173 165 158 151 144 

General Motors 217 206 196 186 176 168 161 154 147 

 

ZZZZZZZZ See TSD chapter 1. 
MY 2017 and Later - Regulatory Impact Analysis 

10-3 

Honda 215 204 194 184 175 167 159 152 145 

Hyundai 214 203 193 183 174 166 158 151 144 

Kia 209 198 188 178 169 162 154 147 141 

Lotus 203 193 183 174 165 157 150 143 137 

Mazda 212 202 192 182 173 165 158 150 144 

Mitsubishi 203 193 183 174 165 157 150 143 137 

Nissan 215 205 195 185 175 167 160 153 146 

Porsche 218 208 197 187 178 170 162 155 148 

Subaru 207 197 187 178 168 161 154 147 140 

Suzuki 202 192 182 173 164 157 150 143 136 

Tata 228 217 205 195 185 176 169 161 153 

Toyota 214 204 194 184 174 166 159 152 145 

Volkswagen 187 178 168 159 151 144 137 131 125 

Fleet 214 203 193 184 174 166 159 152 145 

Table 10-2 – Projected Level of Targets - Trucks 

MY 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin NA NA NA NA NA NA NA NA NA 

BMW 278 267 259 250 231 220 209 199 189 

Chrysler/Fiat 293 283 276 267 248 235 224 213 203 

Daimler 288 278 269 261 241 229 218 208 197 

Ferrari NA NA NA NA NA NA NA NA NA 

Ford 316 308 304 297 277 263 251 239 227 

Geely 276 264 256 247 228 217 207 196 187 

General Motors 313 305 298 289 267 254 242 230 218 

Honda 285 275 267 258 239 227 216 205 195 

Hyundai 270 258 250 242 223 212 202 192 182 

Kia 286 276 267 258 238 226 215 204 194 

Lotus NA NA NA NA NA NA NA NA NA 

Mazda 274 263 255 247 228 217 206 196 186 

Mitsubishi 254 242 235 227 209 199 189 180 171 

Nissan 293 284 278 270 252 240 228 217 207 

Porsche 287 274 266 258 238 227 216 205 195 

Subaru 248 236 229 221 204 194 184 175 166 

Suzuki 253 241 234 226 208 198 188 179 170 

Tata 274 262 254 246 227 216 206 195 186 

Toyota 295 285 278 270 253 241 229 218 207 

Volkswagen 281 270 261 253 234 222 211 201 191 

Fleet 299 289 283 274 255 243 231 219 209 

Table 10-3 – Projected Level of Targets – Fleet (sales weighted) 

MY 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin 220 209 199 189 179 171 163 156 149 

BMW 232 221 211 201 189 180 171 163 156 

Chrysler/Fiat 258 247 238 227 212 202 192 182 173 

Daimler 242 233 223 213 200 191 183 174 166 

Ferrari 221 210 200 189 179 172 164 156 149 

Ford 259 250 242 232 218 207 198 188 179 

Geely 236 224 215 205 192 183 174 166 158 

General Motors 258 248 240 230 215 205 195 186 177 

Honda 238 226 217 207 194 185 176 167 159 
Chapter 10 

10-4 

Hyundai 222 211 200 190 180 171 163 156 149 

Kia 217 207 197 187 176 168 160 153 146 

Lotus 203 193 183 174 165 157 150 143 137 

Mazda 224 213 203 194 182 174 166 158 151 

Mitsubishi 213 203 193 184 173 165 157 150 143 

Nissan 235 225 216 207 195 186 177 169 161 

Porsche 254 242 234 224 209 199 190 181 172 

Subaru 220 209 200 191 179 171 163 155 148 

Suzuki 206 195 186 177 167 160 153 146 139 

Tata 259 247 237 228 212 202 192 182 174 

Toyota 246 235 226 217 204 194 185 176 168 

Volkswagen 203 194 185 176 166 158 151 143 137 

Fleet 244 234 225 215 202 192 183 174 166 

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 Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved Shortfall 

Aston Martin 243 - 243 243 325 - 82 

BMW 239 292 252 253 244 286 2 

Chrysler/Fiat 244 307 274 276 229 301 - 

Daimler 247 301 264 265 267 324 21 

Ferrari 244 - 244 244 371 - 128 

Ford 238 331 277 280 230 317 - 

Geely 236 289 254 256 230 291 - 

General Motors 240 324 276 279 230 312 - 

Honda 238 299 256 258 221 297 - 

Hyundai 236 285 242 243 227 267 - 

Kia 231 299 238 239 219 301 - 

Lotus 225 - 225 225 255 - 30 

Mazda 235 289 245 246 226 274 - 

Mitsubishi 225 271 234 235 221 239 - 

Nissan 238 309 256 258 226 303 - 

Porsche 241 299 271 273 268 323 25 

Subaru 230 266 241 242 235 226 - 

Suzuki 224 270 228 229 215 253 - 

Tata 250 288 274 276 274 354 52 

Toyota 237 308 264 267 216 312 - 

Volkswagen 233 294 244 245 220 294 - 

Fleet 238 312 264 266 227 306 - 

 
MY 2017 and Later - Regulatory Impact Analysis 

10-5 

Table 10-5 Reference Case Targets and Projected Shortfall in MY 2025 

Manufacturer Car 

Target 

Truck 

Target 

Fleet Target 

(Sales Weighted) 

Fleet 

Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved Shortfall 

Aston Martin 243 - 243 243 326 - 83 

BMW 239 292 251 252 246 286 3 

Chrysler/Fiat 243 308 272 274 228 302 - 

Daimler 247 301 264 266 267 323 21 

Ferrari 244 - 244 244 373 - 129 

Ford 238 331 276 279 230 317 - 

Geely 236 289 253 255 231 291 - 

General 

Motors 240 323 275 278 230 311 - 

Honda 238 299 255 257 221 301 - 

Hyundai 236 285 242 242 227 267 - 

Kia 231 298 237 238 219 301 - 

Lotus 225 - 225 225 256 - 31 

Mazda 235 289 244 246 226 275 - 

Mitsubishi 225 271 234 235 220 239 - 

Nissan 238 310 256 258 226 304 - 

Porsche 242 299 271 273 269 323 26 

Subaru 230 266 241 242 234 226 - 

Suzuki 225 270 228 229 215 253 - 

Tata 249 288 274 275 274 354 52 

Toyota 237 308 263 265 217 312 - 

Volkswagen 233 294 244 245 220 293 - 

Fleet 238 312 263 265 227 306 - 

 
Chapter 10 

10-6 

10.3.1.2 Final rule 

Table 10-6 Final rule Targets and Projected Shortfall in MY 2021 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 179 - 179 179 179 - 0 

BMW 176 231 189 190 179 223 - 

Chrysler/Fiat 180 248 212 215 184 242 - 

Daimler 182 241 200 202 172 261 - 

Ferrari 179 - 179 179 212 - 33 

Ford 175 277 218 222 191 258 - 

Geely 173 228 192 194 174 226 - 

General Motors 176 267 215 219 188 253 - 

Honda 175 239 194 196 179 230 - 

Hyundai 174 223 180 180 177 197 - 

Kia 169 238 176 177 172 217 - 

Lotus 165 - 165 165 163 - - 

Mazda 173 228 182 184 178 206 - 

Mitsubishi 165 209 173 174 173 179 - 

Nissan 175 252 195 197 184 230 - 

Porsche 178 238 209 212 161 252 - 

Subaru 168 204 179 180 186 169 - 

Suzuki 164 208 167 168 165 186 - 

Tata 185 227 212 213 149 275 20 

Toyota 174 253 204 207 180 245 - 

Volkswagen 171 234 182 184 172 229 - 

Fleet 175 255 203 205 182 243 - 

 
MY 2017 and Later - Regulatory Impact Analysis 

10-7 

Table 10-7 Final rule Targets and Projected Shortfall in MY 2025 

Manufacturer Car Target Truck Target Fleet Target 

(Sales Weighted) 

Fleet Target 

 (VMT 

and Sales 

weighted) 

