Draft Regulatory Impact Analysis:

 

Proposed Rulemaking for 2017-2025 

Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate 

Average Fuel Economy Standards
EPA-420-D-11-004 

November 2011

Assessment and Standards Division

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency

Draft Regulatory Impact Analysis:

 

Proposed Rulemaking for 2017-2025 

Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate 

Average Fuel Economy Standards
i

TABLE OF CONTENTS

EXECUTIVE SUMMARY ........................................................................................V

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 ..................................................................1-15

1.4 Use of the Lumped Parameter Approach in Determining Package Effectiveness...1-

45

1.4.1 Background......................................................................................................1-45

1.4.2 Role of the model .............................................................................................1-46

1.4.3 Overview of the lumped parameter model....................................................1-46

1.5 Lumped Parameter Model Methodology.................................................................1-49

1.5.1 Changes to the LP model for the proposed rulemaking ..............................1-49

1.5.2 Development of the model...............................................................................1-50

1.5.3 Baseline loss categories....................................................................................1-50

1.5.4 Baseline fuel efficiency by vehicle class .........................................................1-52

1.5.5 Identification and calibration of individual technologies ............................1-54

1.5.6 Example build-up of LP package...................................................................1-56

1.5.7 Calibration of LP results to vehicle simulation results ................................1-59

1.5.8 Notable differences between LP model and Ricardo results .......................1-63

1.5.9 Comparison of results to real-world examples .............................................1-65

2 EPA’S VEHICLE SIMULATION TOOL................................................... 2-1

2.1 Introduction..................................................................................................................2-1

2.1.1 Background........................................................................................................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 the Proposed Rule...........................................2-7

2.3.2 Off-Cycle Credit Calculation..........................................................................2-11

2.4 On-Going and Future Work .....................................................................................2-14
ii

2.4.1 Simulation Tool Validation.............................................................................2-14

2.4.2 Simulation Tool Upgrade................................................................................2-15

3 RESULTS OF PROPOSED AND ALTERNATIVE STANDARDS ........ 3-1

3.1 Introduction..................................................................................................................3-1

3.2 OMEGA model overview ............................................................................................3-1

3.3 OMEGA Model Structure...........................................................................................3-4

3.4 Model Inputs.................................................................................................................3-5

3.4.1 Market Data .......................................................................................................3-5

3.4.2 Technology Data ..............................................................................................3-11

3.5 The Scenario File........................................................................................................3-14

3.5.1 Reference Scenario ..........................................................................................3-14

3.5.2 Control Scenarios ............................................................................................3-21

3.6 Fuels and reference data ...........................................................................................3-22

3.7 OMEGA model calculations .....................................................................................3-22

3.8 Analysis Results..........................................................................................................3-24

3.8.1 Targets and Achieved Values .........................................................................3-24

3.8.1 Penetration of Selected Technologies.............................................................3-36

3.8.2 Projected Technology Penetrations in Reference Case................................3-38

3.8.3 Projected Technology Penetrations in Proposal case...................................3-44

3.8.4 Projected Technology Penetrations in Alternative Cases............................3-50

3.8.5 Additional Detail on Mass Reduction Technology .......................................3-74

3.8.6 Air Conditioning Cost .....................................................................................3-74

3.8.7 Stranded Capital..............................................................................................3-75

3.9 Per Vehicle Costs 2021 and 2025 ..............................................................................3-78

3.10 Alternative Program Stringencies..........................................................................3-81

3.11 Comparative cost of advanced technologies under credit scenarios...................3-84

3.12 How Many of Today’s Vehicles Can Meet or Surpass the Proposed MY 2017-

2025 CO2 Footprint-based Targets with Current Powertrain Designs? .......................3-85

3.13 Analysis of Ferrari & Chrysler/Fiat ......................................................................3-91

3.14 Cost Sensitivities ......................................................................................................3-91

3.14.1 Overview...........................................................................................................3-91

3.14.2 Mass Sensitivity ...............................................................................................3-93

3.14.3 Battery Sensitivity ...........................................................................................3-94
iii

3.14.4 ICM Sensitivity ................................................................................................3-95

3.14.5 Learning Rate Sensitivity................................................................................3-97

3.14.6 Summary of Sensitivity Impacts ....................................................................3-97

3.14.7 NAS report .......................................................................................................3-98

4 PROJECTED IMPACTS ON EMISSIONS, FUEL CONSUMPTION, AND

SAFETY ................................................................................................................... 4-1

4.1 Introduction..................................................................................................................4-1

4.2 Analytic Tools Used .....................................................................................................4-2

4.3 Inputs to the emissions analysis..................................................................................4-2

4.3.1 Methods ..............................................................................................................4-2

4.3.2 Activity................................................................................................................4-4

4.3.3 Scenarios...........................................................................................................4-12

4.3.4 Emission Results ..............................................................................................4-21

4.3.5 Fuel Consumption Impacts.............................................................................4-25

4.3.6 GHG and Fuel Consumption Impacts from Alternatives............................4-26

4.4 Safety Analysis ...........................................................................................................4-27

4.5 Sensitivity Cases.........................................................................................................4-28

4.5.1 Rebound............................................................................................................4-28

4.5.2 EV impacts .......................................................................................................4-30

4.6 Calculation of Impacts from An Electric Vehicle ...................................................4-31

5 VEHICLE PROGRAM COSTS AND FUEL SAVINGS........................... 5-1

5.1 Costs per Vehicle..........................................................................................................5-1

5.2 Annual Costs of the Proposed National Program.....................................................5-8

5.3 Cost per Ton of Emissions Reduced...........................................................................5-9

5.4 Reduction in Fuel Consumption and its Impacts....................................................5-10

5.4.1 What Are the Projected Changes in Fuel Consumption?............................5-10

5.4.2 What are the Fuel Savings to the Consumer?...............................................5-11

5.5 Consumer Payback Period and Lifetime Savings on New Vehicle Purchases.....5-13

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-

15
iv

6.2 Air Quality Impacts of Non-GHG Pollutants..........................................................6-27

6.2.1 Current Levels of Non-GHG Pollutants........................................................6-27

6.2.2 Impacts on Future Air Quality.......................................................................6-29

6.3 Quantified and Monetized Non-GHG Health and Environmental Impacts ........6-32

6.3.1 Economic Value of Reductions in Criteria Pollutants .................................6-33

6.3.2 Human Health and Environmental Benefits for the Final Rule .................6-37

6.4 Changes in Atmospheric CO2 Concentrations, Global Mean Temperature, Sea

Level Rise, and Ocean pH Associated with the Proposed Rule’s GHG Emissions

Reductions............................................................................................................................6-43

6.4.1 Introduction .....................................................................................................6-43

6.4.2 Projected Change in Atmospheric CO2 Concentrations, Global Mean

Surface Temperature and Sea Level Rise ....................................................................6-45

6.4.3 Projected Change in Ocean pH......................................................................6-50

6.4.4 Summary of Climate Analyses .......................................................................6-52

7 OTHER ECONOMIC AND SOCIAL IMPACTS...................................... 7-1

7.1 Monetized CO2 Estimates............................................................................................7-3

7.2 Summary of Costs and Benefits................................................................................7-10

8 VEHICLE SALES AND EMPLOYMENT IMPACTS.............................. 8-1

8.1 Vehicle Sales Impacts ..................................................................................................8-1

8.1.1 Vehicle Sales Impacts and Payback Period.....................................................8-1

8.1.2 Consumer Vehicle Choice Modeling................................................................8-4

8.2 Employment Impacts.................................................................................................8-17

8.2.1 Introduction .....................................................................................................8-17

8.2.2 Approaches to Quantitative Employment Analysis .....................................8-19

8.2.3 Employment analysis of this proposal ...........................................................8-23

8.2.4 Effects on Employment for Auto Dealers......................................................8-29

8.2.5 Effects on Employment in the Auto Parts Sector .........................................8-30

8.2.6 Effects on Employment for Fuel Suppliers ...................................................8-30

8.2.7 Effects on Employment due to Impacts on Consumer Expenditures.........8-30

8.2.8 Summary ..........................................................................................................8-31

9 SMALL BUSINESS FLEXIBILITY ANALYSIS ...................................... 9-1
v

Executive Summary

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

Safety Administration (NHTSA) are issuing a joint notice of proposed rulemaking (NPRM) to

establish standards for light-duty highway vehicles that will reduce greenhouse gas emissions

(GHG) and improve fuel economy. EPA is proposing greenhouse gas emissions standards

under the Clean Air Act, and NHTSA is proposing Corporate Average Fuel Economy

standards under the Energy Policy and Conservation Act (EPCA), as amended. These

proposed standards apply to passenger cars, light-duty trucks, and medium-duty passenger

vehicles, covering model years (MY) 2017 through 2025. The proposed 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 proposed GHG program. These proposed 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 proposed National

Program will result in approximately 1,967 million metric tons of CO2 equivalent emission

reductions and approximately 3.9 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.AThe 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 NPRM 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").
vi

Table 1 EPA’s Estimated 2017-2025 Model Year Lifetime Discounted Costs,

Benefits, and Net Benefits assuming the $22/ton SCC Valuea,b,c,d

(Billions of 2009 dollars)

Lifetime Present Valuec– 3% Discount Rate

Program Costs $140

Fuel Savings $444

Benefits $117

Net Benefitsd$421

Annualized Valuee– 3% Discount Rate

Annualized costs $6.43

Annualized fuel savings $20.3

Annualized benefits $5.36

Net benefits $19.3

Lifetime Present Valuec

- 7% Discount Rate

Program Costs $138

Fuel Savings $347

Benefits $101

Net Benefitsd$311

Annualized Valuee– 7% Discount Rate

Annualized costs $10.64

Annualized fuel savings $26.7

Annualized benefits $6.35

Net benefits $22.4

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 2009 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

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 2009 dollar terms). The lifetime

present values shown here are the present values of each MY in its first

year summed across MYs.

d Net benefits reflect the fuel savings plus benefits minus costs.

e 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%.
vii

This draft Regulatory Impact Analysis (DRIA) contains supporting documentation to

the EPA rulemaking. NHTSA has prepared their own preliminary RIA (PRIA) in support of

their rulemaking (this can be found in NHTSA’s docket for the rulemaking, NHTSA-2010-

0131). While the two rulemakings 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 and EISA, 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—e.g., development of

technology costs and effectiveness—the supporting documentation is contained in the draft

joint Technical Support Document (draft joint TSD can be found in EPA’s docket EPA-HQOAR-2010-0799). Therefore, this DRIA should be viewed as a companion document to the

draft 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 draft 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 draft Joint TSD.

Chapter 2: The development and application of the EPA vehicle simulation tool 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 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. For the off-cycle credits, EPA based its analysis on manufacturer

data, 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. The credit will be 0.6 g/mile for cars and 1.0 g/mile for trucks when the reduction in

aerodynamic drag is around 3%.
viii

Chapter3: This chapter provides the methodology from and results of the technical

assessment of the future vehicle scenarios presented in this proposal. 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: This chapter documents EPA’s analysis of the emission, fuel consumption

and safety impacts of the proposed emission standards for light duty vehicles. This proposal,

if finalized, will 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 proposed rule.

This analysis quantifies the proposed program’s impacts on the greenhouse gases (GHGs)

carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and hydrofluorocarbons (HFC134a); 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. DRIA 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. The

program costs and fuel savings associated with EPA’s proposed 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 (and fuel savings) of the proposal through

2050 and for each of the model years 2017 through 2025 that are effected by the proposal.

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 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 up-front costs in less than four years for people purchasing

new vehicles with cash. 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.
ix

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 proposed rule will affect future climate change. Since GHGs

are well-mixed in the atmosphere and have long atmospheric lifetimes, changes in GHG

emissions will affect atmospheric concentrations of greenhouse gases and future climate for

decades to millennia, depending on the gas. This section provides estimates of the projected

change in atmospheric CO2 concentrations based on the emission reductions estimated for this

proposed rule, compared to the reference case. In addition, this section analyzes the response

to the changes in GHG concentrations of the following climate-related variables: global mean

temperature, sea level rise, and ocean pH. See Chapter 4 in this DRIA 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 proposed standards. To adequately assess

these impacts, full-scale photochemical air quality modeling is necessary to project changes in

atmospheric concentrations of PM2.5, ozone and air toxics. The length of time needed to

prepare the necessary emissions inventories, in addition to the processing time associated with

the modeling itself, has precluded us from performing air quality modeling for this proposal.

However, for the final rule, a national-scale air quality modeling analysis will be performed to

analyze the impacts of the vehicle standards on PM2.5, ozone, and selected air toxics (i.e.,

benzene, formaldehyde, acetaldehyde, acrolein and 1,3-butadiene).

The atmospheric chemistry related to ambient concentrations of PM2.5, ozone and air

toxics is very complex, and making predictions based solely on emissions changes is

extremely difficult. However, based on the magnitude of the emissions changes predicted to

result from the proposed vehicle standards (as shown in Chapter 4), we expect that there will

be an improvement in ambient air quality, pending a more comprehensive analysis for the

final rule.

Chapter 7: Other Economic and Social Impacts. This Chapter outlines a number of

additional impacts that contribute to the overall costs and benefits associated with the

proposed GHG standards. These impacts affect people outside the markets for vehicles and

their use; these effects are termed “external” and include the climate impacts, energy security

impacts, and the effects on traffic, accidents, and noise due to additional driving.

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

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

of a particular energy source. This reduction in risk is a measure of improved U.S. energy

security.
x

SCC and GHG Benefits: EPA uses four estimates of the dollar value of marginal

reductions in CO2 emissions—known as the social cost of carbon—to calculate total

monetized CO2 benefits. Specifically, total monetized benefits in each year are calculated by

multiplying the SCC by the reductions in CO2 for that year. EPA uses four different SCC

values to generate different estimates of total CO2 benefits and capture some of the

uncertainties involved in regulatory impact analysis. The central value is the average SCC

across models at the 3 percent discount rate. For purposes of capturing the uncertainties

involved in regulatory impact analysis, we emphasize the importance and value of considering

the full range. Chapter 7 also presents an analysis of the CO2 benefits over the model year

lifetimes of the 2017 through 2025 model year vehicles.

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

and associated reduced fuel consumption. Lower fuel consumption would, presumably, result

in fewer trips to the filling station to refuel and, thus, time saved. The rebound effect,

discussed in detail in Chapter 4 of the draft joint TSD, produces additional benefits to vehicle

owners in the form of consumer surplus from the increase in vehicle-miles driven, but may

also increase the societal costs associated with traffic congestion, motor vehicle crashes, and

noise. These effects are likely to be relatively small in comparison to the value of fuel saved

as a result of the standards, but they are nevertheless important to include.

Chapter 7 also presents a summary of the total costs, total benefits, and net benefits

expected under the proposed rule. Table 2 presents these economics impacts. We note that

several of the cost and benefit categories we would typically discuss in an RIA are considered

joint economic assumptions shared between EPA and NHTSA and are therefore discussed in

more detail in EPA and NHTSA’s draft Joint TSD Chapter 4.

Table 2 Monetized Benefits Associated with the Proposed Program (Millions, 2009$)

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

Technology Costs $2,300 $8,470 $35,700 $39,800 $44,600 $551,000 $243,000

Fuel Savings $570 $7,060 $85,800 $144,000 $187,000 $1,510,000 $579,000

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $101 $1,240 $15,600 $29,000 $40,700 $275,000 $124,000

3% (avg SCC) $141 $1,730 $22,000 $40,400 $55,600 $413,000 $263,000

2.5% (avg SCC) $173 $2,120 $26,700 $48,700 $65,900 $534,000 $384,000

3% (95th %ile) $250 $3,100 $40,500 $75,100 $102,000 $764,000 $614,000

Monetized Net Benefits at each assumed SCC value c

5% (avg SCC) -$1,630 -$166 $65,600 $133,000 $183,000 $1,230,000 $460,000

3% (avg SCC) -$1,590 $325 $72,000 $144,000 $198,000 $1,370,000 $599,000

2.5% (avg SCC) -$1,560 $712 $76,800 $153,000 $208,000 $1,490,000 $719,000

3% (95th %ile) -$1,480 $1,690 $90,500 $179,000 $244,000 $1,720,000 $950,000

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 DRIA Chapter 7.2 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-

$67; and for 95th percentile SCC at 3%: $70-$140. DRIA Chapter 7.2 also presents these SCC estimates.

c Net Benefits equal Fuel Savings minus Technology Costs plus Benefits.
xi

Chapter 8: Vehicle Sales and Employment. 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. We discuss how the payback period expected for the proposed standards (less

than 3 years in 2021, less than 4 years in 2025) is expected to affect vehicle sales.

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 to increased production of fuel-saving technologies; employment in the

auto industry will also be affected by changes in vehicle sales and by the labor intensity of the

new technologies relative to the old technologies. Effects on other sectors vary. Employment

for auto dealers as well as auto parts manufacturing will be affected by any changes in vehicle

sales. Parts manufacturers may face increased labor demand due to production of the new

technologies. Employment is expected to be reduced in petroleum-related sectors due to

reduced fuel demand. 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. It should be noted that none

of these analyses was used in the benefit-cost analysis of the proposed standards, but they

provide a fuller picture of the impacts of this rule.

Chapter 9: Small Business Flexibility Analysis. EPA’s analysis of the small

business impacts due to EPA’s proposed rulemaking. EPA is proposing to exempt domestic

and foreign businesses that meet small business size definitions established by the Small

Business Administration.

2017 Draft Regulatory Impact Analysis

1-1

1 Technology Packages, Cost and Effectiveness

1.1 Overview of Technology

The proposed 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 2017-2025 timeframe at

reasonable per vehicle cost 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.

It is well known that CO2 is a stable compound produced by the complete combustion

of the 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, 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 that were described in the

2012-2016 rule (TSD and RIA) that are also common to this rule. While we expect

significant penetration of these technologies within the 2016 timeframe, there will be some

technologies that will have continued improvement, and others that are only partially

implemented into the fleet by 2016. We describe those technologies for which we expect to
2017 Draft Regulatory Impact Analysis

1-2

see continued improvement—engine friction reduction, lower rolling resistance tires—in

Chapter 3 of the draft 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 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 MY 2012-2016 standards . There are

also other advanced technologies under development (that were not projected to be available

to meet 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 it is likely 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 (2017 through

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 average, their

entire product line more than twice during that timeframe, we have assumed two full redesign
2017 Draft Regulatory Impact Analysis

1-3

cycles in the 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 our 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, 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 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 2012-2016 final rule and this proposal. The vast majority of

the emission reductions associated with this proposal would result from the increased use of

these technologies. EPA also believes the proposal would 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 proposal 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.
2017 Draft Regulatory Impact Analysis

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

2012-2016 FRM, the 2010 Technical Assessment Report (TAR), tear-down analysis done by

FEV and the 2008 as well as 2010 Ricardo studies. In addition, we considered confidential

data submitted by vehicle manufacturers in response to NHTSA’s request 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 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. 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.1The results of these efforts

especially the results of the FEV tear-down and Ricardo studies were used extensively in this

proposal as described in detail in Chapter 3 of the draft 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 draft joint TSD. There we present direct manufacturing

costs, indirect costs and total costs for each technology in each MY 2017 through 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.

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 draft

Joint TSD. For the majority of the other technologies considered in this proposal, the

agencies have relied on the 2012-2016 final rule and sources described there for estimates of

DMC.

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
2017 Draft Regulatory Impact Analysis

1-5

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 draft Joint TSD Chapter 3.2.2. Stranded capital

is also discussed in this draft RIA at Chapter 3.8.7 and Chapter 5.1.

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 recent heavy-duty GHG

final rule (see 76 FR 57320). Our old 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

draft Joint TSD.

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 1.2-14 present the costs associated with the

technologies we believe would be the enabling technologies for compliance with the proposed
2017 Draft Regulatory Impact Analysis

1-6

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 proposal. That is, technologies such as lower rolling resistance tires and

level 1 aerodynamic treatments are expected to exceed 85 percent penetration by 2016 so they

cannot be added “again” to comply with the 2017-2025 proposed 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 for some technologies in an effort to provide better granularity

in our estimates. This is easily seen in Table 1.2-1 and Table 1.2-2 where we list costs for

technologies by engine configuration—in-line or “I” versus “V”—and/or by number of

cylinders. In the 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 the proposal. 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 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 1.2-14 presents 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 1.2-8 through Table 1.2-12). 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 for 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 of the draft joint TSD. We note that the approach described there is a

departure from our earlier efforts where the weight increase of the electrification components

was not fully recognized. Importantly, that had little impact on the analysis used to support

the 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
2017 Draft Regulatory Impact Analysis

1-7

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 draft joint TSD, along with a more detailed

description of the comprehensive technical evaluation underlying the estimates.

Table 1.2-1 Costs for Engine Technologies (2009$)

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 $45 $42 $42 $41 $40 $40 $39 $38

CCP-OHC-V $91 $90 $84 $83 $82 $80 $79 $78 $76

CCP-OHV-V $46 $45 $42 $42 $41 $40 $40 $39 $38

CVVL-OHC-I4 $240 $237 $216 $212 $209 $206 $203 $200 $197

CVVL-OHC-V6 $440 $434 $396 $389 $383 $377 $372 $366 $360

CVVL-OHC-V8 $480 $473 $432 $425 $418 $412 $405 $399 $393

DCP-OHC-I $94 $92 $84 $83 $81 $80 $79 $78 $77

DCP-OHC-V $201 $198 $181 $178 $175 $173 $170 $167 $165

DCP-OHV-V $102 $101 $92 $90 $89 $87 $86 $85 $84

Deac-V6 $192 $189 $173 $170 $167 $165 $162 $160 $157

Deac-V8 $216 $213 $194 $191 $188 $185 $182 $180 $177

DVVL-OHC-I4 $160 $158 $144 $142 $139 $137 $135 $133 $131

DVVL-OHC-V6 $232 $229 $209 $205 $202 $199 $196 $193 $190

DVVL-OHC-V8 $332 $327 $298 $293 $289 $284 $280 $276 $271

EFR1-I3 $44 $44 $42 $42 $42 $42 $42 $42 $42

EFR1-I4 $58 $58 $56 $56 $56 $56 $56 $56 $56

EFR1-V6 $87 $87 $84 $84 $84 $84 $84 $84 $84

EFR1-V8 $116 $116 $111 $111 $111 $111 $111 $111 $111

EFR2-I3 $95 $95 $95 $95 $95 $95 $95 $95 $91

EFR2-I4 $124 $124 $124 $124 $124 $124 $124 $124 $119

EFR2-V6 $182 $182 $182 $182 $182 $182 $182 $182 $175

EFR2-V8 $240 $240 $240 $240 $240 $240 $240 $240 $230

EGR-I $303 $298 $294 $290 $285 $281 $277 $274 $247

EGR-V $303 $298 $294 $290 $285 $281 $277 $274 $247

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

Stoich GDI-I4 $274 $270 $246 $242 $238 $234 $231 $227 $224

Stoich GDI-I4>I3 $274 $270 $246 $242 $238 $234 $231 $227 $224

Stoich GDI-V6 $413 $407 $370 $364 $359 $353 $348 $343 $338

Stoich GDI-V8 $497 $490 $445 $438 $431 $425 $418 $412 $406

V6 OHV to V6 DOHC $676 $661 $599 $585 $571 $558 $549 $540 $532

V6 SOHC to V6 DOHC $212 $209 $190 $187 $184 $181 $179 $176 $173

V8 OHV to V8 DOHC $740 $724 $656 $640 $625 $611 $601 $592 $583

V8 SOHC 3V to V8

DOHC $153 $151 $137 $135 $133 $131 $129 $127 $125

V8 SOHC to V8 DOHC $245 $241 $219 $216 $212 $209 $206 $203 $200

VVTI-OHC-I $46 $45 $42 $42 $41 $40 $40 $39 $38

VVTI-OHC-V $91 $90 $84 $83 $82 $80 $79 $78 $76

VVTI-OHV-V $46 $45 $42 $42 $41 $40 $40 $39 $38

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
2017 Draft Regulatory Impact Analysis

1-8

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 (2009$)

Technology BMEP 2017 2018 2019 2020 2021 2022 2023 2024 2025

I4 to I3 wT 18 bar $424 $420 $357 $353 $350 $346 $342 $338 $335

I4 to I3 wT 24 bar $685 $677 $650 $642 $635 $627 $620 $613 $547

I4 to I3 wT 27 bar $1,205 $1,189 $1,155 $1,140 $1,126 $1,111 $1,097 $1,083 $972

I4 DOHC to I4

DOHC wT 18 bar $478 $472 $418 $412 $407 $401 $396 $390 $385

I4 DOHC to I4

DOHC wT 24 bar $738 $728 $710 $701 $692 $683 $674 $665 $598

I4 DOHC to I4

DOHC wT 27 bar $1,259 $1,241 $1,216 $1,199 $1,183 $1,167 $1,151 $1,135 $1,022

V6 DOHC to I4

wT 18 bar $248 $250 $159 $161 $163 $164 $166 $168 $170

V6 DOHC to I4

wT 24 bar $509 $507 $451 $449 $448 $446 $444 $442 $382

V6 DOHC to I4

wT 27 bar $1,029 $1,019 $957 $948 $939 $930 $921 $913 $807

V6 SOHC to I4

wT 18 bar $330 $329 $251 $250 $250 $249 $248 $247 $246

V6 SOHC to I4

wT 24 bar $591 $586 $544 $539 $535 $530 $526 $522 $459

V6 SOHC to I4

wT 27 bar $1,111 $1,098 $1,049 $1,037 $1,026 $1,014 $1,003 $992 $884

V6 OHV to I4

DOHC wT 18 bar $903 $887 $805 $789 $775 $760 $749 $737 $726

V6 OHV to I4

DOHC wT 24 bar $1,163 $1,143 $1,097 $1,078 $1,060 $1,042 $1,026 $1,012 $938

V6 OHV to I4

DOHC wT 27 bar $1,683 $1,656 $1,602 $1,576 $1,551 $1,526 $1,504 $1,482 $1,363

V8 DOHC to V6

DOHC wT 18 bar $741 $733 $631 $624 $616 $609 $602 $595 $588

V8 DOHC to V6

DOHC wT 24 bar $1,180 $1,165 $1,124 $1,110 $1,097 $1,084 $1,071 $1,058 $946

V8 DOHC to I4

DOHC wT 27 bar $787 $792 $716 $720 $725 $729 $726 $723 $622

V8 SOHC to V6

DOHC wT 18 bar $835 $824 $738 $727 $717 $707 $697 $687 $677

V8 SOHC to V6

DOHC wT 24 bar $1,274 $1,256 $1,231 $1,214 $1,197 $1,181 $1,165 $1,149 $1,035

V8 SOHC to I4

DOHC wT 27 bar $906 $905 $842 $842 $841 $840 $834 $828 $724

V8 SOHC 3V to

V6 DOHC wT 18 bar $800 $790 $698 $688 $679 $670 $661 $652 $644

V8 SOHC 3V to

V6 DOHC wT 24 bar $1,238 $1,222 $1,191 $1,175 $1,160 $1,144 $1,130 $1,115 $1,002

V8 SOHC 3V to

I4 DOHC wT 27 bar $861 $863 $795 $796 $797 $799 $793 $788 $686

V8 OHV to V6

DOHC wT 18 bar $1,323 $1,301 $1,180 $1,159 $1,138 $1,118 $1,101 $1,084 $1,067

V8 OHV to V6

DOHC wT 24 bar $1,762 $1,733 $1,673 $1,646 $1,618 $1,592 $1,569 $1,547 $1,426

V8 OHV to I4

DOHC wT 27 bar $1,155 $1,143 $1,108 $1,097 $1,085 $1,074 $1,061 $1,048 $938

DOHC=dual overhead cam; I3=inline 3 cylinder; I4=inline 4 cylinder; OHV=overhead valve; SOHC=single overhead cam;
2017 Draft Regulatory Impact Analysis

1-9

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 (2009$)

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

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

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

5sp AT $103 $101 $95 $94 $92 $91 $89 $88 $86

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

6sp DCT-dry -$115 -$111 -$130 -$126 -$122 -$118 -$115 -$111 -$108

6sp DCT-wet -$81 -$78 -$91 -$89 -$86 -$84 -$81 -$79 -$76

6sp MT -$168 -$163 -$170 -$166 -$162 -$158 -$154 -$150 -$146

8sp AT $61 $60 $55 $54 $53 $52 $52 $51 $50

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

8sp DCT-wet $47 $46 $46 $45 $44 $44 $43 $43 $38

HEG $248 $242 $236 $231 $225 $220 $216 $213 $200

TORQ $29 $29 $27 $27 $26 $26 $25 $25 $24

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 (2009$)

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

EPS/EHPS $108 $106 $100 $98 $96 $95 $93 $92 $90

IACC $87 $86 $81 $80 $78 $77 $76 $75 $73

IACC2 $141 $139 $131 $129 $127 $124 $122 $120 $118

Stop-start (12V)

for Subcompact,

Small car

$394 $385 $348 $340 $332 $324 $317 $310 $303

Stop-start (12V)

for Large car,

Minivan, Small

truck

$446 $436 $395 $385 $376 $368 $359 $351 $343

Stop-start (12V)

for Large truck $490 $479 $433 $423 $413 $403 $394 $385 $376

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 (2009$)

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aero1 $48 $47 $45 $44 $43 $42 $42 $41 $40

Aero2 $210 $207 $201 $198 $195 $192 $190 $187 $173

LDB $73 $73 $70 $70 $70 $70 $70 $70 $70

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

LRRT2 $72 $72 $60 $60 $50 $48 $47 $46 $43

SAX $96 $94 $89 $88 $86 $85 $83 $82 $81

Aero1=aerodynamic treatments level 1; Aero2=aero level 2; LDB=low drag brakes; LRRT1=lower rolling resistance tires
2017 Draft Regulatory Impact Analysis

1-10

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 (2009$)

Vehicle Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact/

Small car $2,936 $2,893 $2,627 $2,587 $2,547 $2,509 $2,471 $2,433 $2,397

Large car $3,595 $3,543 $3,218 $3,168 $3,120 $3,072 $3,026 $2,980 $2,936

Minivan $2,942 $2,900 $2,633 $2,593 $2,553 $2,514 $2,476 $2,439 $2,402

Small truck $2,967 $2,924 $2,656 $2,615 $2,575 $2,536 $2,497 $2,460 $2,423

Large truck $4,114 $4,054 $3,682 $3,625 $3,570 $3,515 $3,462 $3,410 $3,359

All costs are incremental to the baseline case.

Table 1.2-7 Costs for P2-Hybird Technology (2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 10% 5% $3,489 $3,435 $3,027 $2,977 $2,928 $2,880 $2,834 $2,788 $2,595

Subcompact 15% 10% $3,452 $3,399 $2,995 $2,945 $2,897 $2,850 $2,804 $2,759 $2,567

Subcompact 20% 15% $3,415 $3,363 $2,962 $2,913 $2,866 $2,819 $2,774 $2,729 $2,540

Small car 10% 5% $3,665 $3,609 $3,180 $3,128 $3,076 $3,026 $2,977 $2,930 $2,725

Small car 15% 10% $3,625 $3,569 $3,145 $3,093 $3,042 $2,993 $2,944 $2,897 $2,696

Small car 20% 15% $3,585 $3,530 $3,110 $3,058 $3,008 $2,959 $2,912 $2,865 $2,666

Large car 10% 5% $4,196 $4,132 $3,640 $3,580 $3,521 $3,464 $3,408 $3,353 $3,121

Large car 15% 10% $4,131 $4,068 $3,583 $3,524 $3,466 $3,410 $3,355 $3,301 $3,073

Large car 20% 15% $4,067 $4,004 $3,527 $3,469 $3,412 $3,356 $3,302 $3,249 $3,025

Minivan 10% 5% $4,148 $4,084 $3,597 $3,538 $3,480 $3,423 $3,368 $3,314 $3,085

Minivan 15% 10% $4,092 $4,029 $3,548 $3,489 $3,432 $3,376 $3,322 $3,268 $3,044

Minivan 20% 15% $4,036 $3,974 $3,498 $3,440 $3,384 $3,329 $3,275 $3,223 $3,002

Small truck 10% 5% $4,005 $3,943 $3,473 $3,416 $3,360 $3,305 $3,252 $3,200 $2,979

Small truck 15% 10% $3,953 $3,892 $3,428 $3,371 $3,316 $3,262 $3,209 $3,158 $2,940

Small truck 20% 15% $3,900 $3,841 $3,382 $3,326 $3,271 $3,218 $3,166 $3,116 $2,901

Minivan-towing 10% 4% $4,286 $4,219 $3,728 $3,666 $3,606 $3,547 $3,489 $3,433 $3,185

Minivan-towing 15% 9% $4,228 $4,162 $3,677 $3,616 $3,556 $3,498 $3,441 $3,386 $3,142

Minivan-towing 20% 14% $4,170 $4,105 $3,626 $3,566 $3,507 $3,450 $3,394 $3,339 $3,099

Large truck 10% 4% $4,417 $4,349 $3,844 $3,780 $3,717 $3,657 $3,597 $3,539 $3,282

Large truck 15% 9% $4,358 $4,290 $3,792 $3,728 $3,667 $3,607 $3,548 $3,491 $3,238

Large truck 20% 14% $4,298 $4,232 $3,739 $3,677 $3,616 $3,557 $3,499 $3,443 $3,194

WR=weight reduction.

All costs are incremental to the baseline case.

Table 1.2-8 Costs for Plug-in Hybrid Technology with 20 Mile EV Range, or PHEV20

(2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 10% 3% $11,325 $10,191 $9,521 $8,607 $8,558 $8,510 $8,463 $8,417 $6,976

Subcompact 15% 8% $11,099 $9,985 $9,332 $8,433 $8,386 $8,339 $8,293 $8,248 $6,832

Subcompact 20% 13% $10,874 $9,779 $9,143 $8,260 $8,214 $8,168 $8,124 $8,080 $6,688

Small car 10% 3% $12,143 $10,945 $10,206 $9,240 $9,186 $9,133 $9,081 $9,030 $7,509

Small car 15% 8% $11,890 $10,714 $9,993 $9,045 $8,993 $8,941 $8,890 $8,841 $7,348

Small car 20% 13% $11,637 $10,483 $9,781 $8,851 $8,800 $8,749 $8,700 $8,652 $7,187

Large car 10% 3% $15,448 $13,989 $12,971 $11,793 $11,719 $11,646 $11,575 $11,505 $9,657

Large car 15% 8% $15,020 $13,597 $12,613 $11,464 $11,392 $11,322 $11,253 $11,185 $9,382
2017 Draft Regulatory Impact Analysis

1-11

Large car 20% 13% $14,593 $13,205 $12,255 $11,135 $11,065 $10,997 $10,931 $10,866 $9,107

Minivan 10% 3% $14,951 $13,511 $12,559 $11,397 $11,327 $11,259 $11,193 $11,127 $9,301

Minivan 15% 8% $14,577 $13,169 $12,245 $11,110 $11,042 $10,976 $10,911 $10,848 $9,062

Minivan 20% 13% $14,203 $12,827 $11,932 $10,822 $10,757 $10,693 $10,630 $10,568 $8,824

Small truck 10% 3% $14,138 $12,766 $11,878 $10,771 $10,706 $10,643 $10,581 $10,520 $8,779

Small truck 15% 8% $13,796 $12,454 $11,591 $10,508 $10,445 $10,384 $10,323 $10,264 $8,561

Small truck 20% 13% $13,453 $12,141 $11,304 $10,246 $10,185 $10,125 $10,066 $10,008 $8,344

WR=weight reduction.

All costs are incremental to the baseline case.

Table 1.2-9 Costs for Plug-in Hybrid Technology with 40 Mile EV Range, or PHEV40

(2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 15% 2% $14,293 $12,717 $12,043 $10,775 $10,726 $10,677 $10,630 $10,584 $8,571

Subcompact 20% 7% $14,067 $12,510 $11,854 $10,601 $10,553 $10,506 $10,460 $10,415 $8,427

Small car 15% 3% $15,655 $13,929 $13,190 $11,802 $11,748 $11,695 $11,643 $11,593 $9,390

Small car 20% 8% $15,248 $13,568 $12,847 $11,495 $11,443 $11,391 $11,341 $11,291 $9,146

Large car 15% 2% $20,644 $18,411 $17,386 $15,589 $15,514 $15,441 $15,369 $15,298 $12,450

Large car 20% 7% $20,000 $17,836 $16,844 $15,102 $15,030 $14,959 $14,889 $14,821 $12,061

Minivan 15% 2% $19,848 $17,677 $16,720 $14,973 $14,903 $14,835 $14,768 $14,702 $11,932

Minivan 20% 7% $19,382 $17,257 $16,328 $14,619 $14,551 $14,485 $14,419 $14,356 $11,644

Small truck 15% 3% $18,637 $16,589 $15,702 $14,054 $13,989 $13,926 $13,864 $13,803 $11,189

Small truck 20% 8% $18,139 $16,145 $15,282 $13,678 $13,615 $13,553 $13,493 $13,433 $10,888

WR=weight reduction.

All costs are incremental to the baseline case.

Table 1.2-10 Costs for Full Electric Vehicle Technology with 75 Mile Range, or EV75

(2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 10% 10% $15,236 $13,009 $13,001 $11,219 $11,211 $11,203 $11,199 $11,194 $8,253

Subcompact 15% 15% $14,914 $12,718 $12,712 $10,955 $10,949 $10,944 $10,941 $10,937 $8,055

Subcompact 20% 20% $14,592 $12,427 $12,424 $10,691 $10,688 $10,685 $10,683 $10,681 $7,857

Small car 10% 10% $16,886 $14,484 $14,466 $12,541 $12,525 $12,508 $12,498 $12,488 $9,245

Small car 15% 15% $16,442 $14,086 $14,071 $12,183 $12,169 $12,155 $12,146 $12,138 $8,976

Small car 20% 20% $15,999 $13,688 $13,676 $11,825 $11,813 $11,802 $11,795 $11,788 $8,707

Large car 10% 10% $20,727 $17,834 $17,805 $15,486 $15,458 $15,432 $15,414 $15,397 $11,430

Large car 15% 15% $19,962 $17,147 $17,123 $14,867 $14,844 $14,822 $14,807 $14,793 $10,965

Large car 20% 20% $19,198 $16,460 $16,441 $14,248 $14,229 $14,212 $14,200 $14,189 $10,501

Minivan 10% 10% $20,440 $17,543 $17,520 $15,198 $15,177 $15,156 $15,142 $15,129 $11,206

Minivan 15% 15% $19,716 $16,896 $16,877 $14,618 $14,601 $14,584 $14,573 $14,562 $10,772

Minivan 20% 20% $18,993 $16,249 $16,235 $14,038 $14,025 $14,012 $14,003 $13,995 $10,337

Small truck 10% 10% $19,874 $17,112 $17,082 $14,868 $14,840 $14,813 $14,795 $14,778 $10,977

Small truck 15% 15% $19,223 $16,530 $16,504 $14,347 $14,322 $14,299 $14,283 $14,268 $10,587

Small truck 20% 20% $18,572 $15,949 $15,927 $13,826 $13,805 $13,785 $13,772 $13,759 $10,197

WR=weight reduction.

All costs are incremental to the baseline case.

Table 1.2-11 Costs for Full Electric Vehicle Technology with 100 Mile Range, or EV100

(2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 10% 4% $18,157 $15,513 $15,502 $13,385 $13,375 $13,365 $13,359 $13,352 $9,850
2017 Draft Regulatory Impact Analysis

1-12

Subcompact 15% 9% $17,733 $15,135 $15,126 $13,046 $13,038 $13,031 $13,025 $13,021 $9,596

Subcompact 20% 14% $17,309 $14,757 $14,750 $12,707 $12,702 $12,696 $12,692 $12,689 $9,343

Small car 10% 5% $20,332 $17,433 $17,412 $15,090 $15,071 $15,052 $15,040 $15,028 $11,122

Small car 15% 10% $19,775 $16,939 $16,922 $14,650 $14,633 $14,617 $14,607 $14,596 $10,793

Small car 20% 15% $19,219 $16,446 $16,431 $14,210 $14,196 $14,182 $14,173 $14,165 $10,464

Large car 10% 5% $24,009 $20,660 $20,626 $17,942 $17,910 $17,879 $17,858 $17,839 $13,244

Large car 15% 10% $23,170 $19,911 $19,881 $17,269 $17,242 $17,215 $17,198 $17,181 $12,740

Large car 20% 15% $22,331 $19,161 $19,136 $16,597 $16,574 $16,552 $16,537 $16,523 $12,236

Minivan 10% 4% $24,740 $21,235 $21,207 $18,398 $18,372 $18,346 $18,330 $18,314 $13,566

Minivan 15% 9% $24,070 $20,634 $20,610 $17,857 $17,835 $17,813 $17,799 $17,785 $13,160

Minivan 20% 14% $23,400 $20,032 $20,013 $17,316 $17,298 $17,280 $17,269 $17,257 $12,754

Small truck 10% 2% $24,324 $20,938 $20,902 $18,189 $18,155 $18,123 $18,102 $18,081 $13,428

Small truck 15% 7% $23,698 $20,378 $20,346 $17,686 $17,656 $17,627 $17,608 $17,590 $13,051

Small truck 20% 12% $23,072 $19,818 $19,790 $17,183 $17,157 $17,131 $17,115 $17,098 $12,674

WR=weight reduction.

All costs are incremental to the baseline case.

Table 1.2-12 Costs for Full Electric Vehicle Technology with 150 Mile Range, or EV150

(2009$)

Vehicle Class Applied

WR

Net

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

Subcompact 20% 2% $23,714 $20,242 $20,230 $17,450 $17,439 $17,428 $17,421 $17,414 $12,836

Small car 20% 3% $26,621 $22,785 $22,763 $19,691 $19,671 $19,651 $19,639 $19,626 $14,502

Large car 20% 2% $32,589 $27,974 $27,936 $24,239 $24,204 $24,170 $24,148 $24,127 $17,873

Minivan 20% 2% $34,229 $29,311 $29,281 $25,342 $25,315 $25,288 $25,270 $25,253 $18,668

Small truck 20% 0% $33,589 $28,831 $28,793 $24,980 $24,944 $24,910 $24,887 $24,865 $18,419

WR=weight reduction.

All costs are incremental to the baseline case.

Table1.2-13 Costs for EV/PHEV In-home Chargers (2009$)

Technology Vehicle

Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

PHEV20

Charger All $78 $65 $65 $55 $55 $55 $55 $55 $41

PHEV40

Charger

Subcompact $410 $344 $344 $291 $291 $291 $291 $291 $214

Small car $476 $399 $399 $338 $338 $338 $338 $338 $249

Larger car

Minivan

Small truck

$521 $437 $437 $369 $369 $369 $369 $369 $272

EV Charger All $521 $437 $437 $369 $369 $369 $369 $369 $272

Charger

labor All $1,009 $1,009 $1,009 $1,009 $1,009 $1,009 $1,009 $1,009 $1,009

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.
2017 Draft Regulatory Impact Analysis

1-13

Table 1.2-14 Costs for 10% and 20% Weight Reduction for the 19 Vehicle Typesa

(2009$)

Vehicle

Type

Base

Weight

Applied

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

1 2615 10% $141 $137 $128 $125 $122 $119 $117 $115 $113

20% $628 $614 $600 $587 $574 $561 $553 $545 $495

2 2907 10% $156 $153 $143 $139 $136 $132 $130 $128 $126

20% $698 $683 $667 $653 $638 $624 $615 $606 $550

3 3316 10% $178 $174 $163 $159 $155 $151 $148 $146 $144

20% $797 $779 $761 $744 $728 $712 $702 $692 $627

4 3357 10% $181 $176 $165 $161 $157 $153 $150 $148 $145

20% $807 $788 $771 $754 $737 $721 $710 $700 $635

5 3711 10% $200 $195 $182 $178 $173 $169 $166 $163 $161

20% $892 $872 $852 $833 $815 $797 $785 $774 $702

6 4007 10% $215 $210 $197 $192 $187 $182 $179 $176 $173

20% $963 $941 $920 $899 $880 $860 $848 $836 $758

7 3535 10% $190 $185 $173 $169 $165 $161 $158 $156 $153

20% $849 $830 $812 $793 $776 $759 $748 $737 $669

8 3845 10% $207 $202 $189 $184 $179 $175 $172 $169 $166

20% $924 $903 $883 $863 $844 $826 $814 $802 $727

9 4398 10% $237 $231 $216 $210 $205 $200 $197 $194 $190

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

10 4550 10% $245 $239 $223 $218 $212 $207 $204 $200 $197

20% $1,093 $1,069 $1,045 $1,021 $999 $977 $963 $949 $861

11 4784 10% $257 $251 $235 $229 $223 $218 $214 $211 $207

20% $1,149 $1,123 $1,098 $1,074 $1,050 $1,027 $1,012 $998 $905

12 4162 10% $224 $218 $204 $199 $194 $189 $186 $183 $180

20% $1,000 $977 $955 $934 $914 $894 $881 $868 $787

13 5169 10% $278 $271 $254 $247 $241 $235 $231 $227 $224

20% $1,242 $1,214 $1,187 $1,160 $1,135 $1,110 $1,094 $1,078 $978

14 5020 10% $270 $263 $246 $240 $234 $228 $225 $221 $217

20% $1,206 $1,179 $1,153 $1,127 $1,102 $1,078 $1,062 $1,047 $949

15 3598 10% $193 $189 $177 $172 $168 $164 $161 $158 $156

20% $865 $845 $826 $808 $790 $773 $761 $750 $680

16 4389 10% $236 $230 $215 $210 $205 $200 $196 $193 $190

20% $1,055 $1,031 $1,008 $985 $964 $942 $929 $915 $830

17 5271 10% $283 $277 $259 $252 $246 $240 $236 $232 $228

20% $1,267 $1,238 $1,210 $1,183 $1,157 $1,132 $1,115 $1,099 $997

18 4251 10% $229 $223 $209 $203 $198 $193 $190 $187 $184

20% $1,021 $998 $976 $954 $933 $913 $900 $887 $804

19 5269 10% $283 $276 $258 $252 $246 $240 $236 $232 $228

20% $1,266 $1,237 $1,210 $1,183 $1,157 $1,131 $1,115 $1,099 $996

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 1.2-15 through Table 1.2-19 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.
2017 Draft Regulatory Impact Analysis

1-14

Table 1.2-15 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

applicable) n.a. 6.5 6.0 4.7 5.7

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 1.2-16 Transmission Technology Effectiveness

Technology

Absolute CO2 Reduction (% from baseline vehicle)

Small

Car

Large

Car Minivan SmallTruck

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 1.2-17 Hybrid Technology Effectiveness

Technology

Absolute CO2 Reduction (% from baseline vehicle)

Small

Car

Large

Car Minivan SmallTruck

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)
2017 Draft Regulatory Impact Analysis

1-15

***Based on utility factors used for 20-mile (40%) and 40-mile (63%) range PHEV

Table 1.2-18 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

Table 1.2-19 Other Vehicle Technology Effectiveness

Technology

Absolute CO2 Reduction (% from baseline vehicle)

Small

Car

Large

Car Minivan SmallTruck

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 often be simply 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 draft joint TSD.

As was done in the 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
2017 Draft Regulatory Impact Analysis

1-16

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 this analysis, EPA has used the same 19 vehicle types that were used in the 2010

TAR. 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 seven vehicle classes: Subcompact, Small car, Large car, Minivan, Minivan with towing,

Small truck, and Large truck. Note that, for the current assessment and representing an

update since the 2010 TAR, EPA has created a new vehicle class called “minivan-towing” or

a minivan (or MPV or Multi-Purpose Vehicle) with towing capability (as defined below)

which allows for greater differentiation of costs for this popular class of vehicles (such as the

Ford Edge, Honda Odyssey, Jeep Grand Cherokee).B Note also that our seven vehicle classes

are not meant to correlate one-to-one with consumer-level vehicle classes. For example, we

have many sport utility and cross-over utility vehicles (SUVs and CUVs) in our “Minivan”

and “Minivan-towing” vehicle classes. We are not attempting to inappropriately mix

minivans with SUVs or equating the two in terms of features and utility, but we are grouping

them with respect to technology effectiveness and some technology costs. The seven

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 minivantowing 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 minivan-towing vehicle type will get technology-specific cost results

for that vehicle class. The same is true for vehicles mapped into the other vehicle classes.

This is true only for applicable technologies, i.e., those costs developed on a vehicle class

basis such as advanced diesel, hybrid and other electrified powertrains (see Table 1.2-6

through Table1.2-13 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—smaller footprints for smaller vehicles and larger footprints for larger vehicles—

which served as the basis for the 2012-2016 GHG standards and the standards in this

proposal. 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.

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

1.5L 4V

DOHC I4 4sp AT Subcompact Subcompact car I4 Ford Focus, ChevyAveo, Honda Fit No

2

2.4L 4V

DOHC I4 4sp AT Small car Compact car I4 Chevy Cobalt,

Honda Civic, Mazda No

B Note the distinction between “vehicle type” and “vehicle class.” We have the same 19 vehicle types as were

used in the 2010 TAR but have added a 7th vehicle class where the TAR used six.
2017 Draft Regulatory Impact Analysis

1-17

3

3

2.4L 4V

DOHC I4 4sp AT Small car Midsize car/SmallMPV I4

Ford Fusion, Honda

Accord, Toyota

Camry

No

4

3.0L 4V

DOHC V6 4sp AT Minivan Compact car/SmallMPV V6

Dodge Caliber,

Subaru Impreza,

VW Jetta

No

5

3.3L 4V

DOHC V6 4sp AT Large car Midsize/Large car V6

Dodge Avenger,

Ford Fusion, Honda

Accord

No

6

4.5L 4V

DOHC V8 4sp AT Large car Midsize car/Large carV8

BMW 750, Ford

Mustang, Cadillac

STS

No

7

2.6L 4V

DOHC I4

(I5)

4sp AT Minivan Midsize MPV/Small

truck I4

Jeep Compass, Ford

Escape, Nissan

Rogue

No

8

3.7L 2V

SOHC V6 4sp AT Small truck Midsize MPV/Smalltruck V6

Jeep Liberty, Ford

Ranger No

9

4.0L 2V

SOHC V6 4sp AT Minivantowing Large MPV V6

Dodge Durango,

Jeep Commander,

Ford Explorer

Yes

10 4.7L 2V

SOHC V8 4sp AT Minivantowing Large MPV V8

Dodge Durango,

Jeep Grand

Cherokee, Ford

F150

Yes

11 4.2L 2V

SOHC V6 4sp AT Large truck Large truck/van V6 Dodge Ram 1500,

Ford F150 Yes

12 3.8L 2V

OHV V6 4sp AT Large truck Large truck/MPV V6

Chrysler Town &

Country, Chevy

Silverado

Yes

13 5.7L 2V

OHV V8 4sp AT Large truck Large truck/van V8 Dodge Ram 1500,

Chevy Silverado Yes

14 5.4L 3V

SOHC V8 4sp AT Large truck Large truck/van V8 Ford Explorer, Ford

F150 Yes

15 5.7L 2V

OHV V8 4sp AT Large car Large car V8 Chrysler 300, Chevy

Corvette No

16 3.5L 4V

DOHC V6 4sp AT Minivantowing Large MPV V6

Ford Edge, Chevy

Equinox, Honda

Odyssey

Yes

17 4.6L 4V

DOHC V8 4sp AT Minivantowing Large MPV V8

Jeep Grand

Cherokee, Nissan

Armada, VW

Touareg

Yes

18 4.0L 4V

DOHC V6 4sp AT Large truck Large truck/van V6

Ford F150, Nissan

Frontier, Toyota

Tacoma

Yes

19 5.6L 4V

DOHC V8 4sp AT Large truck Large truck/van V8 Nissan Titan, Toyota

Tundra Yes

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

models as described in Chapter 1 of the joint TSD. In this discussion, when we refer to

“baseline” vehicle we refer to the “baseline” configuration of the given vehicle type. So, we
2017 Draft Regulatory Impact Analysis

1-18

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 in an

effort 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-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 2017-2025

reference case (i.e., the fleet as it is expected to exist as a result of the 2012-2016 final rule

which serves as the starting point for the larger analysis supporting this proposal). But again,

the discussion here is focused solely on building packages. Therefore, while the baseline

vehicle that defines the vehicle type is relevant, the baseline and reference case fleets of real

vehicles are relevant to the discussion presented later in Chapter 3 of this draft 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 most minivan

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.CFor 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

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

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.
2017 Draft Regulatory Impact Analysis

1-19

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. As a result of the discretization of our vehicle types, we believe that some

towing vehicle models have been mapped into non-towing vehicle types while some nontowing vehicle models may have been mapped into towing vehicle types. One prime example

is the Ford Escape mentioned above. We have mapped all Escapes into non-towing vehicle

types. This is done because the primary driver behind the vehicle type into which a vehicle is

mapped is 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 would put Atkinson-HEVs on some vehicle models that are more

properly treated as towing vehiclesD, and would put turbocharged/downsized HEVs on some

vehicle models that are more properly treated as non-towing vehicles. Table 1.3-2 shows

some of these vehicle models that have been mapped into a non-towing vehicle type even

though they may be towing vehicles (the right column). The table also shows some vehicle

models that have been mapped into a towing vehicle type even though they may not be

towing vehicles (the left column). The vehicles in the right column may experience some loss

of towing 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 may, when converted to

HEV, be costlier and slightly less effective (less CO2 reduction) since they would be

converted to turbocharged/downsized HEVs rather than Atkinson-HEVs. Accurate data on

towing specification is difficult to find, we hope to have better data on towing capacity for the

final rule analysis and may create new vehicle types to more properly model towing and nontowing vehicles.

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.
2017 Draft Regulatory Impact Analysis

1-20

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

Vehicles

Non-towing vehicles mapped into towing

vehicle types

Towing vehicles mapped into non-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

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

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 towing trips. As such, we

believe that the buyers will experience no practical loss of utility.

To prepare inputs for the OMEGA model, EPA builds a “master-set” of technology

packages. The master-set of packages for each vehicle type are meant to reflect the most

likely technology packages manufacturers would consider when determining their plans for

complying with future standards. In other words, they are meant to reflect the most cost

effective groups of technologies—those that provide the best trade-off of costs versus fuel

consumption improvements. This is done by grouping reasonable technologies in all possible

permutations and 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
2017 Draft Regulatory Impact Analysis

1-21

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 presuming that auto makers would

first concentrate efforts on conventional gasoline engine and transmission technologies paired

with some level of mass reduction to improve fuel consumption. Mass reduction varied from

no mass reduction up to 20 percent as the maximum considered in this analysis.E

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 draft RIA and the OMEGA model phase-in

caps are shown in Chapter 3.5 of the joint TSD.

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 draft 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

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.F

E

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 proposed 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 draft RIA.

F 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 and to increase their costs since heavier

batteries and motors are now part of the packages.
2017 Draft Regulatory Impact Analysis

1-22

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 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 14

primary categories of conventional gasoline engine technologies. These are:

1. Our “anytime technologies” (ATT).GThese consist of low friction lubes, engine

friction reduction, aggressive shift logic, early torque converter lock-up (automatic

transmission only). ATT is broken into two levels:

- ATT, which consists of our first level of low friction lubes, engine friction

reduction, aggressive shift logic, early torque converter lock-up (automatic

transmission only) and low drag brakes.

- ATT2, which consists of our second level of low friction lubes and engine

friction reduction (collectively referred to as EFR2), aggressive shift logic,

along with the same early torque converter lock-up (automatic transmission

only) and low drag brakes that are part of ATT.

2. Our “Other” conventional technologies. These consist of improved accessories,

electric power steering (EPS) or electrohydraulic power steering (EHPS, used for

large trucks), aerodynamic improvements and lower rolling resistance tires. The

“other” technology category is broken into two levels:

G 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. This grouping also serves to minimize the number of packages to be considered in our

modeling process. As explained in the text, we have built roughly 40,000 packages. Grouping the anytime and

other technologies together and treating them, essentially, as four technologies (ATT, ATT2, Other1, Other2)

when building packages helps to keep the number of packages lower. If we considered each “anytime” and

“other” technology separately, we would have had to build well over 200,000 packages which becomes

unworkable given the analytical tools at our disposal.
2017 Draft Regulatory Impact Analysis

1-23

- Other1, which consists of our first level of improved accessories, our first level

of aerodynamic improvements and our first level of lower rolling resistance

tires along with EPS/EHPS. This category also includes the high efficiency

gearbox technology (HEG).

- Other2, which consists of our second levels of improved accessories,

aerodynamic improvements and lower rolling resistance tires along with the

same EPS/EHPS and HEG that are part of Other1.

3. Variable valve timing (VVT) consisting of coupled cam phasing (CCP, for OHV

and SOHC engines) and dual cam phasing (DCP, for DOHC engines)

4. Variable valve lift (VVL) consisting of discrete variable valve lift (DVVL, for

DOHC engines)

5. Cylinder deactivation (Deac, considered for OHV and SOHC engines)

6. Gasoline direct injection (GDI)

7. Turbocharging and downsizing (TDS, which always includes a conversion to GDI)

with and without cooled EGR. Note that 27 bar BMEP engines must include the

addition of cooled EGR in our analysis.

8. Stop-start

9. Secondary axle disconnect (SAX)

10. Conversion to advance 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.

11. Mass reduction consisting of 0%, 5%, 10%, 15% and 20%.

In this first step, we also considered the 6 primary transmission technologies. These

are:

12. 6 and 8 speed automatic transmissions (6sp AT/8sp AT)

13. 6 and 8 speed dual clutch transmissions with wet clutch (6sp wet-DCT/8sp wetDCT)

14. 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
2017 Draft Regulatory Impact Analysis

1-24

due to low end torque demands at launch (another example of how the proposed 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. Unlike the TAR analysis, we have limited towing vehicle types to

use of automatic transmissions (both 6 and 8 speed). Like 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 2010 TAR, we have added dry versus wet DCTs

depending on the baseline weight of 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 type 4 and only in

packages with 20% weight reduction applied. Only then was this vehicle type light enough to

add the dry-DCT.
2017 Draft Regulatory Impact Analysis

1-25

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 (as we describe below). Together these result in a decrease in

effectiveness potential for the heavier towing vehicle types compared to the non-towing

vehicle types. This discrepancy is one justification for the adjustment to the 2017-2025 truck

curves (in comparison to the 2012-2016 curves) as described in Chapter 2 of the draft joint

TSD and in preamble Section III.D.6.b.H

H While it’s also offset by more mass reduction capability on these vehicles, the curve analysis did not assume an

uneven distribution of mass reduction throughout the fleet.
2017 Draft Regulatory Impact Analysis

1-26

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 Subcompact I4 2615 6/8 speed dry-DCT

2 Small car I4 2907 6/8 speed dry-DCT

3 Small car I4 3316 6/8 speed dry-DCT

4 Minivan V6 3357 6/8 speed wet-DCT 6/8 speed dry-DCT

5 Large car V6 3711 6/8 speed wet-DCT

6 Large car V8 4007 6/8 speed wet-DCT

7 Minivan I4 3535 6/8 speed dry-DCT

8 Small truck V6 3845 6/8 speed wet-DCT

9 Minivan-towing V6 4398 6/8 speed AT

10 Minivan-towing V8 4550 6/8 speed AT

11 Large truck V6 4784 6/8 speed AT

12 Large truck V6 4162 6/8 speed AT

13 Large truck V8 5169 6/8 speed AT

14 Large truck V8 5020 6/8 speed AT

15 Large car V8 3598 6/8 speed wet-DCT

16 Minivan-towing V6 4389 6/8 speed AT

17 Minivan-towing V8 5271 6/8 speed AT

18 Large truck V6 4251 6/8 speed AT

19 Large truck V8 5269 6/8 speed AT

For example, vehicle type 4 is equipped with a 4 speed automatic transmission in the

baseline. In a package consisting of a 0% to 15% mass reduction, we believe this vehicle type

could convert to a wet-DCT because the lighter weight results in reduced low end torque

demand thus making the wet-DCT feasible. Upon reaching 20% mass reduction, the vehicle

type could employ a dry-DCT because the even lighter weight (3357 less 20% equals 2686

pounds, which is less than our 2800 pound cutoff) results in further reduction in low end

torque demand.

We start by first building a “preliminary-set” of non-electrified (i.e., gasoline and

diesel) packages for each vehicle type consisting of nearly every combination of each of the

14 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 ATT but no weight reduction or transmission changes.

We then add Other1 (less HEG), again with no weight reduction or transmission changes (we

do not consider the addition of HEG without a simultaneous improvement in the transmission

itself). The next package would add HEG and a transmission improvement. The subsequent

packages would iterate on nearly all possible combinations with the result being just under

2000 packages per vehicle type. Table 1.3-4 shows a subset of packages built for vehicle type

5, a midsized/large car with a 3.3L 4 valve DOHC V6 in the baseline. These are package

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
2017 Draft Regulatory Impact Analysis

1-27

transmission. So this table represents roughly one-tenth of the non-electrified packages built

for vehicle type 5.