Car 

Achieved 

Truck 

Achieved 

Shortfall 

Aston Martin 149 - 149 149 148 - - 

BMW 147 189 156 157 143 197 - 

Chrysler/Fiat 149 203 173 175 154 196 - 

Daimler 152 197 166 168 134 231 - 

Ferrari 149 - 149 149 157 - 8 

Ford 146 227 179 182 158 211 - 

Geely 144 187 158 159 138 197 - 

General Motors 147 218 177 180 158 203 - 

Honda 145 195 159 161 149 186 - 

Hyundai 144 182 149 149 147 161 - 

Kia 141 194 146 147 142 182 - 

Lotus 137 - 137 137 137 - - 

Mazda 144 186 151 152 147 170 - 

Mitsubishi 137 171 143 144 144 141 - 

Nissan 146 207 161 163 151 193 - 

Porsche 148 195 172 174 115 220 - 

Subaru 140 166 148 149 151 145 - 

Suzuki 136 170 139 139 138 145 - 

Tata 153 186 174 175 108 239 19 

Toyota 145 207 168 170 148 202 - 

Volkswagen 142 191 151 152 138 203 - 

Fleet 145 209 167 169 150 200 - 

 
Chapter 10 

10-8 

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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,824 $3,385 $4,559 $5,741 $6,909 $7,009 $7,055 $7,056 $6,478 

BMW $432 $786 $1,057 $1,314 $1,582 $2,012 $2,397 $2,763 $2,851 

Chrysler/Fiat $229 $359 $471 $576 $684 $1,012 $1,309 $1,597 $1,715 

Daimler $852 $1,590 $2,163 $2,710 $3,268 $3,704 $4,086 $4,434 $4,371 

Ferrari $1,740 $3,287 $4,454 $5,629 $6,789 $7,450 $8,019 $8,511 $8,244 

Ford $146 $257 $344 $425 $510 $824 $1,107 $1,375 $1,500 

Geely-Volvo $276 $496 $668 $835 $1,006 $1,514 $1,981 $2,420 $2,609 

GM $169 $295 $390 $482 $578 $883 $1,158 $1,419 $1,534 

Honda $138 $241 $321 $396 $475 $755 $1,007 $1,246 $1,357 

Hyundai $171 $307 $413 $513 $617 $907 $1,168 $1,416 $1,520 

Kia $151 $273 $370 $461 $555 $826 $1,070 $1,301 $1,402 

Lotus $1,050 $1,952 $2,633 $3,314 $3,990 $4,134 $4,240 $4,316 $4,031 

Mazda $229 $415 $553 $692 $834 $1,149 $1,432 $1,699 $1,795 

Mitsubishi $262 $484 $660 $832 $1,005 $1,333 $1,632 $1,908 $1,998 

Nissan $161 $283 $380 $471 $566 $893 $1,189 $1,469 $1,598 

Porsche $44 $61 $2,233 $3,149 $4,884 $5,524 $6,076 $6,575 $6,465 

Subaru $235 $438 $588 $736 $885 $1,273 $1,622 $1,951 $2,081 

Suzuki $54 $65 $522 $707 $1,078 $1,409 $1,705 $1,981 $2,064 

Tata-JLR $43 $58 $2,871 $4,129 $6,398 $7,034 $7,579 $8,062 $7,810 

Toyota $90 $161 $219 $271 $327 $599 $844 $1,079 $1,200 

Volkswagen $292 $531 $713 $891 $1,073 $1,481 $1,849 $2,194 $2,317 

Fleet $195 $346 $464 $577 $695 $1,011 $1,295 $1,565 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $0 $0 $0 $0 $0 $0 $0 $0 $0 

BMW $53 $198 $299 $408 $680 $1,588 $1,565 $1,536 $1,391 

Chrysler/Fiat $76 $217 $313 $455 $809 $1,268 $1,773 $2,230 $2,465 

Daimler $34 $160 $258 $358 $615 $2,817 $2,362 $1,914 $1,377 

Ferrari $0 $0 $0 $0 $0 $0 $0 $0 $0 

Ford $58 $153 $198 $307 $688 $990 $1,406 $1,809 $2,008 

Geely-Volvo $48 $205 $298 $401 $657 $1,217 $1,386 $1,540 $1,555 

GM $30 $128 $223 $342 $673 $1,142 $1,643 $2,113 $2,351 

Honda $40 $182 $290 $408 $710 $960 $1,385 $1,785 $1,993 

Hyundai $73 $266 $377 $508 $846 $1,041 $1,411 $1,754 $1,918 

Kia $69 $218 $360 $508 $863 $904 $1,198 $1,477 $1,603 

Lotus $0 $0 $0 $0 $0 $0 $0 $0 $0 
MY 2017 and Later - Regulatory Impact Analysis 

10-9 

Mazda $100 $366 $513 $709 $1,220 $1,345 $1,788 $2,206 $2,389 

Mitsubishi $216 $474 $617 $804 $1,290 $1,573 $2,076 $2,540 $2,743 

Nissan $92 $259 $379 $574 $1,100 $1,100 $1,566 $2,016 $2,246 

Porsche $21 $67 $197 $276 $636 $4,026 $3,181 $2,366 $1,464 

Subaru $202 $413 $527 $674 $1,062 $1,248 $1,561 $1,849 $1,956 

Suzuki $31 $71 $572 $735 $1,570 $1,699 $2,250 $2,758 $2,983 

Tata-JLR $20 $65 $363 $516 $1,243 $5,477 $4,569 $3,679 $2,609 

Toyota $40 $177 $271 $379 $665 $746 $1,115 $1,465 $1,655 

Volkswagen $57 $274 $425 $603 $1,053 $1,299 $1,479 $1,646 $1,660 

Fleet $50 $178 $272 $398 $751 $1,144 $1,535 $1,902 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,824 $3,385 $4,559 $5,741 $6,909 $7,009 $7,055 $7,056 $6,478 

BMW $344 $650 $881 $1,104 $1,373 $1,920 $2,216 $2,496 $2,534 

Chrysler/Fiat $156 $291 $396 $518 $744 $1,126 $1,517 $1,879 $2,050 

Daimler $601 $1,152 $1,579 $1,989 $2,454 $3,426 $3,546 $3,645 $3,433 

Ferrari $1,740 $3,287 $4,454 $5,629 $6,789 $7,450 $8,019 $8,511 $8,244 

Ford $109 $213 $282 $376 $585 $892 $1,230 $1,553 $1,708 

Geely-Volvo $198 $396 $542 $686 $887 $1,417 $1,788 $2,135 $2,268 

GM $110 $223 $318 $422 $619 $993 $1,365 $1,715 $1,883 

Honda $108 $223 $312 $400 $546 $812 $1,112 $1,397 $1,536 

Hyundai $159 $302 $409 $513 $644 $922 $1,195 $1,454 $1,564 

Kia $142 $267 $369 $466 $588 $833 $1,083 $1,318 $1,421 

Lotus $1,050 $1,952 $2,633 $3,314 $3,990 $4,134 $4,240 $4,316 $4,031 

Mazda $206 $406 $546 $695 $902 $1,182 $1,492 $1,784 $1,895 

Mitsubishi $253 $482 $652 $827 $1,059 $1,377 $1,714 $2,025 $2,136 

Nissan $144 $277 $379 $497 $703 $945 $1,285 $1,608 $1,762 

Porsche $32 $64 $1,170 $1,650 $2,668 $4,744 $4,570 $4,385 $3,863 

Subaru $225 $430 $569 $717 $940 $1,266 $1,604 $1,920 $2,043 

Suzuki $52 $65 $526 $709 $1,116 $1,432 $1,748 $2,043 $2,136 

Tata-JLR $28 $62 $1,272 $1,825 $3,111 $6,069 $5,714 $5,347 $4,589 

Toyota $71 $167 $239 $312 $455 $652 $943 $1,219 $1,365 

Volkswagen $249 $485 $661 $839 $1,069 $1,448 $1,782 $2,095 $2,199 

Fleet $145 $288 $398 $515 $714 $1,055 $1,375 $1,677 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 2040 2050

$/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 

Combined $145 $288 $398 $515 $714 $1,055 $1,375 $1,677 $1,807 $1,788 $1,785 $1,785 
Chapter 10 

10-10 

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 truck 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 to 

2012 at 3% and 7% Discount Rates (2010 dollars)