Table 1.3-4 A Subset of 2025 MY Non-HEV/PHEV/EV Packages Built for Vehicle Type

5 (Midsize/Large car 3.3L DOHC V6)a

Prelim

Pkg #

Weight

rdxn Package contents Transmission 2025MY

Cost CO2%

50000 base 3.3L 4V DOHC V6 4sp AT $0 0%

50001 base 4V DOHC V6 +LUB+EFR1+LDB+ASL 4sp AT $184 7%

50002 base 4V DOHC V6 +LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1 4sp AT $394 13%

50395b5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG 6sp DCT-wet $558 24%

50396 5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+ DCP 6sp DCT-wet $723 27%

50397 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL

6sp DCT-wet $913 29%

50398 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI

6sp DCT-wet $1,060 28%

50399 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI

6sp DCT-wet $1,250 30%

50400 5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+ DCP+SS 6sp DCT-wet $1,066 28%

50401 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SS

6sp DCT-wet $1,256 30%

50402 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS

6sp DCT-wet $1,404 29%

50403 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS

6sp DCT-wet $1,593 31%

50404 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+SAX

6sp DCT-wet $804 27%

50405 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SAX

6sp DCT-wet $993 29%

50406 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX

6sp DCT-wet $1,141 28%

50407 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX

6sp DCT-wet $1,331 30%

50408 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+SS+SAX

6sp DCT-wet $1,147 28%

50409 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SS+SAX

6sp DCT-wet $1,337 30%

50410 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX

6sp DCT-wet $1,484 30%

50411 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX

6sp DCT-wet $1,674 31%

50412 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18

6sp DCT-wet $998 34%

50413 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS18

6sp DCT-wet $1,129 35%

50414 5% 4V DOHC I4 6sp DCT-wet $1,341 35%
2017 Draft Regulatory Impact Analysis

1-28

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS18

50415 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS18

6sp DCT-wet $1,472 36%

50416 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18

6sp DCT-wet $1,079 35%

50417 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS18

6sp DCT-wet $1,210 36%

50418 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS18

6sp DCT-wet $1,422 36%

50419 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS18

6sp DCT-wet $1,553 36%

50420 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24

6sp DCT-wet $1,210 37%

50421 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS24

6sp DCT-wet $1,341 37%

50422 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS24

6sp DCT-wet $1,554 38%

50423 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS24

6sp DCT-wet $1,685 38%

50424 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24

6sp DCT-wet $1,291 38%

50425 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS24

6sp DCT-wet $1,422 38%

50426 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS24

6sp DCT-wet $1,634 38%

50427 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24

6sp DCT-wet $1,765 38%

50428 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG 6sp DCT-wet $646 27%

50429 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP 6sp DCT-wet $810 30%

50430 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL 6sp DCT-wet $1,000 32%

50431 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI 6sp DCT-wet $1,148 31%

50432 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI 6sp DCT-wet $1,338 33%

50433 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+SS 6sp DCT-wet $1,153 31%

50434 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SS 6sp DCT-wet $1,343 33%

50435 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS 6sp DCT-wet $1,491 32%

50436 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS 6sp DCT-wet $1,681 34%

50437 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+SAX 6sp DCT-wet $891 31%

50438 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SAX 6sp DCT-wet $1,081 33%

50439 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX 6sp DCT-wet $1,228 32%

50440 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX 6sp DCT-wet $1,418 34%

50441 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+SS+SAX 6sp DCT-wet $1,234 32%
2017 Draft Regulatory Impact Analysis

1-29

50442 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+SS+SAX 6sp DCT-wet $1,424 34%

50443 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX 6sp DCT-wet $1,571 33%

50444 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX 6sp DCT-wet $1,761 35%

50445 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18 6sp DCT-wet $1,058 37%

50446 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS18 6sp DCT-wet $1,189 38%

50447 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS18 6sp DCT-wet $1,401 38%

50448 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS18 6sp DCT-wet $1,532 39%

50449 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18 6sp DCT-wet $1,138 38%

50450 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS18 6sp DCT-wet $1,269 39%

50451 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS18 6sp DCT-wet $1,481 39%

50452 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS18 6sp DCT-wet $1,612 39%

50453 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24 6sp DCT-wet $1,270 40%

50454 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS24 6sp DCT-wet $1,401 40%

50455 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS24 6sp DCT-wet $1,613 40%

50456 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS24 6sp DCT-wet $1,744 41%

50457 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24 6sp DCT-wet $1,351 40%

50458 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS24 6sp DCT-wet $1,482 40%

50459 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS24 6sp DCT-wet $1,694 41%

50460 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24 6sp DCT-wet $1,825 41%

50461 5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG 6sp DCT-wet $772 29%

50462 5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+ DCP 6sp DCT-wet $937 31%

50463 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL

6sp DCT-wet $1,127 33%

50464 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI

6sp DCT-wet $1,274 32%

50465 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI

6sp DCT-wet $1,464 34%

50466 5% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+ DCP+SS 6sp DCT-wet $1,280 32%

50467 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SS

6sp DCT-wet $1,470 34%

50468 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS

6sp DCT-wet $1,618 33%

50469 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS

6sp DCT-wet $1,807 35%

50470 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+SAX

6sp DCT-wet $1,017 32%

50471 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SAX

6sp DCT-wet $1,207 33%
2017 Draft Regulatory Impact Analysis

1-30

50472 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX

6sp DCT-wet $1,355 33%

50473 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX

6sp DCT-wet $1,545 34%

50474 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+SS+SAX

6sp DCT-wet $1,361 33%

50475 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SS+SAX

6sp DCT-wet $1,550 34%

50476 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX

6sp DCT-wet $1,698 34%

50477 5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX

6sp DCT-wet $1,888 35%

50478 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18

6sp DCT-wet $1,212 38%

50479 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS18

6sp DCT-wet $1,343 39%

50480 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS18

6sp DCT-wet $1,555 39%

50481 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS18

6sp DCT-wet $1,686 39%

50482 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18

6sp DCT-wet $1,293 38%

50483 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS18

6sp DCT-wet $1,424 39%

50484 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS18

6sp DCT-wet $1,636 39%

50485 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS18

6sp DCT-wet $1,767 40%

50486 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24

6sp DCT-wet $1,424 40%

50487 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS24

6sp DCT-wet $1,555 40%

50488 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS24

6sp DCT-wet $1,768 41%

50489 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS24

6sp DCT-wet $1,899 41%

50490 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24

6sp DCT-wet $1,505 41%

50491 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS24

6sp DCT-wet $1,636 41%

50492 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS24

6sp DCT-wet $1,848 41%

50493 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24

6sp DCT-wet $1,979 41%

50494 5% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG 6sp DCT-wet $860 32%

50495 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ DCP 6sp DCT-wet $1,024 35%
2017 Draft Regulatory Impact Analysis

1-31

50496 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ DCP+DVVL 6sp DCT-wet $1,214 36%

50497 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ DCP+GDI 6sp DCT-wet $1,362 35%

50498 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI

6sp DCT-wet $1,552 37%

50499 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ DCP+SS 6sp DCT-wet $1,367 35%

50500 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SS

6sp DCT-wet $1,557 37%

50501 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS

6sp DCT-wet $1,705 36%

50502 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS

6sp DCT-wet $1,895 38%

50503 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ DCP+SAX 6sp DCT-wet $1,105 35%

50504 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SAX

6sp DCT-wet $1,295 37%

50505 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX

6sp DCT-wet $1,442 36%

50506 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX

6sp DCT-wet $1,632 38%

50507 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+SS+SAX

6sp DCT-wet $1,448 36%

50508 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+SS+SAX

6sp DCT-wet $1,638 37%

50509 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX

6sp DCT-wet $1,785 37%

50510 5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX

6sp DCT-wet $1,975 38%

50511 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18 6sp DCT-wet $1,272 41%

50512 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS18 6sp DCT-wet $1,403 41%

50513 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS18 6sp DCT-wet $1,615 41%

50514 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS18 6sp DCT-wet $1,746 42%

50515 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18 6sp DCT-wet $1,352 41%

50516 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS18 6sp DCT-wet $1,483 42%

50517 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS18 6sp DCT-wet $1,695 42%

50518 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS18 6sp DCT-wet $1,826 42%

50519 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24 6sp DCT-wet $1,484 43%

50520 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS24 6sp DCT-wet $1,615 43%

50521 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS24 6sp DCT-wet $1,827 43%

50522 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS24 6sp DCT-wet $1,958 44%

50523 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24 6sp DCT-wet $1,565 43%

50524 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+ 6sp DCT-wet $1,696 43%
2017 Draft Regulatory Impact Analysis

1-32

DCP+DVVL+GDI+SAX+TDS24

50525 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS24 6sp DCT-wet $1,908 44%

50526 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24 6sp DCT-wet $2,039 44%

50527 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+EGR

6sp DCT-wet $1,457 39%

50528 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS24+EGR

6sp DCT-wet $1,588 39%

50529 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS24+EGR

6sp DCT-wet $1,801 40%

50530 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS24+EGR

6sp DCT-wet $1,932 40%

50531 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+EGR

6sp DCT-wet $1,538 40%

50532 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS24+EGR

6sp DCT-wet $1,669 40%

50533 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS24+EGR

6sp DCT-wet $1,881 40%

50534 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24+EGR

6sp DCT-wet $2,012 41%

50535 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+EGR 6sp DCT-wet $1,517 42%

50536 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS24+EGR 6sp DCT-wet $1,648 42%

50537 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS24+EGR 6sp DCT-wet $1,860 43%

50538 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS24+EGR 6sp DCT-wet $1,991 43%

50539 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+EGR 6sp DCT-wet $1,598 42%

50540 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS24+EGR 6sp DCT-wet $1,729 43%

50541 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS24+EGR 6sp DCT-wet $1,941 43%

50542 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24+EGR 6sp DCT-wet $2,072 43%

50543 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR

6sp DCT-wet $1,671 42%

50544 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS24+EGR

6sp DCT-wet $1,802 42%

50545 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS24+EGR

6sp DCT-wet $2,015 43%

50546 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS24+EGR

6sp DCT-wet $2,146 43%

50547 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+EGR

6sp DCT-wet $1,752 43%

50548 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS24+EGR

6sp DCT-wet $1,883 43%

50549 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS24+EGR

6sp DCT-wet $2,095 43%

50550 5% 4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+ 6sp DCT-wet $2,226 44%
2017 Draft Regulatory Impact Analysis

1-33

DCP+DVVL+GDI+SS+SAX+TDS24+EGR

50551 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR 6sp DCT-wet $1,731 45%

50552 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS24+EGR 6sp DCT-wet $1,862 45%

50553 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS24+EGR 6sp DCT-wet $2,074 46%

50554 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS24+EGR 6sp DCT-wet $2,205 46%

50555 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+EGR 6sp DCT-wet $1,811 45%

50556 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS24+EGR 6sp DCT-wet $1,942 45%

50557 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS24+EGR 6sp DCT-wet $2,155 46%

50558 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS24+EGR 6sp DCT-wet $2,286 46%

50559 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS27+EGR

6sp DCT-wet $1,882 40%

50560 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS27+EGR

6sp DCT-wet $2,013 40%

50561 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS27+EGR

6sp DCT-wet $2,225 41%

50562 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS27+EGR

6sp DCT-wet $2,356 41%

50563 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS27+EGR

6sp DCT-wet $1,963 40%

50564 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS27+EGR

6sp DCT-wet $2,094 40%

50565 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS27+EGR

6sp DCT-wet $2,306 41%

50566 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS27+EGR

6sp DCT-wet $2,437 41%

50567 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS27+EGR 6sp DCT-wet $1,942 43%

50568 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+TDS27+EGR 6sp DCT-wet $2,073 43%

50569 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+TDS27+EGR 6sp DCT-wet $2,285 43%

50570 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+TDS27+EGR 6sp DCT-wet $2,416 43%

50571 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS27+EGR 6sp DCT-wet $2,022 43%

50572 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SAX+TDS27+EGR 6sp DCT-wet $2,153 43%

50573 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SS+SAX+TDS27+EGR 6sp DCT-wet $2,365 44%

50574 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+SS+SAX+TDS27+EGR 6sp DCT-wet $2,496 44%

50575 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS27+EGR

6sp DCT-wet $2,096 43%

50576 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS27+EGR

6sp DCT-wet $2,227 43%

50577 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS27+EGR

6sp DCT-wet $2,439 44%

50578 5% 4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+ 6sp DCT-wet $2,570 44%
2017 Draft Regulatory Impact Analysis

1-34

DCP+DVVL+GDI+SS+TDS27+EGR

50579 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS27+EGR

6sp DCT-wet $2,177 43%

50580 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS27+EGR

6sp DCT-wet $2,308 43%

50581 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS27+EGR

6sp DCT-wet $2,520 44%

50582 5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS27+EGR

6sp DCT-wet $2,651 44%

50583 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS27+EGR 6sp DCT-wet $2,156 45%

50584 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+TDS27+EGR 6sp DCT-wet $2,287 45%

50585 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+TDS27+EGR 6sp DCT-wet $2,499 46%

50586 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+TDS27+EGR 6sp DCT-wet $2,630 46%

50587 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS27+EGR 6sp DCT-wet $2,236 46%

50588 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS27+EGR 6sp DCT-wet $2,367 46%

50589 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS27+EGR 6sp DCT-wet $2,579 46%

50590 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS27+EGR 6sp DCT-wet $2,710 46%

51963 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DSL-Adv 6sp DCT-wet $3,602 40%

51964 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DSL-Adv+SAX 6sp DCT-wet $3,683 40%

51965 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DSL-Adv 6sp DCT-wet $3,816 43%

51966 5% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DSL-Adv+SAX 6sp DCT-wet $3,896 43%

a Excludes packages with weight reduction of 0%, 10%, 15%, 20% and 8speed DCT.

b The jump from package # 50002 to 50395 represents the packages built with 0% weight reduction

which are intentionally not included in the table.

As stated, this preliminary-set of packages is meant to maintain utility relative to the

baseline vehicle. Having built nearly 2000 packages for each vehicle type suggests 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 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 in Section 3.3.1.11 of the draft 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.

This preliminary-set of conventional gasoline packages (roughly 2000 packages per

vehicle type) was then ranked within vehicle type by TARF. This is done by first calculating

the TARF of each package relative to the baseline package. The package with the best TARF

is selected as OMEGA package #1 (or, more accurately, #501 for vehicle type 5, #101 for

vehicle type 1, etc.). The remaining packages for vehicle type 5 are then ranked again by
2017 Draft Regulatory Impact Analysis

1-35

TARF, this time relative to OMEGA package #501. The best package is selected as OMEGA

package #502, etc. Table 1.3-6 illustrates this process, while Table 1.3-5 presents 2008

baseline data used in the TARF ranking process.

Table 1.3-5 Lifetime VMT & Baseline CO2 used for TARF Ranking in the Package

Building Process

Vehicle

Type

Vehicle

class

Base

engine

Car/

Truck a

Lifetime

VMT

Base CO2

(g/mi)b

1 Subcompact I4 C 190,971 241.0

2 Small car I4 C 190,971 236.8

3 Small car I4 C 190,971 274.2

4 Minivan V6 C 190,971 310.5

5 Large car V6 C 190,971 335.5

6 Large car V8 C 190,971 387.7

7 Minivan I4 T 221,199 309.9

8 Small truck V6 C 190,971 385.1

9 Minivan-towing V6 T 221,199 421.7

10 Minivan-towing V8 T 221,199 437.5

11 Large truck V6 T 221,199 422.5

12 Large truck V6 T 221,199 357.6

13 Large truck V8 T 221,199 447.7

14 Large truck V8 T 221,199 480.0

15 Large car V8 C 190,971 386.7

16 Minivan-towing V6 T 221,199 375.6

17 Minivan-towing V8 T 221,199 463.3

18 Large truck V6 T 221,199 403.0

19 Large truck V8 T 221,199 477.6

a Designation here matters only for lifetime VMT determination in the package building and ranking process.

b

Sales weighted CO2 within vehicle type.

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.
2017 Draft Regulatory Impact Analysis

1-36

Table 1.3-6 Illustration of the TARF Ranking Process, Vehicle Type 5, 2025MY Costs

Preli

m

Pkg#

Weig

ht

rdxn

Package contents Tran

s

Cost

Fuel

Saving

s

a

Net

Cost

CO2

Rdx

n

(%)

CO2

Rdxn

(gram

s)

TARF

b

Round 1 (determine TARF relative to baseline package #50000)

5000

0

base 3.3L 4V DOHC V6 4sp

AT $0 $0 $0 0% 0 -

5000

1

base 4V DOHC V6 +LUB+EFR1+LDB+ASL 4sp

AT $184 $841 -$657 7% 23

-

0.152

8

5000

2

base 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1

4sp

AT $394 $1,567

-

$1,17

3

13% 42

-

0.146

4

5039

5

5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+H

EG

6sp

DCT

-wet

$558 $2,946 $2,38

8

24% 79

-

0.158

5

5039

6

5%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+H

EG+ DCP

6sp

DCT

-wet

$723 $3,349

-

$2,62

6

27% 90

-

0.153

3

Packages not shown for ease of presentation

5042

7

5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+H

EG+ DCP+DVVL+GDI+SS+SAX+TDS24

6sp

DCT

-wet

$1,76

5

$4,808

-

$3,04

3

38% 128.7

-

0.123

8

5042

8

5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG

6sp

DCT

-wet

$646 $3,419

-

$2,77

4

27% 91.6

-

0.158

6

5042

9

5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP

6sp

DCT

-wet

$810 $3,795

-

$2,98

5

30% 101.6

-

0.153

8

Etc...remaining packages have larger TARFs so are not shown; #50428 becomes the new base; all packages with lower effectiveness than 50428

are eliminated from further consideration

Round 2 (determine net cost, CO2 reduction & TARF relative to new base package #50428)

5042

8

5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG

6sp

DCT

-wet

$0 $0 $0 0% 0.0 -

5042

7

5%

4V DOHC I4

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+H

EG+ DCP+DVVL+GDI+SS+SAX+TDS24

6sp

DCT

-wet

$1,12

0

$1,388 -$269 11% 37.2

-

0.037

9

5042

9

5%

4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP

6sp

DCT

-wet

$165 $376 -$211 3% 10.1

-

0.109

9

Packages not shown for ease of presentation

5062

3

5%

4V DOHC

I4+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1

+HEG+DCP+DVVL+GDI+SS+SAX+TDS24

8sp

DCT

-wet

$1,23

4

$1,598 -$364 13% 42.8

-

0.044

6

5062

4

5%

4V DOHC

V6+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HE

G

8sp

DCT

-wet

$115 $365 -$251 3% 9.8

-

0.134

3

5062

5

5%

4V DOHC

V6+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HE

G+DCP

8sp

DCT

-wet

$279 $695 -$416 6% 18.6

-

0.117

0

Etc...remaining packages have larger TARFs so are not shown; #50624 becomes the new base; all packages with lower effectiveness than 50624

are eliminated from further consideration

Round 3 (determine net cost, CO2 reduction & TARF relative to new base package #50624)

5062

4

5%

4V DOHC

V6+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HE

G

8sp

DCT

-wet

$0 $0 $0 0% 0.0 -

Etc. Further ranking rounds not shown for ease of presentation

a

Fuel savings calculated based on the effectiveness of the package, the energy content of the fuel and AEO 2011 reference case fuel prices

(gasoline, diesel, electric). Fuel savings are considered for the first 5 years of life assuming VMT consistent with our car/truck VMT estimates

excluding any rebound driving and are discounted at 3%.

b TARF units are $/kg, so a multiplicative factor of 1000 is included to convert g/mile to kg/mile.

As illustrated in Table 1.3-6, the TARF ranking process eliminates most packages in favor of

more cost effective packages. The packages that remain after the TARF ranking process are
2017 Draft Regulatory Impact Analysis

1-37

then included in the master-set of packages for each vehicle type. These packages are shown

for vehicle type 5 in Table 1.3-7, along with their new OMEGA package # identifier.

Table 1.3-7 Master-set of 2025 MY Non-HEV/PHEV/EV Packages for Vehicle Type 5

(Midsize/Large car 3.3L DOHC V6)

Prelim

Pkg#

OMEGA

Pkg#

Weight

Rdxn Package contents Transmission Cost CO2%

50000 500 base 3.3L 4V DOHC V6 4sp AT $0 0%

50428 501 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG 6sp DCT-wet $646 27%

50624 502 5% 4V DOHC V6

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG 8sp DCT-wet $760 30%

50445 503 5%

4V DOHC I4

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18

6sp DCT-wet $1,058 37%

50641 504 5%

4V DOHC I4

+EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18

8sp DCT-wet $1,172 39%

50707 505 5%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18

8sp DCT-wet $1,386 43%

51099 506 10%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18

8sp DCT-wet $1,507 44%

51107 507 10%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24

8sp DCT-wet $1,719 46%

51139 508 10%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR

8sp DCT-wet $1,966 48%

51491 509 15%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18

8sp DCT-wet $1,741 46%

51531 510 15%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR

8sp DCT-wet $2,200 49%

51883 511 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18

8sp DCT-wet $2,048 47%

51923 512 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR

8sp DCT-wet $2,507 51%

51887 513 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18

8sp DCT-wet $2,128 48%

51927 514 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+EGR

8sp DCT-wet $2,588 51%

51888 515 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SAX+TDS18

8sp DCT-wet $2,259 48%

51890 516 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+SS+SAX+TDS18

8sp DCT-wet $2,602 49%

51929 517 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS24+EGR

8sp DCT-wet $2,931 52%

51961 518 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SS+SAX+TDS27+EGR

8sp DCT-wet $3,356 52%

51994 519 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DSL-Adv+SAX

8sp DCT-wet $4,673 49%
2017 Draft Regulatory Impact Analysis

1-38

The next packages after the advanced gasoline and diesel packages are the HEVs. We

noted above that we have considered applying only the P2 HEV for this analysis. As done

with non-electrified packages, we began with a preliminary-set of HEV packages that 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 vehicle types 8 and 15 are SOHC and OHV, respectively,. Therefore, 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 draft Joint

TSD. As a result, we have built more HEV packages for towing vehicle types than for nontowing types. Lastly, we built 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 in 2016 allowed in the analysis. This maximum allowed weight reduction was 15%

for the 2021MY and 20% for 2025 based on the technology penetration caps set forth and

explained i n Chapter 3 of the joint TSD. Table 1.3-8 shows the HEV packages built for

vehicle types 5 and, for comparison, 10 which is a towing vehicle type.

Table 1.3-8 HEV Packages Built for Vehicle Types 5 (3.3L DOHC V6) and 10 (4.7L

SOHC V8)

Prelim

Pkg#

Weight

Rdxn Package contents Transmission 2025Cost CO2%

500 base 3.3L 4V DOHC V6 4sp AT $0 0.0%

501 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV

6sp DCT-wet $4,937 51.9%

502 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX

6sp DCT-wet $5,018 52.3%

503 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV 6sp DCT-wet $5,024 54.3%

504 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX 6sp DCT-wet $5,105 54.7%

505 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV

6sp DCT-wet $5,151 55.6%

506 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX

6sp DCT-wet $5,231 55.9%

507 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV 6sp DCT-wet $5,238 57.7%

508 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX 6sp DCT-wet $5,319 58.1%

509 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV

8sp DCT-wet $5,051 53.7%

510 20.0% 4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+ 8sp DCT-wet $5,132 54.1%
2017 Draft Regulatory Impact Analysis

1-39

DCP+DVVL+GDI+ATKCS+HEV+SAX

511 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV 8sp DCT-wet $5,139 56.0%

512 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX 8sp DCT-wet $5,219 56.3%

513 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV

8sp DCT-wet $5,265 57.2%

514 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX

8sp DCT-wet $5,346 57.5%

515 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV 8sp DCT-wet $5,353 59.3%

516 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX 8sp DCT-wet $5,433 59.6%

1000 base 4.7L 2V SOHC V8 4sp AT $0 0.0%

1001 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18+HEV

6sp AT $5,749 45.7%

1002 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18+HEV

6sp AT $5,829 46.3%

1003 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+HEV

6sp AT $6,107 47.8%

1004 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+HEV

6sp AT $6,187 48.3%

1005 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18+HEV 6sp AT $5,836 48.0%

1006 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18+HEV 6sp AT $5,916 48.5%

1007 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+HEV 6sp AT $6,194 49.9%

1008 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+HEV 6sp AT $6,274 50.4%

1009 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18+HEV

6sp AT $5,963 49.5%

1010 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18+HEV

6sp AT $6,043 50.0%

1011 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+HEV

6sp AT $6,321 51.2%

1012 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+HEV

6sp AT $6,401 51.7%

1013 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18+HEV 6sp AT $6,050 51.6%

1014 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18+HEV 6sp AT $6,130 52.1%

1015 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+HEV 6sp AT $6,408 53.3%

1016 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+HEV 6sp AT $6,488 53.7%

1017 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR+HEV 6sp AT $6,655 54.9%

1018 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+EGR+HEV 6sp AT $6,735 55.4%

1019 20.0% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS27+EGR+HEV 6sp AT $6,084 55.4%

1020 20.0% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS27+EGR+HEV 6sp AT $6,164 55.8%

1021 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18+HEV

8sp AT $5,807 47.7%

1022 20.0% 4V DOHC V6 8sp AT $5,887 48.2%
2017 Draft Regulatory Impact Analysis

1-40

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18+HEV

1023 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+HEV

8sp AT $6,165 49.6%

1024 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+HEV

8sp AT $6,245 50.0%

1025 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS18+HEV 8sp AT $5,894 49.9%

1026 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS18+HEV 8sp AT $5,975 50.4%

1027 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+TDS24+HEV 8sp AT $6,252 51.7%

1028 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC+EPS+Aero1+LRRT1+HEG+

DCP+GDI+SAX+TDS24+HEV 8sp AT $6,333 52.1%

1029 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18+HEV

8sp AT $6,021 51.3%

1030 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18+HEV

8sp AT $6,101 51.7%

1031 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+HEV

8sp AT $6,379 52.9%

1032 20.0%

4V DOHC V6

+LUB+EFR1+LDB+ASL+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+HEV

8sp AT $6,459 53.3%

1033 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18+HEV 8sp AT $6,108 53.4%

1034 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18+HEV 8sp AT $6,188 53.8%

1035 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+HEV 8sp AT $6,466 54.9%

1036 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+HEV 8sp AT $6,547 55.2%

1037 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR+HEV 8sp AT $6,713 56.5%

1038 20.0% 4V DOHC V6 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS24+EGR+HEV 8sp AT $6,794 56.8%

1039 20.0% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS27+EGR+HEV 8sp AT $6,142 56.9%

1040 20.0% 4V DOHC I4 +EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS27+EGR+HEV 8sp AT $6,222 57.2%

Note: Prelim Pkg #s 500-516 are for vehicle type 5, #s 1000-1040 are for vehicle type 10.

Note also that any automatic transmission that has been improved from the base 4sp AT also includes early torque converter

lockup even though that technology is not specifically listed in the package contents. This is the only technology that does

not appear in the package content descriptions.

We then ranked the preliminary-set of HEV packages according to TARF as described above

to generate the most cost effective set of HEV packages for each vehicle type that would then

be included in the master-set of packages. The TARF ranking process eliminated most

packages in favor of more cost effective packages. These packages are shown for vehicle

types 5 and 10 (as examples) in
2017 Draft Regulatory Impact Analysis

1-41

Table 1.3-9, along with their new OMEGA package # identifier.
2017 Draft Regulatory Impact Analysis

1-42

Table 1.3-9 Master-set of 2025 MY HEV Packages for Vehicle Types 5 (3.3L DOHC V6)

& 10 (4.7L SOHC V8)

Prelim

Pkg#

OMEGA

Pkg#

Weight

rdxn Package contents Transmission 2025MY

Cost CO2%

515 520 20%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV

8sp DCT-wet $5,353 59.3%

516 521 20%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+DVVL+GDI+ATKCS+HEV+SAX

8sp DCT-wet $5,433 59.6%

1033 1018 20%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS18+HEV

8sp AT $6,108 53.4%

1037 1019 20%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS24+EGR+HEV

8sp AT $6,713 56.5%

1039 1020 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+TDS27+EGR+HEV

8sp AT $6,142 56.9%

1034 1021 20%

4V DOHC V6

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS18+HEV

8sp AT $6,188 53.8%

1040 1022 20%

4V DOHC I4

+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+

DCP+GDI+SAX+TDS27+EGR+HEV

8sp AT $6,222 57.2%

The last step was to build the PHEVs (also known as REEVs) and EVs for vehicle

types 1 through 8 and 15. The other vehicle types were not considered 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 2 primary types of PHEV packages and 3

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 draft 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 1.2-12. Table 1.3-10 shows the PHEV

and EV packages built for the 2025MY in this proposal (note that PHEVs are shown as

REEVs in the table). Note that the PHEV and EV packages are included directly in the

master-set of packages for a 2025MY OMEGA run. We have not built a long preliminary-set

of PHEVs and EVs and ranked them based on TARF to determine which packages to include

in the master-set. This is because for each MYof interest we built the

REEV20/REEV40/EV75/EV100/EV150 with the maximum allowed weight reduction
2017 Draft Regulatory Impact Analysis

1-43

(applied weight reduction) of 20% even though for MYs 2016 and 2021 the maximum

allowed weight reduction under our phase-in caps was 10% and 15% for those MYs.I We

have done this 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 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 2016 and 2021 MYs than we have considered likely for other vehicle technologies.

Table 1.3-10 Master-set of 2025 MY PHEV (REEV) & EV Packages for all Vehicle

Types

OMEG

A

Pkg#

Appli

ed

Weig

ht

Rdxn

Package contents Trans 2025

Cost CO2%

120 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV20

8sp

DCTdry$9,489 74.8%

121 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV40

8sp

DCTdry$11,402 84.5%

122 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $10,056 100.0%

123 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $11,542 100.0%

124 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $15,036 100.0%

223 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV20

8sp

DCTdry$10,044 75.6%

224 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV40

8sp

DCTdry$12,211 85.0%

225 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $10,962 100.0%

226 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $12,719 100.0%

227 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $16,757 100.0%

323 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV20

8sp

DCTdry$10,121 75.6%

324 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV40

8sp

DCTdry$12,288 85.0%

325 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $11,039 100.0%

326 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $12,796 100.0%

327 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $16,834 100.0%

421 20.0%

4V DOHC

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV20

8sp

DCTwet$12,135 74.8%

422 20.0% 4V DOHC 8sp $15,186 84.5%

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 draft RIA.
2017 Draft Regulatory Impact Analysis

1-44

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV40

DCTwet

423 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,677 100.0%

424 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $15,094 100.0%

425 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $21,008 100.0%

522 20.0%

4V DOHC

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV20

8sp

DCTwet$12,485 75.2%

523 20.0%

4V DOHC

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV40

8sp

DCTwet$15,670 84.7%

524 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,908 100.0%

525 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $14,643 100.0%

526 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $20,280 100.0%

621 20.0%

4V DOHC

V8+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV20

8sp

DCTwet$12,747 75.2%

622 20.0%

4V DOHC

V8+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV40

8sp

DCTwet$15,931 84.7%

623 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,964 100.0%

624 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $14,699 100.0%

625 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $20,336 100.0%

723 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV20

8sp

DCTdry$11,799 74.9%

724 20.0%

4V DOHC

I4+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+G

DI+ATKCS+REEV40

8sp

DCTdry$14,851 84.5%

725 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,711 100.0%

726 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $15,128 100.0%

727 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $21,041 100.0%

822 20.0%

4V DOHC

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV20

8sp

DCTwet$11,747 74.0%

823 20.0%

4V DOHC

V6+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV40

8sp

DCTwet$14,522 84.0%

824 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,864 100.0%

825 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $15,107 100.0%

826 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $20,852 100.0%

1521 20.0%

4V DOHC

V8+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV20

8sp

DCTwet$12,670 75.2%

1522 20.0%

4V DOHC

V8+EFR2+LDB+ASL2+IACC2+EPS+Aero2+LRRT2+HEG+DCP+DVVL+

GDI+ATKCS+REEV40

8sp

DCTwet$15,854 84.7%

1523 20.0% EV75 mile+IACC2+Aero2+LRRT2+EPS N/A $12,886 100.0%

1524 20.0% EV100 mile+IACC2+Aero2+LRRT2+EPS N/A $14,622 100.0%

1525 20.0% EV150 mile+IACC2+Aero2+LRRT2+EPS N/A $20,259 100.0%

Note that the net weight reduction of these packages as a percent can be determined by cross-referencing the applied weight reduction shown

here with the proper cost table (PHEV20/40, EV75/100/150) shown in Section 1.2 and the vehicle class information shown in Table 1.3-1.

The end result is a master-set of roughly 25 packages for each vehicle type. Because

of the large number of total packages and the difficulty of presenting them all here, we have

placed in the docket (EPA-HQ-OAR-2010-0799) a memorandum that contains the master-set

of packages used for our 2016MY, 2021MY and 2025MY OMEGA runs.2

The remaining package building step in developing a set of OMEGA inputs is to rank

the master-set of packages according to TARF. The end result of this ranking is a ranked-set

of OMEGA packages that includes the package progression that OMEGA must follow when

determining which package to employ next. The package progression is key because
2017 Draft Regulatory Impact Analysis

1-45

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.

The docket memorandum mentioned earlier also contains a ranked-set of packages for each of

the master-sets we have created.3The ranked-set also includes the package progression

information. 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) Standards4.” 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.

In one of the most thorough technical responses to the NRC report, Patton et al5

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 2012-2016 light duty

vehicle GHG and CAFE standards, and has been improved to reflect updates required for the

proposed 2017-2025 light duty GHG rule.
2017 Draft Regulatory Impact Analysis

1-46

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 2012-2016 Light Duty Vehicle GHG rule6, 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 report7.

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,

 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)
2017 Draft Regulatory Impact Analysis

1-47

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 technologiesJ.

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.

J

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.
2017 Draft Regulatory Impact Analysis

1-48

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 12VStop-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% GHGreduction vs 2008 Ricardo baseline fuel consumption 0.022 gal/mi

33.9% GHGreduction 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

4Von 2VBaseline 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 IEratio, 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 12VSS

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
2017 Draft Regulatory Impact Analysis

1-49

The LP model has been updated from the MYs 2012-2016 final rule to support the

MYs 2017-2025 proposed 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 proposed rulemaking

The LP model was updated in conjunction with this rulemaking to provide more

flexibility in assessment of 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 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.
2017 Draft Regulatory Impact Analysis

1-50

 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 2012-2016 light-duty vehicle GHG rule.

Further simulation work was conducted by Ricardo from 2010-2011 to support EPA’s

analysis for the 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 baselineKvehicle simulation output files for the FTP and

HWFE test cycles helped quantify the distribution of fuel energy losses in the baseline LP

K 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.
2017 Draft Regulatory Impact Analysis

1-51

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

vehiclesL

 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,

Heywood8) and prior success with values used in the LP model for the 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:

L

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
2017 Draft Regulatory Impact Analysis

1-52

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 classM.

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:

=௟௘௨௙ܧ

௟௘௘௛ ௪ܧ

௅Ȁ஽ߟ ൈ௘௡௚௜௡௘ ߟ

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:

M

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
2017 Draft Regulatory Impact Analysis

1-53

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 regenN)

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 baselineO

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

N 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.

O 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
2017 Draft Regulatory Impact Analysis

1-54

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.
2017 Draft Regulatory Impact Analysis

1-55

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
2017 Draft Regulatory Impact Analysis

1-56

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 1.5-1Pand

provides a total of 14.9% reduction in GHG.

Table 1.5-1

P

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
2017 Draft Regulatory Impact Analysis

1-57

 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
2017 Draft Regulatory Impact Analysis

1-58

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
2017 Draft Regulatory Impact Analysis

1-59

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 proposed 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 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%
2017 Draft Regulatory Impact Analysis

1-60

simulation package results (for conventional stop-start and P2 hybrid packagesQ) compared to

the lumped parameter estimates.

Figure 1.5-4 Comparison of LP to simulation results for Small Car class

Q 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 P2Hybrid
2017 Draft Regulatory Impact Analysis

1-61

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 P2Hybrid

0

10

20

30

40

50

60

mpg

Large Car Nominal Results

Ricardo

LP results

ConventionalSS P2Hybrid
2017 Draft Regulatory Impact Analysis

1-62

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 P2Hybrid

0

5

10

15

20

25

30

35

40

45

50

mpg

Large MPV Nominal Results

Ricardo

LP results

ConventionalSS P2Hybrid
2017 Draft Regulatory Impact Analysis

1-63

Figure 1.5-9 Comparison of LP to simulation results for Truck class

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.

0

5

10

15

20

25

30

35

mpg

Truck Nominal Results

Ricardo

LP results

ConventionalSS P2Hybrid
2017 Draft Regulatory Impact Analysis

1-64

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

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)
2017 Draft Regulatory Impact Analysis

1-65

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

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
2017 Draft Regulatory Impact Analysis

1-66

References

2

“OMEGA Master-sets and Ranked-sets of Packages,” Memorandum to Air Docket EPAHQ-OAR-2010-0799 from Todd Sherwood, November 10, 2011.

3

“OMEGA Master-sets and Ranked-sets of Packages,” Memorandum to Air Docket EPAHQ-OAR-2010-0799 from Todd Sherwood, November 10, 2011.

4 National Research Council, “Effectiveness and Impact of Corporate Average Fuel Economy

(CAFE) Standards”, National Academy Press, 2002.

5

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.

6 Ref OMEGA description in RIA of 2012-2016 final rule

7 U.S. EPA, “Project Report: 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, Date TBD, Report# TBD, Docket EPA-HQ-OAR-2010-0799.

8 Heywood, J. Internal Combustion Engine Fundamentals. Figures 13-9 and 13-10, p. 723.

McGraw-Hill, 1988.
2017 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 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 it

this is a very time-consuming and resource-intensive task). As a result, over the past year,

EPA has begun to develop 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-reviewed9and has also recently been published.10 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)(1).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 develop the light duty regulatory

program but not for certification since it is not only feasible but common practice to certify

light duty vehicles based on chassis-based vehicle testing. For light-duty vehicles, EPA has

been developing this simulation tool for non-hybrid, hybrid, and electric vehicles, which is

capable of simulating a wide range of conventional and advanced engines, transmissions, and

vehicle technologies over various driving cycles. 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 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, It

is also capable of simulating non-hybrid vehicles with a Dual-Clutch Transmission (DCT),

under warmed-up conditions only. Additional simulation capabilities such as automatic

transmissions, cold-start conditions, engine start-stops, and hybrid/electric vehicles are being

developed by EPA for the final rule. In this proposal, we are using the current simulation tool

in a more limited manner: to determine the maximum credit potential for A/C efficiency and
Chapter 2

2-2

to determine the default credit value for the pre-defined active aerodynamic and electrical

load off-cycle technologies. See section 2.3 below.

The simulation tool 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 this tool in MATLAB/Simulink environment with a completely

open source code. For the 2017 to 2025 GHG proposal, EPA used the simulation tool to

quantify the amount of GHG emissions reduced by improvements in A/C systems and offcycle 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 the light-duty (LD) vehicle

simulation model, 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 prompts the user to enter desired inputs such as vehicle type,

engine technology type, driving cycle, etc. Then, it initializes all necessary vehicle model

parameters including engine maps, transmission gear ratios, and vehicle road load parameters.

After the initialization, the script runs the Simulink vehicle model over the desired driving

cycles. Upon completing the simulation, 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 version of the vehicle simulation tool is still in an early development

stage and provides only a handful of simulation capabilities in terms of vehicle types, engine

and transmission technologies, and driving cycles, it is undergoing constant upgrades and

improvements to include more technology choices and simulation flexibilities. In fact, the
2017 Regulatory Impact Analysis

2-3

first official version of the tool will have a Graphical User Interface (GUI) which will allow

the user to choose from different technologies and other simulation options while making the

use of the tool much easier and straightforward. The Section 2.4.2 will discuss and address

these additional choices and simulation capabilities that are being planned for the improved

version of the tool.

9

“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.

10 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.
Chapter 2

Figure

2-4

2.2-1 LD Vehicle Simulation Tool
2017 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 can be 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.
2017 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 the LD vehicle simulation tool,

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 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 the Proposed Rule

As mentioned previously, EPA used the vehicle simulation tool for the proposed 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 -- one 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. 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

engine maps were obtained by reverse-engineering the vehicle simulation results provided by
Chapter 2

2-8

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.11,12 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.131415 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
2017 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 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.16 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 end 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
Chapter 2

2-10

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 NREL14 and NESCCAF15 cited above.

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

very 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
2017 Regulatory Impact Analysis

2-11

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

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.R

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

R

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

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.

EPA is proposing to limit 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 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.
2017 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
Chapter 2

2-14

when additional cooling is not required by the engine. This reduces the drag of the vehicle,

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 proposing to provide a credit for active aerodynamic

technologies assuming that any of these technologies will achieve an aerodynamic drag of at

least 3% improvement. The proposed default value for the credit will be 0.6 g/mile for cars

and 1.0 g/mile for trucks, in accordance with the simulation results in

Table 2.3-3. It is conceivable that some systems can achieve better performance.

Manufacturers may apply for greater credit for better performing systems through the normal

application process described in Section III.C.5.b of the preamble to the proposed rule..

2.4 On-Going and Future Work

2.4.1 Simulation Tool Validation

Since the EPA’s full vehicle simulation tool is still in the development phase, it has

not been fully validated against actual 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 which had

been calibrated and tuned with Ricardo’s simulation results for a 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 the EPA’s full vehicle simulation tool 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
2017 Regulatory Impact Analysis

2-15

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, a full validation of the tool will be performed using

actual vehicle test data before the final rule.

2.4.2 Simulation Tool Upgrade

As mentioned previously, the EPA’s full light-duty vehicle simulation tool is still in

the development phase. 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 assistance in further analysis for the final rule.

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 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 will 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

add hybrid electric vehicle (HEV) simulation capabilities. The HEV simulation tool is being

currently developed within the EPA for power-split and P2 configurations. For both nonhybrid 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

References

11 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.

12 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.

13 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.

14 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.

15 Northeast States Center for a Clean Air Future, “Reducing Greenhouse Gas Emissions from

Light-Duty Motor Vehicles,” September, 2004.

16 EPA and DOT, “Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate

Average Fuel Economy Standards: Final Rule,” May 7, 2010.
2017 Draft Regulatory Impact Analysis

3-1

3 Results of Proposed and Alternative Standards

3.1 Introduction

This chapter provides the methodology from and results of the technical assessment of

the future vehicle scenarios presented in this proposal. 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.

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 16-18 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.S

EPA has described OMEGA’s specific methodologies and algorithms previously in

the model documentation,17 the model is publically available on the EPA website,18 and it has

recently been peer reviewed.19

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 target has been met, OMEGA reports out the cost and

societal benefits of doing so. OMEGA is capable of modeling two GHGs; carbon

dioxide (CO2) from fuel use and HFC refrigerant emissions from the air conditioning

S 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 model. A/C

improvements are relatively 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.
Chapter 3

3-2

(A/C) system. 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 spreadsheet output files also 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.T While 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.U

OMEGA can be used to model either a single vehicle model or any number

vehicle models. Vehicles can be those of specific manufacturers as in this analysis or

generic fleet-average vehicles as in the 2010 Technical Assessment Report supporting

the MY 2017-2025 NOI. Because OMEGA is an accounting model, the vehicles can

be described using only a relatively few number of terms. The most important of

these terms are the vehicle’s baseline 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. Information required 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.VIn 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 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 “cap”). The effectiveness, cost,

application limits of each technology package can also vary over time.W A list of

technologies or packages is provided for each vehicle type, providing the connection

to the specific vehicles being modeled and a description of these packages can be

found in Chapter 1 of this draft RIA (DRIA)

T Vehicle simulation models may be used in creating the inputs to OMEGA as discussed in Draft Joint TSD

Chapter 3 as well as Chapter 1 of the Draft RIA.

U While OMEGA does not model changes in vehicle sales, Draft RIA Chapter 8 discusses this topic.

V A vehicle’s footprint is the product of its track width and wheelbase, usually specified in terms of square feet.

W “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.
2017 Draft Regulatory Impact Analysis

3-3

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

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 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 constraints, as

discussed in Draft Joint TSD 3) to vehicles until the sales-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

also 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

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.

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 specific information about the technology

added to each vehicle and the resulting costs and emissions. Average costs and
Chapter 3

emissions per vehicle by manufacturer and industry

vehicle class.

3.3 OMEGA Model Structure

OMEGA includes several comp

assist users in preparing a baseline vehicle forecast,

packages,Yand calculating the degree to which technology is present on baseline vehicles.

The OMEGA core model collates this information and produces estimates of increases in

vehicle cost and CO2 reduction. Based on the OMEGA core model output, the technology

penetration of the new vehicle mix and the scenario impacts (fuel savings, emission impacts,

and other monetized benefits) are calculated by post

processors are Microsoft Excel spreadsheets and visual basic programs, while the

core model is an executable program written in the C# language.

OMEGA is designed to be flexible in a number of ways. Very few numerical values

are hard-coded in the model, and consequently, the model relies heavily on its input files. The

model utilizes five input files: Market, Technology, Fuels, Scenario, and Reference.

3.3-1 shows the (simplified) information flow through OMEGA, and how these fi

Figure 3.3-1

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 req

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

X

Joint Draft TSD Chapter 1

Y DRIA Chapter 1

3-4

emissions per vehicle by manufacturer and industry-wide are also determined for each

OMEGA Model Structure

OMEGA includes several components, including a number of pre-processors that

assist users in preparing a baseline vehicle forecast,Xcreating and ranking technology

and calculating the degree to which technology is present on baseline vehicles.

The OMEGA core model collates this information and produces estimates of increases in

reduction. Based on the OMEGA core model output, the technology

netration of the new vehicle mix and the scenario impacts (fuel savings, emission impacts,

and other monetized benefits) are calculated by post-processors. The pre- and post

processors are Microsoft Excel spreadsheets and visual basic programs, while the

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

coded in the model, and consequently, the model relies heavily on its input files. The

l 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

wide are also determined for each

processors that

creating and ranking technology

and calculating the degree to which technology is present on baseline vehicles.

The OMEGA core model collates this information and produces estimates of increases in

reduction. Based on the OMEGA core model output, the technology

netration of the new vehicle mix and the scenario impacts (fuel savings, emission impacts,

and postprocessors are Microsoft Excel spreadsheets and visual basic programs, while the OMEGA

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

l utilizes five input files: Market, Technology, Fuels, Scenario, and Reference. Figure

les interact.

OMEGA utilizes four basic sets of input data. The first, the market file, is a

uired 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
2017 Draft Regulatory Impact Analysis

3-5

which set of technologies can be applied to that vehicle. Chapter 1 of the Joint TSD contains

a description of how the vehicle reference fleets 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 2008 baseline fleet must be input. This prevents the model from adding technologies to

vehicles already having these technologies in the baseline. It also avoids the situation, for

example, where the model might try to add a basic engine improvement to a current hybrid

vehicle. Section 3.4.1.2 of this Draft Regulatory Impact Analysis (RIA) contains a detailed

discussion of how EPA accounts for technology present in the baseline fleet in OMEGA.

The second type of input data, the technology file is a description of the technologies

available to manufacturers, primarily their cost, effectiveness, and electricity consumption.

This information was described in Chapter 1 of this Draft RIA and Chapter 3 of the Draft

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 DRIA.

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 Draft Joint TSD.

The fourth type of data describes the CO2 emission standards being modeled. These

include the MY 2016 standards and the proposed standards. As described in more detail in

Chapter 5 of the Draft Joint TSD and briefly in section 3.8.5 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 3.8.5, below.

The input files used in this analysis, as well as the current version of the OMEGA core

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 Draft

Joint.TSD and in this RIA.

3.4 Model Inputs

3.4.1 Market Data

3.4.1.1 Vehicle platforms

As discussed in Draft Joint TSD Chapter 3 and in Chapter 1 of the DRIA, 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
Chapter 3

3-6

utility classes (Table 3.4-2) 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 Type # Name Cam Engine

1 Subcompact Car DOHC I4

2 Compact Car I4 DOHC I4

3 Midsize Car/Small MPV (unibody) DOHC I4

4 Compact Car/Small MPV (unibody) DOHC V6

5 Midsize/Large Car DOHC V6

6 Midsize Car/Large Car DOHC V8

7 Mid-sized MPV (unibody)/Small Truck DOHC I4

8 Midsize MPV (unibody)/Small Truck SOHC V6

9 Large MPV (unibody) SOHC V8

10 Large MPV (unibody) SOHC V8

11 Large Truck (+ Van) SOHC V6

12 Large Truck + Large MPV OHV V6

13 Large Truck (+ Van) OHV V8

14 Large Truck (+Van) SOHC3V V8

15 Large Car OHV V8

16 Large MPV (unibody) DOHC V6

17 Large MPV (unibody) DOHC V8

18 Large Truck (+ Van) DOHC V6

19 Large Truck (+ Van) DOHC V8

Table 3.4-2 Vehicle Types in the Technical Assessment Analysis

Utility

Class #

Utility Class Vehicle Use 1

Footprint Criteria Structure Criteria

1 Subcompact Auto Car Footprint <43 --

2 Compact Auto Car 43<=Footprint<46 --

3 Mid Size Auto Car 46<=Footprint<53 --

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.
2017 Draft Regulatory Impact Analysis

3-7

3.4.1.2 Accounting for technology already on vehicles

As mentioned above for the market data input file utilized by OMEGA, which

characterizes the vehicle fleet, our modeling accounts for the fact that many 2008 MY

vehicles are already equipped with one or more of the technologies discussed in Draft Joint

TSD 3. Because of the choice to apply technologies in packages, and because 2008 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 2008 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 2008 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 of each technology

package’s incremental effectiveness and incremental cost is affected by the technology

already present on the baseline vehicle. In this step , we calculate the degree to which a

technology package’s effectiveness is already present on the baseline vehicle, and produces a

value for each package termed the technology effectiveness basis, or TEB. 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 TEB for each applicable technology package is

determined as follows:
Chapter 3

3-8









































































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

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 MY 2008 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.Z

Z The OMEGA TEB/CEB calculator used in the analysis of the proposal did not properly calculate CEBs for

vehicles where a more efficient and less expensive engine was placed in a vehicle. We estimate that this issue
2017 Draft Regulatory Impact Analysis

3-9

3.4.1.3 Accounting for Net Mass Reduction and Safety related Mass reduction

For this analysis, EPA modified its application of mass reduction to be similar to that

used by NHTSA in the CAFE model analysis. In this methodology, and in contrast to the

approach taken in the MY 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 includes no net additional fatalities to the fleet.

Manufacturers may not necessarily apply mass reduction in this manner, but EPA

demonstrates that a technically feasible and economically practicable 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 2011 Kahane

report and the related adjustments for improvements in federal motor vehicle safety standards

(FMVSS) as discussed in Section II.G of the Preamble to the proposed rule, and are subject to

the same caveats.AA Changes to these coefficients would change the projected amount of

mass reduction projected for the fleet.

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 coefficients used in the analysis, reducing weight from trucks above 4,594 pounds and

from minivans, reduces fatalities. By contrast, this analysis implies 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

).

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 1.44% 12.38 0.884 1.58E-12

PC above 3106 0.47% 10.33 0.884 4.29E-13

LT below 4594 0.52% 14.77 0.884 6.79E-13

LT above 4594 -0.39% 14.43 0.884 -4.97E-13

Minivan -0.46% 8.30 0.884 -3.38E-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

causes an overestimate of compliance costs by approximately $25 across the fleet in MY 2025, and will update

the model appropriately in the final rulemaking.

AA Please note that the OMEGA safety assessment was performed with a draft version of the FMVSS adjustment

, that raises the impact of the coefficients by approximately 1% relative to the analysis conducted by NHTSA,

which uses an FMVSS adjustment of 0.874
Chapter 3

3-10

The mass reduction scoping tool contains the entire fleet discussed in TSD 1, along

with their curb weight, and their passenger car, light truck, minivan classification according to

the criteria in the 2011 Kahane report. Using this tool, EPA determined that a simulation of

fatality neutrality could result by assuming that no passenger car was light-weighted below

3,000 pounds, and no light trucks were reduced below 4,594 pounds. These values were

determined iteratively, with the end product a fatality neutral analysis. Vehicles above these

weight could have their weight reduced through mass reduction technology in the OMEGA

model. The per vehicle 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. This was not done in the MYs 2012-2016 analysis or in

the technical assessment report. A table of these weight impacts is presented in Draft Joint

TSD Chapter 3. EPA did not include a weight penalty for dieselization, but will consider

including such impacts in the final rulemaking. The per-vehicle limit on weight reduction

determined above is for net mass reduction, not for 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 2008 fleet.

To implement this schema, each vehicle in the 2008 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.
2017 Draft Regulatory Impact Analysis

3-11

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.

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 have an associated list of technology packages, costs and effectivenesses.BB

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.CC

The processes to build and rank technology packages for the technology file are described in

detail in Chapter 1 of the DRIA.

For this analysis, a separate technology file was developed for each 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 Draft Joint TSD Chapter 3, and also differ due to the different

limits on maximum penetrations of technologies.

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.

Equation 3.4-3 – Calculation of New CO2

 

AIE TEB

CO AIE CO t

t



 





1

12

2

1

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

BB 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.

CC 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 Draft Joint TSD.
Chapter 3

3-12

the baseline. Cost can be calculated for the application of the 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

t CostCostt1 TechCost CEB)1(*

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.

In light of the complex set of technology caps used in this analysis, EPA modified the

methodology used to generate the OMEGA technology input file relative to previous

analyses. As background, for both the MY 2012-2016 rulemaking analysis and the Technical

Assessment Report supporting the 2017-2025 NOI, the technology caps generally fell into a

few broad numeric categories. As an example, in the analysis supporting the 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

draft TSD, there are both more technologies and more technology cap levels considered in

this proposal. Thus, it was more difficult to construct packages with uniform sets of caps. As

a means of doing so in this proposal, these caps were incorporated into the OMEGA modeling

in one of two ways. Major engine technologies such as turbo-charging and downsizing,

hybridization, electrification and dieselization were directly controlled through caps in the

technology file. Maximum penetration rates of other technology 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.

For reference case runs, EPA used three sets of TEB/CEB files in order to model the

input caps.

- Set A is a normally Ranked Master-set (30%)

- Set B removes 8sp trans, IACC2, Aero2 from Set A (55%)
2017 Draft Regulatory Impact Analysis

3-13

- Set C removes CCP, DCP, Deac, DVVL, CCC, GDI, TDS18, TDS24, 6sp trans,

EPS, Stop-start, SAX, Aero1, DSL-Adv from Set B (15%)

For proposal and alternative runs in MY 2021, EPA used four sets of TEB/CEB files

in order to model the input caps.

- Set A is a normally Ranked Master-set (60%)

- Set B removes HEG & EFR2 from Set A (15%)

- Set C removes LRRT2 from Set B (5%)

- Set D removes 8sp trans, Aero2, IACC2 from Set C (20%)

In creating the OMEGA input market file and technology file, these sets were then

weighted together according to the fractions listed next to each set above. As an example,

eight speed transmissions are capped at 80% in 2021 (see TSD 3). When weighted together,

set D, which removes eight speed transmissions only gets 20% of the weighting in the cost,

effectiveness and electric conversion fraction. Using this method, in the OMEGA input file,

the cost, effectiveness and electricity consumption of each package was calculated to reflect

the weighted cost and effectiveness of each package after accounting for the weighting of the

sets.DD The technology penetrations are also calculated using the weighting of each set.

Using the combination of the set weighting, and the technology cap feature in the technology

input file, EPA reflects the analytic constraints. For the final rulemaking, EPA intends to

simplify this process. When a technology package is applied to fewer than 100% of the sales

of a vehicle model due to the market penetration cap, OMEGA tracks the sales volume of

vehicles with each technology package applied.

OMEGA also tracks electrical consumption 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

DD Please note that incremental effectiveness values were not simply weighted together, as the resulting rates

would not be correct. Therefore, EPA calculated the accurate CO2 and backcalculated the appropriate

incremental effectiveness values.
Chapter 3

3-14

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 =

୉୪ୣୡ୲୰୧ୡ୧୲୷ୡ୭୬ୱ୳୫ ୮୲୧୭୬

ሺ୥େ୓ଶ୰ୣୢ୳ୡ୲୧୭୬כ భమౝ౨౗ౣ ి

రరృ౨౗ౣ ౩ిో మ ∗ 

భౝ౗ౢౢ౥౤౜౫౛ౢ

ి౗౨ౘ౥౤ౙ౥౤౪౛౤౪౥౜౜౫౛ౢ ∗ 

యరబవౘ౪౫౦౛౨ౡ౭ ౞

ు౤౛౨ౝ౯ౙ౥౤౪౛౤౪౥౜ౝ౗౩౥ౢ౟౤౛ሺౘ౪౫ሻ)

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

3.5 The Scenario File

3.5.1 Reference Scenario

In order to determine the technology costs associated with this NPRM, EPA

performed three separate modeling exercises. The first was to determine the costs associated

with meeting the MY 2016 CO2 regulations. EPA considers the MY 2016 CO2 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 NPRM

alone, EPA seeks to subtract out any costs associated with meeting any existing standards

related to GHG emissions.

EPA assumes that in the absence of the proposed GHG and CAFE standards, the

reference case fleet in MY 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 MY 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.
2017 Draft Regulatory Impact Analysis

3-15

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 MY 2017-2025. One

important element supporting the proposed approach is that AEO2011 projects relatively

stable gasoline prices over the next 15 years. The average actual gasoline price in the U.S. for

the first nine months of 2011 of $3.57 per gallon ($3.38 in 2009 dollars)EE. However, the

AEO2011 reference case projects a 2011 price of $2.80 per gallon (in 2009 dollars), well

below actual prices. AEO2011 projects prices to be $3.25 in 2017, rising slightly to $3.54 per

gallon in 2025 (which is less than a 4 cent/year increase on average). 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.

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

seminal reference on new light-duty vehicle CO2 emissions and fuel economy.FF 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,GG 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 ) and

have 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 overcompliance 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 overcomplied during the entire 18-year period.

17 Previous OMEGA documentation for versions used in MYs 2012-2016 Final Rule (EPA420-B-09-035), Interim Joint TAR (EPA-420-B-10-042)

18 http://www.epa.gov/oms/climate/models.htm

19 EPA-420-R-09-016, September 2009.

EE The Energy Information Administration estimated the average regular unleaded gasoline price in the U.S. for

the first nine months of 2011 was $3.57 per gallon.

FF Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 through

2010, November 2010, available at www.epa.gov/otaq/fetrends.htm.

GG There are no EPA LD GHG emissions regulations prior to MY 2012.
Chapter 3

3-16

Table 3.5-1

Fuel Economy Data for Selected Manufacturers, 1986-2003—Cars

Year Standard GM Ford Chrysler

SalesWeighted

average

Delta Vehicle

Weight Toyota Honda Nissan

SalesWeighted

average

Delta Vehicle

Weight

Vehicle

Weight

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 2706 439

1987 27.5 27.2 26.5 27.7 27.1 -0.4 3149 32.9 32.8 29.3 31.5 4.0 2782 368

1988 27.5 28.1 27.0 28.5 27.8 0.3 3157 32.7 31.8 30.6 31.8 4.3 2779 378

1989 27.5 27.4 26.9 28.0 27.3 -0.2 3207 31.8 31.3 30.2 31.2 3.7 2822 385

1990 27.5 27.3 26.3 27.4 27.0 -0.5 3298 30.4 30.4 28.4 29.9 2.4 2943 355

1991 27.5 27.2 27.2 27.5 27.2 -0.3 3252 30.6 30.3 29.0 30.1 2.6 2950 303

1992 27.5 26.7 26.7 27.7 26.8 -0.7 3329 28.9 30.9 29.9 29.9 2.4 3051 279

1993 27.5 27.3 27.8 27.9 27.6 0.1 3269 29.0 32.2 29.1 30.1 2.6 3071 198

1994 27.5 27.5 27.1 26.2 27.2 -0.3 3334 29.1 32.1 29.8 30.3 2.8 3084 250

1995 27.5 27.3 27.6 28.2 27.6 0.1 3330 30.0 32.8 29.2 30.8 3.3 3102 228

1996 27.5 27.9 26.3 27.2 27.3 -0.2 3388 29.5 31.8 30.2 30.5 3.0 3126 262

1997 27.5 28.2 26.9 27.2 27.6 0.1 3353 29.8 32.1 29.6 30.6 3.1 3122 230

1998 27.5 27.6 27.3 28.3 27.6 0.1 3347 30.2 32.0 30.2 30.9 3.4 3249 98

1999 27.5 27.4 27.2 27.0 27.3 -0.2 3429 30.4 30.9 29.6 30.4 2.9 3280 148

2000 27.5 27.6 27.1 27.6 27.4 -0.1 3448 30.5 31.0 28.0 30.2 2.7 3258 190

2001 27.5 28.1 26.8 27.6 27.6 0.1 3463 31.3 32.2 28.3 31.0 3.5 3233 230

2002 27.5 28.5 27.1 27.0 27.8 0.3 3442 30.7 32.0 28.9 30.8 3.3 3303 140

2003 27.5 28.6 26.7 28.5 27.9 0.4 3506 32.4 32.7 27.9 31.5 4.0 3276 230

Average 1986-2003 -0.1 3.3 262
2017 Draft Regulatory Impact Analysis

3-17

Table 3.5-2

Fuel Economy Data for Selected Manufacturers, 1986-2003—Trucks

Year Standard GM Ford Chrysler

SalesWeighted

average

Delta Vehicle

Weight Toyota Honda Nissan

SalesWeighted

average

Delta Vehicle

Weight

Vehicle

Weight

delta

1986 20.0 20.2 20.3 20.7 20.3 0.3 3917 26.1 24.7 25.5 5.5 3240 677

1987 20.5 20.5 20.5 21.3 20.7 0.2 3876 25.9 23.5 24.9 4.4 3259 617

1988 20.5 20.2 20.6 21.4 20.6 0.1 3961 24.4 22.7 23.8 3.3 3352 609

1989 20.5 20.4 20.1 21.0 20.5 0.0 4016 23.2 23.7 23.3 2.8 3420 596

1990 20.0 19.8 20.2 21.4 20.3 0.3 4102 21.8 25.3 23.2 3.2 3528 574

1991 20.2 21.2 20.5 21.1 20.9 0.7 4026 22.4 24.8 23.1 2.9 3628 397

1992 20.2 20.3 20.2 21.3 20.5 0.3 4132 21.9 24.0 22.5 2.3 3620 512

1993 20.4 20.3 20.8 21.2 20.7 0.3 4141 22.1 23.7 22.7 2.3 3637 505

1994 20.5 20.2 20.8 20.5 20.5 0.0 4204 22.0 20.2 22.9 22.3 1.8 3711 494

1995 20.6 20.1 20.6 20.1 20.3 -0.3 4248 21.2 25.5 22.4 22.0 1.4 3797 452

1996 20.7 20.8 20.8 20.2 20.6 -0.1 4295 23.1 22.2 22.9 23.0 2.3 3678 617

1997 20.7 20.4 20.2 20.2 20.3 -0.4 4445 22.6 24.7 22.3 22.8 2.1 3734 711

1998 20.7 21.2 20.2 20.0 20.5 -0.2 4376 23.4 25.5 22.3 23.5 2.8 3762 614

1999 20.7 20.3 19.8 19.9 20.0 -0.7 4508 23.0 25.2 21.2 23.1 2.4 3943 564

2000 20.7 20.7 20.0 20.4 20.4 -0.3 4456 22.0 25.0 20.8 22.2 1.5 4098 359

2001 20.7 20.4 20.1 19.5 20.0 -0.7 4591 22.3 24.7 20.7 22.3 1.6 4125 465

2002 20.7 19.8 20.2 20.0 20.0 -0.7 4686 22.2 25.3 20.7 22.5 1.8 4149 537

2003 20.7 20.2 20.0 20.9 20.3 -0.4 4738 22.0 24.8 21.9 22.9 2.2 4195 544

Average 1986-2003 -0.1 2.6 547
Chapter 3

3-18

Since the MY 2012-2016 standards are footprint-based, every major manufacturer is

expected to be constrained by the new standards in 2016 and manufacturers of small vehicles

will not routinely over-comply as they had with the past universal standards.HH Thus, the

historical evidence and the footprint-based design of the 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. There are

additional factors that reinforce the historical evidence. 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 overcompliance for the overall fleet.20

Figure 3.5-2 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.II All projections of actual

GHG emissions and fuel economy performance in 2016 or any other future year are

projections, of course, and it is plausible that actual GHG emissions and fuel economy

performance in 2017-2025, absent more stringent standards, could be lower than projected if

there are shifts from car market share to truck market share, or to higher footprint levels.

Figure 3.5-2 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)

HH With the notable exception of manufacturers who only market electric vehicles or other limited product lines.

II 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.

-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/Horsepowerchangesrelativeto1975

Vehiclemilespergallon(adjusted)

MPG

weight

horsepower

22.0 mpg

22.5 mpg

102 hp

3203 lbs

220 hp

4009 lb
2017 Draft Regulatory Impact Analysis

3-19

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 caps. 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

alternative refrigerant cost 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 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 decline over time as a result of the learning effects

discussed in the RIA Chapter 1. To reflect this learning progression, but also that the

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 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 (Table 3.5-3 ). In the OMEGA projections, the vast

majority of companies do not use EVs or PHEVs to comply with the MY 2016 standards.

Five smaller companies under the technology restrictions set forth in this analysis, cannot

comply with the MY 2016 standards.JJ This finding is consistent with the MY 2012-2016 rule

analysis, and are Daimler, Geely-Volvo, Volkswagen, Porsche and Tata (which is comprised

of Jaguar and Land Rover vehicles in the U.S. fleet). 21

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 make

8%-10% of their fleet EVs and PHEVs by MY 2016. As an alternative to this choice, these

JJ 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. As described in 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.
Chapter 3

3-20

companies could exceed the 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 MY 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 proposed later MY standards.

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.

KK Again, this analytic choice increases the incremental costs of the MY 2017-MY 2025

program for these companies.

Table 3.5-3 – MY 2016 EV+PHEV Penetrations

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)

Daimler 17 - $1,506 8%

Geely-Volvo 18 - $1,869 9%

Porsche 46 23 $2,570 11%

Tata 20 - $1,826 10%

Volkswagen 10 - $645 5%

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.LL The Temporary

Leadtime Allowance Alternative Standards (TLAAS), as analyzed in RIA chapter 5 of the

KK In the OMEGA analysis, only BMW’s MY 2016 compliance costs increase (by ~$350) because EV and

PHEV technology was made unavailable.

LL The credit available for producing FFVs will have expired, although the real world usage credits will be

available.
2017 Draft Regulatory Impact Analysis

3-21

MY 2012-2016 rule, is projected have an impact of approximately 0.1 g/mile in MY 2016,

and expire afterwards. Therefore, no incentive credits are projected to be available to the

reference case. Off-cycle credits, which are designed to be environmentally neutral, would

only lower costs. These credits are not modeled here due to the difficult in predicting

manufacturers use of these credits under the MY 2016 program.

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

proposal 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 MY 2012-

2016 and proposed MY 2017-2025 GHG rules.

3.5.2 Control Scenarios

Similar to the reference scenario, OMEGA runs were conducted in 2021 and 2025 for

the proposal and 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 Draft Joint TSD Chapter 2. As

in MYs 2012-2016, the curves from that joint fitting process 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 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 for cars and

24.4 for light trucks..

EPA’s NPRM 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 is related to the standard level proposed. For purposes of

this cost modeling, we assume that this cap is never reached. This does not imply that EPA

has proposed a cap based on this criteria. The proposed PH/EV multipliers were not modeled

in this analysis, but may be considered in the final rule analysis. A discussion of the potential

impacts of these credits can be found in preamble section III.B and DRIA 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.MM

MM The costs for PHEVs and EVs in this rule reflect those costs discussed in Draft Joint TSD Chapter 3, and do

not reflect any tax incentives, as the availability of those tax incentives in this time frame is uncertain.
Chapter 3

3-22

The proposed credit for HEV and performance based pickups was also not modeled in

this proposal analysis of costs. Off-cycle credits, which are not modeled here, could only

reduce costs. A discussion of the potential impacts on a g/mile and total tons basis can be

found in DRIA chapter 4.

Like the reference case, car truck trading was allowed without limit. Depending on

comment and other new input, these proposed flexibilities may be modeled differently for the

final rule.

3.6 Fuels and reference data

Fuels data was based on AEO fuel prices, as documented in Chapter 4 of the Draft

Joint TSD. Estimates of carbon and energy content per gallon of liquid fuel are consistent

with the MY 2012-2016 rule analysis.

VMT used in the payback analysis, which is used for calculating TARFs, was

determined using the EPA benefits post-processor. As the general VMT formula used in this

proposal is dependent on a vehicle’s fuel cost per mile (see Draft Joint TSD Chapter 4), this

was determined in an iterative process.

3.7 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 proposal, and all alternatives allow

for averaging across a manufacturer’s cars and trucks, 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 proposed regulations which govern credit trading between these two vehicle classes.NN

The model then works with one manufacturer at a time to add technologies until that

manufacturer meets its applicable proposed 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 purchasers 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

NN The analysis for the control cases in this proposal was run with slightly different lifetime VMT estimates than

those proposed in the regulation. The impact is on the cost estimates is small and varies by manufacturer.
2017 Draft Regulatory Impact Analysis

3-23

five years.OO 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).PP Any residual value of the additional

technology which might remain when the vehicle is sold is not considered for this analysis.

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). 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 3.7-1 – Calculation of Manufacturer-Based Cost Effectiveness

= ௧݂ݑܽ݊ ܯ݂݂ܧݐݏ݋ܥ

ο݄ܶ݁ܿݐݏ݋ܥെ οܨܵ

οܥܱଶݔܸܯ ܶ௚௘௥௨௟௔௧௢௥௬

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.22 Please note that the

TARF equation does not consider attributes other than cost effectiveness 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 before the plug-in hybrid. The current TARF does not reflect potential consumer

concerns with the range limits of the electric vehicle, although it could be modified to do so.

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

OO For a fuller discussion of this topic see Section III.H

PP 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

3-24

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

component of the manufacturer-based net cost-effectiveness equation is not a measure of the

social cost of this proposed 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. QQ

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 draft RIA

contains further detail on the values of manufacturer-based net cost-effectiveness for the

various technology packages.