Calendar

Year

Sales $/unit $Million/year

Cars Trucks $/car $/truck Cars Trucks Combined

2017 10,213,312 5,598,788 $195 $50 $1,990 $280 $2,300 

2018 10,088,966 5,516,434 $346 $178 $3,490 $980 $4,490 

2019 10,139,761 5,522,339 $464 $272 $4,700 $1,500 $6,230 

2020 10,194,353 5,435,847 $577 $398 $5,880 $2,160 $8,050 

2021 10,310,594 5,419,506 $695 $751 $7,160 $4,070 $11,200 

2022 10,455,061 5,432,139 $1,011 $1,144 $10,600 $6,220 $16,800 

2023 10,593,727 5,413,473 $1,295 $1,535 $13,700 $8,310 $22,000 

2024 10,811,530 5,435,470 $1,565 $1,902 $16,900 $10,300 $27,200 

2025 10,981,082 5,473,718 $1,675 $2,071 $18,400 $11,300 $29,700 

2030 11,467,094 5,591,140 $1,660 $2,055 $19,000 $11,500 $30,500 

2040 12,264,435 5,910,536 $1,660 $2,055 $20,400 $12,100 $32,400 

2050 13,122,182 6,323,905 $1,660 $2,055 $21,800 $13,000 $34,700 

NPV, 3% $292,000 $172,000 $463,000 

NPV, 7% $132,000 $76,200 $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 2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum 

3% 

Car $1,960 $3,440 $4,640 $5,800 $7,060 $10,400 $13,500 $16,700 $18,100 $81,600 

Truck $276 $965 $1,480 $2,130 $4,010 $6,130 $8,190 $10,200 $11,200 $44,500 

Fleet $2,260 $4,430 $6,140 $7,940 $11,100 $16,500 $21,700 $26,900 $29,300 $126,000 

7% 

Car $1,930 $3,370 $4,550 $5,690 $6,930 $10,200 $13,300 $16,400 $17,800 $80,100 

Truck $271 $947 $1,450 $2,090 $3,940 $6,010 $8,040 $10,000 $11,000 $43,700 

Fleet $2,220 $4,340 $6,030 $7,790 $10,900 $16,200 $21,300 $26,400 $28,800 $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. 
MY 2017 and Later - Regulatory Impact Analysis 

10-11 

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 LRRT1 LRRT2 Diesel EV PHEV Total 

Car Truck Car Truck Car Truck Car Truck Car Truck Car Truck Vehicle 

2017 $0 $0 $25 $15 -$1 $0 -$1 $0 $0 $0 $24 $15 $39 

2018 -$4 -$2 $75 $45 -$3 -$1 -$3 $0 $1 $0 $67 $42 $108 

2019 -$11 -$7 $150 $88 -$5 -$1 -$5 $0 $1 $0 $130 $80 $210 

2020 -$22 -$13 $249 $143 -$8 -$2 -$9 $0 $2 $0 $212 $128 $340 

2021 -$37 -$21 $377 $213 -$13 -$3 -$14 $0 $3 $0 $317 $189 $505 

2022 -$54 -$30 $516 $287 -$16 -$4 -$21 $0 $4 $0 $430 $253 $683 

2023 -$73 -$40 $669 $363 -$19 -$4 -$29 $0 $5 $0 $554 $319 $872 

2024 -$94 -$51 $836 $446 -$21 -$5 -$41 $0 $6 $0 $688 $390 $1,080 

2025 -$116 -$62 $1,010 $533 -$21 -$5 -$54 -$1 $7 $0 $826 $465 $1,290 

2030 -$217 -$111 $1,790 $914 -$22 -$7 -$117 -$2 $11 $0 $1,440 $794 $2,240 

2040 -$348 -$175 $2,800 $1,400 -$20 -$10 -$203 -$3 $15 $0 $2,240 $1,220 $3,460 

2050 -$413 -$214 $3,310 $1,710 -$20 -$11 -$244 -$4 $18 $0 $2,650 $1,480 $4,130 

NPV, 3% -$3,700 -$1,900 $30,400 $15,600 -$322 -$120 -$2,060 -$33 $180 $0 $24,500 $13,500 $38,000 

NPV, 7% -$1,450 -$747 $12,100 $6,220 -$150 -$51 -$790 -$12 $75 $0 $9,780 $5,410 $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 Tires Diesel EV PHEV Total $Million per MY 

$/c $/t $/c $/t $/c $/t $/c $/t $/c $/t $/veh $/c $/t $/veh 

2017 $25 $27 -$1 $0 -$1 $0 $0 $0 $24 $27 $25 $243 $149 $392 

2018 $47 $50 -$2 -$1 -$2 $0 $0 $0 $44 $50 $46 $445 $274 $719 

2019 $70 $74 -$3 -$1 -$3 $0 $1 $0 $65 $73 $68 $658 $403 $1,060 

2020 $93 $98 -$4 -$1 -$4 $0 $1 $0 $86 $96 $90 $879 $524 $1,400 

2021 $116 $124 -$4 -$2 -$5 $0 $1 $0 $108 $122 $112 $1,110 $659 $1,770 

2022 $126 $135 -$4 -$2 -$7 $0 $1 $0 $116 $133 $122 $1,220 $721 $1,940 

2023 $137 $146 -$3 -$2 -$9 $0 $1 $0 $126 $144 $132 $1,340 $778 $2,120 

2024 $147 $158 -$2 -$1 -$11 $0 $1 $0 $135 $156 $142 $1,460 $847 $2,310 

2025 $158 $170 -$1 -$1 -$13 -$1 $1 $0 $145 $168 $153 $1,590 $919 $2,510 

Sum $919 $980 -$23 -$11 -$55 -$1 $8 $0 $849 $968 $890 $8,940 $5,280 $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 Tires Diesel EV PHEV Total $Million per MY 

$/c $/t $/c $/t $/c $/t $/c $/t $/c $/t $/veh $/c $/t $/veh 

2017 $20 $21 -$1 $0 -$1 $0 $0 $0 $18 $20 $19 $188 $114 $302 

2018 $37 $39 -$1 -$1 -$1 $0 $0 $0 $34 $38 $36 $345 $210 $555 

2019 $54 $57 -$2 -$1 -$2 $0 $1 $0 $50 $56 $52 $509 $309 $818 

2020 $72 $75 -$3 -$1 -$3 $0 $1 $0 $67 $74 $69 $680 $403 $1,080 

2021 $89 $94 -$3 -$1 -$4 $0 $1 $0 $83 $93 $86 $854 $503 $1,360 

2022 $98 $103 -$3 -$1 -$5 $0 $1 $0 $90 $102 $94 $945 $551 $1,500 

2023 $106 $111 -$2 -$1 -$7 $0 $1 $0 $98 $110 $102 $1,040 $596 $1,630 

2024 $114 $121 -$2 -$1 -$9 $0 $1 $0 $105 $120 $110 $1,130 $650 $1,780 

2025 $122 $129 -$1 -$1 -$10 $0 $1 $0 $112 $128 $117 $1,230 $700 $1,930 

Sum $711 $750 -$18 -$9 -$42 -$1 $6 $0 $657 $741 $686 $6,920 $4,040 $11,000 

Note: Costs include maintenance incurred during rebound miles; results correspond to the 2008 baseline fleet. 
Chapter 10 

10-12 

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 Car Truck 

Total 

Annual 

Costs 

2017 $2,020 $295 $2,330 

2018 $3,550 $1,020 $4,600 

2019 $4,830 $1,580 $6,440 

2020 $6,090 $2,290 $8,390 

2021 $7,480 $4,260 $11,700 

2022 $11,000 $6,470 $17,400 

2023 $14,300 $8,630 $22,900 

2024 $17,600 $10,700 $28,300 

2025 $19,200 $11,800 $31,000 

2030 $20,500 $12,300 $32,700 

2040 $22,600 $13,400 $35,900 

2050 $24,400 $14,500 $38,800 

NPV, 3% $317,000 $185,000 $501,000 

NPV, 7% $141,000 $81,600 $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 MY  2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum 

3% 

Cars $2,210 $3,880 $5,290 $6,680 $8,170 $11,600 $14,900 $18,100 $19,700 $90,600 

Trucks $425 $1,240 $1,880 $2,660 $4,670 $6,850 $8,970 $11,000 $12,100 $49,800 

Combined $2,650 $5,150 $7,200 $9,340 $12,800 $18,500 $23,800 $29,200 $31,800 $140,000 

7% 

Cars $2,120 $3,720 $5,060 $6,370 $7,780 $11,200 $14,300 $17,500 $19,000 $87,000 

Trucks $386 $1,160 $1,760 $2,500 $4,440 $6,560 $8,630 $10,700 $11,700 $47,700 

Combined $2,520 $4,900 $6,840 $8,870 $12,200 $17,700 $22,900 $28,100 $30,700 $135,000 

Note: Results correspond to the 2010 baseline fleet. 
MY 2017 and Later - Regula

10-13 

10.5 Technology Penetrations 

10.5.1 Projected Technology Penetrations in Reference Case 

Table 10-19 Reference Car Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -7% 1% 40% 15% 0% 9% 0% 53% 26% 12% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -6% -5% 1% 44% 15% 0% 13% 2% 49% 27% 8% 0% 15% 15% 0% 0% 55% 0% 30% 0Chrysler/Fiat -5% -5% 0% 68% 15% 0% 11% 4% 48% 26% 1% 0% 15% 0% 0% 0% 0% 0% 30% 0Daimler -8% -7% 1% 40% 15% 0% 0% 52% 18% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ferrari -8% -7% 1% 40% 15% 0% 0% 0% 20% 79% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -3% -3% 0% 35% 0% 0% 18% 5% 43% 23% 5% 0% 0% 3% 0% 0% 0% 0% 29% 0Geely -4% -3% 1% 47% 15% 0% 10% 3% 48% 26% 5% 0% 15% 15% 0% 0% 56% 0% 30% 0General Motors -6% -6% 0% 27% 5% 0% 8% 1% 53% 28% 5% 0% 0% 0% 0% 0% 0% 0% 30% 0Honda -1% -1% 0% 0% 0% 0% 1% 1% 50% 26% 3% 0% 0% 6% 0% 0% 0% 0% 0% 0Hyundai -2% -2% 0% 3% 0% 0% 6% 1% 53% 24% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia 0% 0% 0% 0% 0% 0% 0% 0% 66% 12% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus -1% 0% 1% 42% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -1% -1% 0% 11% 0% 0% 3% 1% 47% 25% 14% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -3% -3% 0% 46% 7% 0% 11% 3% 46% 25% 7% 0% 2% 0% 0% 0% 0% 0% 30% 0Nissan -1% -1% 0% 28% 0% 0% 3% 10% 44% 27% 2% 0% 0% 1% 0% 0% 0% 0% 26% 0Porsche -5% -4% 1% 43% 15% 0% 0% 40% 15% 22% 24% 0% 15% 15% 0% 0% 55% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -5% -5% 0% 32% 9% 0% 1% 1% 47% 25% 13% 0% 1% 0% 0% 0% 0% 0% 30% 0Suzuki -1% -1% 0% 48% 7% 0% 1% 1% 49% 27% 9% 0% 0% 0% 0% 0% 0% 0% 30% 0Tata -9% -8% 1% 43% 15% 0% 10% 0% 55% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -1% -1% 0% 2% 0% 0% 10% 2% 52% 16% 2% 0% 10% 12% 0% 0% 0% 0% 0% 0Volkswagen -4% -3% 0% 68% 13% 0% 14% 2% 50% 26% 8% 0% 13% 3% 0% 0% 56% 0% 30% 0Fleet -3% -3% 0% 24% 4% 0% 8% 4% 49% 24% 4% 0% 4% 4% 0% 0% 6% 0% 18% 0
Chapter 10 

10-14 

Table 10-20 Reference Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -8% -8% 1% 62% 13% 0% 69% 30% 0% 0% 1% 0% 13% 15% 0% 0% 66% 0% 30% 0Chrysler/Fiat -8% -8% 0% 51% 15% 0% 64% 28% 2% 1% 4% 0% 15% 0% 0% 0% 8% 0% 30% 0Daimler -9% -8% 1% 60% 14% 0% 1% 99% 0% 0% 0% 0% 14% 15% 0% 0% 64% 0% 30% 0Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -8% -8% 0% 25% 2% 0% 60% 26% 7% 4% 0% 0% 0% 1% 0% 0% 0% 0% 30% 0Geely -8% -8% 1% 70% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 63% 0% 30% 0General Motors -8% -8% 0% 29% 6% 0% 64% 28% 3% 2% 0% 0% 6% 0% 0% 0% 0% 0% 30% 0Honda -4% -4% 0% 47% 0% 0% 46% 24% 10% 6% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0Hyundai -4% -4% 0% 0% 0% 0% 43% 15% 27% 15% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia -3% -3% 0% 0% 0% 0% 36% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -7% -7% 0% 58% 0% 0% 47% 20% 17% 9% 2% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -8% -8% 0% 70% 15% 0% 29% 12% 33% 18% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Nissan -4% -4% 0% 44% 2% 0% 37% 29% 13% 7% 1% 0% 0% 0% 0% 0% 0% 0% 30% 0Porsche -8% -8% 1% 65% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 64% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -8% -8% 0% 44% 2% 0% 7% 4% 45% 24% 5% 0% 2% 0% 0% 0% 0% 0% 30% 0Suzuki 0% 0% 0% 70% 15% 0% 15% 8% 40% 22% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Tata -8% -7% 1% 60% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 58% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 13% 0% 0% 51% 25% 6% 3% 3% 0% 9% 3% 0% 0% 0% 0% 9% 0Volkswagen -8% -8% 0% 61% 12% 0% 69% 30% 1% 0% 0% 0% 12% 0% 0% 0% 67% 0% 30% 0Fleet -7% -7% 0% 33% 5% 0% 55% 28% 7% 4% 1% 0% 6% 2% 0% 0% 6% 0% 23% 0
MY 2017 and Later - Regula

10-15 

Table 10-21 Reference Fleet (Sales-Weighted) Technology Penetration in MY 20

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -7% 1% 40% 15% 0% 9% 0% 53% 26% 12% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -6% -5% 1% 48% 14% 0% 26% 9% 38% 21% 7% 0% 14% 15% 0% 0% 57% 0% 30% 0Chrysler/Fiat -6% -6% 0% 60% 15% 0% 36% 15% 26% 14% 2% 0% 15% 0% 0% 0% 4% 0% 30% 0Daimler -8% -7% 1% 46% 15% 0% 0% 66% 13% 21% 0% 0% 15% 15% 0% 0% 58% 0% 30% 0Ferrari -8% -7% 1% 40% 15% 0% 0% 0% 20% 79% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -5% -5% 0% 31% 1% 0% 36% 14% 28% 15% 3% 0% 0% 2% 0% 0% 0% 0% 29% 0Geely -6% -5% 1% 55% 15% 0% 31% 12% 31% 17% 3% 0% 15% 15% 0% 0% 59% 0% 30% 0General Motors -7% -7% 0% 28% 6% 0% 32% 12% 32% 17% 3% 0% 2% 0% 0% 0% 0% 0% 30% 0Honda -2% -2% 0% 15% 0% 0% 15% 8% 38% 20% 2% 0% 0% 4% 0% 0% 0% 0% 0% 0Hyundai -2% -2% 0% 3% 0% 0% 10% 3% 50% 23% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia -1% -1% 0% 0% 0% 0% 4% 3% 59% 11% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus -1% 0% 1% 42% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -2% -2% 0% 19% 0% 0% 11% 4% 41% 23% 12% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -4% -4% 0% 51% 9% 0% 14% 5% 44% 24% 6% 0% 4% 0% 0% 0% 0% 0% 30% 0Nissan -2% -2% 0% 32% 1% 0% 11% 15% 36% 22% 2% 0% 0% 1% 0% 0% 0% 0% 27% 0Porsche -7% -6% 1% 54% 15% 0% 36% 35% 7% 10% 11% 0% 15% 15% 0% 0% 60% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -6% -6% 0% 36% 7% 0% 3% 2% 46% 25% 11% 0% 2% 0% 0% 0% 0% 0% 30% 0Suzuki -1% -1% 0% 50% 7% 0% 2% 1% 48% 26% 8% 0% 1% 0% 0% 0% 0% 0% 30% 0Tata -8% -7% 1% 54% 15% 0% 48% 19% 20% 11% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -2% -2% 0% 6% 0% 0% 25% 11% 35% 11% 3% 0% 9% 9% 0% 0% 0% 0% 4% 0Volkswagen -4% -4% 0% 67% 13% 0% 24% 7% 41% 21% 7% 0% 13% 2% 0% 0% 58% 0% 30% 0Fleet -4% -4% 0% 27% 4% 0% 24% 12% 34% 17% 3% 0% 5% 3% 0% 0% 6% 0% 20% 0
Chapter 10 