3.8 Analysis Results

3.8.1 Targets and Achieved Values

3.8.1.1 Reference Case

QQ 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 proposal, this factor was not included

in our determination of manufacturer-based net cost-effectiveness in the analyses.
2017 Draft Regulatory Impact Analysis

3-25

Table 3.8-1 Reference Case Targets and Projected Shortfall in MY 2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 222 - 222 222 342 - 119

BMW 228 285 245 243 234 284 3

Chrysler/Fiat 230 295 261 259 224 300 -

Daimler 239 301 256 254 251 329 17

Ferrari 235 - 235 235 386 - 152

Ford 230 306 259 256 232 304 -

Geely 232 280 248 247 246 302 17

General Motors 229 308 271 268 227 310 -

Honda 222 283 243 241 214 297 -

Hyundai 223 280 236 235 224 277 -

Kia 225 291 241 239 222 299 -

Lotus 206 - 206 206 241 - 35

Mazda 223 276 233 232 228 253 -

Mitsubishi 219 270 238 237 222 265 -

Nissan 227 294 249 247 222 303 -

Porsche 206 287 227 225 251 333 46

Spyker 222 280 231 230 249 325 30

Subaru 216 267 229 228 229 230 0

Suzuki 208 272 221 219 209 268 -

Tata 250 273 262 261 244 322 24

Tesla 206 - 206 206 - - -

Toyota 221 293 251 249 209 309 -

Volkswagen 219 296 236 234 222 326 9

Fleet 225 297 253 250 222 304 1
Chapter 3

3-26

Table 3.8-2 Reference Case Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 222 - 222 222 342 - 119

BMW 228 286 245 243 234 287 4

Chrysler/Fiat 229 294 259 257 225 299 -

Daimler 239 302 255 253 253 329 17

Ferrari 235 - 235 235 386 - 152

Ford 230 304 255 253 232 301 -

Geely 232 280 248 246 245 302 16

General Motors 229 307 269 266 226 307 -

Honda 222 282 242 240 214 299 -

Hyundai 223 280 236 234 224 277 -

Kia 224 292 240 239 221 303 -

Lotus 206 - 206 206 241 - 35

Mazda 223 277 233 232 228 255 -

Mitsubishi 219 270 238 236 222 265 -

Nissan 227 292 248 246 222 302 -

Porsche 206 287 226 224 251 333 46

Spyker 222 280 230 229 249 325 30

Subaru 216 267 229 227 228 231 0

Suzuki 208 272 220 219 209 268 -

Tata 250 273 261 260 244 322 22

Tesla 206 - 206 206 - - -

Toyota 221 292 249 247 209 309 -

Volkswagen 219 296 236 234 222 326 9

Fleet 225 295 251 248 222 303 1
2017 Draft Regulatory Impact Analysis

3-27

3.8.1.1 Proposal and Alternatives

Table 3.8-3 Proposal Targets and Projected Shortfall in MY 2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 171 - 171 171 193 - 22

BMW 175 236 193 191 181 222 -

Chrysler/Fiat 176 246 211 208 182 239 -

Daimler 184 252 203 201 180 263 -

Ferrari 181 - 181 181 220 - 39

Ford 177 261 209 205 188 243 -

Geely 178 230 196 194 176 234 -

General Motors 176 261 222 218 188 250 -

Honda 170 233 192 190 174 225 -

Hyundai 171 230 185 183 177 210 -

Kia 172 242 190 188 180 218 -

Lotus 157 - 157 157 157 - -

Mazda 171 226 182 181 178 197 -

Mitsubishi 168 220 188 186 178 204 -

Nissan 174 247 199 197 175 243 -

Porsche 157 237 179 176 149 260 -

Spyker 170 230 180 179 164 258 -

Subaru 165 217 179 177 180 177 -

Suzuki 158 222 171 170 162 207 -

Tata 193 223 209 208 169 243 -

Tesla 157 - 157 157 - - -

Toyota 169 245 202 199 175 238 -

Volkswagen 167 247 186 184 163 258 -

Fleet 173 249 203 199RR 178 239 -

RR While OMEGA does not model changes in vehicle sales, Draft RIA Chapter 8 discusses this topic.
Chapter 3

3-28

Table 3.8-4 Proposal Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 142 - 142 142 142 - -

BMW 146 194 160 159 145 196 -

Chrysler/Fiat 146 201 172 170 148 199 -

Daimler 153 208 167 166 146 230 -

Ferrari 150 - 150 150 159 - 9

Ford 147 213 170 167 153 200 -

Geely 148 189 162 160 141 204 -

General Motors 146 213 181 178 146 212 -

Honda 142 191 158 156 143 186 -

Hyundai 142 188 153 151 145 178 -

Kia 143 199 157 155 146 189 -

Lotus 131 - 131 131 131 - -

Mazda 142 186 150 149 145 172 -

Mitsubishi 139 180 154 153 144 171 -

Nissan 145 202 164 162 143 204 -

Porsche 131 195 146 144 119 231 -

Spyker 141 188 149 148 133 231 -

Subaru 137 177 147 146 147 149 -

Suzuki 132 181 141 140 132 179 -

Tata 161 182 172 171 134 208 -

Tesla 131 - 131 131 - - -

Toyota 141 200 165 163 140 201 -

Volkswagen 139 203 154 152 133 225 -

Fleet 144 203 166 163 144 202 -
2017 Draft Regulatory Impact Analysis

3-29

Table 3.8-5 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY

2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 171 - 171 171 193 0 22

BMW 175 256 199 197 186 230 -

Chrysler/Fiat 176 267 221 217 195 248 -

Daimler 184 273 209 206 188 263 -

Ferrari 181 - 181 181 220 0 39

Ford 177 284 217 213 196 253 -

Geely 178 250 203 201 186 235 -

General Motors 176 283 234 229 200 262 -

Honda 170 253 199 196 182 229 -

Hyundai 171 250 189 187 180 216 -

Kia 172 263 195 193 185 224 -

Lotus 157 - 157 157 157 0 -

Mazda 171 245 186 184 181 204 -

Mitsubishi 168 238 195 192 184 212 -

Nissan 174 267 206 203 185 245 -

Porsche 157 258 184 181 154 264 -

Spyker 170 249 183 181 168 259 -

Subaru 165 235 184 182 184 182 -

Suzuki 158 241 175 173 167 207 -

Tata 193 242 219 217 190 244 -

Tesla 157 - 157 157 0 0 -

Toyota 169 266 211 207 180 251 -

Volkswagen 167 268 191 188 170 258 -

Fleet 173 270 211 207 186 248 -
Chapter 3

3-30

Table 3.8-6 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY

2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 171 0 171 171 193 0 22

BMW 175 217 188 186 173 222 -

Chrysler/Fiat 176 227 201 199 176 227 -

Daimler 184 232 198 196 172 263 -

Ferrari 181 0 181 181 220 0 39

Ford 177 241 201 199 181 234 -

Geely 178 212 190 189 167 234 -

General Motors 176 241 211 208 175 242 -

Honda 170 215 186 184 169 216 -

Hyundai 171 212 181 179 171 210 -

Kia 172 223 185 184 174 217 -

Lotus 157 0 157 157 157 0 -

Mazda 171 208 178 177 174 195 -

Mitsubishi 168 202 181 180 170 198 -

Nissan 174 228 193 191 167 240 -

Porsche 157 219 174 172 143 259 -

Spyker 170 212 177 176 161 258 -

Subaru 165 199 174 173 173 177 -

Suzuki 158 204 168 167 159 204 -

Tata 193 205 199 199 162 231 -

Tesla 157 0 157 157 0 0 -

Toyota 169 226 194 192 167 228 -

Volkswagen 167 228 181 180 158 257 -

Fleet 173 230 195 193 171 232 -
2017 Draft Regulatory Impact Analysis

3-31

Table 3.8-7 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY 2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 190 0 190 190 193 0 2

BMW 196 236 208 206 197 233 -

Chrysler/Fiat 197 247 221 219 195 249 -

Daimler 205 253 218 217 198 267 -

Ferrari 202 0 202 202 220 0 18

Ford 197 262 222 219 199 259 -

Geely 199 231 210 209 191 244 -

General Motors 196 262 231 229 199 259 -

Honda 190 233 205 203 187 239 -

Hyundai 191 231 200 199 191 231 -

Kia 192 243 205 204 195 235 -

Lotus 176 0 176 176 176 0 -

Mazda 191 226 198 197 195 210 -

Mitsubishi 187 220 200 199 189 215 -

Nissan 194 248 213 211 188 259 -

Porsche 176 238 192 190 166 264 -

Spyker 190 230 196 196 184 260 -

Subaru 184 217 193 192 191 196 -

Suzuki 177 222 186 185 177 217 -

Tata 215 223 219 219 190 244 -

Tesla 176 0 176 176 0 0 -

Toyota 189 246 214 211 184 252 -

Volkswagen 187 248 201 199 184 258 -

Fleet 193 250 215 213 191 251 -
Chapter 3

3-32

Table 3.8-8 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2021

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car Achieved Truck Achieved

Shortfall

Aston Martin 151 0 151 151 0 193 42

BMW 155 236 179 177 222 178 -

Chrysler/Fiat 156 247 201 197 227 201 -

Daimler 163 253 188 185 262 188 -

Ferrari 160 0 160 160 0 220 60

Ford 157 262 196 192 228 196 -

Geely 158 231 183 180 234 183 -

General Motors 156 262 213 208 243 212 -

Honda 150 233 179 176 212 179 -

Hyundai 151 231 170 167 201 170 -

Kia 152 243 175 173 213 175 -

Lotus 139 0 139 139 0 139 -

Mazda 151 226 166 164 190 166 -

Mitsubishi 148 220 176 173 197 175 -

Nissan 154 248 186 183 232 186 -

Porsche 139 238 165 162 257 172 7

Spyker 150 230 164 162 258 164 -

Subaru 146 217 165 163 167 165 -

Suzuki 140 222 157 155 201 157 -

Tata 171 223 199 197 231 199 -

Tesla 139 0 139 139 0 0 -

Toyota 150 246 191 187 225 191 -

Volkswagen 148 248 171 168 254 171 -

Fleet 153 250 191 187 229 190 -
2017 Draft Regulatory Impact Analysis

3-33

Table 3.8-9 Alternative 1- (Trucks +20) Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 142 0 142 142 0 142 -

BMW 146 213 166 164 202 165 -

Chrysler/Fiat 146 221 182 178 207 182 -

Daimler 153 228 173 170 230 173 -

Ferrari 150 0 150 150 0 159 9

Ford 147 234 177 174 204 177 -

Geely 148 207 168 166 204 167 -

General Motors 146 234 192 188 219 192 -

Honda 142 210 164 162 194 163 -

Hyundai 142 207 157 155 184 157 -

Kia 143 218 161 159 194 161 -

Lotus 131 0 131 131 0 131 -

Mazda 142 204 154 152 172 154 -

Mitsubishi 139 198 161 159 176 161 -

Nissan 145 221 170 168 213 170 -

Porsche 131 214 151 149 231 151 -

Spyker 141 207 151 150 232 151 -

Subaru 137 195 152 150 157 152 -

Suzuki 132 200 145 143 183 144 -

Tata 161 200 181 179 215 181 -

Tesla 131 0 131 131 0 0 -

Toyota 141 220 173 170 206 173 -

Volkswagen 139 223 158 156 225 158 -

Fleet 144 223 173 170 208 173 -
Chapter 3

3-34

Table 3.8-10 Alternative 2- (Trucks -20) Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 142 0 142 142 142 0 -

BMW 146 174 154 153 136 196 -

Chrysler/Fiat 146 182 163 161 136 191 -

Daimler 153 187 162 161 138 230 -

Ferrari 150 0 150 150 159 0 9

Ford 147 192 163 161 146 194 -

Geely 148 170 156 155 130 204 -

General Motors 146 192 170 168 138 200 -

Honda 142 172 152 150 137 180 -

Hyundai 142 170 148 148 140 178 -

Kia 143 179 152 151 142 183 -

Lotus 131 0 131 131 131 0 -

Mazda 142 167 147 146 141 170 -

Mitsubishi 139 162 148 147 134 171 -

Nissan 145 182 157 156 134 203 -

Porsche 131 175 142 140 113 231 -

Spyker 141 170 146 145 130 231 -

Subaru 137 159 143 142 141 149 -

Suzuki 132 163 138 137 128 179 -

Tata 161 164 162 162 126 198 -

Tesla 131 0 131 131 0 0 -

Toyota 141 181 157 155 130 196 -

Volkswagen 139 183 149 148 127 225 -

Fleet 144 183 158 157 136 195 -
2017 Draft Regulatory Impact Analysis

3-35

Table 3.8-11 Alternative 3- (Cars +20) Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 162 0 162 162 162 0 -

BMW 166 194 174 173 162 202 -

Chrysler/Fiat 166 201 183 181 159 208 -

Daimler 174 208 183 182 165 233 -

Ferrari 171 0 171 171 171 0 -

Ford 168 213 183 182 165 217 -

Geely 169 189 175 175 158 210 -

General Motors 167 213 191 189 161 218 -

Honda 161 191 171 170 157 196 -

Hyundai 162 188 168 167 160 193 -

Kia 163 199 172 171 164 195 -

Lotus 149 0 149 149 149 0 -

Mazda 162 186 166 166 162 183 -

Mitsubishi 159 180 167 166 158 182 -

Nissan 165 202 177 176 155 221 -

Porsche 149 195 160 159 137 231 -

Spyker 161 188 165 165 153 232 -

Subaru 156 177 162 161 163 157 -

Suzuki 150 181 156 155 150 183 -

Tata 183 182 182 182 149 215 -

Tesla 149 0 149 149 0 0 -

Toyota 160 200 177 175 155 207 -

Volkswagen 159 203 169 167 152 225 -

Fleet 164 203 178 177 159 211 -
Chapter 3

3-36

Table 3.8-12 Alternative 4- (Cars -20) Targets and Projected Shortfall in MY 2025

Manufacturer Car Target Truck Target

Fleet Target

(VMT and

Sales Weighted)

Fleet Target (Sales

weighted)

Car

Achieved

Truck

Achieved Shortfall

Aston Martin 122 0 122 122 137 0 15

BMW 126 194 146 144 123 196 -

Chrysler/Fiat 126 201 162 158 135 191 -

Daimler 132 208 152 149 125 229 -

Ferrari 130 0 130 130 159 0 30

Ford 127 213 157 153 137 191 -

Geely 128 189 148 146 120 204 -

General Motors 126 213 171 167 139 200 -

Honda 122 191 145 142 130 173 -

Hyundai 122 188 137 135 126 175 -

Kia 123 199 142 139 129 182 -

Lotus 112 0 112 112 112 0 -

Mazda 122 186 134 133 128 158 -

Mitsubishi 120 180 142 140 127 165 -

Nissan 125 202 150 147 128 195 -

Porsche 112 195 133 130 101 229 -

Spyker 122 188 132 130 114 230 -

Subaru 118 177 133 131 132 138 -

Suzuki 113 181 127 125 116 168 -

Tata 139 182 161 159 122 198 -

Tesla 112 0 112 112 0 0 -

Toyota 121 200 153 150 127 190 -

Volkswagen 120 203 138 136 114 224 -

Fleet 124 203 153 150 129 192 -

3.8.1 Penetration of Selected Technologies

OMEGA model projected penetrations of selected technologies by manufacturer,

model year, and car/truck class are presented on the following pages. These tables show

results of both the reference case, the proposed standards as well as the four alternatives.

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. As described in 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.

Most obviously, while no manufacturer is actually restricted by the technology caps

modeled in this analysis, a smaller manufacturer with only a few vehicle platforms may

pursue a single technology path.
2017 Draft Regulatory Impact Analysis

3-37

The technology penetrations presented here are absolute, and include baseline

technologies. The analyses shown here represent a single path towards compliance, of which

there are many. The breadth of technology options in the Technical Assessment Report

analysis reflected these opportunities.

Table 3.8-13 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

20 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.

21 See 75 FR at 25457.

22 See OMEGA documentation at http://www.epa.gov/otaq/climate/models.htm.
Chapter 3

3-38

3.8.2 Projected Technology Penetrations in Reference Case

Table 3.8-14 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

EFR2

DI

DSL

Aston Martin -10% -9% 1% 42% 15% 0% 0% 0% 59% 25% 16% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

BMW -9% -8% 1% 47% 15% 0% 12% 0% 48% 26% 13% 0% 15% 15% 0% 0% 57% 0% 30% 0% 77% 13%

Chrysler/Fiat -7% -7% 0% 62% 13% 0% 4% 1% 53% 28% 3% 0% 1% 0% 0% 0% 0% 0% 10% 0% 75% 0%

Daimler -9% -8% 1% 43% 15% 0% 4% 11% 51% 28% 7% 0% 15% 15% 0% 0% 57% 0% 30% 0% 71% 14%

Ferrari -8% -8% 1% 42% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Ford -8% -8% 0% 50% 10% 0% 23% 8% 36% 20% 7% 0% 7% 2% 0% 0% 0% 0% 20% 0% 59% 0%

Geely -9% -8% 1% 52% 15% 0% 22% 4% 46% 25% 3% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

General Motors -7% -7% 0% 42% 7% 0% 19% 9% 36% 19% 6% 0% 2% 0% 0% 0% 0% 0% 16% 0% 50% 0%

Honda -2% -2% 0% 0% 0% 0% 13% 0% 50% 22% 12% 0% 0% 3% 0% 0% 0% 0% 5% 0% 0% 0%

Hyundai -3% -3% 0% 14% 0% 0% 15% 7% 39% 21% 7% 0% 0% 0% 0% 0% 0% 0% 5% 0% 14% 0%

Kia -2% -2% 0% 0% 0% 0% 9% 2% 46% 25% 9% 0% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0%

Lotus -1% 0% 1% 54% 15% 0% 0% 0% 10% 5% 85% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Mazda -4% -4% 0% 16% 10% 0% 16% 4% 37% 20% 17% 0% 3% 0% 0% 0% 0% 0% 26% 0% 26% 0%

Mitsubishi -6% -6% 0% 68% 15% 0% 5% 0% 50% 27% 9% 0% 15% 2% 0% 0% 0% 0% 26% 0% 85% 0%

Nissan -5% -5% 0% 23% 4% 0% 5% 1% 50% 27% 5% 0% 0% 1% 0% 0% 0% 0% 21% 0% 27% 0%

Porsche -4% -4% 1% 45% 15% 0% 5% 0% 25% 14% 56% 0% 15% 15% 0% 0% 57% 0% 30% 0% 75% 13%

Spyker -10% -9% 1% 57% 15% 0% 13% 0% 48% 26% 13% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Subaru -6% -6% 0% 39% 2% 0% 2% 0% 40% 22% 27% 0% 2% 0% 0% 0% 0% 0% 20% 0% 39% 0%

Suzuki 0% 0% 0% 68% 15% 0% 1% 0% 49% 27% 11% 0% 15% 2% 0% 0% 0% 0% 25% 0% 85% 0%

Tata -10% -9% 1% 42% 15% 0% 15% 0% 55% 30% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 0% 0% 0% 7% 2% 42% 22% 7% 0% 0% 15% 0% 0% 0% 0% 6% 0% 8% 0%

Volkswagen -8% -7% 1% 48% 15% 0% 10% 0% 50% 26% 14% 0% 15% 15% 0% 0% 57% 0% 30% 0% 86% 13%

Fleet -5% -5% 0% 27% 6% 0% 12% 4% 43% 23% 8% 0% 4% 6% 0% 0% 8% 0% 15% 0% 37% 2%
2017 Draft Regulatory Impact Analysis

3-39

Table 3.8-15 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

EFR2

DI

DSL

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -9% -9% 1% 60% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 75% 0

Chrysler/Fiat -7% -7% 0% 25% 7% 0% 56% 26% 6% 3% 3% 0% 7% 0% 0% 0% 0% 0% 27% 0% 32% 0

Daimler -10% -9% 1% 64% 13% 0% 55% 45% 0% 0% 0% 0% 13% 15% 0% 0% 57% 0% 30% 0% 64% 0

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -7% -7% 0% 56% 15% 0% 42% 21% 14% 8% 3% 0% 15% 2% 0% 0% 0% 0% 24% 0% 71% -

Geely -10% -9% 1% 57% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 0

General Motors -8% -8% 0% 33% 9% 0% 57% 28% 4% 2% 0% 0% 9% 0% 0% 0% 0% 0% 27% 0% 42% -

Honda -4% -4% 0% 19% 0% 0% 49% 19% 21% 11% 0% 0% 0% 0% 0% 0% 0% 0% 14% 0% 19% -

Hyundai -5% -5% 0% 85% 0% 0% 61% 27% 5% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 85% -

Kia -4% -4% 0% 39% 0% 0% 66% 30% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 16% 0% 39% -

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -10% -10% 0% 73% 15% 0% 49% 19% 19% 10% 2% 0% 10% 0% 0% 0% 0% 0% 17% 0% 88% -

Mitsubishi -10% -10% 0% 68% 15% 0% 39% 19% 19% 11% 0% 0% 15% 2% 0% 0% 0% 0% 30% 0% 85% -

Nissan -5% -5% 0% 57% 15% 0% 54% 23% 12% 7% 2% 0% 10% 0% 0% 0% 0% 0% 22% 0% 72% -

Porsche -10% -9% 1% 59% 15% 0% 69% 30% 0% 0% 1% 0% 15% 15% 0% 0% 57% 0% 30% 0% 87% 0

Spyker -3% -2% 1% 57% 15% 0% 55% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 0

Subaru -10% -10% 0% 70% 15% 0% 18% 7% 37% 20% 8% 0% 5% 0% 0% 0% 0% 0% 8% 0% 85% -

Suzuki -8% -8% 0% 67% 15% 0% 58% 24% 11% 6% 0% 0% 15% 3% 0% 0% 0% 0% 30% 0% 85% -

Tata -7% -6% 1% 55% 15% 0% 51% 20% 19% 10% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 0

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -3% -3% 0% 13% 0% 0% 56% 24% 7% 4% 3% 0% 0% 5% 0% 0% 0% 0% 21% 0% 19% -

Volkswagen -10% -9% 1% 57% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 87% 0

Fleet -6% -6% 0% 37% 8% 0% 54% 25% 9% 5% 2% 0% 7% 3% 0% 0% 5% 0% 23% 0% 46% 0
Chapter 3

3-40

Table 3.8-16 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

EFR2

DI

DSL

Aston Martin -10% -9% 1% 42% 15% 0% 0% 0% 59% 25% 16% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

BMW -9% -8% 1% 51% 15% 0% 28% 8% 36% 19% 9% 0% 15% 15% 0% 0% 57% 0% 30% 0% 77% 13%

Chrysler/Fiat -7% -7% 0% 45% 11% 0% 27% 12% 31% 17% 3% 0% 4% 0% 0% 0% 0% 0% 18% 0% 56% 0%

Daimler -9% -8% 1% 48% 14% 0% 16% 19% 39% 21% 5% 0% 14% 15% 0% 0% 57% 0% 30% 0% 69% 16%

Ferrari -8% -8% 1% 42% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Ford -7% -7% 0% 52% 12% 0% 30% 12% 29% 16% 6% 0% 10% 2% 0% 0% 0% 0% 21% 0% 63% 0%

Geely -9% -8% 1% 54% 15% 0% 37% 12% 32% 17% 2% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

General Motors -7% -7% 0% 37% 8% 0% 38% 18% 20% 11% 3% 0% 6% 0% 0% 0% 0% 0% 21% 0% 46% 0%

Honda -3% -3% 0% 6% 0% 0% 24% 6% 41% 19% 8% 0% 0% 2% 0% 0% 0% 0% 8% 0% 6% 0%

Hyundai -3% -3% 0% 28% 0% 0% 24% 11% 32% 17% 6% 0% 0% 0% 0% 0% 0% 0% 4% 0% 28% 0%

Kia -3% -3% 0% 9% 0% 0% 22% 9% 35% 19% 7% 0% 0% 0% 0% 0% 0% 0% 8% 0% 9% 0%

Lotus -1% 0% 1% 54% 15% 0% 0% 0% 10% 5% 85% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Mazda -5% -5% 0% 26% 11% 0% 22% 7% 34% 19% 14% 0% 4% 0% 0% 0% 0% 0% 24% 0% 37% 0%

Mitsubishi -8% -7% 0% 68% 15% 0% 17% 7% 39% 22% 6% 0% 15% 2% 0% 0% 0% 0% 27% 0% 85% 0%

Nissan -5% -5% 0% 33% 8% 0% 20% 8% 39% 21% 4% 0% 3% 1% 0% 0% 0% 0% 21% 0% 41% 0%

Porsche -6% -5% 1% 48% 15% 0% 20% 7% 19% 10% 43% 0% 15% 15% 0% 0% 57% 0% 30% 0% 78% 13%

Spyker -9% -8% 1% 57% 15% 0% 19% 4% 41% 23% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Subaru -7% -7% 0% 46% 5% 0% 6% 2% 39% 22% 22% 0% 3% 0% 0% 0% 0% 0% 17% 0% 50% 0%

Suzuki -2% -1% 0% 67% 15% 0% 11% 4% 42% 23% 9% 0% 15% 3% 0% 0% 0% 0% 26% 0% 85% 0%

Tata -8% -8% 1% 48% 15% 0% 33% 10% 37% 20% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 5% 0% 0% 26% 10% 28% 15% 5% 0% 0% 12% 0% 0% 0% 0% 12% 0% 12% 0%

Volkswagen -8% -7% 1% 50% 15% 0% 22% 6% 40% 21% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0% 86% 13%

Fleet -5% -5% 0% 30% 7% 0% 27% 11% 31% 16% 6% 0% 5% 5% 0% 0% 7% 0% 18% 0% 40% 2%
2017 Draft Regulatory Impact Analysis

3-41

Table 3.8-17 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

EFR2

DI

DSL

Aston

Martin -10% -9% 1% 42% 15% 0% 0% 0% 59% 25% 16% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

BMW -9% -8% 1% 47% 15% 0% 12% 0% 48% 26% 13% 0% 15% 15% 0% 0% 57% 0% 30% 0% 77% 13%

Chrysler/Fiat -7% -7% 0% 48% 13% 0% 3% 1% 53% 29% 3% 0% 1% 0% 0% 0% 0% 0% 15% 0% 61% 0%

Daimler -9% -8% 1% 43% 15% 0% 4% 11% 52% 28% 6% 0% 15% 15% 0% 0% 57% 0% 30% 0% 71% 14%

Ferrari -8% -8% 1% 42% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Ford -8% -8% 0% 51% 10% 0% 24% 8% 35% 19% 7% 0% 8% 1% 0% 0% 0% 0% 19% 0% 61% 0%

Geely -9% -8% 1% 52% 15% 0% 22% 4% 46% 25% 3% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

General

Motors -7% -7% 0% 43% 7% 0% 18% 8% 37% 20% 6% 0% 2% 0% 0% 0% 0% 0% 15% 0% 51% 0%

Honda -2% -2% 0% 0% 0% 0% 13% 0% 50% 22% 12% 0% 0% 3% 0% 0% 0% 0% 5% 0% 0% 0%

Hyundai -2% -2% 0% 13% 0% 0% 14% 7% 39% 21% 7% 0% 0% 0% 0% 0% 0% 0% 5% 0% 13% 0%

Kia -2% -2% 0% 0% 0% 0% 9% 2% 46% 25% 9% 0% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0%

Lotus -1% 0% 1% 54% 15% 0% 0% 0% 10% 5% 85% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Mazda -4% -4% 0% 15% 10% 0% 16% 4% 38% 20% 18% 0% 3% 0% 0% 0% 0% 0% 26% 0% 26% 0%

Mitsubishi -6% -6% 0% 68% 15% 0% 4% 0% 50% 27% 9% 0% 15% 2% 0% 0% 0% 0% 26% 0% 85% 0%

Nissan -5% -5% 0% 23% 4% 0% 5% 1% 50% 27% 5% 0% 0% 1% 0% 0% 0% 0% 20% 0% 27% 0%

Porsche -4% -4% 1% 45% 15% 0% 5% 0% 25% 14% 56% 0% 15% 15% 0% 0% 57% 0% 30% 0% 75% 13%

Spyker -10% -9% 1% 57% 15% 0% 13% 0% 48% 26% 13% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Subaru -6% -6% 0% 47% 2% 0% 2% 0% 40% 22% 27% 0% 2% 0% 0% 0% 0% 0% 17% 0% 47% 0%

Suzuki 0% 0% 0% 68% 15% 0% 1% 0% 49% 27% 11% 0% 15% 2% 0% 0% 0% 0% 25% 0% 85% 0%

Tata -10% -9% 1% 42% 15% 0% 15% 0% 55% 30% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 0% 0% 0% 7% 1% 52% 12% 7% 0% 0% 16% 0% 0% 0% 0% 6% 0% 8% 0%

Volkswagen -8% -7% 1% 48% 15% 0% 9% 0% 50% 26% 14% 0% 15% 15% 0% 0% 57% 0% 30% 0% 86% 13%

Fleet -5% -5% 0% 27% 6% 0% 12% 4% 45% 21% 8% 0% 4% 6% 0% 0% 8% 0% 15% 0% 36% 2%
Chapter 3

3-42

Table 3.8-18 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

EFR2

DI

DSL

Aston Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -9% -8% 1% 60% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 76% 13%

Chrysler/Fiat -7% -7% 0% 26% 8% 0% 56% 26% 6% 3% 3% 0% 8% 0% 0% 0% 0% 0% 27% 0% 34% 0%

Daimler -10% -9% 1% 64% 13% 0% 55% 45% 0% 0% 0% 0% 13% 15% 0% 0% 57% 0% 30% 0% 64% 24%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -7% -7% 0% 57% 15% 0% 41% 20% 15% 8% 3% 0% 15% 3% 0% 0% 0% 0% 24% 0% 71% 0%

Geely -10% -9% 1% 57% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

General Motors -8% -8% 0% 36% 10% 0% 58% 28% 4% 2% 0% 0% 10% 0% 0% 0% 0% 0% 26% 0% 46% 0%

Honda -4% -4% 0% 15% 0% 0% 49% 18% 21% 11% 0% 0% 0% 0% 0% 0% 0% 0% 16% 0% 15% 0%

Hyundai -5% -5% 0% 85% 0% 0% 60% 27% 5% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 85% 0%

Kia -5% -5% 0% 28% 0% 0% 66% 30% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 20% 0% 28% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -10% -10% 0% 72% 15% 0% 50% 20% 17% 9% 2% 0% 11% 0% 0% 0% 0% 0% 17% 0% 87% 0%

Mitsubishi -10% -10% 0% 68% 15% 0% 39% 19% 19% 11% 0% 0% 15% 2% 0% 0% 0% 0% 30% 0% 85% 0%

Nissan -5% -5% 0% 57% 12% 0% 54% 23% 12% 7% 2% 0% 11% 0% 0% 0% 0% 0% 23% 0% 70% 0%

Porsche -10% -9% 1% 59% 15% 0% 69% 30% 0% 0% 1% 0% 15% 15% 0% 0% 57% 0% 30% 0% 87% 13%

Spyker -3% -2% 1% 57% 15% 0% 55% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Subaru -10% -10% 0% 70% 15% 0% 18% 7% 37% 20% 8% 0% 5% 0% 0% 0% 0% 0% 8% 0% 85% 0%

Suzuki -8% -8% 0% 67% 15% 0% 58% 24% 11% 6% 0% 0% 15% 3% 0% 0% 0% 0% 30% 0% 85% 0%

Tata -7% -6% 1% 55% 15% 0% 51% 20% 19% 10% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -3% -3% 0% 9% 0% 0% 55% 23% 7% 4% 3% 0% 0% 6% 0% 0% 0% 0% 22% 0% 16% 0%

Volkswagen -10% -9% 1% 57% 15% 0% 70% 30% 0% 0% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 87% 13%

Fleet -6% -6% 0% 36% 8% 0% 54% 25% 9% 5% 2% 0% 7% 3% 0% 0% 5% 0% 23% 0% 46% 1%
2017 Draft Regulatory Impact Analysis

3-43

Table 3.8-19 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

EFR2

DI

DSL

Aston Martin -10% -9% 1% 42% 15% 0% 0% 0% 59% 25% 16% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

BMW -9% -8% 1% 51% 15% 0% 28% 8% 36% 19% 9% 0% 15% 15% 0% 0% 57% 0% 30% 0% 77% 13%

Chrysler/Fiat -7% -7% 0% 38% 11% 0% 26% 12% 33% 18% 3% 0% 4% 0% 0% 0% 0% 0% 20% 0% 49% 0%

Daimler -9% -8% 1% 48% 14% 0% 15% 18% 40% 22% 5% 0% 14% 15% 0% 0% 57% 0% 30% 0% 70% 16%

Ferrari -8% -8% 1% 42% 15% 0% 14% 0% 52% 28% 5% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Ford -7% -7% 0% 53% 12% 0% 29% 12% 29% 16% 6% 0% 10% 2% 0% 0% 0% 0% 21% 0% 64% 0%

Geely -9% -8% 1% 53% 15% 0% 36% 12% 33% 18% 2% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

General Motors -7% -7% 0% 40% 9% 0% 37% 18% 21% 11% 3% 0% 6% 0% 0% 0% 0% 0% 21% 0% 49% 0%

Honda -3% -3% 0% 4% 0% 0% 23% 5% 42% 19% 8% 0% 0% 2% 0% 0% 0% 0% 8% 0% 4% 0%

Hyundai -3% -3% 0% 27% 0% 0% 24% 11% 32% 18% 6% 0% 0% 0% 0% 0% 0% 0% 4% 0% 27% 0%

Kia -3% -3% 0% 6% 0% 0% 21% 8% 36% 20% 7% 0% 0% 0% 0% 0% 0% 0% 9% 0% 6% 0%

Lotus -1% 0% 1% 54% 15% 0% 0% 0% 10% 5% 85% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Mazda -5% -5% 0% 25% 11% 0% 22% 7% 34% 19% 15% 0% 4% 0% 0% 0% 0% 0% 25% 0% 36% 0%

Mitsubishi -7% -7% 0% 68% 15% 0% 16% 6% 40% 22% 6% 0% 15% 2% 0% 0% 0% 0% 27% 0% 85% 0%

Nissan -5% -5% 0% 33% 7% 0% 20% 8% 39% 21% 4% 0% 3% 1% 0% 0% 0% 0% 21% 0% 40% 0%

Porsche -6% -5% 1% 48% 15% 0% 19% 6% 20% 11% 44% 0% 15% 15% 0% 0% 57% 0% 30% 0% 78% 13%

Spyker -9% -8% 1% 57% 15% 0% 18% 4% 42% 23% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Subaru -7% -7% 0% 52% 5% 0% 6% 2% 39% 21% 23% 0% 3% 0% 0% 0% 0% 0% 15% 0% 55% 0%

Suzuki -1% -1% 0% 67% 15% 0% 11% 4% 42% 23% 9% 0% 15% 3% 0% 0% 0% 0% 26% 0% 85% 0%

Tata -9% -8% 1% 48% 15% 0% 32% 9% 38% 21% 0% 0% 15% 15% 0% 0% 57% 0% 30% 0% 72% 13%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 3% 0% 0% 25% 9% 36% 9% 5% 0% 0% 12% 0% 0% 0% 0% 12% 0% 11% 0%

Volkswagen -8% -7% 1% 50% 15% 0% 21% 6% 40% 21% 11% 0% 15% 15% 0% 0% 57% 0% 30% 0% 86% 13%

Fleet -5% -5% 0% 30% 6% 0% 26% 11% 33% 15% 6% 0% 5% 5% 0% 0% 7% 0% 18% 0% 39% 2%
Chapter 3

3-44

3.8.3 Projected Technology Penetrations in Proposal case

Table 3.8-20 Proposal 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -8% 4% 40% 26% 2% 0% 0% 18% 70% 10% 59% 28% 30% 2% 0% 0% 75% 80% 59% 98% 0%

Chrysler/Fiat -8% -8% 0% 89% 7% 1% 1% 2% 19% 76% 3% 60% 3% 0% 0% 0% 0% 75% 80% 60% 97% 0%

Daimler -13% -8% 5% 37% 21% 4% 0% 0% 17% 69% 5% 55% 25% 30% 8% 0% 0% 75% 80% 55% 91% 1%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -8% -8% 0% 76% 15% 1% 5% 21% 13% 52% 7% 59% 16% 2% 0% 0% 0% 74% 79% 59% 92% 0%

Geely -13% -9% 4% 43% 15% 8% 3% 11% 15% 61% 2% 55% 22% 30% 8% 0% 0% 75% 80% 55% 92% 0%

General

Motors -7% -7% 0% 48% 9% 1% 6% 23% 13% 52% 6% 60% 5% 0% 0% 0% 0% 75% 80% 60% 59% 0%

Honda -4% -4% 0% 15% 0% 0% 0% 0% 17% 68% 12% 58% 0% 3% 0% 0% 0% 73% 77% 58% 15% 0%

Hyundai -5% -5% 0% 38% 0% 0% 4% 18% 14% 56% 7% 60% 0% 0% 0% 0% 0% 75% 80% 60% 38% 0%

Kia -3% -3% 0% 20% 0% 0% 2% 7% 17% 66% 9% 60% 0% 0% 0% 0% 0% 31% 33% 60% 20% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -5% -5% 0% 80% 20% 0% 3% 12% 14% 54% 17% 60% 7% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -9% -8% 0% 64% 30% 0% 0% 0% 18% 74% 8% 60% 30% 6% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 65% 9% 0% 1% 3% 18% 73% 5% 59% 10% 1% 0% 0% 0% 74% 79% 59% 75% 0%

Porsche -8% -3% 5% 14% 25% 3% 0% 0% 12% 47% 27% 52% 27% 30% 14% 15% 4% 75% 80% 52% 86% 0%

Spyker -15% -9% 7% 27% 25% 3% 0% 0% 16% 64% 8% 53% 27% 30% 12% 4% 0% 75% 80% 53% 88% 0%

Subaru -8% -8% 0% 68% 30% 0% 0% 0% 15% 59% 26% 60% 30% 2% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -1% 0% 1% 46% 30% 0% 0% 0% 18% 73% 9% 60% 30% 24% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -15% -10% 6% 42% 7% 11% 0% 0% 18% 72% 0% 54% 19% 30% 10% 0% 0% 75% 80% 54% 90% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -3% -3% 0% 23% 0% 0% 1% 3% 15% 60% 7% 51% 0% 15% 0% 0% 0% 17% 18% 51% 24% 0%

Volkswagen -10% -7% 3% 34% 29% 1% 0% 0% 17% 67% 10% 57% 29% 30% 6% 1% 0% 75% 80% 57% 94% 0%

Fleet -6% -6% 1% 45% 10% 1% 2% 9% 15% 61% 8% 57% 9% 8% 1% 0% 0% 62% 67% 57% 60% 0%
2017 Draft Regulatory Impact Analysis

3-45

Table 3.8-21 Proposal 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -14% -12% 2% 73% 24% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -7% -7% 0% 35% 25% 3% 17% 69% 2% 9% 3% 60% 27% 0% 0% 0% 0% 75% 80% 60% 62% 0%

Daimler -15% -13% 2% 80% 14% 6% 20% 80% 0% 0% 0% 60% 20% 30% 0% 0% 0% 75% 80% 60% 89% 11%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -8% -8% 0% 75% 17% 6% 14% 55% 5% 21% 3% 59% 23% 2% 0% 0% 0% 73% 78% 59% 98% 0%

Geely -15% -13% 2% 72% 27% 2% 20% 80% 0% 0% 0% 60% 28% 30% 0% 0% 35% 75% 80% 60% 100% 0%

General

Motors -8% -8% 0% 33% 19% 5% 19% 75% 1% 5% 0% 60% 24% 0% 0% 0% 0% 75% 80% 60% 57% 0%

Honda -8% -8% 0% 72% 0% 0% 12% 50% 8% 30% 0% 60% 0% 0% 0% 0% 0% 75% 80% 60% 72% 0%

Hyundai -10% -10% 0% 70% 30% 0% 18% 72% 2% 8% 0% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -9% -9% 0% 99% 0% 0% 20% 79% 0% 0% 1% 60% 0% 0% 0% 0% 0% 75% 80% 60% 99% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -14% -14% 0% 70% 30% 0% 13% 51% 7% 27% 2% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -15% -14% 1% 66% 30% 0% 13% 52% 7% 28% 0% 60% 30% 4% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 73% 24% 3% 15% 61% 5% 18% 2% 60% 27% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Porsche -15% -13% 2% 55% 24% 8% 20% 79% 0% 0% 1% 60% 32% 30% 0% 0% 55% 75% 80% 60% 87% 13%

Spyker -4% -2% 2% 76% 19% 6% 20% 80% 0% 0% 0% 60% 24% 30% 0% 0% 70% 75% 80% 60% 100% 0%

Subaru -15% -15% 0% 70% 30% 0% 5% 20% 13% 54% 8% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -12% -12% 0% 64% 30% 0% 16% 63% 4% 16% 0% 60% 30% 6% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -11% -9% 2% 66% 10% 10% 13% 53% 6% 24% 0% 58% 20% 30% 4% 0% 9% 75% 80% 58% 96% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -4% -4% 0% 66% 0% 3% 16% 63% 3% 11% 3% 57% 3% 5% 0% 0% 0% 71% 76% 57% 69% 0%

Volkswagen -14% -12% 2% 73% 23% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Fleet -8% -8% 0% 59% 14% 4% 16% 65% 3% 13% 2% 59% 17% 4% 0% 0% 1% 74% 79% 59% 76% 0%
Chapter 3

3-46

Table 3.8-22 Proposal 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -13% -9% 4% 49% 25% 2% 5% 21% 13% 52% 8% 59% 28% 30% 1% 0% 0% 75% 80% 59% 99% 0%

Chrysler/Fiat -7% -7% 0% 64% 15% 2% 8% 32% 11% 45% 3% 60% 14% 0% 0% 0% 0% 75% 80% 60% 81% 0%

Daimler -14% -10% 4% 48% 20% 4% 5% 20% 13% 52% 4% 57% 24% 30% 6% 0% 0% 75% 80% 57% 91% 3%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -8% -8% 0% 75% 16% 3% 8% 32% 10% 42% 6% 59% 18% 2% 0% 0% 0% 74% 79% 59% 94% 0%

Geely -13% -10% 3% 52% 18% 6% 8% 32% 10% 42% 1% 57% 24% 30% 6% 0% 11% 75% 80% 57% 94% 0%

General

Motors -8% -8% 0% 41% 14% 3% 12% 49% 7% 29% 3% 60% 14% 0% 0% 0% 0% 75% 80% 60% 58% 0%

Honda -5% -5% 0% 33% 0% 0% 4% 15% 14% 56% 8% 59% 0% 2% 0% 0% 0% 73% 78% 59% 33% 0%

Hyundai -6% -6% 0% 45% 6% 0% 7% 29% 12% 46% 6% 60% 6% 0% 0% 0% 0% 75% 80% 60% 51% 0%

Kia -4% -4% 0% 37% 0% 0% 6% 23% 13% 51% 7% 60% 0% 0% 0% 0% 0% 41% 44% 60% 37% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -6% -6% 0% 78% 22% 0% 5% 19% 12% 50% 14% 60% 11% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -11% -10% 1% 64% 30% 0% 5% 18% 14% 58% 5% 60% 30% 6% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 68% 14% 1% 5% 21% 14% 56% 4% 60% 15% 1% 0% 0% 0% 75% 80% 60% 83% 0%

Porsche -10% -5% 4% 24% 25% 4% 5% 19% 9% 36% 21% 54% 29% 30% 11% 11% 16% 75% 80% 54% 86% 3%

Spyker -14% -8% 6% 34% 24% 3% 3% 11% 14% 55% 7% 54% 27% 30% 10% 3% 10% 75% 80% 54% 90% 0%

Subaru -10% -9% 0% 69% 30% 0% 1% 5% 14% 58% 22% 60% 30% 1% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -3% -2% 1% 49% 30% 0% 3% 11% 16% 63% 7% 60% 30% 21% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -13% -9% 4% 54% 9% 11% 7% 26% 12% 48% 0% 56% 19% 30% 7% 0% 4% 75% 80% 56% 93% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -3% -3% 0% 40% 0% 1% 7% 26% 10% 41% 5% 53% 1% 12% 0% 0% 0% 38% 40% 53% 41% 0%

Volkswagen -11% -8% 3% 42% 27% 1% 4% 16% 13% 54% 8% 57% 29% 30% 5% 0% 0% 75% 80% 57% 95% 0%

Fleet -7% -6% 0% 50% 11% 2% 7% 28% 11% 44% 6% 58% 12% 7% 1% 0% 0% 66% 71% 58% 65% 0%
2017 Draft Regulatory Impact Analysis

3-47

Table 3.8-23 Proposal 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -15% -9% 5% 0% 56% 5% 0% 0% 0% 81% 9% 90% 61% 28% 10% 0% 0% 100% 100% 90% 90% 0%

Chrysler/Fiat -12% -9% 2% 8% 70% 2% 0% 2% 0% 95% 2% 99% 72% 19% 1% 0% 0% 100% 100% 99% 99% 0%

Daimler -17% -10% 7% 1% 43% 9% 0% 0% 0% 80% 5% 85% 52% 32% 15% 0% 0% 100% 100% 85% 84% 1%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -11% 2% 15% 64% 4% 0% 27% 0% 62% 6% 95% 68% 13% 4% 0% 0% 99% 99% 95% 95% 0%

Geely -16% -9% 7% 3% 31% 16% 0% 13% 0% 71% 1% 85% 47% 41% 15% 0% 0% 100% 100% 85% 85% 0%

General

Motors -12% -11% 2% 11% 68% 2% 0% 28% 0% 65% 4% 97% 70% 15% 3% 0% 0% 100% 100% 97% 97% 0%

Honda -5% -5% 0% 24% 73% 0% 0% 0% 0% 85% 12% 97% 73% 3% 0% 0% 0% 97% 97% 97% 97% 0%

Hyundai -8% -8% 0% 25% 75% 0% 0% 22% 0% 71% 7% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -5% -5% 0% 43% 57% 0% 0% 7% 0% 84% 9% 100% 33% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -8% -6% 2% 4% 73% 0% 0% 14% 0% 71% 12% 98% 73% 21% 2% 0% 0% 100% 100% 98% 98% 0%

Mitsubishi -11% -9% 2% 4% 69% 0% 0% 0% 0% 88% 6% 94% 69% 21% 6% 0% 0% 100% 100% 94% 94% 0%

Nissan -8% -7% 1% 8% 73% 0% 0% 4% 0% 90% 4% 98% 73% 18% 1% 0% 0% 99% 99% 98% 98% 0%

Porsche -9% -3% 6% 0% 35% 1% 0% 0% 0% 53% 24% 78% 36% 29% 22% 13% 0% 100% 100% 78% 78% 0%

Spyker -19% -11% 8% 0% 47% 1% 0% 0% 0% 76% 6% 82% 48% 29% 18% 5% 0% 100% 100% 82% 82% 0%

Subaru -10% -9% 2% 5% 70% 0% 0% 0% 0% 73% 22% 95% 70% 20% 5% 0% 0% 100% 100% 95% 95% 0%

Suzuki -1% 0% 1% 0% 66% 0% 0% 0% 0% 83% 8% 91% 66% 25% 9% 0% 0% 100% 100% 91% 91% 0%

Tata -20% -11% 9% 0% 14% 26% 0% 0% 0% 85% 0% 85% 41% 44% 15% 0% 0% 100% 100% 85% 85% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -6% -5% 0% 20% 61% 1% 0% 3% 0% 75% 7% 84% 62% 17% 0% 0% 0% 84% 84% 84% 84% 0%

Volkswagen -11% -8% 4% 0% 60% 1% 0% 0% 0% 78% 10% 88% 60% 26% 12% 1% 0% 100% 100% 88% 88% 0%

Fleet -9% -8% 2% 14% 65% 2% 0% 11% 0% 75% 7% 93% 66% 15% 4% 0% 0% 96% 96% 93% 93% 0%
Chapter 3

3-48

Table 3.8-24 Proposal 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -18% -11% 7% 30% 61% 10% 0% 100% 0% 0% 0% 100% 70% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Chrysler/Fiat -13% -12% 1% 26% 61% 8% 0% 86% 0% 10% 2% 99% 70% 10% 1% 0% 0% 100% 100% 99% 99% 0%

Daimler -20% -12% 8% 46% 35% 19% 0% 100% 0% 0% 0% 100% 54% 50% 0% 0% 0% 100% 100% 100% 92% 8%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -12% -9% 3% 27% 40% 20% 0% 67% 0% 27% 2% 96% 60% 31% 3% 1% 0% 98% 98% 96% 96% 0%

Geely -20% -12% 8% 28% 66% 6% 0% 100% 0% 0% 0% 100% 72% 50% 0% 0% 0% 100% 100% 100% 100% 0%

General

Motors -12% -12% 0% 31% 51% 15% 0% 93% 0% 6% 0% 99% 66% 3% 1% 0% 0% 100% 100% 99% 99% 0%

Honda -15% -15% 0% 23% 75% 0% 0% 62% 0% 38% 0% 100% 75% 2% 0% 0% 0% 100% 100% 100% 100% 0%

Hyundai -20% -19% 1% 23% 74% 0% 0% 90% 0% 9% 0% 99% 74% 2% 1% 0% 0% 100% 100% 99% 99% 0%

Kia -14% -14% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -20% -18% 1% 17% 74% 0% 0% 66% 0% 32% 1% 100% 74% 8% 0% 0% 0% 100% 100% 100% 100% 0%

Mitsubishi -20% -18% 2% 16% 71% 0% 0% 65% 0% 31% 0% 96% 71% 9% 4% 0% 0% 100% 100% 96% 96% 0%

Nissan -9% -6% 3% 24% 59% 9% 0% 77% 0% 20% 1% 98% 68% 24% 2% 0% 0% 100% 100% 98% 98% 0%

Porsche -20% -12% 8% 39% 34% 28% 0% 99% 0% 0% 1% 100% 61% 50% 0% 0% 6% 100% 100% 100% 100% 0%

Spyker -5% -2% 3% 35% 46% 19% 0% 100% 0% 0% 0% 100% 65% 50% 0% 0% 19% 100% 100% 100% 100% 0%

Subaru -20% -17% 3% 6% 74% 0% 0% 25% 0% 69% 6% 99% 74% 19% 1% 0% 0% 100% 100% 99% 99% 0%

Suzuki -16% -16% 0% 20% 72% 0% 0% 79% 0% 17% 0% 97% 72% 5% 3% 0% 0% 100% 100% 97% 97% 0%

Tata -14% -7% 6% 33% 18% 33% 0% 66% 0% 26% 0% 92% 50% 41% 8% 0% 0% 100% 100% 92% 92% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -10% -10% 0% 27% 59% 8% 0% 78% 0% 14% 2% 94% 67% 7% 0% 0% 0% 94% 94% 94% 94% 0%

Volkswagen -19% -11% 7% 31% 58% 11% 0% 100% 0% 0% 0% 100% 69% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Fleet -13% -11% 1% 27% 57% 11% 0% 81% 0% 15% 1% 98% 67% 13% 1% 0% 0% 99% 99% 98% 97% 0%
2017 Draft Regulatory Impact Analysis

3-49

Table 3.8-25 Proposal 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -15% -10% 6% 8% 58% 6% 0% 26% 0% 59% 7% 92% 64% 34% 8% 0% 0% 100% 100% 92% 92% 0%

Chrysler/Fiat -12% -11% 2% 16% 66% 5% 0% 38% 0% 58% 2% 99% 71% 15% 1% 0% 0% 100% 100% 99% 99% 0%

Daimler -18% -11% 7% 11% 41% 11% 0% 23% 0% 62% 4% 88% 53% 36% 12% 0% 0% 100% 100% 88% 86% 2%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -11% 2% 18% 56% 9% 0% 39% 0% 51% 4% 95% 65% 19% 4% 0% 0% 99% 99% 95% 95% 0%

Geely -17% -10% 7% 11% 42% 13% 0% 39% 0% 50% 1% 89% 54% 44% 11% 0% 0% 100% 100% 89% 89% 0%

General

Motors -12% -11% 1% 21% 60% 8% 0% 59% 0% 37% 2% 98% 68% 10% 2% 0% 0% 100% 100% 98% 98% 0%

Honda -8% -8% 0% 24% 73% 0% 0% 18% 0% 71% 8% 98% 73% 3% 0% 0% 0% 98% 98% 98% 98% 0%

Hyundai -10% -10% 0% 25% 75% 0% 0% 35% 0% 58% 6% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -7% -7% 0% 39% 61% 0% 0% 27% 0% 66% 7% 100% 42% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -10% -8% 2% 6% 73% 0% 0% 23% 0% 65% 11% 98% 73% 19% 2% 0% 0% 100% 100% 98% 98% 0%

Mitsubishi -14% -12% 2% 8% 70% 0% 0% 21% 0% 69% 4% 95% 70% 17% 5% 0% 0% 100% 100% 95% 95% 0%

Nissan -8% -7% 1% 13% 69% 3% 0% 25% 0% 69% 3% 98% 72% 20% 2% 0% 0% 100% 100% 98% 98% 0%

Porsche -11% -5% 7% 8% 35% 7% 0% 21% 0% 42% 19% 83% 41% 34% 17% 10% 1% 100% 100% 83% 83% 0%

Spyker -17% -10% 7% 5% 47% 3% 0% 13% 0% 66% 6% 84% 50% 32% 16% 5% 2% 100% 100% 84% 84% 0%

Subaru -13% -10% 2% 6% 71% 0% 0% 6% 0% 72% 19% 96% 71% 19% 4% 0% 0% 100% 100% 96% 96% 0%

Suzuki -4% -3% 1% 3% 67% 0% 0% 14% 0% 72% 6% 92% 67% 22% 8% 0% 0% 100% 100% 92% 92% 0%

Tata -17% -9% 8% 15% 16% 29% 0% 31% 0% 58% 0% 88% 45% 43% 12% 0% 0% 100% 100% 88% 88% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -7% -7% 0% 23% 60% 4% 0% 30% 0% 53% 5% 88% 64% 13% 0% 0% 0% 88% 88% 88% 88% 0%

Volkswagen -13% -8% 5% 6% 60% 3% 0% 20% 0% 63% 8% 90% 62% 31% 10% 1% 0% 100% 100% 90% 90% 0%

Fleet -11% -9% 2% 19% 62% 5% 0% 34% 0% 55% 5% 94% 66% 15% 3% 0% 0% 97% 97% 94% 94% 0%
Chapter 3

3-50

3.8.4 Projected Technology Penetrations in Alternative Cases

Table 3.8-26 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

EFR2

DI

DSL

Aston Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -9% 3% 49% 26% 2% 0% 0% 17% 69% 12% 59% 28% 22% 1% 0% 0% 75% 80% 59% 99% 0%

Chrysler/Fiat -7% -7% 0% 33% 0% 1% 1% 2% 19% 76% 3% 60% 1% 0% 0% 0% 0% 75% 80% 60% 33% 0%

Daimler -13% -9% 4% 42% 21% 4% 0% 0% 17% 70% 8% 57% 25% 28% 4% 0% 0% 75% 80% 57% 95% 1%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -8% -8% 0% 44% 7% 1% 5% 21% 13% 52% 7% 59% 8% 2% 0% 0% 0% 74% 79% 59% 52% 0%

Geely -13% -9% 3% 47% 15% 8% 3% 11% 16% 63% 2% 57% 22% 26% 5% 0% 0% 75% 80% 57% 95% 0%

General

Motors -7% -7% 0% 13% 0% 0% 6% 23% 13% 52% 6% 60% 0% 0% 0% 0% 0% 75% 80% 60% 12% 0%

Honda -2% -2% 0% 15% 0% 0% 0% 0% 17% 68% 12% 58% 0% 3% 0% 0% 0% 25% 27% 58% 15% 0%

Hyundai -4% -4% 0% 33% 0% 0% 4% 18% 14% 56% 7% 60% 0% 0% 0% 0% 0% 75% 80% 60% 33% 0%

Kia -2% -2% 0% 20% 0% 0% 2% 7% 17% 66% 9% 60% 0% 0% 0% 0% 0% 6% 7% 60% 20% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -4% -4% 0% 68% 19% 0% 3% 12% 14% 54% 17% 60% 6% 0% 0% 0% 0% 75% 80% 60% 86% 0%

Mitsubishi -7% -7% 0% 75% 25% 0% 0% 0% 18% 73% 9% 60% 25% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 32% 0% 0% 1% 3% 18% 73% 5% 59% 0% 1% 0% 0% 0% 74% 79% 59% 32% 0%

Porsche -8% -3% 5% 16% 25% 3% 0% 0% 12% 47% 29% 53% 27% 30% 12% 15% 0% 75% 80% 53% 88% 0%

Spyker -15% -9% 6% 29% 25% 3% 0% 0% 16% 65% 8% 53% 27% 30% 11% 3% 0% 75% 80% 53% 89% 0%

Subaru -7% -7% 0% 95% 5% 0% 0% 0% 15% 59% 27% 60% 5% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -1% 0% 1% 57% 30% 0% 0% 0% 18% 72% 10% 60% 30% 13% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -15% -11% 4% 57% 7% 11% 0% 0% 20% 79% 0% 59% 19% 23% 2% 0% 0% 75% 80% 59% 98% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 23% 0% 0% 1% 3% 60% 15% 7% 51% 0% 15% 0% 0% 0% 2% 2% 51% 24% 0%

Volkswagen -10% -7% 3% 38% 29% 1% 0% 0% 17% 69% 11% 58% 29% 30% 3% 1% 0% 75% 80% 58% 97% 0%

Fleet -6% -5% 1% 31% 5% 1% 2% 9% 23% 53% 8% 57% 6% 7% 1% 0% 0% 53% 57% 57% 41% 0%
2017 Draft Regulatory Impact Analysis

3-51

Table 3.8-27 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -14% -13% 0% 73% 24% 3% 20% 80% 0% 0% 1% 60% 27% 6% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -7% -7% 0% 28% 3% 3% 17% 69% 2% 9% 3% 60% 6% 0% 0% 0% 0% 75% 80% 60% 34% 0%

Daimler -15% -13% 2% 80% 14% 6% 20% 80% 0% 0% 0% 60% 20% 30% 0% 0% 0% 75% 80% 60% 89% 11%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -7% -7% 0% 40% 16% 6% 14% 55% 5% 21% 3% 59% 22% 2% 0% 0% 0% 73% 78% 59% 62% 0%

Geely -15% -13% 2% 72% 27% 2% 20% 80% 0% 0% 0% 60% 28% 30% 0% 0% 0% 75% 80% 60% 100% 0%

General

Motors -8% -8% 0% 40% 0% 0% 19% 75% 1% 5% 0% 60% 0% 0% 0% 0% 0% 75% 80% 60% 40% 0%

Honda -6% -6% 0% 66% 0% 0% 12% 50% 8% 30% 0% 60% 0% 0% 0% 0% 0% 75% 80% 60% 66% 0%

Hyundai -10% -10% 0% 100% 0% 0% 18% 72% 2% 8% 0% 60% 0% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -5% -5% 0% 99% 0% 0% 20% 79% 0% 0% 1% 60% 0% 0% 0% 0% 0% 75% 80% 60% 99% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -10% -10% 0% 79% 21% 0% 13% 51% 7% 27% 2% 60% 21% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -11% -11% 0% 70% 30% 0% 13% 52% 7% 28% 0% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 82% 15% 3% 15% 61% 5% 18% 2% 60% 18% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Porsche -15% -13% 2% 78% 13% 8% 20% 79% 0% 0% 1% 60% 22% 30% 0% 0% 25% 75% 80% 60% 100% 0%

Spyker -4% -2% 2% 76% 19% 6% 20% 80% 0% 0% 0% 60% 24% 30% 0% 0% 46% 75% 80% 60% 100% 0%

Subaru -11% -11% 0% 70% 30% 0% 5% 20% 13% 54% 8% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -12% -12% 0% 64% 30% 0% 16% 63% 4% 16% 0% 60% 30% 6% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -11% -9% 2% 66% 10% 10% 13% 53% 6% 24% 0% 58% 20% 30% 4% 0% 0% 75% 80% 58% 96% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -3% -3% 0% 66% 0% 0% 16% 63% 6% 7% 3% 57% 0% 5% 0% 0% 0% 13% 14% 57% 66% 0%

Volkswagen -14% -12% 2% 73% 23% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Fleet -7% -7% 0% 57% 6% 2% 16% 65% 4% 12% 2% 59% 7% 4% 0% 0% 0% 62% 66% 59% 64% 0%
Chapter 3

3-52

Table 3.8-28 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -10% 2% 55% 25% 2% 5% 21% 13% 51% 9% 59% 28% 18% 1% 0% 0% 75% 80% 59% 99% 0%

Chrysler/Fiat -7% -7% 0% 31% 1% 1% 8% 32% 11% 45% 3% 60% 3% 0% 0% 0% 0% 75% 80% 60% 34% 0%

Daimler -13% -10% 4% 52% 20% 4% 5% 20% 13% 53% 6% 58% 24% 28% 3% 0% 0% 75% 80% 58% 93% 3%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -7% -7% 0% 43% 10% 3% 8% 32% 10% 42% 6% 59% 13% 2% 0% 0% 0% 74% 79% 59% 55% 0%

Geely -13% -10% 3% 55% 18% 6% 8% 32% 11% 44% 1% 58% 24% 27% 3% 0% 0% 75% 80% 58% 97% 0%

General

Motors -7% -7% 0% 26% 0% 0% 12% 49% 7% 29% 3% 60% 0% 0% 0% 0% 0% 75% 80% 60% 26% 0%

Honda -4% -4% 0% 31% 0% 0% 4% 15% 14% 56% 8% 59% 0% 2% 0% 0% 0% 40% 43% 59% 31% 0%

Hyundai -5% -5% 0% 47% 0% 0% 7% 29% 12% 46% 6% 60% 0% 0% 0% 0% 0% 75% 80% 60% 47% 0%

Kia -3% -3% 0% 37% 0% 0% 6% 23% 13% 51% 7% 60% 0% 0% 0% 0% 0% 22% 23% 60% 37% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -5% -5% 0% 70% 19% 0% 5% 19% 12% 50% 14% 60% 8% 0% 0% 0% 0% 75% 80% 60% 89% 0%

Mitsubishi -8% -8% 0% 73% 27% 0% 5% 18% 14% 57% 6% 60% 27% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -5% -5% 0% 47% 5% 1% 5% 21% 14% 56% 4% 60% 6% 1% 0% 0% 0% 75% 80% 60% 53% 0%

Porsche -9% -5% 4% 31% 22% 4% 5% 19% 9% 36% 22% 55% 26% 30% 9% 11% 6% 75% 80% 55% 91% 0%

Spyker -14% -8% 6% 36% 24% 3% 3% 11% 14% 55% 7% 54% 27% 30% 9% 2% 7% 75% 80% 54% 91% 0%

Subaru -8% -8% 0% 89% 11% 0% 1% 5% 14% 57% 22% 60% 11% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -3% -2% 1% 58% 30% 0% 3% 11% 16% 62% 8% 60% 30% 12% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -13% -10% 3% 61% 9% 11% 7% 26% 13% 52% 0% 58% 19% 26% 3% 0% 0% 75% 80% 58% 97% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 40% 0% 0% 7% 26% 39% 12% 5% 53% 0% 12% 0% 0% 0% 6% 7% 53% 40% 0%

Volkswagen -11% -8% 3% 45% 27% 1% 4% 16% 14% 55% 9% 59% 29% 30% 2% 0% 0% 75% 80% 59% 98% 0%

Fleet -6% -6% 0% 40% 5% 1% 7% 28% 17% 39% 6% 58% 6% 6% 0% 0% 0% 56% 60% 58% 49% 0%
2017 Draft Regulatory Impact Analysis

3-53

Table 3.8-29 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -8% 5% 37% 26% 2% 0% 0% 17% 68% 10% 57% 28% 30% 6% 0% 0% 75% 80% 57% 94% 0%

Chrysler/Fiat -10% -9% 0% 67% 28% 1% 1% 2% 19% 76% 2% 60% 29% 1% 0% 0% 0% 75% 80% 60% 98% 0%

Daimler -13% -8% 5% 33% 21% 4% 0% 0% 17% 69% 5% 55% 25% 30% 8% 4% 0% 75% 80% 55% 91% 1%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -10% -10% 0% 68% 27% 1% 5% 21% 13% 53% 7% 59% 28% 4% 0% 0% 0% 74% 79% 59% 98% 0%

Geely -13% -8% 5% 37% 15% 8% 3% 11% 15% 60% 2% 54% 22% 30% 9% 6% 0% 75% 80% 54% 91% 0%

General

Motors -10% -10% 0% 68% 28% 1% 6% 23% 13% 52% 5% 60% 29% 1% 0% 0% 0% 75% 80% 60% 98% 0%

Honda -4% -4% 0% 48% 0% 0% 0% 0% 17% 68% 12% 58% 0% 3% 0% 0% 0% 73% 77% 58% 48% 0%

Hyundai -5% -5% 0% 53% 7% 0% 4% 18% 14% 56% 7% 60% 7% 0% 0% 0% 0% 75% 80% 60% 60% 0%

Kia -4% -4% 0% 20% 0% 0% 2% 7% 17% 66% 9% 60% 0% 0% 0% 0% 0% 75% 80% 60% 20% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -6% -6% 0% 69% 30% 0% 3% 12% 14% 55% 16% 60% 30% 1% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -9% -7% 2% 45% 30% 0% 0% 0% 19% 75% 6% 60% 30% 25% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -6% -6% 0% 69% 30% 0% 1% 3% 18% 73% 5% 59% 30% 1% 0% 0% 0% 74% 79% 59% 99% 0%

Porsche -8% -3% 5% 11% 25% 3% 0% 0% 12% 46% 26% 50% 27% 30% 16% 15% 14% 75% 80% 50% 84% 0%

Spyker -16% -8% 7% 24% 25% 3% 0% 0% 16% 65% 7% 53% 27% 30% 12% 7% 0% 75% 80% 53% 88% 0%

Subaru -8% -7% 2% 53% 30% 0% 0% 0% 15% 61% 23% 60% 30% 17% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -1% 0% 1% 40% 30% 0% 0% 0% 18% 73% 8% 60% 30% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -15% -9% 6% 38% 7% 11% 0% 0% 18% 71% 0% 53% 19% 30% 11% 3% 0% 75% 80% 53% 89% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -4% -4% 0% 23% 0% 0% 1% 3% 15% 60% 7% 51% 0% 15% 0% 0% 0% 63% 68% 51% 24% 0%

Volkswagen -10% -7% 4% 32% 29% 1% 0% 0% 16% 66% 10% 55% 29% 30% 8% 1% 0% 75% 80% 55% 92% 0%

Fleet -7% -7% 1% 50% 17% 1% 2% 9% 15% 61% 8% 57% 18% 9% 1% 0% 0% 72% 77% 57% 73% 0%
Chapter 3

3-54

Table 3.8-30 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -14% -12% 2% 73% 24% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -9% -9% 0% 71% 25% 3% 17% 69% 2% 9% 3% 60% 27% 2% 0% 0% 0% 75% 80% 60% 100% 0%

Daimler -15% -13% 2% 80% 14% 6% 20% 80% 0% 0% 0% 60% 20% 30% 0% 0% 0% 75% 80% 60% 89% 11%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -9% -8% 1% 66% 17% 6% 14% 55% 5% 22% 2% 59% 23% 16% 0% 0% 0% 74% 79% 59% 98% 0%

Geely -15% -13% 2% 72% 27% 2% 20% 80% 0% 0% 0% 60% 28% 30% 0% 0% 35% 75% 80% 60% 100% 0%

General

Motors -10% -10% 0% 51% 20% 5% 19% 75% 1% 5% 0% 60% 25% 2% 0% 0% 0% 75% 80% 60% 77% 0%

Honda -8% -8% 0% 78% 22% 0% 12% 50% 8% 30% 0% 60% 22% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Hyundai -10% -10% 0% 70% 30% 0% 18% 72% 2% 8% 0% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -9% -9% 0% 91% 9% 0% 20% 79% 0% 0% 1% 60% 9% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -15% -15% 0% 69% 30% 0% 13% 51% 7% 27% 2% 60% 30% 1% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -15% -13% 2% 58% 30% 0% 13% 52% 7% 27% 0% 59% 30% 11% 1% 0% 0% 75% 80% 59% 99% 0%

Nissan -6% -6% 0% 73% 24% 3% 15% 61% 5% 18% 1% 60% 27% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Porsche -15% -13% 2% 48% 27% 8% 20% 79% 0% 0% 1% 60% 36% 30% 0% 0% 53% 75% 80% 60% 83% 17%

Spyker -4% -2% 2% 76% 19% 6% 20% 80% 0% 0% 0% 60% 24% 30% 0% 0% 70% 75% 80% 60% 100% 0%

Subaru -15% -15% 0% 70% 30% 0% 5% 20% 13% 54% 8% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -12% -12% 0% 63% 30% 0% 16% 63% 4% 16% 0% 59% 30% 6% 1% 0% 0% 75% 80% 59% 99% 0%

Tata -11% -9% 3% 59% 10% 10% 13% 53% 6% 23% 0% 57% 20% 30% 6% 5% 9% 75% 80% 57% 94% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -5% -5% 0% 73% 13% 3% 16% 63% 3% 11% 3% 57% 15% 5% 0% 0% 0% 71% 76% 57% 88% 0%

Volkswagen -14% -12% 2% 73% 23% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 31% 75% 80% 60% 100% 0%

Fleet -9% -9% 0% 66% 19% 4% 16% 65% 3% 13% 1% 59% 23% 7% 0% 0% 1% 74% 79% 59% 91% 0%
2017 Draft Regulatory Impact Analysis

3-55

Table 3.8-31 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -13% -9% 4% 46% 25% 2% 5% 21% 12% 50% 7% 58% 28% 30% 4% 0% 0% 75% 80% 58% 96% 0%

Chrysler/Fiat -9% -9% 0% 69% 27% 2% 8% 32% 11% 45% 3% 60% 28% 2% 0% 0% 0% 75% 80% 60% 99% 0%

Daimler -14% -9% 5% 45% 20% 4% 5% 20% 13% 52% 4% 56% 24% 30% 6% 3% 0% 75% 80% 56% 90% 3%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -10% -9% 1% 67% 23% 3% 8% 32% 11% 42% 5% 59% 26% 8% 0% 0% 0% 74% 79% 59% 98% 0%

Geely -14% -10% 4% 48% 18% 6% 8% 32% 10% 41% 1% 56% 24% 30% 6% 4% 11% 75% 80% 56% 94% 0%

General

Motors -10% -10% 0% 59% 24% 3% 12% 49% 7% 29% 3% 60% 27% 2% 0% 0% 0% 75% 80% 60% 88% 0%