10-16 

Table 10-22 Reference Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -7% 1% 55% 15% 0% 9% 0% 53% 26% 12% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -6% -5% 1% 59% 15% 0% 13% 2% 49% 27% 8% 0% 15% 15% 0% 0% 55% 0% 30% 0Chrysler/Fiat -5% -5% 0% 68% 15% 0% 10% 4% 48% 26% 1% 0% 15% 0% 0% 0% 0% 0% 30% 0Daimler -8% -7% 1% 55% 15% 0% 0% 52% 18% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ferrari -8% -7% 1% 55% 15% 0% 0% 0% 20% 79% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -3% -3% 0% 35% 0% 0% 18% 5% 43% 23% 5% 0% 0% 3% 0% 0% 0% 0% 29% 0Geely -4% -4% 1% 60% 15% 0% 10% 3% 48% 26% 4% 0% 15% 15% 0% 0% 56% 0% 30% 0General Motors -6% -6% 0% 27% 5% 0% 7% 1% 54% 28% 5% 0% 0% 0% 0% 0% 0% 0% 30% 0Honda -1% -1% 0% 0% 0% 0% 1% 1% 50% 26% 3% 0% 0% 6% 0% 0% 0% 0% 0% 0Hyundai -2% -2% 0% 3% 0% 0% 6% 1% 53% 24% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia 0% 0% 0% 0% 0% 0% 0% 0% 71% 8% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus -1% 0% 1% 57% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -1% -1% 0% 11% 0% 0% 3% 0% 47% 25% 13% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -3% -3% 0% 46% 7% 0% 11% 3% 46% 25% 7% 0% 2% 0% 0% 0% 0% 0% 30% 0Nissan -1% -1% 0% 28% 0% 0% 3% 10% 44% 27% 2% 0% 0% 1% 0% 0% 0% 0% 28% 0Porsche -5% -4% 1% 58% 15% 0% 0% 40% 15% 22% 24% 0% 15% 15% 0% 0% 55% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -5% -5% 0% 34% 10% 0% 1% 0% 47% 25% 14% 0% 1% 0% 0% 0% 0% 0% 30% 0Suzuki -1% -1% 0% 48% 7% 0% 1% 1% 49% 27% 9% 0% 0% 0% 0% 0% 0% 0% 30% 0Tata -9% -8% 1% 58% 15% 0% 10% 0% 55% 30% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -1% -1% 0% 0% 0% 0% 10% 2% 52% 16% 2% 0% 9% 12% 0% 0% 0% 0% 0% 0Volkswagen -4% -3% 0% 70% 13% 0% 14% 2% 50% 26% 8% 0% 13% 1% 0% 0% 55% 0% 30% 0Fleet -3% -3% 0% 25% 4% 0% 8% 4% 49% 24% 4% 0% 4% 4% 0% 0% 6% 0% 18% 0
MY 2017 and Later - Regula

10-17 

Table 10-23 Reference Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -8% -8% 1% 64% 13% 0% 69% 30% 0% 0% 1% 0% 13% 15% 0% 0% 66% 0% 30% 0Chrysler/Fiat -8% -8% 0% 51% 15% 0% 64% 27% 2% 1% 4% 0% 15% 0% 0% 0% 2% 0% 30% 0Daimler -9% -8% 1% 65% 14% 0% 1% 99% 0% 0% 0% 0% 14% 15% 0% 0% 64% 0% 30% 0Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -8% -8% 0% 27% 0% 0% 60% 26% 7% 4% 0% 0% 0% 1% 0% 0% 0% 0% 30% 0Geely -8% -8% 1% 77% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 63% 0% 30% 0General Motors -8% -8% 0% 30% 6% 0% 64% 28% 4% 2% 1% 0% 6% 0% 0% 0% 0% 0% 30% 0Honda -4% -4% 0% 27% 0% 0% 46% 24% 10% 6% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0Hyundai -4% -4% 0% 0% 0% 0% 42% 15% 28% 15% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia -3% -3% 0% 0% 0% 0% 35% 30% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -7% -7% 0% 59% 0% 0% 48% 20% 17% 9% 2% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -9% -9% 0% 70% 15% 0% 29% 12% 33% 18% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Nissan -4% -4% 0% 44% 2% 0% 37% 29% 12% 7% 1% 0% 0% 0% 0% 0% 0% 0% 30% 0Porsche -8% -8% 1% 70% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 64% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -9% -9% 0% 44% 2% 0% 7% 4% 45% 24% 5% 0% 2% 0% 0% 0% 0% 0% 30% 0Suzuki 0% 0% 0% 70% 15% 0% 15% 8% 40% 22% 0% 0% 15% 0% 0% 0% 0% 0% 30% 0Tata -8% -7% 1% 73% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 58% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 12% 0% 0% 51% 25% 6% 3% 3% 0% 9% 3% 0% 0% 0% 0% 9% 0Volkswagen -8% -8% 0% 61% 12% 0% 69% 30% 1% 0% 0% 0% 12% 0% 0% 0% 67% 0% 30% 0Fleet -7% -6% 0% 32% 5% 0% 55% 28% 7% 4% 1% 0% 6% 2% 0% 0% 5% 0% 23% 0
Chapter 10 

10-18 

Table 10-24 Reference Fleet (Sales-Weighted) Technology Penetration in MY 20

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -8% -7% 1% 55% 15% 0% 9% 0% 53% 26% 12% 0% 15% 15% 0% 0% 55% 0% 30% 0BMW -6% -5% 1% 60% 14% 0% 25% 8% 39% 21% 7% 0% 14% 15% 0% 0% 57% 0% 30% 0Chrysler/Fiat -6% -6% 0% 60% 15% 0% 34% 14% 28% 15% 3% 0% 15% 0% 0% 0% 1% 0% 30% 0Daimler -8% -7% 1% 58% 15% 0% 0% 66% 13% 21% 0% 0% 15% 15% 0% 0% 58% 0% 30% 0Ferrari -8% -7% 1% 55% 15% 0% 0% 0% 20% 79% 0% 0% 15% 15% 0% 0% 55% 0% 30% 0Ford -5% -5% 0% 32% 0% 0% 35% 13% 28% 15% 3% 0% 0% 2% 0% 0% 0% 0% 29% 0Geely -6% -5% 1% 66% 15% 0% 30% 12% 32% 17% 3% 0% 15% 15% 0% 0% 59% 0% 30% 0General Motors -7% -7% 0% 28% 6% 0% 31% 12% 32% 17% 3% 0% 2% 0% 0% 0% 0% 0% 30% 0Honda -2% -2% 0% 8% 0% 0% 14% 7% 39% 20% 2% 0% 0% 4% 0% 0% 0% 0% 0% 0Hyundai -2% -2% 0% 3% 0% 0% 10% 3% 50% 23% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0Kia 0% 0% 0% 0% 0% 0% 4% 3% 64% 7% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0Lotus -1% 0% 1% 57% 15% 0% 0% 0% 15% 0% 85% 0% 15% 15% 0% 0% 55% 0% 30% 0Mazda -2% -2% 0% 19% 0% 0% 11% 4% 42% 23% 11% 0% 0% 0% 0% 0% 0% 0% 30% 0Mitsubishi -4% -4% 0% 50% 8% 0% 14% 4% 44% 24% 6% 0% 4% 0% 0% 0% 0% 0% 30% 0Nissan -2% -2% 0% 32% 0% 0% 11% 15% 36% 22% 2% 0% 0% 1% 0% 0% 0% 0% 28% 0Porsche -7% -6% 1% 64% 15% 0% 36% 35% 7% 10% 11% 0% 15% 15% 0% 0% 60% 0% 30% 0Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -6% -6% 0% 37% 8% 0% 3% 2% 46% 25% 11% 0% 2% 0% 0% 0% 0% 0% 30% 0Suzuki -1% -1% 0% 50% 8% 0% 2% 1% 48% 26% 8% 0% 1% 0% 0% 0% 0% 0% 30% 0Tata -8% -7% 1% 67% 15% 0% 47% 19% 21% 11% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -2% -2% 0% 4% 0% 0% 25% 10% 36% 12% 3% 0% 9% 9% 0% 0% 0% 0% 4% 0Volkswagen -4% -4% 0% 68% 13% 0% 24% 7% 41% 21% 6% 0% 13% 1% 0% 0% 57% 0% 30% 0Fleet -4% -4% 0% 28% 4% 0% 23% 12% 35% 17% 3% 0% 5% 3% 0% 0% 6% 0% 20% 0
MY 2017 and Later - Regula

10-19 

10.5.2 Projected Technology Penetrations in Final rule case 

Table 10-25 Final rule Car Technology Penetrations in MY 2021

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -14% -9% 6% 9% 15% 15% 0% 0% 8% 70% 6% 59% 25% 30% 16% 15% 30% 75% 24% 6BMW -8% -7% 1% 44% 28% 8% 0% 0% 13% 73% 6% 58% 30% 11% 7% 0% 42% 75% 55% 6Chrysler/Fiat -6% -6% 0% 74% 23% 1% 3% 11% 19% 67% 1% 56% 22% 0% 0% 0% 3% 75% 77% 5Daimler -14% -11% 3% 17% 30% 14% 0% 9% 1% 79% 0% 59% 30% 22% 10% 6% 42% 75% 46% 6Ferrari -14% -8% 6% 9% 15% 15% 0% 0% 1% 83% 0% 59% 25% 30% 16% 15% 30% 75% 24% 6Ford -4% -4% 0% 35% 5% 1% 3% 13% 19% 57% 5% 32% 3% 3% 0% 0% 0% 73% 74% 1Geely -7% -6% 1% 55% 30% 6% 2% 8% 14% 69% 4% 59% 30% 5% 5% 0% 42% 75% 56% 6General Motors -6% -6% 0% 42% 13% 1% 0% 2% 22% 71% 5% 40% 8% 0% 0% 0% 0% 75% 79% 3Honda -2% -2% 0% 16% 0% 0% 0% 2% 22% 67% 3% 9% 0% 6% 0% 0% 0% 71% 75% 2Hyundai -3% -3% 0% 18% 0% 0% 1% 3% 23% 68% 5% 23% 0% 0% 0% 0% 0% 75% 79% 2Kia 0% 0% 0% 2% 0% 0% 0% 0% 23% 69% 7% 0% 0% 0% 0% 0% 0% 75% 71% 0Lotus -3% 0% 3% 15% 30% 14% 0% 0% 0% 42% 47% 59% 30% 18% 11% 12% 36% 75% 42% 5Mazda -2% -2% 0% 22% 20% 0% 0% 1% 21% 64% 14% 54% 3% 0% 0% 0% 0% 75% 80% 5Mitsubishi -4% -4% 0% 73% 27% 0% 2% 7% 19% 65% 7% 52% 27% 0% 0% 0% 6% 75% 78% 5Nissan -3% -3% 0% 32% 11% 0% 1% 8% 20% 68% 2% 31% 1% 1% 0% 0% 0% 74% 78% 3Porsche -10% -7% 3% 7% 29% 15% 0% 7% 0% 66% 11% 55% 30% 25% 16% 8% 33% 75% 36% 5Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -6% -6% 0% 83% 17% 0% 0% 1% 20% 65% 13% 54% 10% 0% 0% 0% 0% 75% 80% 4Suzuki -1% -1% 0% 73% 27% 0% 0% 1% 21% 70% 7% 57% 27% 0% 0% 0% 1% 75% 79% 5Tata -16% -10% 6% 6% 19% 15% 0% 0% 9% 75% 0% 59% 26% 29% 16% 15% 30% 75% 27% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -2% -2% 0% 20% 0% 0% 1% 4% 20% 61% 2% 0% 10% 12% 0% 0% 0% 66% 12% 0Volkswagen -5% -5% 1% 57% 26% 2% 1% 3% 15% 72% 6% 60% 26% 4% 2% 0% 39% 75% 61% 6Fleet -4% -4% 0% 34% 10% 1% 1% 5% 20% 66% 4% 30% 9% 4% 1% 0% 5% 73% 65% 2
Chapter 10 

10-20 

Table 10-26 Final rule Truck Technology Penetrations in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -15% -14% 1% 55% 28% 8% 20% 80% 0% 0% 1% 60% 29% 0% 0% 0% 69% 75% 56% 6Chrysler/Fiat -9% -8% 0% 30% 24% 5% 18% 74% 1% 3% 3% 59% 28% 0% 0% 0% 11% 75% 64% 4Daimler -15% -14% 1% 56% 29% 9% 8% 92% 0% 0% 0% 60% 30% 0% 0% 0% 68% 75% 58% 6Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -8% -8% 0% 20% 8% 10% 17% 70% 3% 9% 0% 60% 14% 1% 0% 0% 0% 75% 75% 1Geely -15% -14% 1% 60% 30% 10% 20% 80% 0% 0% 0% 60% 30% 0% 0% 0% 68% 75% 59% 6General Motors -9% -9% 0% 27% 10% 9% 19% 75% 1% 5% 0% 58% 12% 0% 0% 0% 0% 75% 67% 2Honda -9% -9% 0% 61% 17% 0% 16% 65% 5% 14% 0% 52% 0% 0% 0% 0% 0% 75% 65% 1Hyundai -9% -9% 0% 38% 10% 0% 10% 40% 12% 37% 0% 40% 0% 0% 0% 0% 0% 75% 70% 1Kia -5% -5% 0% 95% 0% 0% 20% 80% 0% 0% 0% 40% 0% 0% 0% 0% 0% 75% 40% 0Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -11% -10% 0% 78% 17% 0% 13% 54% 8% 23% 2% 60% 17% 0% 0% 0% 13% 75% 67% 3Mitsubishi -12% -11% 1% 73% 27% 0% 8% 33% 15% 44% 0% 60% 27% 0% 0% 0% 29% 75% 68% 5Nissan -7% -7% 0% 69% 17% 3% 13% 63% 6% 17% 1% 60% 10% 0% 0% 0% 0% 75% 69% 3Porsche -15% -14% 1% 61% 30% 9% 20% 80% 0% 0% 0% 60% 30% 2% 0% 0% 68% 75% 57% 6Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -11% -11% 0% 75% 25% 0% 3% 11% 20% 61% 5% 60% 25% 0% 0% 0% 3% 75% 77% 4Suzuki -1% 0% 1% 48% 30% 0% 5% 22% 15% 58% 0% 60% 30% 22% 0% 0% 23% 75% 61% 5Tata -13% -12% 1% 55% 30% 15% 20% 80% 0% 0% 0% 60% 30% 30% 0% 0% 66% 75% 66% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -4% -4% 0% 57% 0% 4% 17% 66% 3% 8% 3% 38% 11% 3% 0% 0% 0% 73% 55% 0Volkswagen -15% -14% 1% 52% 27% 7% 20% 80% 0% 0% 0% 60% 29% 0% 0% 0% 69% 75% 54% 6Fleet -8% -8% 0% 41% 12% 6% 17% 69% 3% 9% 1% 54% 14% 1% 0% 0% 7% 75% 65% 2
MY 2017 and Later - Regula

10-21 

Table 10-27 Final rule Fleet Technology Penetration in MY 2021 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -14% -9% 6% 9% 15% 15% 0% 0% 8% 70% 6% 59% 25% 30% 16% 15% 30% 75% 24% 6BMW -10% -9% 1% 46% 28% 8% 5% 19% 10% 56% 5% 59% 30% 9% 6% 0% 48% 75% 55% 6Chrysler/Fiat -7% -7% 0% 53% 23% 2% 10% 41% 10% 37% 2% 57% 25% 0% 0% 0% 7% 75% 71% 4Daimler -14% -12% 2% 29% 29% 13% 3% 34% 1% 55% 0% 60% 30% 16% 7% 4% 50% 75% 50% 6Ferrari -14% -8% 6% 9% 15% 15% 0% 0% 1% 83% 0% 59% 25% 30% 16% 15% 30% 75% 24% 6Ford -6% -6% 0% 29% 6% 5% 9% 37% 12% 37% 3% 44% 7% 2% 0% 0% 0% 73% 75% 1Geely -9% -8% 1% 57% 30% 7% 8% 32% 9% 45% 2% 59% 30% 3% 3% 0% 51% 75% 57% 6General Motors -7% -7% 0% 36% 11% 4% 8% 33% 13% 42% 3% 48% 10% 0% 0% 0% 0% 75% 74% 2Honda -4% -4% 0% 30% 5% 0% 5% 21% 17% 51% 2% 22% 0% 4% 0% 0% 0% 72% 72% 2Hyundai -4% -4% 0% 21% 1% 0% 2% 8% 21% 64% 5% 25% 0% 0% 0% 0% 0% 75% 78% 2Kia -1% -1% 0% 12% 0% 0% 2% 9% 21% 62% 7% 4% 0% 0% 0% 0% 0% 75% 67% 0Lotus -3% 0% 3% 15% 30% 14% 0% 0% 0% 42% 47% 59% 30% 18% 11% 12% 36% 75% 42% 5Mazda -3% -3% 0% 32% 19% 0% 3% 11% 19% 57% 12% 55% 6% 0% 0% 0% 2% 75% 77% 5Mitsubishi -5% -5% 0% 73% 27% 0% 3% 12% 18% 61% 6% 54% 27% 0% 0% 0% 11% 75% 76% 5Nissan -4% -4% 0% 42% 12% 1% 4% 22% 16% 55% 2% 39% 3% 1% 0% 0% 0% 74% 76% 3Porsche -13% -10% 2% 36% 29% 12% 10% 45% 0% 32% 5% 57% 30% 13% 7% 4% 51% 75% 47% 5Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -8% -8% 0% 80% 20% 0% 1% 4% 20% 64% 11% 56% 15% 0% 0% 0% 1% 75% 79% 4Suzuki -1% -1% 1% 71% 27% 0% 1% 3% 20% 69% 7% 57% 27% 2% 0% 0% 3% 75% 78% 5Tata -14% -11% 3% 37% 26% 15% 13% 51% 3% 27% 0% 60% 29% 30% 6% 5% 53% 75% 52% 6Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 34% 0% 2% 7% 27% 13% 41% 3% 15% 10% 9% 0% 0% 0% 69% 28% 0Volkswagen -7% -6% 1% 56% 26% 3% 4% 17% 13% 59% 5% 60% 27% 4% 2% 0% 45% 75% 60% 6Fleet -6% -5% 0% 37% 11% 3% 7% 27% 14% 47% 3% 39% 11% 3% 0% 0% 6% 73% 65% 2
Chapter 10 