Honda -5% -5% 0% 58% 7% 0% 4% 15% 14% 56% 8% 59% 7% 2% 0% 0% 0% 73% 78% 59% 64% 0%

Hyundai -6% -6% 0% 57% 12% 0% 7% 29% 12% 46% 6% 60% 12% 0% 0% 0% 0% 75% 80% 60% 68% 0%

Kia -5% -5% 0% 35% 2% 0% 6% 23% 13% 51% 7% 60% 2% 0% 0% 0% 0% 75% 80% 60% 38% 0%

Lotus -3% 0% 3% 16% 30% 0% 0% 0% 9% 35% 46% 54% 30% 30% 11% 13% 0% 75% 80% 54% 89% 0%

Mazda -7% -7% 0% 69% 30% 0% 5% 19% 13% 50% 13% 60% 30% 1% 0% 0% 0% 75% 80% 60% 100% 0%

Mitsubishi -11% -9% 2% 50% 30% 0% 5% 18% 15% 58% 4% 60% 30% 20% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -6% -6% 0% 70% 28% 1% 5% 21% 14% 56% 4% 60% 29% 1% 0% 0% 0% 75% 80% 60% 99% 0%

Porsche -10% -5% 5% 20% 25% 4% 5% 19% 9% 35% 20% 52% 29% 30% 13% 11% 23% 75% 80% 52% 84% 4%

Spyker -14% -8% 6% 31% 24% 3% 3% 11% 14% 55% 6% 54% 27% 30% 10% 6% 10% 75% 80% 54% 90% 0%

Subaru -10% -9% 1% 57% 30% 0% 1% 5% 15% 60% 20% 60% 30% 13% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -3% -2% 1% 44% 30% 0% 3% 11% 16% 63% 7% 60% 30% 26% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -13% -9% 4% 48% 9% 11% 7% 26% 12% 47% 0% 55% 19% 30% 8% 4% 4% 75% 80% 55% 92% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -5% -5% 0% 42% 5% 1% 7% 26% 10% 41% 5% 53% 6% 12% 0% 0% 0% 66% 71% 53% 49% 0%

Volkswagen -11% -8% 3% 41% 27% 1% 4% 16% 13% 52% 8% 56% 29% 30% 6% 0% 6% 75% 80% 56% 94% 0%

Fleet -8% -7% 1% 55% 18% 2% 7% 28% 11% 44% 5% 58% 20% 8% 1% 0% 1% 73% 78% 58% 79% 0%
Chapter 3

3-56

Table 3.8-32 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -11% 1% 67% 26% 2% 0% 0% 17% 68% 14% 60% 28% 4% 1% 0% 0% 75% 80% 60% 99% 0%

Chrysler/Fiat -7% -7% 0% 33% 0% 1% 1% 2% 19% 76% 3% 60% 1% 0% 0% 0% 0% 75% 80% 60% 33% 0%

Daimler -13% -10% 2% 57% 21% 4% 0% 0% 18% 71% 8% 58% 25% 14% 3% 0% 0% 75% 80% 58% 96% 1%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -8% -8% 0% 51% 0% 0% 5% 21% 13% 52% 7% 59% 0% 2% 0% 0% 0% 74% 79% 59% 50% 0%

Geely -13% -9% 3% 49% 15% 8% 3% 11% 16% 65% 2% 58% 22% 26% 3% 0% 0% 75% 80% 58% 97% 0%

General

Motors -7% -7% 0% 12% 0% 0% 6% 23% 13% 52% 6% 60% 0% 0% 0% 0% 0% 75% 80% 60% 13% 0%

Honda -2% -2% 0% 15% 0% 0% 0% 0% 38% 47% 12% 58% 0% 3% 0% 0% 0% 0% 0% 58% 15% 0%

Hyundai -3% -3% 0% 33% 0% 0% 4% 18% 20% 50% 7% 60% 0% 0% 0% 0% 0% 0% 0% 60% 33% 0%

Kia -2% -2% 0% 12% 0% 0% 2% 7% 83% 0% 9% 60% 0% 0% 0% 0% 0% 0% 0% 60% 12% 0%

Lotus -2% 0% 2% 27% 30% 0% 0% 0% 7% 26% 57% 54% 30% 30% 11% 3% 0% 75% 80% 54% 89% 0%

Mazda -4% -4% 0% 19% 1% 0% 3% 12% 14% 54% 17% 60% 1% 0% 0% 0% 0% 75% 80% 60% 21% 0%

Mitsubishi -6% -6% 0% 68% 15% 0% 0% 0% 18% 73% 9% 60% 0% 0% 0% 0% 0% 75% 80% 60% 83% 0%

Nissan -5% -5% 0% 32% 0% 0% 1% 3% 18% 73% 5% 59% 0% 1% 0% 0% 0% 45% 48% 59% 32% 0%

Porsche -7% -3% 4% 23% 25% 3% 0% 0% 11% 43% 34% 53% 27% 30% 12% 8% 0% 75% 80% 53% 88% 0%

Spyker -15% -10% 5% 36% 25% 3% 0% 0% 17% 67% 9% 56% 27% 30% 6% 0% 0% 75% 80% 56% 94% 0%

Subaru -6% -6% 0% 68% 3% 0% 0% 0% 15% 59% 27% 60% 3% 0% 0% 0% 0% 75% 80% 60% 71% 0%

Suzuki 0% 0% 0% 100% 0% 0% 0% 0% 18% 71% 12% 60% 0% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -15% -11% 4% 57% 7% 11% 0% 0% 20% 79% 0% 59% 19% 23% 2% 0% 0% 75% 80% 59% 98% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 19% 0% 0% 1% 3% 73% 2% 7% 51% 0% 15% 0% 0% 0% 0% 0% 51% 24% 0%

Volkswagen -9% -7% 2% 58% 29% 1% 0% 0% 17% 68% 15% 60% 29% 12% 1% 0% 0% 75% 80% 60% 99% 0%

Fleet -6% -5% 0% 32% 4% 0% 2% 9% 31% 46% 8% 58% 4% 5% 1% 0% 0% 43% 46% 58% 38% 0%
2017 Draft Regulatory Impact Analysis

3-57

Table 3.8-33 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -13% -13% 0% 73% 24% 3% 20% 80% 0% 0% 1% 60% 27% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -7% -7% 0% 28% 3% 1% 17% 69% 2% 9% 3% 60% 4% 0% 0% 0% 0% 75% 80% 60% 33% 0%

Daimler -15% -14% 1% 80% 14% 6% 20% 80% 0% 0% 0% 60% 20% 16% 0% 0% 0% 75% 80% 60% 89% 11%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -7% -7% 0% 44% 0% 4% 14% 55% 5% 21% 3% 59% 4% 2% 0% 0% 0% 73% 78% 59% 49% 0%

Geely -15% -15% 0% 72% 27% 2% 20% 80% 0% 0% 0% 60% 28% 3% 0% 0% 0% 75% 80% 60% 100% 0%

General

Motors -8% -8% 0% 42% 0% 0% 19% 75% 1% 5% 0% 60% 0% 0% 0% 0% 0% 75% 80% 60% 42% 0%

Honda -4% -4% 0% 66% 0% 0% 12% 50% 8% 30% 0% 60% 0% 0% 0% 0% 0% 26% 28% 60% 66% 0%

Hyundai -5% -5% 0% 100% 0% 0% 18% 72% 2% 8% 0% 60% 0% 0% 0% 0% 0% 15% 16% 60% 100% 0%

Kia -4% -4% 0% 99% 0% 0% 20% 79% 0% 0% 1% 60% 0% 0% 0% 0% 0% 0% 0% 60% 99% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -10% -10% 0% 53% 19% 0% 13% 51% 7% 27% 2% 60% 19% 0% 0% 0% 0% 75% 80% 60% 72% 0%

Mitsubishi -10% -10% 0% 81% 19% 0% 13% 52% 7% 28% 0% 60% 19% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Nissan -4% -4% 0% 59% 0% 3% 15% 61% 5% 18% 2% 60% 3% 0% 0% 0% 0% 75% 80% 60% 62% 0%

Porsche -15% -13% 2% 78% 13% 8% 20% 79% 0% 0% 1% 60% 22% 30% 0% 0% 25% 75% 80% 60% 100% 0%

Spyker -4% -2% 2% 76% 19% 6% 20% 80% 0% 0% 0% 60% 24% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Subaru -10% -10% 0% 23% 10% 0% 5% 20% 13% 54% 8% 60% 10% 0% 0% 0% 0% 75% 80% 60% 32% 0%

Suzuki -8% -8% 0% 70% 30% 0% 16% 63% 4% 16% 0% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -11% -9% 2% 66% 10% 10% 13% 53% 6% 24% 0% 58% 20% 30% 4% 0% 0% 75% 80% 58% 96% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -3% -3% 0% 66% 0% 0% 16% 63% 11% 2% 3% 57% 0% 5% 0% 0% 0% 13% 14% 57% 66% 0%

Volkswagen -14% -12% 2% 73% 23% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Fleet -6% -6% 0% 55% 2% 1% 16% 65% 5% 11% 2% 59% 4% 3% 0% 0% 0% 54% 58% 59% 59% 0%
Chapter 3

3-58

Table 3.8-34 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -12% 0% 69% 25% 2% 5% 21% 13% 50% 10% 60% 28% 3% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -7% -7% 0% 31% 1% 1% 8% 32% 11% 45% 3% 60% 2% 0% 0% 0% 0% 75% 80% 60% 33% 0%

Daimler -13% -11% 2% 63% 20% 4% 5% 20% 13% 53% 6% 59% 24% 15% 2% 0% 0% 75% 80% 59% 94% 3%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -7% -7% 0% 49% 0% 2% 8% 32% 10% 42% 6% 59% 2% 2% 0% 0% 0% 74% 79% 59% 50% 0%

Geely -13% -11% 2% 56% 18% 6% 8% 32% 11% 45% 2% 59% 24% 19% 2% 0% 0% 75% 80% 59% 98% 0%

General

Motors -8% -8% 0% 27% 0% 0% 12% 49% 7% 29% 3% 60% 0% 0% 0% 0% 0% 75% 80% 60% 27% 0%

Honda -3% -3% 0% 31% 0% 0% 4% 15% 29% 42% 8% 59% 0% 2% 0% 0% 0% 8% 9% 59% 31% 0%

Hyundai -3% -3% 0% 47% 0% 0% 7% 29% 16% 42% 6% 60% 0% 0% 0% 0% 0% 3% 3% 60% 47% 0%

Kia -3% -3% 0% 31% 0% 0% 6% 23% 64% 0% 7% 60% 0% 0% 0% 0% 0% 0% 0% 60% 31% 0%

Lotus -2% 0% 2% 27% 30% 0% 0% 0% 7% 26% 57% 54% 30% 30% 11% 3% 0% 75% 80% 54% 89% 0%

Mazda -5% -5% 0% 25% 5% 0% 5% 19% 12% 50% 14% 60% 5% 0% 0% 0% 0% 75% 80% 60% 30% 0%

Mitsubishi -8% -8% 0% 73% 17% 0% 5% 18% 14% 57% 6% 60% 7% 0% 0% 0% 0% 75% 80% 60% 89% 0%

Nissan -5% -5% 0% 40% 0% 1% 5% 21% 14% 56% 4% 60% 1% 1% 0% 0% 0% 54% 58% 60% 41% 0%

Porsche -9% -6% 3% 36% 22% 4% 5% 19% 8% 33% 26% 55% 26% 30% 9% 6% 6% 75% 80% 55% 91% 0%

Spyker -13% -9% 5% 42% 24% 3% 3% 11% 14% 58% 8% 57% 27% 30% 5% 0% 0% 75% 80% 57% 95% 0%

Subaru -7% -7% 0% 57% 5% 0% 1% 5% 14% 57% 22% 60% 5% 0% 0% 0% 0% 75% 80% 60% 62% 0%

Suzuki -1% -1% 0% 95% 5% 0% 3% 11% 15% 61% 10% 60% 5% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Tata -13% -10% 3% 61% 9% 11% 7% 26% 13% 52% 0% 58% 19% 26% 3% 0% 0% 75% 80% 58% 97% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -2% -2% 0% 37% 0% 0% 7% 26% 49% 2% 5% 53% 0% 12% 0% 0% 0% 5% 6% 53% 40% 0%

Volkswagen -10% -8% 2% 61% 27% 1% 4% 16% 14% 54% 12% 60% 29% 16% 0% 0% 0% 75% 80% 60% 100% 0%

Fleet -6% -6% 0% 40% 3% 1% 7% 28% 22% 34% 6% 58% 4% 4% 0% 0% 0% 47% 50% 58% 46% 0%
2017 Draft Regulatory Impact Analysis

3-59

Table 3.8-35 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -12% -7% 5% 30% 26% 2% 0% 0% 16% 65% 9% 54% 28% 30% 10% 1% 0% 75% 80% 54% 90% 0%

Chrysler/Fiat -10% -9% 0% 67% 28% 1% 1% 2% 19% 76% 2% 60% 29% 1% 0% 0% 0% 75% 80% 60% 97% 0%

Daimler -14% -7% 6% 26% 21% 4% 0% 0% 17% 66% 5% 53% 25% 30% 12% 6% 0% 75% 80% 53% 87% 1%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -11% -10% 1% 58% 27% 1% 5% 21% 13% 53% 6% 59% 28% 14% 0% 0% 0% 74% 79% 59% 98% 0%

Geely -14% -8% 6% 31% 15% 8% 3% 11% 15% 59% 1% 53% 22% 30% 11% 10% 0% 75% 80% 53% 89% 0%

General

Motors -10% -10% 0% 68% 28% 1% 6% 23% 13% 52% 6% 60% 29% 0% 0% 0% 0% 75% 80% 60% 97% 0%

Honda -4% -4% 0% 70% 14% 0% 0% 0% 17% 68% 12% 58% 5% 3% 0% 0% 0% 73% 77% 58% 84% 0%

Hyundai -6% -6% 0% 67% 30% 0% 4% 18% 14% 57% 7% 60% 30% 3% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -4% -4% 0% 69% 12% 0% 2% 7% 17% 66% 9% 60% 2% 0% 0% 0% 0% 75% 80% 60% 81% 0%

Lotus -4% 0% 4% 0% 15% 15% 0% 0% 9% 36% 39% 50% 30% 30% 16% 15% 30% 75% 80% 50% 75% 9%

Mazda -7% -5% 2% 43% 30% 0% 3% 12% 14% 58% 11% 59% 30% 26% 1% 0% 0% 75% 80% 59% 98% 0%

Mitsubishi -9% -6% 3% 37% 30% 0% 0% 0% 18% 73% 6% 58% 30% 30% 3% 0% 0% 75% 80% 58% 97% 0%

Nissan -7% -6% 1% 56% 30% 0% 1% 3% 18% 73% 4% 60% 30% 14% 0% 0% 0% 75% 80% 60% 99% 0%

Porsche -8% -3% 5% 0% 12% 15% 0% 0% 12% 46% 26% 50% 27% 30% 16% 15% 27% 75% 80% 50% 72% 11%

Spyker -16% -7% 9% 14% 25% 3% 0% 0% 16% 64% 6% 52% 27% 30% 13% 15% 4% 75% 80% 52% 87% 0%

Subaru -9% -6% 2% 42% 30% 0% 0% 0% 16% 64% 18% 59% 30% 27% 1% 0% 0% 75% 80% 59% 99% 0%

Suzuki -2% 0% 2% 33% 30% 0% 0% 0% 17% 69% 7% 56% 30% 30% 7% 0% 0% 75% 80% 56% 93% 0%

Tata -15% -9% 6% 37% 7% 11% 0% 0% 18% 71% 0% 53% 19% 30% 11% 3% 0% 75% 80% 53% 89% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -4% -4% 0% 28% 1% 0% 1% 3% 15% 60% 7% 51% 1% 15% 0% 0% 0% 63% 68% 51% 30% 0%

Volkswagen -11% -6% 4% 25% 29% 1% 0% 0% 16% 64% 9% 53% 29% 30% 11% 5% 1% 75% 80% 53% 89% 0%

Fleet -8% -7% 1% 51% 21% 1% 2% 9% 15% 61% 7% 57% 20% 12% 2% 1% 0% 72% 77% 57% 82% 0%
Chapter 3

3-60

Table 3.8-36 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -14% -12% 2% 73% 24% 3% 20% 80% 0% 0% 0% 60% 27% 30% 0% 0% 0% 75% 80% 60% 100% 0%

Chrysler/Fiat -9% -9% 0% 71% 25% 3% 17% 69% 2% 9% 3% 60% 27% 2% 0% 0% 0% 75% 80% 60% 100% 0%

Daimler -15% -13% 2% 80% 14% 6% 20% 80% 0% 0% 0% 60% 20% 30% 0% 0% 19% 75% 80% 60% 89% 11%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -10% -8% 2% 65% 17% 6% 14% 55% 5% 22% 2% 59% 23% 25% 1% 0% 0% 74% 79% 59% 98% 0%

Geely -15% -13% 2% 72% 27% 2% 20% 80% 0% 0% 0% 60% 28% 30% 0% 0% 35% 75% 80% 60% 100% 0%

General

Motors -10% -10% 0% 47% 20% 5% 19% 75% 1% 5% 0% 60% 25% 2% 0% 0% 0% 75% 80% 60% 73% 0%

Honda -9% -9% 0% 70% 30% 0% 12% 50% 8% 30% 0% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Hyundai -15% -15% 0% 67% 30% 0% 18% 72% 2% 8% 0% 60% 30% 3% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -9% -9% 0% 70% 30% 0% 20% 79% 0% 0% 1% 60% 30% 0% 0% 0% 0% 75% 80% 60% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -15% -13% 2% 59% 30% 0% 13% 51% 7% 27% 1% 60% 30% 11% 0% 0% 0% 75% 80% 60% 99% 0%

Mitsubishi -15% -13% 2% 57% 30% 0% 13% 52% 7% 27% 0% 59% 30% 11% 2% 0% 0% 75% 80% 59% 98% 0%

Nissan -7% -6% 1% 66% 24% 3% 15% 61% 4% 18% 1% 60% 27% 13% 0% 0% 0% 75% 80% 60% 100% 0%

Porsche -15% -13% 2% 30% 25% 15% 20% 79% 0% 0% 1% 60% 40% 30% 0% 0% 40% 75% 80% 60% 70% 30%

Spyker -4% -2% 2% 76% 19% 6% 20% 80% 0% 0% 0% 60% 24% 30% 0% 0% 70% 75% 80% 60% 100% 0%

Subaru -15% -12% 3% 47% 30% 0% 5% 20% 14% 56% 6% 60% 30% 23% 0% 0% 0% 75% 80% 60% 100% 0%

Suzuki -12% -12% 0% 61% 30% 0% 16% 63% 4% 15% 0% 59% 30% 6% 2% 0% 0% 75% 80% 59% 98% 0%

Tata -11% -9% 3% 59% 10% 10% 13% 53% 6% 23% 0% 57% 20% 30% 6% 5% 9% 75% 80% 57% 94% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -5% -5% 0% 71% 21% 3% 16% 63% 3% 11% 3% 57% 24% 5% 0% 0% 0% 71% 76% 57% 95% 0%

Volkswagen -14% -12% 2% 61% 29% 3% 20% 80% 0% 0% 0% 60% 32% 30% 0% 0% 63% 75% 80% 60% 93% 7%

Fleet -9% -9% 1% 62% 22% 4% 16% 65% 3% 13% 1% 59% 26% 9% 0% 0% 2% 74% 79% 59% 91% 0%
2017 Draft Regulatory Impact Analysis

3-61

Table 3.8-37 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

EFR2

DI

DSL

Aston

Martin -16% -7% 9% 0% 0% 15% 0% 0% 15% 61% 7% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

BMW -13% -8% 4% 42% 25% 2% 5% 21% 12% 48% 6% 55% 28% 30% 8% 1% 0% 75% 80% 55% 92% 0%

Chrysler/Fiat -9% -9% 0% 69% 27% 2% 8% 32% 11% 45% 3% 60% 28% 2% 0% 0% 0% 75% 80% 60% 98% 0%

Daimler -14% -9% 5% 40% 20% 4% 5% 20% 12% 50% 3% 54% 24% 30% 9% 5% 5% 75% 80% 54% 88% 3%

Ferrari -13% -7% 6% 0% 0% 15% 0% 0% 16% 65% 2% 50% 15% 30% 16% 15% 15% 75% 80% 50% 60% 24%

Ford -10% -9% 1% 60% 23% 3% 8% 32% 11% 42% 5% 59% 26% 18% 0% 0% 0% 74% 79% 59% 98% 0%

Geely -14% -9% 5% 43% 18% 6% 8% 32% 10% 41% 1% 55% 24% 30% 8% 7% 11% 75% 80% 55% 92% 0%

General

Motors -10% -10% 0% 58% 24% 3% 12% 49% 7% 29% 3% 60% 27% 1% 0% 0% 0% 75% 80% 60% 85% 0%

Honda -6% -6% 0% 70% 19% 0% 4% 15% 14% 56% 8% 59% 12% 2% 0% 0% 0% 73% 78% 59% 89% 0%

Hyundai -8% -8% 0% 67% 30% 0% 7% 29% 12% 47% 5% 60% 30% 3% 0% 0% 0% 75% 80% 60% 100% 0%

Kia -5% -5% 0% 69% 16% 0% 6% 23% 13% 51% 7% 60% 9% 0% 0% 0% 0% 75% 80% 60% 85% 0%

Lotus -4% 0% 4% 0% 15% 15% 0% 0% 9% 36% 39% 50% 30% 30% 16% 15% 30% 75% 80% 50% 75% 9%

Mazda -8% -6% 2% 46% 30% 0% 5% 19% 13% 52% 10% 59% 30% 23% 1% 0% 0% 75% 80% 59% 99% 0%

Mitsubishi -11% -9% 3% 44% 30% 0% 5% 18% 14% 57% 4% 59% 30% 23% 2% 0% 0% 75% 80% 59% 98% 0%

Nissan -7% -6% 1% 59% 28% 1% 5% 21% 14% 56% 3% 60% 29% 14% 0% 0% 0% 75% 80% 60% 99% 0%

Porsche -10% -5% 5% 7% 15% 15% 5% 19% 9% 35% 20% 52% 30% 30% 13% 11% 30% 75% 80% 52% 72% 16%

Spyker -14% -7% 8% 23% 24% 3% 3% 11% 14% 55% 5% 53% 27% 30% 12% 13% 13% 75% 80% 53% 88% 0%

Subaru -10% -8% 3% 43% 30% 0% 1% 5% 16% 62% 15% 59% 30% 26% 1% 0% 0% 75% 80% 59% 99% 0%

Suzuki -3% -2% 1% 38% 30% 0% 3% 11% 15% 59% 6% 56% 30% 26% 6% 0% 0% 75% 80% 56% 94% 0%

Tata -13% -9% 5% 48% 9% 11% 7% 26% 12% 47% 0% 55% 19% 30% 8% 4% 4% 75% 80% 55% 92% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -5% -5% 0% 45% 9% 1% 7% 26% 10% 41% 5% 53% 10% 12% 0% 0% 0% 66% 71% 53% 55% 0%

Volkswagen -11% -7% 4% 32% 29% 1% 4% 16% 13% 51% 7% 55% 30% 30% 9% 4% 14% 75% 80% 55% 90% 1%

Fleet -8% -7% 1% 55% 21% 2% 7% 28% 11% 44% 5% 58% 22% 11% 1% 0% 1% 73% 78% 58% 85% 0%
Chapter 3

3-62

Table 3.8-38 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -14% -10% 5% 3% 58% 5% 0% 0% 0% 81% 10% 91% 63% 25% 9% 0% 0% 100% 100% 91% 91% 0%

Chrysler/Fiat -10% -10% 0% 24% 71% 3% 0% 2% 0% 95% 2% 100% 74% 2% 0% 0% 0% 100% 100% 100% 99% 0%

Daimler -17% -11% 6% 1% 46% 9% 0% 0% 0% 83% 5% 88% 56% 32% 12% 0% 0% 100% 100% 88% 88% 1%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -12% -12% 0% 25% 68% 4% 0% 27% 0% 65% 6% 98% 71% 3% 1% 0% 0% 99% 99% 98% 98% 0%

Geely -16% -10% 6% 3% 32% 18% 0% 13% 0% 74% 1% 88% 50% 35% 12% 0% 0% 100% 100% 88% 88% 0%

General

Motors -10% -10% 0% 18% 70% 3% 0% 28% 0% 66% 6% 100% 73% 0% 0% 0% 0% 100% 100% 100% 91% 0%

Honda -4% -4% 0% 28% 60% 0% 0% 0% 0% 85% 12% 97% 36% 3% 0% 0% 0% 97% 97% 97% 89% 0%

Hyundai -5% -5% 0% 25% 75% 0% 0% 22% 0% 71% 7% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -4% -4% 0% 28% 45% 0% 0% 7% 0% 84% 9% 100% 21% 0% 0% 0% 0% 100% 100% 100% 73% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -8% -7% 1% 13% 73% 0% 0% 14% 0% 71% 14% 98% 73% 13% 2% 0% 0% 100% 100% 98% 98% 0%

Mitsubishi -11% -9% 2% 5% 73% 0% 0% 0% 0% 91% 7% 98% 73% 20% 2% 0% 0% 100% 100% 98% 98% 0%

Nissan -8% -7% 0% 20% 73% 0% 0% 4% 0% 90% 4% 98% 73% 6% 1% 0% 0% 99% 99% 98% 98% 0%

Porsche -9% -3% 6% 0% 38% 1% 0% 0% 0% 54% 27% 81% 39% 29% 19% 13% 0% 100% 100% 81% 81% 0%

Spyker -19% -12% 7% 0% 49% 1% 0% 0% 0% 76% 7% 83% 50% 29% 17% 4% 0% 100% 100% 83% 83% 0%

Subaru -10% -9% 2% 10% 71% 0% 0% 0% 0% 72% 23% 96% 71% 15% 4% 0% 0% 100% 100% 96% 96% 0%

Suzuki -1% 0% 1% 0% 67% 0% 0% 0% 0% 84% 8% 92% 67% 25% 8% 0% 0% 100% 100% 92% 92% 0%

Tata -20% -12% 8% 0% 16% 31% 0% 0% 0% 91% 0% 91% 47% 44% 9% 0% 0% 100% 100% 91% 91% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -4% -4% 0% 46% 20% 1% 0% 3% 0% 75% 7% 84% 15% 16% 0% 0% 0% 84% 84% 84% 68% 0%

Volkswagen -11% -8% 3% 0% 63% 1% 0% 0% 0% 81% 10% 91% 64% 26% 9% 1% 0% 100% 100% 91% 91% 0%

Fleet -8% -7% 1% 23% 57% 2% 0% 11% 0% 76% 8% 94% 54% 9% 2% 0% 0% 96% 96% 94% 88% 0%
2017 Draft Regulatory Impact Analysis

3-63

Table 3.8-39 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -17% -13% 4% 30% 61% 10% 0% 100% 0% 0% 0% 100% 70% 30% 0% 0% 0% 100% 100% 100% 100% 0%

Chrysler/Fiat -12% -12% 0% 24% 62% 8% 0% 86% 0% 11% 3% 99% 70% 0% 1% 0% 0% 100% 100% 99% 94% 0%

Daimler -20% -12% 8% 46% 35% 19% 0% 100% 0% 0% 0% 100% 54% 50% 0% 0% 0% 100% 100% 100% 92% 8%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -12% -10% 2% 32% 41% 20% 0% 67% 0% 27% 2% 96% 61% 26% 2% 0% 0% 98% 98% 96% 96% 0%

Geely -20% -12% 8% 28% 66% 6% 0% 100% 0% 0% 0% 100% 72% 50% 0% 0% 0% 100% 100% 100% 100% 0%

General

Motors -12% -12% 0% 15% 53% 15% 0% 93% 0% 7% 0% 100% 67% 2% 0% 0% 0% 100% 100% 100% 84% 0%

Honda -10% -10% 0% 25% 75% 0% 0% 62% 0% 38% 0% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Hyundai -17% -17% 0% 25% 75% 0% 0% 90% 0% 10% 0% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -10% -10% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -20% -18% 1% 17% 75% 0% 0% 66% 0% 32% 1% 100% 75% 8% 0% 0% 0% 100% 100% 100% 100% 0%

Mitsubishi -20% -18% 2% 16% 73% 0% 0% 65% 0% 34% 0% 98% 73% 9% 2% 0% 0% 100% 100% 98% 98% 0%

Nissan -8% -8% 0% 24% 60% 9% 0% 77% 0% 22% 1% 99% 70% 8% 1% 0% 0% 100% 100% 99% 99% 0%

Porsche -20% -12% 8% 39% 34% 28% 0% 99% 0% 0% 1% 100% 61% 50% 0% 0% 6% 100% 100% 100% 100% 0%

Spyker -5% -2% 3% 35% 46% 19% 0% 100% 0% 0% 0% 100% 65% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Subaru -20% -20% 0% 25% 75% 0% 0% 25% 0% 67% 8% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Suzuki -16% -16% 0% 20% 73% 0% 0% 79% 0% 19% 0% 98% 73% 5% 2% 0% 0% 100% 100% 98% 98% 0%

Tata -13% -7% 6% 33% 20% 33% 0% 66% 0% 29% 0% 95% 53% 41% 5% 0% 0% 100% 100% 95% 95% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -6% -6% 0% 28% 59% 8% 0% 78% 0% 14% 3% 94% 67% 6% 0% 0% 0% 94% 94% 94% 94% 0%

Volkswagen -19% -11% 7% 31% 58% 11% 0% 100% 0% 0% 0% 100% 69% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Fleet -11% -10% 1% 24% 57% 11% 0% 81% 0% 16% 1% 98% 68% 9% 0% 0% 0% 99% 99% 98% 94% 0%
Chapter 3

3-64

Table 3.8-40 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -15% -11% 5% 10% 59% 6% 0% 26% 0% 59% 8% 93% 65% 26% 7% 0% 0% 100% 100% 93% 93% 0%

Chrysler/Fiat -11% -11% 0% 24% 67% 5% 0% 38% 0% 59% 3% 100% 72% 1% 0% 0% 0% 100% 100% 100% 97% 0%

Daimler -17% -11% 7% 11% 44% 12% 0% 23% 0% 64% 4% 91% 55% 36% 9% 0% 0% 100% 100% 91% 89% 2%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -12% -11% 1% 27% 59% 9% 0% 39% 0% 53% 5% 97% 68% 10% 1% 0% 0% 98% 98% 97% 97% 0%

Geely -17% -11% 6% 11% 43% 14% 0% 39% 0% 52% 1% 92% 57% 39% 8% 0% 0% 100% 100% 92% 92% 0%

General

Motors -11% -11% 0% 17% 62% 9% 0% 59% 0% 38% 3% 100% 71% 1% 0% 0% 0% 100% 100% 100% 88% 0%

Honda -6% -6% 0% 27% 65% 0% 0% 18% 0% 71% 8% 98% 48% 2% 0% 0% 0% 98% 98% 98% 92% 0%

Hyundai -8% -8% 0% 25% 75% 0% 0% 35% 0% 59% 6% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -6% -6% 0% 28% 51% 0% 0% 27% 0% 66% 7% 100% 32% 0% 0% 0% 0% 100% 100% 100% 79% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -10% -9% 1% 13% 73% 0% 0% 23% 0% 64% 11% 98% 73% 12% 2% 0% 0% 100% 100% 98% 98% 0%

Mitsubishi -14% -12% 2% 9% 73% 0% 0% 21% 0% 72% 5% 98% 73% 16% 2% 0% 0% 100% 100% 98% 98% 0%

Nissan -8% -7% 0% 21% 69% 3% 0% 25% 0% 70% 3% 98% 72% 7% 1% 0% 0% 99% 99% 98% 98% 0%

Porsche -11% -5% 6% 8% 37% 7% 0% 21% 0% 43% 21% 85% 44% 34% 15% 10% 1% 100% 100% 85% 85% 0%

Spyker -17% -11% 7% 5% 49% 3% 0% 13% 0% 66% 6% 85% 52% 32% 15% 3% 0% 100% 100% 85% 85% 0%

Subaru -12% -11% 1% 13% 72% 0% 0% 6% 0% 71% 20% 97% 72% 12% 3% 0% 0% 100% 100% 97% 97% 0%

Suzuki -4% -3% 1% 3% 68% 0% 0% 14% 0% 73% 7% 93% 68% 22% 7% 0% 0% 100% 100% 93% 93% 0%

Tata -17% -10% 7% 15% 18% 32% 0% 31% 0% 62% 0% 93% 50% 43% 7% 0% 0% 100% 100% 93% 93% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -5% -5% 0% 40% 34% 4% 0% 30% 0% 52% 5% 88% 34% 12% 0% 0% 0% 88% 88% 88% 77% 0%

Volkswagen -13% -9% 4% 6% 62% 3% 0% 20% 0% 65% 8% 93% 65% 31% 7% 1% 0% 100% 100% 93% 93% 0%

Fleet -9% -8% 1% 24% 57% 5% 0% 34% 0% 56% 6% 96% 59% 9% 2% 0% 0% 97% 97% 96% 90% 0%
2017 Draft Regulatory Impact Analysis

3-65

Table 3.8-41 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -15% -9% 6% 0% 52% 4% 0% 0% 0% 77% 8% 85% 57% 28% 15% 0% 0% 100% 100% 85% 85% 0%

Chrysler/Fiat -12% -9% 3% 6% 64% 2% 0% 2% 0% 88% 2% 92% 66% 20% 8% 0% 0% 100% 100% 92% 92% 0%

Daimler -17% -10% 7% 1% 42% 6% 0% 0% 0% 78% 4% 83% 48% 32% 17% 2% 0% 100% 100% 83% 82% 1%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -11% 3% 11% 61% 4% 0% 29% 0% 59% 4% 92% 65% 19% 7% 0% 0% 99% 99% 92% 92% 0%

Geely -17% -8% 8% 3% 30% 11% 0% 13% 0% 68% 1% 81% 41% 41% 19% 3% 0% 100% 100% 81% 81% 0%

General

Motors -13% -10% 3% 7% 64% 2% 0% 28% 0% 62% 3% 93% 66% 20% 7% 0% 0% 100% 100% 93% 93% 0%

Honda -6% -6% 1% 14% 71% 0% 0% 0% 0% 85% 10% 96% 71% 13% 1% 0% 0% 97% 97% 96% 96% 0%

Hyundai -8% -8% 0% 16% 73% 0% 0% 22% 0% 71% 6% 98% 73% 9% 2% 0% 0% 100% 100% 98% 98% 0%

Kia -6% -6% 0% 25% 75% 0% 0% 7% 0% 84% 9% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -8% -6% 2% 4% 70% 0% 0% 14% 0% 69% 12% 95% 70% 21% 5% 0% 0% 100% 100% 95% 95% 0%

Mitsubishi -11% -8% 3% 0% 64% 0% 0% 0% 0% 84% 6% 89% 64% 25% 11% 0% 0% 100% 100% 89% 89% 0%

Nissan -8% -6% 2% 2% 69% 0% 0% 4% 0% 87% 4% 94% 69% 24% 5% 0% 0% 99% 99% 94% 94% 0%

Porsche -9% -3% 7% 0% 30% 1% 0% 0% 0% 56% 21% 77% 31% 29% 23% 17% 0% 100% 100% 77% 77% 0%

Spyker -19% -11% 8% 0% 45% 1% 0% 0% 0% 76% 6% 82% 46% 29% 18% 7% 0% 100% 100% 82% 82% 0%

Subaru -11% -8% 2% 3% 68% 0% 0% 0% 0% 74% 19% 93% 68% 22% 7% 0% 0% 100% 100% 93% 93% 0%

Suzuki -2% 0% 2% 0% 63% 0% 0% 0% 0% 81% 7% 88% 63% 25% 12% 0% 0% 100% 100% 88% 88% 0%

Tata -20% -10% 10% 0% 14% 22% 0% 0% 0% 81% 0% 81% 37% 44% 19% 0% 0% 100% 100% 81% 81% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -7% -6% 1% 8% 58% 1% 0% 3% 0% 74% 5% 82% 59% 30% 3% 0% 0% 85% 85% 82% 82% 0%

Volkswagen -12% -8% 4% 0% 57% 0% 0% 0% 0% 75% 9% 84% 57% 26% 16% 1% 0% 100% 100% 84% 84% 0%

Fleet -10% -8% 2% 8% 63% 2% 0% 11% 0% 73% 6% 90% 64% 21% 7% 0% 0% 96% 96% 90% 90% 0%
Chapter 3

3-66

Table 3.8-42 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -18% -11% 7% 30% 61% 10% 0% 100% 0% 0% 0% 100% 70% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Chrysler/Fiat -13% -12% 2% 26% 59% 8% 0% 87% 0% 10% 2% 99% 68% 21% 1% 2% 0% 100% 100% 99% 98% 0%

Daimler -20% -12% 8% 46% 35% 19% 0% 100% 0% 0% 0% 100% 54% 50% 0% 0% 0% 100% 100% 100% 92% 8%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -12% -8% 4% 27% 38% 20% 0% 67% 0% 25% 2% 94% 58% 38% 5% 1% 0% 98% 98% 94% 94% 0%

Geely -20% -12% 8% 28% 66% 6% 0% 100% 0% 0% 0% 100% 72% 50% 0% 0% 0% 100% 100% 100% 100% 0%

General

Motors -14% -9% 5% 31% 51% 15% 0% 93% 0% 6% 0% 99% 66% 39% 1% 0% 0% 100% 100% 99% 99% 0%

Honda -15% -14% 1% 15% 73% 0% 0% 62% 0% 37% 0% 98% 73% 10% 2% 0% 0% 100% 100% 98% 98% 0%

Hyundai -20% -19% 1% 23% 74% 0% 0% 90% 0% 9% 0% 99% 74% 2% 1% 0% 0% 100% 100% 99% 99% 0%

Kia -18% -18% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -20% -18% 1% 17% 74% 0% 0% 66% 0% 31% 1% 99% 74% 8% 1% 0% 0% 100% 100% 99% 99% 0%

Mitsubishi -20% -18% 2% 16% 71% 0% 0% 65% 0% 31% 0% 96% 71% 9% 4% 0% 0% 100% 100% 96% 96% 0%

Nissan -9% -6% 3% 24% 59% 9% 0% 77% 0% 20% 1% 98% 68% 27% 2% 0% 0% 100% 100% 98% 98% 0%

Porsche -20% -12% 8% 39% 34% 28% 0% 99% 0% 0% 1% 100% 61% 50% 0% 0% 6% 100% 100% 100% 100% 0%

Spyker -5% -2% 3% 35% 46% 19% 0% 100% 0% 0% 0% 100% 65% 50% 0% 0% 19% 100% 100% 100% 100% 0%

Subaru -20% -17% 3% 6% 74% 0% 0% 25% 0% 69% 6% 99% 74% 19% 1% 0% 0% 100% 100% 99% 99% 0%

Suzuki -16% -16% 0% 20% 72% 0% 0% 79% 0% 17% 0% 97% 72% 5% 3% 0% 0% 100% 100% 97% 97% 0%

Tata -14% -7% 7% 33% 10% 33% 0% 66% 0% 26% 0% 92% 43% 41% 8% 7% 8% 100% 100% 92% 92% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -10% -9% 1% 25% 58% 8% 0% 78% 0% 14% 2% 94% 66% 17% 0% 0% 0% 94% 94% 94% 94% 0%

Volkswagen -19% -11% 7% 31% 58% 11% 0% 100% 0% 0% 0% 100% 69% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Fleet -13% -10% 3% 26% 56% 11% 0% 81% 0% 15% 1% 97% 67% 27% 1% 0% 0% 99% 99% 97% 97% 0%
2017 Draft Regulatory Impact Analysis

3-67

Table 3.8-43 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

EFR2

DI

DSL

Aston

Martin -20% -6% 14% 0% 0% 9% 0% 0% 0% 75% 2% 77% 9% 50% 23% 18% 0% 100% 100% 77% 77% 0%

BMW -15% -9% 6% 8% 55% 6% 0% 26% 0% 57% 6% 89% 60% 34% 11% 0% 0% 100% 100% 89% 89% 0%

Chrysler/Fiat -13% -10% 3% 15% 62% 5% 0% 39% 0% 54% 2% 95% 67% 20% 5% 1% 0% 100% 100% 95% 95% 0%

Daimler -18% -10% 7% 11% 41% 9% 0% 23% 0% 60% 3% 87% 50% 36% 13% 1% 0% 100% 100% 87% 85% 2%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -10% 3% 16% 54% 9% 0% 40% 0% 49% 3% 92% 63% 25% 6% 0% 0% 99% 99% 92% 92% 0%

Geely -18% -10% 8% 11% 41% 10% 0% 39% 0% 48% 1% 87% 50% 44% 13% 2% 0% 100% 100% 87% 87% 0%

General

Motors -13% -9% 4% 18% 58% 8% 0% 59% 0% 35% 2% 96% 66% 29% 4% 0% 0% 100% 100% 96% 96% 0%

Honda -9% -8% 1% 14% 72% 0% 0% 18% 0% 71% 7% 96% 72% 12% 1% 0% 0% 98% 98% 96% 96% 0%

Hyundai -11% -10% 0% 18% 74% 0% 0% 35% 0% 58% 5% 99% 74% 7% 1% 0% 0% 100% 100% 99% 99% 0%

Kia -8% -8% 0% 25% 75% 0% 0% 27% 0% 66% 7% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus -4% 0% 4% 0% 44% 0% 0% 0% 0% 36% 44% 80% 44% 25% 20% 11% 0% 100% 100% 80% 80% 0%

Mazda -10% -8% 2% 6% 71% 0% 0% 23% 0% 63% 10% 96% 71% 19% 4% 0% 0% 100% 100% 96% 96% 0%

Mitsubishi -14% -11% 3% 5% 66% 0% 0% 21% 0% 66% 4% 91% 66% 20% 9% 0% 0% 100% 100% 91% 91% 0%

Nissan -9% -6% 2% 8% 66% 3% 0% 25% 0% 67% 3% 95% 69% 25% 4% 0% 0% 100% 100% 95% 95% 0%

Porsche -12% -5% 7% 8% 31% 7% 0% 21% 0% 44% 17% 82% 38% 34% 18% 13% 1% 100% 100% 82% 82% 0%

Spyker -17% -10% 7% 5% 45% 3% 0% 13% 0% 66% 5% 84% 48% 32% 16% 6% 2% 100% 100% 84% 84% 0%

Subaru -13% -10% 3% 3% 69% 0% 0% 6% 0% 73% 16% 94% 69% 22% 6% 0% 0% 100% 100% 94% 94% 0%

Suzuki -4% -3% 1% 3% 65% 0% 0% 14% 0% 70% 6% 90% 65% 22% 10% 0% 0% 100% 100% 90% 90% 0%

Tata -17% -9% 9% 15% 13% 27% 0% 31% 0% 55% 0% 86% 40% 43% 14% 3% 4% 100% 100% 86% 86% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -8% -7% 1% 14% 58% 4% 0% 30% 0% 52% 4% 86% 62% 25% 2% 0% 0% 88% 88% 86% 86% 0%

Volkswagen -13% -8% 5% 6% 57% 3% 0% 20% 0% 60% 7% 87% 60% 31% 13% 1% 0% 100% 100% 87% 87% 0%

Fleet -11% -8% 3% 14% 60% 5% 0% 34% 0% 54% 4% 92% 65% 23% 5% 0% 0% 97% 97% 92% 92% 0%
Chapter 3

3-68

Table 3.8-44 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

EFR2

DI

DSL

Aston

Martin -20% -8% 12% 0% 0% 22% 0% 0% 0% 73% 4% 77% 22% 50% 23% 5% 0% 100% 100% 77% 77% 0%

BMW -14% -11% 3% 14% 62% 5% 0% 0% 0% 83% 12% 95% 67% 15% 5% 0% 0% 100% 100% 95% 95% 0%

Chrysler/Fiat -10% -9% 0% 24% 71% 3% 0% 2% 0% 95% 2% 100% 74% 2% 0% 0% 0% 100% 100% 100% 99% 0%

Daimler -16% -12% 4% 12% 50% 9% 0% 0% 0% 85% 7% 92% 60% 21% 8% 0% 0% 100% 100% 92% 92% 1%

Ferrari -14% -6% 8% 0% 0% 13% 0% 0% 0% 76% 1% 77% 13% 50% 23% 14% 0% 100% 100% 77% 77% 0%

Ford -12% -12% 0% 26% 68% 4% 0% 27% 0% 65% 7% 98% 72% 2% 0% 0% 0% 99% 99% 98% 98% 0%

Geely -16% -11% 5% 3% 35% 20% 0% 13% 0% 79% 1% 93% 55% 35% 7% 0% 0% 100% 100% 93% 93% 0%

General

Motors -11% -11% 0% 18% 70% 3% 0% 28% 0% 66% 6% 100% 73% 0% 0% 0% 0% 100% 100% 100% 91% 0%

Honda -4% -4% 0% 12% 36% 0% 0% 0% 0% 85% 12% 97% 11% 3% 0% 0% 0% 97% 97% 97% 48% 0%

Hyundai -5% -5% 0% 31% 28% 0% 0% 22% 0% 71% 7% 100% 24% 0% 0% 0% 0% 100% 100% 100% 59% 0%

Kia -4% -4% 0% 14% 5% 0% 0% 7% 0% 84% 9% 100% 5% 0% 0% 0% 0% 100% 100% 100% 19% 0%

Lotus -3% 0% 3% 0% 53% 0% 0% 0% 0% 31% 53% 85% 53% 25% 15% 6% 0% 100% 100% 85% 85% 0%

Mazda -5% -5% 0% 25% 75% 0% 0% 14% 0% 68% 18% 100% 47% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Mitsubishi -11% -10% 1% 12% 75% 0% 0% 0% 0% 93% 7% 100% 75% 13% 0% 0% 0% 100% 100% 100% 100% 0%

Nissan -6% -6% 0% 25% 74% 0% 0% 4% 0% 91% 5% 99% 56% 1% 0% 0% 0% 99% 99% 99% 99% 0%

Porsche -8% -3% 5% 0% 45% 1% 0% 0% 0% 52% 31% 83% 46% 29% 17% 8% 0% 100% 100% 83% 83% 0%

Spyker -19% -13% 6% 0% 53% 6% 0% 0% 0% 80% 8% 88% 59% 29% 12% 0% 0% 100% 100% 88% 88% 0%

Subaru -10% -10% 0% 25% 74% 0% 0% 0% 0% 72% 27% 99% 74% 0% 1% 0% 0% 100% 100% 99% 99% 0%

Suzuki 0% 0% 0% 25% 69% 0% 0% 0% 0% 83% 11% 94% 69% 0% 6% 0% 0% 100% 100% 94% 94% 0%

Tata -20% -12% 8% 0% 16% 32% 0% 0% 0% 92% 0% 92% 48% 44% 8% 0% 0% 100% 100% 92% 92% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -4% -4% 0% 11% 17% 1% 0% 3% 0% 75% 7% 84% 14% 16% 0% 0% 0% 84% 84% 84% 29% 0%

Volkswagen -11% -8% 2% 15% 66% 2% 0% 0% 0% 80% 14% 93% 67% 11% 7% 0% 0% 100% 100% 93% 93% 0%

Fleet -8% -7% 1% 18% 50% 2% 0% 11% 0% 76% 8% 95% 46% 6% 1% 0% 0% 96% 96% 95% 73% 0%
2017 Draft Regulatory Impact Analysis

3-69

Table 3.8-45 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -17% -13% 4% 30% 61% 10% 0% 100% 0% 0% 0% 100% 70% 30% 0% 0% 0% 100% 100% 100% 100% 0%

Chrysler/Fiat -12% -12% 0% 24% 62% 8% 0% 86% 0% 11% 3% 100% 71% 0% 0% 0% 0% 100% 100% 100% 94% 0%

Daimler -20% -14% 6% 46% 35% 19% 0% 100% 0% 0% 0% 100% 54% 38% 0% 0% 0% 100% 100% 100% 92% 8%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -11% -10% 0% 32% 43% 20% 0% 67% 0% 28% 2% 97% 63% 5% 0% 0% 0% 97% 97% 97% 97% 0%

Geely -20% -15% 5% 28% 66% 6% 0% 100% 0% 0% 0% 100% 72% 30% 0% 0% 0% 100% 100% 100% 100% 0%

General

Motors -12% -12% 0% 16% 52% 15% 0% 93% 0% 6% 0% 100% 67% 2% 0% 0% 0% 100% 100% 100% 85% 0%

Honda -9% -9% 0% 25% 75% 0% 0% 62% 0% 38% 0% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Hyundai -10% -10% 0% 25% 75% 0% 0% 90% 0% 10% 0% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Kia -9% -9% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 74% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -15% -15% 0% 25% 75% 0% 0% 66% 0% 32% 2% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Mitsubishi -20% -19% 1% 22% 75% 0% 0% 65% 0% 35% 0% 100% 75% 3% 0% 0% 0% 100% 100% 100% 100% 0%

Nissan -7% -7% 0% 30% 61% 9% 0% 77% 0% 22% 2% 100% 70% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Porsche -20% -12% 8% 39% 34% 28% 0% 99% 0% 0% 1% 100% 61% 50% 0% 0% 6% 100% 100% 100% 100% 0%

Spyker -5% -2% 3% 35% 46% 19% 0% 100% 0% 0% 0% 100% 65% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Subaru -20% -20% 0% 25% 75% 0% 0% 25% 0% 67% 8% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Suzuki -16% -16% 0% 20% 73% 0% 0% 79% 0% 19% 0% 98% 73% 5% 2% 0% 0% 100% 100% 98% 98% 0%

Tata -13% -7% 6% 33% 20% 33% 0% 66% 0% 29% 0% 95% 53% 41% 5% 0% 0% 100% 100% 95% 95% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -6% -6% 0% 28% 59% 8% 0% 78% 0% 14% 3% 94% 67% 6% 0% 0% 0% 94% 94% 94% 94% 0%

Volkswagen -19% -11% 7% 31% 58% 11% 0% 100% 0% 0% 0% 100% 69% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Fleet -11% -10% 1% 25% 58% 11% 0% 81% 0% 16% 1% 98% 68% 6% 0% 0% 0% 98% 98% 98% 94% 0%
Chapter 3

3-70

Table 3.8-46 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

EFR2

DI

DSL

Aston

Martin -20% -8% 12% 0% 0% 22% 0% 0% 0% 73% 4% 77% 22% 50% 23% 5% 0% 100% 100% 77% 77% 0%

BMW -15% -12% 3% 18% 62% 6% 0% 26% 0% 61% 9% 97% 68% 19% 3% 0% 0% 100% 100% 97% 97% 0%

Chrysler/Fiat -11% -11% 0% 24% 67% 5% 0% 38% 0% 59% 3% 100% 72% 1% 0% 0% 0% 100% 100% 100% 97% 0%

Daimler -17% -12% 5% 20% 47% 12% 0% 23% 0% 66% 5% 94% 58% 25% 6% 0% 0% 100% 100% 94% 92% 2%

Ferrari -14% -6% 8% 0% 0% 13% 0% 0% 0% 76% 1% 77% 13% 50% 23% 14% 0% 100% 100% 77% 77% 0%

Ford -11% -11% 0% 28% 60% 9% 0% 39% 0% 53% 5% 98% 69% 3% 0% 0% 0% 98% 98% 98% 98% 0%

Geely -17% -12% 5% 11% 44% 16% 0% 39% 0% 55% 1% 95% 60% 33% 5% 0% 0% 100% 100% 95% 95% 0%

General

Motors -11% -11% 0% 17% 62% 9% 0% 59% 0% 38% 3% 100% 70% 1% 0% 0% 0% 100% 100% 100% 89% 0%

Honda -5% -5% 0% 16% 48% 0% 0% 18% 0% 71% 8% 98% 30% 2% 0% 0% 0% 98% 98% 98% 63% 0%

Hyundai -6% -6% 0% 30% 38% 0% 0% 35% 0% 59% 6% 100% 35% 0% 0% 0% 0% 100% 100% 100% 68% 0%

Kia -5% -5% 0% 16% 20% 0% 0% 27% 0% 66% 7% 100% 20% 0% 0% 0% 0% 100% 100% 100% 36% 0%

Lotus -3% 0% 3% 0% 53% 0% 0% 0% 0% 31% 53% 85% 53% 25% 15% 6% 0% 100% 100% 85% 85% 0%

Mazda -7% -7% 0% 25% 75% 0% 0% 23% 0% 62% 15% 100% 52% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Mitsubishi -14% -13% 1% 15% 75% 0% 0% 21% 0% 74% 5% 100% 75% 10% 0% 0% 0% 100% 100% 100% 100% 0%

Nissan -7% -7% 0% 26% 70% 3% 0% 25% 0% 70% 4% 99% 60% 1% 0% 0% 0% 99% 99% 99% 99% 0%

Porsche -10% -5% 5% 8% 43% 7% 0% 21% 0% 41% 25% 87% 49% 34% 13% 6% 1% 100% 100% 87% 87% 0%

Spyker -17% -12% 6% 5% 52% 8% 0% 13% 0% 70% 7% 90% 60% 32% 10% 0% 0% 100% 100% 90% 90% 0%

Subaru -12% -12% 0% 25% 75% 0% 0% 6% 0% 71% 23% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Suzuki -3% -3% 0% 24% 70% 0% 0% 14% 0% 72% 9% 95% 70% 1% 5% 0% 0% 100% 100% 95% 95% 0%

Tata -17% -10% 7% 15% 18% 32% 0% 31% 0% 63% 0% 93% 51% 43% 7% 0% 0% 100% 100% 93% 93% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -5% -5% 0% 17% 32% 4% 0% 30% 0% 52% 5% 88% 33% 12% 0% 0% 0% 88% 88% 88% 53% 0%

Volkswagen -12% -9% 3% 18% 64% 3% 0% 20% 0% 64% 11% 95% 68% 19% 5% 0% 0% 100% 100% 95% 95% 0%

Fleet -9% -8% 1% 20% 53% 5% 0% 34% 0% 56% 6% 96% 54% 6% 1% 0% 0% 97% 97% 96% 80% 0%
2017 Draft Regulatory Impact Analysis

3-71

Table 3.8-47 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

EFR2

DI

DSL

Aston

Martin -20% -5% 15% 0% 0% 5% 0% 0% 0% 76% 1% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

BMW -15% -8% 7% 0% 48% 1% 0% 0% 0% 74% 7% 81% 48% 28% 19% 4% 0% 100% 100% 81% 81% 0%

Chrysler/Fiat -12% -8% 4% 2% 64% 2% 0% 2% 0% 88% 2% 92% 66% 24% 8% 0% 0% 100% 100% 92% 92% 0%

Daimler -17% -8% 9% 0% 38% 2% 0% 0% 0% 76% 4% 81% 39% 32% 19% 9% 0% 100% 100% 81% 80% 0%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -9% 5% 9% 58% 4% 0% 29% 0% 57% 4% 89% 61% 28% 10% 0% 0% 99% 99% 89% 89% 0%

Geely -17% -7% 9% 3% 26% 6% 0% 13% 0% 68% 1% 81% 32% 41% 19% 12% 0% 100% 100% 81% 81% 0%

General

Motors -13% -10% 3% 7% 64% 2% 0% 28% 0% 62% 3% 93% 67% 20% 7% 0% 0% 100% 100% 93% 93% 0%

Honda -6% -5% 1% 12% 68% 0% 0% 0% 0% 83% 10% 92% 68% 16% 4% 0% 0% 97% 97% 92% 92% 0%

Hyundai -9% -7% 1% 5% 67% 0% 0% 22% 0% 66% 5% 92% 67% 20% 8% 0% 0% 100% 100% 92% 92% 0%

Kia -6% -5% 1% 5% 71% 0% 0% 7% 0% 83% 6% 96% 71% 20% 4% 0% 0% 100% 100% 96% 96% 0%

Lotus -5% 0% 5% 0% 32% 0% 0% 0% 0% 45% 32% 77% 32% 25% 23% 20% 0% 100% 100% 77% 77% 0%

Mazda -8% -6% 2% 4% 62% 0% 0% 14% 0% 62% 10% 87% 62% 22% 13% 0% 0% 100% 100% 87% 87% 0%

Mitsubishi -11% -7% 4% 0% 61% 0% 0% 0% 0% 80% 6% 86% 61% 25% 14% 0% 0% 100% 100% 86% 86% 0%

Nissan -8% -6% 2% 1% 66% 0% 0% 4% 0% 84% 3% 91% 66% 25% 9% 0% 0% 99% 99% 91% 91% 0%

Porsche -10% -3% 8% 0% 0% 14% 0% 0% 0% 67% 10% 77% 14% 41% 23% 22% 14% 100% 100% 77% 77% 0%

Spyker -19% -10% 10% 0% 34% 1% 0% 0% 0% 74% 5% 78% 35% 29% 22% 14% 0% 100% 100% 78% 78% 0%

Subaru -11% -7% 3% 0% 63% 0% 0% 0% 0% 71% 17% 88% 63% 25% 12% 0% 0% 100% 100% 88% 88% 0%

Suzuki -2% 0% 2% 0% 55% 0% 0% 0% 0% 78% 6% 85% 55% 25% 15% 4% 0% 100% 100% 85% 85% 0%

Tata -20% -10% 10% 0% 14% 20% 0% 0% 0% 79% 0% 79% 35% 44% 21% 0% 0% 100% 100% 79% 79% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -7% -6% 1% 8% 57% 1% 0% 3% 0% 73% 5% 81% 58% 30% 5% 0% 0% 85% 85% 81% 81% 0%

Volkswagen -12% -6% 6% 0% 47% 0% 0% 0% 0% 73% 8% 82% 47% 26% 18% 8% 0% 100% 100% 82% 82% 0%

Fleet -10% -7% 3% 6% 59% 1% 0% 11% 0% 71% 5% 88% 61% 24% 9% 1% 0% 96% 96% 88% 88% 0%
Chapter 3

3-72

Table 3.8-48 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

EFR2

DI

DSL

Aston

Martin NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

BMW -18% -11% 7% 30% 61% 10% 0% 100% 0% 0% 0% 100% 70% 50% 0% 0% 0% 100% 100% 100% 100% 0%

Chrysler/Fiat -13% -12% 2% 26% 59% 8% 0% 87% 0% 10% 2% 99% 68% 21% 1% 2% 0% 100% 100% 99% 98% 0%

Daimler -20% -12% 8% 46% 35% 19% 0% 100% 0% 0% 0% 100% 54% 50% 0% 0% 27% 100% 100% 100% 92% 8%

Ferrari NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Ford -13% -8% 5% 27% 38% 20% 0% 67% 0% 24% 2% 93% 58% 44% 5% 1% 0% 98% 98% 93% 93% 0%

Geely -20% -12% 8% 28% 66% 6% 0% 100% 0% 0% 0% 100% 72% 50% 0% 0% 0% 100% 100% 100% 100% 0%

General

Motors -14% -9% 5% 31% 51% 15% 0% 93% 0% 6% 0% 99% 66% 39% 1% 0% 0% 100% 100% 99% 99% 0%

Honda -16% -13% 3% 15% 71% 0% 0% 62% 0% 34% 0% 96% 71% 17% 4% 0% 0% 100% 100% 96% 96% 0%

Hyundai -20% -19% 1% 23% 74% 0% 0% 90% 0% 8% 0% 99% 74% 7% 1% 0% 0% 100% 100% 99% 99% 0%

Kia -18% -18% 0% 25% 75% 0% 0% 99% 0% 0% 1% 100% 75% 0% 0% 0% 0% 100% 100% 100% 100% 0%

Lotus NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Mazda -20% -13% 7% 17% 72% 0% 0% 66% 0% 29% 1% 97% 72% 42% 3% 0% 0% 100% 100% 97% 97% 0%

Mitsubishi -20% -15% 5% 16% 70% 0% 0% 65% 0% 30% 0% 95% 70% 25% 5% 0% 0% 100% 100% 95% 95% 0%

Nissan -10% -6% 4% 24% 57% 9% 0% 77% 0% 19% 1% 96% 67% 39% 4% 0% 0% 100% 100% 96% 96% 0%

Porsche -20% -12% 8% 39% 11% 50% 0% 99% 0% 0% 1% 100% 61% 50% 0% 0% 50% 100% 100% 100% 100% 0%

Spyker -5% -2% 3% 35% 46% 19% 0% 100% 0% 0% 0% 100% 65% 50% 0% 0% 50% 100% 100% 100% 100% 0%

Subaru -20% -15% 5% 6% 69% 0% 0% 25% 0% 64% 5% 94% 69% 25% 6% 0% 0% 100% 100% 94% 94% 0%

Suzuki -16% -9% 7% 20% 72% 0% 0% 79% 0% 17% 0% 97% 72% 45% 3% 0% 0% 100% 100% 97% 97% 0%

Tata -14% -7% 7% 33% 10% 33% 0% 66% 0% 26% 0% 92% 43% 41% 8% 7% 8% 100% 100% 92% 92% 0%

Tesla NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Toyota -11% -8% 2% 25% 57% 8% 0% 79% 0% 13% 2% 94% 65% 26% 1% 0% 0% 96% 96% 94% 94% 0%

Volkswagen -19% -11% 7% 31% 58% 11% 0% 100% 0% 0% 0% 100% 69% 50% 0% 0% 50% 100% 100% 100% 100% 0%

Fleet -14% -10% 4% 26% 55% 11% 0% 81% 0% 14% 1% 97% 66% 33% 2% 0% 2% 99% 99% 97% 97% 0%
2017 Draft Regulatory Impact Analysis

3-73

Table 3.8-49 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

EFR2

DI

DSL

Aston

Martin -20% -5% 15% 0% 0% 5% 0% 0% 0% 76% 1% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

BMW -16% -9% 7% 8% 51% 3% 0% 26% 0% 55% 5% 86% 54% 34% 14% 3% 0% 100% 100% 86% 86% 0%

Chrysler/Fiat -13% -10% 3% 13% 62% 5% 0% 39% 0% 54% 2% 95% 67% 23% 5% 1% 0% 100% 100% 95% 95% 0%

Daimler -18% -9% 8% 11% 37% 6% 0% 23% 0% 59% 3% 85% 43% 36% 15% 7% 6% 100% 100% 85% 83% 2%

Ferrari -15% -6% 9% 0% 0% 5% 0% 0% 0% 77% 0% 77% 5% 50% 23% 22% 5% 100% 100% 77% 77% 0%

Ford -13% -8% 5% 15% 52% 9% 0% 41% 0% 47% 3% 90% 60% 33% 9% 0% 0% 99% 99% 90% 90% 0%

Geely -18% -9% 9% 11% 38% 6% 0% 39% 0% 48% 0% 87% 44% 44% 13% 8% 0% 100% 100% 87% 87% 0%

General

Motors -13% -9% 4% 18% 58% 8% 0% 59% 0% 35% 2% 96% 67% 29% 4% 0% 0% 100% 100% 96% 96% 0%

Honda -9% -7% 2% 13% 69% 0% 0% 18% 0% 68% 7% 94% 69% 16% 4% 0% 0% 98% 98% 94% 94% 0%

Hyundai -11% -10% 1% 9% 69% 0% 0% 35% 0% 54% 4% 94% 69% 17% 6% 0% 0% 100% 100% 94% 94% 0%

Kia -9% -8% 1% 9% 72% 0% 0% 27% 0% 65% 5% 97% 72% 16% 3% 0% 0% 100% 100% 97% 97% 0%

Lotus -5% 0% 5% 0% 32% 0% 0% 0% 0% 45% 32% 77% 32% 25% 23% 20% 0% 100% 100% 77% 77% 0%

Mazda -10% -7% 3% 6% 64% 0% 0% 23% 0% 57% 9% 89% 64% 25% 11% 0% 0% 100% 100% 89% 89% 0%

Mitsubishi -14% -10% 4% 5% 64% 0% 0% 21% 0% 63% 4% 89% 64% 25% 11% 0% 0% 100% 100% 89% 89% 0%

Nissan -9% -6% 3% 8% 63% 3% 0% 25% 0% 65% 2% 92% 66% 29% 7% 0% 0% 100% 100% 92% 92% 0%

Porsche -13% -5% 8% 8% 2% 22% 0% 21% 0% 53% 8% 82% 24% 43% 18% 17% 22% 100% 100% 82% 82% 0%

Spyker -18% -9% 9% 5% 36% 3% 0% 13% 0% 64% 4% 81% 39% 32% 19% 12% 7% 100% 100% 81% 81% 0%

Subaru -13% -9% 4% 1% 65% 0% 0% 6% 0% 69% 15% 90% 65% 25% 10% 0% 0% 100% 100% 90% 90% 0%

Suzuki -5% -2% 3% 3% 58% 0% 0% 14% 0% 68% 5% 87% 58% 28% 13% 3% 0% 100% 100% 87% 87% 0%

Tata -17% -9% 9% 15% 13% 26% 0% 31% 0% 54% 0% 85% 39% 43% 15% 3% 4% 100% 100% 85% 85% 0%

Tesla 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%

Toyota -8% -7% 1% 14% 57% 4% 0% 31% 0% 51% 4% 86% 61% 28% 3% 0% 0% 89% 89% 86% 86% 0%

Volkswagen -13% -7% 6% 6% 49% 2% 0% 20% 0% 59% 7% 85% 51% 31% 15% 7% 10% 100% 100% 85% 85% 0%

Fleet -11% -8% 3% 12% 58% 4% 0% 34% 0% 53% 4% 91% 63% 27% 7% 1% 1% 97% 97% 91% 91% 0%
Chapter 3

3-74

3.8.5 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 five vehicle classes.

Table 3.8-50 Aggregation of Vehicle types for Mass Reduction Presentation

Vehicle Type Aggregated Type

1 Subcompact/Compact

2 Subcompact/Compact

3 Midsize Car

4 Subcompact/Compact

5 Midsize Car

6 Large Car

7 Midsize MPV/Small Truck

8 Midsize MPV/Small Truck

9 Large Truck/MPV/SUV

10 Large Truck/MPV/SUV

11 Large Truck/MPV/SUV

12 Large Truck/MPV/SUV

13 Large Truck/MPV/SUV

14 Large Truck/MPV/SUV

15 Large Car

16 Large Truck/MPV/SUV

17 Large Truck/MPV/SUV

18 Large Truck/MPV/SUV

19 Large Truck/MPV/SUV

After aggregations here are the weight reductions by vehicle class.

Net Mass Reduction

Reference Control

Category MY 2021 MY 2025 MY 2021 MY 2025

Subcompact/Compact -2% -2% -2% -3%

Midsize Car -7% -7% -8% -12%

Midsize MPV/Small Truck -5% -5% -7% -11%

Large Truck/MPV/SUV -7% -7% -8% -13%

Large Car -8% -8% -9% -9%

Fleet -5% -5% -6% -9%

3.8.6 Air Conditioning Cost

As previously referenced, once the OMEGA costs were determined, the estimated air

conditioning costs, as discussed in Chapter 5 of the draft Joint TSD were added onto the total

cost. These costs are shown below.
2017 Draft Regulatory Impact Analysis

3-75

Table 3.8-51 Total Costs for A/C Control Used in This Proposal (2009$)

Car/

Truck Case 2021 2025

Car

Reference $67 $63

Control $78 $69

Total $145 $132

Truck

Reference $51 $48

Control $94 $84

Total $145 $132

Fleet Total $145 $132

3.8.7 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

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 3.8-53 ). 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) depend both on the stranded technology and

the replacing technology.

Table 3.8-52 Potential Stranded Capital Costs

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
Chapter 3

3-76

DSTGDI=Downsized, turbocharged engine with stoichiometric gasoline direct injection.

For 2008-2016, the eight year stranded capital costs were used. For 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. 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 2021, and then 27 bar BMEP engine technology in 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 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

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.
2017 Draft Regulatory Impact Analysis

3-77

Table 3.8-53 Estimated Potential Stranded CapitalSS

MY2016 MY 2021 MY 2025

Manufacturer Engine Transmission Total Engine Transmission Total Engine Transmission Total

Aston Martin $60 $15 $75 $21 $8 $29 $14 $3 $17

BMW $20 $3 $23 $29 $15 $45 $3 $4 $7

Chrysler/Fiat $54 $- $54 $21 $14 $35 $9 $5 $15

Daimler $18 $6 $24 $17 $9 $27 $6 $4 $10

Ferrari $5 $1 $6 $22 $12 $34 $16 $3 $19

Ford $8 $- $8 $17 $12 $29 $6 $5 $10

Geely $14 $0 $14 $22 $16 $38 $8 $5 $13

General

Motors

$12 $- $12 $8 $10 $18 $16 $5 $21

Honda $2 $- $2 $11 $10 $21 $11 $4 $15

Hyundai $- $- $- $11 $6 $17 $14 $4 $18

Kia $- $- $- $25 $16 $41 $16 $4 $21

Lotus $40 $- $40 $15 $0 $16 $1 $2 $2

Mazda $5 $- $5 $32 $15 $47 $14 $4 $18

Mitsubishi $- $- $- $25 $15 $40 $12 $4 $16

Nissan $6 $- $6 $10 $9 $19 $9 $4 $14

Porsche $23 $1 $25 $15 $10 $24 $4 $3 $7

Spyker $45 $- $45 $14 $8 $22 $3 $3 $6

Subaru $3 $- $3 $18 $7 $25 $7 $3 $10

Suzuki $20 $- $20 $19 $8 $27 $2 $4 $6

Tata $14 $4 $18 $20 $12 $32 $14 $4 $19

Tesla $- $- $- $- $- $- $- $- $-

Toyota $0 $- $0 $17 $6 $23 $14 $9 $23

Volkswagen $13 $2 $16 $21 $10 $31 $2 $4 $6

Fleet $9 $0 $10 $15 $10 $25 $11 $5 $16

SS 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.
Chapter 3

3-78

3.9 Per Vehicle Costs 2021 and 2025

As described above, the per-vehicle technology costs for this program alone must

account for any cost that are incurred due to compliance with existing vehicle programs. EPA

first used OMEGA to calculate costs reflected in the existing 2012-2016 program, which is

the reference case for this analysis. The OMEGA estimates indicate that, on average,

manufacturers will need to spend $830 to meet the 2016MY standards in the 2021MY, and

$776 to meet the 2016MY standards in the 2025MY per vehicle. Reference case costs,

inclusive of AC costs, are provided in Table 3.9-1 .

Table 3.9-1 Reference Case Costs

Company 2021 2025

Cars Trucks Fleet Cars Trucks Fleet

Aston Martin $2,589 $0 $2,589 $2,376 $0 $2,376

BMW $1,988 $2,220 $2,049 $1,827 $2,029 $1,880

Chrysler/Fiat $921 $945 $931 $803 $896 $843

Daimler $2,227 $2,290 $2,243 $2,058 $2,118 $2,072

Ferrari $2,470 $0 $2,470 $2,270 $0 $2,270

Ford $893 $1,220 $1,004 $856 $1,134 $942

Geely-Volvo $2,159 $2,199 $2,172 $1,987 $2,031 $2,000

GM $859 $910 $884 $815 $869 $840

Honda $320 $465 $365 $314 $440 $351

Hyundai $441 $785 $511 $424 $761 $491

Kia $384 $641 $442 $374 $576 $417

Lotus $1,691 $0 $1,691 $1,563 $0 $1,563

Mazda $611 $1,091 $696 $578 $1,032 $654

Mitsubishi $1,046 $1,235 $1,112 $981 $1,162 $1,041

Nissan $409 $1,021 $598 $391 $931 $551

Porsche $1,934 $1,935 $1,934 $1,783 $1,777 $1,781

Spyker-Saab $2,000 $2,460 $2,066 $1,840 $2,272 $1,896

Subaru $714 $1,080 $801 $721 $1,016 $787

Suzuki $1,068 $1,328 $1,115 $1,004 $1,251 $1,046

Tata-JLR $2,529 $2,529 $2,529 $2,324 $2,338 $2,331

Tesla $0 $0 $0 $0 $0 $0

Toyota $290 $462 $357 $273 $436 $332

VW $1,870 $1,909 $1,878 $1,717 $1,747 $1,723

Fleet $776 $930 $830 $728 $873 $776

EPA then used OMEGA to calculate the costs of meeting the proposed standards in

the years 2021 and 2025, which are shown in Table 3.9-2 . EPA has accounted for the cost to

meet the standards in the reference case. In other words, Table 3.9-2 contains per-vehicle

costs that are incremental to the reference case costs shown in Table 3.9-1 .
2017 Draft Regulatory Impact Analysis

3-79

Table 3.9-2 Control Case Costs for the Proposed Standards MY 2021 (2009$)

Company 2021 Costs 2021 Sales

Cars Trucks Fleet Cars Truck Fleet

Aston

Martin $6,424 $0 $6,424 1,058 - 1,058

BMW $945 $915 $937 359,098 128,724 487,822

Chrysler/Fiat $569 $853 $698 421,013 348,613 769,626

Daimler $1,949 $956 $1,702 300,378 99,449 399,827

Ferrari $6,351 $0 $6,351 7,059 - 7,059

Ford $655 $776 $696 1,401,617 714,181 2,115,798

Geely-Volvo $2,035 $1,086 $1,741 92,726 41,768 134,494

GM $502 $680 $590 1,564,277 1,530,020 3,094,297

Honda $467 $756 $556 1,198,880 535,916 1,734,796

Hyundai $614 $884 $669 613,355 156,466 769,821

Kia $483 $927 $582 331,319 95,432 426,751

Lotus $3,324 $0 $3,324 278 - 278

Mazda $924 $897 $919 274,740 59,227 333,967

Mitsubishi $813 $998 $877 65,851 35,309 101,160

Nissan $759 $662 $729 912,629 408,029 1,320,658

Porsche $5,455 $1,328 $4,482 36,475 11,242 47,716

Spyker-Saab $3,335 $898 $2,986 21,294 3,560 24,854

Subaru $1,017 $922 $994 230,780 72,773 303,553

Suzuki $1,160 $1,000 $1,132 95,725 20,767 116,492

Tata-JLR $2,220 $1,648 $1,935 58,677 58,153 116,830

Tesla $0 $0 $0 28,623 - 28,623

Toyota $332 $713 $481 1,903,706 1,215,539 3,119,245

VW $1,624 $797 $1,457 585,607 148,734 734,341

Fleet $718 $764 $734 10,505,165 5,683,902 16,189,066
Chapter 3

3-80

Table 3.9-3 Control Case Costs for the Proposed Standards MY 2025 (2009$)

Company 2025 2025 Sales

Cars Trucks Fleet Cars Truck Fleet

Aston

Martin $6,862 $0 $6,862 1,182 - 1,182

BMW $2,251 $1,959 $2,174 405,256 145,409 550,665

Chrysler/Fiat $1,914 $2,212 $2,043 436,479 331,762 768,241

Daimler $2,931 $1,952 $2,707 340,719 101,067 441,786

Ferrari $7,109 $0 $7,109 7,658 - 7,658

Ford $2,051 $2,463 $2,178 1,540,109 684,476 2,224,586

Geely-Volvo $3,228 $2,040 $2,876 101,107 42,588 143,696

GM $2,209 $1,834 $2,030 1,673,936 1,524,008 3,197,943

Honda $1,452 $1,937 $1,595 1,340,321 557,697 1,898,018

Hyundai $1,677 $1,988 $1,739 677,250 168,136 845,386

Kia $1,442 $1,675 $1,491 362,783 97,653 460,436

Lotus $3,705 $0 $3,705 316 - 316

Mazda $2,196 $1,806 $2,131 306,804 61,368 368,172

Mitsubishi $2,114 $2,171 $2,133 73,305 36,387 109,692

Nissan $1,997 $2,212 $2,060 1,014,775 426,454 1,441,229

Porsche $5,827 $2,054 $5,012 40,696 11,219 51,915

Spyker-Saab $4,001 $1,468 $3,670 23,130 3,475 26,605

Subaru $2,236 $2,087 $2,202 256,970 74,722 331,692

Suzuki $2,307 $1,832 $2,225 103,154 21,374 124,528

Tata-JLR $3,255 $2,653 $2,976 65,418 56,805 122,223

Tesla $0 $0 $0 31,974 - 31,974

Toyota $1,399 $1,631 $1,483 2,108,053 1,210,016 3,318,069

VW $2,618 $2,048 $2,506 630,163 154,284 784,447

Fleet $1,942 $1,954 $1,946 11,541,558 5,708,900 17,250,459

EPA estimates that the additional technology required for manufacturers to meet the

GHG standards for this proposed rule will cost on average $734/vehicle and $1,946/vehicle in

the 2021 and 2025 MYs, respectively. These costs include our estimates of stranded capital

and costs associated with the A/C program from above.
2017 Draft Regulatory Impact Analysis

3-81

The OMEGA results project that under the primary proposal approximately 1% of the

vehicles sold in MYs 2017-2025 will be EVs or PHEVs.

Table 3.9-4 Sales by Technology

MY ICE Sales HEV Sales EV+PHEV

Sales Total Sales

2017 14,940,135 840,896 25,290 15,806,322

2018 14,648,056 878,510 49,845 15,576,410

2019 14,575,393 928,488 74,778 15,578,658

2020 14,795,940 998,265 101,734 15,895,939

2021 14,991,075 1,068,478 129,513 16,189,066

2022 14,804,015 1,417,930 217,827 16,439,772

2023 14,573,553 1,773,810 308,127 16,655,489

2024 14,385,507 2,146,396 402,185 16,934,087

2025 14,214,379 2,535,818 500,263 17,250,459

Total 131,928,053 12,588,590 1,809,560 146,326,204

Fraction 90% 9% 1% 100%

3.10Alternative Program Stringencies

Table 3.10-1 Control Case Costs for the Alternative 1 (Trucks +20) Standards (2009$)

Company 2021 2025

Cars Trucks Fleet Cars Trucks Fleet

Aston Martin $6,424 $0 $6,424 $6,862 $0 $6,862

BMW $553 $76 $427 $1,945 $1,320 $1,780

Chrysler/Fiat $121 $490 $280 $1,247 $1,728 $1,455

Daimler $1,344 $954 $1,255 $2,461 $1,952 $2,345

Ferrari $6,351 $0 $6,351 $7,109 $0 $7,109

Ford $371 $361 $368 $1,445 $2,180 $1,671

Geely-Volvo $1,290 $953 $1,190 $2,515 $2,040 $2,374

GM $99 $316 $202 $1,296 $1,418 $1,355

Honda $305 $665 $411 $1,249 $1,515 $1,327

Hyundai $528 $680 $559 $1,491 $1,580 $1,509

Kia $395 $791 $479 $1,234 $1,464 $1,282

Lotus $3,324 $0 $3,324 $3,705 $0 $3,705

Mazda $809 $536 $763 $1,918 $1,777 $1,895

Mitsubishi $491 $540 $507 $1,706 $1,865 $1,758

Nissan $403 $602 $462 $1,674 $1,478 $1,616

Porsche $4,929 $953 $4,070 $5,244 $2,054 $4,555

Spyker-Saab $2,981 $805 $2,696 $3,630 $1,397 $3,338

Subaru $790 $682 $766 $2,052 $1,486 $1,925

Suzuki $867 $1,000 $890 $2,147 $1,588 $2,051

Tata-JLR $688 $1,567 $1,097 $2,506 $2,143 $2,337

Tesla $0 $0 $0 $0 $0 $0

Toyota $233 $472 $320 $1,000 $1,366 $1,133

VW $1,092 $797 $1,034 $2,197 $2,048 $2,168

Fleet $436 $487 $453 $1,484 $1,580 $1,516
Chapter 3

3-82

Table 3.10-2 Control Case Costs for the Alternative 2 (Trucks -20) Standards (2009$)

Company 2021 2025

Cars Trucks Fleet Cars Trucks Fleet

Aston Martin $6,424 $0 $6,424 $6,862 $0 $6,862

BMW $1,511 $915 $1,354 $2,840 $1,959 $2,607

Chrysler/Fiat $841 $1,498 $1,125 $2,570 $2,808 $2,673

Daimler $2,579 $956 $2,208 $3,475 $1,952 $3,127

Ferrari $6,351 $0 $6,351 $7,109 $0 $7,109

Ford $1,010 $1,404 $1,131 $2,558 $2,923 $2,670

Geely-Volvo $3,006 $1,086 $2,437 $4,181 $2,040 $3,546

GM $1,121 $1,126 $1,123 $2,753 $3,013 $2,877

Honda $640 $1,043 $758 $1,854 $2,307 $1,987

Hyundai $815 $884 $829 $2,008 $1,988 $2,004

Kia $628 $988 $704 $1,635 $2,011 $1,715

Lotus $3,324 $0 $3,324 $3,705 $0 $3,705

Mazda $1,135 $1,000 $1,113 $2,440 $1,882 $2,347

Mitsubishi $1,358 $1,438 $1,384 $2,775 $2,171 $2,574

Nissan $1,066 $793 $985 $2,561 $2,311 $2,487

Porsche $6,182 $1,400 $5,148 $6,421 $2,054 $5,477

Spyker-Saab $3,709 $898 $3,342 $4,250 $1,468 $3,887

Subaru $1,434 $922 $1,319 $2,558 $2,087 $2,452

Suzuki $1,407 $1,194 $1,370 $2,561 $1,832 $2,436

Tata-JLR $2,800 $2,845 $2,821 $3,981 $3,563 $3,787

Tesla $0 $0 $0 $0 $0 $0

Toyota $494 $999 $678 $2,009 $2,023 $2,014

VW $2,032 $914 $1,812 $3,072 $2,048 $2,871

Fleet $1,055 $1,121 $1,077 $2,443 $2,501 $2,462
2017 Draft Regulatory Impact Analysis

3-83

Table 3.10-3 Control Case Costs for the Alternative 3 (Cars +20) Standards (2009$)

Company 2021 2025

Cars Trucks Fleet Cars Trucks Fleet

Aston Martin $6,424 $0 $6,424 $5,348 $0 $5,348

BMW -$240 -$154 -$218 $1,108 $1,320 $1,164

Chrysler/Fiat $121 $471 $272 $1,242 $1,663 $1,424

Daimler $592 $481 $567 $1,623 $1,591 $1,616

Ferrari $6,351 $0 $6,351 $6,292 $0 $6,292

Ford $278 $156 $240 $1,322 $1,246 $1,299

Geely-Volvo $923 $44 $662 $1,946 $1,420 $1,790

GM $109 $394 $245 $1,350 $1,456 $1,400

Honda $215 $476 $292 $913 $1,428 $1,064

Hyundai $304 $374 $318 $1,016 $1,156 $1,044

Kia $253 $597 $326 $776 $1,397 $908

Lotus $2,114 $0 $2,114 $2,628 $0 $2,628

Mazda $356 $350 $355 $1,251 $1,118 $1,229

Mitsubishi $284 $409 $326 $1,371 $1,501 $1,414

Nissan $323 $202 $287 $1,323 $1,063 $1,246

Porsche $3,732 $953 $3,131 $4,135 $2,054 $3,685

Spyker-Saab $1,733 $625 $1,588 $2,431 $1,397 $2,296

Subaru $574 $151 $478 $1,375 $1,486 $1,400

Suzuki $321 $381 $331 $1,341 $1,588 $1,383

Tata-JLR $688 $1,567 $1,097 $2,336 $2,143 $2,246

Tesla $0 $0 $0 $0 $0 $0

Toyota $201 $466 $298 $780 $1,334 $982

VW $36 $797 $186 $1,230 $2,048 $1,391

Fleet $244 $390 $292 $1,161 $1,394 $1,238
Chapter 3

3-84

Table 3.10-4 Control Case Costs for the Alternative 4 (Cars -20) Standards (2009$)

Company 2021 2025

Cars Trucks Fleet Cars Trucks Fleet

Aston Martin $6,424 $0 $6,424 $7,231 $0 $7,231

BMW $2,583 $915 $2,143 $3,956 $1,959 $3,428

Chrysler/Fiat $866 $1,505 $1,142 $2,718 $2,808 $2,757

Daimler $3,734 $1,025 $3,114 $4,693 $2,044 $4,087

Ferrari $6,351 $0 $6,351 $7,109 $0 $7,109

Ford $1,333 $1,878 $1,501 $3,235 $3,169 $3,214

Geely-Volvo $4,111 $1,086 $3,215 $5,262 $2,040 $4,307

GM $1,064 $1,017 $1,042 $2,689 $3,013 $2,843

Honda $909 $1,194 $993 $2,224 $2,777 $2,387

Hyundai $1,335 $1,440 $1,356 $2,901 $2,249 $2,771

Kia $1,049 $1,126 $1,066 $2,500 $2,064 $2,408

Lotus $4,861 $0 $4,861 $4,812 $0 $4,812

Mazda $2,064 $1,420 $1,957 $3,312 $3,117 $3,279

Mitsubishi $1,926 $1,555 $1,803 $3,184 $2,780 $3,050

Nissan $1,485 $1,429 $1,469 $2,965 $2,938 $2,957

Porsche $6,519 $1,678 $5,473 $7,428 $2,299 $6,320

Spyker-Saab $5,406 $898 $4,817 $5,814 $1,575 $5,261

Subaru $1,959 $1,724 $1,906 $3,091 $2,866 $3,040

Suzuki $2,276 $1,410 $2,128 $3,324 $3,032 $3,274

Tata-JLR $2,877 $2,845 $2,862 $4,291 $3,563 $3,953

Tesla $0 $0 $0 $0 $0 $0

Toyota $563 $1,099 $758 $2,140 $2,449 $2,252

VW $3,267 $1,166 $2,854 $4,438 $2,219 $4,001

Fleet $1,415 $1,275 $1,369 $2,923 $2,760 $2,869

3.11Comparative 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 3.11-1 shows the cost per gram per mile of going from the 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 incentives.

Table 3.11-1 Gram/mile cost of advanced technologies

Reference

Case CO2

MY 2021

CO2

(Proposed)

Delta

g/mile

Delta

Cost^

$ per

g/mile
2017 Draft Regulatory Impact Analysis

3-85

OMEGA projection of

average 2021 Car in

Proposal Case

225 178 47 $625 $ 13

EV100 (45 sqft, VT 3,

no multiplier) 263 0 263 $16,066 $61

EV100 (45 sqft, VT 3,

1.5 multiplier) 263 0 395 $16,066 $41

OMEGA projection of

average 2021 Truck in

Proposal Case

297 239 58 $654 $11

HEV (65 sqft, VT 13,

no credit) 344 243 101 $6,264 $62

HEV (65 sqft, VT 13,

20 g credit) 344 223 121 $6,264 $52

^Note that we use average reference case cost of $704 for cars and $858 for trucks, not the vehicle specific cost.

If these vehicles reference case costs were higher than average, then their costs under the proposal 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. The HEV in this example has an effectiveness of 51.4%

relative to a baseline (no technology) vehicle with a CO2 of 500 g/mile.

HEVs and EVs, regardless of their cost-effectiveness, are more effective than the

conventional technologies, and retain that advantage. Further in MY 2025, when the average

cost per gram/mile is higher, these technologies are more cost effective.

3.12 How Many of Today’s Vehicles Can Meet or Surpass the Proposed MY 2017-2025

CO2 Footprint-based Targets with Current Powertrain Designs?

As part of its evaluation of the feasibility of the proposed standards , EPA evaluated

all MY2011 and MY2012 vehicles sold in the U.S. today against the proposed CO2 footprintbased standard curves to determine which of these vehicles would meet or be lower than the

proposed MY 2017 –MY 2025 footprint-based CO2 targets assuming air conditioning credit

generation consistent with today’s proposal. Under the proposed 2017-2025 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 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 assuming

such credits does not affect consideration of cost and leadtime for use of these other
Chapter 3

3-86

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. 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 MY2017 proposed

CO2 targets, and a few meet even the proposed MY2025 CO2 targets.

Using publicly available dataTT, 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 MY2011 vehicles and some MY2012

vehicles. EPA gathered vehicle footprint data from EPA reports,23 manufacturer submitted

CAFE reports, and manufacturer websites. .

Table 3.12-1 shows that a significant number of vehicles sold today would meet or be

lower than the proposed footprint-based CO2 targets with current powertrain designs,

assuming air conditioning credit generation consistent with our proposal. The table

highlights the vehicles with CO2 emissions that meet or are lower than the applicable

proposed footprint targets from MY 2017 to 2025 in green, and shows the percentage below

the proposed 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 proposed 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

proposed MY2017 footprint CO2 target with only simple improvements in air conditioning

systems.

Vehicles that are above, but within 5%, of the proposed 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

proposed targets through at least 2021. This includes two engine options (the 3.7L V6 and the

3.5L V6), and three wheelbase optionsUU.

EPA also receives projected sales data prior to each model year from each

manufacturer. Based on this data, approximately 7% of MY2011 sales will be vehicles that

would meet or be better than the proposed MY 2017 targets for those vehicles, requiring only

improvements in air conditioning systems. In addition, nearly 30% of projected MY2011

sales would be within 10% of the proposed MY2017 footprint CO2 target with only simple

improvements to air conditioning systems, a full six model years before the proposed standard

would take effect.

TT www.fueleconomy.gov

UU The F-150 engine and wheelbase combinations listed in Table 3.12-1 correspond to models that are currently

available. Not all possible engine and wheelbase combinations are produced.
2017 Draft Regulatory Impact Analysis

3-87

With improvements to air conditioning systems, the most efficient gasoline internal

combustion engines would meet the MY 2020 proposed footprint targets. After MY2020, the

only current vehicles that continue to meet the proposed footprint-based CO2 targets

(assuming improvements in air conditioning) are hybrid-electric, plug-in hybrid-electric, and

fully electric vehicles. However, the proposed MY 2020 standards will not be in effect for

another nine 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 Chapter

3 of the draft Joint TSD and as discussed in Preamble Section III.D) assuming air conditioner

improvements . Today’s Toyota Prius, Ford Fusion Hybrid, Chevrolet Volt, Nissan Leaf,

Honda Civic Hybrid, and Hyundai Sonata Hybrid all meet or surpass the proposed footprintbased CO2 targets through MY2025. In fact, the current Prius, Volt, and Leaf meet the

proposed 2025 CO2 targets without air conditioning credits.

This assessment of MY2011 and MY2012 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 proposed standards. Notably, based on the OMEGA modeling,

we project that the MY2017-2025 standards can primarily be achieved by advanced gasoline

vehicles – for example, in MY2025, we project more than 80 percent of the new vehicles

could be advanced gasoline powertrains. The assessment of MY2011 and MY2012 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 proposed standards (i.e., model years

2017-2020) 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 proposed 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 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

draft 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 MY2016, there are also many of the key technologies we project as being

needed to achieve the proposed 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 MY2016, and which

even by MY2021 will still be constrained. These include important powertrain technologies
Chapter 3

3-88

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 then 5

years, typically on the order of 10 years or more. Thus major powertrain technologies

generally take longer to penetrate the new vehicle fleet then can be done in a 5-year redesign

cycle. As detailed in Chapter 3.5 of the draft 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 brakemean 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 project that 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 proposed

MY2017-2025 standards and the product development and introduction process which is

fairly standard in the automotive industry today, our assessment of the MY2011 and MY2012

vehicles in comparison to the proposed standards supports our overall feasibility assessment,

and reinforces our assessment of the lead time needed for the industry to achieve the proposed

standards.

23 EPA’s “Light Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel

Economy Trends Report, 1975 through 2010”
2017 Draft Regulatory Impact Analysis

3-89

Table 3.12-1 Vehicles that Meet or Exceed Proposed Targets With Current Powertrain Designs

2017 2018 2019 2020 2021 2022 2023 2024 2025

2011 Mercedes-Benz Smart fortwo (cabriolet) 123.9 0.0 26.8 EV A1 n/a Two Seaters C 100% 100% 100% 100% 100% 100% 100% 100% 100%

2011 Mercedes-Benz Smart fortwo (coupe) 123.9 0.0 26.8 EV A1 n/a Two Seaters C 100% 100% 100% 100% 100% 100% 100% 100% 100%

2011 Nissan LEAF 141.7 0.0 44.7 EV A1 n/a Midsize Cars C 100% 100% 100% 100% 100% 100% 100% 100% 100%

2011 Chevrolet VOLT 48.4 56.0 45.3 PHEV CVT 1.4 Compact Cars C 76% 76% 75% 73% 72% 71% 69% 68% 66%

2011 Toyota PRIUS 70.8 125.6 44.2 HEV CVT 1.8 Midsize Cars C 46% 44% 42% 40% 37% 34% 31% 28% 24%

2012 Honda CIVIC HYBRID 63.1 140.9 43.5 HEV A5 1.5 Compact Cars C 38% 35% 33% 30% 27% 23% 20% 16% 12%

2011 Hyundai SONATA HYBRID 52.2 170.3 48.0 HEV A6 2.4 Midsize Cars C 30% 28% 24% 21% 18% 14% 10% 6% 1%

2012 Ford FUSION HYBRID FWD 54.2 164.0 45.6 HEV CVT 2.5 Midsize Cars C 30% 27% 24% 21% 17% 13% 9% 5% 0%

2012 Lincoln MKZ HYBRID FWD 54.2 164.0 45.6 HEV CVT 2.5 Midsize Cars C 30% 27% 24% 21% 17% 13% 9% 5% 0%

2011 Lexus CT 200h 57.5 154.6 42.6 HEV CVT 1.8 Compact Cars C 30% 27% 24% 21% 17% 13% 9% 5% 0%

2011 Honda INSIGHT 57.3 155.1 40.8 HEV CVT 1.3 Compact Cars C 27% 24% 21% 17% 13% 9% 5% 0% -4%

2011 Toyota HIGHLANDER HYBRID 4WD 38.7 229.7 48.8 HEV CVT 3.5 Sport Utility Vehicle T 21% 20% 18% 16% 10% 6% 1% -4%

2011 Lexus RX 450h AWD 38.6 230.4 48.6 HEV CVT 3.5 Sport Utility Vehicle T 21% 19% 18% 16% 10% 5% 0% -5%

2011 Honda CIVIC CNG 37.5 175.7 43.4 CNG A5 1.8 Subcompact Cars C 21% 17% 14% 10% 6% 1% -3%

2011 Chevrolet SILVERADO 2WD HYBRID 28.5 311.4 67.3 HEV CVT 6.0 Standard Pick-up Trucks T 14% 14% 14% 13% 7% 2% -3%

2011 GMC SIERRA 2WD HYBRID 28.5 311.4 67.3 HEV CVT 6.0 Standard Pick-up Trucks T 14% 14% 14% 13% 7% 2% -3%

2011 Chevrolet SILVERADO 4WD HYBRID 28.4 313.2 67.3 HEV CVT 6.0 Standard Pick-up Trucks T 13% 13% 14% 12% 6% 1% -3%

2011 GMC SIERRA 4WD HYBRID 28.4 313.2 67.3 HEV CVT 6.0 Standard Pick-up Trucks T 13% 13% 14% 12% 6% 1% -3%

2011 Nissan ALTIMA HYBRID 46.7 190.3 46.3 HEV CVT 2.5 Midsize Cars C 19% 15% 12% 8% 3% -1%

2011 Toyota CAMRY HYBRID 45.9 193.4 46.9 HEV CVT 2.4 Midsize Cars C 18% 15% 11% 7% 3% -2%

2011 Lexus HS 250h 47.3 188.0 44.5 HEV CVT 2.4 Compact Cars C 17% 13% 9% 5% 1% -4%

2012 Ford ESCAPE HYBRID AWD 39.0 227.6 43.3 HEV CVT 2.5 Sport Utility Vehicle T 13% 12% 10% 8% 1% -4%

2011 Honda CR-Z 50.1 177.3 39.5 HEV CVT 1.5 Two Seaters C 15% 12% 8% 4% -1%

2011 Mercedes-Benz Smart fortwo (cabriolet) 49.5 179.5 26.8 Gasoline A5 1.0 Two Seaters C 14% 11% 7% 2% -2%

2011 Mercedes-Benz Smart fortwo (coupe) 49.5 179.5 26.8 Gasoline A5 1.0 Two Seaters C 14% 11% 7% 2% -2%

2012 Hyundai ELANTRA 44.7 198.7 45.2 Gasoline M6 1.8 Midsize Cars C 13% 9% 5% 1% -4%

2012 Hyundai ELANTRA 44.4 200.2 45.2 Gasoline A6 1.8 Midsize Cars C 12% 9% 4% 0% -5%

2011 Toyota TACOMA 2WD 30.2 294.5 55.9 Gasoline M5 2.7 Small Pick-up Trucks T 9% 7% 5% 3% -4%

2011 Toyota SIENNA 29.4 302.0 56.1 Gasoline A6 2.7 Minivan T 7% 5% 3% 1%

2012 Chevrolet CRUZE ECO 44.4 200.3 44.8 Gasoline M6 1.4 Midsize Cars C 11% 8% 3% -1%

2011 Lexus RX 450h 40.4 220.2 48.6 HEV CVT 3.5 Sport Utility Vehicle C 9% 6% 1% -3%

2012 Ford Focus SFE FWD 43.6 203.7 44.2 Gasoline A6 2.0 Compact Cars C 9% 5% 0% -4%

2012 Honda CIVIC HF 44.3 200.6 43.4 Gasoline A5 1.8 Compact Cars C 9% 5% 0% -4%

2011 Honda ODYSSEY 2WD 29.0 306.7 55.9 Gasoline A6 3.5 Minivan T 5% 3% 1% -1%

2011 Ford RANGER 2WD 31.2 284.5 50.6 Gasoline M5 2.3 Small Pick-up Trucks T 4% 2% 0% -2%

2012 Ford ESCAPE HYBRID FWD 44.1 201.4 43.3 HEV CVT 2.5 Sport Utility Vehicle C 8% 4% 0% -5%

2011 Toyota TACOMA 2WD 28.3 313.6 55.9 Gasoline A4 2.7 Small Pick-up Trucks T 3% 1% -1% -4%

2012 Hyundai ACCENT 45.5 195.1 41.7 Gasoline A6 1.6 Compact Cars C 8% 4% -1% -5%

2012 Infiniti M35h 38.8 229.1 49.1 HEV A7 3.5 Midsize Cars C 6% 2% -2%

2012 Honda CIVIC 43.0 206.7 43.4 Gasoline A5 1.8 Compact Cars C 6% 2% -3%

2012 Ford FOCUS FWD 42.1 211.0 44.2 Gasoline A6 2.0 Compact Cars C 5% 1% -4%

2011 Honda CR-Z 44.9 197.9 39.5 HEV M6 1.5 Two Seaters C 5% 1% -4%

2011 Ford Fiesta SFE FWD 44.9 198.0 39.3 Gasoline A6 1.6 Subcompact Cars C 5% 1% -4%

2012 Fiat 500 44.5 199.6 34.7 Gasoline M5 1.4 Minicompact Cars C 4% 0% -5%

2011 Cadillac ESCALADE 2WD HYBRID 28.5 311.4 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2% -5%

2011 Chevrolet TAHOE 2WD HYBRID 28.5 311.4 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2% -5%

2011 GMC YUKON 2WD HYBRID 28.5 311.4 54.8 HEV CVT 6.0 Sport Utility Vehicle T 2% -1% -2% -5%

2011 Chevrolet TAHOE 4WD HYBRID 28.4 313.2 54.8 HEV CVT 6.0 Sport Utility Vehicle T 1% -1% -3%

2011 GMC YUKON 4WD HYBRID 28.4 313.2 54.8 HEV CVT 6.0 Sport Utility Vehicle T 1% -1% -3%

2012 Chevrolet CRUZE ECO 40.9 217.1 44.8 Gasoline A6 1.4 Midsize Cars C 4% -1% -5%

2012 Volkswagen Passat 46.4 219.5 45.3 Diesel M6 2.0 Midsize Cars C 3% -1%

2012 Honda CIVIC 41.8 212.4 43.4 Gasoline M5 1.8 Compact Cars C 3% -1%

2012 Ford FOCUS FWD 41.1 216.1 44.2 Gasoline A6 2.0 Compact Cars C 3% -2%

Model

Year Manufacturer Vehicle

Unadjusted

Fuel Economy

(mpg)

Footprint

(ft2)

Tailpipe CO2

(ft2)

Powertrain

Type Vehicle Class Car/Truck

Compliance

Transmission

Engine

Displacement

(L)
Chapter 3

3-90

2017 2018 2019 2020 2021 2022 2023 2024 2025

2011 Ford Fiesta FWD 44.0 202.2 39.3 Gasoline A6 1.6 Subcompact Cars C 3% -2%

2012 Buick LACROSSE 38.1 233.3 48.0 Gasoline A6 2.4 Midsize Cars C 3% -2%

2011 Kia FORTE ECO 40.7 218.3 44.5 Gasoline A6 2.0 Midsize Cars C 2% -2%

2012 Chevrolet CRUZE 40.4 219.8 44.8 Gasoline M6 1.4 Midsize Cars C 2% -2%

2011 Mini Mini Cooper 43.6 203.9 38.8 Gasoline M6 1.6 Minicompact Cars C 2% -3%

2012 Chevrolet CRUZE 40.1 221.7 44.8 Gasoline A6 1.4 Midsize Cars C 1% -3%

2012 Buick REGAL 38.1 233.3 46.8 Gasoline A6 2.4 Midsize Cars C 0% -4%

2011 Ford F150 PICKUP 2WD 23.9 372.3 75.9 Gasoline A6 3.5 Standard Pick-up Trucks T -4% -4% -4% -4% -4% -4%

2011 Ford F150 PICKUP 2WD 23.9 372.3 72.8 Gasoline A6 3.5 Standard Pick-up Trucks T -4% -4% -4% -4% -4% -5%

2011 Ford F150 PICKUP 2WD 23.9 372.3 67.2 Gasoline A6 3.5 Standard Pick-up Trucks T -4% -4% -4% -4% -5%

2011 Ford F150 PICKUP 2WD 24.4 363.8 67.2 Gasoline A6 3.7 Standard Pick-up Trucks T -2% -1% -1% -1% -3%

2011 Cadillac ESCALADE 4WD HYBRID 28.0 317.4 54.8 HEV CVT 6.0 Sport Utility Vehicle T 0% -3% -4%

2011 GMC YUKON DENALI HYBRID 4WD 28.0 317.4 54.8 HEV CVT 6.0 Sport Utility Vehicle T 0% -3% -4%

2011 Mercedes-Benz ML450 HYBRID 4MATIC 29.6 300.4 51.0 HEV CVT 3.5 Sport Utility Vehicle T -1% -3% -5%

2012 Ford TRANSIT CONNECT FWD 31.1 286.0 47.9 Gasoline M5 1.6 Special Purpose Vehicle T -1% -3% -5%

2011 Mini Mini Cooper Countryman 41.0 216.8 43.0 Gasoline M6 1.6 Compact Cars C 0% -5%

2012 Volkswagen Jetta 46.1 220.7 43.8 Gasoline A6 2.0 Compact Cars C 0% -5%

2011 Ford Fiesta FWD 42.7 208.1 39.3 Gasoline A6 2.7 Subcompact Cars C 0% -5%

2012 Volkswagen Jetta 46.1 220.9 43.8 Diesel CVT 3.5 Compact Cars C 0% -5%

2011 Toyota VENZA AWD 30.2 294.3 48.8 Gasoline A4 2.0 Sport Utility Vehicle T -2% -5%

2011 Nissan QUEST 27.2 326.7 55.9 Gasoline M6 2.0 Minivan T -1% -4%

2011 Nissan FRONTIER 2WD 27.4 324.8 54.8 Gasoline M5 2.5 Small Pick-up Trucks T -3% -5%

2011 Mazda MAZDA2 42.6 208.6 39.4 Gasoline M5 1.5 Compact Cars C -1% -5%

2012 Ford Transit Connect Van 30.5 291.8 47.9 Gasoline A4 2.0 Vans, Cargo Types T -3%

2011 Toyota SIENNA 26.7 333.0 56.1 Gasoline A6 3.5 Minivan T -3%

2012 Kia SORENTO 4WD 30.6 290.8 47.1 Gasoline A6 2.4 Sport Utility Vehicle T -4%

2012 Kia SPORTAGE 4WD 31.0 286.9 46.0 Gasoline A6 2.4 Sport Utility Vehicle T -5%

2011 Suzuki EQUATOR 2WD 27.3 325.3 54.0 Gasoline M5 2.5 Small Pick-up Trucks T -4%

2011 Toyota YARIS 42.6 208.7 39.9 Gasoline M5 1.5 Subcompact Cars C -1%

2012 Volkswagen Passat 44.6 228.2 45.3 Diesel A6 2.0 Midsize Cars C -1%

2011 Honda FIT 42.5 208.9 39.9 Gasoline A5 1.5 Small Station Wagons C -1%

2012 Nissan SENTRA 39.5 224.9 44.3 Gasoline CVT 2.0 Midsize Cars C -1%

2011 Toyota COROLLA 41.0 217.0 42.5 Gasoline M5 1.8 Compact Cars C -1%

2012 Ford FOCUS FWD 39.4 225.4 44.2 Gasoline M5 2.0 Compact Cars C -2%

2012 Hyundai SONATA 36.5 243.3 48.0 Gasoline A6 2.4 Large Cars C -2%

2011 Kia OPTIMA 36.5 243.8 48.1 Gasoline M6 2.4 Midsize Cars C -2%

2012 Hyundai SONATA 36.5 243.6 48.0 Gasoline M6 2.4 Large Cars C -2%

2011 Kia FORTE 38.9 228.4 44.5 Gasoline A6 2.0 Midsize Cars C -2%

2011 Toyota YARIS 41.9 212.2 39.9 Gasoline A4 1.5 Subcompact Cars C -2%

2011 Kia OPTIMA 36.3 245.1 48.1 Gasoline A6 2.4 Midsize Cars C -2%

2012 Chevrolet CRUZE 38.5 230.5 44.8 Gasoline M6 1.8 Midsize Cars C -3%

2011 Mini Mini Cooper 41.7 213.3 38.8 Gasoline A6 1.6 Minicompact Cars C -3%

2012 Volkswagen GOLF 46.1 220.7 42.4 Diesel A6 2.0 Compact Cars C -3%

2012 Volkswagen GOLF 46.1 220.9 42.4 Diesel M6 2.0 Compact Cars C -4%

2011 Kia RIO 41.1 216.0 41.3 Gasoline M5 1.6 Compact Cars C -4%

2012 Volkswagen JETTA SPORTWAGEN 46.1 220.9 42.3 Diesel M6 2.0 Small Station Wagons C -4%

2011 Kia FORTE KOUP 38.3 232.1 44.6 Gasoline A6 2.0 Compact Cars C -4%

2012 Audi A6 35.4 251.0 48.6 Gasoline CVT 2.0 Midsize Cars C -4%

2011 Nissan VERSA 40.8 217.9 41.4 Gasoline CVT 1.8 Midsize Cars C -4%

2011 Hyundai ENTOURAGE 26.8 331.6 54.8 Gasoline A6 3.5 Minivan T -5%

2012 Kia SEDONA 26.8 331.6 54.7 Gasoline A6 3.5 Minivan T -5%

2011 Mini Mini Clubman 41.0 216.8 40.1 Gasoline M6 1.6 Subcompact Cars C -5%

2011 Mini Mini Convertible 41.0 216.8 38.8 Gasoline M6 1.6 Minicompact Cars C -5%

2012 Audi A3 46.1 220.7 41.8 Diesel A6 2.0 Small Station Wagons C -5%

Car/

Truck

Compliance

Footprint

(ft2)

Powertrain

Type Transmission

Engine

Displacement

(L)

Vehicle Class Model

Year Manufacturer Vehicle

Unadjusted

Fuel Economy

(mpg)

Tailpipe CO2

(ft2)
2017 Draft Regulatory Impact Analysis

3-91

3.13 Analysis of Ferrari & Chrysler/Fiat

Note that in the primary analyses, Ferrari is shown as a separate entity, but in this sideanalysis, 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. 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.

Also note that in Section III.B., EPA is requesting comment on the concept of allowing

companies that are able to demonstrate "operational independence" to be eligible for SVM

alternative standards. If EPA were to adopt such provisions, and Ferrari were to qualify, they

would likely petition for an alternative standard under the proposed SVM provisions, rather

than comply as part of Chrysler/Fiat.

Under the MY 2025 OMEGA projections, Ferrari falls short of its 2025 target (150

grams/mile CO2) by nine grams. VV 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 $7,100 relative to the MY 2016 standards.

If Ferrari is included in the Chrysler/Fiat GHG compliance fleet, Chrylser/Fiat’s

starting 2008 CO2 is 2 grams higher (347.6 vs. 345.6). As a result, the cost of complying with

the reference case standards would increase by approximately $65, and the cost of complying

with the proposed standards would increase by $91 for a net average increase in MY 2025

compliance costs of $36 per vehicle for Chrysler/Fiat. Net program costs would not change

significantly.

3.14Cost Sensitivities

3.14.1 Overview

We have conducted several sensitivity analyses on a variety of input parameters. For

the analyses presented in and 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

VV Assuming that Ferrari complied with the primary proposed standards.
Chapter 3

3-92

Table 3.14-1 , with the summarized results in

Table 3.14-10 . Additional sensitivities with regard to benefits are shown in DRIA

Chapter 4.
2017 Draft Regulatory Impact Analysis

3-93

Table 3.14-1 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.

3.14.2 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 3.14-2 along with the cost equation

used for each side of the mass reduction cost sensitivity.

Table 3.14-2 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

We did not re-rank OMEGA packages for the mass reduction cost sensitivities but

rather used the same input files used for our primary analysis with new mass costs. This

should have no impact on the results other than making them conservative since re-ranking

packages would serve to move the modeling to more cost effective technologies, thus

reducing the cost impact of this sensitivity. Because mass reduction is a cost effective

technology, even with higher costs, OMEGA would still choose a similar degree of mass

reduction given the stringency of the MY 2025 standards. By contrast, even with lower costs,

mass reduction would still be limited by the fatality analysis. As a result, the mass reduction

sensitivity does not have any impact on the relative ranking of packages or the engine and

hybridization technologies that would be added in response to our proposed standards.

The high mass cost inputs increased the average compliance costs of the reference

case by $31 and the control case by $133, for a net average per-vehicle cost increase of $102
Chapter 3

3-94

in MY 2025. The low mass cost inputs decreased the average compliance costs of the

reference case by $31 and the control case by $133 for a net average cost decrease of $102

These impacts would be greater on manufacturers that use more mass reduction technology,

and less on those that use less.

3.14.3 Battery Sensitivity

For the battery pack cost sensitivities, we decreased/increased the battery pack DMCs

by the amounts shown in Table 3.14-3. As presented in Chapter 3 of the draft 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 3.14-3 and

Table 3.14-5 show the linear regressions used for our low side and high side sensitivity

analyses, respectively, while Table 3.14-4 presents the linear regressions used for our primary

analysis (as presented in Chapter 3 of the draft joint TSD).

Table 3.14-3 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net

Weight Reduction used for Low Side Sensitivity (2009$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Subcompact -$159x+$645 -$690x+$2,082 -$694x+$2,919 -$1,080x+$4,339 -$1,651x+$5,088 -$1,615x+$6,633

Small car -$196x+$682 -$798x+$2,197 -$1,675x+$3,229 -$1,626x+$4,710 -$2,279x+$5,603 -$2,480x+$7,350

Large car -$270x+$777 -$1,255x+$2,664 -$2,521x+$4,153 -$2,768x+$5,738 -$3,215x+$6,481 -$3,016x+$8,791

Minivan -$265x+$763 -$1,152x+$2,637 -$1,672x+$4,028 -$2,784x+$5,761 -$2,472x+$6,731 -$3,653x+$9,397

Small truck -$249x+$740 -$1,071x+$2,514 -$1,955x+$3,829 -$2,518x+$5,463 -$2,377x+$6,436 $9,003

Minivan+towing -$265x+$836

Large truck -$285x+$868

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 (-

$159)x(15%)+$645=$621.

The small truck EV150 regression has no slope since the net weight reduction is always 0 due to the 20% weight

reduction hit.

The agencies did not regress PHEV or EV costs for the minivan+towing and large truck vehicle classes since we

do not believe these vehicle classes would use the technologies.

Table 3.14-4 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net

Weight Reduction used for the Primary Analysis (2009$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Subcompact -$177x+$716 -$862x+$2,602 -$867x+$3,649 -$1,350x+$5,424 -$2,064x+$6,360 -$2,019x+$8,292

Small car -$218x+$758 -$998x+$2,746 -$2,093x+$4,037 -$2,033x+$5,888 -$2,849x+$7,004 -$3,100x+$9,187

Large car -$300x+$864 -$1,568x+$3,331 -$3,152x+$5,192 -$3,460x+$7,173 -$4,019x+$8,101 -$3,770x+$10,989

Minivan -$294x+$848 -$1,439x+$3,296 -$2,090x+$5,035 -$3,480x+$7,201 -$3,090x+$8,414 -$4,566x+$11,746

Small truck -$277x+$822 -$1,338x+$3,143 -$2,444x+$4,787 -$3,148x+$6,828 -$2,971x+$8,045 $11,253

Minivan+towing -$294x+$929

Large truck -$317x+$964

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 (-

$177)x(15%)+$716=$689.

The small truck EV150 regression has no slope since the net weight reduction is always 0 due to the 20% weight

reduction hit.

The agencies did not regress PHEV or EV costs for the minivan+towing and large truck vehicle classes since we

do not believe these vehicle classes would use the technologies.
2017 Draft Regulatory Impact Analysis

3-95

Table 3.14-5 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net

Weight Reduction used for the High Side Sensitivity (2009$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Subcompact -$194x+$788 -$1,034x+$3,123 -$1,041x+$4,378 -$1,620x+$6,509 -$2,477x+$7,632 -$2,423x+$9,950

Small car -$240x+$834 -$1,197x+$3,295 -$2,512x+$4,844 -$2,439x+$7,065 -$3,419x+$8,404 -$3,720x+$11,024

Large car -$330x+$950 -$1,882x+$3,997 -$3,782x+$6,230 -$4,152x+$8,607 -$4,823x+$9,722 -$4,524x+$13,187

Minivan -$324x+$933 -$1,727x+$3,955 -$2,508x+$6,042 -$4,176x+$8,641 -$3,708x+$10,097 -$5,479x+$14,096

Small truck -$304x+$904 -$1,606x+$3,771 -$2,933x+$5,744 -$3,777x+$8,194 -$3,565x+$9,654 $13,504

Minivan+towing -$324x+$1,022

Large truck -$348x+$1,061

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 (-$194)x(15%)+$788=$759.

The small truck EV150 regression has no slope since the net weight reduction is always 0 due to the 20% weight

reduction hit.

The agencies did not regress PHEV or EV costs for the minivan+towing and large truck vehicle classes since we do

not believe these vehicle classes would use the technologies.

For the battery pack sensitivities, unlike the mass reduction sensitivities, we did rerank OMEGA packages. However, we started with the master-sets of packages as described

in Chapter 1 of this draft RIA for both the 2016MY and 2025MY and did not start with

preliminary-sets of packages and conduct a full package building/ranking process. Using the

master-sets of packages, we inserted new battery pack costs, re-ranked the master-sets of

packages to get the proper ordering of packages for each sensitivity case, then ran OMEGA.

As noted above, this should have no impact on the results other than making them

conservative since starting with a preliminary-set of packages and re-ranking would serve to

move the modeling to more cost effective technologies, thus reducing the cost impact.

The high battery cost inputs increased the average compliance costs of the reference

case by $2 and the control case by $63, for a net cost increase of $61. In the high case, the

penetration of EVs decreased from 2.8% to 2.2%, as companies chose more cost effective

options. MY 2025 HEV penetration increased from 15% to 17%. The low battery cost inputs

decreased the average compliance costs of the reference case by $2 and the control case by

$103 for a net decrease of $101. In the low cost case, the MY 2025 penetration of EVs

increased to 3.9%, while the HEV penetration decreased to 10%. In general, changing the

battery costs shifted the choice between HEVs and EVs. As both EVs and HEVs are less cost

effective (in this set of inputs) than conventional technologies, the penetrations of non-battery

dependent technologies was little changed.

3.14.4 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

draft joint TSD and provide details in a memorandum to the docket (EPA-HQ-OAR-2010-
Chapter 3

3-96

0799).WW 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 3.14-6 and Table 3.14-8 show the ICMs used

for the low side and high side sensitivity analyses, respectively, while Table 3.14-7 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 3.14-6 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 3.14-7 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

Table 3.14-8 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

Like done for the battery pack sensitivities, we re-ranked OMEGA packages using the

master-sets of packages as described in Chapter 1 of this draft RIA for the 2016MY and

2025MY. We did not start with preliminary-sets of packages and conduct a full package

building/ranking process. Using the master-sets of packages, we inserted new costs

calculated using the low/high IMCs, re-ranked the master-sets of packages to get the proper

ordering of packages for each sensitivity case, then ran OMEGA. As noted above, this should

have no impact on the results other than making them conservative since starting with a

preliminary-set of packages and re-ranking would serve to move the modeling to more cost

effective technologies, thus reducing the cost impact.

WW “Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,”

Helfand, G., and Sherwood, T., Memorandum dated August 2009.
2017 Draft Regulatory Impact Analysis

3-97

The high ICM inputs increased the average compliance costs of the reference case by

$63 and the control case by $312, for a net average per-vehicle cost increase of $249 in MY

2025. The low ICM inputs decreased the average compliance costs of the reference case by

$105 and the control case by $349 for a net average cost decrease of $244.

3.14.5 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 draft

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

3.14-9 shows how we have adjusted these learning rates for both the low and high side

sensitivities.

Table 3.14-9 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%

For the learning sensitivity, we re-ranked OMEGA packages using the master-sets of

packages as described in Chapter 1 of this draft RIA for the 2016MY and 2025MY. We did

not start with preliminary-sets of packages and conduct a full package building/ranking

process. Using the master-sets of packages, we inserted new costs calculated using the

low/high learning rates, re-ranked the master-sets of packages to get the proper ordering of

packages for each sensitivity case, then ran OMEGA. As noted above, this should have no

impact on the results other than making them conservative since starting with a preliminaryset of packages and re-ranking would serve to move the modeling to more cost effective

technologies, thus reducing the cost impact.

The high learning inputs increased the average compliance costs of the reference case

by $48 and the control case by $154, for a net average per-vehicle cost increase of $106 in

MY 2025. The low ICM inputs decreased the average compliance costs of the reference

case by $63 and the control case by $158 for a net average cost decrease of $97.

3.14.6 Summary of Sensitivity Impacts

The average per-vehicle impacts of the sensitivity runs are shown in

Table 3.14-10. Note that the majority of these impacts are less than $100 relative to

the primary analysis costs. The ICM impacts are larger.
Chapter 3

3-98

Table 3.14-10 Summary of Per-vehicle Cost Impacts of Sensitivity Analyses in MY 2025

relative to Primary Analysis

Sensitivity Title Low ($) High ($)

Primary Case $1946

Mass Cost -$102 $102

Battery Cost $101 $61

ICM $244 $249

Learning Rates $97 $106

3.14.7 NAS report

We note that EPA has decided not to base a sensitivity case on the 2010 National

Academy of Science Report “Assessment of Technologies for Improving Light-Duty Vehicle

Fuel Economy, Assessment for Fuel Economy Technologies for Light-Duty Vehicles” (The

National Academies Press, June 2010).

As discussed in detail in Chapter 3 of the draft Joint Technical Support Document for

this proposal, 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 proposal, 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 proposal, 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. 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 & NHTSA

assessment for this proposal. For example, there are a number of technologies that EPA and

NHTSA discuss in Chapter 3 of the draft 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, aerodynamic drag improvement, and improved internals for

automatic transmissions. The 2010 NAS report provides cost and effectiveness estimates for
2017 Draft Regulatory Impact Analysis

3-99

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 2012-2016

rulemaking. However, for the 2017-2025 proposals, where EPA and NHTSA are using a

model year 2008 baseline set of vehicles, the agencies estimate that for each these

technologies two increments of improvement can be implemented across the fleet between

2008 and 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 2012-2016 rule. But, for the 2017-2025 proposal,

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 additioal

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 proposal, 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 draft 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”24, this report has been peer reviewed and the peer review report and the

response to peer review comments are available in the EPA docket EPA-HQ-OAR2010-0799.
Chapter 3

3-100

- Argonne National Laboratories 2011 Report “Modeling the Performance and

Cost of Lithium-Ion Batteries for Electric-Drive Vehicles”25 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-OAR2010-0799.

- 2011 FEV Report “Light-Duty Technology Cost Analysis Power-split and P2

HEV Case Studies.”26 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”27. 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”28, this report has been peer reviewed,

and a copy of the report and the peer review report are available in the EPA docket

EPA-HQ-OAR-2010-0799.

- EPA vehicle fuel economy certification data from MY2011 and MY2012

vehicles, including for example the MY2011 Ford F-150 with the 3.5L Ecoboost

engine, MY2011 Sonata Hybrid, MY2012 Infiniti M35h hybrid, and several other

advanced technology production vehicles.

- “EBDI - Application of a Fully Flexible High BMEP Downsized Spark

Ignited Engine.” Society of Autmotive Engineers (SAE) Technical Paper No. 2010-

01-0587, Cruff, L., Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M.,

2010.29

- “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., 2010.30

- “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., 2010.31

- “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., 2010.32 EPA-HQ-OAR2010-0799

- “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., 2009.33
2017 Draft Regulatory Impact Analysis

3-101

- “Documentation of the Development of Indirect Cost Multipliers for Three Automotive

Technologies,” EPA Technical Memorandum, Helfand, G., Sherwood, T., August 2009.34

- 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.

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 proposal, 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 this proposal.

EPA requests comment on our overall approach for this proposal of basing our

assessment on technology feasibility, lead time, costs and effectiveness on the full range of

information described in the draft 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 also requests comment specifically on EPA’s use of the 2011 Ricardo study

(listed above), and we seek comment on 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 request comment on the 2011 Ricardo

Study and the Ricardo response to comments report with respect to the peer review conducted

on the draft Ricardo report. 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 draft Joint Technical Support Document.
Chapter 3

3-102

References

24 2011 Ricardo Report “Computer Simulation of Light-duty Vehicle Technologies for

Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe”

25 Argonne National Laboratories 2011 Report “Modeling the Performance and Cost of

Lithium-Ion Batteries for Electric-Drive Vehicles”

26 2011 FEV Report “Light-Duty Technology Cost Analysis Power-split and P2 HEV Case

Studies”

27 2011 FEV Report “Light-Duty Technology Cost Analysis: Advanced 8-speed

Transmissions”

28 2010 Lotus Engineer Study “An Assessment of Mass Reduction Opportunities for a 2017 –

2020 Model Year Vehicle Program”

29 “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

30 “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

31 “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

32 “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

33 “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

34 “Documentation of the Development of Indirect Cost Multipliers for Three Automotive

Technologies,” EPA Technical Memorandum, Helfand, G., Sherwood, T., August 2009
2017 Draft Regulatory Impact Analysis

4-1

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 proposed 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.

Mobile sources represent a large and growing share of U.S. GHG emissions and

include light-duty vehicles, light-duty trucks, medium duty passenger vehicles, heavy duty

trucks, airplanes, railroads, marine vessels and a variety of other sources. In 2007, all 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, lightduty trucks, medium-duty passenger vehicles, heavy-duty trucks, buses, and motorcycles

account for 23% of total U.S. GHGs.

This proposal, if finalized, 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 proposed rule. This

analysis quantifies the proposed 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 are also shown in this chapter.

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 discussed 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.

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 Draft Joint TSD.
Chapter 4

4-2

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 proposal. 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). 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

Draft Joint Technical Support Document. Specifically, Draft Joint TSD Chapter 1 discusses

the development of the vehicle fleet, Draft Joint TSD Chapter 2 discusses the attribute based

curves which define the CO2 targets, Draft Joint TSD Chapter 3 discusses the technologies

which may be used to meet those targets,XX and Draft 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.

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, YY (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 4.3-16).ZZ EPA additionally accounted for the greenhouse gas impacts of additional

vehicle miles travelled (VMT) due to the "rebound" effect discussed in Section III.H.

XX Specifically, the power consumption of plug-in hybrid and battery electric vehicles are discussed in Draft

Joint TSD Chapter 3 and used in this analysis. Mass reduction, an input to the mass-safety analysis, is also

discussed therein.

YY 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 proposed action.

ZZ As discussed in TSD Chapter 4, the 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.
2017 Draft Regulatory Impact Analysis

4-3

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 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 proposed program would be small

compared to total U.S. inventories across all sectors.

As discussed in the preamble, while 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 fleetaverage emissions level.35 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 2 - Emissions

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

SO2, which is largely controlled by the sulfur content of the fuel, is an exception to this

basic equation. As discussed inTSD 4, decreasing the quantity of fuel consumed decreases

tailpipe SO2 emissions roughly proportionally to the decrease in fuel consumed.

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. The

global warming potentials used in this rule are consistent with 100-year time frame values in

the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report
Chapter 4

4-4

(FAR). 36 At this time, the 100-year global warming potential values from the 1996 IPCC

Second Assessment Report are used in the official U.S. greenhouse gas inventory submission

to the United Nations Framework Convention on Climate Change (per the reporting

requirements under that international convention, which were last updated in 2006) . The

FAR values were used in the MYs 2012-2016 light duty rule and the MY 2014-2018 Medium

and Heavy duty rule.

Table 4.3-1 Global Warming Potentials for the Inventory GHGs

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 proposal in calendar years 2020,

2030, 2040 and 2050. We also present the emission impacts over the estimated full lifetime

of MYs 2017-2025.AAA The program was quantified as the difference in mass emissions

between a control case under proposed standards and a reference case as described in Section

4.3.3.

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 Draft Joint TSD. For MYs

between 2025 and 2035, EPA used the Volpe Center run of the NEMs model (discussed

in Draft Joint TSD Chapter 1) in order to project the sales of cars and trucks by “preMY 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.

AAA The “full lifetime” is the timespan 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.
2017 Draft Regulatory Impact Analysis

4-5

Table 4.3-2 MY 2011 and later Car and Truck DefinitionsBBB

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 schedulesCCC

TSD 4 also describes the derivation of the survival schedule, which is shown below.

The proportions of passenger cars and light trucks expected to remain in service at each age

are drawn from a 2006 NHTSA study, and are shown in (Table 4.3-16)DDD, 37 Note that these

survival rates were calculated against the pre-MY 2011 definitions of cars and light trucks,

because the NHTSA study has not been updated since 2006. Because the agencies are

unaware of a better data source, these values were used unchanged, and are the same values

used in the MYs 2012-2016 rule and the interim Joint TAR. No changes in survival rates were

explicitly projected into the future.

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.EEE

BBB While the formal definitions are lengthy, brief summaries of the classifications are shown here.

CCC A lengthier discussion of both survival and mileage schedules are provides in Draft Joint TSD Chapter 4

DDD The maximum age of cars and light trucks was defined as the age when the number remaining in service has

declined to approximately two percent of those originally produced. Based on an examination of recent

registration data for previous model years, typical maximum ages appear to be 26 years for passenger cars and

36 years for light trucks.

EEE Historic values are derived from the Fuel Economy Trends report (http://www.epa.gov/otaq/fetrends.htm),

future values are discussed in Table 4.3-7.
Chapter 4

4-6

Table 4.3-3 Survival Rates

VEHICLE AGE

ESTIMATED

SURVIVAL

FRACTION

CARS

ESTIMATED

SURVIVAL

FRACTION

LIGHT TRUCKS

1 0.9950 0.9950

2 0.9900 0.9741

3 0.9831 0.9603

4 0.9731 0.9420

5 0.9593 0.9190

6 0.9413 0.8913

7 0.9188 0.8590

8 0.8918 0.8226

9 0.8604 0.7827

10 0.8252 0.7401

11 0.7866 0.6956

12 0.7170 0.6501

13 0.6125 0.6042

14 0.5094 0.5517

15 0.4142 0.5009

16 0.3308 0.4522

17 0.2604 0.4062

18 0.2028 0.3633

19 0.1565 0.3236

20 0.1200 0.2873

21 0.0916 0.2542

22 0.0696 0.2244

23 0.0527 0.1975

24 0.0399 0.1735

25 0.0301 0.1522

26 0.0227 0.1332

27 0 0.1165

28 0 0.1017

29 0 0.0887

30 0 0.0773

31 0 0.0673

32 0 0.0586

33 0 0.0509

34 0 0.0443

35 0 0.0385

36 0 0.0334
2017 Draft Regulatory Impact Analysis

4-7

4.3.2.3 VMT schedules

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 calendar year 2001 cars and light trucks of

various ages were developed by NHTSA from the Federal Highway Administration’s 2001

National Household Transportation Survey (NHTS), and are reported in(Table 4.3-4). These

estimates represent the typical 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.
Chapter 4

4-8

Table 4.3-4 CY 2001 Mileage Schedules

VEHICLE AGE

ESTIMATED

VEHICLE MILES

TRAVELED

CARS

ESTIMATED

VEHICLE MILES

TRAVELED

LIGHT TRUCKS

1 14,231 16,085

2 13,961 15,782

3 13,669 15,442

4 13,357 15,069

5 13,028 14,667

6 12,683 14,239

7 12,325 13,790

8 11,956 13,323

9 11,578 12,844

10 11,193 12,356

11 10,804 11,863

12 10,413 11,369

13 10,022 10,879

14 9,633 10,396

15 9,249 9,924

16 8,871 9,468

17 8,502 9,032

18 8,144 8,619

19 7,799 8,234

20 7,469 7,881

21 7,157 7,565

22 6,866 7,288

23 6,596 7,055

24 6,350 6,871

25 6,131 6,739

26 5,940 6,663

27 0 6,648

28 0 6,648

29 0 6,648

30 0 6,648

31 0 6,648

32 0 6,648

33 0 6,648

34 0 6,648

35 0 6,648

36 0 6,648
2017 Draft Regulatory Impact Analysis

4-9

4.3.2.4 Adjusting vehicle use for years after 2001

For this rulemaking, the agencies updated the estimates of average vehicle use

reported in Table 4-4 using forecasts of future fuel prices reported in the AEO 2011 Reference

Case and projected fuel economy levels in the reference and control cases.FFF This adjustment

accounts for the difference between the average retail price per gallon of fuel forecast during

each calendar year over the expected lifetimes of model year 2017-25 passenger cars and light

trucks, and the average price that prevailed when the NHTS was conducted in 2001.GGG This

adjustment also accounts for the potential rebound effect from vehicles of a specific age in

future years having higher fuel economy than vehicles of the same age in 2001 (discussed

further in section 4.5.1). Like the survival schedule, this VMT schedule was not adjusted for

differences in car and truck definitions post MY 2011.

Specifically, 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; see section

4.5.1) was applied to the difference between the combination of each future year’s fuel prices

and vehicle fuel economy and those prevailing in 2001.HHH,III

The estimates of annual miles driven by passenger cars and light trucks at each age

were also adjusted to reflect projected future growth trends in average use for vehicles of all

ages. In order to develop reasonable estimates of future growth in the average number of

miles driven by cars and light trucks of all ages, the agencies calculated the rate of growth in

the reference mileage schedules necessary for total car and light truck travel to increase at the

rate forecast in the AEO 2011 Reference Case. The growth rate in average annual car and

light truck use produced by this calculation is approximately 1.1 percent per year through

2030, and 0.5% per year from 2031-2050.JJJ As shown in TSD 4, this roughly calibrates the

total calculated VMT to an extrapolation from AEO 2011. This growth was applied to the

mileage figures reported in Table 4.3-4 (after adjusting them as described previously for

future fuel prices, fuel economy, and expected vehicle survival rates) to estimate average

annual mileage during each calendar year analyzed and during the expected lifetimes of

FFF Historic values are derived from the Fuel Economy Trends report (http://www.epa.gov/otaq/fetrends.htm),

future values are discussed in Table 4.3-7

GGG Under the assumption that people tend to drive more as the cost of driving decreases, the higher fuel prices

that are forecast for future years would be expected to reduce average vehicle use. We assume that fuel prices

will be the same in both the reference and control cases;, however, in section III.H.7 of the preamble to the

proposed rule, we discuss the potential for this proposal to decrease world oil prices.

HHH See Draft Joint TSD Chapter 4

III For our VMT analysis, we assume consumers respond the same way to changes in fuel efficiency and fuel

prices. Consistent with this assumption, we use the same elasticity to measure consumer responses to changes in

fuel prices as we do to measure the rebound effect of consumers driving more in response to increased fuel

efficiency. See section Draft Joint TSD Chapter 4 and section 4.5.1 for additional discussion.

JJJ 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.
Chapter 4

4-10

model year 2017-25 cars and light trucksKKK The net impact resulting from these two separate

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. Observed

aggregate VMT in recent years has actually declined, but it is unclear if the underlying cause

is general shift in behavior or a response to a set of temporary economic conditions. The

agencies intend to consider new data on the VMT growth estimates as it becomes available,

and are requesting comment on this topic.LLL

Because the effects of fuel prices, fuel economy, and growth in average vehicle use

differ for each year, these adjustments result in different VMT schedules for each future year.

While the adjustment for future fuel prices generally reduces average mileage at each age

from the values tabulated from the 2001 NHTS, the adjustment for expected future growth in

average vehicle use and improvements in fuel economy increases it. The net impact resulting

from these separate adjustments is continued growth over time in the average number of miles

that vehicles of each age are driven, although at slower rates.

4.3.2.5 Final VMT equation

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 2001 NHTS to

derive the estimates of average miles driven by vehicles of each model year (denoted MY)

during future calendar years (denoted CY) that are used in this analysis. The equation has

three multiplicative components; the CY2001 VMT by age, the adjustment for a growth rate,

and the adjustment for changes in fuel prices and fuel economy.

Equation 3 – VMT growth

ܸܯ ܶ௡௘௟௔௖ௗ௔௥௬௘௔௥௫ǡ௔௚௘௬ ൌ ൫ܸ௬൯כ)ͳ൅ ܩܴͳ)

௒ௌଵ כሺͳ൅ ܩ ʹሻ௒ௌଶ כሺͳെ ܴ כሺܥܨܲܯଶ଴଴ଵǡ௬

ଵǡ௬଴଴ଶܯܲܥܨ௫ǡ௬ሻȀܯܲܥܨ െ

Where:

Vy = Average miles driven in CY 2001 (from NHTSA analysis of 2001 NHTS data) by a vehicle of age

y during 2001

GR1 = Growth Rate for average miles driven by vehicles of each age from 2001 to 2030

YS1 = Lesser of (Years since 2001) and (29).

GR2 = Growth Rate for average miles driven by vehicles of each age from 2030 to 2050

YS2 = Greater of (Years since 2030) and (0).