10-22 

Table 10-28 Final rule Car Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 0% 4% 40% 0% 0% 0% 75% 2% 77% 44% 23% 23% 10% 17% 100% 17% 7BMW -10% -8% 2% 5% 57% 17% 0% 0% 0% 81% 3% 85% 74% 5% 15% 0% 35% 100% 35% 8Chrysler/Fiat -10% -9% 1% 23% 73% 2% 0% 12% 0% 86% 1% 98% 74% 0% 2% 0% 6% 100% 65% 9Daimler -17% -13% 4% 2% 60% 11% 0% 0% 0% 78% 0% 78% 70% 3% 22% 3% 25% 100% 25% 7Ferrari -17% -8% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Ford -6% -6% 0% 22% 67% 5% 0% 16% 0% 78% 3% 97% 71% 3% 0% 0% 0% 97% 86% 9Geely -8% -6% 2% 8% 64% 14% 0% 9% 0% 76% 2% 87% 75% 1% 13% 0% 38% 100% 38% 8General Motors -8% -8% 0% 23% 70% 5% 0% 2% 0% 95% 3% 100% 74% 0% 0% 0% 0% 100% 84% 10Honda -3% -3% 0% 27% 67% 0% 0% 2% 0% 89% 3% 94% 46% 6% 0% 0% 0% 94% 94% 9Hyundai -4% -4% 0% 35% 64% 1% 0% 4% 0% 91% 5% 100% 25% 0% 0% 0% 0% 100% 100% 10Kia 0% 0% 0% 4% 67% 0% 0% 0% 0% 93% 7% 100% 4% 0% 0% 0% 0% 100% 100% 10Lotus -3% 0% 3% 3% 70% 0% 0% 0% 0% 52% 26% 78% 70% 4% 22% 2% 26% 100% 26% 7Mazda -3% -3% 0% 24% 75% 0% 0% 2% 0% 87% 10% 99% 75% 0% 1% 0% 1% 100% 86% 9Mitsubishi -7% -6% 1% 22% 75% 0% 0% 9% 0% 85% 3% 97% 75% 0% 3% 0% 6% 100% 68% 9Nissan -3% -3% 0% 25% 74% 0% 0% 6% 0% 91% 1% 99% 74% 1% 0% 0% 0% 99% 82% 9Porsche -13% -7% 6% 0% 26% 21% 0% 0% 0% 75% 2% 77% 46% 15% 23% 16% 11% 100% 11% 7Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -10% -9% 1% 19% 75% 0% 0% 2% 0% 88% 8% 98% 75% 4% 2% 0% 1% 100% 65% 9Suzuki -2% -1% 1% 18% 75% 0% 0% 2% 0% 87% 6% 95% 75% 2% 5% 0% 1% 100% 62% 9Tata -20% -11% 9% 0% 2% 3% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 7Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -3% -3% 0% 41% 41% 1% 0% 4% 0% 82% 2% 88% 28% 12% 0% 0% 0% 88% 88% 8Volkswagen -7% -5% 2% 11% 66% 6% 0% 4% 0% 82% 4% 90% 70% 2% 10% 0% 36% 100% 40% 9Fleet -6% -5% 1% 25% 64% 3% 0% 5% 0% 87% 3% 95% 57% 4% 2% 0% 4% 97% 80% 9
MY 2017 and Later - Regula

10-23 

Table 10-29 Final rule Truck Technology Penetrations in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NBMW -20% -18% 1% 18% 64% 11% 0% 100% 0% 0% 0% 100% 68% 0% 0% 0% 50% 100% 50% 10Chrysler/Fiat -12% -11% 1% 16% 67% 15% 0% 93% 0% 4% 2% 100% 75% 6% 0% 0% 48% 100% 50% 10Daimler -20% -19% 2% 15% 63% 17% 0% 100% 0% 0% 0% 100% 70% 0% 0% 0% 50% 100% 50% 10Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NFord -13% -13% 0% 8% 57% 33% 0% 87% 0% 12% 0% 99% 74% 1% 0% 0% 22% 99% 89% 9Geely -20% -19% 2% 13% 63% 23% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10General Motors -14% -13% 1% 11% 47% 28% 0% 93% 0% 7% 0% 100% 75% 0% 0% 0% 59% 100% 64% 10Honda -12% -11% 1% 25% 75% 0% 0% 81% 0% 19% 0% 100% 75% 0% 0% 0% 23% 100% 60% 10Hyundai -15% -15% 0% 25% 75% 0% 0% 50% 0% 50% 0% 100% 75% 0% 0% 0% 0% 100% 100% 10Kia -11% -11% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 10Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NMazda -19% -18% 1% 25% 75% 0% 0% 68% 0% 30% 2% 100% 75% 0% 0% 0% 34% 100% 66% 10Mitsubishi -19% -18% 2% 10% 75% 0% 0% 41% 0% 55% 0% 95% 75% 10% 5% 0% 20% 100% 45% 9Nissan -11% -10% 1% 19% 69% 12% 0% 77% 0% 23% 1% 100% 75% 0% 0% 0% 38% 100% 60% 10Porsche -20% -18% 1% 16% 66% 19% 0% 100% 0% 0% 0% 100% 75% 0% 0% 0% 50% 100% 50% 10Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -20% -19% 0% 25% 75% 0% 0% 13% 0% 81% 5% 100% 75% 0% 0% 0% 7% 100% 93% 10Suzuki -2% 0% 2% 7% 69% 0% 0% 27% 0% 61% 0% 89% 69% 13% 11% 0% 14% 100% 39% 8Tata -17% -15% 2% 0% 50% 50% 0% 100% 0% 0% 0% 100% 75% 50% 0% 0% 50% 100% 50% 10Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -8% -8% 0% 18% 67% 12% 0% 83% 0% 11% 3% 97% 74% 3% 0% 0% 0% 97% 94% 9Volkswagen -20% -18% 1% 20% 65% 6% 0% 100% 0% 0% 0% 100% 65% 0% 0% 0% 50% 100% 50% 10Fleet -13% -12% 1% 15% 62% 19% 0% 86% 0% 12% 1% 99% 74% 2% 0% 0% 33% 99% 71% 9
Chapter 10 