KKK 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.

LLL 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 slow down. See the 2009 National Household Travel Survey (http://nhts.ornl.gov/2009/pub/stt.pdf)

and National transportation Statistics
2017 Draft Regulatory Impact Analysis

4-11

R= Magnitude of consumer responsiveness to changes in fuel cost per mile, equivalent to the rebound

effect rate assumption and expressed as an elasticity (-0.10)

FCPMx,y = Fuel cost per mile of a vehicle of age y in calendar year x

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:

ܥܨܲܯ௡௘௟௔஼ௗ௔௥௬௘௔௥௫ ൌ ܥܧ௬ ܧכܲ௫ ൅ ܥܩ௬ ܩכܲ௫ ൅ ܥܦ௬ ܦכܲ௫

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

Table 4.3-5 presents the EPA’s estimates of the average number of miles driven by

model year 2021 and 2025 cars and light trucks at over their estimated average lifetimes.

While these values may appear large relative to current vehicles, the full useful life of MY

2025 vehicles (36 years) ends in CY 2061. A more extensive discussion of the VMT schedule

development relative to AEO and current data is presented in TSD 4. The control case VMT

schedules, due to the lower cost per mile, have somewhat higher VMT.

Table 4.3-5–Reference VMT used in EPA’s analyses

MY 2021 MY 2025

Cars Light

Trucks

Cars Light

Trucks

204,668 242,576 210,898 249,713

4.3.2.6 Non CO2 Emission Factors

As documented in Draft Joint TSD Chapter 4, emission factors for this analysis were

derived from several sources. A more complete documentation of these sources is provided in

that chapter. Tailpipe emission factors other than CO2 were derived from MOVES 2010a.38

Upstream emission factors for petroleum refining, transport and distribution were derived

from EPA’s “Impact spreadsheet” based on Argon National Labs Greet 1.8.39, 40 Electricity

related emission factors for were derived from EPA’s Integrated Planning Model (IPM).

These emission factors were used as inputs to the OMEGA post-processor.41
Chapter 4

4-12

4.3.3 Scenarios

4.3.3.1 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 for 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.

Table 4.3-6 – A/C Credits

Reference Control

Car Truck Car Truck

IndirectDirectTotalIndirectDirectTotalIndirectDirectTotalIndirectDirectTotal

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 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.

4.3.3.2 Reference Case

As described in DRIA chapter 3 and Preamble III.D, we assume a flat reference case

of MY 2016 standards. No additional compliance flexibilities were explicitly modeled for the
2017 Draft Regulatory Impact Analysis

4-13

MY 2016 standards. The EPA flexible fueled vehicle (FFV) credit expires before MY

2016.MMM 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 incentive credits are

projected to be available to the reference case. Off-cycle credits, which are designed to be

environmentally neutral, would only lower costs. These credits are not modeled here due to

the difficult in predicting manufacturers use of these credits under the MY 2016 program.

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 DRIA Chapter 3.

CO2 emission rates for MY 2016, 2021 and 2025 were taken from OMEGA model

outputs. Intermediate years were interpolated, and CO2 g/mile rates past MY 2025 were kept

the same. Two cycle CO2 emission rates for the reference case are shown below, and

continue changing on a fleet basis due to mix shifts (Table 4.3-7). As no EVs were modeled,

there is no increase in electricity consumption in the reference Case. The air conditioning

impacts as discussed in Section 4.3.3.1 were also incorporated.

Table 4.3-7 – Reference Case Two Cycle CO2

MY Car Truck Fleet

2017 233 314 263

2018 233 315 263

2019 233 315 262

2020 233 315 262

2021 233 315 262

2022 233 315 261

2023 232 315 261

2024 232 314 260

2025 232 314 259

4.3.3.3 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 car-truck trading,

but the results should be environmentally equivalent. A/C refrigerant and efficiency

credit estimates are discussed in Section4.3.3.1, while the EV/PHEV/FCV multiplier

credit and pickup related credits are discussed in the following sections. Off-cycle

MMM The credit available for producing FFVs will have expired, although the real world usage credits will be

available.
Chapter 4

4-14

credits were not modeled explicitly, as they have been designed to be environmentally

neutral. These estimates are summarized in Table 4.3-8 through
2017 Draft Regulatory Impact Analysis

4-15

Table 4.3-10. 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 onroad gap. See TSD 5 for more details.

Table 4.3-8 Passenger Cars (Grams per mile)

Model

Year

Projected

CO2

Compliance

Target

EV/PHEV/FCV

Multiplier

A/C

Refrigerant

A/C

Efficiency

Projected

2-cycle

CO2

2016 (base) 225 -- 5.4 4.8 235

2017 213 2.2 7.8 5.0 228

2018 202 2.1 9.3 5.0 219

2019 192 2.0 10.8 5.0 210

2020 182 1.5 12.3 5.0 201

2021 173 1.0 13.8 5.0 193

2022 165 -- 13.8 5.0 184

2023 158 -- 13.8 5.0 177

2024 151 -- 13.8 5.0 169

2025 144 -- 13.8 5.0 163

Table 4.3-9 Light Trucks (Grams per mile)

Model

Year

Projected

CO2

Compliance

Target

EV/PHEV/FCV

Multiplier

Pickup

Mild

HEV +

Perf

Pickup

Strong

HEV +

Perf

A/C

Refrigerant

A/C

Efficiency

2-

cycle

CO2

(3)

2017 295 0.0 0.3 0.0 7.0 5.0 307

2018 285 0.0 0.4 0.1 11.0 6.5 303

2019 277 0.1 0.6 0.2 13.4 7.2 299

2020 270 0.1 0.7 0.2 15.3 7.2 293

2021 250 0.0 0.8 0.4 17.2 7.2 275

2022 237 -- -- 0.5 17.2 7.2 262

2023 225 -- -- 0.6 17.2 7.2 250

2024 214 -- -- 0.6 17.2 7.2 239

2025 203 -- -- 0.7 17.2 7.2 228
Chapter 4

4-16

Table 4.3-10 Combined Cars and Trucks (Grams per mile)

Model

Year

Projected

CO2

Compliance

Target

EV/PHEV/FCV

Multiplier

Pickup

Mild

HEV

+ Perf

Pickup

Strong

HEV

+ Perf

A/C

Refrigerant

A/C

Efficiency

2-cycle

CO2

2017 243 1.4 0.1 0.0 7.5 5.0 257

2018 232 1.3 0.2 0.0 9.9 5.5 249

2019 223 1.3 0.2 0.1 11.7 5.8 242

2020 213 1.0 0.3 0.1 13.4 5.8 234

2021 200 0.6 0.3 0.1 15.0 5.8 222

2022 190 -- -- 0.2 15.0 5.8 211

2023 181 -- -- 0.2 15.0 5.8 202

2024 172 -- -- 0.2 14.9 5.7 193

2025 163 -- -- 0.2 14.9 5.7 184

4.3.3.3.1 EV/PHEV/FCVs

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 proposed. For purposes of this modeling, we

assume that this cap is never reached. This does not imply that EPA has proposed 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 4.5.2of the DRIA Costs beyond MY 2025 assume no

technology changes on the vehicles, and implicitly assume EVs used for compliance remain at

zero gram/mile.NNN Upstream emissions from electric vehicles, regardless of the zero-gram

mile credit, are always modeled in this analysis.

For the benefits analysis, 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. 2017 EV penetrations were setat 1% of the fleet. PHEV sales, as

projected by OMEGA, are not significant.OOO

NNN The costs for PHEVs and EVs in this rule reflect those costs discussed in Draft Joint TSD Chapter 3, and do

not reflect any tax incentives, as the availability of those tax incentives in this time frame is uncertain.

OOO 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 2017-2021 timeframe.
2017 Draft Regulatory Impact Analysis

4-17

Table 4.3-11 – EV Fraction of the MY Fleets

Model Year Cars Truck EV

multiplier

2017 1.0% 0.0% 2

2018 1.0% 0.0% 2

2019 1.1% 0.0% 2

2020 1.1% 0.0% 1.75

2021 1.1% 0.0% 1.5

2022 1.7% 0.3% 0

2023 2.4% 0.5% 0

2024 3.0% 0.8% 0

2025 3.6% 1.0% 0

The EV multiplier credit was calculated by following formula

Equation 4 – Impact of EV multiplier

GHG Target with multiplier = (GHG Target without multiplier * (Total MY Sales + Multiplier *

Number of EV sales))/Total sales

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 5 – Impact of EV multiplier: example

GHG Target with multiplier =

(172.8 * (10.5 million+ 1.5 * 1.1% EV sales * 10.5 million sales))/10.5 million sales

= 173.8 or a delta of 1.0 grams.

4.3.3.3.2 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. As we did not consider these credits directly in

the OMEGA cost modeling, we did two post-process exercises to project likely benefits.
Chapter 4

4-18

Table 4.3-12 Pickup Trucks as a Fraction of the Fleet

Model Year Projected Sales of

Full Size Pickup

TrucksPPP

Pickup Trucks

(of Trucks)

Pickup Trucks

(of Fleet)

Trucks

(of fleet)

2017 1,240,844 21% 8% 37%

2018 1,186,474 21% 8% 36%

2019 1,133,605 20% 7% 36%

2020 1,157,114 21% 7% 35%

2021 1,122,173 20% 7% 35%

2022 1,103,058 19% 7% 35%

2023 1,045,507 18% 6% 34%

2024 1,011,897 18% 6% 34%

2025 1,002,806 18% 6% 33%

Based on these fleet fractions, and the credit available the maximum potential credit

can be calculated.

Table 4.3-13 Maximum Potential Impact of Pickup Credits on Truck Fleet

Model

Year

Mild

HEV

Credit

Mild

HEV

Max

impact

(Trucks)

Strong

HEV

Credit

Strong HEV Max

Credit (Trucks)

2017 10.0 2.1 20.0 4.3

2018 10.0 2.1 20.0 4.2

2019 10.0 2.0 20.0 4.1

2020 10.0 2.1 20.0 4.1

2021 10.0 2.0 20.0 3.9

2022 0.0 0.0 20.0 3.9

2023 0.0 0.0 20.0 3.7

2024 0.0 0.0 20.0 3.6

2025 0.0 0.0 20.0 3.5

Not every pickup truck will get these credits. For the each credit, there is a minimum

fleet fraction required for a manufacturer to receive the credit. For the mild credit, we

assumed that one-half of this minimum percentage received the credit. For the strong HEV

credit we assumed that 0% received the credit in MY 2017, 10% received the credit in MY

2021, and 20% received the credit in MY 2025. Because these penetrations are in all cases

PPP These totals include 1 model with 30,000 sales which would not be classified as a full size pickup under the

proposal. Therefore, the credit impact is be overstated by about 3%.
2017 Draft Regulatory Impact Analysis

4-19

higher than the penetrations projected by the OMEGA model, we consider these estimates to

adequately bound the potential utilization of the performance based credit.

Table 4.3-14 – Pickup Credits

MY Mild

Credit(%

of

Pickups)

Truck

Credit

from

Mild

(g/mile)

Strong

Credit(%

of

Pickups)

Truck

Credit

from

Strong

(g/mile)

2017 30% 0.3 0% 0.0

2018 40% 0.4 2% 0.1

2019 55% 0.6 4% 0.2

2020 70% 0.7 6% 0.2

2021 80% 0.8 10% 0.4

2022 13% 0.5

2023 15% 0.6

2024 18% 0.6

2025 20% 0.7

4.3.3.4 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 the Interim

Joint TAR, and are 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 2010.42,43 The upstream emission factor is applied to total electricity

production, rather than simply power consumed at the wheel. QQQ It is assumed that

electrically power vehicles drive the same drive schedule as the rest of the fleet.

QQQ 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.
Chapter 4

4-20

Table 4.3-15 Average Electricity Consumption

Average 2 cycle

Electricity

Consumption for the

fleet (kwh/mile)

Model

Year

Cars Trucks

2017 0.000 0.000

2018 0.001 0.000

2019 0.001 0.000

2020 0.002 0.000

2021 0.002 0.000

2022 0.004 0.001

2023 0.005 0.002

2024 0.007 0.002

2025 0.009 0.003
2017 Draft Regulatory Impact Analysis

4-21

4.3.4 Emission Results

4.3.4.1 Calendar Year Analyses

Table 4.3-16 Impacts of Program on GHG Emissions

Calendar Year: 2020 2030 2040 2050

Net Delta*

-29 -297 -462 -547

Net CO2

-24 -268 -420 -497

Net other GHG -4 -29 -42 -50

Downstream -24 -249 -389 -461

CO2 (excluding A/C) -19 -224 -355 -421

A/C – indirect CO2

-1 -3 -4 -4

A/C – direct HFCs -4 -21 -30 -36

CH4 (rebound effect) 0 0 0 0

N2O (rebound effect) 0 0 0 0

Gasoline Upstream -6 -63 -100 -119

CO2

-5 -55 -87 -103

CH4

-1 -8 -12 -15

N2O

0 0 0 0

Electricity Upstream 1 15 27 32

CO2

1 15 26 32

CH4

0 0 0 0

N2O

0 0 0 0
Chapter 4

4-22

Table 4.3-17 Annual Criteria 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 -12,467 -0.1% -135,566 -1.1%

CO 21,242 0.0% 397,861 0.7%

NOX -2,449 0.0% -16,008 -0.2%

PM2.5 -351 0.0% -3,123 -0.1%

SOX -1,650 0.0% -9,443 -0.1%

Downstream VOC 379 0.0% 8,623 0.1%

CO 22,212 0.0% 405,260 0.7%

NOX 779 0.0% 14,872 0.1%

PM2.5 63 0.0% 1,023 0.0%

SOX -449 0.0% -5,051 -0.1%

Fuel Production

and Distribution

VOC -12,860 -0.1% -144,503 -1.1%

CO -1,229 0.0% -13,810 0.0%

NOX -3,846 0.0% -43,215 -0.4%

PM2.5 -524 0.0% -5,890 -0.1%

SOX -2,353 0.0% -26,443 -0.3%

Electricity VOC 14 0.0% 6,411 0.1%

CO 259 0.0% 6,411 0.0%

NOX 617 0.0% 12,335 0.1%

PM2.5 110 0.0% 1,743 0.0%

SOX 1,153 0.0% 22,051 0.3%
2017 Draft Regulatory Impact Analysis

4-23

Table 4.3-18 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 2 0.02% 47 0.4%

Acetaldehyde 4 0.00% 112 0.2%

Acrolein 0 0.01% -6 0.0%

Benzene -15 -0.01% -26 0.0%

Formaldehyde -5 0.00% 3 0.0%

Downstream 1,3- Butadiene 2 0.02% 49 0.4%

Acetaldehyde 6 0.01% 124 0.2%

Acrolein 0 0.01% 5 0.0%

Benzene 13 0.01% 285 0.1%

Formaldehyde 5 0.00% 118 0.1%

Fuel Production

and Distribution

1,3- Butadiene 0 0.00% -3 0.0%

Acetaldehyde -1 0.00% -15 0.0%

Acrolein 0 -0.01% -15 0.0%

Benzene -28 -0.01% -313 -0.1%

Formaldehyde -10 0.00% -115 -0.1%

Electricity 1,3- Butadiene 0 0.00% 2 0.0%

Acetaldehyde 0 0.00% 3 0.0%

Acrolein 0 0.01% 4 0.0%

Benzene 0 0.00% 2 0.0%

Formaldehyde 0 0.00% 1 0.0%
Chapter 4

4-24

4.3.4.2 Model Year Analyses

Table 4.3-19 Projected Net GHG Deltas (MMTCO2eq per model year)

MY Downstream Upstream

(Gasoline) Electricity Total

CO2e

2017 -24 -6 1 -29

2018 -58 -14 2 -70

2019 -90 -21 3 -108

2020 -125 -30 4 -151

2021 -181 -44 5 -220

2022 -226 -56 9 -273

2023 -268 -68 13 -322

2024 -311 -79 18 -372

2025 -354 -91 23 -422

Total -1,637 -408 77 -1,967

Table 4.3-20 Projected Net Non-GHG Deltas (MMT per model year)

Criteria Emission Impacts of Program (short tons)

MY VOC CO NOx PM2.5 SO2RRR

2017 -11,990 49,315 -1,324 -290 -1,569

2018 -27,915 115,855 -3,324 -723 -4,108

2019 -43,158 179,978 -5,169 -1,132 -6,425

2020 -60,523 253,119 -7,281 -1,603 -9,107

2021 -89,344 373,036 -11,199 -2,450 -14,516

2022 -114,882 477,853 -12,202 -2,821 -14,590

2023 -138,334 574,020 -12,700 -3,112 -13,918

2024 -162,591 673,516 -13,127 -3,413 -13,086

2025 -187,085 774,071 -13,404 -3,705 -12,006

Sum -835,821 3,470,763 -79,729 -19,247 -89,322

Model Year Lifetime Air Toxic Emissions (short tons)

MY Benzene 1,3 Butadiene Formaldehyde Acetaldehyde Acrolein

2017 15 7 7 17 1

2018 35 17 17 40 2

RRR 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.
2017 Draft Regulatory Impact Analysis

4-25

2019 54 26 27 63 3

2020 76 37 38 88 4

2021 112 54 56 130 6

2022 141 69 71 166 8

2023 167 83 84 200 11

2024 194 97 98 234 13

2025 222 112 113 269 16

Sum 1,014 502 512 1,207 63

4.3.5 Fuel Consumption Impacts

The fuel consumption analyses relied on the same set of fleet and activity inputs as the

emission analysis. Because the OMEGA penetrations of diesel technology are small (<1% in

MY 2025), EPA modeled the entire fleet as 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 a lower energy

content . This topic is further discussed in TSD 4.

Table 4.3-21 Calendar Year Fuel Consumption Impacts

CY Fuel Delta

(Billion

Gallons

petroleum

gasoline)

Fuel Delta

(Billion

Barrels

petroleum

gasoline)

Electricity

Delta

(Billion

kwh)

2020 -2 -0.1 1

2030 -26 -0.6 26

2040 -40 -1.0 46

2050 -48 -1.1 55

Sum 2017-

2050 -942 -22.4 1,023
Chapter 4

4-26

Table 4.3-22 Model Year Fuel Consumption Impacts

MY Fuel Delta

( Billion

Gallons

petroleum

gasoline)

Fuel Delta

(Billion

Barrels

petroleum

gasoline)

Electricity Delta

(Billion kwh)

2017 -2 -0.1 2

2018 -6 -0.1 3

2019 -9 -0.2 5

2020 -12 -0.3 6

2021 -18 -0.4 8

2022 -23 -0.5 16

2023 -27 -0.6 23

2024 -32 -0.8 32

2025 -37 -0.9 40

Sum -165 -3.9 135

4.3.6 GHG and Fuel Consumption Impacts from Alternatives

Table 4.3-23 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 -29 -297 -462 -547 -2.3 -25.6 -40.4 -47.9

A - Cars +20

g/mile -20 -248 -396 -471 -1.4 -20.3 -33.0 -39.2

B - Cars -20

g/mile -35 -335 -511 -604 -2.9 -30.8 -48.1 -56.9

C - Trucks +20

g/mile -28 -275 -431 -510 -2.2 -23.0 -36.5 -43.3

D - Trucks -20

g/mile -39 -322 -492 -582 -3.2 -28.6 -44.4 -52.7
2017 Draft Regulatory Impact Analysis

4-27

Table 4.3-24 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 -1,967 -165 -3.9

A - Cars +20

g/mile -1,567 -125 -3.0

B - Cars -20

g/mile -2,283 -202 -4.8

C - Trucks +20

g/mile -1,788 -146 -3.5

D - Trucks -20

g/mile -2,254 -194 -4.6

4.4 Safety Analysis

As described in Preamble Section II.G and DRIA 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 DRIA Chapter 3.

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

135 137 142 149 155 160 166 172 178 1,395

Light

trucks -160 -156 -153 -154 -156 -157 -158 -158 -159 -1,411

Total -24 -18 -12 -5 -1 3 8 14 19 -16

Control

Case

Passenger

cars

133 132 133 138 141 169 198 230 264 1,538

Light

trucks -160 -157 -155 -156 -160 -192 -224 -257 -292 -1,754

Total -28 -25 -22 -19 -19 -24 -26 -27 -28 -217

Delta

Passenger

cars

-3 -5 -8 -11 -14 8 32 58 86 143

Light

trucks -1 -1 -2 -2 -4 -35 -67 -99 -133 -343

Total -3 -7 -10 -13 -18 -27 -34 -41 -47 -201
Chapter 4

4-28

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.SSS 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.TTT 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 proposal means that a 10 percent decrease in fuel cost per

mile is expected to result in a 1 percent increase in VMT.UUU

As described in TSD 4 and section 4.3.2 of this DRIA, we estimate the VMT impact

from consumer responses to changes in fuel prices and fuel efficiency in both the control and

reference cases against CY 2001 NHTS data.VVV Below, we use the same 1.1% per-vehicle

VMT growth rates as in the primary case. As shown in Equation 3 – VMT growth, varying

the rebound rate changes both the control and reference VMT schedules. Therefore, this

sensitivity varies the total amount of both reference and control VMT.WWW

SSS 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.

TTT 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.

UUU 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.

VVV As discussed in above in 4.3.2.4, we assume consumers respond the same way to changes in fuel efficiency

and fuel prices. Consistent with this assumption, we use the same elasticity to measure consumer responses to

changes in fuel prices as we do to measure the rebound effect of consumers driving more in response to

increased fuel efficiency.

WWW One important validation of the VMT equations used in this analysis is a strong resemblance to total

historic VMT data, and future AEO projections. Changing the rebound rate without changing the growth rates

may weaken that relationship, which is why they must be evaluated together when parameterizing the equation.

Consequently, while this sensitivity analysis varies the rebound rate in isolation, were any of these rebound rates

to be used for the primary analysis,EPA would also revisit the per-vehicle VMT growth rates.
2017 Draft Regulatory Impact Analysis

4-29

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% 2,342 197 352 30

5% 2,155 181 325 28

10% 1,967 165 297 26

15% 1,780 149 270 23

20% 1,593 133 242 21

In the analysis, EPA applies the rebound rate to the change in the fuel cost of driving

in future years relative to CY 2001 NHTS values for all MYs, all ages, and in both the

reference and control cases. This allows the agency to directly tie the future VMT schedules

back to known source data. A major benefit of this approach is the consistency, in that future

values of the fuel cost per mile in both the reference and control cases are always compared

back against the same reference point. However, it also means that the theoretical consumer

is comparing back against 2001 driving costs, which may not be an accurate representation of

the real-world process (i.e., in practice, consumers are more likely to be internalizing a change

in the fuel cost of driving that they experienced recently, rather than 15-40 years ago).

An alternative approach considered by the agency is to calculate the rebound effect

relative to the reference case. Thus, the reference case would be calculated relative to the

2001 data, and the control case would be calculated relative to the derived reference VMT

schedules. Tying the rebound effect to a shifting reference point (the current year of the

reference case) would make VMT increase proportional to the difference in the fuel cost per

mile between the two cases, rather than the difference being proportional to the CY 2001 fuel

cost per mile. However, this change implies that consumers are responding to the impact of

changes in fuel cost per mile differently between the reference case and the control case than

between the reference case and CY 2001. Hence the absolute change in VMT from a given

rebound rate is different for the control case in this alternative approach.

The current application of the rebound effect in both the reference and control cases is

demonstrated in Equation 2. The alternative approach for evaluating the rebound effect in the

control case would be to use Equation 5 (below) after calculating the reference case with

Equation 2. Following this alternative approach would also allow EPA to vary the rebound

effect in the control case while holding the reference case VMT constant for sensitivity cases.
Chapter 4

4-30

Equation 5 – Rebound Equation Relative to the Reference Case

ܸܯ ܶ௡௘௟௔௖ௗ௔௥௬௘௔௥௑ǡ௔௚௘௒ = (ܸܴݔǡݕ ( כ ሺͳെ ܴ

∗ 

ܥܨܲܯ௘௖௡௘௥௘௙௘௥௔஼௦௘௬௘௔௥௫ǡ௬ െ ܥܨܲܯ௡௢௖௧௥௢௟஼௔௟௘௡ௗ௔௥௬௘௔௥௫ǡ௬

ܥܨܲܯ௘௖௡௘௥௘௙௘௥௔௖௦௘௬௘௔௥௫ǡ௬

)

Where:

VRx,y = Average miles driven in a vehicle of age y in calendar year x in the reference case

R= Magnitude of the rebound effect, expressed as an elasticity (e.g., -0.10)

FCPMx,y = Fuel cost per mile of a vehicle of age y in calendar year x

EPA may consider alternative methods of implementing the rebound effect in the final

rulemaking.

4.5.2 EV impacts

In section III.C.2 of the preamble, EPA presented an analysis of the GHG impacts of

the EV zero gram/mile and EV/PHEV multiplier impacts on the cumulative GHG savings

from the fleet. In this projection, EPA varied the number of electric and plug-in hybrid

electric assumed in the future fleet.

This projection of the impact of the EV/PHEV/FCV incentives on the overall program

GHG emissions reductions assumes that EPA would have proposed 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, it is clear that not allowing a 0 gram per mile

compliance value would change the technology mix and cost projected for the proposed

standard.

To conduct this analysis, EPA first ran the OMEGA model post-processor assuming

that no vehicles operated on wall electricity. Thus, the 2 cycle standard was simply the

CO2 targets adjusted for air conditioning and the pickup related credits (Table 4.3-8,

Table 4.3-9). 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 final

scenario, involving 2 million EVs+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 ~1.495 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.
2017 Draft Regulatory Impact Analysis

4-31

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+PHEVs in order to approximate the lesser

reliance of EVs on electric power. XXX

In table 4.5-2, the number of metric tons represents the number of additional tons that

would be reduced if the standards stayed the same and there was no 0 gram per mile

compliance value. The percentage change represents the ratio of the cumulative decrease in

GHG emissions reductions from the prior column to the total cumulative GHG emissions

reductions associated with the proposed standards and the proposed 0 gram per mile

compliance value.

If EPA proposed the exact same tailpipe standards, and provided no additional

flexibilities, the program impacts would be estimated at 2,180 MMT if there were no electric

vehicles or plug-in electric vehicles used for compliance.

4.5-2 – EV/PHEV Impacts

Scenario Cumulative

EV/PHEV/FCV

Sales 2017-2025

Cumulative

EV/PHEV/FCV

Sales

2022-2025

Cumulative

Decrease in

GHG Emissions

Reductions

2017-2025

Percentage

Decrease in

GHG Emissions

Reductions

2017-

2025

No EV/PHEVs 0 0 0 millon metric tons 0

EPA OMEGA

model

projection

1.9 million 1.3 million 80 million metric

tons

3.6%

EPA alternative

projection

2.8 million 2.0 million 110 million metric

tons

5.4%

4.6 Calculation of Impacts from An Electric Vehicle

As one illustrative example, using the most recent national average electricity GHG

emissions factors to calculate upstream fuel production and distribution GHG emissions, the

Nissan Leaf would have an upstream GHG emissions value of 161 grams per mile. This is

calculated as follows.

XXX While have PHEVs rather than EVs would also change the multiplier, this 2.0 million vehicle scenario is

meant to approximate the impacts of a larger fleet of electric vehicles.
Chapter 4

4-32

 The Leaf consumes 238 watt-hours of electricity per mile over the EPA city and highway

tests. Note that the EPA electricity consumption values reported here and on the fuel

economy label include both the electricity needed to propel the vehicle as well as vehicle

charging losses, which are typically on the order of 10 percent. This EPA test value is divided

by 0.7 to get the official label value for the 2011 Leaf of 340 watt-hours per mile, or 0.34

kilowatt-hours per mile.

 To reflect average electricity grid/transmission losses of about 7 percent, we divide the 238

watt-hours per mile by 0.93 to get 256 watt-hours per mile, which is the amount of electricity

that would to be generated at the powerplant to power the Leaf for one mile.

 Multiplying the 256 watt-hours/mile value by a nationwide average electricity upstream GHG

emissions rate (powerplant plus feedstock) of 0.628 grams GHG per watt-hour at the

powerplantYYY to get 161 grams GHG per mile.

YYY The most recent nationwide average electricity upstream GHG rate of 0.628 grams GHG per watt-hour at the

powerplant was calculated from 2007 nationwide powerplant data for CO2, CH4, and N2O emissions from

eGRID2010 at http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html, converting to CO2-e using

Global Warming Potentials of 25 for CH4 and 298 for N2O, yielding a value of 0.592 grams GHG per watt-hour

generated at the powerplant, and multiplying by a factor of 1.06 to account for GHG emissions associated with

feedstock extraction, transportation, and processing (based on Argonne National Laboratory's The Greenhouse

Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model, Version 1.8c.0, available at

http://www.transportation.anl.gov/modeling simulation/GREET/). EPA Docket EPA-HQ-OAR-2009-0472. Of

course, EVs sold in various areas of the country would have different upstream GHG gram per mile values. For

example, using an average California electricity upstream GHG rate of 0.349 grams GHG per watt-hour at the

powerplant, from the same EPA eGRID 2010 database, would yield a Leaf upstream GHG emissions value of

89 grams per mile.
2017 Draft Regulatory Impact Analysis

4-33

References

35Historically, manufacturers have reduced precious metal loading in catalysts in order to reduce costs. See

http://www.platinum.matthey.com/media-room/our-view-on-.-.-./thrifting-of-precious-metals-in-autocatalysts/

Accessed 11/08/2011. Alternatively, manufacturers could also modify vehicle calibration.

36 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

37 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage

Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed Sept. 9, 2011).

38EPA MOVES 2010a. August 2010. http://www.epa.gov/otaq/models/moves/index.htm

39 Craig Harvey, EPA, “Calculation of Upstream Emissions for the GHG Vehicle Rule.” 2009. Docket ID:

EPA-HQ-OAR-2009-0472-0216

40 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-2009-0472-0215

41 OMEGA Benefits post-processor.

42 Argonne National Laboratory’s The Greenhouse Gases, Regulated Emissions, and Energy Use in

Transportation (GREET) Model, Version 1.8c.0, available at

http://www.transportation.anl.gov/modeling_simulation/GREET/). EPA Docket EPA-HQ-OAR-2009-0472.

43 EPA. eGrid 2010, http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html

2017 Draft 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 proposed

vehicle program. The presentation here summarizes the vehicle level costs associated with

the new technologies expected to be added to meet the proposed GHG standards, including

hardware costs to comply with the proposed 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.

The presentation here summarizes the outputs of the OMEGA model that were

discussed in some detail in Chapter 3 of this draft 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 draft RIA, and Chapter 3 of the draft

Joint TSD.

5.1 Costs per Vehicle

To develop costs per vehicle, EPA has used the same methodology as that used in the

recent 2012-2016 final rule and the 2010 TAR. 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 2012-2016 final rule. All of these individual technology costs are described

in detail in Chapter 3 of the draft joint TSD. Also described there are the ICMs used in this

proposal and the ways the ICMs have been updated and revised since the 2012-2016 final rule

which results in considerably higher indirect costs in this proposal than estimated in the 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 2012-2016 final rule which applied learning to both direct and indirect costs.

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 proposal). Again, this is

detailed in Chapter 3 of the draft joint TSD.

EPA then 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). 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 draft RIA. These packages are then used as inputs to the OMEGA model

to estimate the most cost effective means of compliance with the proposed 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
Chapter 5

5-2

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 5 years.

Further, we do not expect manufacturers to redesign or refresh 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 draft 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 (2009$)

Company 2021MY 2025MY

Car Truck Combined Car Truck Combined

Aston Martin $6,400 $73 $6,400 $6,850 $59 $6,850

BMW $905 $860 $893 $2,249 $1,942 $2,168

Chrysler/Fiat $540 $808 $661 $1,905 $2,187 $2,027

Daimler $1,928 $945 $1,683 $2,926 $1,942 $2,701

Ferrari $6,323 $73 $6,323 $7,095 $59 $7,095

Ford $631 $736 $667 $2,046 $2,443 $2,168

Geely-Volvo $2,003 $1,038 $1,703 $3,220 $2,017 $2,864

GM $489 $651 $569 $2,193 $1,803 $2,007

Honda $451 $724 $536 $1,442 $1,912 $1,580

Hyundai $602 $857 $654 $1,664 $1,960 $1,723

Kia $448 $875 $543 $1,426 $1,645 $1,473

Lotus $3,314 $73 $3,314 $3,708 $59 $3,708

Mazda $882 $887 $883 $2,184 $1,797 $2,119

Mitsubishi $778 $947 $837 $2,103 $2,145 $2,117

Nissan $745 $632 $710 $1,988 $2,189 $2,048

Porsche $5,436 $1,293 $4,460 $5,825 $2,038 $5,007

Spyker-Saab $3,318 $865 $2,967 $4,000 $1,452 $3,667

Subaru $998 $887 $971 $2,230 $2,067 $2,194

Suzuki $1,139 $963 $1,108 $2,306 $1,816 $2,222

Tata-JLR $2,194 $1,606 $1,901 $3,242 $2,625 $2,955

Tesla $0 $0 $0 $0 $0 $0

Toyota $314 $680 $457 $1,381 $1,598 $1,460

Volkswagen $1,599 $755 $1,428 $2,618 $2,032 $2,503

Fleet $697 $728 $708 $1,931 $1,928 $1,930
2017 Draft 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

proposal, those target CO2 levels were presented in Chapter 3 of this draft 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 by MY for Cars (g/mi)

Company 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin 233 223 214 206 197 189 182 174 167 161

BMW 239 229 220 211 202 194 186 179 172 165

Chrysler/Fiat 243 230 221 212 204 195 187 179 172 165

Daimler 248 239 230 220 211 203 194 187 179 172

Ferrari 245 235 226 217 208 200 191 184 176 169

Ford 239 231 221 212 204 196 188 180 173 166

Geely-Volvo 243 232 223 214 205 197 189 182 174 167

GM 241 230 220 211 203 195 187 179 172 165

Honda 232 223 214 205 197 189 181 174 167 160

Hyundai 233 224 215 206 198 190 182 175 168 161

Kia 235 226 216 208 199 191 183 176 169 162

Lotus 216 208 199 191 183 176 169 162 156 149

Mazda 232 223 214 206 197 190 182 175 168 161

Mitsubishi 230 220 211 203 194 187 179 172 165 158

Nissan 236 227 218 209 201 193 185 178 171 164

Prosche 216 208 199 191 183 176 169 162 156 149

Spyker-Saab 232 223 214 205 197 189 181 174 167 160

Subaru 226 217 208 200 192 184 176 169 163 156

Suzuki 218 209 201 193 185 177 170 163 157 150

Tata-JLR 260 250 240 230 221 212 203 195 187 180

Tesla 216 208 199 191 183 176 169 162 156 149

Toyota 231 222 213 204 196 188 181 173 166 160

Volkswagen 229 220 211 202 194 186 179 171 165 158

Fleet 235 226 217 208 200 192 184 176 169 162
Chapter 5

5-4

Table 5.1-3 Target CO2 Levels 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

BMW 294 295 289 284 278 261 249 238 228 218

Chrysler/Fiat 307 305 301 296 289 271 259 247 236 226

Daimler 306 311 306 301 295 277 265 254 243 232

Ferrari 0 0 0 0 0 0 0 0 0 0

Ford 318 317 313 309 305 287 274 261 249 237

Geely-Volvo 292 290 284 279 272 255 244 233 223 213

GM 324 321 316 311 306 286 273 261 249 238

Honda 292 292 286 282 275 258 246 236 225 215

Hyundai 290 289 283 278 272 255 244 233 223 213

Kia 301 301 297 292 285 267 255 244 233 223

Lotus 0 0 0 0 0 0 0 0 0 0

Mazda 282 284 277 272 266 251 240 230 220 210

Mitsubishi 281 278 271 267 261 244 233 223 213 204

Nissan 306 305 300 295 288 272 260 248 237 226

Prosche 298 298 292 287 280 262 251 240 229 219

Spyker-Saab 291 290 283 278 272 255 243 233 222 213

Subaru 278 275 268 264 257 241 231 220 211 201

Suzuki 283 281 274 269 263 247 236 225 215 206

Tata-JLR 284 282 275 270 264 247 236 226 216 206

Tesla 0 0 0 0 0 0 0 0 0 0

Toyota 306 304 298 293 288 270 258 247 235 225

Volkswagen 304 307 301 296 290 272 260 249 238 227

Fleet 309 307 302 298 292 274 262 250 238 228

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 draft 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 draft 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 while the latter scalar was used for the interpolations between 2021 and 2025.

These scalars are shown in Table 5.1-4.
2017 Draft 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 draft 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 draft 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 (2009$)

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $66 $57 $48 $39 $29 $26 $23 $20 $17

BMW $28 $32 $36 $40 $45 $35 $26 $16 $7

Chrysler/Fiat $50 $46 $42 $38 $35 $30 $25 $20 $15

Daimler $25 $25 $26 $26 $27 $23 $18 $14 $10

Ferrari $12 $17 $23 $28 $34 $30 $27 $23 $19

Ford $12 $17 $21 $25 $29 $25 $20 $15 $10

Geely-Volvo $19 $24 $29 $33 $38 $32 $25 $19 $13

GM $13 $14 $16 $17 $18 $19 $20 $20 $21

Honda $6 $10 $14 $17 $21 $20 $18 $17 $15

Hyundai $3 $7 $10 $14 $17 $17 $18 $18 $18

Kia $8 $16 $25 $33 $41 $36 $31 $26 $21

Lotus $35 $30 $26 $21 $16 $12 $9 $6 $2

Mazda $14 $22 $30 $39 $47 $40 $33 $25 $18

Mitsubishi $8 $16 $24 $32 $40 $34 $28 $22 $16

Nissan $9 $12 $14 $17 $19 $18 $16 $15 $14

Prosche $25 $25 $25 $25 $24 $20 $16 $11 $7

Spyker-Saab $40 $36 $31 $27 $22 $18 $14 $10 $6

Subaru $7 $12 $16 $20 $25 $21 $18 $14 $10

Suzuki $21 $22 $24 $25 $27 $22 $16 $11 $6

Tata-JLR $21 $24 $26 $29 $32 $29 $25 $22 $19

Tesla $0 $0 $0 $0 $0 $0 $0 $0 $0

Toyota $5 $10 $14 $19 $23 $23 $23 $23 $23

Volkswagen $19 $22 $25 $28 $31 $25 $18 $12 $6

Fleet $12 $16 $19 $22 $26 $23 $21 $18 $16

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.
Chapter 5

5-6

Table 5.1-6 Control Case Costs by Manufacturer by MY including AC & Stranded

Capital Costs -- Cars (2009$)

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,708 $3,157 $4,245 $5,341 $6,424 $7,353 $7,383 $7,409 $6,862

BMW $263 $470 $631 $787 $945 $1,442 $1,794 $2,125 $2,251

Chrysler/Fiat $215 $326 $411 $482 $569 $1,047 $1,401 $1,758 $1,914

Daimler $476 $914 $1,254 $1,604 $1,949 $2,482 $2,730 $2,950 $2,931

Ferrari $1,634 $3,080 $4,170 $5,267 $6,351 $7,367 $7,487 $7,598 $7,109

Ford $168 $318 $436 $542 $655 $1,147 $1,525 $1,885 $2,051

Geely-Volvo $553 $1,007 $1,351 $1,691 $2,035 $2,633 $2,938 $3,226 $3,228

GM $152 $259 $343 $419 $502 $1,066 $1,527 $1,975 $2,209

Honda $126 $228 $312 $387 $467 $808 $1,072 $1,329 $1,452

Hyundai $155 $293 $404 $507 $614 $999 $1,283 $1,555 $1,677

Kia $123 $230 $318 $399 $483 $818 $1,076 $1,324 $1,442

Lotus $887 $1,635 $2,200 $2,764 $3,324 $3,847 $3,905 $3,960 $3,705

Mazda $232 $441 $609 $765 $924 $1,399 $1,738 $2,068 $2,196

Mitsubishi $217 $397 $539 $674 $813 $1,292 $1,642 $1,975 $2,114

Nissan $193 $366 $502 $629 $759 $1,212 $1,543 $1,861 $1,997

Prosche $1,420 $2,657 $3,590 $4,527 $5,455 $6,243 $6,268 $6,291 $5,827

Spyker-Saab $893 $1,643 $2,209 $2,774 $3,335 $3,944 $4,082 $4,211 $4,001

Subaru $269 $496 $674 $844 $1,017 $1,497 $1,820 $2,129 $2,236

Suzuki $314 $572 $772 $965 $1,160 $1,639 $1,943 $2,228 $2,307

Tata-JLR $586 $1,086 $1,466 $1,844 $2,220 $2,798 $3,055 $3,297 $3,255

Tesla $0 $0 $0 $0 $0 $0 $0 $0 $0

Toyota $89 $161 $222 $275 $332 $682 $972 $1,250 $1,399

Volkswagen $427 $790 $1,070 $1,348 $1,624 $2,116 $2,371 $2,608 $2,618

Fleet $194 $353 $479 $595 $718 $1,165 $1,492 $1,806 $1,942
2017 Draft Regulatory Impact Analysis

5-7

Table 5.1-7 Control Case Costs by Manufacturer by MY including AC & Stranded

Capital Costs -- Trucks (2009$)

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $0 $0 $0 $0 $0 $0 $0 $0 $0

BMW -$148 $91 $253 $443 $915 $1,331 $1,610 $1,873 $1,959

Chrysler/Fiat $43 $194 $313 $465 $853 $1,352 $1,724 $2,070 $2,212

Daimler -$284 -$62 $150 $370 $956 $1,364 $1,629 $1,877 $1,952

Ferrari $0 $0 $0 $0 $0 $0 $0 $0 $0

Ford $106 $243 $339 $422 $776 $1,345 $1,814 $2,269 $2,463

Geely-Volvo -$117 $168 $347 $557 $1,086 $1,497 $1,749 $1,984 $2,040

GM $123 $235 $321 $401 $680 $1,096 $1,414 $1,712 $1,834

Honda -$104 $78 $213 $371 $756 $1,194 $1,509 $1,816 $1,937

Hyundai -$132 $93 $248 $433 $884 $1,316 $1,608 $1,890 $1,988

Kia -$93 $86 $249 $453 $927 $1,254 $1,449 $1,636 $1,675

Lotus $0 $0 $0 $0 $0 $0 $0 $0 $0

Mazda -$317 -$4 $185 $378 $897 $1,262 $1,496 $1,734 $1,806

Mitsubishi -$144 $130 $298 $501 $998 $1,464 $1,776 $2,072 $2,171

Nissan $58 $194 $294 $400 $662 $1,184 $1,616 $2,026 $2,212

Prosche -$142 $196 $409 $667 $1,328 $1,702 $1,886 $2,059 $2,054

Spyker-Saab -$71 $163 $305 $471 $898 $1,171 $1,318 $1,456 $1,468

Subaru -$112 $137 $292 $472 $922 $1,377 $1,689 $1,982 $2,087

Suzuki -$104 $159 $323 $515 $1,000 $1,364 $1,583 $1,788 $1,832

Tata-JLR -$233 $197 $472 $817 $1,648 $2,144 $2,403 $2,646 $2,653

Tesla $0 $0 $0 $0 $0 $0 $0 $0 $0

Toyota $42 $194 $301 $400 $713 $1,062 $1,312 $1,547 $1,631

Volkswagen -$121 $70 $224 $384 $797 $1,254 $1,589 $1,914 $2,048

Fleet $55 $198 $305 $417 $764 $1,200 $1,525 $1,834 $1,954
Chapter 5

5-8

Table 5.1-8 Control Case Costs by Manufacturer by MY including AC & Stranded

Capital Costs – Combined Fleet (2009$)

Company 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin $1,708 $3,157 $4,245 $5,341 $6,424 $7,353 $7,383 $7,409 $6,862

BMW $154 $370 $531 $696 $937 $1,413 $1,746 $2,058 $2,174

Chrysler/Fiat $137 $266 $367 $475 $698 $1,179 $1,541 $1,893 $2,043

Daimler $287 $671 $980 $1,297 $1,702 $2,226 $2,478 $2,704 $2,707

Ferrari $1,634 $3,080 $4,170 $5,267 $6,351 $7,367 $7,487 $7,598 $7,109

Ford $147 $293 $403 $501 $696 $1,208 $1,614 $2,003 $2,178

Geely-Volvo $345 $746 $1,039 $1,339 $1,741 $2,297 $2,585 $2,858 $2,876

GM $138 $247 $332 $410 $590 $1,080 $1,473 $1,850 $2,030

Honda $55 $182 $281 $382 $556 $922 $1,201 $1,472 $1,595

Hyundai $97 $253 $372 $492 $669 $1,062 $1,347 $1,622 $1,739

Kia $75 $198 $303 $411 $582 $910 $1,155 $1,391 $1,491

Lotus $887 $1,635 $2,200 $2,764 $3,324 $3,847 $3,905 $3,960 $3,705

Mazda $134 $362 $534 $696 $919 $1,377 $1,697 $2,012 $2,131

Mitsubishi $91 $304 $455 $614 $877 $1,349 $1,687 $2,007 $2,133

Nissan $151 $312 $437 $558 $729 $1,204 $1,565 $1,910 $2,060

Prosche $1,052 $2,077 $2,840 $3,618 $4,482 $5,262 $5,321 $5,377 $5,012

Spyker-Saab $755 $1,431 $1,936 $2,444 $2,986 $3,582 $3,721 $3,851 $3,670

Subaru $178 $410 $582 $755 $994 $1,470 $1,790 $2,096 $2,202

Suzuki $239 $498 $692 $885 $1,132 $1,592 $1,881 $2,153 $2,225

Tata-JLR $178 $644 $972 $1,333 $1,935 $2,494 $2,752 $2,994 $2,976

Tesla $0 $0 $0 $0 $0 $0 $0 $0 $0

Toyota $71 $174 $253 $324 $481 $820 $1,096 $1,358 $1,483

Volkswagen $316 $644 $898 $1,153 $1,457 $1,947 $2,218 $2,472 $2,506

Fleet $146 $299 $418 $533 $734 $1,176 $1,503 $1,815 $1,946

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 Proposed Standards

(2009$)

Model Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 2040 2050

$/car $194 $353 $479 $595 $718 $1,165 $1,492 $1,806 $1,942 $1,926 $1,926 $1,926

$/truck $55 $198 $305 $417 $764 $1,200 $1,525 $1,834 $1,954 $1,938 $1,938 $1,938

Combined $146 $299 $418 $533 $734 $1,176 $1,503 $1,815 $1,946 $1,930 $1,929 $1,929

5.2 Annual Costs of the Proposed National Program

The costs presented here represent the costs for newly added technology to comply

with the proposed 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 5.1-9 above result in the total annual costs presented in Table
2017 Draft Regulatory Impact Analysis

5-9

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. Note also that the costs presented here represent

costs estimated to occur presuming that the proposed MY 2025 standard would continue in

perpetuity. Any changes to the proposed standards would be considered as part of a future

rulemaking. In other words, the proposed 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 Costs & Costs of the Program Discounted back to

2012 at 3% and 7% Discount Rates (2009 dollars)

Calendar

Year

Sales $/unit $Million/year

Cars Trucks $/car $/truck Cars Trucks Combined

2017 9,987,667 5,818,655 $194 $55 $1,940 $322 $2,300

2018 9,905,364 5,671,046 $353 $198 $3,500 $1,130 $4,660

2019 9,995,696 5,582,962 $479 $305 $4,780 $1,700 $6,510

2020 10,291,562 5,604,377 $595 $417 $6,120 $2,340 $8,470

2021 10,505,165 5,683,902 $718 $764 $7,540 $4,340 $11,900

2022 10,735,777 5,703,996 $1,165 $1,200 $12,500 $6,840 $19,300

2023 10,968,003 5,687,486 $1,492 $1,525 $16,400 $8,680 $25,000

2024 11,258,138 5,675,949 $1,806 $1,834 $20,300 $10,400 $30,700

2025 11,541,560 5,708,899 $1,942 $1,954 $22,400 $11,200 $33,600

2030 12,535,870 5,986,092 $1,926 $1,938 $24,100 $11,600 $35,700

2040 14,097,092 6,505,226 $1,926 $1,938 $27,100 $12,600 $39,800

2050 15,822,370 7,301,371 $1,926 $1,938 $30,500 $14,100 $44,600

NPV, 3% $373,000 $177,000 $551,000

NPV, 7% $165,000 $78,300 $243,000

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 draft Joint TSD.

5.3 Cost per Ton of Emissions Reduced

EPA has calculated the cost per ton of GHG reductions associated with the proposed

GHG standards on a CO2eq basis using the costs and the emissions reductions described in

Section III.F. 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). 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.
Chapter 5

5-10

Table 5.3-1 Annual Cost per Metric Ton of CO2eq Reduced (2009 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 $6,120 $4,840 19 $318 $67

2030 $24,100 $54,300 183 $132 -$165

2040 $27,100 $91,200 284 $95 -$226

2050 $30,500 $117,000 332 $92 -$260

Trucks

2020 $2,340 $2,340 10 $245 $0

2030 $11,600 $34,000 114 $102 -$196

2040 $12,600 $57,500 178 $71 -$252

2050 $14,100 $76,000 215 $66 -$288

Combined

2020 $8,470 $7,180 29 $294 $45

2030 $35,700 $88,300 297 $120 -$177

2040 $39,800 $149,000 462 $86 -$236

2050 $44,600 $193,000 547 $81 -$271

5.4 Reduction in Fuel Consumption and its Impacts

5.4.1 What Are the Projected Changes in Fuel Consumption?

The proposed 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 proposed GHG standards, electricity consumption

would increase slightly due to the small penetration of EVs and PHEVs (1-3% for the 2021

and 2025 MYs). The fuel savings includes both the gasoline consumption reductions and the

electricity 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 of the draft 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

proposed CO2 standards, including the A/C credit program, and include increased

consumption resulting from the rebound effect.
2017 Draft Regulatory Impact Analysis

5-11

Table 5.4-1 Fuel Consumption Impacts of the Proposed Standards and A/C Credit

Programs

Calendar Year

Petroleum-based

Gasoline Reference

(million gallons)

Petroleum-based

Gasoline Reduced

(million gallons)

Electricity Increased

(million kWh)a

2017 130,544 194 115

2018 129,503 641 345

2019 128,680 1,326 695

2020 128,229 2,277 1,177

2021 128,387 3,673 1,796

2022 128,599 5,424 2,952

2023 129,312 7,520 4,673

2024 130,087 9,919 6,980

2025 131,289 12,658 9,911

2030 140,602 25,581 24,298

2040 159,582 40,391 42,369

2050 184,136 47,883 51,123

Total 5,079,096 941,839 951,392

a Electricity increase by vehicles not by power plants.

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 proposed 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 2011 Final Release.ZZZ AEO is a

standard reference used by NHTSA and EPA and many other government agencies to

estimate the projected price of fuel. The agencies also used the AEO’s fuel price estimate 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.AAAA In addition, since future fuel prices are not known

with certainty, there could be a distribution of possible fuel price outcomes, as opposed to a

ZZZ In the Preface to AEO 2011, the Energy Information Administration describes the reference case used in

AEO 2011. They state that, “Projections by EIA are not statements of what will happen but of what might

happen, given the assumptions and methodologies used for any particular scenario. The Reference case

projection is a business-as-usual trend estimate, given known technology and technological and demographic

trends.

AAAA 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 DRIA). 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

5-12

set of known higher price- and a set of known lower price-pathways. For the final rule, EPA

may to do a sensitivity analysis on future fuel prices.

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 $82 million in 2017 and $17 billion by 2050. These results

are shown in Table 5.4-1. Note that in Chapter 7 of this DRIA, the overall benefits and costs

of the proposal are presented and, for that reason, only the pre-tax fuel savings are presented

there.

Table 5.4-1 Undiscounted Annual Fuel Savings & Fuel Savings Discounted back to 2012

at 3% and 7% Discount Rates (millions of 2009 dollars)

Calendar

Year

Gasoline

Savings

(pre-tax)

Gasoline

Savings

(taxed)

Electricity

Costs

Total Fuel

Savings

(pre-tax)

Total Fuel

Savings

(taxed)

2017 $581 $663 $11.1 $570 $652

2018 $1,950 $2,230 $32.8 $1,920 $2,200

2019 $4,120 $4,670 $66.0 $4,060 $4,600

2020 $7,180 $8,110 $113 $7,060 $7,990

2021 $11,600 $13,100 $172 $11,400 $12,900

2022 $17,400 $19,700 $286 $17,100 $19,400

2023 $24,400 $27,500 $458 $24,000 $27,000

2024 $32,700 $36,800 $691 $32,000 $36,100

2025 $42,400 $47,200 $1,000 $41,400 $46,200

2030 $88,300 $98,100 $2,550 $85,800 $95,600

2040 $149,000 $164,000 $4,850 $144,000 $159,000

2050 $193,000 $210,000 $6,350 $187,000 $204,000

NPV, 3% $1,550,000 $1,720,000 $47,800 $1,510,000 $1,670,000

NPV, 7% $596,000 $660,000 $17,800 $579,000 $642,000

Annual values represent undiscounted values; net present values represent annual costs discounted to 2012.

As shown in Table 5.4-1, the agencies are projecting that consumers would realize

very large fuel savings as a result of the proposed standards. These calculations are based on

the assumption, discussed in Preamble Section III.D., that the fuel economy of vehicles would

be constant at MY 2016 levels in the absence of the rule. As discussed further in Chapter

8.1.2.6 of this DRIA, 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. Regardless 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 provide actual money in consumers’ pockets.
2017 Draft Regulatory Impact Analysis

5-13

5.5 Consumer Payback Period and Lifetime Savings on New Vehicle Purchases

Another factor of interest is the payback period that consumers would experience on

the purchase of a new vehicle that meets the proposed standards. In other words, how long

would it take for the expected fuel savings to outweigh the increased cost of a new vehicle?

For example, a new 2025 MY vehicle is estimated to cost $1,946 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 upfront costs?

Table 5.5-1 provides the answer to this question for a vehicle purchaser who pays for

the new vehicle upfront in cash (we discuss later in this section the payback period for

consumers who finance the new vehicle purchase with a loan). 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 draft Joint TSD. The control case includes

fuel savings associated with A/C controls. Not included here are the possible A/C-related

maintenance savings as discussed in Chapter 5 of the draft joint TSD. 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. To

estimate the upfront vehicle cost (i.e., the lifetime increased cost discounted back to

purchase), 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.44 Car/truck

fleet weighting is handled as described in Chapter 1 of the draft Joint TSD. The present value

of the increased vehicle costs shown in the table are $2,189 at a 3% discount rate and $2,180

at a 7% discount rate. As can be seen in the table, it will take just over 3.5 years at a 3%

discount rate, and just under 4 years at a 7% discount rate, for the cumulative discounted fuel

savings to exceed the present value of increased vehicle costs.

Table 5.5-1 Payback Period on a 2025 MY New Vehicle Purchase via Cash (2009

dollars)

Year of

Ownership

Increased Vehicle

Costa

(undiscounted)

Annual Fuel

Savingsb

(undiscounted)

Cumulative

Discounted Fuel

Savings at 3%

Cumulative

Discounted Fuel

Savings at 7%

1 -$2,087 $643 $634 $622

2 -$31 $634 $1,240 $1,195

3 -$26 $630 $1,826 $1,728

4 -$21 $614 $2,379 $2,213

5 -$16 $601 $2,906 $2,656

6 -$11 $572 $3,392 $3,051

7 -$6 $543 $3,840 $3,401

8 -$1 $512 $4,250 $3,709

a

Increased vehicle cost due to the rule is $1,946; the value here includes nationwide average sales tax of

5.32% and increased insurance premiums of 1.85% in year one decreasing to 0% by year 9. Both of these

percentages are discussed in Section 8.1.1 of this DRIA. This results in a present value of increased costs

of $2,189 at 3% discounting and $2,180 at 7% discounting. These present value costs are used in
Chapter 5

5-14

determining the payback period.

b Calculated using AEO 2011 reference case fuel prices including taxes.

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 of July, 2011, the national

average interest rate for a 5 year new car loan was 5.52 percent.45 If the increased vehicle

cost is spread out over 5 years at 5.52 percent, the analysis would look like that shown in

Table 5.5-2. As can be seen in this table, the fuel savings immediately outweigh the increased

payments on the car loan, amounting to $145 in discounted net savings (3% discount rate) in

the first year and similar savings for the next four years although savings decline somewhat

due to reduced VMT as the average vehicle ages. Results are similar using a 7% discount

rate. This means that for every month that the average owner is making a payment for the

financing of the average new vehicle their monthly fuel savings would be greater than the

increase in the loan payments. This amounts to a savings on the order of $12 per month

throughout the duration of the 5 year loan. Note that in year six when the car loan is paid off,

the net savings equal the fuel savings less the increased insurance premiums (as would be the

case for the remaining years of ownership).

Table 5.5-2 Payback Period on a 2025 MY New Vehicle Purchase via Credit (2009

dollars)

Year of

Ownership

Increased Vehicle

Costa

(undiscounted)

Annual Fuel

Savingsb

(undiscounted)

Annual Discounted

Net Savings at 3%c

Annual Discounted

Net Savings at 7% c

1 -$489 $643 $145 $133

2 -$488 $634 $133 $117

3 -$487 $630 $127 $107

4 -$485 $614 $109 $88

5 -$484 $601 $96 $74

6 -$11 $572 $477 $387

7 -$6 $543 $443 $346

8 -$1 $512 $409 $308

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 5.52 percent.

b Calculated using AEO 2011 reference case fuel prices including taxes.

c Note that the cumulative discounted fuel savings are identical to those shown in Table 5.5-1. Here we

show discounted net savings.

The lifetime fuel savings and net savings can also be calculated for those who

purchase the vehicle using cash and for those who purchase the vehicle with credit. This

calculation applies to the vehicle owner who retains the vehicle for its entire life and drives

the vehicle each year at the rate equal to the national projected average. The results are

shown in Table 5.5-3. In either case, the present value of the lifetime net savings is greater

than $4,200 at a 3% discount rate, or $2,900 at a 7% discount rate.
2017 Draft Regulatory Impact Analysis

5-15

Table 5.5-3 Lifetime Discounted Net Savings on a 2025 MY New Vehicle Purchase (2009

dollars)

Purchase

Option

Increased Discounted

Vehicle Cost

Lifetime Discounted

Fuel Savings b

Lifetime Discounted

Net Savings

3% discount rate

Cash $2,189 $6,568 $4,378

Credit a$2,310 $6,568 $4,258

7% discount rate

Cash $2,180 $5,154 $2,972

Credit a$2,147 $5,154 $3,004

a Assumes a 5 year loan at 5.52 percent.

b

Fuel savings here were calculated using AEO 2011 reference case fuel prices

including taxes.

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

5-16

References

44 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in

“Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf,

accessed 8/22/11, Table 14..

45 “National Auto Loan Rates for July 21, 2011,”

http://www.bankrate.com/finance/auto/national-auto-loan-rates-for-july-21-2011.aspx,

accessed 7/26/11.
2017 Draft 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 will discuss the health effects associated with non-GHG pollutants,

specifically: particulate matter, ozone, nitrogen oxides (NOX), sulfur oxides (SOX), carbon

monoxide and air toxics. These pollutants will not be directly regulated by the standards, but

the standards will affect emissions of these pollutants and precursors.

6.1.1.1 Background on Particulate Matter

Particulate matter (PM) is a generic term for a broad class of chemically and

physically diverse substances. It can be principally characterized as discrete particles that

exist in the condensed (liquid or solid) phase spanning several orders of magnitude in size.

Since 1987, EPA has delineated that subset of inhalable particles small enough to penetrate to

the thoracic region (including the tracheobronchial and alveolar regions) of the respiratory

tract (referred to as thoracic particles).BBBB Current National Ambient Air Quality Standards

(NAAQS) use PM2.5 as the indicator for fine particles (with PM2.5 generally referring to

particles with a nominal mean aerodynamic diameter less than or equal to 2.5 micrometers

(μm)), and use PM10 as the indicator for purposes of regulating the coarse fraction of PM10

(referred to as thoracic coarse particles or coarse-fraction particles; generally including

particles with a nominal mean aerodynamic diameter greater than 2.5 μm and less than or

equal to 10 μm, or PM10-2.5). Ultrafine particles (UFPs) are a subset of fine particles,

generally less than 100 nanometers (0.1 μm) in diameter. 

Particles span many sizes and shapes and consist of numerous different components.

Particles originate from sources and are also formed through atmospheric chemical reactions;

the former are often referred to as “primary” particles, and the latter as “secondary” particles.

In addition, there are also physical, non-chemical reaction mechanisms that contribute to

secondary particles. Particle pollution also varies by time of year and location and is affected

by several weather-related factors, such as temperature, clouds, humidity, and wind. A

further layer of complexity comes from a particle’s ability to shift between solid/liquid and

gaseous phases, which is influenced by concentration, meteorology, and 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.46

BBBB 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.CCCC 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).DDDD

The ISA concludes that ambient concentrations of PM are associated with a number of

adverse health effects.EEEE 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.

47 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.48 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.

CCCC 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.

DDDD The ISA is available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546

EEEE 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.
2017 Draft Regulatory Impact Analysis

6-3

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.

49 The ISA concludes that available

evidence is suggestive of a causal relationship between short-term exposures to PM10-2.5 and

cardiovascular 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.

50

6.1.1.2.4 Effects Associated with Ultrafine Particles

The ISA concludes that the evidence is suggestive of a causal relationship between

short-term exposures to UFPs and cardiovascular effects, including changes in heart rhythm

and vasomotor function (the ability of blood vessels to expand and contract).51

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.52

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. Groundlevel ozone is produced and destroyed in a cyclical set of chemical reactions, many of which

are sensitive to temperature and sunlight. When ambient temperatures and sunlight levels

remain high for several days and the air is relatively stagnant, ozone and its precursors can

build up and result in more ozone than typically 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)
Chapter 6

6-4

sources to local ambient ozone concentrations can be significant, even some areas where manmade VOC emissions are relatively low can be NOX-limited.

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide

(NO) with ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle

continues, the NO2 forms additional ozone. The importance of this reaction depends, in part,

on the relative concentrations of NOX, VOC, and ozone, all of which change with time and

location. When NOX levels are relatively high and VOC levels relatively low, NOX forms

inorganic nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOClimited.” Under these conditions, VOC reductions are effective in reducing ozone, but NOX

reductions can actually increase local ozone under certain circumstances. Even in VOClimited 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.FFFF

These health effects are well documented and are critically assessed in the EPA ozone air

quality criteria document (ozone AQCD) and EPA staff paper.53,54 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.55 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.

FFFF 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.
2017 Draft Regulatory Impact Analysis

6-5

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.56, 57, 58, 59, 60, 61

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung

inflammation and can aggravate preexisting respiratory diseases, such as asthma.62, 63, 64, 65, 66

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the

lung, impairment of lung defense mechanisms, and possibly irreversible changes in lung

structure, which over time could affect premature aging of the lungs and/or the development

of chronic respiratory illnesses, such as emphysema and chronic bronchitis.67, 68, 69, 70

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.71 For example, summer camp studies have

reported statistically significant reductions in lung function in children who are active

outdoors.72, 73, 74, 75, 76, 77, 78, 79 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.80, 81, 82, 83

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.84 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
Chapter 6

6-6

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 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 have 

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.85 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. The ISA also draws two broad conclusions regarding airway
2017 Draft Regulatory Impact Analysis

6-7

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. 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.86 The ISA concludes that

ambient concentrations of CO are associated with a number of adverse health effects.GGGG

This section provides a summary of the health effects associated with exposure to ambient

concentrations of CO.HHHH

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 shortterm 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

GGGG 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.

HHHH 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.
Chapter 6

6-8

that available data are inadequate to conclude that a causal relationship exists between longterm 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.

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 copollutants such as ozone, SO2, and PM in two-pollutant models and found that CO risk

estimates were generally robust, although this limited evidence makes it difficult to

disentangle effects attributed to CO itself from those of the larger complex air pollution

mixture. Controlled human exposure studies have not extensively evaluated the effect of CO

on respiratory morbidity. Animal studies at levels of 50-100 ppm CO show preliminary

evidence of altered pulmonary vascular remodeling and oxidative injury. The 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.87 These compounds include, but are not limited to, benzene, 1,3-butadiene,

formaldehyde, acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene.
2017 Draft Regulatory Impact Analysis

6-9

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 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.IIII

Noncancer health effects can result from chronic,JJJJ subchronic,KKKK or acuteLLLL

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.88

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.89 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.90,91,92 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.93,94

IIII 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.

JJJJ 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).

KKKK Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an

organism.

LLLL Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.
Chapter 6

6-10

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.95,96 The most sensitive noncancer effect observed in humans, based on current data,

is the depression of the absolute lymphocyte count in blood.97,98 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.99,100,101,102 EPA’s IRIS program has not yet evaluated these new data.

6.1.1.9.2 1,3-Butadiene

EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.103,104

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.105,106,107 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-butadieneinduced 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.108

6.1.1.9.3 Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based

on evidence in humans and in rats, mice, hamsters, and monkeys.109 Substantial additional

research since that time informs current scientific understanding of the health effects

associated with exposure to formaldehyde. These include recently published research

conducted by the National Cancer Institute (NCI) which found an increased risk of

nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among

workers exposed to formaldehyde.110,111 In an analysis of the lymphohematopoietic cancer

mortality from an extended follow-up of these workers, NCI confirmed an association

between lymphohematopoietic cancer risk and peak formaldehyde exposures.112 A recent

NIOSH study of garment workers also found increased risk of death due to leukemia among

workers exposed to formaldehyde.113 Extended follow-up of a cohort of British chemical

workers did not find evidence of an increase in nasopharyngeal or lymphohematopoietic

cancers, but a continuing statistically significant excess in lung cancers was reported.114

In the past 15 years there has been substantial research on the inhalation dosimetry for

formaldehyde in rodents and primates by the Chemical Industry Institute of Toxicology (CIIT,

now renamed the Hamner Institutes for Health Sciences), with a focus on use of rodent data

for refinement of the quantitative cancer dose-response assessment.115,116,117 CIIT’s risk

assessment of formaldehyde incorporated mechanistic and dosimetric information on

formaldehyde. These data were modeled using a biologically-motivated two-stage clonal

growth model for cancer and also a point of departure based on a Benchmark Dose approach.

However, it should be noted that recent research published by EPA indicates that when two-
2017 Draft Regulatory Impact Analysis

6-11

stage modeling assumptions are varied, resulting dose-response estimates can vary by several

orders of magnitude.118,119,120,121 These findings are not supportive of interpreting the CIIT

model results as providing a conservative (health protective) estimate of human risk.122 EPA

research also examined the contribution of the two-stage modeling for formaldehyde towards

characterizing the relative weights of key events in the mode-of-action of a carcinogen. For

example, the model-based inference in the published CIIT study that formaldehyde’s direct

mutagenic action is not relevant to the compound’s tumorigenicity was found not to hold

under variations of modeling assumptions.123

Based on the developments of the last decade, in 2004, the working group of the IARC

concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient

evidence in humans and sufficient evidence in experimental animals - a higher classification

than previous IARC evaluations. After reviewing the currently available epidemiological

evidence, the IARC (2006) characterized the human evidence for formaldehyde

carcinogenicity as “sufficient,” based upon the data on nasopharyngeal cancers; the

epidemiologic evidence on leukemia was characterized as “strong.”124

Formaldehyde exposure also causes a range of noncancer health effects, including

irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from

repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal

epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest that

formaldehyde may also cause airway inflammation – including eosinophil infiltration into the

airways. There are several studies that suggest that formaldehyde may increase the risk of

asthma – particularly in the young.125,126

The above-mentioned rodent and human studies, as well as mechanistic information

and their analyses, were evaluated in EPA’s recent Draft Toxicological Review of

Formaldehyde – Inhalation Assessment through the Integrated Risk Information System

(IRIS) program. This draft IRIS assessment was released in June 2010 for public review and

comment and external peer review by the National Research Council (NRC). The NRC

released their review report in April 2011

(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.127 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.128,129 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.130 In short-term (4 week) rat studies, degeneration of

olfactory epithelium was observed at various concentration levels of acetaldehyde

exposure.131,132 Data from these studies were used by EPA to develop an inhalation reference
Chapter 6

6-12

concentration. Some asthmatics have been shown to be a sensitive subpopulation to

decrements in functional expiratory volume (FEV1 test) and bronchoconstriction upon

acetaldehyde inhalation.133 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.134 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.135

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.136 Lesions to

the lungs and upper respiratory tract of rats, rabbits, and hamsters have been observed after

subchronic exposure to acrolein.137 Acute exposure effects in animal studies report bronchial

hyperresponsiveness.138 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.139 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 carcinogenicity.140 The IARC determined in 1995 that acrolein was not

classifiable as to its carcinogenicity in humans.141

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.MMMM142 Animal studies

have reported respiratory tract tumors from inhalation exposure to benzo[a]pyrene and

MMMM 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.
2017 Draft Regulatory Impact Analysis

6-13

alimentary tract and liver tumors from oral exposure to benzo[a]pyrene. 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.143 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).144,145 EPA has not yet evaluated

these studies.

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. EPA released an external review draft of a reassessment of the inhalation

carcinogenicity of naphthalene based on a number of recent animal carcinogenicity studies.146

The draft reassessment completed external peer review.147 Based on external peer review

comments received, additional analyses are being undertaken. This 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.148

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.149

Naphthalene also causes a number of chronic non-cancer effects in animals, including

abnormal cell changes and growth in respiratory and nasal tissues.150

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 this proposal. Mobile

source air toxic compounds that would potentially be impacted include ethylbenzene,

propionaldehyde, toluene, and xylene. Information regarding the health effects of these

compounds can be found in EPA’s IRIS database.NNNN

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

NNNN U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris
Chapter 6

6-14

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.151 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.152 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.153 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.154

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 evidence as “suggestive” of a causal association, and an independent

epidemiological literature review by Adar and Kaufman (2007) concludes that there is

“consistent evidence” linking traffic-related pollution and adverse cardiovascular health

outcomes.155

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.156

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.157 Therefore, at current population of

approximately 309 million, assuming that population and housing are similarly distributed,
2017 Draft Regulatory Impact Analysis

6-15

there are over 48 million people in the U.S. living near such sources. The HEI report also

notes that in two North American cities, Los Angeles and Toronto, over 40% of each city’s

population live within 500 meters of a highway or 100 meters of a major road. It also notes

that about 33% of each city’s population resides within 50 meters of major roads. Together,

the evidence suggests that a large U.S. population lives in areas with elevated traffic-related

air pollution.

People living near roads are often socioeconomically disadvantaged. According to the

2007 American Housing Survey, a renter-occupied property is over twice as likely as an

owner-occupied property to be located near a highway with 4+ lanes, railroad or airport. In

the same survey, the median household income of rental housing occupants was less than half

that of owner-occupants ($28,921/$59,886). Numerous studies in individual urban areas

report higher levels of traffic-related air pollutants in areas with high minority or poor

populations.158,159,160

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.161 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.162,163,164 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.161

6.1.2 Environmental Effects Associated with Exposure to Non-GHG Pollutants

In this section we will discuss the environmental effects associated with non-GHG

pollutants, specifically: particulate matter, ozone, NOX, SOX and air toxics.

6.1.2.1 Visibility Degradation

Airborne particles degrade visibility by scattering and absorbing light. Good visibility

increases the quality of life where individuals live and work, and where they engage in

recreational activities.

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
Chapter 6

6-16

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.

6.1.2.1.1 Visibility Monitoring

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other

Federal land managers, and State organizations in the U.S., the U.S. EPA has supported

visibility monitoring in national parks and wilderness areas since 1988. The monitoring

network was originally established at 20 sites, but it has now been expanded to 110 sites that

represent all but one of the 156 Mandatory Federal Class I areas across the country (see

Figure 6.1-1). This long-term visibility monitoring network is known as IMPROVE

(Interagency Monitoring of Protected Visual Environments).

IMPROVE provides direct measurement of fine particles that contribute to visibility

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical

and scene measurements at some of the sites. Aerosol measurements are taken for PM10 and

PM2.5mass, 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
2017 Draft Regulatory Impact Analysis

6-17

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.165

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.166 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.”167 Like carbon dioxide (CO2)

and other gaseous substances, ozone enters plant tissues primarily through apertures (stomata)

in leaves in a process called “uptake.”168 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.169,170 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,171 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.172,173
Chapter 6

6-18

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)174,175,176 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.177

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

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 ozoneexposure/tree-response functions by exposing tree seedlings to different ozone levels and

measuring reductions in growth as “biomass loss.” Typically, seedlings are used because they

are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms

of susceptibility to ozone within the leaves of seedlings and mature trees are identical, though

the magnitude of the effect may be higher or lower depending on the tree species.178

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.
2017 Draft Regulatory Impact Analysis

6-19

Ozone also has been conclusively shown to cause discernible injury to forest

trees.179,180 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.181,182

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.183 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.184,185,186 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 longterm 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.187 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.188 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

typical of those found in the United States.”189 In addition, economic studies have shown

reduced economic benefits as a result of predicted reductions in crop yields associated with

observed ozone levels.190,191,192

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.193 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 Recent Ozone Visible Foliar Injury Data for 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)
Chapter 6

6-20

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.194,195 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.196,197

The highest percentages of observed high and severe foliar injury, those which are

most likely to be associated with tree or ecosystem-level responses, are primarily found in the

Mid-Atlantic and Southeast regions. In EPA Region 3 (which comprises the States of

Pennsylvania, West Virginia, Virginia, Delaware, Maryland and Washington D.C.), 12% of

ozone-sensitive plants showed signs of high or severe foliar damage, and in Regions 2 (States

of New York, New Jersey), and 4 (States of North Carolina, South Carolina, Kentucky,

Tennessee, Georgia, Florida, Alabama, and Mississippi) the values were 10% and 7%,

respectively. The sum of high and 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.
2017 Draft Regulatory Impact Analysis

6-21

Figure 6.1-2 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab

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.198 The presence of diagnostic visible ozone injury on indicator plants
Chapter 6

6-22

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.199

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.200,201,202

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.203

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 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
2017 Draft Regulatory Impact Analysis

6-23

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.204 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.205 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.206

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 forests of the Colorado Front Range; and red alder forests in the

Cascade Mountains in Washington.
Chapter 6

6-24

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.207

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.208 Investigation of trace metals near

roadways and industrial facilities indicate that a substantial load of heavy metals can

accumulate on vegetative surfaces. Copper, zinc, and nickel have been documented to cause

direct toxicity to vegetation under field conditions. Little research has been conducted on the

effects associated with mixtures of contaminants found in ambient PM. While metals

typically exhibit low solubility, limiting their bioavailability and direct toxicity, chemical

transformations of metal compounds occur in the environment, particularly in the presence of

acidic or other oxidizing species. These chemical changes influence the mobility and toxicity

of metals in the environment. Once taken up into plant tissue, a metal compound can undergo

chemical changes, exert toxic effects on the plant itself, accumulate and be passed along to

herbivores or can re-enter the soil and further cycle in the environment. Although there has

been no direct evidence of a physiological association between tree injury and heavy metal

exposures, heavy metals have been implicated because of similarities between metal
2017 Draft Regulatory Impact Analysis

6-25

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.209 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.210,211 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.212

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.213,214 Over

fifty percent of the mercury in the Chesapeake Bay has been attributed to atmospheric

deposition.215 Overall, the National Science and Technology Council identifies atmospheric

deposition as the primary source of mercury to aquatic systems.216 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.217,218 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.219 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.220 Polycyclic aromatic hydrocarbons (PAHs) are a

class of POM that contains compounds which are known or suspected carcinogens.
Chapter 6

6-26

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.221

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.222,223 Analyses of PAH deposition in

Chesapeake and Galveston Bay indicate that dry deposition and gas exchange from the

atmosphere to the surface water predominate.224,225 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.226 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.227 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.228 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.229

Cousins et al. estimate that more than ninety percent of semi-volatile organic

compound (SVOC) emissions in the United Kingdom deposit on soil.230 An analysis of PAH

concentrations near a Czechoslovakian roadway indicated that concentrations were thirty

times greater than background.231

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
2017 Draft Regulatory Impact Analysis

6-27

compounds (VOCs), some of which are considered air toxics, have long been suspected to

play a role in vegetation damage.232 In laboratory experiments, a wide range of tolerance to

VOCs has been observed.233 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.234

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.235,236,237

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 DRIA presents the projected emissions changes due to the proposed

rule. Once the emissions changes are projected the next step is to look at how the ambient air

quality would be impacted by those emissions changes. Although the purpose of this proposal

is to address greenhouse gas emissions, this proposed rule would also impact emissions of

criteria and air toxics. Section 6.2.1 describes current ambient levels of PM, ozone and some

air toxics without the standards being proposed in this rule. No air quality modeling was done

for this DRIA to project the impacts of the proposed rule. EPA plans to conduct such

modeling, however, and those plans are discussed in Section 6.2.2.

6.2.1 Current Levels of Non-GHG Pollutants

6.2.1.1 Particulate Matter

As described in Section 6.1.1.2, exposure to PM2.5 causes adverse health effects, and

the U.S. government has set national standards to provide requisite protection against those

health effects. There are two U.S. national ambient air quality standards (NAAQS) for PM2.5:

an annual standard (15 μg/m3

) and a 24-hour standard (35 μg/m3). The most recent revisions

to these standards were in 1997 and 2006. In 2005 the U.S. EPA designated nonattainment

areas for the 1997 PM2.5 NAAQS (70 FR 19844, April 14, 2005).OOOO As of April 21, 2011,

approximately 88 million people live in the 39 areas that are designated as nonattainment for

the 1997 PM2.5 NAAQS. These PM2.5 nonattainment areas are comprised of 208 full or

partial counties. 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

OOOO 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

6-28

include 32 areas composed of 121 full or partial counties with a population of over 70 million.

In total, there are 54 PM2.5 nonattainment areas composed of 245 counties with a population

of 101 million people.

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

areas into compliance in the future. Most 1997 PM2.5 nonattainment areas are required to

attain the 1997 PM2.5 NAAQS in the 2010 to 2015 time frame and then be required to

maintain the 1997 PM2.5 NAAQS thereafter.238 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 be required to maintain the 2006 24-hour PM2.5 NAAQS thereafter.239 The vehicle

standards proposed here first apply to model year 2017 vehicles.

6.2.1.2 Ozone

As described in Section 6.1.1.4, ozone causes adverse health effects, and the U.S.

government has set national standards to protect against those health effects. The primary

NAAQS for 8-hour ozone was set at 0.075 ppm in 2008. The previous 8-hour ozone standard,

set in 1997, had been 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).PPPP As of August 30, 2011 there

are 44 1997 8-hour ozone nonattainment areas comprised of 242 full or partial counties with a

total population of over 118 million.240 Nonattainment designations for the 2008 8-hour

ozone standard are currently under development.

States with ozone nonattainment areas are required to take action to bring those areas

into compliance 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 and then to maintain it

thereafter.QQQQ The attainment dates associated with the potential nonattainment areas based

on the 2008 8-hour ozone NAAQS will likely be in the 2015 to 2035 timeframe, depending on

the severity of the problem in each area. In addition, EPA is working to complete the current

review of the ozone NAAQS by mid-2014. The attainment dates associated with the potential

nonattainment areas based on the 2014 8-hour ozone NAAQS will likely be in the 2019 to

2036 timeframe, depending on the severity of the problem in each area. As mentioned above,

the vehicle standards proposed here first apply to model year 2017 vehicles.

PPPP 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.

QQQQ The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area is designated as severe and will

have to attain before June 15, 2021. The South Coast Air Basin has requested to be reclassified as an extreme

nonattainment area which will make its attainment date June 15, 2024. The San Joaquin Valley Air Basin 8-hour

ozone nonattainment area is designated as serious and will have to attain before June 15, 2013. The San Joaquin

Valley Air Basin has requested to be reclassified as an extreme nonattainment area which will make its

attainment date June 15, 2024.
2017 Draft Regulatory Impact Analysis

6-29

6.2.1.3 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.RRRR,SSSS,241 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.242 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.243

6.2.2 Impacts on Future Air Quality

Air quality models use mathematical and numerical techniques to simulate the

physical and chemical processes that affect air pollutants as they disperse and react in the

atmosphere. Based on inputs of meteorological data and source information, these models are

designed to characterize primary pollutants that are emitted directly into the atmosphere and

secondary pollutants that are formed as a result of complex chemical reactions within the

atmosphere. Photochemical air quality models have become widely recognized and routinely

utilized tools for regulatory analysis by assessing the effectiveness of control strategies.

These models are applied at multiple spatial scales from local, regional, national, and global.

Section 6.2.2.1 provides more detail on the photochemical model, the Community Multi-scale

Air Quality (CMAQ) model, which will be utilized for the final rule analysis.

6.2.2.1 Community Multi-Scale Air Quality (CMAQ) Modeling Plans

Full-scale photochemical air quality modeling is necessary to accurately project levels

of PM2.5, ozone and air toxics. For the final rule, a national-scale air quality modeling

analysis will be performed to analyze the impacts of the vehicle standards on PM2.5, ozone,

and selected air toxics (i.e., benzene, formaldehyde, acetaldehyde, acrolein and 1,3-

butadiene). The length of time needed to prepare the necessary emissions inventories, in

addition to the processing time associated with the modeling itself, has precluded us from

performing air quality modeling for this proposal.

RRRR 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.

SSSS 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.
Chapter 6

6-30

Section III.G of the preamble presents projections of the changes in criteria pollutant

and air toxics emissions due to the proposed vehicle standards; the basis for those estimates is

set out in Chapter 4 of the DRIA. The atmospheric chemistry related to ambient

concentrations of PM2.5, ozone and air toxics is very complex, and making predictions based

solely on emissions changes is extremely difficult. However, based on the magnitude of the

emissions changes predicted to result from the proposed vehicle standards, we expect that

there will be an improvement in ambient air quality, pending a more comprehensive analysis

for the final rule.

For the final rule, EPA intends to use a Community Multi-scale Air Quality (CMAQ)

modeling platform as the tool for the air quality modeling. The CMAQ modeling system is a

comprehensive three-dimensional grid-based Eulerian air quality model designed to estimate

the formation and fate of oxidant precursors, primary and secondary PM concentrations and

deposition, and air toxics, over regional and urban spatial scales (e.g., over the contiguous

U.S.).244,245,246 The CMAQ model is a well-known and well-established tool and is commonly

used by EPA for regulatory analyses, for instance the 2012-2016 final rule, and by States in

developing attainment demonstrations for their State Implementation Plans.247 The CMAQ

model (version 4.7) was most recently peer-reviewed in February of 2009 for the U.S.

EPA.248 The CMAQ model also has been used in numerous national and international

applications.249,250,251

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. EPA intends to use the most recent version of CMAQ, which reflects updates

to version 4.7 to improve the underlying science.252 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.

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. boundary

within which air quality is modeled at the 36 kilometer (km) grid cell level and two 12 km

boundaries (an Eastern US and a Western US domain) within which air quality is modeled at

the 12 km grid cell level. These are 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).
2017 Draft Regulatory Impact Analysis

6-31

Figure 6.2-1 CMAQ 12-km Eastern and Western US modeling domains

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 are derived from annual simulations of the Pennsylvania State

University / National Center for Atmospheric Research Mesoscale Model.253 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.254 The meteorology for the national 36 km grid and the 12 km Eastern

and Western U.S. grids are developed by EPA and will be described in more detail within the

final RIA and the technical support document for the final rule air quality modeling.TTTT The

meteorological outputs from MM5 are 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.255

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.256 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 will be run with a grid resolution of 2 degree x 2.5 degree (latitudelongitude) and 20 vertical layers. The predictions will be used to provide one-way dynamic

TTTT In addition background information can be found in the final RIA and TSD for the 2012-2016 final rule,

http://www.epa.gov/otaq/climate/regulations.htm#1-1.

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary
Chapter 6

6-32

boundary conditions at three-hour intervals and an initial concentration field for the 36 km

CMAQ simulations. The future base conditions from the 36 km coarse grid modeling will be

used as the initial/boundary state for all subsequent 12 km finer grid modeling.

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 proposed 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 proposed standards are also significant sources of mobile source air pollution such as

direct PM, NOx, VOCs and air toxics. The proposed standards would affect exhaust

emissions of these pollutants from vehicles. They would 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 would result from the proposed 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

proposed standard because a failure to adequately consider these ancillary co-pollutant

impacts could lead to an incorrect assessment of their net costs and benefits. Moreover, copollutant 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.

EPA typically quantifies and monetizes the health and environmental impacts related

to both PM and ozone exposure in its regulatory impact analyses (RIAs), when possible. To

estimate these impacts, EPA conducts full-scale photochemical modeling to provide the

needed spatial and temporal detail to estimate the changes in ambient levels of these

pollutants and their associated health and welfare impacts. However, we were unable to do so

in time for this proposal, as explained above. EPA attempts to make emissions and air quality

modeling decisions early in the analytical process so that we can complete the photochemical

air quality modeling and use that data to inform the health and environmental impacts

analysis. Resource and time constraints precluded the Agency from completing this work in

time for the proposal. Instead, EPA is using PM2.5-related benefits-per-ton values as an

interim approach to estimating the PM2.5-related benefits of the proposal. We also provide a

complete characterization of the health and environmental impacts that will be quantified and

monetized for the final rulemaking.

This section is split into two sub-sections: the first presents the PM2.5-related benefitsper-ton values used to monetize the PM2.5-related co-benefits associated with the proposal; the

second explains what PM2.5- and ozone-related health and environmental impacts EPA will

quantify and monetize in the analysis for the final rule. EPA bases its analyses on peerreviewed studies of air quality and health and welfare effects and peer-reviewed studies of the

monetary values of public health and welfare improvements, and is generally consistent with

benefits analyses performed for the analysis of the final Transport Rule,257 the final 2012-

2016 MY Light-Duty Vehicle Rule, 258 and the final Portland Cement National Emissions

Standards for Hazardous Air Pollutants (NESHAP) RIA.259
2017 Draft Regulatory Impact Analysis

6-33

Though EPA is characterizing the changes in emissions associated with toxic

pollutants, we will not be able to quantify or monetize the human health effects associated

with air toxic pollutants for either the proposal or the final rule analyses. Please refer to

Chapter 4.5 of this DRIA for more information about the air toxics emissions impacts

associated with the proposed standards.

6.3.1 Economic Value of Reductions in Criteria Pollutants

As described in Chapter 4.5, the proposed standards would reduce emissions of

several criteria and toxic pollutants and precursors. In this 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 toxic pollutants, nor does it monetize all of the

potential health and welfare effects associated with PM2.5.

This analysis uses a “benefit-per-ton” method to estimate a selected suite of PM2.5-

related health benefits described below. These PM2.5 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 its precursors (such as NOx, SOx,

and VOCs), from a specified source. Ideally, the human health benefits would be estimated

based on changes in ambient PM2.5 as determined by full-scale air quality modeling.

However, this modeling was not possible in the timeframe for this proposal, but EPA plans to

perform such modeling for the final rulemaking.

The dollar-per-ton estimates used in this analysis are provided in Table 6.3-1. In the

summary of costs and benefits, Chapter 7.4 of this RIA, we present the monetized value of

PM-related improvements associated with the proposal.

Table 6.3-1 Benefits-per-ton Values (2009$) Derived Using the American Cancer

Society Cohort Study for PM-related Premature Mortality (Pope et al., 2002)aand a 3%

Discount Rateb

Yearc All Sourcesd

Stationary (Non-EGU)

Sourcese

Mobile Sources

SOX VOC NOX Direct PM2.5 NOX Direct PM2.5

Estimated Using a 3 Percent Discount Rateb

2015 $29,000 $1,200 $4,800 $230,000 $5,000 $280,000

2020 $32,000 $1,300 $5,300 $250,000 $5,500 $300,000

2030 $38,000 $1,600 $6,300 $290,000 $6,600 $360,000

2040 $44,000 $1,900 $7,500 $340,000 $7,900 $430,000

Estimated Using a 7 Percent Discount Rateb

2015 $27,000 $1,100 $4,400 $210,000 $4,600 $250,000

2020 $29,000 $1,200 $4,800 $220,000 $5,000 $280,000

2030 $34,000 $1,400 $5,700 $260,000 $6,000 $330,000

2040 $40,000 $1,700 $6,800 $310,000 $7,200 $390,000

a The benefit-per-ton estimates presented in this table are based on an estimate of premature mortality derived

from the ACS study (Pope et al., 2002). If the benefit-per-ton estimates were based on the Six-Cities study

(Laden et al., 2006), the values would be approximately 245% (nearly two-and-a-half times larger. See below

for a description of these studies.
Chapter 6

6-34

b The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the

valuation of premature mortality to account for a twenty-year segmented cessation lag.

c Benefit-per-ton values were estimated for the years 2015, 2020, and 2030. For intermediate years, such as

2017 (the year the standards begin), we interpolated exponentially. For years beyond 2030 (including 2040),

EPA and NHTSA extrapolated exponentially based on the growth between 2020 and 2030.

d Note that the benefit-per-ton value for SOx is based on the value for Stationary (Non-EGU) sources; no SOx

value was estimated for mobile sources. The benefit-per-ton value for VOCs was estimated across all sources.

e Non-EGU denotes stationary sources of emissions other than electric generating units (EGUs).

The benefit per-ton technique has been used in previous analyses, including EPA’s

2012-2016 Light-Duty Vehicle Greenhouse Gas Rule,260 and the Portland Cement National

Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA.261 Table 6.3-2 shows the

quantified and unquantified PM2.5-related co-benefits captured in those benefit-per-ton

estimates.

Table 6.3-2 Human Health and Welfare Effects of PM2.5

Pollutant /

Effect

Quantified and Monetized

in Primary Estimates

Unquantified Effects

Changes in:

PM2.5 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 cost-benefit analysis that accompanied the NO2 NAAQS,UUUU,262

the benefits estimates utilize the concentration-response functions as reported in the

epidemiology literature. To calculate the total monetized impacts associated with quantified

health impacts, EPA applies values derived from a number of sources. For premature

mortality, EPA applies a value of a statistical life (VSL) derived from the mortality valuation

literature. For certain health impacts, such as chronic bronchitis and a number of respiratoryrelated ailments, EPA applies willingness-to-pay estimates derived from the valuation

literature. For the remaining health impacts, EPA applies values derived from current cost-ofillness and/or wage estimates.

UUUU 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. Note that the cost-benefit analysis was prepared solely for purposes

of fulfilling analysis requirements under Executive Order 12866 and was not considered, or otherwise played

any part, in the decision to revise the NO2 NAAQS.
2017 Draft Regulatory Impact Analysis

6-35

A more detailed description of the benefit-per-ton estimates is provided in Chapter 4

of the Draft Joint TSD that accompanies this rulemaking. 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)VVVV,WWWW accompanying the recent final

ozone NAAQS RIA (U.S. EPA, 2008). Readers can also refer to Fann et al. (2009)XXXX for a

detailed description of the benefit-per-ton methodology.YYYY

As described in the documentation for the benefit per-ton estimates cited above,

national per-ton estimates were developed for selected pollutant/source category

combinations. The per-ton values calculated therefore apply only to tons reduced from those

specific pollutant/source combinations (e.g., NO2 emitted from mobile sources; direct PM

emitted from stationary sources). Our estimate of PM2.5 benefits is therefore based on the

total direct PM2.5 and PM2.5-related precursor emissions controlled by sector and multiplied by

each per-ton value.

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.

EPA will conduct full-scale air quality modeling for the final rulemaking in an effort

to capture this variability.

 This analysis assumes that all fine particles, regardless of their chemical composition,

are equally potent in causing premature mortality. This is an important assumption,

because PM2.5 produced via transported precursors emitted from stationary sources

may differ significantly from direct PM2.5 released from diesel engines and other

industrial sources, but 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

VVVV 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>.

WWWW Note that the cost-benefit analysis was prepared solely for purposes of fulfilling analysis requirements

under Executive Order 12866 and was not considered, or otherwise played any part, in the decision to revise the

Ozone NAAQS.

XXXX 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.

YYYY 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; and (2) We have revised the Value of a Statistical Life to equal

$6.3 million (year 2000$), up from an estimate of $5.5 million (year 2000$) used in the June 2009 report. Please

refer to the following website for updates to the dollar-per-ton estimates:

http://www.epa.gov/air/benmap/bpt.html
Chapter 6

6-36

health benefits from reducing fine particles in areas with varied 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 benefit categories that EPA was unable to quantify due to

limitations associated with using benefits-per-ton estimates, several of which could be

substantial. Because the NOX and VOC emission reductions associated with this

proposal are also precursors to ozone, reductions in NOX and VOC would also reduce

ozone formation and the health effects associated with ozone exposure.

Unfortunately, ozone-related benefits-per-ton estimates do not exist due to issues

associated with the complexity of the atmospheric air chemistry and nonlinearities

associated with ozone formation. The PM-related benefits-per-ton estimates also do

not include any human welfare or ecological benefits. Please refer to Chapter 6.3.2

for a description of the agency’s plan to quantify and monetize the PM- and ozonerelated health impacts planned for the FRM and a description of the unquantified copollutant benefits associated with this rulemaking.

 There are many uncertainties associated with the health impact functions used in this

modeling effort. These include: within-study variability (the precision with which a

given study estimates the relationship between air quality changes and health effects);

across-study variation (different published studies of the same pollutant/health effect

relationship typically do not report identical findings and in some instances the

differences are substantial); the application of concentration-response functions

nationwide (does not account for any relationship between region and health effect, to

the extent that such a relationship exists); extrapolation of impact functions across

population (we assumed that certain health impact functions applied to age ranges

broader than that considered in the original epidemiological study); and various

uncertainties in the concentration-response function, including causality and

thresholds. These uncertainties may under- or over-estimate benefits.

 EPA has investigated methods to characterize uncertainty in the relationship between

PM2.5 exposure and premature mortality. EPA’s final PM2.5 NAAQS analysis provides

a more complete picture about the overall uncertainty in PM2.5 benefits estimates. For

more information, please consult the PM2.5 NAAQS RIA (Table 5.5).263

 The benefit-per-ton estimates used in this analysis incorporate projections of key

variables, including atmospheric conditions, source level emissions, population, health

baselines and incomes, technology. These projections introduce some uncertainties to

the benefit per ton estimates.

 As described above, using the benefit-per-ton value derived from the ACS study

(Pope et al., 2002) alone provides an incomplete characterization of PM2.5 benefits.

When placed in the context of the Expert Elicitation results, this estimate falls toward

the lower end of the distribution. By contrast, the estimated PM2.5 benefits using the

coefficient reported by Laden in that author’s reanalysis of the Harvard Six Cities

cohort fall toward the upper end of the Expert Elicitation distribution results (Laden et

al., 2006).

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a

good indication of local or regional air quality and health impacts, as there may be localized

impacts associated with the proposed rulemaking. Additionally, the atmospheric chemistry

related to ambient concentrations of PM2.5, ozone and air toxics is very complex. Full-scale

photochemical modeling is therefore necessary to provide the needed spatial and temporal

detail to more completely and accurately estimate the changes in ambient levels of these

pollutants and their associated health and welfare impacts. As discussed above, timing and
2017 Draft Regulatory Impact Analysis

6-37

resource constraints precluded EPA from conducting a full-scale photochemical air quality

modeling analysis in time for the NPRM. For the final rule, however, a national-scale air

quality modeling analysis will be performed to analyze the impacts of the standards on PM2.5,

ozone, and selected air toxics. The benefits analysis plan for the final rulemaking is discussed

in the next section.

6.3.2 Human Health and Environmental Benefits for the Final Rule

6.3.2.1 Human Health and Environmental Impacts

As noted above, to model the ozone and PM air quality benefits for the final rule, EPA

plans to use the Community Multiscale Air Quality (CMAQ) model (see Chapter 6.2.2.1 for a

description of the CMAQ model). The modeled ambient air quality data will serve as an input

to the Environmental Benefits Mapping and Analysis Program (BenMAP).264 BenMAP is a

computer program developed by EPA that integrates a number of the modeling elements used

in previous RIAs (e.g., interpolation functions, population projections, health impact

functions, valuation functions, analysis and pooling methods) to translate modeled air

concentration estimates into health effects incidence estimates and monetized benefits

estimates.

Table 6.3-3 lists the PM- and ozone-related health effect exposure-response functions

we will use to quantify the non-GHG incidence impacts associated with the final light-duty

vehicles standard.
Chapter 6

6-38

Table 6.3-3: 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)265–

Non-accidental

Huang et al (2005)266 - Cardiopulmonary

Schwartz (2005)267 – Non-accidental

Meta-analyses:

Bell et al (2005)268 – All cause

Ito et al (2005)269 – Non-accidental

Levy et al (2005)270 – All cause

All ages

Premature mortality —

cohort study, all-cause

PM2.5 Pope et al. (2002)271

Laden et al. (2006)272

>29 years

>25 years

Premature mortality, total

exposures

PM2.5 Expert Elicitation (IEc, 2006)273 >24 years

Premature mortality —

all-cause

PM2.5 Woodruff et al. (1997)274 Infant (<1 year)

Chronic Illness

Chronic bronchitis PM2.5 Abbey et al. (1995)275 >26 years

Nonfatal heart attacks PM2.5 Peters et al. (2001)276 Adults (>18

years)

Hospital Admissions

Respiratory

O3

Pooled estimate:

Schwartz (1995) - ICD 460-519 (all resp)277

Schwartz (1994a; 1994b) - ICD 480-486

(pneumonia)278,279

Moolgavkar et al. (1997) - ICD 480-487

(pneumonia)280

Schwartz (1994b) - ICD 491-492, 494-496

(COPD)

Moolgavkar et al. (1997) – ICD 490-496

(COPD)

>64 years

Burnett et al. (2001)281 <2 years

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496

(COPD)282

Ito (2003)—ICD 490-496 (COPD)283

>64 years

PM2.5 Moolgavkar (2000)—ICD 490-496

(COPD)284

20–64 years

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM2.5 Sheppard (2003)—ICD 493 (asthma)285 <65 years

Cardiovascular PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 390-429 (all

cardiovascular)

>64 years
2017 Draft Regulatory Impact Analysis

6-39

Ito (2003)—ICD 410-414, 427-428

(ischemic heart disease, dysrhythmia, heart

failure)

PM2.5 Moolgavkar (2000)—ICD 390-429 (all

cardiovascular)

20–64 years

Asthma-related ER visits O3 Pooled estimate:

Peel et al (2005)286

Wilson et al (2005)287

All ages

All ages

Asthma-related ER visits

(con’t)

PM2.5 Norris et al. (1999)288 0–18 years

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996)289 8–12 years

Upper respiratory

symptoms

PM2.5 Pope et al. (1991)290 Asthmatics, 9–11

years

Lower respiratory

symptoms

PM2.5 Schwartz and Neas (2000)291 7–14 years

Asthma exacerbations PM2.5 Pooled estimate:

Ostro et al. (2001)292 (cough, wheeze and

shortness of breath)

Vedal et al. (1998)293 (cough)

6–18 yearsa

Work loss days PM2.5 Ostro (1987)294 18–65 years

School absence days

O3

Pooled estimate:

Gilliland et al. (2001)295

Chen et al. (2000)296

5–17 yearsb

Minor Restricted Activity

Days (MRADs)

O3 Ostro and Rothschild (1989)297 18–65 years

PM2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al.

(1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we

extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in

the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in

the Analytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—

2020. EPA-SAB-COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the

Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies

Press.

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.

6.3.2.2 Monetized Estimates of Impacts of Reductions in Co-Pollutants

Table 6.3-4 presents the monetary values we will apply to changes in the incidence of

health and welfare effects associated with reductions in non-GHG pollutants that will occur

when these GHG control strategies are finalized.
Chapter 6

6-40

Table 6.3-4: Valuation Metrics Used in BenMAP to Estimate Monetary Co-Benefits

Endpoint Valuation Method Valuation (2009$)

Premature mortality Assumed Mean VSL $7,850,000

Chronic Illness

Chronic Bronchitis WTP: Average Severity $424,193

Myocardial Infarctions,

Nonfatal

Medical Costs Over 5 Years. Varies by age and

discount rate. Russell (1998)298

---

Medical Costs Over 5 Years. Varies by age and

discount rate. Wittels (1990)299

---

Hospital Admissions

Respiratory, Age 65+ COI: Medical Costs + Wage Lost $26,433

Respiratory, Ages 0-2 COI: Medical Costs $11,149

Chronic Lung Disease (less

Asthma)

COI: Medical Costs + Wage Lost $17,827

Pneumonia COI: Medical Costs + Wage Lost $21,161

Asthma COI: Medical Costs + Wage Lost $9,555

Cardiovascular COI: Medical Costs + Wage Lost (20-64) $32,806

COI: Medical Costs + Wage Lost (65-99) $30,520

ER Visits, Asthma COI: Smith et al. (1997)300 $449

COI: Standford et al. (1999)301 $376

Other Health Endpoints

Acute Bronchitis WTP: 6 Day Illness, CV Studies $444

Upper Respiratory Symptoms WTP: 1 Day, CV Studies $31

Lower Respiratory Symptoms WTP: 1 Day, CV Studies $20

Asthma Exacerbation WTP: Bad Asthma Day, Rowe and Chestnut (1986)

302

$54

Work Loss Days Median Daily Wage, County-Specific ---

Minor Restricted Activity

Days

WTP: 1 Day, CV Studies $64

School Absence Days Median Daily Wage, Women 25+ $93

Worker Productivity Median Daily Wage, Outdoor Workers, CountySpecific---

Environmental Endpoints

Recreational Visibility WTP: 86 Class I Areas ---

Source: Dollar amounts for each valuation method were extracted from BenMAP and adjusted to year 2009

dollars (from year 2000 dollars) using the Consumer Price Urban Index (CPI-U). For endpoints valued using

measures of VSL, WTP, or are wage-based, we use the CPI-U for “all items”: 214.537 (2009) and 172.2 (2000).

For endpoints valued using a Cost-of-Illness measure, we use the CPI-U for “medical care”: 375.613 (2009) and

260.8 (2000)..

6.3.2.3 Other Unquantified Health and Environmental Impacts

In addition to the co-pollutant health and environmental impacts we plan to quantify

for the analysis of the Light-Duty Vehicle GHG standard, there are a number of other health

and human welfare endpoints that we will not be able to quantify because of current

limitations in the methods or available data. These impacts are associated with emissions of

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

ethanol), ambient ozone, and ambient PM2.5 exposures. For example, we have not quantified

a number of known or suspected health effects linked with ozone and PM for which

appropriate health impact functions are not available or which do not provide easily
2017 Draft Regulatory Impact Analysis

6-41

interpretable outcomes (i.e., changes in heart rate variability). In addition, we are currently

unable to quantify a number of known welfare effects, including reduced acid and particulate

deposition damage to cultural monuments and other materials, and environmental benefits due

to reductions of impacts of eutrophication in coastal areas. Table 6.3-5 lists these

unquantified health and environmental impacts.

Although there will be impacts associated with air toxic pollutant emission changes

that result from this action, we do not attempt to monetize those impacts. This is primarily

because 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.303

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,304 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.”305 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 this action.ZZZZ We seek public comment

to inform how the Agency might do this in the future.

Table 6.3-5: Unquantified and Non-Monetized Potential Effects

POLLUTANT/EFFECTS EFFECTS NOT INCLUDED IN ANALYSIS - CHANGES IN:

Ozone Healtha Chronic respiratory damage

Premature aging of the lungs

Non-asthma respiratory emergency room visits

ZZZZ 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

6-42

Exposure to UVb (+/-)d

Ozone Welfare Yields for

-commercial forests

-some fruits and vegetables

-non-commercial crops

Damage to urban ornamental plants

Impacts on recreational demand from damaged forest aesthetics

Ecosystem functions

Exposure to UVb (+/-)

PM HealthbPremature mortality - short term exposuresc

Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic bronchitis

Non-asthma respiratory emergency room visits

Exposure to UVb (+/-)

PM Welfare Residential and recreational visibility in non-Class I areas

Soiling and materials damage

Damage to ecosystem functions

Exposure to UVb (+/-)

Nitrogen and Sulfate

Deposition Welfare

Commercial forests due to acidic sulfate and nitrate deposition

Commercial freshwater fishing due to acidic deposition

Recreation in terrestrial ecosystems due to acidic deposition

Existence values for currently healthy ecosystems

Commercial fishing, agriculture, and forests due to nitrogen deposition

Recreation in estuarine ecosystems due to nitrogen deposition

Ecosystem functions

Passive fertilization

CO Health Behavioral effects

Hydrocarbon (HC)/Toxics

Healthe

Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde, ethanol)

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, ethanol)

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 Welfaref Direct toxic effects to animals

Bioaccumulation in the food chain

Damage to ecosystem function

Odor

a

In addition to primary economic endpoints, there are a number of biological responses that have been

associated with ozone health effects including increased airway responsiveness to stimuli, inflammation in the

lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection. The

public health impact of these biological responses may be partly represented by our quantified endpoints.
2017 Draft Regulatory Impact Analysis

6-43

b

In addition to primary economic endpoints, there are a number of biological responses that have been

associated with PM health effects including morphological changes and altered host defense mechanisms. The

public health impact of these biological responses may be partly represented by our quantified endpoints.

c While some of the effects of short-term exposures are likely to be captured in the estimates, there may be

premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis.

However, the PM mortality results derived from the expert elicitation do take into account premature mortality

effects of short term exposures.

d May result in benefits or disbenefits.

e Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act.

Please refer to Chapter 6.1 for additional information on the health effects of air toxics.

f

Please refer to Chapter 6.1for additional information on the welfare effects of air toxics.

6.4 Changes in Atmospheric CO2 Concentrations, Global Mean Temperature, Sea

Level Rise, and Ocean pH Associated with the Proposed Rule’s GHG Emissions

Reductions

6.4.1 Introduction

The impact of GHG emissions on the climate has been reviewed in the 2012-2016

light-duty rulemaking and recent heavy-duty GHG rulemaking. See 75 FR at 25491; 76 FR at

57294. This section briefly discusses again some of the climate impact context for

transportation emissions. These previous discussions noted that 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 2009 (27 percent of total emissions).306

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”) AAAAA as the primary scientific and technical

basis for the Endangerment and Cause or Contribute Findings for Greenhouse Gases Under

Section 202(a) of the Clean Air Act (74 FR 66496, December 15, 2009). These assessments

comprehensively address the scientific issues 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.

AAAAA 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.
Chapter 6

6-44

Based on these assessments, the Administrator determined, in essence, 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 for which the endangerment finding was

made. 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.

More recent assessments have produced 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.”307 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 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

coral bleaching and erosion will increase due both to warming and ocean acidification, and

that sea level will rise 1.6 to 3.3 feet by 2100 in a 3 degree C scenario. 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
2017 Draft Regulatory Impact Analysis

6-45

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 proposed rule will affect future climate change. Since GHGs

are well-mixed in the atmosphere and have long atmospheric lifetimes, changes in GHG

emissions will affect atmospheric concentrations of greenhouse gases and future climate for

decades to millennia, depending on the gas. This section provides estimates of the projected

change in atmospheric CO2 concentrations based on the emission reductions estimated for this

proposed rule, compared to the reference case. In addition, this section analyzes the response

to the changes in GHG concentrations of the following climate-related variables: global mean

temperature, sea level rise, and ocean pH. See Chapter 4 in this DRIA for the estimated net

reductions in global emissions over time by GHG.BBBBB

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 proposed rule, EPA

estimated changes in projected atmospheric CO2 concentrations, global mean surface

temperature and sea-level rise to 2100 using the GCAM (Global Change Assessment Model,

formerly MiniCAM), integrated assessment modelCCCCC,308 coupled with the MAGICC

(Model for the Assessment of Greenhouse-gas Induced Climate Change) simple climate

model.DDDDD,309,310 GCAM was used to create the globally and temporally consistent set of

BBBBB Due to timing constraints, the modeling analysis in this section was conducted with preliminary estimates

of the emissions reductions projected from this proposal, which were similar to the final estimates presented in

Chapter 4 of this DRIA.

CCCCC 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.

DDDDD 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
Chapter 6

6-46

climate relevant emissions required for running MAGICC. MAGICC was then used to

estimate the projected change in relevant climate variables over time. Given the magnitude of

the estimated emissions reductions associated with the proposal, a simple climate model such

as MAGICC is appropriate for estimating the atmospheric and climate response.

6.4.2.1 Methodology

Emissions reductions associated with this proposal were evaluated with respect to a

baseline reference case. An emissions scenario was developed by applying the estimated

emissions reductions from the proposed rule relative to the baseline to the GCAM reference

(no climate policy) scenario (used as the basis for the Representative Concentration Pathway

RCP4.5).311 Specifically, the annual CO2, N2O, CH4, HFC-134a, NOx, CO, and SO2

emissions reductions estimated from this proposal were applied as net reductions to the

GCAM global baseline net emissions for each substance.EEEEE The emissions reductions past

2050 for all emissions were scaled with total U.S. road transportation fuel consumption from

the GCAM reference scenario. This was chosen as a simple scale factor given that both direct

and upstream emissions changes are included in the emissions reduction scenario provided.

Road transport fuel consumption past 2050 does not change significantly and thus emissions

reductions remain relatively constant from 2050 through 2100.

The GCAM reference scenario312 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.

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.

EEEEE Due to timing constraints, the modeling analysis in this section was conducted with preliminary estimates

of the emissions reductions projected from this proposal, which were similar to the final estimates presented in

Chapter 4 of this DRIA.
2017 Draft Regulatory Impact Analysis

6-47

The GCAM reference scenario uses non-CO2 and pollutant emissions implemented as

described in Smith and Wigley (2006); land-use change emissions as described in Wise et al.

(2009); and updated base-year estimates of global GHG emissions. This scenario was created

as part of the Climate Change Science Program (CCSP) effort to develop a set of long-term

global emissions scenarios that incorporate an update of economic and technology data and

utilize improved scenario development tools compared to the IPCC Special Report on

Emissions Scenarios (SRES) (IPCC 2000).

Using MAGICC 5.3 v2,313 the change in atmospheric CO2 concentrations, global

mean temperature, and sea level were projected at five-year time steps to 2100 for both the

reference (no climate policy) scenario and the emissions reduction scenario specific to the

proposed 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.FFFFF The range as illustrated in Chapter 10, Box 10.2,

Figure 2 of the IPCC’s Working Group I is approximately consistent with the 10-90%

probability distribution of the individual cumulative distributions of climate sensitivity.314

Other uncertainties, such as uncertainties regarding the carbon cycle, ocean heat uptake,

different baseline 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) which were 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

FFFFF 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 “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/.
Chapter 6

6-48

warming effect and the interaction of black carbon (and other co-emitted aerosol species) with

clouds. 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 proposed standards. See generally, EPA, Response to Comments to the Endangerment

Finding Vol. 9 section 9.1.6.1 and the discussion of black carbon in the endangerment finding

at 74 FR at 66520. Additionally, the magnitude of PM2.5 emissions changes (and therefore,

black carbon emission changes) related to these proposed standards are small in comparison

to the changes in the pollutants which have been included in the MAGICC model simulations.

To compute the changes in atmospheric CO2 concentration, global mean temperature,

and sea level rise specifically attributable to the impacts of the proposal, the difference in

emissions between the proposal and the baseline scenario was subtracted from the GCAM

reference emissions scenario. As a result of the emissions reductions from the proposed rule

relative to the baseline case, the concentration of atmospheric CO2 is projected to be reduced

by approximately 3.3 to 3.7 parts per million by volume (ppmv), the global mean temperature

is projected to be reduced by approximately 0.008-0.018°C, and global mean sea level rise is

projected to be reduced by approximately 0.07-0.17 cm by 2100. For 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 proposal compared to the baseline case.

Figure 6.4-2 provides the estimated change in projected global mean temperatures associated

with the proposal. Figure 6.4-3 provides the estimated reductions in global mean sea level

rise associated with the proposal. The range of reductions in global mean temperature and sea

level rise due to uncertainty in climate sensitivity is larger than that for CO2 concentrations

because CO2 concentrations are only weakly coupled to climate sensitivity through the

dependence on temperature of the rate of ocean absorption of CO2, whereas the magnitude of

temperature change response to CO2 changes (and therefore sea level rise) is more tightly

coupled to climate sensitivity in the MAGICC model.
2017 Draft Regulatory Impact Analysis

6-49

Figure 6.4-1 Projected Reductions in Atmospheric CO2 Concentrations (parts per

million by volume) from the Proposed Rule (climate sensitivity (CS) cases ranging from

1.5-6.0°C)

Figure 6.4-2 Projected Reductions in Global Mean Surface Temperatures from the

Proposed Rule (climate sensitivity (CS) cases ranging from 1.5-6.0°C)

-4.0

-3.0

-2.0

-1.0

0.0

2000 2020 2040 2060 2080 2100

ppmv

Change in CO2 Concentration

(Proposed Rule - Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0

-0.020

-0.016

-0.012

-0.008

-0.004

0.000

2000 2020 2040 2060 2080 2100

degree C

Change in Global Mean Temperature

(Proposed Rule - Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0
Chapter 6

6-50

Figure 6.4-3 Projected Reductions in Global Mean Sea Level Rise from the Proposed

Rule (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 are small relative to the change in temperature (1.8 – 4.8 oC) and sea level rise (23

– 55 cm) from 1990 to 2100 from the MAGICC simulations for the GCAM reference case.

However, this is to be expected given the magnitude of emissions reductions expected from

the proposal in the context of global emissions. 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-asusual 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.315 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 proposal, EPA analyzes another key climate-related variable and calculates

projected change in ocean pH for tropical waters. For this analysis, changes in ocean pH are

related to the change in the atmospheric concentration of carbon dioxide (CO2) resulting from

-0.200

-0.160

-0.120

-0.080

-0.040

0.000

2000 2020 2040 2060 2080 2100

cm

Change in Global Mean Sea Level Rise

(Proposed Rule - Baseline)

CS 1.5

CS 2.0

CS 2.5

CS 3.0

CS 4.5

CS 6.0
2017 Draft Regulatory Impact Analysis

6-51

the emissions reductions associated with the proposed rule. GGGGG EPA used the proposal

developed for CO2 System Calculations CO2SYS,316 version 1.05, a proposal which performs

calculations relating parameters of the carbon dioxide (CO2) system in seawater. The

proposal 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 proposal 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)317 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 proposed rule relative to the baseline with a CO2

concentration of 781.419, 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 proposed emissions standards relative to the baseline

scenario pH was +0.0018 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 proposed rule’s

emissions reduction 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)318, refit by Dickson and Millero

(1987)319

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)320 [Lewis and Wallace (1998)321 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).322 Switching this parameter to Khoo et al. (1977)

instead of Dickson (1990) had no effect on the calculated result].

GGGGG Due to timing constraints, the modeling analysis in this section was conducted with preliminary estimates

of the emissions reductions projected from this proposal, which were highly similar to the final estimates

presented in Chapter 4 of this DRIA.
Chapter 6

6-52

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).323 Based

on the projected atmospheric CO2 concentration reductions that would result from this

proposed rule (784.868 ppmv for a climate sensitivity of 3.0), the modeling program

calculates an increase in ocean pH of approximately 0.0018 pH units in 2100. Thus, this

analysis indicates the projected decrease in atmospheric CO2 concentrations from the

proposed standards yields an increase in ocean pH. Table 6.4-1 contains the projected

changes in ocean pH based the change in atmospheric CO2 concentrations which were

derived from the MAGICC modeling.

Table 6.4-1 Impact of the Proposal’s GHG Emissions Reductions On Ocean pHa

CLIMATE

SENSITIVITY

DIFFERENCE

IN CO2

a

YEAR PROJECTED

CHANGE

3.0 -3.45 ppm 2100 +0.0018

a

represents the change in atmospheric CO2 concentrations in 2100 based on the difference from the proposed

rule relative to the base case from the GCAM reference scenario used in the MAGICC modeling.

6.4.4 Summary of Climate Analyses

EPA’s analysis of the impact of the emissions reductions from this proposal on global

climate conditions is intended to quantify these potential reductions using the best available

science. While EPA’s modeling results of the impact of this proposal alone show small

differences in climate effects (CO2 concentration, global mean temperature, sea level rise, and

ocean pH), in comparison to the total projected changes, they yield results that are repeatable

and directionally consistent within the modeling frameworks used. The results are

summarized in Table 6.4-2, Impact of GHG Emissions Reductions On Projected Changes in

Global Climate Associated with the Proposal.

These projected reductions are proportionally representative of changes to U.S. GHG

emissions in the transportation sector. While not formally estimated for this proposal, a

reduction in projected global mean temperature 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 proposed rule 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
2017 Draft Regulatory Impact Analysis

6-53

global risks of climate change, which complicates quantification and cost-benefits

assessments. Changes in climate variables are a meaningful proxy for changes in the risk of

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 GHG Emissions Reductions On Projected Changes in Global

Climate Associated with the Proposal (based on a range of climate sensitivities from 1.5-

6°C)

VARIABLE UNITS YEAR PROJECTED CHANGE

Atmospheric CO2 Concentration ppmv 2100 -3.29 to -3.68

Global Mean Surface Temperature oC 2100 -0.0076 to -0.0184

Sea Level Rise cm 2100 -0.074 to -0.166

Ocean pH pH units 2100 +0.0018a

a The value for projected change in ocean pH is based on a climate sensitivity of 3.0.
Chapter 6

6-54

References

46 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.

47 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 2.3.1.1. Docket EPA-HQ-OAR-2010-

0799.

48 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 2.3.1.2. Docket EPA-HQ-OAR-2010-

0799.

49 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 2.3.4. Docket EPA-HQ-OAR-2010-

0799.

50 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. Table 2-6. Docket EPA-HQ-OAR-2010-0799.

51 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 2.3.5.1. Docket EPA-HQ-OAR-2010-

0799.

52 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. Table 2-6. Docket EPA-HQ-OAR-2010-0799.

53 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.

54 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

55 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-OAR2010-0799.

56 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.

57 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.

58 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.

59 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.

60 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.

61 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

62 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.
2017 Draft Regulatory Impact Analysis

6-55

63 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.

64 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.

65 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.

66 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.

67 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.

68 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.

69 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.

70 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.

71 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.

72 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.

73 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.

74 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-OAR2010-0799.

75 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-OAR2010-0799.

76 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.

77 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.

78 Spektor, D. M., Thurston, G.D., Mao, J., He, D., Hayes, C., Lippmann, M. (1991). Effects of single- and

multiday ozone exposures on respiratory function in active normal children. Environ. Res, 55,107-122. Docket

EPA-HQ-OAR-2010-0799.
Chapter 6

6-56

79 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.

80 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.

81 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.

82 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.

83 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.

84 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.

85 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.

86 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.

87 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.

88 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 EPAHQ-OAR-2010-0799.

89 U.S. EPA (2011) 2005 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005. Docket

EPA-HQ-OAR-2010-0799.

90 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.

91 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.

92 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.

93 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.

94 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens

available at: http://ntp.niehs.nih.gov/go/16183.

95 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197.

EPA-HQ-OAR-2010-0799
2017 Draft Regulatory Impact Analysis

6-57

96 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541-554.

Docket EPA-HQ-OAR-2010-0799.

97 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.

98 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

Assessment, Washington DC. This material is available electronically at http://www.epa.gov/iris/subst/0276.htm.

Docket EPA-HQ-OAR-2010-0799.

99 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.

100 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-OAR2010-0799.

101 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.

102 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.

103 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-OAR2010-0799.

104 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-OAR2010-0799.

105 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-OAR2010-0799.

106 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.

107 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens

available at: http://ntp.niehs.nih.gov/go/16183.

108 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.

109 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from

Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. Docket EPA-HQ-OAR2010-0799.

110 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.

111 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 EPAHQ-OAR-2010-0799.
Chapter 6

6-58

112 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.

113 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.

114 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.

115 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447.

Docket EPA-HQ-OAR-2010-0799.

116 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human

respiratory tract cancer risks of inhaled formaldehyde: Dose-response predictions derived from biologicallymotivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296. Docket EPAHQ-OAR-2010-0799.

117 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and doseresponse assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research

Triangle Park, NC. Docket EPA-HQ-OAR-2010-0799.

118 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde

with Relevance to Other Biologically-Based Dose Response (BBDR) Models. U.S. Environmental Protection

Agency, Washington, D.C., EPA/600/R-08/103, 2008. Docket EPA-HQ-OAR-2010-0799.

119 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of

formaldehyde-induced cancer risk: identification of key issues. Risk Anal 28(4):907-923. Docket EPA-HQOAR-2010-0799.

120 Subramaniam RP; Crump KS; Van Landingham C; et. al. (2007) Uncertainties in the CIIT model for

formaldehyde-induced carcinogenicity in the rat: A limited sensitivity analysis–I. Risk Anal, 27: 1237–1254.

Docket EPA-HQ-OAR-2010-0799.

121 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for

formaldehyde-induced respiratory cancer in humans. Ann Occup Hyg 52:481-495. Docket EPA-HQ-OAR-2010-

0799.

122 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for

formaldehyde-induced respiratory cancer in humans. Ann Occup Hyg 52:481-495. Docket EPA-HQ-OAR-2010-

0799.

123 Subramaniam RP; Crump KS; Van Landingham C; et. al. (2007) Uncertainties in the CIIT model for

formaldehyde-induced carcinogenicity in the rat: A limited sensitivity analysis–I. Risk Anal, 27: 1237–1254.

Docket EPA-HQ-OAR-2010-0799.

124 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tertButoxypropan-2-ol. Monographs Volume 88. World Health Organization, Lyon, France. Docket EPA-HQOAR-2010-0799.

125 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Formaldehyde.

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

http://www.atsdr.cdc.gov/toxprofiles/tp111.html. Docket EPA-HQ-OAR-2010-0799.

126 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the

joint sponsorship of the United Nations Environment Programme, the International Labour Organization, and the

World Health Organization, and produced within the framework of the Inter-Organization Programme for the

Sound Management of Chemicals. Geneva. Docket EPA-HQ-OAR-2010-0799.

127 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.
2017 Draft Regulatory Impact Analysis

6-59

128 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens

available at: http://ntp.niehs.nih.gov/go/16183.

129 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.

130 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.

131 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.

132 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.

133 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.

134 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.

135 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.

136 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.

137 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.

138 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.

139 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Immediate sensory nerve-mediated

respiratory responses to irritants in healthy and allergic airway-diseased mice. J Appl Physiol

94(4):1563-1571. Docket EPA-HQ-OAR-2010-0799.

140 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.

141 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.
Chapter 6

6-60

142 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.

143 U.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.

144 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.

145 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.

146 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.

147 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.

148 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.

149 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.

150 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

151 Zhou, Y.; Levy, J.I. (2007) Factors influencing the spatial extent of mobile source air pollution impacts: a

meta-analysis. BMC Public Health 7: 89. doi:10.1186/1471-2458-7-89. Docket EPA-HQ-OAR-2010-0799.

152 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.

153 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.

154 Holguin, F. (2008) Traffic, outdoor air pollution, and asthma. Immunol Allergy Clinics North Am 28: 577-

588.

155 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.

156 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.

157 U.S. Census Bureau (2008) American Housing Survey for the United States in 2007. Series H-150 (National

Data), Table 1A-6. [Accessed at http://www.census.gov/hhes/www/housing/ahs/ahs07/ahs07.html on January

22, 2009]

158 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 EPA-HQ-OAR-2010-0799.
2017 Draft Regulatory Impact Analysis

6-61

159 Wier, M.; Sciammas, C.; Seto, E.; Bhatia, R.; Rivard, T. (2009) Health, traffic, and environmental justice:

collaborative research and community action in San Francisco, California. Am J Public Health 99: S499-S504.

Docket EPA-HQ-OAR-2010-0799.

160 Forkenbrock, D.J. and L.A. Schweitzer, Environmental Justice and Transportation Investment Policy. Iowa

City: University of Iowa, 1997.

161 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.

162 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.

163 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.

164 Wu, Y.; Batterman, S. (2006) Proximity of schools in Detroit, Michigan to automobile and truck traffic. J

Exposure Sci Environ Epidemiol 16: 457-470. Docket EPA-HQ-OAR-2010-0799.

165 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. pg 9-19 through 9-23. Docket EPA-HQ-OAR2010-0799.

166 U.S. EPA. 1999. The Benefits and Costs of the Clean Air Act, 1990-2010. Prepared for U.S. Congress by

U.S. EPA, Office of Air and Radiation, Office of Policy Analysis and Review, Washington, DC, November;

EPA report no. EPA410-R-99-001. Docket EPA-HQ-OAR-2010-0799.

167 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

168 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences for growth.”

Trends in Ecology and Evolution 1:15-18. Docket EPA-HQ-OAR-2010-0799.

169 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

170 Tingey, D.T., and Taylor, G.E. (1982) Variation in plant response to ozone: a conceptual model of

physiological events. In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air Pollution in Agriculture

and Horticulture. (pp.113-138). London, UK: Butterworth Scientific. Docket EPA-HQ-OAR-2010-0799.

171 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

172 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

173 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

174 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

175 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.

176 Winner, W.E. (1994). Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4),

651-661. Docket EPA-HQ-OAR-2010-0799.

177 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

178 Chappelka, A.H., Samuelson, L.J. (1998). Ambient ozone effects on forest trees of the eastern United States:

a review. New Phytologist, 139, 91-108. Docket EPA-HQ-OAR-2010-0799
Chapter 6

6-62

179 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

180 Fox, S., Mickler, R. A. (Eds.). (1996). Impact of Air Pollutants on Southern Pine Forests, Ecological Studies.

(Vol. 118, 513 pp.) New York: Springer-Verlag.

181 De Steiguer, J., Pye, J., Love, C. (1990). Air Pollution Damage to U.S. Forests. Journal of Forestry, 88(8),

17-22. Docket EPA-HQ-OAR-2010-0799.

182 Pye, J.M. (1988). Impact of ozone on the growth and yield of trees: A review. Journal of Environmental

Quality, 17, 347-360. Docket EPA-HQ-OAR-2010-0799.

183 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

184 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

185 McBride, J.R., Miller, P.R., Laven, R.D. (1985). Effects of oxidant air pollutants on forest succession in the

mixed conifer forest type of southern California. In: Air Pollutants Effects On Forest Ecosystems, Symposium

Proceedings, St. P, 1985, p. 157-167. Docket EPA-HQ-OAR-2010-0799.

186 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest

ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory

(EPA600-D-82-276). Docket EPA-HQ-OAR-2010-0799.

187 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

188 Grulke, N.E. (2003). The physiological basis of ozone injury assessment attributes in Sierran conifers. In A.

Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the Sierra Nevada: Distribution and

effects on forests. (pp. 55-81). New York, NY: Elsevier Science, Ltd. Docket EPA-HQ-OAR-2010-0799.

189 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA,

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0799.

190 Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. (1985). Implications of environmental policy for U.S.

agriculture: the case of ambient ozone standards. Journal of Environmental Management, 20, 321-331. Docket

EPA-HQ-OAR-2010-0799.

191 Adams, R. M., Hamilton, S. A., McCarl, B. A. (1986). The benefits of pollution control: the case of ozone

and U.S. agriculture. American Journal of Agricultural Economics, 34, 3-19. Docket EPA-HQ-OAR-2010-

0799.

192 Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. (1989). A reassessment of the economic effects of

ozone on U.S. agriculture. Journal of the Air Pollution Control Association, 39, 960-968. Docket EPA-HQOAR-2010-0799.

193 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic losses.

U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park. Under contract to RADIAN

Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10. Docket EPA-HQ-OAR-2010-0799.

194 White, D., Kimerling, A.J., Overton, W.S. (1992). Cartographic and geometric component of a global

sampling design for environmental monitoring. Cartography and Geographic Information Systems, 19, 5-22.

Docket EPA-HQ-OAR-2010-0799.

195 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.

196 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 EPAHQ-OAR-2010-0799.
2017 Draft Regulatory Impact Analysis

6-63

197 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants. EPA/600/R05/004aF-cF. Docket EPA-HQ-OAR-2010-0799.

198 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants. EPA/600/R05/004aF-cF. Docket EPA-HQ-OAR-2010-0799.

199 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.

200 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.

201 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 EPAHQ-OAR-2010-0799.

202 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria

(Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Docket EPA-HQ-OAR-2010-0799.

203 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria

(Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Docket EPA-HQ-OAR-2010-0799.

204 Environmental Protection Agency (2003). Response Of Surface Water Chemistry to the Clean Air Act

Amendments of 1990. National Health and Environmental Effects Research Laboratory, Office of Research and

Development, U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA 620/R–03/001. Docket

EPA-HQ-OAR-2010-0799.

205 Fenn, M.E. and Blubaugh, T.J. (2005) Winter Deposition of Nitrogen and Sulfur in the Eastern Columbia

River Gorge National Scenic Area, USDA Forest Service. Docket EPA-HQ-OAR-2010-0799.

206 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of change. Ambio 31: 64–

71. Docket EPA-HQ-OAR-2010-0799.

207 Bricker, Suzanne B., et al., National Estuarine Eutrophication Assessment, Effects of Nutrient Enrichment in

the Nation’s Estuaries, National Ocean Service, National Oceanic and Atmospheric Administration, September,

1999. Docket EPA-HQ-OAR-2010-0799.

208 Smith, W.H. 1991. “Air pollution and Forest Damage.” Chemical Engineering News, 69(45): 30-43. Docket

EPA-HQ-OAR-2010-0799.

209 Gawel, J.E.; Ahner, B.A.; Friedland, A.J.; and Morel, F.M.M. 1996. “Role for heavy metals in forest decline

indicated by phytochelatin measurements.” Nature, 381: 64-65. Docket EPA-HQ-OAR-2010-0799.

210 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-OAR2010-0799.

211 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.

Docket EPA-HQ-OAR-2010-0799.

212 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-

0799

213 Mason, R.P. and Sullivan, K.A. 1997. “Mercury in Lake Michigan.” Environmental Science & Technology,

31: 942-947. (from Delta Report “Atmospheric deposition of toxics to the Great Lakes”). Docket EPA-HQOAR-2010-0799.

214 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-OAR2010-0799.
Chapter 6

6-64

215 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.

216 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.

217 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. Docket EPA-HQ-OAR-2010-0799.

218 Rice, K.C. 1999. “Trace Element Concentrations in Streambed Sediment Across the Conterminous United

States.” Environmental Science & Technology, 33: 2499-2504. Docket EPA-HQ-OAR-2010-0799.

219 Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. “Implications of Platinum-Group Element Accumulation along

U.S. Roads from Catalytic-Converter Attrition.” Environ. Sci. Technol. 35: 3816-3822. Docket EPA-HQ-OAR2010-0799.

220 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources of Polycyclic

Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina. Docket

EPA-HQ-OAR-2010-0799.

221 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources of Polycyclic

Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina. Docket

EPA-HQ-OAR-2010-0799.

222 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:

3039-3046. Docket EPA-HQ-OAR-2010-0799.

223 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 EPA-HQ-OAR-2010-0799.

224 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. “Fate of Atmospherically Deposited Polycyclic

Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & Technology, 35, 2178-2183.

Docket EPA-HQ-OAR-2010-0799.

225 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-HQOAR-2010-0799.

226 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 EPA-HQ-OAR-2010-0799.

227 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. “Fate of Atmospherically Deposited Polycyclic

Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & Technology, 35, 2178-2183.

Docket EPA-HQ-OAR-2010-0799.

228 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.

229 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.

230 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 EPA-HQ-OAR-2010-0799.

231 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.
2017 Draft Regulatory Impact Analysis

6-65

232 U.S. EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants. EPA/600/3-91/001.

Docket EPA-HQ-OAR-2010-0799.

233 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD Sharpe. 2003.

Effects of VOCs on herbaceous plants in an open-top chamber experiment. Environ. Pollut. 124:341-343.

Docket EPA-HQ-OAR-2010-0799.

234 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD Sharpe. 2003.

Effects of VOCs on herbaceous plants in an open-top chamber experiment. Environ. Pollut. 124:341-343.

Docket EPA-HQ-OAR-2010-0799.

235 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.

236 Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and transformation of benzene and toluene by plant

leaves. Ecotox. Environ. Safety 37:24-29. Docket EPA-HQ-OAR-2010-0799.

237 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.

238 U.S. EPA. (2007). PM2.5 National Ambient Air Quality Standard Implementation Rule (Final). Washington,

DC: U.S. EPA. Retrieved on May 14, 2009 from Docket EPA-HQ-OAR-2003-0062 at

http://www.regulations.gov/.72 FR 20586.

U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

0190 at http://www.regulations.gov/.

239 PM Standards Revision – 2006: Timeline. Retrieved on March 19, 2009 from

http://www.epa.gov/oar/particlepollution/naaqsrev2006.html#timeline

240 US EPA: 8-hour Ozone Nonattainment Areas. http://www.epa.gov/oar/oaqps/greenbk/gnsum.html

241 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment.

http://www.epa.gov/ttn/atw/nata2005/05pdf/sum_results.pdf.

242 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007)

243 US EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis. EPA

document number 420-R-07-002, February 2007.

244 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of

EPA Models-3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/030, Office of

Research and Development).

245 Byun, D.W., and Schere, K.L., 2006. Review of the Governing Equations, Computational Algorithms, and

Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J. Applied

Mechanics Reviews, 59 (2), 51-77.

246 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.

247 US EPA (2010). Regulatory Impact Analysis of the Final Rulemaking to Establish Light-Duty Vehicle

Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards. EPA document number

420-R-10-009, April 2010.

248 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., http://www.epa.gov/amad/peer/2009_AMAD_PeerReviewReport.pdf.
Chapter 6

6-66

249 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). Simulating regionalscale ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment,

38(17), 2627-2638. Docket EPA-HQ-OAR-2009-0472-11350

250 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. Docket EPA-HQ-OAR-2009-0472-11341

251 U.S. Environmental Protection Agency. (2008). Technical support document for the final locomotive/marine

rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards, Air Quality Assessment Division. Docket EPA-HQ-OAR-2009-

0472-11329

252 CMAQ v4.7.1 release notes are available at: http://www.cmaq-model.org/

253 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.

254 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.

255 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:

http://www.cmascenter.org/. Docket EPA-HQ-OAR-2009-0472-1915

256 Yantosca, B. (2004). GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group,

Harvard University, Cambridge, MA, October 15, 2004.

257 U.S. Environmental Protection Agency. (2011). Regulatory Impact Analysis (RIA) for the final Transport

Rule Docket ID No. EPA-HQ-OAR-2009-0491. June 2011.