10-24 

Table 10-30 Final rule Fleet Technology Penetration in MY 2025 

Mass Tech 

Applied 

True 

Mass 

Mass 

Penalty 

TDS18 

TDS24 

TDS27 

AT6 

AT8 

DCT6 

DCT8 

MT 

HEG 

EGR 

HEV 

EV 

PHEV 

SS 

LRRT2 

IACC2 

Aston Martin -16% -11% 6% 0% 4% 40% 0% 0% 0% 75% 2% 77% 44% 23% 23% 10% 17% 100% 17% 77BMW -12% -10% 2% 8% 59% 15% 0% 22% 0% 64% 3% 88% 73% 4% 12% 0% 38% 100% 38% 88Chrysler/Fiat -11% -10% 1% 20% 70% 8% 0% 48% 0% 49% 1% 99% 75% 3% 1% 0% 25% 100% 58% 99Daimler -18% -15% 3% 6% 61% 13% 0% 31% 0% 54% 0% 85% 70% 2% 15% 2% 33% 100% 33% 85Ferrari -17% -8% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 5% 77Ford -9% -9% 0% 16% 63% 16% 0% 45% 0% 51% 2% 98% 72% 2% 0% 0% 9% 98% 87% 98Geely -12% -10% 2% 10% 64% 17% 0% 38% 0% 51% 1% 91% 75% 0% 9% 0% 42% 100% 42% 9General Motors -11% -10% 1% 18% 60% 15% 0% 41% 0% 57% 2% 100% 74% 0% 0% 0% 25% 100% 76% 10Honda -5% -5% 0% 27% 69% 0% 0% 24% 0% 70% 2% 96% 54% 4% 0% 0% 6% 96% 85% 96Hyundai -5% -5% 0% 34% 65% 1% 0% 9% 0% 86% 5% 100% 30% 0% 0% 0% 0% 100% 100% 10Kia -1% -1% 0% 6% 68% 0% 0% 10% 0% 84% 6% 100% 11% 0% 0% 0% 0% 100% 100% 10Lotus -3% 0% 3% 3% 70% 0% 0% 0% 0% 52% 26% 78% 70% 4% 22% 2% 26% 100% 26% 78Mazda -6% -5% 1% 24% 75% 0% 0% 13% 0% 77% 9% 99% 75% 0% 1% 0% 6% 100% 82% 99Mitsubishi -9% -8% 1% 20% 75% 0% 0% 15% 0% 79% 3% 97% 75% 2% 3% 0% 9% 100% 64% 97Nissan -5% -4% 1% 23% 73% 3% 0% 24% 0% 74% 1% 99% 74% 1% 0% 0% 10% 99% 77% 99Porsche -17% -13% 4% 8% 46% 20% 0% 52% 0% 36% 1% 89% 61% 7% 11% 8% 31% 100% 31% 89Spyker NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NSubaru -13% -12% 1% 20% 75% 0% 0% 5% 0% 86% 7% 99% 75% 3% 1% 0% 3% 100% 73% 99Suzuki -2% -1% 1% 17% 75% 0% 0% 4% 0% 85% 6% 94% 75% 3% 6% 0% 2% 100% 61% 94Tata -18% -14% 5% 0% 32% 32% 0% 62% 0% 29% 0% 91% 49% 50% 9% 8% 33% 100% 33% 9Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NToyota -5% -5% 0% 33% 50% 5% 0% 33% 0% 56% 3% 91% 45% 9% 0% 0% 0% 91% 90% 9Volkswagen -9% -8% 2% 13% 66% 6% 0% 21% 0% 68% 3% 92% 69% 1% 8% 0% 39% 100% 42% 92Fleet -8% -7% 1% 21% 63% 8% 0% 32% 0% 62% 2% 97% 62% 3% 1% 0% 14% 98% 77% 97
MY 2017 and Later - Regulatory Impact Analysis 

10-25 

10.6 GHG Impacts 

The GHG reductions and fuel savings are shown in this section. 

Table 10-31 Calendar year GHG impacts 

Calendar Year: 2020 2030 2040 2050 

Net Reduction*-28 -262 -423 -506 

Net CO2 -24 -238 -387 -464 

Net other GHG -4 -24 -35 -42 

Downstream Reduction -23 -213 -344 -412 

CO2 (excluding A/C) -18 -193 -314 -377 

A/C – indirect CO2 -1 -3 -4 -5 

 A/C – direct HFCs -3 -18 -26 -30 

 CH4 (rebound effect) 0 0 0 0 

 N2O (rebound effect) 0 0 0 0 

Gasoline Upstream Reduction -5 -55 -89 -106 

CO2 -5 -48 -77 -93 

CH4 -1 -7 -11 -13 

N2O 0 0 0 0 

Electricity Upstream Increase 1 6 10 12 

CO2 0 5 9 10 

CH4 0 1 1 2 

N2O 0 0 0 0 

Table 10-32 Model year GHG impacts 

MY Downstream Upstream 

Gasoline Upstream Electricity Total CO2e 

2017 -27 -7 1 -33 

2018 -59 -14 1 -72 

2019 -90 -21 2 -110 

2020 -124 -29 3 -150 

2021 -174 -42 4 -213 

2022 -216 -54 5 -264 

2023 -254 -64 7 -312 

2024 -295 -75 8 -362 

2025 -334 -86 10 -411 

Sum -1,573 -394 41 -1,926 

Monetized values of CO2 reductions associated with the 2010 baseline are presented in 

Section 10.7 below. 
Chapter 10 

10-26 

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 CO2 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 

Petroleum-based 

Gasoline Reference 

(million gallons) 

Petroleum-based 

Gasoline Reduced 

(million gallons) 

Electricity Increased 

(million kWh)

2017 125,346 209 92 

2018 124,204 645 273 

2019 123,247 1,297 544 

2020 122,408 2,181 904 

2021 121,775 3,441 1,357 

2022 121,386 5,016 1,972 

2023 121,210 6,866 2,751 

2024 121,293 8,984 3,698 

2025 121,645 11,353 4,809 

2030 125,979 22,017 9,911 

2040 139,497 35,838 16,748 

2050 157,428 42,960 20,036 

Total 5,186,805 833,756 383,605 

Note: The electricity increase shown is that needed to charge EVs/PHEVs, not that generated by power plants; 

results correspond to 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 

Gasoline 

Savings 

(pre-tax) 

Gasoline 

Savings 

(taxed) 

Electricity 

Costs 

Total Fuel 

Savings 

(pre-tax) 

Total Fuel 

Savings 

(taxed) 

2017 $704 $794 $8.5 $696 $786 

2018 $2,190 $2,460 $25.2 $2,160 $2,430 

2019 $4,480 $5,040 $49.9 $4,430 $4,990 

2020 $7,650 $8,570 $83 $7,570 $8,480 

2021 $12,200 $13,600 $126 $12,100 $13,500 

2022 $17,800 $19,900 $186 $17,600 $19,700 

2023 $24,400 $27,200 $263 $24,100 $26,900 

2024 $32,200 $35,800 $358 $31,800 $35,500 

2025 $41,400 $45,900 $472 $40,900 $45,400 

2030 $84,200 $92,900 $1,030 $83,100 $91,900 

2040 $145,000 $159,000 $1,900 $143,000 $157,000 

2050 $190,000 $205,000 $2,460 $188,000 $203,000 

NPV, 3% $1,510,000 $1,650,000 $19,200 $1,490,000 $1,630,000 

NPV, 7% $578,000 $634,000 $7,270 $571,000 $627,000 

Note: Annual values represent undiscounted values; net present values represent annual costs discounted to 

2012; results correspond to the 2010 baseline fleet. 
MY 2017 and Later - Regulatory Impact Analysis 

10-27 

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 2017 2018 2019 2020 2021 2022 2023 2024 2025 Sum

3% 

Car $7,380 $13,700 $20,400 $27,300 $34,800 $42,700 $50,100 $58,200 $66,100 $321,000 

Truck $158 $2,330 $4,310 $6,990 $14,900 $20,700 $26,300 $30,000 $38,000 $144,000 

Total $7,540 $16,000 $24,700 $34,300 $49,700 $63,400 $76,400 $88,200 $104,000 $465,000 

7% 

Car $5,670 $10,600 $15,700 $21,000 $26,700 $32,900 $38,500 $44,800 $50,900 $247,000 

Truck $119 $1,770 $3,270 $5,300 $11,300 $15,700 $19,900 $22,900 $28,800 $109,000 

Total $5,790 $12,400 $19,000 $26,300 $38,000 $48,600 $58,400 $67,700 $79,700 $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 

Analysis using the MY 2008 

based market forecast. 

Analysis using the MY 2010 

based market forecast 

MY 2025 Per Vehicle Average 

Costs relative to the MY 2016 

standard reference case($) 

$1,836 $1,785 

MY Lifetime GHG emission 

reductions (MMT CO2eq) 1,956 1,926 

MY Lifetime Fuel savings (B. 

Barrels) 3.9 3.8 

CY 2030 GHG emission 

reductions (MMT CO2eq) 271 262 

CY 2030 Fuel savings (B. 

Barrels) 0.55 0.53