258 U.S. Environmental Protection Agency. (2010). Final Rulemaking to Establish Light-Duty Vehicle

Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards: Regulatory Impact

Analysis, Assessment and Standards Division, Office of Transportation and Air Quality, EPA-420-R-10-009,

April 2010. Available on the internet: http://www.epa.gov/otaq/climate/regulations/420r10009.pdf

259 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

260 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-R10-009

261 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-OAR2009-0472-0241
2017 Draft Regulatory Impact Analysis

6-67

262 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis

(RIA). Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the

Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 15,

2010. EPA-HQ-OAR-2009-0472-0237U.S. Environmental Protection Agency (U.S. EPA). 2009.

263 U.S. Environmental Protection Agency. October 2006. Final Regulatory Impact Analysis

(RIA) for the Final National Ambient Air Quality Standards for Particulate Matter. Prepared

by: Office of Air and Radiation.

264 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/

benmodels.html.

265 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities, 1987-2000. JAMA,

2004. 292(19): p. 2372-8.

266 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for summer ozone

exposure and cardio-respiratory mortality. Environmetrics. 16: 547-562.

267 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control for

temperature? Am. J. Respir. Crit. Care Med. 171: 627-631.

268 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.

269 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between ozone and daily mortality: analysis

and meta-analysis. Epidemiology. 16(4): p. 446-57.

270 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.

271 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.

272 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.

273 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared

for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle

Park, NC. August.

274 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.

275 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). Estimated LongTerm Ambient Concentrations of PM(10) and Development of Respiratory Symptoms in a Nonsmoking

Population. Archives of Environmental Health. 50(2): 139-152.

276 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.

277 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for

respiratory disease. Thorax. 50(5):531-538.

278 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul,

Minnesota. Arch Environ Health. 49(5):366-374.

279 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J

Respir Crit Care Med. 150(3):648-655.
Chapter 6

6-68

280 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.

281 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.

282 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.

283 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.

284 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.

285 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.

286 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.

287 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.

288 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.

289 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 ChildrenRespiratory Symptoms. Environmental Health Perspectives 104(5):500-505.

290 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.

291 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.

292 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.

293 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.

294 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of

Environmental Economics Management 14:87-98.

295 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.

296 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhal

Toxicol 12(11):997-1016.

297 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An

Observational Study of Multiple Pollutants. Environmental Research 50:238-247.

298 Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. (1998). Direct Medical Costs

of Coronary Artery Disease in the United States. American Journal of Cardiology 81(9):1110-1115.
2017 Draft Regulatory Impact Analysis

6-69

299 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. (1990). Medical Costs of Coronary Artery Disease in

the United States. American Journal of Cardiology 65(7):432-440.

300 Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. (1997). A

National Estimate of the Economic Costs of Asthma. American Journal of Respiratory and Critical Care

Medicine 156(3 Pt 1):787-793.

301 Stanford, R., T. McLaughlin, and L.J. Okamoto. (1999). The Cost of Asthma in the Emergency

Department and Hospital. American Journal of Respiratory and Critical Care Medicine 160(1):211-215.

302 Rowe, R.D., and L.G. Chestnut. (1986). Oxidants and Asthmatics in Los Angeles: A Benefits Analysis—

Executive Summary. Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental

Protection Agency, Office of Policy Analysis. EPA-230-09-86-018. Washington, DC.

303 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 –

an SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html.

304 U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act from

1990 to 2020. Office of Air and Radiation, Washington, DC. March. Available on the Internet at

<http://www.epa.gov/air/sect812/feb11/fullreport.pdf>.

305 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. Benefits of

Reducing Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. July. Available at

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPACOUNCIL-08-001-unsigned.pdf>.

306 U.S. EPA (2011) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009. EPA 430-R-11-005.

307 National Research Council (NRC) (2010). Advancing the Science of Climate Change. National Academy

Press. Washington, DC. Docket EPA-HQ-OAR-2010-0799.

308 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.

309 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.

310 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.

311 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)

312 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-HQOAR-2010-0799.

313 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.

314 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
Chapter 6

6-70

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-HQOAR-2010-0799.

315 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.

316 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC105. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy,

Oak Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.

317 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC105. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy,

Oak Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.

318 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.

319 Dickson, A. G. and F. J. Millero. 1987. A comparison of the equilibrium constants for the dissociation of

carbonic acid in seawater media. Deep-Sea Res. 34, 1733-1743. (Corrigenda. Deep-Sea Res. 36, 983). Docket

EPA-HQ-OAR-2010-0799.

320 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.

321 Lewis, E., and D. W. R. Wallace. 1998. Program Developed for CO2 System Calculations. ORNL/CDIAC105. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy,

Oak Ridge, Tennessee. Docket EPA-HQ-OAR-2010-0799.

322 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.

323 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.
2017 Draft 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 proposed GHG

standards. We 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 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 would require different fuel efficiency

improvements. EPA’s proposed 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 proposing 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 restrictions on

transfer of credits between car and truck fleets), primarily because NHTSA is statutorily

prohibited from considering some flexibilities when establishing CAFE standards,HHHHH while

EPA is not. Also, manufacturers may opt to pay a civil penalty in lieu of actually meeting CAFE

standards, but they cannot pay a fine to avoid complying with EPA’s proposed 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 harmonized the programs as much as possible, and this proposal

to continue the National Program would 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.

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 proposed standard. Note, however, that the values presented in

this table are summaries of the inputs used for the agencies’ respective models. See draft Joint

TSD Chapter 4 for expanded discussion and details on each of these joint economic and other

values.

This Chapter includes an expanded description of the agencies’ approach to the

monetization of CO2 emission reductions. Though the underlying unit values are consistent with

those used in NHTSA’s analysis of the proposed CAFE standards, the specific stream of CO2-

HHHHH See 49 U.S.C. 32902(h).
Chapter 7

7-2

related benefits are unique to each program and EPA’s benefits are therefore presented in section

7.1.

Table 7.1-6.4-1 Joint Economic and other Values for Benefits Computations (2009$)

Fuel Economy Rebound Effect 10%

"Gap" between test and on-road MPG for liquid-fueled vehicles 20%

“Gap” between test and on-road wall electricity consumption for

electric and plug-in hybrid electric vehicles 30%

Value of refueling time per ($ per vehicle-hour) $ 22.02

Average tank volume refilled during refueling stop 57%

Annual growth in average vehicle use 1% through 2030,

0.5% thereafter

Fuel Prices (2017-50 average, $/gallon)

Retail gasoline price $3.71

Pre-tax gasoline price $3.35

Economic Benefits from Reducing Oil Imports ($/gallon)

"Monopsony" Component $ 0.00

Price Shock Component $ 0.185 in 2025

Military Security Component $ 0.00

Total Economic Costs ($/gallon) $ 0.185 in 2025

Emission Damage Costs (2020, $/short ton)

Carbon monoxide $ 0

Volatile organic compounds (VOC) $ 1,300

Nitrogen oxides (NOX) – vehicle use $ 5,500

Nitrogen oxides (NOX) – fuel production and distribution $ 5,300

Particulate matter (PM2.5) – vehicle use $ 300,000

Particulate matter (PM2.5) – fuel production and distribution $ 250,000

Sulfur dioxide (SO2) $ 32,000

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 $ 0.080

External Costs from Additional Light Truck Use ($/vehicle-mile)

Congestion $0.049

Accidents $0.027

Noise $0.001

Total External Costs $0.077

Discount Rates Applied to Future Benefits 3%, 7%
2017 Draft Regulatory Impact Analysis

7-3

7.1 Monetized CO2 Estimates

We assigned a dollar value to reductions in carbon dioxide (CO2) emissions using recent

estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized

damages associated with an incremental increase in carbon emissions in a given year. 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.324

The interagency group selected four SCC values for use in regulatory analyses, which

we have applied in this analysis: $5, $22, $36, and $67 per metric ton of CO2 emissionsIIIII,JJJJJ in

the year 2010, and in 2009 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. Treating climate sensitivity probabilistically results in more high

temperature outcomes, which in turn 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 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

IIIII The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles

and impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC,

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13).

JJJJJ The SCC estimates were converted from 2007 dollars to 2008 dollars using a GDP price deflator (1.021) and

again to 2009 dollars using a GDP price deflator (1.009) 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

on the physical and biological environment, and (4) the translation of these environmental

impacts into economic damages.325 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. The interagency group hopes that over

time researchers and modelers will work to fill these gaps and that the SCC estimates used for

regulatory analysis by the Federal government will continue to evolve with improvements in

modeling. Additional details on these limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to updating the current

estimates as the science and economic understanding of climate change and its impacts on

society improves over time. Specifically, the interagency group has set a preliminary goal of

revisiting the SCC values in the next few years or at such time as substantially updated models

become available, and to continue to support research in this area.

Applying the global SCC estimates, shown in Table 7.1-2
2017 Draft Regulatory Impact Analysis

7-5

Table 7.1-, to the estimated reductions in CO2 emissions under the proposed standards, we

estimate the dollar value of the GHG related benefits for each analysis year. 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%.KKKKK The

SCC estimates are presented in and the associated CO2 benefit estimates for each calendar year

are shown in Tables 7.1-3.

KKKKK It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be

discounted at rates that differ from those used to develop the SCC estimates.
Chapter 7

7-6

Table 7.1-2 Social Cost of CO2 2017-2050a(2009 dollars)

YEAR

DISCOUNT RATE AND STATISTIC

5% AVERAGE 3% AVERAGE 2.5% AVERAGE 3% 95TH

PERCENTILE

2017 $6.36 $25.59 $40.94 $78.28

2020 $7.01 $27.10 $42.98 $83.17

2025 $8.53 $30.43 $47.28 $93.11

2030 $10.05 $33.75 $51.58 $103.06

2035 $11.57 $37.08 $55.88 $113.00

2040 $13.09 $40.40 $60.19 $122.95

2045 $14.63 $43.34 $63.59 $131.66

2050 $16.18 $46.27 $66.99 $140.37

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 2009 dollars)

YEAR

5%

(AVERAGE SCC =

$6 IN 2017)

3%

(AVERAGE SCC =

$26 IN 2017)

2.5%

(AVERAGE SCC =

$41 IN 2017)

3%

(95TH PERCENTILE =

$78 IN 2017)

2017 $13 $53 $85 $162

2018 $45 $179 $286 $549

2019 $97 $378 $602 $1,160

2020 $171 $662 $1,050 $2,030

2021 $289 $1,100 $1,730 $3,360

2022 $443 $1,650 $2,600 $5,060

2023 $635 $2,330 $3,650 $7,150

2024 $866 $3,130 $4,890 $9,600

2025 $1,140 $4,070 $6,320 $12,500

2030 $2,690 $9,040 $13,800 $27,600

2040 $5,490 $17,000 $25,300 $51,600

2050 $8,050 $23,000 $33,300 $69,800

NPVb$32,800 $172,000 $292,000 $522,000

a The SCC values are dollar-year and emissions-year specific.

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, 2.5 percent) is used to

calculate net present value of SCC for internal consistency. Refer to SCC TSD for more detail.

We also conducted a separate analysis of the CO2 benefits over the model year lifetimes

of the 2017 through 2025 model year vehicles. In contrast to the calendar year analysis, the

model year lifetime analysis shows the impacts of the proposed 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 DRIA. 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

social cost of carbon 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
2017 Draft Regulatory Impact Analysis

7-7

future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for

internal consistency.

Table 7.1-4 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 2009 dollars)

YEAR MY

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025 SUM

2017 $13 $0 $0 $0 $0 $0 $0 $0 $0 $13

2018 $13 $32 $0 $0 $0 $0 $0 $0 $0 $45

2019 $13 $32 $51 $0 $0 $0 $0 $0 $0 $97

2020 $13 $32 $52 $74 $0 $0 $0 $0 $0 $171

2021 $13 $33 $53 $76 $114 $0 $0 $0 $0 $289

2022 $13 $33 $53 $77 $116 $151 $0 $0 $0 $443

2023 $13 $33 $53 $77 $118 $153 $187 $0 $0 $635

2024 $13 $32 $53 $77 $118 $155 $190 $227 $0 $866

2025 $12 $32 $52 $77 $118 $156 $193 $230 $270 $1,141

2026 $12 $31 $51 $76 $117 $156 $193 $234 $274 $1,143

2027 $11 $29 $49 $74 $115 $154 $192 $233 $276 $1,134

2028 $10 $28 $47 $72 $112 $152 $190 $233 $276 $1,120

2029 $9 $26 $45 $69 $108 $147 $186 $229 $275 $1,094

2030 $8 $23 $42 $66 $105 $142 $182 $226 $273 $1,066

2031 $7 $19 $36 $60 $98 $136 $175 $219 $266 $1,016

2032 $6 $16 $30 $52 $90 $128 $167 $209 $257 $955

2033 $5 $13 $25 $44 $78 $117 $156 $200 $245 $885

2034 $4 $11 $21 $36 $66 $102 $143 $187 $234 $804

2035 $3 $9 $17 $30 $56 $86 $124 $171 $219 $716

2036 $3 $7 $14 $25 $46 $72 $105 $148 $200 $622

2037 $2 $6 $12 $20 $39 $60 $88 $126 $173 $527

2038 $2 $5 $9 $16 $32 $50 $74 $105 $147 $441

2039 $2 $4 $8 $13 $26 $41 $61 $88 $123 $367

2040 $1 $4 $6 $11 $21 $34 $51 $73 $102 $304

2041 $1 $3 $6 $9 $18 $28 $41 $60 $85 $252

2042 $1 $3 $5 $8 $15 $23 $34 $50 $71 $209

2043 $1 $2 $4 $6 $13 $20 $28 $41 $58 $173

2044 $1 $2 $3 $5 $11 $17 $24 $34 $48 $145

2045 $1 $1 $2 $5 $9 $14 $21 $29 $40 $122

2046 $1 $1 $2 $3 $8 $12 $18 $25 $33 $103

2047 $1 $1 $2 $3 $6 $11 $15 $21 $29 $88

2048 $0 $1 $2 $2 $5 $8 $13 $18 $25 $74

2049 $0 $1 $1 $2 $5 $7 $9 $16 $21 $62

2050 $0 $1 $1 $2 $4 $6 $8 $11 $18 $52

NPV,

5% $142 $344 $552 $802 $1,230 $1,610 $1,980 $2,390 $2,810 $11,900

a The SCC values are dollar-year and emissions-year specific. Note that annual data extend to 2052 for the 2017MY

and to 2060 for the 2025MY. These data are not shown but are included in the NPV values.
Chapter 7

7-8

Table 7.1-5 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 2009 dollars)

YEAR MY

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025 SUM

2017 $53 $0 $0 $0 $0 $0 $0 $0 $0 $53

2018 $53 $127 $0 $0 $0 $0 $0 $0 $0 $179

2019 $52 $126 $200 $0 $0 $0 $0 $0 $0 $378

2020 $51 $126 $199 $286 $0 $0 $0 $0 $0 $662

2021 $51 $124 $200 $287 $434 $0 $0 $0 $0 $1,096

2022 $50 $123 $197 $287 $433 $563 $0 $0 $0 $1,652

2023 $48 $120 $195 $284 $433 $564 $689 $0 $0 $2,334

2024 $46 $117 $191 $280 $427 $563 $687 $822 $0 $3,134

2025 $44 $113 $186 $275 $422 $556 $688 $822 $964 $4,070

2026 $42 $108 $180 $267 $413 $549 $679 $822 $964 $4,024

2027 $39 $102 $171 $257 $400 $535 $668 $809 $960 $3,942

2028 $36 $96 $163 $246 $386 $520 $654 $799 $948 $3,848

2029 $31 $87 $152 $234 $367 $500 $633 $778 $933 $3,716

2030 $27 $76 $140 $220 $351 $478 $613 $760 $916 $3,580

2031 $22 $64 $120 $200 $327 $453 $580 $728 $885 $3,379

2032 $19 $53 $100 $171 $297 $421 $549 $689 $846 $3,145

2033 $16 $44 $83 $143 $256 $383 $510 $651 $801 $2,885

2034 $13 $36 $68 $118 $214 $329 $462 $603 $755 $2,599

2035 $11 $29 $56 $96 $178 $277 $398 $547 $701 $2,293

2036 $9 $24 $45 $79 $147 $230 $335 $472 $635 $1,976

2037 $7 $19 $36 $64 $122 $190 $279 $396 $547 $1,661

2038 $6 $16 $29 $51 $99 $157 $230 $330 $460 $1,379

2039 $5 $13 $24 $41 $81 $129 $191 $273 $383 $1,139

2040 $5 $12 $20 $34 $66 $105 $156 $226 $316 $938

2041 $4 $10 $17 $28 $54 $85 $127 $185 $262 $771

2042 $3 $8 $14 $24 $45 $70 $104 $150 $214 $633

2043 $2 $7 $12 $20 $39 $59 $85 $123 $174 $521

2044 $2 $5 $10 $16 $33 $51 $72 $101 $142 $432

2045 $2 $4 $7 $14 $27 $43 $63 $85 $117 $361

2046 $2 $4 $6 $9 $23 $36 $52 $74 $98 $304

2047 $2 $3 $5 $8 $17 $31 $44 $62 $86 $256

2048 $1 $3 $5 $7 $15 $22 $38 $52 $71 $213

2049 $1 $3 $4 $6 $13 $19 $27 $45 $60 $178

2050 $1 $2 $4 $5 $11 $17 $24 $32 $52 $149

NPV,

5% $598 $1,430 $2,260 $3,240 $4,900 $6,350 $7,730 $9,200 $10,700 $46,400

a The SCC values are dollar-year and emissions-year specific. Note that annual data extend to 2052 for the 2017MY

and to 2060 for the 2025MY. These data are not shown but are included in the NPV values.
2017 Draft Regulatory Impact Analysis

7-9

Table 7.1-6 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 2009 dollars)

YEAR MY

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025 SUM

2017 $85 $0 $0 $0 $0 $0 $0 $0 $0 $85

2018 $84 $202 $0 $0 $0 $0 $0 $0 $0 $286

2019 $83 $201 $318 $0 $0 $0 $0 $0 $0 $602

2020 $81 $199 $316 $453 $0 $0 $0 $0 $0 $1,050

2021 $80 $196 $316 $454 $684 $0 $0 $0 $0 $1,730

2022 $78 $193 $310 $451 $680 $885 $0 $0 $0 $2,597

2023 $76 $188 $306 $445 $679 $882 $1,078 $0 $0 $3,654

2024 $72 $182 $298 $437 $666 $877 $1,072 $1,282 $0 $4,888

2025 $69 $175 $289 $427 $656 $864 $1,068 $1,277 $1,498 $6,324

2026 $65 $167 $278 $414 $640 $850 $1,051 $1,273 $1,492 $6,231

2027 $61 $158 $263 $397 $618 $825 $1,031 $1,248 $1,481 $6,082

2028 $55 $147 $251 $378 $594 $800 $1,005 $1,229 $1,458 $5,918

2029 $48 $134 $233 $358 $562 $766 $971 $1,193 $1,430 $5,696

2030 $41 $116 $213 $337 $537 $731 $936 $1,161 $1,399 $5,472

2031 $34 $97 $183 $305 $498 $691 $884 $1,109 $1,348 $5,149

2032 $28 $80 $152 $260 $451 $640 $834 $1,046 $1,286 $4,779

2033 $24 $66 $126 $217 $388 $580 $773 $986 $1,213 $4,372

2034 $19 $54 $103 $178 $324 $498 $699 $912 $1,141 $3,928

2035 $16 $44 $84 $145 $269 $417 $601 $825 $1,056 $3,457

2036 $13 $36 $68 $119 $221 $346 $504 $709 $955 $2,971

2037 $11 $29 $55 $96 $182 $285 $418 $595 $821 $2,491

2038 $9 $24 $44 $77 $149 $235 $345 $494 $688 $2,064

2039 $8 $20 $36 $62 $120 $192 $285 $407 $571 $1,701

2040 $7 $17 $30 $50 $98 $156 $233 $337 $471 $1,398

2041 $6 $14 $26 $41 $80 $127 $189 $275 $388 $1,145

2042 $5 $12 $21 $35 $67 $104 $154 $222 $317 $936

2043 $4 $10 $17 $29 $58 $87 $126 $181 $257 $769

2044 $3 $7 $15 $24 $48 $76 $106 $149 $209 $636

2045 $3 $6 $10 $20 $40 $62 $92 $125 $172 $530

2046 $2 $5 $9 $13 $34 $52 $76 $109 $144 $445

2047 $2 $5 $8 $12 $24 $45 $64 $90 $125 $374

2048 $2 $4 $7 $10 $21 $32 $55 $75 $104 $310

2049 $2 $4 $6 $9 $19 $28 $39 $65 $87 $258

2050 $2 $3 $5 $8 $17 $25 $35 $47 $75 $216

NPV,

2.5% $968 $2,310 $3,640 $5,190 $7,820 $10,100 $12,300 $14,600 $16,900 $73,800

a The SCC values are dollar-year and emissions-year specific. Note that annual data extend to 2052 for the 2017MY

and to 2060 for the 2025MY. These data are not shown but are included in the NPV values.
Chapter 7

7-10

Table 7.1-7 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 2009 dollars)

YEAR MY

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025 SUM

2017 $162 $0 $0 $0 $0 $0 $0 $0 $0 $162

2018 $161 $388 $0 $0 $0 $0 $0 $0 $0 $549

2019 $160 $387 $613 $0 $0 $0 $0 $0 $0 $1,160

2020 $158 $385 $611 $877 $0 $0 $0 $0 $0 $2,031

2021 $156 $382 $613 $881 $1,330 $0 $0 $0 $0 $3,361

2022 $152 $376 $604 $879 $1,326 $1,725 $0 $0 $0 $5,064

2023 $148 $368 $598 $870 $1,328 $1,726 $2,110 $0 $0 $7,149

2024 $142 $358 $584 $858 $1,308 $1,723 $2,104 $2,517 $0 $9,595

2025 $135 $345 $569 $841 $1,293 $1,701 $2,104 $2,516 $2,951 $12,455

2026 $129 $329 $549 $818 $1,264 $1,680 $2,077 $2,515 $2,948 $12,309

2027 $120 $312 $522 $787 $1,224 $1,636 $2,043 $2,473 $2,935 $12,052

2028 $110 $293 $498 $751 $1,180 $1,590 $1,997 $2,442 $2,898 $11,759

2029 $95 $266 $465 $714 $1,121 $1,527 $1,934 $2,377 $2,850 $11,350

2030 $81 $232 $426 $673 $1,072 $1,461 $1,871 $2,320 $2,796 $10,932

2031 $68 $194 $366 $610 $998 $1,383 $1,772 $2,221 $2,701 $10,314

2032 $57 $162 $306 $523 $906 $1,285 $1,675 $2,101 $2,583 $9,597

2033 $48 $133 $253 $436 $780 $1,167 $1,556 $1,985 $2,442 $8,800

2034 $39 $110 $207 $359 $654 $1,004 $1,409 $1,840 $2,302 $7,925

2035 $32 $89 $170 $294 $544 $843 $1,215 $1,668 $2,135 $6,990

2036 $26 $72 $138 $240 $448 $702 $1,021 $1,438 $1,936 $6,021

2037 $22 $59 $111 $194 $370 $579 $849 $1,208 $1,667 $5,059

2038 $19 $48 $90 $156 $303 $479 $701 $1,005 $1,400 $4,200

2039 $16 $40 $73 $126 $245 $392 $581 $830 $1,165 $3,468

2040 $14 $35 $61 $102 $200 $318 $476 $687 $962 $2,855

2041 $12 $29 $52 $84 $164 $260 $386 $562 $796 $2,345

2042 $10 $24 $43 $73 $137 $213 $316 $457 $651 $1,924

2043 $7 $21 $36 $59 $120 $179 $260 $373 $529 $1,584

2044 $7 $14 $30 $49 $99 $156 $218 $307 $432 $1,313

2045 $6 $13 $20 $42 $83 $129 $190 $258 $356 $1,097

2046 $5 $11 $18 $27 $71 $108 $158 $225 $299 $923

2047 $5 $10 $16 $24 $50 $94 $132 $187 $261 $778

2048 $4 $9 $14 $21 $44 $67 $114 $157 $216 $647

2049 $4 $8 $12 $19 $39 $59 $82 $135 $182 $540

2050 $3 $7 $11 $17 $35 $52 $73 $98 $157 $452

NPV,

3% $1,830 $4,380 $6,920 $9,910 $15,000 $19,400 $23,600 $28,100 $32,700 $142,000

a The SCC values are dollar-year and emissions-year specific. Note that annual data extend to 2052 for the 2017MY

and to 2060 for the 2025MY. These data are not shown but are included in the NPV values.

7.2 Summary of Costs and Benefits

In this section, EPA presents a summary of costs, benefits, and net benefits of the

proposed program. Table 7.2-1 shows the estimated annual monetized costs of the proposed

program for the indicated calendar years. The table also shows the net present values of those
2017 Draft Regulatory Impact Analysis

7-11

costs for the calendar years 2012-2050 using both 3 percent and 7 percent discount rates.LLLLL

Table 7.2-2 shows the estimated annual monetized fuel savings of the proposed 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 7.2-3 shows the annual

reductions in petroleum-based imports and the monetized energy security benefits of the

proposed 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 7.2-1 Undiscounted Annual Costs & Costs of the Proposed Program Discounted

Back to 2012 at 3% and 7% Discount Rates (Millions, 2009$) a

2017 2020 2030 2040 2050 NPV, Years

2012-2050, 3%

Discount Rate

NPV, Years

2012-2050, 7%

Discount Rate

Technology

Costs $2,300 $8,470 $35,700 $39,800 $44,600 $551,000 $243,000

Note:

a Technology costs for separate light-duty vehicle segments can be found in Chapter 5 of this DRIA. Annual costs

shown are undiscounted values.

Table 7.2-2 Undiscounted Annual Fuel Savings & Proposed Program Fuel Savings

Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2009$) a

2017 2020 2030 2040 2050 NPV, Years

2012-2050, 3%

Discount Rate

NPV, Years

2012-2050, 7%

Discount Rate

Fuel Savings

(pre-tax) $570 $7,060 $85,800 $144,000 $187,000 $1,510,000 $579,000

Note:

a

Fuel savings for separate light-duty vehicle segments can be found in Chapter 5 of this DRIA. Annual costs shown

are undiscounted values.

LLLLL 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.
Chapter 7

7-12

Table 7.2-3 Undiscounted Annual Energy Security Benefits, & Proposed Program Benefits

Discounted back to 2012 at 3% and 7% Discount Rates (Millions, 2009$)a

2017 2020 2030 2040 2050

NPV, Years

2012-2050, 3%

Discount Rate

NPV, Years

2012-2050, 7%

Discount Rate

Petroleum-based

imports reduced

(mmb)

4.4 51.5 579 914 1,083

Monetized

benefits $30 $366 $4,810 $7,860 $9,310 $81,500 $31,500

Note:

a EPA developed estimates of energy security premiums (i.e., $/barrel of imported crude oil and finished petroleum

products) as result of the proposed rule for 2020, 2030, 2040 and 2050 using a method developed by the Oak Ridge

National Laboratory. 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 from the propose rule. 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 2011 forecasts that end in

2035, EPA assumed that the post-2035 energy security premium did not change through 2050. Annual costs shown

are undiscounted values.

Table 7.2-4 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 DRIA, 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 change, uncertainty in the

extrapolation of damages to high temperatures, and assumptions regarding risk aversion.

In addition, these monetized GHG benefits exclude the value of net reductions in nonCO2 GHG emissions (CH4, N2O, HFC) expected under this action. Although EPA has not

monetized the benefits of reductions in non-CO2 GHGs, the value of these reductions should not

be interpreted as zero. Rather, the net reductions in non-CO2 GHGs will contribute to this

program’s climate benefits, as explained in Chapter 6.4 of this DRIA.
2017 Draft Regulatory Impact Analysis

7-13

Table 7.2-4 Monetized Undiscounted Annual Benefits & Benefits of the Proposed Program

Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2009$)

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) $13 $171 $2,690 $5,490 $8,050 $32,800 $32,800

3% (avg SCC) $53 $662 $9,040 $17,000 $23,000 $172,000 $172,000

2.5% (avg SCC) $85 $1,050 $13,800 $25,300 $33,300 $292,000 $292,000

3% (95th %ile) $162 $2,030 $27,600 $51,600 $69,800 $522,000 $522,000

Energy Security

Benefits $30 $366 $4,810 $7,860 $9,310 $81,500 $31,500

Accidents,

Congestion, Noise

Costsg

$66 $844 $9,960 $16,900 $22,000 $176,000 $67,700

Increased Travel

Benefits $89 $1,090 $12,900 $23,600 $33,600 $244,000 $92,100

Refueling Time

Savings $25 $301 $3,780 $6,650 $8,800 $68,700 $26,200

PM2.5 Related Impacts

c,d,e $11 $150 $1,360 $2,190 $2,970 $23,800 $9,280

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) $101 $1,240 $15,600 $29,000 $40,700 $275,000 $124,000

3% (avg SCC) $141 $1,730 $22,000 $40,400 $55,600 $413,000 $263,000

2.5% (avg SCC) $173 $2,120 $26,700 $48,700 $65,900 $534,000 $384,000

3% (95th %ile) $250 $3,100 $40,500 $75,100 $102,00

0

$764,000 $614,000

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 DRIA Chapter 7.1 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67;

and for 95th percentile SCC at 3%: $70-$140.

c Note that the co-pollutant impacts associated with the proposed standards presented here do not include the full

complement of endpoints that, if quantified and monetized, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant 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 analysis in time for the proposal. We intend to more fully capture

the co-pollutant benefits for the analysis of the final standards.

d The PM2.5-related 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). See Chapter 6.3.1 of this DRIA. If the benefitper-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be nearly two-and-a-half

times larger. Id.

e The PM2.5-related 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 The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions

expected under this program as discussed above in section 7.1. Although EPA has not monetized changes in non-
Chapter 7

7-14

CO2 GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a

method of quantifying non-CO2 GHG benefits in Section III.H.5 of the preamble.

g The values shown for Accidents, Congestion, and Noise are costs and are treated as negative values in the total

benefits.

Table 7.2-5 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 2012-2050

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 7.2-5 Undiscounted Annual Monetized Net Benefits & Net Benefits of the Proposed

Program Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2009$)

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

Technology Costs $2,300 $8,470 $35,700 $39,800 $44,600 $551,000 $243,000

Fuel Savings $570 $7,060 $85,800 $144,000 $187,000 $1,510,000 $579,000

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $101 $1,240 $15,600 $29,000 $40,700 $275,000 $124,000

3% (avg SCC) $141 $1,730 $22,000 $40,400 $55,600 $413,000 $263,000

2.5% (avg SCC) $173 $2,120 $26,700 $48,700 $65,900 $534,000 $384,000

3% (95th %ile) $250 $3,100 $40,500 $75,100 $102,000 $764,000 $614,000

Monetized Net Benefits at each assumed SCC value c

5% (avg SCC) -$1,630 -$166 $65,600 $133,000 $183,000 $1,230,000 $460,000

3% (avg SCC) -$1,590 $325 $72,000 $144,000 $198,000 $1,370,000 $599,000

2.5% (avg SCC) -$1,560 $712 $76,800 $153,000 $208,000 $1,490,000 $719,000

3% (95th %ile) -$1,480 $1,690 $90,500 $179,000 $244,000 $1,720,000 $950,000

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 DRIA Chapter 7.1 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67;

and for 95th percentile SCC at 3%: $70-$140. DRIA 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 7.2-1 through Table 7.2-5, the model year lifetime analysis below

shows the impacts of the proposed 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 7.2-6 and Tables 7.2-7 at both 3 percent and 7 percent discount rates,

respectively.
2017 Draft Regulatory Impact Analysis

7-15

Table 7.2-6 Monetized Technology Costs, Fuel Savings, Benefits, and Net Benefits

Associated with the Lifetimes of 2017-2025 Model Year Light-Duty Vehicles (Millions,

2009$; 3% Discount Rate)h

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025

MY

Sum

Technology Costs $2,270 $4,590 $6,410 $8,340 $11,700 $19,100 $24,700 $30,300 $33,100 $140,000

Fuel Savings (pretax) $6,060 $14,30

0

$22,400 $31,80

0

$47,300 $61,000 $73,700 $87,000 $100,00

0

$444,000

Energy Security

Benefits $322 $763 $1,200 $1,710 $2,550 $3,310 $4,030 $4,790 $5,560 $24,200

Accidents,

Congestion, Noise

Costsf

$721 $1,740 $2,740 $3,880 $5,600 $7,150 $8,560 $10,000 $11,500 $52,000

Increased Travel

Benefits $1,040 $2,480 $3,850 $5,380 $7,720 $9,770 $11,600 $13,600 $15,500 $70,900

Refueling Time

Savings $262 $618 $967 $1,370 $2,040 $2,650 $3,230 $3,840 $4,470 $19,500

PM2.5 Related

Impactsc,d,e $117 $302 $481 $692 $1,090 $1,210 $1,300 $1,380 $1,450 $8,020

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) $142 $344 $552 $802 $1,230 $1,610 $1,980 $2,390 $2,810 $11,900

3% (avg SCC) $598 $1,430 $2,260 $3,240 $4,900 $6,350 $7,730 $9,200 $10,700 $46,400

2.5% (avg SCC) $968 $2,310 $3,640 $5,190 $7,820 $10,100 $12,300 $14,600 $16,900 $73,800

3% (95th %ile) $1,830 $4,380 $6,920 $9,910 $15,000 $19,400 $23,600 $28,100 $32,700 $142,000

Monetized Net Benefits at each assumed SCC value a, b

5% (avg SCC) $4,960 $12,50

0

$20,300 $29,50

0

$44,600 $53,300 $62,600 $72,600 $85,700 $386,000

3% (avg SCC) $5,420 $13,60

0

$22,100 $32,00

0

$48,300 $58,100 $68,400 $79,400 $93,600 $421,000

2.5% (avg SCC) $5,790 $14,50

0

$23,400 $33,90

0

$51,200 $61,800 $72,900 $84,800 $99,800 $448,000

3% (95th %ile) $6,650 $16,60

0

$26,700 $38,60

0

$58,400 $71,100 $84,300 $98,300 $116,00

0

$516,000

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 DRIA Chapter 7.1 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67;

and for 95th percentile SCC at 3%: $70-$140. DRIA Chapter 7.1 also presents these SCC estimates.

c Note that the co-pollutant impacts associated with the proposed standards presented here do not include the full

complement of endpoints that, if quantified and monetized, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant 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 analysis in time for the proposal. We intend to more fully capture

the co-pollutant benefits for the analysis of the final standards.

d The PM2.5-related 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). See Chapter 6.3.1 of this DRIA. If the benefitper-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be nearly two-and-a-half

times larger. Id.
Chapter 7

7-16

e The PM2.5-related 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 The values shown for Accidents, Congestion, and Noise are costs and are treated as negative values in the net

benefits.

g The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions

expected under this action as discussed above in section 7.1. Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a method of

quantifying non-CO2 GHG benefits in Section III.H.5 of the preamble.

h Model year values are discounted to the first year of each model year; the “Sum” represents those discounted

values summed across model years.

Table 7.2-7 Monetized Technology Costs, Fuel Savings, Benefits, and Net Benefits

Associated with the Lifetimes of 2017-2025 Model Year Light-Duty Vehicles (Millions,

2009$; 7% Discount Rate)h

2017

MY

2018

MY

2019

MY

2020

MY

2021

MY

2022

MY

2023

MY

2024

MY

2025

MY

Sum

Technology Costs $2,220 $4,500 $6,290 $8,190 $11,500 $18,700 $24,200 $29,700 $32,500 $138,000

Fuel Savings (pretax)

$4,720 $11,20

0

$17,500 $24,90

0

$37,000 $47,700 $57,700 $68,100 $78,700 $347,000

Energy Security

Benefits

$250 $593 $934 $1,330 $1,980 $2,580 $3,150 $3,750 $4,360 $18,900

Accidents,

Congestion, Noise

Costsf

$562 $1,360 $2,140 $3,040 $4,390 $5,600 $6,720 $7,880 $9,060 $40,800

Increased Travel

Benefits

$808 $1,930 $3,000 $4,190 $6,010 $7,620 $9,080 $10,600 $12,100 $55,300

Refueling Time

Savings

$203 $481 $754 $1,070 $1,590 $2,070 $2,520 $2,990 $3,480 $15,200

PM2.5 Related

Impactsc,d,e $93 $240 $382 $551 $864 $964 $1,030 $1,100 $1,160 $6,390

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) $142 $344 $552 $802 $1,230 $1,610 $1,980 $2,390 $2,810 $11,900

3% (avg SCC) $598 $1,430 $2,260 $3,240 $4,900 $6,350 $7,730 $9,200 $10,700 $46,400

2.5% (avg SCC) $968 $2,310 $3,640 $5,190 $7,820 $10,100 $12,300 $14,600 $16,900 $73,800

3% (95th %ile) $1,830 $4,380 $6,920 $9,910 $15,000 $19,400 $23,600 $28,100 $32,700 $142,000

Monetized Net Benefits at each assumed SCC value a, b

5% (avg SCC) $3,420 $8,920 $14,700 $21,60

0

$32,800 $38,200 $44,500 $51,300 $61,100 $277,000

3% (avg SCC) $3,880 $10,00

0

$16,400 $24,00

0

$36,400 $43,000 $50,200 $58,100 $69,000 $311,000

2.5% (avg SCC) $4,250 $10,90

0

$17,800 $26,00

0

$39,400 $46,700 $54,800 $63,500 $75,200 $338,000

3% (95th %ile) $5,110 $13,00

0

$21,100 $30,70

0

$46,500 $56,000 $66,100 $77,000 $91,000 $406,000
2017 Draft Regulatory Impact Analysis

7-17

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 DRIA Chapter 7.1 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67;

and for 95th percentile SCC at 3%: $70-$140. DRIA Chapter 7.1 also presents these SCC estimates.

c Note that the co-pollutant impacts associated with the proposed standards presented here do not include the full

complement of endpoints that, if quantified and monetized, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant 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 analysis in time for the proposal. We intend to more fully capture

the co-pollutant benefits for the analysis of the final standards.

d The PM2.5-related 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). See Chapter 6.3.1 of this DRIA. If the benefitper-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be nearly two-and-a-half

times larger. Id

e The PM2.5-related 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 The values shown for Accidents, Congestion, and Noise are costs and are treated as negative values in the net

benefits.

g The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions

expected under this action as discussed above in section 7.1. Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a method of

quantifying non-CO2 GHG benefits in Section III.H.5 of the preamble.

h Model year values are discounted to the first year of each model year; the “Sum” represents those discounted

values summed across model years.

References

324 Docket ID EPA-HQ-OAR-2010-0799, 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://epa.gov/otaq/climate/regulations.htm

325 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.

2017 Draft Regulatory Impact Analysis

8-1

8 Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 Vehicle Sales Impacts and Payback Period

Predicting the effects of this rule on vehicles 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, be expected to discourage sales. On the other hand, the vehicles will have improved

fuel economy and thus lower operating costs, producing lower total costs over the life of

vehicles, which makes them more attractive to consumers. Which of these effects dominates for

potential vehicle buyers when they are considering a purchase will determine the effect on sales.

However, assessing the net effect of these two competing effects is complex and uncertain, as it

rests on how consumers value fuel savings at the time of purchase and the extent to which

manufacturers and dealers reflect them in the purchase price. The empirical literature does not

provide clear evidence on whether consumers fully consider the value of fuel savings at the time

of purchase. It also generally does not speak to the efficiency of manufacturing and dealer

pricing decisions. Thus, for the proposal we do not provide quantified estimates of potential sales

impacts. Rather, we solicit comment on the issues raised here and on methods for estimating the

effect of this rule on vehicle sales.

For years, consumers have been gaining experience with the benefits that accrue to them

from owning and operating vehicles with greater fuel efficiency. Many households already own

vehicles with a fairly wide range of fuel economy, and thus already have an opportunity to learn

about the value of fuel economy on their own. Among two-vehicle households, for example, the

least fuel-efficient vehicle averages just over 22 mpg (EPA test rating), and the range between

this and the fuel economy of their other vehicle averages nearly 7 mpg. Among households that

own 3 or more vehicles, the typical range of the fuel economy they offer is much wider.

Consumer demand may have shifted towards such vehicles, not only because of higher fuel

prices but also if many consumers are learning about the value of purchases based not only on

initial costs but also on the total cost of owning and operating a vehicle over its lifetime. This

type of learning should continue before and during the model years affected by this rule,

particularly given the new fuel economy labels that clarify potential economic effects and should

therefore reinforce that learning..

Today’s proposed rule, combined with the new and easier-to-understand fuel economy

label required to be on all new vehicles beginning in 2012, may increase sales above baseline

levels by hastening this very type of consumer learning. As more consumers experience, as a

result of the rule, the savings in time and expense from owning more fuel efficient vehicles,

demand may shift yet further in the direction of the vehicles mandated under the rule. This

social learning can take place both within and across households, as consumers learn from one

another.

First and most directly, the time and fuel savings associated with operating more fuel

efficient vehicles may be more salient to individuals who own them, which might cause their

subsequent purchase decisions to shift closer to minimizing the total cost of ownership over the

lifetime of the vehicle.
Chapter 8

8-2

Second, this appreciation may spread across households through word of mouth and other

forms of communications.

Third, as more motorists experience the time and fuel savings associated with greater fuel

efficiency, the price of used cars will better reflect such efficiency, further reducing the cost of

owning more efficient vehicles for the buyers of new vehicles (since the resale price will

increase).

If these induced learning effects are strong, the rule could potentially increase total

vehicle sales over time. It is not possible to quantify these learning effects years in advance and

that effect may be speeded or slowed by other factors that enter into a consumer’s valuation of

fuel efficiency in selecting vehicles. The possibility that the rule will (after a lag for consumer

learning) increase sales need not rest on the assumption that automobile manufacturers are

failing to pursue profitable opportunities to supply the vehicles that consumers demand. In the

absence of the rule, no individual automobile manufacturer would find it profitable to move

toward the more efficient vehicles mandated under the rule. In particular, no individual company

can fully internalize the future boost to demand resulting from the rule. If one company were to

make more efficient vehicles, counting on consumer learning to enhance demand in the future,

that company would capture only a fraction of the extra sales so generated, because the learning

at issue is not specific to any one company's fleet. Many of the extra sales would accrue to that

company's competitors.

In other words, consumer learning about the benefits of fuel efficient vehicles involves

positive externalities (spillovers) from one company to the othersMMMMM. These positive

externalities may lead to benefits for manufacturers as a whole. We emphasize that this

discussion has been tentative and qualified. To be sure, social learning of related kinds has been

identified in a number of contexts.326 Comments are invited on the discussion offered here, with

particular reference to any relevant empirical findings.

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by

comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel

savings. We assumed that the costs for the fuel-saving technologies would be passed along fully

to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take into

account several factors that would affect consumer costs: the increased sales tax that consumers

would pay, the increase in insurance premiums, the increase in loan payments that buyers would

face, and a higher resale value, with all of these factors due to the higher up-front cost of the

vehicle. Those calculations resulted in an adjusted increase in costs to consumers. We then

assumed that consumers considered the present value of five years of fuel savings in their vehicle

purchase, which is consistent with the length of a typical new light-duty vehicle loan, and is

MMMMM Industrywide positive spillovers of this type are hardly unique to this situation. In many industries,

companies form trade associations to promote industry-wide public goods. For example, merchants in a given locale

may band together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can

increase output.
2017 Draft Regulatory Impact Analysis

8-3

similar to the average time that a new vehicle purchaser holds onto the vehicle.NNNNN 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.OOOOO 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, we present 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. The calculation of the payback period is discussed in DRIA Chapter 5.3. Table

8.1-1 shows the estimated payback period for MY 2021 and 2025. We present MY 2021

because it is the last year before the mid-term review impacts, if any, will take place, and MY

2025 because it is the last year of the program. The payback period in 2021 is shorter than that

in 2025, because the technologies required to meet the proposed MY 2021 standards are more

cost-effective than those for MY 2025. In all cases, the payback periods are less than 4 years.

Table 8.1-1 Estimated Payback Period for Model Years 2021 and 2025 (Years)

Model

Year

Estimated

Payback Period

for Cash

Purchase, 3%

Discount Rate

Estimated

Payback Period

for Cash

Purchase, 7%

Discount Rate

Estimated

Payback Period

for Purchase on

Credit, 3%

Discount Rate

Estimated

Payback Period

for Purchase on

Credit, 7%

Discount Rate

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including the full range of

considerations and assumptions which influence market behavior and outcomes; and associated

uncertainties. We welcome comments on the methodology described here for quantitative

estimates of the effects of this proposal on sales and its appropriateness for this rulemaking; we

also welcome proposals for other methods.

NNNNN In this proposal, 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, model year, and car vs. truck).

OOOOO 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

8-4

We here provide further detail on some of the assumptions included in the payback

analysis. We seek comment on all these factors as well. The analysis starts with the increase in

costs estimated by the OMEGA model. We assume that these costs are fully passed along to

consumers. This assumption is appropriate for cost increases in perfectly competitive markets.

In less than perfectly competitive markets, though, it is possible that the cost increase is split

between consumers and automakers, and possibly suppliers, and the price may not increase as

much as costs.327 Thus, the assumption of full cost pass-through is possibly an overestimate.

The next step in the analysis is to adjust this cost increase for other effects on the

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and

vehicle financing costs.

The increase in insurance costs is estimated from the average value of collision plus

comprehensive insurance as a proportion of average new vehicle price. Collision plus

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The

Insurance Information Institute328 provides the average value of collision plus comprehensive

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S.

Department of Energy, was $23,334.329 Dividing the insurance cost by the average price of a

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use information

on depreciation of vehicle value from the same U.S. Department of Energy report to estimate a

reduction in insurance costs, due to reduction in the estimated value of the vehicle, over 9 years.

Calculating the average increase in sales tax starts with the vehicle sales tax for each state

in 2006, the most recent source identified.330 The sales tax per state was then multiplied by the

2010 population of the state;331 those values were summed and divided by total U.S. population,

to give a population-weighted sales tax. That estimate of the state sales taxes for vehicles in the

U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when the vehicle is

purchased.

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51

percent.332 We use this loan rate to calculate the increase in vehicle costs due to financing a loan.

8.1.2 Consumer Vehicle Choice ModelingPPPPP

An alternative to the vehicle sales analysis 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

PPPPP 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.
2017 Draft Regulatory Impact Analysis

8-5

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 may be limited.

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,

but, given the known limitations and uncertainties of vehicle choice models, EPA has not

conducted an analysis using these models for this proposal.

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.

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 compliance costs compared to models that hold fleet mix

constant, since it predicts changes in the fleet mix that can affect 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 is 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
Chapter 8

8-6

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.QQQQQ 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,333 Greene et al.,334 and McManus.335

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 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,336 Bento et al.,337 and Train and Winston.338

While discrete choice modeling appears to be the primary method for consumer choice

modeling, others (such as Kleit339 and Austin and Dinan340) 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.341 Because they draw on

QQQQQ 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).
2017 Draft Regulatory Impact Analysis

8-7

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.342

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 Administration343 and the New Vehicle Market Model developed by NERA

Economic Consulting344 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;345 others consider the choice between new vehicles and an

outside good (possibly including a used vehicle);346 others explicitly consider the relationship

between the new and used vehicle markets.347 Some models include consideration of vehicle

miles traveled,348 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;349 others include varying numbers and kinds of vehicle and consumer attributes.

Some models include only the consumer side of the vehicle market;350 others seek to

represent both consumer and producer decisions.351 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.352 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.353

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.354

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,355 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
Chapter 8

8-8

the effects of voluntary export restraints on Japanese vehicles compared to tariffs and quotas,356

the market acceptability of alternative-fuel vehicles,357 the effects of introduction and exit of

vehicles from markets,358 causes of the decline in market shares of U.S. automakers,359 and the

effects of gasoline taxes360 and “feebates”361 (subsidizing fuel-efficient cars with revenue

collected by taxing fuel-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider the effect of fuel economy on vehicle

purchase decisions. It can appear in various forms.

Some models362 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.

Turrentine and Kurani363 question this assumption; they find, in fact, that consumers do not 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:364 since consumers pay for gallons of fuel, then this measure

can assess fuel savings relatively directly.365 Yet other models multiply fuel consumption per

mile by the cost of fuel to get the cost of driving a mile,366 or they divide fuel economy by fuel

cost to get miles per dollar.367 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.RRRRR 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 research368 presents results that higher fuel prices play a major

role in that decision.

Greene and Liu,369 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

RRRRR 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 4.2.5.2 of the TSD).
2017 Draft Regulatory Impact Analysis

8-9

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 Greene370 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, Gramlich371 includes both 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 studies372 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 studies373 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 studies374 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 which 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
Chapter 8

8-10

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,375 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.376 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 put little weight on benefits from fuel economy in the future and show

high discount rates;

 Consumers do not find the benefits from fuel economy to be sufficiently salient at the

time of purchase, even if it would be in consumers’ economic interest to take account

of those benefits;

 Consumers consider other attributes more important than fuel economy at the time of

vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute;

 Consumers have difficulty in calculating expected fuel savings;

 Consumers may use imprecise rules of thumb when deciding how much fuel

economy to purchase;

 Consumers might associate higher fuel economy with inexpensive, less well designed

vehicles;

 Fuel savings in the future are uncertain, while at the time of purchase the increased

costs of fuel-saving technologies are certain and immediate;

 Consumers may not be able to find the vehicles they want with improved fuel

economy;

 The level of cost savings may be affected by the underlying reasons for the gap:

factors such as transactions costs and differences in quality may not be adequately

measured;377

 There is likely to be variation among consumers in the benefits they get from

improved fuel economy, due to different miles driven and driving styles;

 Consumers may give particular weight to the losses associated with upfront costs, and

less so to the costs over time (a version of the phenomenon of “myopic loss

aversion”).
2017 Draft Regulatory Impact Analysis

8-11

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 underlying causes, and their potential significance for assessing the

potential incremental effects of pollution control standards. We welcome comments on any

aspect of this discussion.

8.1.2.7 Modeling 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.
Chapter 8

8-12

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.SSSSS They may have different

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.378 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, 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 8.1-2 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.

SSSSS 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 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.
2017 Draft Regulatory Impact Analysis

8-13

Table 8.1-2 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 2009$a

Bunch et al. (1993)b$7,600 (1991$) $11,100

Kavelek (1996) for California Energy

Commissionc

$2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for

California Energy Commissiond

$2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same data

as Resource Systems Group (2009)e

$2400 - $8500 (2009$) $2400 - $8500

Hidrue et al. (2011)f$3776 - $10,399 (2009$) $3776 - $10,399

aValues adjusted to 2009$ 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 (tstatistic = 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: tstatistics 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 EVoriented 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
Chapter 8

8-14

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

8.1-2, 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.

8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of

developing a vehicle choice model. 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 draft EPA model uses a nested logit structure common in the vehicle choice

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decision-tree

structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no

decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In its current form, at the initial decision

node, consumers choose between buying a new vehicle or not. Conditional on choosing a new

vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultra-luxury

vehicles. The next set of choices subdivides each of these categories into vehicle type (e.g.,

standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes (small,

medium, and large SUVs, for instance), and then, at the bottom, are the individual vehicle

configurations.

At this bottom level, vehicles that are similar to each other (such as standard

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest is

considered much more likely than substitution across nests, because the vehicles within a nest

are more similar to each other than vehicles in different nests. For instance, a person is more

likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo and a

pickup truck. In addition, substitution is greater at low decision nodes (such as individual

configurations) than at higher decision nodes (such as the buy/no buy decision), because there

are more choices at lower levels than at higher levels.

Parameters for the model (including demand elasticities and the value of fuel economy in

purchase decisions) are being 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,TTTTT assist in

TTTTT 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.
2017 Draft Regulatory Impact Analysis

8-15

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 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 will take 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.UUUUU 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 offset this

price increase. It then calculates changes in total fleet size and in sales mix, at the individualconfiguration 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.

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 proposed standards, because the mix and volume of

vehicles sold changed from the initial levels. To correct this problem, it would be necessary to

feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a new set

of output, which could then be fed back into the vehicle choice model. OMEGA would increase

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 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.

UUUUU As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule,

the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.
Chapter 8

8-16

The model is still undergoing development; EPA will seek peer review on it before it is

utilized. In addition, concerns remain over the ability of any vehicle choice model to make

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice model

to produce reasonable estimates of changes in fleet mix.

8.1.2.9 Summary and Additional Considerations

Consumer vehicle choice modeling in principle can 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 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.

The modeling may 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 compliance costs: because the model

allows consumers to choose among accepting the new vehicle, buying a different vehicle, or not

buying a vehicle, 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. 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.
2017 Draft Regulatory Impact Analysis

8-17

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 proposal 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,VVVVV the price increase measures the loss to the buyer.WWWWW

Assuming that the full technology cost gets passed along to the 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 proposal

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 exploration

of a vehicle choice model for use in the future.

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.”379,XXXXX The

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review”

VVVVV 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.

WWWWW 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 welfare,

unless there are other changes to the vehicle due to the fuel economy improvements that make the vehicle less

desirable to buyers.

XXXXX 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.”
Chapter 8

8-18

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety,

and our environment while promoting economic growth, innovation, competitiveness, and job

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of this

proposal on domestic employment in the auto manufacturing and parts sectors, while

qualitatively discussing how it may affect employment in other sectors more generally.

This proposal 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 proposed standards.380 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 proposed 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 the RIA, this

proposal 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 proposal 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.YYYYY 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.381 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 the regulatory

YYYYY Masur and Posner, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441
2017 Draft Regulatory Impact Analysis

8-19

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.ZZZZZ 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, 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). 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 Shih382 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

ZZZZZ 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.
Chapter 8

8-20

regulatory change on the regulated sector in the medium to longer 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,AAAAAA 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

AAAAAA As will be discussed below, the demand effect in this proposal 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?
2017 Draft Regulatory Impact Analysis

8-21

pollution control standards on industrial facilities that were considered in 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.383

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 shortrun 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, inputoutput 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
Chapter 8

8-22

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.384 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.
2017 Draft Regulatory Impact Analysis

8-23

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 expost 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 proposal

As mentioned above, this program is expected to affect employment in the regulated

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts

suppliers, auto dealers, 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),

and consumers (who will face higher vehicle costs and lower fuel expenditures). 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 proposal on the regulated sector (the auto industry; for reasons discussed below,

we include some quantitative assessment of effects on suppliers to the industry although they are

not regulated directly). It also includes qualitative discussion of the effects of the proposal 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 proposal. Secondly, 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.

As noted above, in a full-employment economy, any changes in employment will result

from people changing jobs or voluntarily entering or exiting the workforce. In a full-
Chapter 8

8-24

employment economy, employment impacts of this proposal 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. The agencies seek comment on other sectors

that are likely to be significantly affected and thus warrant further analysis in the final

rulemaking analysis.

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 proposal 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 unambiguously decreased

employment, there are countervailing effects in the vehicle market due to the fuel savings

resulting from this program. On one hand, this proposal will increase vehicle costs; by itself, this

effect would reduce vehicle sales. On the other hand, this proposal 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 proposal, we do not quantify the demand effect.

8.2.3.1.2 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 new or additional

technologies needed for vehicles to comply with the proposed 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 expenditures are required on

specific activities, as the factor shift effect (discussed below) indicates. For instance, the ratio
2017 Draft Regulatory Impact Analysis

8-25

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 proposal 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 public sources for estimates of employment per $1 million expenditures.

The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix

(ERM),385 which provides direct estimates of the employment per $1 million in sales of goods in

202 sectors. The most recent estimates, used here, are from 2008 (adjusted to 2009$). The

tables used here are adjusted to remove the employment effects of imports through use of the

ratio of domestic production to domestic sales, described above, of 0.667. The values reported

are for Motor Vehicle Manufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing

(NAICS 3363).

The Annual Survey of Manufactures386 (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 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 2009. 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 2009$), 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).

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic

Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the

underlying data come from different years (which may account for some of the differences). The
Chapter 8

8-26

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 8.2-1 Employment per $1 Million Expenditures (2009$) 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 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle

Mfg

0.757 0.505

Economic

Census

Motor Vehicle Mfg 0.674 0.449

Economic

Census

Light Duty Vehicle

Mfg

0.610 0.407

BLS ERM Motor Vehicle Parts

Mfg

3.073 2.049

ASM Motor Vehicle Parts

Mfg

3.093 2.063

Economic

Census

Motor Vehicle Parts

Mfg

2.749 1.833

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, 1.52 workers were needed per $1 million of 2000$,

but only 0.83 workers by 2008 (in 2000$).387 Because the ERM is available annually for 1993-

2008, we used these data to estimate productivity improvements over time. We regressed logged

ERM values on year (to estimate percent change per year) for both the Motor Vehicle

Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4

percent per year productivity improvement in the Motor Vehicle Manufacturing Sector, and a 3.6

percent per year improvement in the Motor Vehicle Parts Manufacturing Sector. We then used

the regression relationship to project the ERM through 2025. In the results presented below,

these projected values (adjusted to 2009$) 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 2009 (the

base year for the ASM); for the Economic Census estimates, we used the ratio of the projected
2017 Draft Regulatory Impact Analysis

8-27

value in the future to the projected value in 2007 (the base year for that estimate). As noted

above, we adjusted the estimate of workers per vehicle for the demand effect in Chapter

8.2.3.1.1, above, using the productivity improvement for the Motor Vehicle Manufacturing

sector; because the estimate of workers per vehicle for the demand effect was based on data from

2001 to 2010, we used the projected value of the ERM in 2005 as the denominator of the ratio.

This is a simple way to examine the relationship between labor required and expenditure and we

seek comment on refining this method.

Table 8.2-2 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 2.049 in 2009 if all the additional costs are in

the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are in the lightduty 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 2009. The results in Table 8.2-2

include the productivity adjustment described above.

While we estimate employment impacts beginning with the first year of the standard

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers hire

staff in anticipation of compliance with the standard.

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector

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 $ 2,300 600 3,600

2018 $ 4,656 1,200 7,000

2019 $ 6,507 1,600 9,400

2020 $ 8,467 1,900 11,800

2021 $ 11,878 2,600 15,900

2022 $ 19,340 4,100 25,000

2023 $ 25,036 5,000 31,200

2024 $ 30,738 5,900 37,000

2025 $ 33,561 6,200 39,000

8.2.3.1.3 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 proposed

standards. For a subset of the technologies, though, EPA-sponsored research (discussed in
Chapter 8

8-28

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.388 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 proposed 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 proposed 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 8.2-3 shows the changes in labor hours in moving from baseline to new fuel-saving

technologies for technologies in the FEV study. It indicates that, in switching from the baseline

to the new technologies, labor use per technology increased: the fuel-saving technologies use

more labor than the baseline technologies. For a subset of the technologies likely to be used to

meet the standards in this proposal, 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 proposed 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 proposal.
2017 Draft Regulatory Impact Analysis

8-29

Table 8.2-3 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

8.2.3.1.4 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 proposed standards on employment for auto dealers depend principally

on the effects of the standards on light duty vehicle sales. 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.

Although this proposal predicts very small penetration of advanced technology 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.
Chapter 8

8-30

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 proposed 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

proposed 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

proposed 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 Chapter 5.3. 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 Chapter 5.3. This new fuel may require additional infrastructure, such as electricity

charging locations. Providing this infrastructure 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 proposed 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.10.b); 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.
2017 Draft Regulatory Impact Analysis

8-31

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 proposal are expected to be found throughout

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel production

and supply, and consumers.

These proposed 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 600 –

3,600 workers 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 is ambiguous and depends on changes in

vehicle sales, which are not quantified for this proposal. 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 fuel production implies less employment in the petroleum sectors.

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.

References
Chapter 8

8-32

326 See Hunt Alcott, Social Norms and Energy Conservation, Journal of Public Economics

(forthcoming 2011), available at http://web.mit.edu/allcott/www/Allcott%20201

1%20JPubEc%20-%20 Social% 20Norms% 20and%20Energy%20Conservation.pdf; Christophe

Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003).

327 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S.

Automobile Market,” Review of Economics and Statistics 82: 316-324 (Docket EPA-HQ-OAR2010-0799).

328 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State,

2007-2008,” http://www.iii.org/media/facts/statsbyissue/auto/, accessed 8/22/11 (Docket EPAHQ-OAR-2010-0799).

329 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in

“Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf,

accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010-0799).

330 Solheim, Mark, 2006 “State Car Tax Rankings,”

http://www.kiplinger.com/features/archives/2006/04/cartax.html, accessed April 23, 2009

(Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799).

331 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of Columbia,

and Puerto Rico: 2010 Census" http://2010.census.gov/news/presskits/apportionment/apport.html .

332“National Auto Loan Rates for July 21, 2011,”

http://www.bankrate.com/finance/auto/national-auto-loan-rates-for-july-21-2011.aspx, accessed

7/26/11 (Docket EPA-HQ-OAR-2010-0799).

333 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951

(Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinelopi Koujianou, “The Effects of the

Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1)

(March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799).

334 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared by

the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., Philip D.

Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of

Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQOAR-2010-0799).

335 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate

Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14,

2006 (Docket EPA-HQ-OAR-2010-0799).
2017 Draft Regulatory Impact Analysis

8-33

336 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799);

Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems

from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political

Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

337 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H.

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799).

338 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007):

1469-1496 (Docket EPA-HQ-OAR-2010-0799).

339 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE)

Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-0799).

340 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

341 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger

H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Train, Kenneth E., and

Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S.

Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket

EPA-HQ-OAR-2010-0799).

342 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799).

343 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared by

the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799).

344 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-2015

CAFE Standards,” 2008, available at

http://www.heartland.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_Analysi

s_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799).

345 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007):

1469-1496 (Docket EPA-HQ-OAR-2010-0799).

346 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799).
Chapter 8

8-34

347 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger

H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799).

348 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H.

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton,

and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of

Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf

, accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799).

349 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the

California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments submitted

to the U.S. Environmental Protection Agency at Regulations.gov, document number EPA-HQOAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799).

350 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007):

1469-1496 (Docket EPA-HQ-OAR-2010-0799)

351 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799).

352 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

353 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing can

learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPAHQ-OAR-2010-0799).

354 Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, “Product Design Responses to

Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of

Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of

California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

355 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799).

356 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951

(Docket EPA-HQ-OAR-2010-0799).

357 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible

Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPA-HQ-OAR2010-0799); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit

Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation

Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799); Greene David L.,
2017 Draft Regulatory Impact Analysis

8-35

“TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak

Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQOAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of

Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak

Ridge National Laboratory for the U.S. Department of Energy, August 2004 (Docket EPA-HQOAR-2010-0799).

358 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal

of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); Berry, Steven,

James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a

Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy

112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

359 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007):

1469-1496 (Docket EPA-HQ-OAR-2010-0799).

360 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H.

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton,

and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of

Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf

, accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799).

361 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan,

“Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis

Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09

(Docket EPA-HQ-OAR-2010-0799); Greene, David L., “Feebates, Footprints and Highway

Safety,” Transportation Research Part D 14 (2009): 375-384.

362 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics

and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

363 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799).

364 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It Worth?”

Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799).

365 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-

1594 (Docket EPA-HQ-OAR-2010-0799).

366 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951

(Docket EPA-HQ-OAR-2010-0799).
Chapter 8

8-36

367 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799).

368 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump:

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper,

http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket EPAHQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 2009.

“How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal:

Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQ-OAR2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on Driving

Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883 (Docket EPAHQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for

Sport Utility Vehicles.” Working Paper. Macalester College,

http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

369 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799).

370 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.”

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799).

371 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S.

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11

(Docket EPA-HQ-OAR-2010-0799).

372 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket

EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI:

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 (Docket

EPA-HQ-OAR-2010-0799).

373 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics

and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-0799); Klier, Thomas, and

Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel

Economy Standard,” Resources for the Future Paper RFF DP 10-50 (December 2010),

http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPA-HQ-OAR-2010-

0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with

Producer and Household Heterogeneity,” working paper, at

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) .
2017 Draft Regulatory Impact Analysis

8-37

374 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

375 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.”

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam

B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and

Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy,

Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPAHQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the Energy

Savings From Home Improvement Investments: Evidence From Monthly Billing Data." The

Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-0799);

Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or Pipeline to the

Future?” Review of Environmental Economics and Policy 3(2): 304-320 (Docket EPA-HQOAR-2010-0799).

376 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel

Economy: A Review of the Literature.” International Review of Environmental and Resource

Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799).

377 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: Déjà

Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: Summary of a

Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010.

http://www.nap.edu/openbook.php?record_id=13023&page=42 (Docket EPA-HQ-OAR-2010-

0799)

378 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,”

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001

(Docket EPA-HQ-OAR-2010-0799).

379 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards.

The White House, Office of the Press Secretary, May 21, 2010. http://www.whitehouse.gov/thepress-office/presidential-memorandum-regarding-fuel-efficiency-standards

380 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on

August 9, 2011.

381 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).

382 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).

383 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156

(Docket EPA-HQ-OAR-2010-0799).
Chapter 8

8-38

384 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand:

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 (Docket

EPA-HQ-OAR-2010-0799).

385 http://www.bls.gov/emp/ep_data_emp_requirements.htm

386 http://www.census.gov/manufacturing/asm/index.html

387 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors

88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufacturing) from “Chainweighted (2000 dollars) real domestic employment requirements table. . . adjusted to remove

imports.”

388FEV, Inc. “Light-Duty Technology Cost Analysis: Power-split and P2 HEV Case Studies,”

Report FEV07-069-303, October 10, 2011 (Docket EPA-HQ-OAR-2010-0799).
2017 Draft 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

proposing to certify that this proposed rule would 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 would not be subject to this proposed rule. We are

proposing to exempt small business entities from the proposed 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 model year certification database. These companies are already certifying their vehicles

for compliance with applicable EPA emissions standards (e.g., Tier 2). We then identified
Chapter 9

9-2

companies that appear to meet the definition of small business provided in the table 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 21 entities that appear to fit

the Small Business Administration (SBA) criterion of a small business. EPA estimates there

are about 4 small vehicle manufacturers, including three electric vehicle manufacturers, 8

independent commercial importers (ICIs), and 9 alternative fuel vehicle converters in the

light-duty vehicle market which may qualify as small businesses.389 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 proposing to exempt from the proposed 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.390 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 proposing to exempt small

businesses from the GHG standards, we are proposing to certify that the rule would 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.

Based on input we have heard from at least one small business vehicle manufacturer,

EPA is proposing to allow small businesses to voluntarily waive their small entity exemption

and optionally certify to the GHG standards. This would allow small entity manufacturers to

earn CO2 credits under the GHG program, if their actual fleetwide CO2 performance was

better than their fleetwide CO2 target standard. EPA is proposing to make the GHG program

opt-in available starting in MY 2014, as the MY 2012, and potentially the MY 2013,

certification process will have already occurred by the time this rulemaking is finalized. EPA

is also proposing that manufacturers certifying to the GHG standards for MY 2014 would be

eligible to generate early credits for vehicles sold in MY 2012 and MY 2013. EPA is

proposing that manufacturers waiving their small entity exemption would be required to meet

all aspects of the GHG standards and program requirements across their entire product line.

However, the exemption waiver would be 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
2017 Draft Regulatory Impact Analysis

9-3

adding this voluntary option does not affect EPA’s determination that the proposed standards

would impose no significant adverse impact on small entities.

References

389 “List of Potential Small Businesses in the Light-duty Vehicle Market,” Memorandum from

Chris Lieske to Docket EPA-HQ-OAR-2010-0799.

390 75 FR 25424, May 7, 2010.